Chat with us, powered by LiveChat Wk1 Assignment Literature (KIm Woods Only) Due in 2 days | Gen Paper
+1(978)310-4246 credencewriters@gmail.com
  

Week 1 Assignment/..Literature Review (Required Question) Due in 2 days.docx

Literature Review Should be write according to MEAL Plan Format (Main idea, Evidence, Analysis, Link, Conclusion)


Assignment: Literature Review

on selected two Topic

Total Page requirement: 4 to 5 Pages

Must be 100% Original Work (due in 2 Days)

Read All instruction carefully & must be answer/included all point

My main research topic is related Pharmaceutical Industrys

Two Selected Topic for Literature Review: 2-2 Pages each

Requirements: 

1. Peer-Reviewed References.  

2. 2021-2022 References only.

3. Use MEAL plan format (literature review)

Subjects/Topic:

         Improve the Investigation Process to Manage Customer Complaints effectively

(2 Pages)

         Organizing Management Structure to Manage Customer Complaints effectively

(2 Pages)


MEAL Plan (Main idea, Evidence, Analysis, Link, Conclusion)

Course name: DDBA Doctoral Study Completion


Peer-Reviewed References

I hv attached Peer reviewed sources according to related Topic, Must be use below sources

Topic 1: Improve the Investigation Process to Manage Customer Complaints effectively

Liu, P.-J., Caspi, E., & Cheng, C.-W. (2021). Complaints matter: Seriousness of elder mistreatment citations in nursing homes nationwide. Journal of Applied Gerontology, 41(4), 908–917. https://doi.org/10.1177/07334648211043063

Neall, A. M., Li, Y., & Tuckey, M. R. (2021). Organizational justice and workplace bullying: Lessons learned from externally referred complaints and investigations. Societies, 11(4), 143. https://doi.org/10.3390/soc11040143

Giardina, T. D., Korukonda, S., Shahid, U., Vaghani, V., Upadhyay, D. K., Burke, G. F., & Singh, H. (2021). Use of patient complaints to identify diagnosis-related safety concerns: A mixed-method evaluation. BMJ Quality & Safety, 30(12), 996–1001. https://doi.org/10.1136/bmjqs-2020-011593

Peterson, L. J., Bowblis, J. R., Jester, D. J., & Hyer, K. (2020). U.S. state variation in frequency and prevalence of Nursing Home Complaints. Journal of Applied Gerontology, 40(6), 582–589. https://doi.org/10.1177/0733464820946673

Topic 2: Organizing Management Structure to Manage Customer Complaints effectively

García-Alcaraz, J. L., Montalvo, F. J., Sánchez-Ramírez, C., Avelar-Sosa, L., Saucedo, J. A., & Alor-Hernández, G. (2019). Importance of organizational structure for TQM success and customer satisfaction. Wireless Networks, 27(3), 1601–1614. https://doi.org/10.1007/s11276-019-02158-5

Von Janda, S., Polthier, A., & Kuester, S. (2021). Do they see the signs? organizational response behavior to customer complaint messages. Journal of Business Research, 137, 116–127. https://doi.org/10.1016/j.jbusres.2021.08.017

Rong, K., Sun, H., Li, D., & Zhou, D. (2021). Matching as service provision of sharing economy platforms: An Information Processing Perspective. Technological Forecasting and Social Change, 171, 120901. https://doi.org/10.1016/j.techfore.2021.120901

Week 1 Assignment/1 Complaints Matter Seriousness of Elder Mistreatment.pdf

https://doi.org/10.1177/07334648211043063

Journal of Applied Gerontology

© The Author(s) 2021
Article reuse guidelines:
sagepub.com/journals-permissions
DOI: 10.1177/07334648211043063
journals.sagepub.com/home/jag

Introduction

Although federal regulations in the United States set the
standards of nursing home care through extensive require-
ments aimed at protecting residents, evidence of widespread
mistreatment is well documented (Castle et al., 2015;
Harrington et al., 2020). For example, a recent systematic
review and meta-analysis (Yon et al., 2019) has shown that
more than half of staff admitted to committing acts of mis-
treatment: psychological abuse, 33%; physical abuse, 14%;
neglect, 12%; and sexual abuse, 2%. Low staffing levels
have been found to contribute to various forms of neglect
(Harrington et al., 2020). This is a source of concern because
half of the nursing homes have been found to have low staff-
ing levels, while at least one-quarter have been found to
have dangerous staffing levels (Harrington et al., 2016)—a
disturbing situation that has been exacerbated during the
coronavirus disease 2019 (COVID-19) pandemic.

Nursing Home Certification Process and
Mistreatment Deficiency Citations

Certification is a requirement for nursing homes to receive
reimbursement for residents’ care under Medicare and/or

Medicaid programs, and one of the strategies to assure the
quality of care and safety of residents. The vast majority of
nursing homes are certified, because nearly all have one or
more residents with care provided that is reimbursed by
Medicaid or Medicare (Centers for Medicare & Medicaid
Services [CMS], 2020c). After initial certification, CMS
requires that nursing homes receive standard recertification
surveys every 9 to 15 months, with a statewide average that
must not exceed 12 months. Surveys consist of an on-site
inspection by a team of surveyors (CMS, 2020b) who gener-
ally come from state survey agencies (SSAs). These survey-
ors monitor the quality of care and assess whether the nursing
home meets federal standards for certification.

1043063 JAGXXX10.1177/07334648211043063Journal of Applied GerontologyLiu et al.
research-article2021

Manuscript received: March 29, 2021; final revision received:
August 7, 2021; accepted: August 12, 2021.

1Purdue University, West Lafayette, IN, USA
2University of Connecticut, Storrs, USA

Corresponding Author:
Eilon Caspi, Institute for Collaboration on Health, Intervention, and
Policy, University of Connecticut, 2006 Hillside Road, Unit 1248, Storrs,
CT 06269, USA.
Email: [email protected]

Complaints Matter: Seriousness of
Elder Mistreatment Citations in
Nursing Homes Nationwide

Pi-Ju Liu1 , Eilon Caspi2, and Ching-Wei Cheng1

Abstract
Mistreatment of nursing home residents is prevalent and leads to harmful consequences. The Centers for Medicare &
Medicaid Services’s (CMS) mission to protect residents’ right to be free from mistreatment is implemented partially through
state survey agencies’ (SSAs) issuance of deficiency citations. The goal of this study was to compare SSA standard surveys
and SSA complaint investigations with regard to the seriousness (scope and severity) of the mistreatment citations issued.
A cumulative link mixed model was built to estimate the differences between standard surveys and complaint investigations
in the seriousness of four core and two secondary mistreatment citations nationwide from 2014 to 2017. In all of the six
mistreatment deficiency citations, complaint investigations were more likely to be determined as more serious compared
with standard surveys. The findings reinforce the importance of strengthening nursing homes’ and CMS/SSA response to
consumers’ concerns and grievances before they escalate into more harmful mistreatment.

Keywords
nursing home, long-term care, elder mistreatment, abuse, neglect, exploitation, deficiency citation, standard survey, complaint
investigation

Mistreatment and Exploitation

2022, Vol. 41(4) 908 –917

Liu et al. 9092 Journal of Applied Gerontology 00(0)

When nursing homes fail to meet minimum standards for
certification, a deficiency citation (also called F-Tag) is
issued for the noncompliance (CMS, 2020a). The Code of
Federal Regulations (CFR) contains an extensive definition
for each deficiency citation. The scope and severity of the
violation identified during the on-site visit are labeled using
12 letters from “A” (lowest scope and severity) through “L”
(highest scope and severity) (CMS, 2017). The severity rep-
resents the extent of harm to the resident, whereas the scope
represents the number of residents affected.

Residents living in long-term care settings are vulnerable
to mistreatment due to cognitive impairment, physical frailty,
and chronic illnesses (Castle, 2011; Wood & Stephens,
2003). The core deficiency citations for mistreatment used in
CMS’s former F-Tags coding system (the ones used by CMS
until November 27, 2017) were F-223, F-224, F-225, and
F-226 (see Table 1 for definitions). A study reviewing these
four deficiency citations as they were issued between 2000
and 2007 found that, annually, 20% of nursing homes nation-
wide received at least one of these mistreatment citations,
while 10% of nursing homes caused actual harm to at least
one resident (Castle, 2011). In a recent report from the U.S.
Government Accountability Office (GAO, 2019), mistreat-
ment F-Tags more than doubled between 2013 and 2017 with
increased serious cases, whereas the total number of all cita-
tions decreased. Physical and mental/verbal abuse, followed
by sexual abuse, were found to be the most common types of
deficiencies. In addition, compared with emotional/verbal
abuse, physical and sexual abuse had a higher level of
seriousness.

Two other violations of federal nursing home regulations
are commonly considered in the practice and research litera-
ture as forms of mistreatment. The first is untreated pressure
sores (F-314) and the other is inappropriate physical restraints
(F-221). These violations are described hereafter as second-
ary mistreatment F-Tags. With regard to the violation to pro-
vide treatment or services to prevent or heal pressure ulcers
(F-314), a substantial portion of pressure sores are consid-
ered to be a form of neglect (Pemberton, 2011). When basic
prevention measures are not implemented and when pressure
ulcers are left untreated, these wounds can worsen to cause
emotional and physical suffering, severe injury, and death
(Di Maio & Di Maio, 2002; Thompson, 2001). It is important
to recognize that not only could pressure sore formation to be
prevented or minimized with appropriate identification and
risk mitigation (Edsberg et al., 2014), almost all pressure
sores can be effectively treated (Lindbloom et al., 2007).
With regard to the violation of the right to be free from physi-
cal restraints (F-221), the Omnibus Budget Reconciliation
Act (1987) states that applying physical restraints is not
acceptable for the purposes of discipline or convenience.
Improper use of physical restraints can cause depression,
reduced social engagement, and cognitive decline (Castle,
2006). Serious injuries resulting from inappropriate physical
restraints can be fatal due to the lack of adequate supervision,

especially among residents with advanced dementia and
poor mobility (Berzlanovich et al., 2012; Miles & Irvine,
1992).

Standard Survey Versus Complaint Investigation

Under federal regulations, CMS oversees all SSAs’ 10
regional offices, which conduct complaint investigations and
standard surveys. Although the standard survey approach
can be helpful in identifying care-related problems and
improving nursing home quality of care and safety, the pro-
cess consists of significant limitations. For example, a U.S.
GAO (2009) study identified weaknesses in the survey meth-
odology and guidance to surveyors in identifying deficien-
cies. These weaknesses contribute to failures to cite serious
deficiencies or citing them at seriousness levels lower than
warranted—a problem known as understatement. Other
related limitations in the survey process include the fact that
on-site standard surveys, taking place approximately annu-
ally, may not reflect nursing home care practices throughout
the year but rather during a narrow time period prior to the
survey. Although standard surveys are intended to be unan-
nounced, U.S. General Accounting Office (2003) stated that
one third of them are predictable in their timing. The predict-
ability allowed nursing homes to add extra staffing or con-
ceal issues that may be problematic during other times of the
year (Williams et al., 2016).

In contrast to standard surveys, complaint investigations
are largely based on complaints filed by residents and fami-
lies, and in some cases, nursing home employees or visitors.
The Office of the Long-Term Care Ombudsman (LTCO) can
assist residents and families in knowing how to file com-
plaints with SSA or independently file complaints with a
resident and/or family’s permission (Troyer & Sause, 2011),
including allegations of abuse and neglect (Bloemen et al.,
2015). Therefore, complaints represent the voice of the resi-
dents, families, or other concerned parties. The nursing home
complaint process is considered “the front-line system for
addressing consumer concerns” (Office of Inspector General
[OIG], 2006, p. 1) and “a critical safeguard to protect vulner-
able residents” (OIG, 2017, p. 1). An OIG (2019a) study
found that nursing homes failed to report many incidents of
potential abuse and neglect to their SSA in accordance with
federal requirements, highlighting the importance of com-
plaints and their timely investigations.

In general, investigations prompted by complaints are
timelier. Compared with standard surveys, complaints are
more likely to be held in a closer proximity to the time in
which the mistreatment occurs. Timeliness of investigations,
or the lack of thereof, has significant implications on survey-
ors’ ability to collect sufficient evidence necessary to sub-
stantiate the allegation and issue a citation (U.S. General
Accounting Office, 1999). The actual timing of on-site com-
plaint investigations varies in part depending on the priority
level assigned to each complaint by the SSA during triage.

910 Journal of Applied Gerontology 41(4)Liu et al. 3

Such determination is based on the alleged conduct’s nature
and the level of harm alleged in the complaint. In general,
allegations triaged at the highest priority levels—“immediate
jeopardy” and “non-immediate jeopardy but high priority”
are required to be investigated on-site by SSA within 2 and
10 working days, respectively (OIG, 2017). That said, com-
plaint-initiated investigations may be delayed, such as when
SSA triages mistreatment allegations at a lower priority
level. Given that a portion of residents’ and families’ com-
plaints are likely filed with SSA without the knowledge of
the nursing home, the timing of on-site investigations is often
unpredictable. This contrasts with standard surveys whereby
nursing homes can often anticipate the general time frame
during which the survey will take place.

An OIG (2017) study pointed to the seriousness of con-
sumer complaints, with nearly 60% of consumer complaints
categorized as “immediate jeopardy” or “non-immediate
jeopardy but high priority” at the triage stage. Also, nearly
60% of more serious deficiencies overall were identified
through complaints (OIG, 2019b). Another study found that
nearly one fourth of complaints were related to mistreatment
and over one third of the allegations were substantiated
(Hansen et al., 2019). Furthermore, other research found that
the number of complaints predicted the number of citations
issued by surveyors, as well as serious deficiency citations
(Stevenson, 2005, 2006).

Gap in the Literature and Current Study

Despite the widely recognized importance of SSA complaint
investigations and the relationship between complaints and
quality of care, only a small number of studies examined
nursing home complaint data nationwide (Hansen et al.,
2019; Peterson et al., 2020; Troyer & Sause, 2011). In addi-
tion, to our knowledge, no study to date has utilized a national
nursing home mistreatment citation data set to compare stan-
dard surveys and complaint investigations with a primary
focus on citations’ scope and severity determinations. A call

for such comparison was made recently by Hansen and col-
leagues (2019) who stated, “complaint-related deficiencies
should be compared to deficiencies received on recent annual
surveys to gain a better understanding of the effect of com-
plaints on quality” (p. 754).

The current study takes a first step toward bridging this
gap in knowledge by comparing the scope and severity of
mistreatment citations issued during standard surveys versus
complaint investigations in nursing homes nationwide. The
limitations inherent in standard surveys and advantages of
complaint investigations as identified in our aforementioned
review of studies and government reports served as the basis
for our hypothesis, such that at the national level, SSA’s mis-
treatment investigations prompted by complaints are more
likely to be issued a deficiency citation at higher scope and
severity levels compared with standard surveys.

Method

Data Source

Through their Freedom of Information Act request to CMS,
the second author obtained a state survey deficiency citations
data set for all CMS-certified nursing homes in 50 states. The
data spanned from Fall 2014 to 2017, up until the last day of
the former CMS’s F-Tag system on November 27, 2017. The
following data elements were included in the CMS data set
obtained and used in this study: (a) Each F-Tag issued in 50
states occupied a row in Excel, and the six mistreatment
F-Tags (detailed in Table 1) were filtered out of the 175 types
of F-Tags to create the data subset used in the study. (b) Each
F-Tag was assigned a scope-severity level between B to L
(no F-Tags at scope-severity A level were received in the
CMS data set). (c) If the F-Tag issued was based on a stan-
dard survey, it was assigned the value 1 for the standard sur-
vey column, otherwise it received a zero. (d) Similarly, if the
F-Tag issued was based on a complaint investigation, it was
assigned the value 1 for the complaint investigation column,

Table 1. Elder Mistreatment Citations Selected for the Study.

F-Tag CFR regulatory group Definition Inclusion criteria

Core elder mistreatment F-tags
F-223 483.13 Free from abuse and involuntary seclusion Abuse
F-224 483.13 Prohibit mistreatment, neglect, and misappropriation of property

(staff treatment of residents)
Abuse, neglect,

exploitation
F-225 483.13 Investigate and report individuals with allegations of abuse,

neglect, and misappropriation of property
Abuse, neglect,

exploitation
F-226 483.13 Develop and implement abuse, neglect, and misappropriation of

property policies and procedures (staff treatment of residents)
Abuse, neglect,

exploitation
Secondary elder mistreatment F-tags
F-221 483.13 Right to be free from physical restraints Physical and psychological

abuse; restraints
F-314 483.25 Provide treatment or services to prevent or heal pressure ulcers Neglect

Note. Regulatory groups 483.13: resident behavior and facility practices; regulatory groups 483.25: quality of care. CFR = Code of Federal Regulations.

Liu et al. 9114 Journal of Applied Gerontology 00(0)

otherwise it received a zero. The data also included (e) the
date of the standard survey or complaint investigation and (f)
the state in which the standard survey or complaint investiga-
tion was conducted. All data elements were received at the
individual nursing home level. The entire CMS data set
received and its subset used in the study are considered by
CMS as public records, so no informed consent was obtained
for the study. The Institutional Review Boards (IRB) of
University of Minnesota and Purdue University determined
that the study is exempt from IRB review and approved the
study.

Mistreatment Deficiency Citations: Seriousness
Levels and Sources

The subset of six mistreatment F-Tags was selected by the
research team based on their direct relevance to various
forms of mistreatment (i.e., abuse, neglect, and/or financial
exploitation) or because they represent violations of federal
standards of care that are commonly considered as forms of
mistreatment (i.e., physical restraints and pressure ulcers).
Out of the 15,045 nursing homes, 10,240 (68%) had at least
one mistreatment F-Tag across the 3-year study period.

For each citation, CMS uses the following four levels to
determine the seriousness of a deficiency: Level 1 (scope and
severity A–C) is “no actual harm with potential for minimal
harm.” This level of deficiency has the potential for causing
no more than a minor negative impact on the residents. Level
2 (scope and severity D–F) is “no actual harm with potential
for more than minimal harm that is not immediate jeopardy.”
Level 3 (scope and severity G–I) is “actual harm that is not
immediate jeopardy.” Level 4 (scope and severity J–L) is
“immediate jeopardy to resident health or safety.”

The initial inspection of the data revealed that the sample
sizes for Level 1 are small across the F-Tags: 4 (0.2%) in
F-223, 4 (0.3%) in F-224, 114 (1.4%) in F-225, 505 (6.5%)
in F-226, and none in F-221, 3 (0.04%) in F-314, with the
total of 630 (2.2%) for all the six F-Tags. Following the clas-
sification approach used in a recent U.S. GAO (2019) report,
we decided to combine Level 1 and Level 2 into one level
entitled hereafter as “no actual harm.” However, this does
not suggest that no harm was caused to a resident as explained
under Table 2. The three seriousness levels used in the study
are described in Table 2.

The CMS data set distinguishes each F-Tag with regard to
whether the deficiency citation was issued as a result of a
standard survey or complaint investigation. This distinction
between standard surveys and complaint investigations as a
data element in the data set allowed us to conduct the com-
parative analysis at the core of the study. Sample size distri-
bution is presented in Table 3. However, in a small number of
F-Tags, the deficiency citation was issued as a result of both
standard survey and complaint investigation. A total of 2,731
citations (9% out of 28,390) were excluded from the data
subset, including 223 (12.1%) in F-223, 196 (12.4%) in
F-224, 864 (10.9%) in F-225, 659 (8.5%) in F-226, 75 (4.3%)
in F-221, and 714 (9.5%) in F-314. We excluded these F-Tags
from the analysis because they cannot be exclusively catego-
rized as either standard survey or complaint investigation.

Statistical Analysis

Cumulative link mixed models (CLMMs; Christensen,
2015), a novel statistical modeling framework appropriate
for studies with ordinal response variables and clustered
observations, were adopted with the logit link function to
investigate how the citation types correlate with the scope
and severity levels. For the “cumulative link” part of the
analysis, we adopted the negative binomial regression frame-
work because it has the least constraints on the data distribu-
tion and therefore provides the most flexibility (Agresti,
2010). The “mixed model” part of the analysis allows us to
have a more accurate inference on the effect of citation types,
which is taken as the sole fixed effect, with the grouped cor-
relation structure accounted for (Snijders & Bosker, 2011).
Considering the variation in the use of nursing home defi-
ciency citations (Castle et al., 2007), besides the citation type
(standard survey, complaint investigation) as the sole fixed
effect, the state and the inspection year were taken as random
effects to account for the correlations between observations.
Data analysis was carried out using R 3.6.1, and statistical
significance was set at p < .05.

Results

Among the core mistreatment deficiency citations (see
Table 3), abuse/involuntary seclusion (F-223) and neglect/
misappropriation (F-224) had more complaint investigations

Table 2. Elder Mistreatment Deficiency Citations’ Scope and Severity Level Used in the Study.

Scope and severity level Category Description

Levels 1 and 2 B–F No actual harm with potential for minimal harm and no actual
harm with potential for more than minimal harm

Level 3 G–I Actual harm that is not immediate jeopardy
Level 4 J–L Immediate jeopardy to resident health or safety

Note. Levels 1 and 2 “no actual harm” consist of a combination of “no actual harm with potential for minimal harm” and “no actual harm with potential
for more than minimal harm that is not immediate jeopardy,” respectively. In addition, the scope and severity letter “A” are not displayed in the table
because it was not included in the data set received by the authors from Centers for Medicare & Medicaid Services; thus, letter “A” was not part of the
study’s analysis.

912 Journal of Applied Gerontology 41(4)Liu et al. 5

(79% and 76%) than standard surveys (21% and 24%), and
close to 60% of all F-Tags were issued at the scope and
severity level 1 or 2. Investigate/report allegations (F-225)
and develop/implement abuse/neglect policies (F-226) were
split equally between complaint investigations (55% and
47%) and standard survey (45% and 53%), and the majority
of all the deficiency citations were found to be at the scope
and severity level 1 or 2 (over 90%). Among the secondary
mistreatment deficiency citations, more were investigated
during standard survey (83% for physical restraints [F-221]
and 71% for pressure ulcers [F-314]), and the majority of the
deficiency citations were found to be at the scope and sever-
ity level 1 or 2 (97% and 77%).

For all six F-Tags, complaint investigations were more
likely to result in higher scope and severity level compared
with standard surveys (p < .001). The comparison of scope
and severity level between standard survey and complaint
investigation is reported in Table 4.

Discussion

The study findings improve our understanding of the seri-
ousness (i.e., scope and severity) of mistreatment deficiency
citations issued by SSAs during complaint investigations in
comparison with the same deficiency citations when issued
during standard surveys. The analysis of this CMS national

Table 3. Sample Size of Mistreatment Deficiency Citations by Citation Type and Scope and Severity Level.

Citation type

Scope and severity level

No actual harm Actual harm Immediate jeopardy Total

F-223
Standard survey 241 49 47 337 20.85%
Complaint investigation 713 332 234s 1,279 79.15%
Total 954 381 281 1,616
59.03% 23.58% 17.39%
F-224
Standard survey 231 61 40 332 23.90%
Complaint investigation 583 220 254 1,057 76.10%
Total 814 281 294 1,389
58.60% 20.23% 21.17%
F-225
Standard survey 3,106 27 51 3,184 45.02%
Complaint investigation 3,617 58 214 3,889 54.98%
Total 6,723 85 265 7,073
95.05% 1.20% 3.75%
F-226
Standard survey 3,679 38 67 3,784 53.19%
Complaint investigation 2,881 138 311 3,330 46.81%
Total 6,560 176 378 7,114
92.21% 2.47% 5.31%
F-221
Standard survey 1,348 11 12 1,371 82.69%
Complaint investigation 255 20 12 287 17.31%
Total 1,603 31 24 1,658
96.68% 1.87% 1.45%
F-314
Standard survey 3,889 937 39 4,865 71.45%
Complaint investigation 1,369 494 81 1,944 28.55%
Total 5,258 1,431 120 6,809
77.22% 21.02% 1.76%
Total
Standard survey 12,494 1,123 256 13,873 54.07%
Complaint investigation 9,418 1,262 1,106 11,786 45.93%
Total 21,912 2,385 1,362 25,659
85.40% 9.29% 5.31%

Note. Mistreatment deficiency citations classified in the Centers for Medicare & Medicaid Services data set as both standard survey and complaint
investigation (n = 2,731) were excluded from the data subset used in the study, and thus they are not displayed in the table.

Liu et al. 9136 Journal of Applied Gerontology 00(0)

data set revealed that in all of the six mistreatment deficiency
citations examined, complaint investigations were more
likely to result in a higher scope and severity level citation
than standard surveys. In addition, a higher number of F-223
(abuse/involuntary seclusion) and F-224 (neglect/misappro-
priation) deficiency citations were the result of complaint
investigations, whereas a higher number of F-221 (physical
restraints) and F-314 (pressure ulcers) were issued during
standard survey. Although our data do not allow us to exam-
ine it, one possibility is that physical restraints and pressure
sores are more easily discovered during standard surveys,
but evidence of mistreatment is more readily detected during
complaint investigations when reported directly to SSA.

We believe that the findings from our study are important
in terms of residents’ and families’ potential ability to hold
nursing homes accountable for mistreatment. As mentioned
earlier, investigations of complaints are generally conducted
closer to the occurrence of the alleged mistreatment and
more likely to be truly unannounced. When an investigation
is conducted in a timely manner, evidence is more likely to
be available to investigators to support issuing a citation at a
higher scope and severity level (U.S. General Accounting
Office, 1999; U.S. GAO, 2011). This is important because
close to two thirds of nursing home residents are estimated to
have cognitive impairment (Gaugler et al., 2014) and delayed
investigation limits their ability to recall details from mis-
treatment incidents. Moreover, during a complaint investiga-
tion, surveyors focus on gathering information directly
related to specific information included in a complaint. This
is different from a standard survey, where a standardized pro-
tocol instructs surveyors to interview a sample of residents
on a broad range of care-related problems. The U.S. GAO
(2019) and other researchers (e.g., Stevenson, 2005, 2006)
have recognized that residents and families might be embar-
rassed or afraid to report mistreatment. Many frail residents,
who are physically dependent on staff for daily personal
care, may fear being perceived as troublemakers if they
report abuse or neglect. Therefore, when a complaint is filed
with SSA, it is reasonable to assume that in many cases the
resident and/or their family may have reached a “breaking

point,” where the mistreatment situation has become intoler-
able and more severe in nature.

Implications for CMS and SSA Oversight

Knowing that complaint investigations of mistreatment alle-
gations generally tend to result in citations with higher seri-
ousness compared with standard surveys warrants closer
attention by CMS to the complaint investigation process and
citations. Government reports suggest that when substanti-
ated mistreatment determined to have lower seriousness is
not adequately addressed, it may escalate to more serious
harm (OIG, 2019c; Office of the Legislative Auditor, 2005).
Specific to complaint investigations, CMS’s guidance to
state surveyors on Complaint Procedures (described in the
agency’s State Operations Manual, July 19, 2019) states that
one of the primary objectives of the federal complaint pro-
cess is prevention. It highlights the importance of identifica-
tion and correction of less serious complaints “to prevent the
escalation of these problems into more serious situations that
would threaten the health, safety, and welfare of the individ-
uals receiving the service.”

CMS should also work with SSAs’ Regional Offices to
strengthen SSAs’ ability to investigate and detect more seri-
ous mistreatment during standard surveys. This could be
achieved, for example, through increase in SSAs’ staffing
levels, unannounced on-site surveys, strengthening survey-
ors’ specialized training in detection of serious mistreatment,
and supervisory reviews of surveys. Other areas in which
SSAs could strengthen their ability to detect serious mis-
treatment during standard surveys are based on areas of
weakness identified previously as contributing to understate-
ment. These include, among others, a large number of inex-
perienced surveyors (U.S. General Accounting Office, 2003),
surveyors with weak investigative skills (U.S. GAO, 2008),
poor investigations and documentation of deficiencies, lim-
ited quality assurance systems, and inadequate audit of sur-
veyors (U.S. General Accounting Office, 2003; OIG, 2019a).
Furthermore, SSA workforce shortages (U.S. GAO, 2009)
and staff turnover (OIG, 2019a) need to be addressed, such

Table 4. Cumulative Link Mixed Models Parameters.

Standard survey threshold parameters Comparison parameters

No actual harm | Actual harm Actual harm | Immediate jeopardy Complaint investigation

Estimate SE Z value
p-value
(>|z|) Estimate SE Z value

p-value
(>|z|) Estimate SE Z value

p-value
(>|z|)

F-223 0.52 (0.24) 2.21 .0273 2.02 (0.24) 8.33 <.0001 0.76 (0.15) 4.97 <.0001
F-224 1.03 (0.21) 4.88 <.0001 2.28 (0.22) 10.42 <.0001 0.69 (0.15) 4.66 <.0001
F-225 3.82 (0.23) 16.64 <0.0001 4.14 (0.23) 17.87 <.0001 0.98 (0.14) 7.04 <.0001
F-226 3.88 (0.24) 15.96 <.0001 4.35 (0.25) 17.71 <.0001 1.38 (0.12) 11.56 <.0001
F-221 4.22 (0.34) 12.51 <.0001 5.22 (0.38) 13.68 <.0001 1.98 (0.31) 6.39 <.0001
F-314 1.41 (0.13) 10.90 <.0001 4.27 (0.15) 28.14 <.0001 0.61 (0.07) 9.23 <.0001

914 Journal of Applied Gerontology 41(4)Liu et al. 7

as through increased funding and guidance to SSAs, as these
factors have been identified as contributing to inadequate
oversight of nursing homes.

In addition, due to the fact that many residents are afraid
of retaliation when considering whether to file a mistreat-
ment complaint against the nursing home (Robison et al.,
2007), CMS should increase SSAs’ efforts to educate con-
sumers about their right to be free from staff retaliation
(CMS, 2017) and strengthen SSAs’ enforcement of this right
(i.e., through the issuance of F-585 “right to voice grievances
without reprisal” and F-600 “right to be free from abuse and
neglect” in the new federal regulations; the latter F-Tag is
applicable because CMS considers retaliation by staff as
abuse). In addition to filing a complaint, concerns related to
staff retaliation can be made to LTCO office, which can
engage in advocacy aimed at protecting the resident against
staff retaliation. This, in turn, may increase the number of
consumers who will be less afraid to report mistreatment
allegations with surveyors during standard surveys. Another
important but largely overlooked means to strengthen sur-
veyors’ ability to detect more serious mistreatment during
standard surveys is through the use of pertinent information
from resident and family satisfaction surveys. When crafted
and administered by independent contractors, such informa-
tion could alert surveyors to potential mistreatment other-
wise not on SSA’s radar during standard surveys (Ejaz et al.,
2003; U.S. GAO, 2016; Williams et al., 2016).

Bridging a Gap in Care Compare’s Five-Star
Quality Rating System

Although citations issued as a result of SSA’s complaint
investigations are factored into CMS’s Care Compare’s (U.S.
GAO, 2016; called Nursing Home Compare prior to
September 3, 2020) Five-Star Quality Rating System, the
rating itself, when calculated, does not distinguish citations
as arising from standard surveys versus complaint investiga-
tions. As a result, the public, researchers, and policymakers
remain limited in their ability to discern the extent to which
the five-star ratings of nursing homes (i.e., both the rating
under health inspections component and under the overall
rating) consist of mistreatment citations resulting from
reports made by residents and their families through com-
plaints, as opposed to those identified by state surveyors dur-
ing standard surveys. Strengthening CMS’s transparency
pertaining to this issue is important because significant dif-
ferences may exist in the scope, severity, and nature of mis-
treatment violations identified by SSA when captured during
the two distinct oversight strategies (Peterson et al., 2020).

Implications for the Office of LTCO

As described in Title VII of the Older Americans Act, our
findings reinforce the critical role of LTCO in fulfilling its
varied duties pertaining to residents and families’ concerns

related to mistreatment in nursing homes. Previous studies
found that the presence of the LTCO at the nursing home
predicted more complaints and better quality of care (Allen
et al., 2003; Cherry, 1991; Nelson, 1995), while abuse reports
and their substantiation also increased (Nelson et al., 1995).
Ombudsman staff often learn about consumers’ concerns,
investigate and help resolve them, and assist consumers in
filing a complaint with the SSA (Bloemen et al., 2015).
However, many LTCO programs are chronically under-
funded and have very low ombudsman-to-LTC bed ratios
(Cooney, 2019; National Ombudsman Reporting System,
2019). Therefore, our study findings indirectly support the
need for increased state and federal funding to LTCO pro-
grams so that a larger number of nursing home residents will
have a well-trained ombudsman advocate when they need
them and thus have stronger protections of their right to be
free from mistreatment (Office of the New York City
Comptroller, 2020).

Limitations and Future Research

The CMS deficiency citation data used in the study have
limitations. First, nursing home characteristics potentially
affecting the study results, such as size, staffing levels, hos-
pital-based, ownership status, and Medicare/Medicaid-
reimbursed rates, were not included in the data set. Future
studies could examine these and other characteristics as
well as identify complaints filed with the assistance of
ombudsmen to detect potential differences in mistreatment
seriousness in further detail. In addition, the data set did not
include F-Tags of scope and severity level A, and we also
excluded the 9% deficiency citations issued as a result
of both standard survey and complaint investigation.
Nonetheless, the study findings did not change when addi-
tional analyses were conducted, no matter whether the 9%
F-Tags were categorized as part of standard survey or com-
plaint investigation.

Furthermore, the data set did not allow us to identify who
filed the mistreatment complaint, nor did we know who the
perpetrators were. Researchers and government reports have
repeatedly recommended distinguishing staff abuse of resi-
dents from harmful resident-to-resident incidents in CMS
deficiency citation and tracking systems (Caspi, 2017;
Castle, 2011; Lachs et al., 2016; U.S. GAO, 2019, 2021), but
CMS has not yet to bridge this major gap. Finally, it was not
possible to distinguish certain subtypes of mistreatment from
others within certain F-tags. For example, it was not possible
to disentangle deficiency citations pertaining to financial
exploitation alone from other forms of mistreatment under
F-224, F-225, and F-226. In the new CMS federal regula-
tions and oversight process, which went into effect on
November 28, 2017, financial exploitation is separated from
other types of abuse. Future research should use data from
the new F-Tag (F-602 Free from misappropriation/exploita-
tion) to shed light on the number of financial exploitation

Liu et al. 9158 Journal of Applied Gerontology 00(0)

citations and their scope and severity levels by standard sur-
vey versus complaint investigations.

Conclusion

Mistreatment complaints are an important barometer of the
quality of care and safety in nursing homes. The study find-
ings should reinforce federal agencies’ recognition of the
critical role of consumers’ complaints in CMS and SSA
oversight process (OIG, 2017). The higher scope-severity
level determinations in citations issued during complaint
investigations at the national level suggest that state investi-
gators were able to establish sufficient evidence to deter-
mine that more serious harm was experienced by the resident
due to the nursing homes’ violation of federal regulation.
The study findings identified the need for CMS and SSAs to
develop strategies to improve state surveyors’ ability to
detect mistreatment, especially during standard surveys. As
importantly, stronger and more proactive measures should
be implemented by nursing homes to listen to the voice of
residents to promptly and adequately respond to their and
their families’ care-related concerns while protecting them
from retaliation. This, in turn, could ultimately enable resi-
dents to realize their federal right to live in safe nursing
homes.

Acknowledgments

We would like to thank Dr. Greg Arling at Purdue Nursing for
reviewing the manuscript and providing extensive suggested edits
for improvement. We would also like to thank the School of
Nursing, University of Minnesota for their support of this study, as
well as Lori Smetanka, executive director, the National Consumer
Voice for Quality Long-Term Care, and Richard Mollot, executive
director, Long Term Care Community Coalition, for their assis-
tance in the interpretation of the main findings.

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect
to the research, authorship, and/or publication of this article.

Funding

The authors disclosed receipt of the following financial support for
research, authorship and/or publication of the article: This research
is funded by institutional review board of University of Minnesota
and purdue University.

Ethics Committee or Institutional Review Board
Approval

Institutional Review Board (IRB) of University of Minnesota (IRB
protocol number 00002737) Purdue University (IRB protocol num-
ber 1902021794) determined that the study is exempt from IRB
review and approved the study.

ORCID iD

Pi-Ju Liu https://orcid.org/0000-0002-8762-5120

Data Availability Statement

The data that support the findings of this study are available from
the authors.

References

Agresti, A. (2010). Analysis of ordinal categorical data (2nd ed.).
Wiley.

Allen, P. D., Klein, W. C., & Gruman, C. (2003). Correlates of
complaints made to the Connecticut long-term care ombuds-
man program. Research on Aging, 25(6), 631–654. https://doi.
org/10.1177/0164027503256691

Berzlanovich, A. M., Schopfer, J., & Keil, W. (2012). Deaths due to
physical restraint. Deutsches Artzteblatt International, 109(3),
27–32. https://doi.org/10.3238/arztebl.2012.0027

Bloemen, E. M., Rosen, T., Clark, S., Nash, D., & Mielenz, T. J.
(2015). Trends in reporting of abuse and neglect to long term
care ombudsmen: Data from the national ombudsman reporting
system from 2006 to 2013. Geriatric Nursing, 36(4), 281–283.
https://doi.org/10.1016/j.gerinurse.2015.03.002

Caspi, E. (2017). A federal survey deficiency citation is needed for
resident-to-resident aggression in U.S. nursing homes. Journal
of Elder Abuse and Neglect, 29(4), 193–212. https://doi.org/10
.1080/08946566.2017.1333939

Castle, N. G. (2011). Nursing home deficiency citations for abuse.
Journal of Applied Gerontology, 30(6), 719–743. https://doi.
org/10.1177/0733464810378262

Castle, N. G. (2006). Mental health outcomes and physical restraint
use in nursing homes {private}. Administration and Policy in
Mental Health and Mental Health Services Research, 33(6),
696–704. https://doi.org/10.1007/s10488-006-0080-0

Castle, N. G., Engberg, J., & Men, A. (2007). Variation in the use
of nursing home deficiency citations. Journal of Healthcare
Quality, 29(6), 12–23. https://doi.org/10.1111/j.1945-1474
.2007.tb00220.x

Castle, N. G., Ferguson-Rome, J. C., & Teresi, J. A. (2015).
Elder abuse in residential long-term care: An update to the
2003 National Research Council report. Journal of Applied
Gerontology, 34(4), 407–443. https://doi.org/10.1177/0733
464813492583

Centers for Medicare & Medicaid Services. (2017). Revisions to
the state operational manual (SOM) chapter 7. https://www.
cms.gov/Regulations-and-Guidance/Guidance/Transmittals/
Downloads/R161SOMA.pdf

Centers for Medicare & Medicaid Services. (2019). State opera-
tions manual: Chapter 5—Complaint procedures. https://
www.cms.gov/Regulations-and-Guidance/Guidance/Manuals/
Downloads/som107c05pdf.pdf

Centers for Medicare & Medicaid Services. (2020a). Glossary.
https://www.cms.gov/apps/glossary/search.asp

Centers for Medicare & Medicaid Services. (2020b). Nursing
home enforcement. https://www.cms.gov/Medicare/Provider-
Enrollment-and-Certification/SurveyCertificationEnforcement/
Nursing-Home-Enforcement

Centers for Medicare & Medicaid Services. (2020c). State opera-
tions manual: Chapter 2—The certification process. https://
www.cms.gov/Regulations-and-Guidance/Guidance/Manuals/
Downloads/som107c02.pdf

Cherry, R. L. (1991). Agents of nursing home quality of care:
Ombudsmen and staff ratios revisited. The Gerontologist,
31(3), 302–308. https://doi.org/10.1093/geront/31.3.302

916 Journal of Applied Gerontology 41(4)Liu et al. 9

Christensen, H. R. B. (2015). Analysis of ordinal data with cumu-
lative link models—Estimation with the R-package ordinal.
https://cran.r-project.org/web/packages/ordinal/vignettes/clm_
article.pdf

Cooney, V. (2019). Increased staffing in ombudsman office could
help prevent elder abuse. https://www.house.leg.state.mn.us/
sessiondaily/SDView.aspx?StoryID=13689

Di Maio, V. J. M., & Di Maio, T. G. (2002). Homicide by decubitus
ulcers. American Journal of Forensic Medicine and Pathology,
23(1), 1–4. https://doi.org/10.1097/00000433-200203000-
00001

Edsberg, L. E., Langemo, D., Baharesstani, M. M., Posthauer, M.
E., & Goldberg, M. (2014). Unavoidable pressure injury: State
of the science and consensus outcomes. Journal of Wound
Ostomy Continence Nursing, 41(4), 313–334.

Ejaz, K. F., Straker, J. K., Fox, K., & Swami, S. (2003). Developing
a satisfaction survey for families of Ohio’s nursing home
residents. The Gerontologist, 43(4), 447–458. https://doi.
org/10.1093/geront/43.4.447

Gaugler, J. E., Yu, F., Davila, H. W., & Shippee, T. (2014).
Alzheimer’s disease and nursing homes. Health Affairs, 33(4),
650–657. https://doi.org/10.1377/hlthaff.2013.1268

Hansen, K. E., Hyer, K., Holup, A. A., Smith, K. M., & Small,
B. J. (2019). Analyses of complaints, investigations of allega-
tions, and deficiency citations in United States nursing homes.
Medical Care Research and Review, 76(6), 736–757. https://
doi.org/10.1177/1077558717744863

Harrington, C., Mollot, R., Edelman, T. S., Wells, J., &
Valanejad, D. (2020). U.S. nursing home violations of inter-
national and domestic human rights standards. International
Journal of Health Services, 50(1), 62–72. https://doi.org
/10.1177/0020731419886196

Harrington, C., Schnelle, J. F., McGregor, M., & Simmons, S. F.
(2016). The need for higher minimum staffing standards in
U.S. nursing homes. Health Services Insights, 9, 13–19. https://
doi.org/10.4137/HSI.S38994

Lachs, M. S., Teresi, A. T., Ramirez, M., Van Haitsma, K., Silver,
S., Eimicke, J. P., Boratgis, G., Sukha, G., Kong, J., Besas, A.
M., Luna, M. R., & Pillemer, K. A. (2016). The prevalence
of resident-to-resident elder mistreatment in nursing homes.
Annals of Internal Medicine, 165(4), 229–236. https://doi.
org/10.7326/M15-1209

Lindbloom, E. J., Brandt, J., Hough, L. D., & Meadows, S. E. (2007).
Elder mistreatment in the nursing home: A systematic review.
Journal of the American Medical Directors Association, 8(9),
610–616. https://doi.org/10.1016/j.jamda.2007.09.001

Miles, S. H., & Irvine, P. (1992). Deaths caused by physical
restraints. The Gerontologist, 32(6), 762–766. https://doi.
org/10.1093/geront/32.6.762

National Ombudsman Reporting System. (2019). Number of LTC
facility beds per paid program staff (FTEs). https://agid.acl.
gov/DataGlance/NORS/

Nelson, H. W. (1995). Long-term care volunteer roles on trial:
Ombudsman effectiveness revisited. Journal of Gerontological
Social Work, 23(3–4), 25–46. https://doi.org/10.1300/J083
V23N03_03

Nelson, H. W., Huber, R., & Walter, K. L. (1995). The relation-
ship between volunteer long-term care ombudsmen and regula-
tory nursing home actions. The Gerontologist, 35(4), 509–514.
https://doi.org/10.1093/geront/35.4.509

Office of Inspector General. (2006). Nursing home complaint inves-
tigations. https://oig.hhs.gov/oei/reports/oei-01-04-00340.pdf

Office of Inspector General. (2017). A few states fell short in timely
investigation of the most serious nursing home complaints:
2011–2015. U.S. Department of Health & Human Services.
https://oig.hhs.gov/oei/reports/oei-01-16-00330.pdf

Office of Inspector General. (2019a). Incidents of potential abuse
and neglect at skilled nursing facilities were not always
reported and investigated. U.S. Department of Health & Human
Services. https://oig.hhs.gov/oas/reports/region1/11600509.
pdf

Office of Inspector General. (2019b). Trends in deficiencies at nurs-
ing homes show that improvements are needed to ensure the
health and safety of residents. https://oig.hhs.gov/oas/reports/
region9/91802010.pdf

Office of Inspector General. (2019c). CMS guidance to State Survey
Agencies on verifying correction of deficiencies needs to be
improved to help ensure the health and safety of nursing home
residents. https://oig.hhs.gov/oas/reports/region9/91802000.
pdf

Office of the Legislative Auditor. (2005). Evaluation report:
Nursing home inspections. https://www.auditor.leg.state.mn.
us/ped/pedrep/0505all.pdf

Office of the New York City Comptroller. (2020). Protecting our
most vulnerable: The case for strengthening New York’s long
term care ombudsman program. https://comptroller.nyc.gov/
wp-content/uploads/documents/Ombudsman-Report.pdf

Omnibus Budget Reconciliation Act of 1987. P.L. 100–203. Subtitle
C. The Nursing Home Reform Act. 42 U.S.C. 1395i-3(a)–(h)
(Medicare); 1396r (a)–(h)(Medicaid).

Pemberton, M. (2011). Why are bedsores not being prioritized in
nursing care? British Journal of Nursing, 20(15), Article S26.

Peterson, L. J., Bowblis, J. R., Jester, D. J., & Hyer, K. (2020). U.S.
state variation in frequency and prevalence of nursing home
complaints. Journal of Applied Gerontology, 40(6), 582–589.
https://doi.org/10.1177/0733464820946673ADDINMendeley
BibliographyCSL_BIBLIOGRAPHY

Robison, J., Gruman, C., Reed, I., Shugrue, N., Kellett, K., Porter,
M., Smith, P., & Jo, A. (2007). Connecticut long-term care
needs assessment: Connecticut long term care ombuds-
man program. University of Connecticut’s Health Center.
https://health.uconn.edu/dev-aging/wp-content/uploads/
sites/102/2017/03/ombudsman_program.pdf

Snijders, T. A. B., & Bosker, R. J. (2011). Multilevel analysis: An
introduction to basic and advanced multilevel modeling (2nd
ed.). SAGE.

Stevenson, D. G. (2005). Nursing home consumer complaints and
their potential role in assessing quality of care. Medical Care,
43, 102–111. https://doi.org/10.1097/00005650-200502000-
00003

Stevenson, D. G. (2006). Nursing home consumer complaints
and quality of care: A national view. Medical Care Research
and Review, 63(3), 347–368. https://doi.org/10.1177/1077
558706287043

Thompson, M. (2001, June). Fatal neglect. Time. http://content.
time.com/time/magazine/article/0,9171,136745,00.html

Troyer, J. L., & Sause, W. L. (2011). Complaints against nursing
homes: Comparing two sources of complaint information and
predictors of complaints. The Gerontologist, 51(4), 516–529.
https://doi.org/10.1093/geront/gnr023

Liu et al. 91710 Journal of Applied Gerontology 00(0)

U.S. General Accounting Office. (1999). Nursing homes: Complaint
investigation processes often inadequate to protect residents.
https://www.gao.gov/assets/hehs-99-80.pdf

U.S. General Accounting Office. (2003). Nursing home quality:
Prevalence of serious problems, while declining, reinforces
importance of enhanced oversight. https://www.gao.gov/
assets/gao-03-561.pdf

U.S. Government Accountability Office. (2008). Nursing homes:
Federal monitoring surveys demonstrate continued understate-
ment of serious care problems and CMS oversight weaknesses.
https://www.gao.gov/assets/gao-08-517.pdf

U.S. Government Accountability Office. (2009). Nursing homes:
Addressing the factors underlying understatement of serious
care problems requires sustained CMS and state commitment.
https://www.gao.gov/assets/gao-10-70.pdf

U.S. Government Accountability Office. (2011). Nursing homes:
More reliable data and consistent guidance would improve
CMS oversight of state complaint investigations. https://www.
gao.gov/assets/gao-11-280.pdf

U.S. Government Accountability Office. (2016). Consumers could
benefit from improvements to the Nursing Home Compare

website and Five-Star Quality Rating System. https://www.
gao.gov/assets/690/681138.pdf

U.S. Government Accountability Office. (2019). Nursing homes:
Improved oversight needed to better protect residents from
abuse. https://www.gao.gov/assets/700/699721.pdf

U.S. Government Accountability Office. (2021). COVID-19 in
nursing homes: HHS has taken steps in response to pandemic,
but several GAO recommendations have not been implemented.
https://www.gao.gov/assets/gao-21-402t.pdf

Williams, A., Straker, J. K., & Applebaum, R. (2016). The nurs-
ing home give star rating: How does it compare to resident
and family view of care? The Gerontologist, 56(2), 234–242.
https://doi.org/10.1093/geront/gnu043

Wood, S., & Stephens, M. (2003). Vulnerability to elder abuse and
neglect in assisted living facilities. The Gerontologist, 43(5),
753–757. https://doi.org/10.1007/BF01514551

Yon, Y., Ramiro-Gonzalez, M., Mikton, C. R., Huber, M., &
Sethi, D. (2019). The prevalence of elder abuse in insti-
tutional settings: A systematic review and meta-analysis.
European Journal of Public Health, 29(1), 58–67. https://doi.
org/10.1093/eurpub/cky093

Week 1 Assignment/2 Organizational Justice and Workplace Bullying.pdf

societies

Article

Organizational Justice and Workplace Bullying: Lessons
Learned from Externally Referred Complaints
and Investigations

Annabelle M. Neall 1,* , Yiqiong Li 2 and Michelle R. Tuckey 3

����������
�������

Citation: Neall, A.M.; Li, Y.; Tuckey,

M.R. Organizational Justice and

Workplace Bullying: Lessons Learned

from Externally Referred Complaints

and Investigations. Societies 2021, 11,

143. https://doi.org/10.3390/

soc11040143

Academic Editors: Margaret Hodgins,

Patricia Mannix McNamara,

Loredana Ivan, George Jennings and

Gregor Wolbring

Received: 30 April 2021

Accepted: 30 November 2021

Published: 3 December 2021

Publisher’s Note: MDPI stays neutral

with regard to jurisdictional claims in

published maps and institutional affil-

iations.

Copyright: © 2021 by the authors.

Licensee MDPI, Basel, Switzerland.

This article is an open access article

distributed under the terms and

conditions of the Creative Commons

Attribution (CC BY) license (https://

creativecommons.org/licenses/by/

4.0/).

1 School of Psychology, University of Queensland, Brisbane 4072, Australia
2 UQ Business School, University of Queensland, Brisbane 4072, Australia; [email protected]
3 UniSA Justice & Society, University of South Australia, Adelaide 5001, Australia;

[email protected]
* Correspondence: [email protected]

Abstract: Workplace bullying is a serious psychosocial risk which, when poorly managed, results in
detrimental outcomes for individuals, organizations, and society. Some of the most common strate-
gies for addressing bullying within the workplace centre on attempts to document and contextualise
the bullying situation—that is, the internal complaint and investigation process. Scholarly inquiries
of these investigative mechanisms, however, are limited, and most have neglected the influence of or-
ganisational justice as an underpinning mechanism in explaining complainant dissatisfaction. Using
evidence from 280 real-life cases of workplace bullying lodged with a peak work, health, and safety
agency, we identify how organizational justice manifests in externally referred cases of workplace
bullying. Specifically, we match complainant evaluations of the internal complaint and investigation
handling process to domains of organisational justice, thereby ascertaining potential threats to efforts
to effectively manage and prevent bullying in the workplace. Four types of justice—distributive,
procedural, interpersonal, and informational—were identified within the cases. Specifically, in cases
of workplace bullying where distributive justice is not upheld (usually by virtue of unsubstantiated
claims), the way in which information is gathered and decisions are made (procedural), the way in
which the parties are treated (interpersonal), and the timeliness and validity of explanations provided
(informational) are all cited by complainants as key factors in their decision to escalate the complaint
to an external investigative body. These results signal the need for timely, clear, and compassionate
investigative processes that validate complainants’ experiences and serve as a tool for rebuilding
trust and repairing damaged relationships in the workplace.

Keywords: workplace bullying; organisational justice; investigations; complaints

1. Introduction

Workplace bullying is a challenging occupational hazard that negatively impacts
the health and wellbeing of individuals, teams, and organizations [1]. While there is no
universally agreed upon definition for workplace bullying, it is commonly regarded as
harassing, offending, or socially excluding actions, repeated regularly over a period of time
(usually six months), between two or more parties who hold different levels of power. Data
from Nielsen et al.’s [2] meta-analysis indicated that approximately 15% of employees are
exposed to some level of workplace bullying globally.

The harmful and detrimental effects of workplace bullying are widely documented in
the academic literature. For individuals, exposure to workplace bullying can trigger mental
and physical health problems, burnout, strain, decreased job satisfaction, and diminished
organizational commitment [3]. The effects of bullying are not just limited to targets—
witnesses of workplace bullying are more likely to report decreased self-esteem and less-
ened job, co-worker, supervisor, and health satisfaction as compared to employees who

Societies 2021, 11, 143. https://doi.org/10.3390/soc11040143 https://www.mdpi.com/journal/societies

Societies 2021, 11, 143 2 of 18

do not witness bullying [4,5]. Organizational functioning is also impaired by workplace
bullying, with several studies demonstrating a significant correlation between workplace
bullying and increased absenteeism and turnover, and decreased productivity [6–8].

As the detrimental effects of workplace bullying become more evident, scholarly
investigation has turned to how best to manage and prevent bullying and its negative
effects. A key strategy in addressing workplace bullying is encouraging victims and
other affected parities (e.g., bystanders and health and safety representatives) to report
bullying behaviour when it occurs via internal reporting mechanisms (e.g., lodging a
complaint) [9,10]. Following a complaint, an internal investigation may be launched to
establish facts about the circumstances leading to the bullying event(s), to provide an
opportunity for all parties to put the events in context, and to determine a response to
the matter that may or may not lead to disciplinary action, in line with the organization’s
behavioural or code of conduct policy [11].

The efficacy of the internal complaint and investigation process remains dubious [9],
with little scholarly study of the extent to which internal workplace bullying complaints are
successful and the factors that allow these internal complaints to be resolved successfully.
For those victims whose cases are not handled or poorly handled, they may resort to
external government authorities for investigation and intervention. In these externally
referred cases, issues concerning organizational justice are highly relevant, because it is
likely for these victims to feel unfairness and frustration about how their cases are dealt by
the internal complaint and investigation process.

Organizational justice is concerned with fairness and consideration in the work-
place [12] and can be defined as “the extent to which employees are treated with justice at
their workplace” [13]. Put simply, “people expect to be treated fairly, and they experience a
shock to their systems when they believe this has not occurred” [14]. In applying the orga-
nizational justice lens to understand workplace bullying, the ‘shock to the system’ might
come from exposure to the behaviour itself and, separately, from how the organization fails
to handle the bullying complaint. In workplace bullying literature, organizational justice
has typically been explored as either an antecedent of perceptions of bullying [15–17],
or as a moderator of the effects of bullying [18]. In contrast, little is known about how
justice reactions or perceptions of injustice are triggered within the bullying complaints
and investigation process. This issue is especially important in the cases for which the
internal investigation process failed to result in a satisfactory solution. Addressing this gap
in knowledge is vital for informing intelligence-led responses to bullying that does occur
within organizations.

Accordingly, in the present research, we analyse a sample of workplace bullying
complaints that were escalated from internal complaints to an external investigation with
the local work health and safety agency. Our research question is: How do issues of organi-
zational justice manifest among the externally refereed workplace bullying cases in relation
to the internal workplace bullying complaint and investigation process? In answering this
question, the contributions of this study are two-fold. First, we generate new knowledge
on the internal workplace bullying complaints and investigation process in a way that has
meaning at the level of individual experiences of the complainants, while also grounding
the findings in key principles of organizational justice to identify transferable insights. Sec-
ond, investigating how justice manifests within the investigation and complaints processes
for the externally refereed workplace bullying cases provides clear intervention points
for optimising the way organizations handle workplace bullying complaints. This has
many flow-on effects, including (a) curbing interpersonal conflict in its early stages (thus
preventing the need for escalation to criminal prosecution), (b) correcting the culture and
norms around what is considered acceptable workplace behaviour between colleagues
(thereby reducing likelihood of bullying in the future), and (c) restoring employee justice
perceptions (which have been shown to have impact on employee performance, wellbeing,
and interpersonal relationships) [19,20].

Societies 2021, 11, 143 3 of 18

1.1. Workplace Bullying Complaint and Investigation Process

A cornerstone strategy for managing workplace bullying often occurs after the bully-
ing has taken place, via a form of complaint or reporting instrument—that is, “when an
individual employee notifies the organization that there is a specific bullying incident with
the expectation of a response to address their situation” [9]. Typically lodged by the target
of the bullying behaviour, complaints can take the form of an informal conversation with, or
email to, management, through to official requests for investigation lodged with a human
resources representative, or even application to Stop Bullying through an employment
tribunal [9,21]. Complaints are often regarded as a means to seek redress from an ordeal,
where actions are implemented following rounds of inquiry. However, research has noted
significant variation in the rate of registered incidents of bullying within organisations
comparative to self-labelled prevalence rates, suggesting several barriers to the reporting
of bullying [22].

While lodging a complaint can itself be a lengthy and nonlinear process, complaints
often trigger a thorough analysis of the offending events. Specifically, a workplace bullying
investigation may be regarded as “a process to determine the facts prior to decision making
by the employer” [23]. Similar to bullying complaints, there have been few academic
studies of the purpose, process, and practice of workplace bullying investigations, despite
a clear, repeated mandate from health and safety bodies to implement fair, objective, and
thorough investigative processes [24]. Potential outcomes of the investigation processes
are varied, from informal attempts to restore the working relationship, through to an
official hearing within the organization, or legal action driven by outside council to apply
punitive consequences to the individuals or organization responsible for the bullying
behaviour [9,25]. However, there is a growing consensus that investigations primarily
serve to demonstrate organisational compliance with policy, legislation, and regulations,
rather than to establish factors and reprimand perpetrators or modify the contributing
individual/system factors that underpin bullying behaviour [23].

When implemented effectively, the process of handling complaints and undertaking
investigations has the potential to re-establish fairness and provides an opportunity to
signal that bullying behaviour is taken seriously in the organization and will not be toler-
ated [26]. If the complaints are not handled or handled poorly by the internal investigation
process, this will have significant impact on employee perceptions of organizational justice,
not only in relation to the outcome of their bullying complaints but also how the complains
is dealt and how they are treated. In other words, organizational justice is a key factor in the
resolution of bullying situations, yet few studies have explored the role of organizational
theory as an explanatory mechanism.

1.2. Organizational Justice and Workplace Bullying

According to fairness heuristic theory, employees rely on justice-relevant information
to understand, evaluate, and react to what is happening in their organization [27–29]
especially when faced with uncertainty [30]. In relation to workplace bullying, employees
who have been exposed to bullying may be unsure if and how such issues can be addressed,
if they can trust their organization in managing the risk and mitigating the negative impact
of bullying, and if lodgement of a bullying complaint will bring negative ramifications
upon their career, personal life, and health.

The justice literature primarily centres on experienced justice, manifested in four
dimensions, namely distributive, procedural, interpersonal, and informational justice [31].
In relation to our study with a focus on internal investigations of workplace bullying for
externally referred workplace bullying complaints, experienced justice describes employees’
perceptions of the treatment they have received from their organizations and the significant
parties in their organizations after lodging a formal bullying complaint internally and prior
to their decision to resort externally. In other words, experienced justice, in our research
context, captures to what extent complainants perceive the investigation procedure itself

Societies 2021, 11, 143 4 of 18

(including the outcome), personal treatment received during the investigation process, and
accounts and explanations offered about the investigation process as fair.

We expect that all four dimensions of justice [31] could manifest in employees’ expe-
rience of the internal investigations of workplace bullying. Procedural justice, referring
to the extent to which decision-making procedures and processes are perceived to be
consistent, bias-free, accurate, correctable, ethical, and representative [32], manifests in
how a bullying complaint is made and is investigated and the way in which an outcome
is determined. for example, the transparency of the process through which decisions
are made and the opportunity for all parties to have meaningful input [33]. Distributive
justice, defined as the perceived fairness of the decision outcomes or the distribution of
outcomes judged in principles of equity, equality, and need [34], is concerned with the
consequences of, or specific decisions made arising from, the investigation. Interpersonal
justice relates to whether individuals are treated in a polite, dignified, and respectful
way [19] by authorities or third parties involved in implementing procedures or determin-
ing outcomes. In relation to our study, interpersonal justice reflects how a complainant is
treated with interpersonal sensitivity throughout the complaint and investigation process.
Informational justice perceptions emerge upon justifications and explanations provided by
organizational authorities [19], with individuals assessing to what extent such justifications
and explanations are accurate, sufficiently justified, and delivered in a timely manner by
means of honest communication. In relation to internal investigation, informational justice
perceptions focus on the accounts and explanation offered to complainant about reasons as
to how certain investigation procedures were chosen and implemented and how certain
investigation outcomes were finalized.

Although the four dimensions of experienced justice perceptions are worthy of study
in their own right, and each has contributed substantially to employee attitudinal and
behavioural outcomes, it does not necessarily mean all four types of justice must be in place
simultaneously to result in justice perceptions. For example, employees are more willing
to accept an unwanted or undesirable outcome if they believe that the decision-making
process used to arrive at such decision was conducted in accordance with the six procedural
justice rules [32], termed the “fair-process” effect [35–38]. When employees perceive
interpersonal and informational justice, employees typically view decisions as fairer, even
if they are unfavourable/undesirable [37]. This suggests that a favourable distributive
outcome is not the only way to make victims feel fair in relation to internal investigation.

These four dimensions of experienced justice perceptions begin to develop after vic-
tims lodge a bullying complaint and are largely influenced by perceptions of predicted or
anticipatory justice, defined as expectations of justice in future events [39,40]. For example,
employees will try to predict whether investigation procedures will be fair (procedural),
if investigation outcome will be fair and impartial (distributive), if they will be treated
respectfully (interpersonal), and if they will be offered with justified explanation (informa-
tional) when they lodge a complaint. A handful of empirical studies show that anticipatory
justice impacts how employees react to organizational changes [30]. If employees question
an organizations’ capacity in handling internal investigation in a fair way and anticipate
the absence of any or all of the four dimensions of justice, this may prevent them from filing
a formal complaint. Rudman, Borgida, and Robertson [41] found that women are more
reluctant to report sexual harassment when they suspect that the investigation process
will not be organized in a fair manner. Victims also have less confidence in an organi-
zations’ ability to deal effectively with workplace bullying incidents, particularly when
perpetrators are their supervisors [42]. In contrast, when employees have high perceptions
of foreseen organizational justice, they are more likely to pursue a lawsuit in response to
sexual harassment in workplace [43].

1.3. Evaluating Bullying Investigations through a Justice Lens

The bullying complaint process has been investigated through various lenses to date,
including disciplinary matters, health and safety, and whistleblowing [9], but scarcely

Societies 2021, 11, 143 5 of 18

through the justice context. Only a handful of studies have explored the notion that victims
of workplace bullying evaluate their experience of the complaint/investigation/outcome
process through a justice lens [16,42,44] and even fewer have connected the complaint/
investigation process to organisational justice—that is, when targets of workplace bullying
make a complaint and do not achieve justice restoration, they feel dissatisfied [45,46]. For
example, Jenkins and colleagues’ [33] study of Australian bullying complainants suggests
that participants who submitted a workers’ compensation claim perceived less organisa-
tional justice in the way their complaint of bullying was managed internally than those
participants who did not submit a claim. However, it is not yet known what forms of
justice are violated through the workplace bullying complaint and investigation process. It
is evident from previous studies of organisational justice that complainants who do not
experience the outcome they want or expect experience a violation of distributive justice,
and that this may apply to cases of workplace bullying. Similarly, when complainants
perceive the investigation process to be biased or dissatisfactory, they may seek recourse to
restore procedural justice. It is possible, however, that there may be multiple violations
within a single case (i.e., possible joint effects)—for example, a complainant who feels
their grievance has not been taken seriously (i.e., poor procedural justice) may seek further
intervention if they believe the organization did not take appropriate action (i.e., a form
of distributive justice) and that their complaint was not conducted in line with how other
complaints had been handled (i.e., informational justice). Similarly, complainants whose
claims are not substantiated (i.e., a lack of distributive justice) may feel scared and unsup-
ported during the investigation process (i.e., interpersonal justice) and find it difficult to
raise a complaint within the organisation (i.e., procedure justice).

The focus of this study was thus to understand how the externally referred workplace
bullying complaints have been handled and subsequently investigated within organiza-
tions through an organizational justice lens. The data represent a rich and contextually
detailed source on how externally referred workplace bullying complainants evaluate the
internal complaint and investigation process, in terms of the restoration of organizational
justice. Specifically, the data were sourced from case files where Australian employees felt
they had been exposed to workplace bullying and who sought further intervention from
the state work health and safety regulator (i.e., SafeWork SA) following an unsuccessful
internal investigation process. These participants filed a request for investigation into
alleged workplace bullying, and the documentation from subsequent investigations was
transcribed and analysed. Examining the data in this way provides insights in optimising
the process for addressing workplace bullying by capturing the key forms of organisational
justice that foster resolution and constructive outcomes.

2. Method
2.1. Data Collection

Data for this study were sourced from the peak health and safety body of one Aus-
tralian state—SafeWork South Australia (SA). SafeWork SA provides advice and education
on work health and safety, enforces legislation pertaining to health safety and workplace
relations, and instigates workplace incidents or suspected violations of the health and
safety act, including a failure to maintain a psychologically healthy workplace. Between
January 2006 and March 2013, over 1200 requests for investigation into alleged workplace
bullying were opened with SafeWork SA. Upon examination of the quality of the case
file materials, it was decided to utilise files from 2010 onwards (totalling 540 cases). Of
these, 55 files were still under investigation with SafeWork SA at the time of data collection,
and the outcome of the investigation was not confirmed. A further 140 files were not
available for transcription, as the hard copy files could not be located. Twenty-nine cases
contained insufficient information about the case and were therefore deemed unsuitable
for examination. An additional 27 cases were related to non-bullying matters, such as
non-psychosocial occupational health and safety breaches, fraud, WorkCover claims, and
common assault. In these cases, investigators from SafeWork SA assisted the complainant

Societies 2021, 11, 143 6 of 18

with finding the correct government department to deal with their issue. These cases were
subsequently removed from the analysis. Thus, a total of 289 files were available, which
were transcribed and analysed in the current study.

The cases contained a variety of information related to the case—we captured evidence
provided by the complainant, email communications, and records and results of the
investigation process from the SafeWork SA investigator. Some cases had more than
480 pages of information; others contained as few as 10 pages. The selected cases were
electronically transcribed onsite by an independent agency and then imported into NVivo
V10 for analysis.

2.2. Data Analysis

Data were thematically analysed [47] according to employee perceptions of bully-
ing complaint and investigation processes and outcomes. Specifically, data were initially
coded to determine the characteristics of the complaint/investigation (i.e., perpetrator role,
reporting personnel, method of complaint submission) and the core aspects of the com-
plaint/investigation process (i.e., the outcome of internal investigation and the reason for
escalation to SafeWork SA). A subsequent round of coding identified different evaluations
of the internal complaint and investigation process (e.g., complaint was not taken seriously,
or investigation was biased towards perpetrator), which were dually coded by the lead
author and research assistant. In the final stage, evaluations were paired to corresponding
domains of organisational justice, with any discrepancies in matching resolved through
discussion with all authors. All data were coded exhaustively (i.e., all forms of justice,
methods of complaint submission, reporting persons, and perpetrators were recorded).

3. Results
3.1. Complaint Characteristics

Data were analysed to determine the gender of the complainant(s) and alleged per-
petrator(s), along with the industry of work where the complaint occurred (see Table 1).
This analysis revealed that women were more likely to be targets of workplace bullying
(n = 166, males = 117) but less likely to perpetrate bullying behaviour (n = 118, males = 161).
Additionally, the most prevalent industries to raise a complaint with SafeWork SA were
health and community services (n = 43); accommodation, cafes, and restaurants (n = 23);
and education (n = 21).

Table 1. Gender of Complainant, Alleged Perpetrator in Relation to the Complaint and Industry that Complaint Occurred.

Complainant Gender n Complainant Industry n

Female 166
Accommodation, cafes,

and restaurants
23

Health and
community services

43

Female and Male 1
Agriculture, forestry,

and fishing
3 Manufacturing 12

Male 117 Communications services 1 Mining 1

Not disclosed 5 Construction 10 Personal and other services 6

Perpetrator Gender n
Cultural and

recreational services
7

Property and
business services

5

Female 118 Education 21 Retail trade 20

Male 161
Electricity, gas, and

water supply
1 Transport and storage 12

Multiple persons (Gender
not specified)

8 * Finance and insurance 7 Wholesale trade 2

Not disclosed 37
Government

administration/defence
10 Not disclosed 105

NOTE: In 68 cases, there was no evidence of an internal complaint being submitted. In 43 cases, the outcome of the complaint was not
disclosed (in case files). In 15 cases, no complaint was not lodged (confirmed) (no reason given). * Refers to number of cases, not individuals.

Societies 2021, 11, 143 7 of 18

The working relationship between the complainant and the alleged perpetrator was
also coded. In 54 cases, the relationship between the complainant and alleged perpetrator
could not be ascertained due to incomplete information; thus, 235 cases were included in
this analysis. In over two-thirds of the cases, a direct supervisory figure was the alleged
perpetrator of the bullying. Managers were the most reported direct supervisory figure,
along with supervisors and team leaders. In one-sixth of cases, the perpetrator was reported
to be a higher-level manager—that is, the alleged perpetrator held some form of power
over the victim but did not necessarily oversee their work on a day-to-day basis. Examples
included CEOs, employers, owners, and directors. Fifteen percent of complainants alleged
that they had been bullied by a colleague or co-worker, while just under five percent of
cases reported bullying from an employee who held another role within the company—for
example, union representatives, human resources personnel, or work health and safety
delegates. More specific descriptions of the complainant/perpetrator relationship and
exemplar quotes can be found in Table 2.

Table 2. Position of Alleged Perpetrator in Relation to the Complaint.

Position of Alleged
Perpetrator

n of Cases Exemplar Quotes

Direct supervisor 165

[Manager]’s behaviour in general is unprofessional; I have heard rumours that
he has spread about me. He is secretive about promotional opportunities and
appears to have favourites, I find [Manager] to be abrupt, rude, untruthful and

he rarely seems to know the answers to anything you ask him.
During the second week of May there were times when his face blushed in

anger while screaming to me. He was losing patience.

Higher level manager 41

CEO received an email of complaint from client; I have not met or committed a
time to respond. CEO aggressively questioned why I did not ring him. I

repeatedly explained I was away and in hospital. He continued the line of
questioning as to who made the commitment—I did not know. He did not

relent, and I became upset. He then demanded a resolution meeting the next
day. He demanded I apologise, or I receive a letter of warning.

When I have tried to discuss with the Director of Nursing, she has made which
effect how I perform my work role—decisions made without my consent or

consultation, she either avoids me, or speaks down to me telling me I have no
idea and that the decision she has made is reasonable. She is rigid and her

decisions are non-negotiable.

Peer or subordinate 35

[Perpetrators] and select others started having pizza on a Friday for lunch and
they would come in and eat it in the office in front of me (I was never asked

whether I wanted to join in).
Since that initial incident most of my colleagues, led by [Colleague], have

deliberately excluded me from the normal workplace interactions and
activities by ignoring my initial greetings (e.g., good morning, hi mate etc.),

going to smoko without me, ceasing conversations when I approach the group,
acting as though I am invisible.

Other roles
(HR, WHS/Union Rep)

10

There have been numerous occasions where [Perpetrator] has used his
influence and position as Union Representative to bully me. [Perpetrator] has

threatened management with industrial action, if management allow me to
work in the control room even though I am fully qualified and deemed

competent to do so.

Multiple Persons
(not specified)

10 *

Ever since I started in marination, [Perpetrator], [Perpetrator], [Perpetrator],
[Perpetrator] and [Perpetrator] and just recently [Perpetrator] have been

harassing every day as soon as we are in the factory. They all sit together and
[expletive] together. You can tell they are talking about you because they keep

turning around or looking over her shoulder giving daggers at you or
sometimes you can hear some of what they say.

Company/Management 4
In general I feel this management has worked subversively, gradually

increasing expectation and workload.

NOTE: In 54 cases, the role of the perpetrator was not disclosed within the cases. * Refers to number of cases, not individuals.

Societies 2021, 11, 143 8 of 18

In 137 cases (out of a possible 174 with sufficient information), the person to whom they
initially reported their experience of bullying to internally was recorded. Exemplar quotes
and a breakdown of the roles of the reporting person are detailed in Table 3. Complainants
were most likely to report bullying their grievance to a direct supervisory figure, while
nearly one in three complaints were lodged with a person holding a health and safety-
related role within the company (e.g., WHS/HR/Union Representative). Nearly one in
six complainants sought out an indirect or higher-level superior to lodge their complaint,
including the CEO, a board member, or director. A small percentage of complainants
specified that they could not make a complaint in their organization, either because they
were not sure of the process for reporting internally, or because the alleged perpetrator was
the assigned reporting person:

Table 3. Position of the Person Who the Bullying Was Reported to Internally.

n of People Exemplar Quotes

Direct Relationship
(Line supervisor)

Manager (Assistant, Café, Centre, Factory, Line,
Maintenance, Practice, Principal, Operations,

Regional, Site, State, Store)
34

I made an appointment with the Principal—told him it was a
formal complaint and asked him to act on it.

Feeling embarrassed, I talked to my boss and told him what
has happened and what has been said. He said that we will

have a meeting involving the people. Meeting never
happened and got avoided.

Employer/Boss 9
Management 5

Supervisor (Agency, Safety) 5

Indirect Relationship
(Higher-order manager)

Director(s)
(Acting, Assistant, Executive, Nursing) 10

I sent a letter of complaint to the CEO.
I sent an email to [Director] again with my concerns of the
treatment I was receiving from [Perpetrator], that he was

totally ignoring me and would speak the girls then ask them
to pass it on to me. Again, no reply.

CEO/Chairman/Board 15
Owner(s) 3

Other Roles

HR (Department, Director, Manager,
Representative, Leader)

39 The following week I reported this to HR who were
unsupportive, and I was even asked “what did you do to

bring this on?”
I immediately brought this to the attention of my onsite

union representative.

Perpetrator (Directly) 9
OHS (Manager, Officer, Representative) 6

Colleague 1
Union Representative 1

NOTE: In 79 cases, the position of the person who the bullying was reported to internally could not be coded, as no internal complaint was
lodged. In 63 cases, the position of the person who the bullying was reported to internally was not disclosed.

“I made a complaint about how bad things were in [Location] to management
only to be told that I’m not to go above [Perpetrator] again. I get a feeling of
hopelessness thinking that how do I make a complaint about my bullier to the
bullier himself?”

Data regarding the method in which the complaint was submitted were also coded
(see Table 4 for an overview of the methods). Complaints about workplace bullying
were commonly made verbally (n = 38), including a small percentage who confronted the
perpetrator directly:

“I confronted [Perpetrator]—who denied it—until I explained I watched her on
the camera.”

A further 95 records were identified where complaints were lodged via hand-written
or electronic mediums, including telephone conversations, formal letters, and emails. Many
complainants noted that their complaint went unacknowledged:

“I brought my side to the attention of [President] in an email but it was never acknowledged.”

A small number of complainants followed their organization’s official procedure with
an incident report form, grievance system, or workplace bullying request (n = 5). There
were no cases that mentioned an online or anonymous reporting system, nor the services
of a specialised grievance officer.

Societies 2021, 11, 143 9 of 18

Table 4. Method of Submitting Complaint Internally.

Complaint Mechanism n of Cases Exemplar Quotes

Verbal Discussion/Complaint 38
I then made a complaint to the operating manager regarding my

manager’s appalling behaviour, but he virtually laughed in my face but
stated he would talk to [Perpetrator].

Email 32
On or about [Date], the applicant informed his manager via email of the
events that had taken place and the ongoing implications to his welfare.

Letter 31
Sent a letter of complaint to [Manager] with above incidences, nothing

was done.

Formal Complaint 20
My two formal complaints in writing were not handled per the

[Organizations]’s policies and procedures.

Phone Call 12
I called and reported to [Person], one of the managers, and he arranged a
meeting for [Date]. When I went to [Location] to meet him, [Manager] was

not there. I was told he was sick.

Meeting 5

A recent meeting with [Manager] on this matter was on [Date] in person
where I requested [Manager] to ask [Perpetrator] to cease his bullying and
harassing behaviour towards me where [Manager] told me I had to accept

that it the way [Perpetrator] is.

Grievance Form 3
The only formal mechanism available to do so by my employer is

“Administrative Grievance Procedure”. There is no “bullying”
complaints mechanism.

Incident Report 2
I attempted to mitigate my situation by lodging an incident report which

went to an external investigator and was not sustained

NOTE: In 68 cases, method of complaint submission was not applicable, as no internal complaint was lodged. In 65 cases, the method of
complaint submission was not disclosed in the case files. In 7 cases, there was confirmation of no internal complaint being lodged; hence,
there was no method of complaint submission.

3.2. Manifestation of Organizational Justice in Internal Workplace Bullying Complaints
and Investigations

When lodging a request for investigation with SafeWork SA, complainants were asked
to provide a detailed written account of the bullying situation in their workplace, including
their original complaint and what action (or inaction) has already been implemented or
considered by the organization. Each evaluation of the complaint and/or investigation
process made by complainants was recorded and coded to types of violations to organi-
sational justice (i.e., distributive, procedural, interpersonal, and informational) [12]. An
overview of the manifestation of organisational justice violations across the cases is shown
in Table 5. In evaluating the internal complaint process, complainants were most likely
to note issues with procedural justice (i.e., lack of sincerity, action, and communication),
while internal investigations triggered perceived violations to distributive justice (i.e., not
substantiating complaints, inappropriate action).

Table 5. Percentage of Cases Represented by Violations to Justice Type.

Process
No. of Cases (with

Sufficient Information)
Justice Type

No. of
Evaluations

No. of Cases
Percentage of Cases Where

Justice Type Manifested

Complaints 163

Procedural 130 124 76%

Distributive 67 65 39%

Interpersonal 39 39 24%

Informational 24 24 15%

Investigations 75

Distributive 55 52 69%

Procedural 46 38 50%

Interpersonal 16 13 17%

Informational 13 13 17%

Societies 2021, 11, 143 10 of 18

Table 6 overviews (in detail) the manifestation of organizational justice in workplace
bullying complaints process. Procedural justice was regularly threatened where due pro-
cess was not followed, i.e., where there was no response or action from the organization
following submission of the complaint (which also triggered perceptions of poor distribu-
tive justice), where the complaint was not properly acknowledged or escalated, where
there was perceived pressure to withdraw the complaint, and when confidentiality of
the complaint process was breached. Similarly, a lack of support from the organization
activated perceptions of diminished interpersonal justice, stemming from perceived pres-
sure to withdraw the complaint and/or accept inappropriate actions resulting from the
complaint process. We explored the potential for a relationship between perceptions of
organisational justice manifestation to perpetrator type and to investigator role but did not
observe any significant associations.

Table 6. Triggers of Justice Reactions in the Internal Complaint Process.

Evaluation Justice Type n of Evaluations Exemplar Quotes

Complaint was
lodged internally

The organization took no action as a
result of a complaint being lodged

Procedural; Distributive 36

Tried to report this to [Manager] on
several occasions only to be told to “Get

used to it, that’s just the way
[Perpetrator] is”

I have bought this up with [Manager]
when trying to get opportunity to defend

myself. I have seen or heard no action
from it.

Complainant felt their grievance was
not taken seriously

(e.g., told to ignore behaviour, nothing
could be done, accept behaviour)

Procedural 25

Union dismissed my complaint as trivial
and took no action whatsoever

It does not matter how often I complain
the leading hand and supervisor do not

listen or believe me when I tell them.

Complainant did not receive a
response following submission

of complaint
Procedural; informational 24

My final complaint has not been
acknowledged despite my repeated

follow up emails and [Organization]’s
policy that management will thoroughly

and promptly investigate every
reported incident

I am lodging this complaint because the
grievance process I initiated with my
employer on [Date] has been ignored.

Inappropriate action was taken as a
result of complaint

(e.g., hours reduced, supported
perpetrator, not handled per policy,

complainant moved to different area of
organisation, threatened complainant)

Distributive; procedural;
interpersonal

21

After bringing complaints forward I was
blamed for this and then they tried to

transfer me to a different store.
My complaint against my manager has

not been dealt with properly

Complaint was
lodged internally

Advised to meet with perpetrator/Sort
it out themselves/Go to SWSA

Procedural; interpersonal 8
I approached the Head of . . . over 6mths
ago in regards to the above and was told

to “sort it out between yourselves”

Complainant felt scared/unsupported
during process

Interpersonal 6

I have suffered anxiety since my
employment at [Organization], to the
point where I was too fearful to take

further action. [Perpetrator]’s behaviour
was unlike anything I have ever

experienced in the workplace and I
would not wish this experience

upon anyone.

Complainant reported that
confidentiality was breached

Procedural 5

You should note that the complaint letter
my partner wrote to [HR Manager] was
then forwarded to the [Perpetrator] and

[Perpetrator], two of the people the
complaint letter was about . . . she said

that [HR Manger] was within his rights to
do this as they are the managers involved

with my work cover claim.

Organisation denied receipt of
complaint/presence of bullying

Distributive; interpersonal 4
[Company] is unwilling to retrain or

acknowledge any victimisation occurred
on site

Bullying behaviour continued even
after action taken

Distributive 3

I have raised my issues with
management on numerous occasions
[Dates] even though in meetings with

management I was assured that serious
steps will be taken to resolve the issue

but still the problem exists

Societies 2021, 11, 143 11 of 18

Table 6. Cont.

Evaluation Justice Type n of Evaluations Exemplar Quotes

Complaint was
not lodged

Lack of trust/confidence in
investigator or investigative system Procedural 6

I did not go to [Higher body] as I am one
on a list of people and feel nothing comes

of it

Did not feel there was a valid internal
reporting mechanism Procedural 5

Since I was harassed by the HR
representative and my department

manager I felt as though I had nowhere
to turn. Who was I meant to report to?

Felt no action would be taken Distributive 2
[Complainant] explains he did not raise
the issues with [Company] as he feels

nothing will happen

Fear of consequences Distributive 1

I have no confidence in my workplace’s
system of dealing with this issue and feel
that by taking it to them it may threaten

my employment there

NOTE: In 68 cases, there was no evidence of internal complaint being submitted. In 43 cases, the outcome of the complaint was not
disclosed (in case files). In 15 cases, no complaint was not lodged (confirmed) (no reason given).

In addition, Table 7 overviews bullying complainants’ evaluations of the internal
investigation process and corresponding threats to organizational justice. Similar to the
complaint process, a failure to follow due process threatened perceived procedural justice,
expressed through difficulty in initiating investigations, continued exposure to bullying
behaviour following a completed investigation, and investigations that were biased, in-
complete, untimely, and fraudulent in nature (the latter two of which also threatened
informational justice). Poor outcomes following the investigation (such as a failure to
validate claims of bullying, continued exposure to workplace bullying, or reallocation to a
different area of the organization) jeopardised distributive justice.

Table 7. Triggers of Justice Reactions in the Internal Investigation Process.

Evaluation Justice Type n of Evaluations Exemplar Quotes

Organization did not substantiate
claims of bullying
following investigation

Distributive 17

I attempted to mitigate my situation by lodging a
formal complaint of bullying which went to an
external investigator and was not sustained. The
Executive Director told me I could lodge an
application for internal review which was
subsequently withdrawn by Workforce Division
and denied under Section 61 & 62 of the Public
Sector Act 2009.
As was discussed during our final meeting on
[Date] the outcome of the investigation was that
my complaint of bullying and harassment against
[Perpetrator] was not substantiated

Inappropriate action
(e.g., moved to different department,
complainant blamed for behaviour,
accused of bullying themselves,
position made redundant, attributed
claims to personality clashes)

Distributive; procedural; interpersonal 16

My treatment by [OHS/HR manager] was very
one sided in which representation in my defence
of sacking was denied. I could not talk back to
defend myself as he just would not listen or
entertain my defence.
In addition, [Complainant], and the other two
complainants consider that other employees who
were also of the same party as [Perpetrator], had
not been dealt with at all.
[Superior] objected to this proposal and had to
point out that simply moving [Perpetrator] to
another department was in no way a satisfactory
way of dealing with such a serious complaint.
Why should the victim have to be taken out of her
professional position, and at this stage of her
career, learn another role?

Investigation was not completed in a
timely matter/per company policy
(e.g., not all witnesses investigated,
did not adhere to policy, protective of
perpetrator, no action taken based
on findings)

Procedural; informational 13

I complained numerous times to [Supervisor]
verbally. My two formal complaints in writing
were not handled per the schools’ policies and
procedures.
At no stage has [Organization] or [Chairman]
followed Policy or Procedures related to the
following (which also forms part of the grievance
I made to my employer).

No outcome/response
from investigation

Procedural 10

The outcome of the internal investigation
provided me with no outcome or resolution.
An investigation was instigated but no outcome
noted in file.

Societies 2021, 11, 143 12 of 18

Table 7. Cont.

Evaluation Justice Type n of Evaluations Exemplar Quotes

Bullying continued
despite investigation

Distributive 9

I have followed [Company]’s policies and
procedures in regard to having these matters
addressed internally but the situation has
continued over a sustained period of time.

Appropriate action taken
(e.g., mediation, perpetrators
retrained or disciplined)

Distributive; procedural 7

Investigation revealed that there was a
breakdown in communication and the working
relationship. [Company] has sent expectation
letters to all managers and to the alleged bully.
[Company] is supplying further coaching to
managers and alleged bully.

Investigation occurred;
claims substantiated

Distributive 6 Claims of bullying were substantiated by SWSA.

NOTE: In 185 cases, there was no evidence of an internal investigation having occurred. In 27 cases, an investigation occurred, but no
record of the outcome was disclosed in file. In 2 cases, an investigation did not occur before complainant lodged a request for investigation
with SafeWork SA.

4. Discussion

In this study, we analysed 289 real-life workplace bullying complaints lodged with a
peak state regulatory body in order to understand how justice reactions are triggered in the
internal bullying complaint and investigation process. In examining complaints that were,
by definition, unable to be resolved internally, our sample comprised many observations
regarding the manifestation of organizational injustice in the way that bullying situations
are handled within organizations. We found evidence that all four types of justice—
distributive, procedural, interpersonal, and informational—play a role in the way that
targets of bullying appraise the internal processes for lodging and investigating complaints.
In other words, in situations where distributive justice is not upheld (such as for the
complaints in our sample that were escalated outside of the organization), the way in
which information is gathered and decisions are made (procedural), the way in which the
parties are treated (interpersonal), and the timeliness and validity of explanations provided
(informational) are all cited by complainants as key factors in their decision to escalate
the complaint.

4.1. Contribution to Knowledge

In making a complaint, targets of bullying are arguably attempting to seek fair reso-
lution of an unfair situation. Based on our findings, both the processes involved, and the
outcomes of the bullying complaint and investigation procedure contain significant trig-
gers of justice reactions that are important to understand. Traditionally, the organizational
justice literature has emphasised the concepts of equity, equality, and need as a foundation
for distributive justice perceptions. Equity relates to the ratio of work inputs and outputs,
for example, the distribution of rewards according to individual effort, often in comparison
with others. Equality is defined as equal access to resources and/or equal distribution of
rewards to all members of a group regardless of individuals’ contributions. Need reflects
favourable allocation of rewards according to individuals’ specific needs [32,48]. In our
study, complainants experienced lapses in all three concepts of distributive justice—namely,
inequitable investigation practices (e.g., a failure to investigate all perpetrators), inequitable
processes (e.g., a lack of organisational response to bullying complaints), and poor need
allocation (i.e., removal or termination of the complainant instead of the perpetrator).

In terms of procedural justice, procedures should be consistent across persons and
time (consistency), based on valid information (accuracy), neutral and impartial (bias
suppression), allow for a mechanism to appeal the procedure and correct poor decisions
(correctability), uphold moral and ethical values (ethicality), and be representative of the
concerns and needs of all persons affected (representativeness) [49,50]. In our study, threats
to procedure justice were noted, whereby investigations were not conducted in a valid (i.e.,
failure to investigate all perpetrators or investigate according to company policy), impartial
(i.e., biased investigations and pressure was placed on complainants to withdraw their
grievance), or ethical manner (i.e., breaches in confidentiality). Additionally, there were few

Societies 2021, 11, 143 13 of 18

courses of correctability for poor decisions (i.e., being investigated for bullying themselves,
transfers, or no action taken upon receipt of complaint or conclusion of investigation) and
no references to representativeness or consistency.

Interpersonal justice, or the importance of receiving polite, dignified, respectful, and
proper interpersonal treatment [19] with sensitivity [51] was highlighted in the current
study, where complainants felt scared, unsupported, and pressured during the investigative
process and deeply unsatisfied with the outcome in many cases (including where they
themselves were disciplined or transferred throughout the company).

In terms of information justice, an authority figure is expected to be candid [19] and
provide thorough, reasonable, timely, and specific information [31]. This surfaced in our
findings by virtue of untimely investigations and failures to adhere to company policy
for dispute resolutions, although it is likely that many complainants failed to acquire the
information that they required to further their case.

Together, our findings regarding the manifestation of justice in the complaint and
investigation process reveal a new element in how workplace bullying complainants
articulate their experience of the complaint’s investigation. Despite the perception of having
been exposed to unfair (bullying) treatment, targets of bullying who make a complaint
expect (or hope) to be able to resolve that treatment in a fair way with the support of their
employer organization. When they are unable to do so, complainants, who already hold
little to no control over their external environment, experience diminished wellbeing and
commitment to the organisation [52]. According to our findings, complainants seem to seek
a sense of validation. It is possible that, for targets of bullying, validation is a core aspect of
the way in which an organization responds to bullying. This was evident in evaluations
of the complaint and investigation process that highlighted a desire to be taken seriously,
to be acknowledged in an appropriate and timely manner, to be treated with dignity and
respect, and ultimately, for their claim of bullying to be substantiated (which only occurred
in 2 out of 280 cases).

Workplace bullying is a type of stressor that functions as a threat to the self [53]
interfering with the basic need for positive regard by others [54]. In this way, seeking
validation through the complaint and investigation process may have the intended function
of restoring the sense of self that was diminished through ongoing bullying exposure.
Extrapolating from our data, it may be that complainants see the process and the outcomes
of the complaint investigation as having the potential to re-establish a lost sense of positive
regard; when this does not happen, it stands out as being very important to complainants.
The role of the workplace bulling complaint and investigation process in validating the
sense of self and positive regard is an interesting phenomenon hinted at by our data that
we believe would be worthwhile exploring in future research.

4.2. Practical Implications

Our findings suggest that bullying complainants covet a validated sense of self in
the way that the complaint is handled and investigated. This is not always possible in
terms of the outcome of a complaint/investigation. It is, however, a worthy and more
achievable goal in terms of the process through which bullying complaints are handled.
Indeed, safeguarding procedural and interpersonal justice will reduce the stress associated
with making a complaint even if the outcome is not what the complainant wants [14].

Although not all claims of bullying will (or should) be substantiated, efforts should
made to reduce distributive injustice. At the heart of this lies a conceptual shift in how
organisations view (and manage) bullying and other forms of harmful interpersonal
behaviour. Specifically, all negative workplace behaviours (even those that do not meet the
legal or official definition of workplace bullying) should be acknowledged, recorded, and
addressed. At its core, workplace bullying is an organisational problem that manifests as
negative interactions between two or more co-workers, and even minor forms of workplace
bullying (i.e., incivility) pose a significant risk to employee health and safety [55]. Taking
care to capture all contextual and nuanced facets of the bullying complaints provides a rich

Societies 2021, 11, 143 14 of 18

source of data regarding the underlying contributors to bullying specific to that workplace.
From this, improvements can be made the work design, coordination, and management
of work, improving outcomes for employees, regardless of whether the claim of bullying
is substantiated.

The most valuable conduit to re-validating employees’ sense of self lies in improving
the processes, policies and investigative structures that contribute to procedural, inter-
personal, and informational justice. Organisations should declare a clear commitment
to protect employees from psychological risks (such as workplace bullying) but avoid
catch-all phrases such as ‘zero tolerance’, as it is not feasible 100% of the time [56] and thus
may threaten procedural justice if not properly enforced.

Ideally, workplace behavioural policies should be informative and direct, incorpo-
rating definitions and examples of what bullying is and is not (updated regularly to
reflect new forms of bullying behaviour, e.g., cyberbullying), references to legislation and
regulations (where available), a clear list of personnel who should be contacted if bul-
lying occurs (including personnel that do not directly oversee the complainant/target)
and the responsibilities and requirements of management and employees, including who
will conduct investigations if deemed necessary [10], thereby limiting potential threats to
informational justice.

Importantly, the internal investigation process (and associated policies) on bullying
should not exist just for the sake of meeting state health and safety regulations. The
presence of a policy and grievance procedure is futile without adhering to the conditions
outlined in it. Each investigation should be treated seriously, by clearly outlining the
consequences of bullying others and consulting with all appropriate parties. Responders
who rely solely on avoidant or transfer measures of responding to workplace bullying send
the message to workers that negative workplace behaviours are not only tolerated, but
potentially encouraged, and spark threats to interpersonal justice within complainants.

Optimising the complaint and investigation process through a justice lens also presents
an opportunity for novel approaches to complainant validation. Specifically, by focussing
on the needs of the harmed (i.e., the bullying targets), and investigating such incidents
collaboratively and with a shared decision-making tactic, allows capacity to rebuild trust
and encourage employee engagement and development at work [57].

4.3. Strengths, Limitations and Future Directions

A key strength of this study lies in the use of richly contextual data—drawing on real-
life case data from a large number of self-identified targets of workplace bullying allows
researchers to tap into the subjective dimension and sense-making process of this complex
issue [58]. Conversely, the dataset is inherently limited by the information available in
the SafeWork SA case files. SafeWork SA’s primary role is to investigate the transparency
and fairness of an organisations’ internal investigation process, and to ascertain whether
the organisation had policies and procedures in place to minimise the risk of exposure to
workplace bullying (in line with Occupational Health and Safety laws). This process did
not necessarily include thorough documentation about the actual investigation process (to
ensure it is fair and transparent). Consequently, some files provided limited or minimal
information about to whom and how complaints were submitted, why the complainant
sought outside intervention and how they evaluated the internal investigation process.
Accordingly, we risk the omission of other factors that underpin evaluation on complaint
and investigation processes that were not documented here. Ideally future studies should
collect this information directly (from the organisation).

Additionally, the data presented here is evidentiary of how ineffective investigations
play out—however it is imprudent to assume that abstaining from these practices or adopt-
ing contradictory actions will yield greater restoration of justice. Academically, evidence
of effective internal investigations is limited, as cases that are successfully managed are
usually done so informally, in house, and before the problem escalates to the point of formal
investigation. Thus, the process and substance of effective complaint and investigation

Societies 2021, 11, 143 15 of 18

procedures remains unclear. Future research should examine the investigation process
of organisations that report successful interventive measures (i.e., internal policies and
investigation processes), to supplement and contrast information on successful practices to
restore justice [59].

In Australia, legislation regarding workplace bullying was harmonised in 2014—one
year after the cases in our analysis were finalised. We note that since that time three key
pieces of guidance material have been released to guide appropriate intervention and
prevention of workplace bullying [24,60,61]. Thus, it is possible that a trickledown effect
of more effective investigative methods is now in place in these organisations—further
research should ascertain the effect of these materials on how investigations are conducted
and subsequent perceptions of organisational justice.

Finally, the data in this study does not allow for comparison or rating of the most
and least severe forms of organisational justice violation, nor a correlational or causational
connection to individual or organisational outcomes. However, such questions can be
readily addressed with appropriate sampling and study methodology.

4.4. Conclusions

Formal complaints investigation is often a core feature of an organisations’ response
to workplace bullying. When executed poorly, complainants of workplace bullying may
perceive diminished organisational fairness and justice, thereby triggering escalation to an
external body for further investigation. In identifying the challenges inherent to effective
internal investigation and resolution, we draw attention to key areas of consideration and
amendment to the internal investigation process (i.e., improved communication practices
between the organisation and complainant(s), fair transparent and timely investigative
processes, and greater organisational support for employees who bring grievances to
light). The nature and implementation of such processes merit further theoretical and
empirical investigation but hold great promise in addressing and reducing occurrences of
workplace bullying.

Author Contributions: Conceptualization, A.M.N., M.R.T. and Y.L.; methodology, M.R.T., Y.L. and
A.M.N.; software, A.M.N.; validation, M.R.T., Y.L. and A.M.N.; formal analysis, A.M.N.; investigation,
A.M.N., M.R.T. and Y.L.; resources, A.M.N. and M.R.T.; data curation, A.M.N.; writing—original
draft preparation, A.M.N., Y.L. and M.R.T.; writing—review and editing, A.M.N., M.R.T. and Y.L.;
visualization, M.R.T.; supervision, M.R.T.; project administration, A.M.N. and M.R.T.; funding
acquisition, M.R.T., Y.L. and A.M.N. All authors have read and agreed to the published version of
the manuscript.

Funding: This research was funded by SafeWork SA WHS Commissioned Research Grant, grant
name Developing a Workplace Bullying Risk Audit Tool.

Institutional Review Board Statement: The study was conducted according to the guidelines of the
Declaration of Helsinki and approved by the Ethics Committee of the University of South Australia
(0000034756).

Informed Consent Statement: Participant consent was waived due to the nature of data.

Data Availability Statement: Data can be made available upon request. Please address requests to
the corresponding author.

Conflicts of Interest: The authors declare no conflict of interest.

References
1. Einarsen, S.; Hoel, H.; Zaph, D.; Cooper, C.L. The concept of bullying and harassment at work: The European tradition. In Bulling

and Harassment in the Workplace: Developments in Theory, Research and Practice, 2nd ed.; Einarsen, S., Hoel, H., Zaph, D., Cooper, C.,
Eds.; CRC Press: Boca Raton, FL, USA, 2010; pp. 3–41.

2. Nielsen, M.B.; Matthiesen, S.B.; Einarsen, S. The impact of methodological moderators on prevalence rates of workplace bullying.
A meta-analysis. J. Occup. Organ. Psychol. 2010, 83, 955–979. [CrossRef]

3. Nielsen, M.B.; Einarsen, S. Outcomes of exposure to workplace bullying: A meta-analytic review. Work Stress 2012, 26, 309–332.
[CrossRef]

Societies 2021, 11, 143 16 of 18

4. Low, K.S.D.; Radhakrishnan, P.; Schneider, K.T.; Rounds, J. The experiences of bystanders of workplace ethnic harassment.
J. Appl. Soc. Psychol. 2007, 37, 2261–2297. [CrossRef]

5. Miner-Rubino, K.; Cortina, L.M. Working in a context of hostility toward women: Implications for employees’ well-being.
J. Occup. Health Psychol. 2004, 9, 107–122. [CrossRef]

6. Hoel, H.; Sheehan, M.J.; Cooper, C.L.; Einarsen, S. Organisational effects of workplace bullying. In Bulling and Harassment in the
Workplace: Developments in Theory, Research and Practice, 2nd ed.; Einarsen, S., Hoel, H., Zaph, D., Cooper, C., Eds.; CRC Press:
Boca Raton, FL, USA, 2010; pp. 129–148.

7. Høgh, A.; Clausen, T.; Bickmann, L.; Hansen, Å.M.; Conway, P.M.; Baernholdt, M. Consequences of workplace bullying for
individuals, organizations and society. In Pathways of Job-Related Negative Behavior; D’Cruz, P., Noronha, E., Baillien, E., Catley, B.,
Harlos, K., Hogh, A., Eds.; Springer: Singapore, 2021; pp. 177–200.

8. Eriksen, T.L.M.; Hogh, A.; Hansen, Ã….M. Long-term consequences of workplace bullying on sickness absence. Labour Econ. 2016,
43, 129–150. [CrossRef]

9. Thompson, N.; Catley, B. Managing workplace bullying complaints: Conceptual influences and the effects of contextual factors.
In Dignity and Inclusion at Work; D’Cruz, P., Noronha, E., Caponecchia, C., Escartín, J., Salin, D., Tuckey, M.R., Eds.; Springer
Nature: Singapore, 2018; Volume 3, pp. 109–146.

10. Vartia, M.; Leka, S. Interventions for the prevention and management of bullying at work. In Bullying and Harassment in the
Workplace: Developments in Theory, Research, and Practice, 2nd ed.; Einarsen, S., Hoel, H., Zapf, D., Cooper, C., Eds.; CRC Press:
Boca Raton, FL, USA, 2011; pp. 359–380.

11. Hoel, H.; Einarsen, S. Investigating complaints of bullying and harassment. In Bullying and Harassment in the Workplace:
Developments in Theory, Research and Practice, 3rd ed.; Einarsen, S., Hoel, H., Zapf, D., Cooper, C., Eds.; CRC Press: Boca Raton, FL,
USA, 2020; pp. 541–562.

12. Greenberg, J. Organizational justice: Yesterday, today, and tomorrow. J. Manag. 1990, 16, 399–432. [CrossRef]
13. Cropanzano, R.; Byrne, Z.S.; Bobocel, D.R.; Rupp, D.E. Moral virtues, fairness heuristics, social entities, and other denizens of

organizational justice. J. Vocat. Behav. 2001, 58, 164–209. [CrossRef]
14. Greenberg, J. Stress fairness to fare no stress: Managing workplace stress by promoting organizational justice. Organ. Dyn. 2004,

33, 352–365. [CrossRef]
15. Eisele, P. Organizational justice and workplace bullying: Validating two instruments and testing their joined relation with

wellbeing. Int. J. Bus. Soc. Sci. 2016, 7, 167–176.
16. Samsudin, E.Z.; Isahak, M.; Rampal, S.; Rosnah, I.; Zakaria, M.I. Organisational antecedents of workplace victimisation: The role

of organisational climate, culture, leadership, support, and justice in predicting junior doctors’ exposure to bullying at work.
Int. J. Health Plan. Manag. 2020, 35, 346–367. [CrossRef]

17. Seyrek, H.; Ekici, D. Nurses’ Perception of organisational justice and its effect on bullying behaviour in the hospitals of turkey.
Hosp. Pract. Res. 2017, 2, 72–78. [CrossRef]

18. Hsu, F.-S.; Liu, Y.-A.; Tsaur, S.-H. The impact of workplace bullying on hotel employees’ well-being: Do organizational justice
and friendship matter? Int. J. Contemp. Hosp. Manag. 2019, 31, 1702–1719. [CrossRef]

19. Bies, R.J.; Moag, J.S. Interactional justice: Communication criteria for fairness. In Research on Negotiation in Organizations;
Sheppard, B., Ed.; JAI Press: Greenwich, CT, USA, 1986; pp. 43–55.

20. Clay-Warner, J.; Reynolds, J.; Roman, P. Organizational justice and job satisfaction: A test of three competing models.
Soc. Justice Res. 2005, 18, 391–409. [CrossRef]

21. Rayner, C. Workplace bullying: Consequences, precursors, and best practices. In Proceedings of the Academy of Management
Meeting 2005, Honolulu, HI, USA, 5–10 August 2005.

22. Song, C.; Wang, G.; Wu, H. Frequency and barriers of reporting workplace violence in nurses: An online survey in China.
Int. J. Nurs. Sci. 2021, 8, 65–70. [CrossRef] [PubMed]

23. Burr, C.; Wyatt, A. Investigation of workplace bullying and harassment complaints. In Dignity and Inclusion at Work; D’Cruz, P.,
Noronha, E., Caponecchia, C., Escartín, J., Salin, D., Tuckey, M.R., Eds.; Springer Nature: Singapore, 2021; pp. 147–180.

24. Safe Work Australia. Guide for Preventing and Responding to Workplace Bullying. 2016. Available online: https://www.
safeworkaustralia.gov.au/doc/guide-preventing-and-responding-workplace-bullying (accessed on 21 May 2021).

25. Catley, B.; Blackwood, K.; Forsyth, D.; Tappin, D.C. Workplace bullying complaints: Lessons for “good HR practice”. Personn. Rev.
2017, 46, 100–114. [CrossRef]

26. Hoel, H.; Einarsen, S. Investigating complaints of bullying and harassment. In Bulling and Harassment in the Workplace: Develop-
ments in Theory, Research and Practice, 2nd ed.; Einarsen, S., Hoel, H., Zaph, D., Cooper, C., Eds.; CRC Press: Boca Raton, FL, USA,
2010; pp. 341–358.

27. Lind, E.A. Fairness heuristic theory: Justice judgments as pivotal cognitions in organizational relations. In Advances in Organiza-
tional Justice; Greenberg, J., Cropanzano, R., Eds.; Stanford University Press: Stanford, CA, USA, 2001; pp. 56–88.

28. Van den Bos, K. Fairness heuristic theory: Assessing the information to which people are reacting has a pivotal role in un-
derstanding organizational justice. In Theoretical and Cultural Perspectives on Organizational Justice; Gilliland, S., Steiner, D.,
Skarlicki, D., Eds.; Information Age: Greenwich, CT, USA, 2001; pp. 63–84.

Societies 2021, 11, 143 17 of 18

29. Van den Bos, K.; Lind, E.A.; Wilke, H.A.M. The psychology of procedural justice and distributive justice viewed from the
perspective of fairness heuristic theory. In Justice in the workplace: From Theory to Practice; Cropanzano, R., Ed.; Erlbaum: Mahwah,
NJ, USA, 2001; Volume 2, pp. 49–66.

30. Rodell, J.B.; Colquitt, J.A. Looking ahead in times of uncertainty: The role of anticipatory justice in an organizational change
context. J. Appl. Psychol. 2009, 94, 989–1002. [CrossRef]

31. Colquitt, J.A. On the dimensionality of organizational justice: A construct validation of a measure. J. Appl. Psychol. 2001, 86,
386–400. [CrossRef]

32. Leventhal, G.S. The distribution of rewards and resources in groups and organizations. In Advances in Experimental Social
Psychology; Berkowitz, L., Walster, W., Eds.; Academic Press: New York, NY, USA, 1976; Volume 9, pp. 91–131.

33. Jenkins, M.; Winefield, H.; Sarris, A. Perceptions of unfairness in the management of bullying complaints: Exploring the
consequences. Int. J. Bus. Adm. 2013, 4, 16–25. [CrossRef]

34. Lam, S.S.K.; Schaubroeck, J.; Aryee, S. Relationship between organizational justice and employee work outcomes: A cross-national
study. J. Organ. Behav. 2001, 23, 1–18. [CrossRef]

35. Folger, R.G.; Cropanzano, R. Organizational Justice and Human Resource Management; Sage: Thousand Oaks, CA, USA, 1998;
Volume 7.

36. Folger, R.G.; Rosenfield, D.; Grove, J.; Corkran, L. Effects of “voice” and peer opinions on responses to inequity. J. Personal. Soc.
Psychol. 1979, 37, 2253. [CrossRef]

37. Thornhill, A.; Saunders, M. Exploring employees’ reactions to strategic change over time: The utilisation of an organizational
justice perspective. Iran. J. Manag. 2003, 24, 66–86.

38. Van den Bos, K.; Lind, E.A.; Vermunt, R.; Wilke, H.A. How do I judge my outcome when I do not know the outcome of others?
The psychology of the fair process effect. J. Personal. Soc. Psychol. 1997, 72, 1034–1046. [CrossRef]

39. Shapiro, D.L.; Kirkman, B. Employees’ reaction to the change to work teams: The influence of “anticipatory” injustice. J. Organ.
Chang. Manag. 1999, 12, 51–67. [CrossRef]

40. Shapiro, D.L.; Kirkman, B.L. Anticipatory injustice: The consequences of expecting injustice in the workplace. In Advances in
Organizational Justice; Greenberg, J., Cropanzano, R., Eds.; New Lexington: Lexington, MA, USA, 2001; pp. 152–178.

41. Rudman, L.A.; Borgida, E.; Robertson, B.A. Suffering in silence: Procedural justice versus gender socialization issues in university
sexual harassment grievence procedures. Basic Appl. Soc. Psychol. 1995, 17, 519–541. [CrossRef]

42. Fox, S.; Stallworth, L.E. Building a framework for two internal organizational approaches to resolving and preventing workplace
bullying: Alternative dispute resolution and training. Consult. Psychol. J. Pract. Res. 2009, 61, 220–241. [CrossRef]

43. Hogler, R.L.; Frame, J.H.; Thornton, G. Workplace sexual harassment law: An empirical analysis of organizational justice and
legal policy. J. Manag. Issues 2002, 14, 234–250.

44. Guglielmi, D.; Mazzetti, G.; Villano, P.; Topa Cantisan, G. The impact of perceived effort-reward imbalance on workplace bullying:
Also a matter of organizational identification. Psychol. Health Med. 2018, 23, 511–516. [CrossRef]

45. Jenkins, M.; Winefield, H.; Sarris, A. Consequences of being accused of workplace bullying: An exploratory study. Int. J. Work
Health Manag. 2011, 4, 33–47. [CrossRef]

46. Cowan, R.L. “Rocking the boat” and “Continuing to fight”: Un/productive justice episodes and the problem of workplace
bullying. Hum. Commun. 2009, 12, 283–301.

47. Braun, V.; Clarke, V. Using thematic analysis in psychology. Qual. Res. Psychol. 2006, 3, 77–101. [CrossRef]
48. Deutsch, M. Distributive Justice; Yale University Press: New Haven, CT, USA, 1985.
49. Colquitt, J.A.; Jackson, C.L. Justice in teams: The context sensitivity of justice rules across individual and team contexts.

J. Appl. Soc. Psychol. 2006, 36, 868–899. [CrossRef]
50. Leventhal, G.S. What should be done with equity theory? New approaches to the study of fairness in social relationships. In

Social Exchange: Advances in Theory and Research; Gergen, K., Greenberg, M., Willis, R., Eds.; Plenum Press: New York, NY, USA,
1980; pp. 27–55.

51. Greenberg, J. The social side of fairness: Interpersonal and informational classes of organizational justice. In Justice in the Workplace:
Approaching Airness in Human Resource Management; Cropanzano, R., Ed.; Erlbaum: Hillsdale, NJ, USA, 1993; pp. 79–103.

52. Reknes, I.; Glambek, M.; Einarsen, S.V. Injustice perceptions, workplace bullying and intention to leave. Empl. Relat. 2020, 43,
1–13. [CrossRef]

53. Tuckey, M.R.; Searle, B.J.; Boyd, C.M.; Winefield, A.H.; Winefield, H.R. Hindrances are not threats: Advancing the multidimen-
sionality of work stress. J. Occup. Health Psychol. 2015, 20, 131–147. [CrossRef]

54. Semmer, N.K.; McGrath, J.E.; Beehr, T.A. Conceptual issues in research on stress and health. In Handbook of Stress Medicine and
Health, 2nd ed.; Cooper, C.L., Ed.; CRC Press: New York, NY, USA, 2005; pp. 1–43.

55. Schilpzand, P.; de Pater, I.E.; Erez, A. Workplace incivility: A review of the literature and agenda for future research.
J. Organ. Behav. 2016, 37, S57–S88. [CrossRef]

56. Salin, D. The prevention of workplace bullying as a question of human resource management: Measures adopted and underlying
organizational factors. Scandin. J. Man. 2008, 24, 221–231. [CrossRef]

57. Kidder, D.L. Restorative justice: Not “rights”, but the right way to heal relationships at work. Int. J. Confl. Manag. 2007, 18, 4–22.
[CrossRef]

Societies 2021, 11, 143 18 of 18

58. John, W.S.; Johnson, P. The pros and cons of data analysis software for qualitative research. J. Nurs. Sch. 2000, 32, 393–397.
[CrossRef] [PubMed]

59. Rayner, C.; Lewis, D. Managing workplace bullying: The role of policies. In Bullying and Harassment in The Workplace;
Einarsen, S.V., Hoel, H., Zaph, D., Cooper, C.L., Eds.; CRC Press: Boca Raton, FL, USA, 2020; pp. 497–519.

60. Safe Work Australia. Preventing Psychological Injury under Work Health and Safety Law: Fact Sheet. 2014. Available
online: https://www.safeworkaustralia.gov.au/doc/preventing-psychological-injury-under-work-health-and-safety-laws-
fact-sheet (accessed on 18 June 2021).

61. Safe Work Australia. Work-Related Psychological Health and Safety: National Guidance Material. 2019. Available on-
line: https://www.safeworkaustralia.gov.au/doc/work-related-psychological-health-and-safety-systematic-approach-meeting-
your-duties (accessed on 13 May 2021).

  • Introduction
    • Workplace Bullying Complaint and Investigation Process
    • Organizational Justice and Workplace Bullying
    • Evaluating Bullying Investigations through a Justice Lens
  • Method
    • Data Collection
    • Data Analysis
  • Results
    • Complaint Characteristics
    • Manifestation of Organizational Justice in Internal Workplace Bullying Complaints and Investigations
  • Discussion
    • Contribution to Knowledge
    • Practical Implications
    • Strengths, Limitations and Future Directions
    • Conclusions
  • References

Week 1 Assignment/3 Use of patient complaints to identify.pdf

Giardina TD, et al. BMJ Qual Saf 2021;30:996–1001. doi:10.1136/bmjqs-2020-011593996

ORIGINAL RESEARCH

1Center for Innovations in
Quality, Effectiveness and
Safety, Michael E. DeBakey
Veterans Affairs Medical Center,
Houston, TX, USA
2Baylor College of Medicine,
Houston, Texas, USA
3Investigator Initiated Research
Operations, Geisinger, Danville,
PA, USA
4Division of Quality, Safety and
Patient Experience, Geisinger,
Danville, PA, USA
5Division of General Internal
Medicine, Geisinger, Danville,
PA, USA

Correspondence to
Dr Traber D Giardina, Baylor
College of Medicine, Houston,
TX 77030, USA;
[email protected] bcm. edu

Received 20 May 2020
Revised 2 February 2021
Accepted 6 February 2021
Published Online First
17 February 2021

To cite: Giardina TD,
Korukonda S, Shahid U, et al.
BMJ Qual Saf
2021;30:996–1001.

Use of patient complaints to identify
diagnosis- related safety concerns: a
mixed- method evaluation

Traber D Giardina ,1,2 Saritha Korukonda,3 Umber Shahid,1,2
Viralkumar Vaghani,1,2 Divvy K Upadhyay,4 Greg F Burke,4,5
Hardeep Singh 1,2

© Author(s) (or their
employer(s)) 2021. Re- use
permitted under CC BY- NC. No
commercial re- use. See rights
and permissions. Published by
BMJ.

ABSTRACT
Background Patient complaints are associated with
adverse events and malpractice claims but underused in
patient safety improvement.
Objective To systematically evaluate the use of patient
complaint data to identify safety concerns related to
diagnosis as an initial step to using this information to
facilitate learning and improvement.
Methods We reviewed patient complaints submitted
to Geisinger, a large healthcare organisation in the USA,
from August to December 2017 (cohort 1) and January
to June 2018 (cohort 2). We selected complaints more
likely to be associated with diagnostic concerns in
Geisinger’s existing complaint taxonomy. Investigators
reviewed all complaint summaries and identified cases
as ’concerning’ for diagnostic error using the National
Academy of Medicine’s definition of diagnostic error.
For all ’concerning’ cases, a clinician- reviewer evaluated
the associated investigation report and the patient’s
medical record to identify any missed opportunities in
making a correct or timely diagnosis. In cohort 2, we
selected a 10% sample of ’concerning’ cases to test this
smaller pragmatic sample as a proof of concept for future
organisational monitoring.
Results In cohort 1, we reviewed 1865 complaint
summaries and identified 177 (9.5%) concerning reports.
Review and analysis identified 39 diagnostic errors. Most
were categorised as ’Clinical Care issues’ (27, 69.2%),
defined as concerns/questions related to the care that
is provided by clinicians in any setting. In cohort 2,
we reviewed 2423 patient complaint summaries and
identified 310 (12.8%) concerning reports. The 10%
sample (n=31 cases) contained five diagnostic errors.
Qualitative analysis of cohort 1 cases identified concerns
about return visits for persistent and/or worsening
symptoms, interpersonal issues and diagnostic testing.
Conclusions Analysis of patient complaint data and
corresponding medical record review identifies patterns
of failures in the diagnostic process reported by patients
and families. Health systems could systematically
analyse available data on patient complaints to monitor
diagnostic safety concerns and identify opportunities for
learning and improvement.

INTRODUCTION
Patient complaints are associated with
adverse events and malpractice claims but
underused in patient safety improvement

efforts.1–4 Patients’ experiences offer rich
information about factors that lead to
adverse events5–9 but existing incident
reporting mechanisms often fail to capture
them. In the USA, while many healthcare
organisations collect and address indi-
vidual patient complaints, few organisa-
tions use systematic or rigorous processes
to review and act on patient complaints
for system- wide learning and improve-
ment.

Literature on the type and frequency of
patient complaints10 is emerging but gaps
in knowledge of patient- reported diag-
nostic safety concerns remain. Diagnostic
errors are frequent and harmful,11 yet they
are under- reported,12 limiting our data on
how and why they occur. While methods
to identify diagnostic errors are still being
refined, currently most measurement
methods are imperfect, unreliable and/or
labour intensive.13 Many of the methods
to identify patient safety issues cannot
specifically identify diagnostic errors.14
There are few methods to study diagnostic
errors that include patient perspectives
even though this was a major recom-
mendation of the National Academy of
Medicine (NAM) report ‘Improving Diag-
nosis in Health Care’.12 It is thus essential
to develop more targeted measurement
methods that are patient centred and have
stronger safety signals.

Conversely, analytical methods to
study patient complaints are getting
more robust. For instance, the Health-
care Complaints Analysis Tool (HCAT)10
is reliable for coding and measuring the
severity of complaints and helps iden-
tify unsafe and hard- to- monitor areas of
care though systematic analysis of patient
complaints.15 Systematic approaches
are similarly needed to analyse patient
complaints related to diagnostic errors.

o
n

M
a
y 7

, 2
0
2

2
b

y g
u
e

st. P
ro

te
cte

d
b

y co
p

yrig
h

t.
h
ttp

://q
u
a
litysa

fe
ty.b

m
j.co

m
/

B
M

J Q
u

a
l S

a
f: first p

u
b

lish
e

d
a

s 1
0

.1
1

3
6

/b
m

jq
s-2

0
2

0
-0

1
1

5
9

3
o

n
1

7
F

e
b
ru

a
ry 2

0
2
1
. D

o
w

n
lo

a
d
e
d
fro

m

997Giardina TD, et al. BMJ Qual Saf 2021;30:996–1001. doi:10.1136/bmjqs-2020-011593

Original research

We thus evaluated the use of patient complaints to
identify diagnosis- related safety concerns as an initial
step to enable their use for learning and improvement.

METHODS
Setting
Geisinger is one of the largest integrated health systems
in the USA serving approximately 4.2 million resi-
dents; many live in rural Pennsylvania. Nearly a fifth
of the population served is elderly (65+). Geisinger
refunds copays and out- of- pocket expenses for certain
care delivery concerns raised by patients.16

Patients reported concerns to Geisinger’s Patient
Experience department via telephone, email or in
person. All patient liaisons are trained in use of commu-
nication, especially skills related to de- escalation and
service recovery17 and enhancing patient experience.
At the time of the study there were approximately 15
patient liaisons. Every complaint is discussed with the
patient and/or family member prior to recording their
summary statement. A patient who emails, writes a
letter or leaves a message is contacted by the ‘patient
liaison’ to discuss the issue. The health system has an
internal policy to respond to initial patient concerns
within 24 hours and provide a written response within
7 days. Summary statements are categorised and
entered into a commercial incident reporting system
used to manage and track patient complaints.

Geisinger uses a locally developed and routinely
updated taxonomy to categorise patient complaints.
This is followed by a time- bound investigation
conducted by the patient liaison to gather details from
the patient’s perspective. Details include information
about visits and interactions with clinicians, responses
from involved clinicians and patient safety teams and
actions to resolve the complaint. Investigation details
are recorded into the incident reporting system.

Design and procedures
The research team reviewed two cohorts of patient
complaints submitted to Geisinger. In both cohorts,
we selected complaints based on an internally devel-
oped categorisation. From a total of 34 categories, the
following categories were selected for inclusion based
on increased likelihood of being associated with diag-
nostic safety concerns:
1. Accident/injury—all issues related to patient injuries.
2. Attitude/behaviour of clinicians/staff—all concerns/ques-

tions related to provider actions denoted as unprofes-
sional or demonstrating poor customer service towards
patient(s).

3. Clinical care issues—all concerns/questions related to the
care that is provided by clinicians in any setting (inpa-
tient/outpatient).

4. Delay in care—any concern/question where a patient ex-
periences a perceived or actual delay in obtaining clinical
care on an inpatient or outpatient basis.

5. Delay in test results—any concern/question where a pa-
tient experiences a perceived or actual delay in having
a medical test performed or resulted on an inpatient or
outpatient basis.

6. Delay in admission/discharge—any concern/question
where a patient experiences a perceived or actual delay
in the scheduled, anticipated or emergent admission to
the hospital for care or a perceived or actual delay in the
discharge process.

Cohort 1 involved review of all complaints that met
the inclusion categorisation above and were submitted
between August and December 2017. In cohort 2,
we reviewed a more pragmatic sample, as proof of
concept, assuming that most healthcare organisations
will only choose to periodically review a random
manageable sample to gain insights. Cohort 2 includes
complaints submitted from January to June 2018 that
met the inclusion categorisation. Figure 1 outlines the
methodology for each cohort.

Figure 1 Patient complaint data flow chart. MODs, missed opportunities in diagnosis.

o
n

M
a
y 7

, 2
0
2

2
b

y g
u
e

st. P
ro

te
cte

d
b

y co
p

yrig
h

t.
h
ttp

://q
u
a
litysa

fe
ty.b

m
j.co

m
/

B
M

J Q
u

a
l S

a
f: first p

u
b

lish
e

d
a

s 1
0

.1
1

3
6

/b
m

jq
s-2

0
2

0
-0

1
1

5
9

3
o

n
1

7
F

e
b
ru

a
ry 2

0
2
1
. D

o
w

n
lo

a
d
e
d
fro

m

998 Giardina TD, et al. BMJ Qual Saf 2021;30:996–1001. doi:10.1136/bmjqs-2020-011593

Original research

In both cohorts, two clinical reviewers (authors US
and VV) independently reviewed all summary state-
ments (one to two sentences only; cohort 1 n=1865;
cohort 2 n=2423) and identified cases that were
‘concerning’ for a diagnostic error (figure 1) using
NAM’s broad definition of diagnostic error (ie, ‘the
failure to (a) establish an accurate and timely explana-
tion of the patient’s health problem(s) or (b) commu-
nicate that explanation to the patient’12). Where the
two reviewers did not agree, the first author (TDG)
would review the summary statement and err on the
side of including the case for further review. Cases
were included as ‘concerning’ if summary statements
included one or more of the following: (A) any
language about a diagnosis (eg, misdiagnosis), (B) any
mention of a potential patient safety issue (eg, delayed
care), and (C) any clinician behaviours related to
communication (eg, did not listen). Complaints related
to only behavioural issues of nursing and/or staff, clini-
cian behaviour (eg, doctor/nurse was rude) and those
unrelated to patient safety were excluded.

In cohort 1, for all ‘concerning’ cases, the associ-
ated investigation reports were evaluated by a clinical
reviewer (US) for a diagnostic error, defined as a missed
opportunity in making a correct or timely diagnosis
(MOD),18 and a timeline of events was created from
the patient perspective. A second independent clin-
ical reviewer (SK) concurrently reviewed the patient’s
medical record for MODs and created a timeline from
the medical record documentation. The reviewer used
the Revised Safer Dx Instrument19 as a framework
to identify diagnostic safety concerns and as a guide
to create a timeline of events. For these MOD cases,
data collected included patient demographics, number
of in- person visits (mean number of visits from initial
symptoms to communication of final diagnosis), type
of provider and specialty, and final diagnosis. Finally,
a multidisciplinary team discussed all MOD cases, and
cases where the two reviewers did not agree to confirm
presence or absence of MODs, irrespective of inves-
tigation outcomes. Cases for which consensus could
not be reached were discussed with the senior author
(HS) for adjudication (cohort 1 n=25, cohort 2 n=6).
In cohort 2, the team followed the same methodology
with a 10% random sample of ‘concerning’ cases (see
figure 1).

Qualitative analysis
We conducted a qualitative inductive content analysis
of cohort 1 confirmed MODs to better understand
complaint details from the patient perspective (eg,
the written summary statements and detailed inves-
tigation notes). Two qualitative methodologists (US
and TDG) familiarised themselves with each of the
MOD cases while reviewing them exclusively from
the perspective of the patient/family/caregiver. At this
stage, they did not consider clinician or the healthcare
system response to complaints and investigations, nor

did they review the medical record for this qualitative
analysis. Each reviewer coded the data independently
and met to discuss all emergent codes. Based on the
discussion, they grouped experiences and identified
salient themes. The analysis was presented to the
research team for further discussion.

RESULTS
In cohort 1, review of 1865 complaint summaries
identified 177 (9.5%) potential diagnostic concerns.
On full analysis of these 177 cases, including inves-
tigation and chart review, we identified 39 MODs
(2.1%); patients were mostly female (n=27), white
(n=39), with a mean age of 44 (SD=28.2, range: 9
months to 91 years). The clinical care concerns cate-
gory was the most common (n=27), followed by delay
in care (n=7), delay in test results (n=2), attitude/
behaviour of provider (n=2) and discharged too soon
(n=1). Most common diagnoses involved were cancer
related (n=4), missed fracture (n=4) and Lyme disease
(n=3). Patients attended a mean of 1.5 visits before
being diagnosed correctly (range: 1–5). More than half
of the MODs occurred in the emergency department
(ED) and primary care (n=15 and n=11, respectively)
(table 1). Research team’s total time investment to
analyse cohort 1 was estimated to be approximately
339 hours.

In cohort 2, a review of 2423 summary statements
identified 310 (12.8%) potentially concerning reports.
Detailed analyses on a random 10% sample (n=31)
identified five MODs; mostly male (n=4) and white

Table 1 Visit characteristics associated with the complaint

Cohort 1 Cohort 2

n % n %

Clinical location of visits*
Emergency department 22 38.6 3 60.0
Primary care/family medicine 16 28.1 1 20.0
Convenient care 6 10.5 – –
Specialty† 6 10.5 1 20.0
Urgent care 4 7.0 – –
Obstetrics- gynaecology 3 5.3 – –
Clinician involved in care*
Physician 40 70.2 4 80.0
Nurse practitioner/physician

assistant
17 29.8 1 20.0

Category of complaint
Clinical care (provider) 27 69.2 2 40.0
Delay in care 7 17.9 1 20.0
Delay in test results 2 5.1 2 40.0
Attitude/behaviour of provider 2 5.1 – –
Discharged too soon 1 2.6 – –
*Total does not equal 39 as some patients presented to more than
one clinician and/or in more than one setting before being correctly
diagnosed.
†Surgery, cardiology, radiology, pulmonology, dermatology.

o
n

M
a
y 7

, 2
0
2

2
b

y g
u
e

st. P
ro

te
cte

d
b

y co
p

yrig
h

t.
h
ttp

://q
u
a
litysa

fe
ty.b

m
j.co

m
/

B
M

J Q
u

a
l S

a
f: first p

u
b

lish
e

d
a

s 1
0

.1
1

3
6

/b
m

jq
s-2

0
2

0
-0

1
1

5
9

3
o

n
1

7
F

e
b
ru

a
ry 2

0
2
1
. D

o
w

n
lo

a
d
e
d
fro

m

999Giardina TD, et al. BMJ Qual Saf 2021;30:996–1001. doi:10.1136/bmjqs-2020-011593

Original research

(n=4), with a mean age of 45 (SD=17.9, range: 21–65
years). Missed diagnoses included: cancer (n=2),
dislocation (n=1), acute renal injury (n=1) and hyper-
glycaemia (n=1) occurring in the ED (n=3), primary
care (n=1) and dermatology (n=1); most involved
physicians (n=4).

Qualitative analysis
Thirty- nine MOD patient complaints from cohort 1
were included in the final qualitative content analysis.
On review of the summary statement and the inves-
tigation report text, three commonalities emerged
across cases despite heterogeneity among diagnoses.
These included: (1) reports of return visits for same
or worsening symptoms (the most salient; n=24); (2)
interpersonal issues; and (3) diagnostic testing issues.
Return visits involved patient- reported situations such
as initial treat- and- release ED visits for a symptom (eg,
abdominal pain), followed by an ED return visit where
a more certain diagnosis (eg, appendicitis) was made.
From the patient/caregiver perspective, several reasons
for such return visits emerged within this theme: first
visit was not helpful, concerns that patient was not
heard, limited or no testing performed and concerns
with notification of test results. Of this subset, five
complaints involved more than one return visit to
resolve the patient’s concerns.

Eighteen of the cases included comments related to
interpersonal problems (eg, ignoring patient/caregiver
suggestions). Testing issues were also found across 12
of the cases and included patient/caregiver concerns
regarding perceptions of overtesting and undertesting,
not communicating or acting on abnormal test results
and imaging misreads. Other concerns were related to
perceptions of being discharged too soon (n=4) and
challenges in obtaining urgent referral for symptoms
(n=1).

DISCUSSION
Patient complaints can be used systematically to iden-
tify diagnostic safety concerns but harvesting this infor-
mation requires considerable time investment from
complaint information and medical record reviews.
In the main cohort, we identified 39 MODs, many of
which were found within ‘clinical care issues’ category
of the taxonomy used by the organisation. Qualitative
analysis of the main cohort’s diagnostic error cases
found that the complaint information from patients/
caregivers often highlighted return visits for persistent
and/or worsening symptoms. Focusing on higher risk
categories within the complaint data (ie, clinical care
issues, delay in care, delay in test results) coupled with
medical record reviews may improve safety signals
from this data source.

Patient complaints to a healthcare system are often
unprompted assessments of care reflective of what
matters to patients and families. Patients, families and
caregivers are in the best position to communicate

about their diagnostic experience. However, patient
complaint mechanisms are not necessarily set up for
safety monitoring.20 While our study has identified a
methodology to improve signal for patient- reported
diagnostic safety concerns in the complaint data, a
national policy on integrating these data for learning
and practice improvement is lacking. A recent system-
atic review of the patient complaint literature identi-
fied multiple mechanisms to ensure patient- centred
complaint data collection and quality improvements—
which includes a reliable coding taxonomy.21 Struc-
tured complaint data provide an opportunity for
health systems to move safety concerns to the appro-
priate department to be addressed and tracked, and
allow them to be nimble in their management and
response.21

Existing complaint taxonomies, such as HCAT,
provide a validated method to categorise complaints
by problem, severity and harm if used by healthcare
systems, and HCAT has been used to identify patient
safety issues.15 22 Our study builds on such efforts
and specifically explores the way patients articulate
and conceptualise diagnostic safety concerns when
filing a complaint. While some complaints include
clear expressions of missed or delayed diagnoses,
other patients express safety concerns about diagnosis
through their experience with return visits, communi-
cation and testing. Analysing these complaints using
this lens can make the healthcare system sensitive
to the nuances of diagnostic safety from the patient
perspective—for instance, focusing on patient return
visits with persistent or worsening symptoms could
be fertile ground for additional exploration. This
work also informs the development of standardised
categorisation mechanisms that capture how patients
express diagnostic safety concerns. This standardi-
sation and the subsequent analysis of patients’ expe-
riences of diagnostic errors may highlight areas for
improvement in the diagnostic process that might
otherwise go undetected and have potential to cause
harm. Organisations that intend to pursue diagnostic
excellence should focus on systematically identifying
patient- reported diagnostic concerns and generate
feedback for learning.23

Several factors may influence whether a patient
will complain24 and not all diagnostic errors will be
captured in patient complaints. Other measurement
strategies have to be used concurrently to capture
them.25 To date, methods to identify diagnostic errors
are still being refined, and currently most measure-
ment methods are imperfect, unreliable and/or labour
intensive.13 However, quantifying and learning from
the experiences of patients who file their complaints
provides insight into how some patients conceptualise
diagnostic concerns and is foundational for improve-
ment. Nevertheless, we show why it is essential to
develop more targeted methods with better signals
that require less review burden.23 26

o
n

M
a
y 7

, 2
0
2

2
b

y g
u
e

st. P
ro

te
cte

d
b

y co
p

yrig
h

t.
h
ttp

://q
u
a
litysa

fe
ty.b

m
j.co

m
/

B
M

J Q
u

a
l S

a
f: first p

u
b

lish
e

d
a

s 1
0

.1
1

3
6

/b
m

jq
s-2

0
2

0
-0

1
1

5
9

3
o

n
1

7
F

e
b
ru

a
ry 2

0
2
1
. D

o
w

n
lo

a
d
e
d
fro

m

1000 Giardina TD, et al. BMJ Qual Saf 2021;30:996–1001. doi:10.1136/bmjqs-2020-011593

Original research

Our study shows how other health systems can
evaluate available data on patient complaints for
diagnostic safety concerns to identify opportunities
for learning and improvement. To our knowledge,
this is the largest assessment of patient complaints to
evaluate diagnostic safety data and while the signal is
not as high as seen in triggers of electronic medical
records,26 27 it is significant. We also provide a proof of
concept for future organisational monitoring. More-
over, information harvested from patient complaints
complements data obtained from medical record
reviews which do not necessarily include an assess-
ment of the breadth of the patient experience. This
study is a first step towards more systematic analysis
of patient complaints by health systems to routinely
identify information on diagnostic safety concerns
that could inform sustainable improvements. Our
research effort to identify signals of diagnostic safety
is thus foundational to creating future learning health
systems that will ultimately use multiple measurement
methods, including the patient voice, to improve diag-
nostic safety.23 28

Our study has several limitations. Patient complaint
summaries and investigations are not first- person
accounts. They are created by a patient liaison, there-
fore subject to any unconscious biases of or interpreta-
tions by the patient liaison. While patient liaisons are
trained to receive patient complaints, the research team
can only assume that the patient liaison has adequately
captured the complaint from the patient’s perspective.
Additionally, these patient complaints are not represen-
tative of the general US patient population, the Geisinger
patient population or of all patients who experience
diagnostic errors. We cannot account for differences
between patients/families that do file a complaint and
those that do not. Because our team included diverse
clinical and patient- centred expertise and we used the
broad NAM definition, we used consensus methods
to determine whether an error occurred and did not
calculate inter- rater reliability. Additionally, the depth
of our qualitative analysis was limited because the orig-
inal data were gathered for informing day- to- day clin-
ical operations and not for research purposes. Finally,
it is not possible to maintain complete blinding when
reviewing cases retrospectively. However, given these
limitations and the scant data on diagnostic concerns
from patients and families, this study provides insight
into leveraging an existing patient- centred data source
for future diagnostic safety measurement.

In conclusion, analysis of patient complaint data
identifies breakdowns in the diagnostic process
reported by patients and families. This work is foun-
dational to advance research and implementation
efforts to better harvest diagnostic safety signals from
patient complaint data. Health systems could system-
atically analyse available data on patient complaints
to monitor diagnostic safety concerns and identify
opportunities for learning and improvement.

Twitter Traber D Giardina @TDGiardina and Hardeep Singh
@HardeepSinghMD

Acknowledgements Our team thanks Cara E Dusick, the
patient liaison manager at Geisinger, for her help guiding the
team on the patient grievances process and for setting up the
infrastructure to identify data for this study. We also thank the
Geisinger Committee to Improve Clinical Diagnosis for their
support on this project.

Contributors All authors contributed equally to the planning,
conduct and reporting of the work described in the article.

Funding This work was supported by the Gordon and Betty
Moore Foundation. It was also supported in part by the
Houston VA HSR&D Center for Innovations in Quality,
Effectiveness and Safety (CIN 13- 413). In addition, TDG is
supported by an Agency for Healthcare Research and Quality
Mentored Career Development Award (K01- HS025474);
and HS is supported by the Veterans Affairs Health Services
Research and Development Service (CRE17- 127 and the
Presidential Early Career Award for Scientists and Engineers
USA 14- 274), the VA National Center for Patient Safety,
and the Agency for Healthcare Research and Quality
(R01HS27363).

Competing interests None declared.

Patient consent for publication Not required.

Ethics approval The study was approved by the Institutional
Review Board of Baylor College of Medicine and Geisinger.

Provenance and peer review Not commissioned; externally
peer reviewed.

Data availability statement No data are available.

Open access This is an open access article distributed in
accordance with the Creative Commons Attribution Non
Commercial (CC BY- NC 4.0) license, which permits others
to distribute, remix, adapt, build upon this work non-
commercially, and license their derivative works on different
terms, provided the original work is properly cited, appropriate
credit is given, any changes made indicated, and the use is non-
commercial. See: http:// creativecommons. org/ licenses/ by- nc/ 4.
0/.

ORCID iDs
Traber D Giardina http:// orcid. org/ 0000- 0002- 9184- 6524
Hardeep Singh http:// orcid. org/ 0000- 0002- 4419- 8974

REFERENCES
1 Harrison R, Walton M, Healy J, et al. Patient complaints

about hospital services: applying a complaint taxonomy to
analyse and respond to complaints. Int J Qual Health Care
2016;28:240–5.

2 Hickson GB, Federspiel CF, et al, JAMA. Patient complaints
and malpractice risk 2002;287:2951–7.

3 de Vos MS, Hamming JF, Chua- Hendriks JJC, et al.
Connecting perspectives on quality and safety: patient- level
linkage of incident, adverse event and complaint data. BMJ
Qual Saf 2019;28:180–9.

4 O’Hara JK, Reynolds C, Moore S, et al. What can patients
tell us about the quality and safety of hospital care?
findings from a UK multicentre survey study. BMJ Qual Saf
2018;27:673–82.

5 Iedema R, Allen S, Britton K, et al. What do patients and
relatives know about problems and failures in care? BMJ Qual
Saf 2012;21:198–205.

6 Scott J, Heavey E, Waring J, et al. Implementing a survey
for patients to provide safety experience feedback following
a care transition: a feasibility study. BMC Health Serv Res
2019;19:613.

o
n

M
a
y 7

, 2
0
2

2
b

y g
u
e

st. P
ro

te
cte

d
b

y co
p

yrig
h

t.
h
ttp

://q
u
a
litysa

fe
ty.b

m
j.co

m
/

B
M

J Q
u

a
l S

a
f: first p

u
b

lish
e

d
a

s 1
0

.1
1

3
6

/b
m

jq
s-2

0
2

0
-0

1
1

5
9

3
o

n
1

7
F

e
b
ru

a
ry 2

0
2
1
. D

o
w

n
lo

a
d
e
d
fro

m

1001Giardina TD, et al. BMJ Qual Saf 2021;30:996–1001. doi:10.1136/bmjqs-2020-011593

Original research

7 Giardina TD, Haskell H, Menon S, et al. Learning from
patients’ experiences related to diagnostic errors is essential for
progress in patient safety. Health Aff 2018;37:1821–7.

8 Zengin S, Al B, Yavuz E, et al. Analysis of complaints lodged
by patients attending a university hospital: a 4- year analysis. J
Forensic Leg Med 2014;22:121–4.

9 Donaldson LJ. The wisdom of patients and families: ignore it
at our peril. BMJ Qual Saf 2015;24:603–4.

10 Reader TW, Gillespie A, Roberts J. Patient complaints in
healthcare systems: a systematic review and coding taxonomy.
BMJ Qual Saf 2014;23:678–89.

11 Singh H, Meyer AND, Thomas EJ. The frequency of
diagnostic errors in outpatient care: estimations from three
large observational studies involving us adult populations. BMJ
Qual Saf 2014;23:727–31.

12 National Academies of Sciences, Engineering, and Medicine.
Improving Diagnosis in Health Care. National Academies Press,
2015.

13 Singh H, Bradford A, Goeschel C. Operational measurement of
diagnostic safety: state of the science. Diagnosis 2021;8:51–65.

14 Graber ML, Trowbridge R, Myers JS, et al. The next
organizational challenge: finding and addressing diagnostic
error. Jt Comm J Qual Patient Saf 2014;40:102–10.

15 Gillespie A, Reader TW. Patient- Centered insights: using health
care complaints to reveal hot spots and blind spots in quality
and safety. Milbank Q 2018;96:530–67.

16 Burke GF. Geisinger’s Refund Promise: Where Things Stand
After One Year. NEJM Catal 2020.

17 Service Recovery Programs. Available: http://www. ahrq. gov/
cahps/ quality- improvement/ improvement- guide/ 6- strategies-
for- improving/ customer- service/ strategy6p- service- recovery.
html [Accessed 21 Sep 2020].

18 Singh H. Editorial: helping health care organizations to define
diagnostic errors as missed opportunities in diagnosis. Jt
Comm J Qual Patient Saf 2014;40:99–AP1.

19 Singh H, Khanna A, Spitzmueller C, et al. Recommendations
for using the revised safer DX instrument to help measure and
improve diagnostic safety. Diagnosis 2019;6:315–23.

20 de Vos MS, Hamming JF, Marang- van de Mheen PJ. The
problem with using patient complaints for improvement. BMJ
Qual Saf 2018;27:758–62.

21 van Dael J, Reader TW, Gillespie A, et al. Learning from
complaints in healthcare: a realist review of academic
literature, policy evidence and front- line insights. BMJ Qual
Saf 2020;29:684- 695.

22 Gillespie A, Reader TW. The healthcare complaints analysis
tool: development and reliability testing of a method for
service monitoring and organisational learning. BMJ Qual Saf
2016;25:937–46.

23 Singh H, Upadhyay DK, Torretti D. Developing health
care organizations that Pursue learning and exploration
of diagnostic excellence: an action plan. Acad Med
2020;95:1172–8.

24 Howard M, Fleming ML, Parker E. Patients do not always
complain when they are dissatisfied: implications for service
quality and patient safety. J Patient Saf 2013;9:224–31.

25 Vincent C, Burnett S, Carthey J. Safety measurement and
monitoring in healthcare: a framework to guide clinical teams
and healthcare organisations in maintaining safety. BMJ Qual
Saf 2014;23:670–7.

26 Murphy DR, Meyer AN, Sittig DF, et al. Application of
electronic trigger tools to identify targets for improving
diagnostic safety. BMJ Qual Saf 2019;28:151–9.

27 Singh H, Giardina TD, Forjuoh SN, et al. Electronic health
record- based surveillance of diagnostic errors in primary care.
BMJ Qual Saf 2012;21:93–100.

28 Meyer AND, Upadhyay DK, Collins CA, et al. A program
to provide clinicians with feedback on their diagnostic
performance in a learning health system. Jt Comm J Qual
Patient Saf 2021;47:120–6.

o
n

M
a
y 7

, 2
0
2

2
b

y g
u
e

st. P
ro

te
cte

d
b

y co
p

yrig
h

t.
h
ttp

://q
u
a
litysa

fe
ty.b

m
j.co

m
/

B
M

J Q
u

a
l S

a
f: first p

u
b

lish
e

d
a

s 1
0

.1
1

3
6

/b
m

jq
s-2

0
2

0
-0

1
1

5
9

3
o

n
1

7
F

e
b
ru

a
ry 2

0
2
1
. D

o
w

n
lo

a
d
e
d
fro

m

  • Use of patient complaints to identify diagnosis-­related safety concerns: a mixed-­method evaluation
    • Abstract
    • Introduction
    • Methods
      • Setting
      • Design and procedures
      • Qualitative analysis
    • Results
      • Qualitative analysis
    • Discussion
    • References

Week 1 Assignment/4 U.S. State Variation in Frequency and Prevalence.pdf

https://doi.org/10.1177/0733464820946673

Journal of Applied Gerontology
2021, Vol. 40(6) 582 –589
© The Author(s) 2020
Article reuse guidelines:
sagepub.com/journals-permissions
DOI: 10.1177/0733464820946673
journals.sagepub.com/home/jag

Living Environments: Satisfaction & Perceptions

Introduction

Consumer complaints are a critical piece of the U.S. govern-
ment’s oversight of nursing home (NH) quality. While NH
quality assurance depends largely on mandated annual sur-
veys, a key role exists for consumers through a federally
established process enabling any concerned party to file a
complaint and potentially initiate an investigation. The com-
plaint process is the only way for consumers (e.g., residents,
family members, ombudsmen, and others concerned about
care) to alert the government to problems in an NH that may
emerge between surveys. However, researchers and govern-
ment oversight officials have found persistent state-by-state
differences in NH complaint processes and rates (Hansen
et al., 2019; Stevenson, 2006; U.S. Government Accountability
Office [U.S. GAO], 1999, 2003, 2011). Variation among the
states raises questions about the fairness of the complaint pro-
cess, meaning the extent to which federal standards are
applied equally across the states, and whether consumers in
every state have an equal chance of having their concerns
heard and investigated.

The objective of this article is to examine the extent to
which NH complaints vary by state. Prior research exam-
ined this question (Hansen et al., 2019; Stevenson, 2006)
but used data collected before publication of government
oversight studies that recommended revisions to the NH
complaint and complaint substantiation processes (U.S.

GAO, 2011, 2015). This study uses recent data to examine
and compare multiple measures of NH complaints by state:
numbers of complaints and substantiated complaints, per-
centages of NHs with at least one complaint and one sub-
stantiated complaint, number of allegations per complaint,
and complaint substantiation rates. This analysis provides
the most comprehensive view yet of NH complaints across
the nation, with an examination of variation by state in num-
bers and percentages of complaints and substantiated com-
plaints in the context of recommendations for more uniform
and consistent state interpretation of federal standards.

Background

Both annual survey and complaint investigation processes are
guided by protocols set at the federal level but carried out at
the state level (U.S. GAO, 2015). The complaint process

946673 JAGXXX10.1177/0733464820946673Journal of Applied GerontologyPeterson et al.
research-article2020

Manuscript received: March 3, 2020; final revision received: July 8,
2020; accepted: July 9, 2020.

1University of South Florida, Tampa, USA
2Miami University, Oxford, OH, USA

Corresponding Author:
Lindsay J. Peterson, School of Aging Studies, University of South Florida,
4202 E. Fowler Ave., MHC 1300, Tampa, FL 33620, USA.
Email: [email protected]

U.S. State Variation in Frequency and
Prevalence of Nursing Home Complaints

Lindsay J. Peterson1 , John R. Bowblis2 ,
Dylan J. Jester1 , and Kathryn Hyer1

Abstract
Consumers play a key role in the U.S. nursing home (NH) oversight through a federally established complaint process.
However, past variation by state in complaint numbers and rates raised questions about the uniformity of the process.
We examined state variation in numbers of complaints at intake and substantiated complaints, percentages of NHs with
at least one complaint and one substantiated complaint, number of allegations per complaint, and complaint substantiation
rates. We found state variation most prominently at the intake level, ranging from 0.4 to 30.4 complaints per NH. The
investigation process appears to reduce this variation: however, variation remains among states in frequency and prevalence
of substantiated complaints. Further work is needed to ensure federal standards concerning the handling of consumer
complaints are applied equally across the states. This includes policies affecting how complaints are initially filed, in addition
to how complaints are investigated.

Keywords
nursing homes, long-term services and supports, quality of care, quality of life, consumers, regulation, complaints

Peterson et al. 583

begins with the intake and prioritization process, during
which the survey agency records details about what was
alleged to have occurred and categorizes each allegation. A
single complaint may contain multiple allegations. Complaints
can be filed by residents, families, or others with concerns
about the care a resident is receiving. Long-term care ombuds-
men are frequently involved, either by advising families on
how to file complaints or filing them independently. Based on
the level of harm alleged, the complaint is assigned a priority
level that determines whether an on-site investigation is to be
conducted and within what time frame, as outlined in the
State Operations Manual (Centers for Medicare and Medicaid
Services [CMS], 2016). If no actual harm is alleged in a com-
plaint, it may be investigated initially offsite through an
administrative review.

As part of the onsite-inspection process, an important
question for the surveyors is whether the allegations in the
complaint are substantiated, which means there are indi-
cations that the practices of the NH are likely inconsistent
with regulatory standards. Only substantiated complaints
are further assessed to determine if any federal regulations
have been violated, and if so, what level of deficiency
citation should be issued. Information on substantiated
complaints that lead to federal deficiency citations is
made public on the federal Nursing Home Compare web-
site (Stevenson, 2005). No data are provided to the public
on complaints that are not officially substantiated. While
excluding invalid or nuisance complaints appears to be
justifiable, as Stevenson (2005) noted it assumes that
complaint intake and substantiation processes are uniform
nationwide and of equal stringency.

Two decades ago, after identifying inconsistencies by
state in complaint intake and investigation, the federal gov-
ernment bolstered complaint protocols. This included estab-
lishing requirements concerning the timely investigation of
complaints alleging serious harm and strengthening federal
oversight of state complaint investigation systems (Institute
of Medicine, 2001; U.S. GAO, 1999). Nevertheless, over-
sight studies continued to find considerable variation in state
processes and rates, including differences in the ease of filing
a complaint, resources and tools available to train intake
staff, how complaints were prioritized for investigation, and
when a complaint should be substantiated (U.S. GAO, 2011,
2015). The U.S. GAO (2015) reported increases in numbers
of complaints filed by consumers nationally and by state
(though they did not analyze substantiation rates), and
described changes to some state complaint processes that
may have affected the numbers of complaints. It reported, for
example, that complaints rose in California after the state
took steps to ensure all complaints were entered into the fed-
eral tracking system and in Michigan after it provided more
options for filing complaints, such as by email. Variation
among states in complaints also may stem from differences
in NH quality or differences in consumers’ motivation or
inclination to complain.

Scholars have suggested that consumer complaints about
NHs could be part of a new strategy to more effectively
ensure NH quality of care (Stevenson, 2006, 2018). However,
any such effort may be problematic if there is variation
among states in NH complaint processes. While the 2015
GAO report highlights some of this variation, the GAO
report utilized data from 2011 to 2014, a period that includes
the introduction of the Minimum Data Set 3.0, Medicare
postacute care reimbursement moving from RUG-III to
RUG-IV, and other major CMS initiatives (e.g., National
Partnership to Improve Dementia Care in Nursing Homes)
that may have increased community awareness of NH qual-
ity. This study adds to the prior work on complaints using
2017 data; data that would reflect changes to the complaint
process made after the 2015 GAO report. In addition, this
study details and compares state variation in the results of
two crucial steps, complaint intake and substantiation, using
multiple measures of complaints. The results will provide
baseline information needed for further research into the col-
lection and use of NH complaint data to best reflect con-
sumer concerns about NH quality, and thereby potentially
improve NH quality.

Methods

Data and Sample Selection

The source of information on allegations and complaints is
the ASPEN Complaints/Incidents Tracking System (ACTS).
These data are based on information collected by state agen-
cies as part of the federally required inspection process. CMS
requires all states to track NH complaint investigations. For
each allegation in a complaint, the state is required to record
how each allegation was handled from the initial reporting of
allegations at intake to closure, including key dates, prioriti-
zation level, overall findings, and proposed action. Most
importantly, ACTS includes whether a complaint allegation
was unsubstantiated or substantiated (CMS, 2016). The
ACTS data used in this analysis are part of a larger data set
that includes data from the Certification and Survey Provider
Enhanced Reports (CASPER) and the Area Health Resource
File (AHRF). CASPER contains data on facility characteris-
tics (e.g., ownership status, occupancy rates), aggregate resi-
dent characteristics, and staffing levels. The AHRF includes
socioeconomic and provider information concerning the
county in which each NH is located.

Our sample is restricted to information about allegations
and complaints filed between November 28, 2016, and
November 27, 2017. We selected these dates due to our
focus on the complaint process. On November 28, 2017,
CMS implemented significant changes to the regulatory
standards NHs must meet, and we wanted to ensure that we
were examining only variation in how states administer the
complaint process, not the response to the new federal poli-
cies. We further restricted the sample to free-standing NHs

584 Journal of Applied Gerontology 40(6)

in the continental United States that did not have missing or
erroneous data for describing the NH.1 A total of 341 NHs
were excluded from the sample: 163 because of staffing data
and 178 because of they could not be linked with the AHRF.
This resulted in an analytic sample of 14,194 free-standing
NHs. We found no evidence that NHs dropped from the
analysis were significantly different from those included in
the analysis.

Allegation and Complaint Outcomes

Each complaint can consist of multiple allegations. All
allegations that are part of the same complaint are identi-
fied by common intake identification numbers. The ACTS
data identify whether each allegation was unsubstantiated
or substantiated.

Given this structure, we calculated six variables for all
NHs in the analytic sample: counts of the number of com-
plaints per NH and per 100 residents, number of substantiated
complaints per NH and per 100 residents, and binary indica-
tors of whether a facility received at least one complaint and
at least one substantiated complaint. The numbers of com-
plaints and substantiated complaints identify the intensity of
complaint (e.g., how many) a facility receives and whether
those complaints rise to level of being substantiated. In con-
trast, the two binary indicator variables indicate prevalence of
complaints by identifying whether the facility received any
complaint and any substantiated complaint. As there can be
multiple allegations in a complaint, we defined a substanti-
ated complaint as being a complaint in which at least one alle-
gation was substantiated (U.S. GAO, 2011).

The second set of outcome variables was restricted to
facilities that received at least one complaint. The first of
these variables was the number of allegations per complaint.
This variable identified the number of specific issues associ-
ated with a single complaint at intake and may reflect vari-
ability in a state’s process for logging a complaint. The
second variable in this set was the percentage of complaints
that are substantiated. Similar to other variables, a complaint
was considered substantiated if at least one allegation was
substantiated. This variable measured the percent of com-
plaints with at least one allegation that had merit in the view
of the inspector.

Empirical Strategy

The empirical strategy was to identify state variation in each
of our eight outcome variables. This was done by calculating
state means for each of our allegation and complaint vari-
ables. We further calculated the means of the state means and
identified the minimum and maximum value and standard
deviation (SD) for each state mean. In addition, we calcu-
lated median values. Finally, we examined whether there was
an association between allegations per complaint and percent
of complaints substantiated.

Results

Number of Complaints and Substantiated
Complaints

There were 14,194 NHs distributed among the U.S. states in
our data, with each state receiving a mean of 5.2 complaints
per NH and 6.5 complaints per 100 residents from November
28, 2016, to November 27, 2017 (see Table 1; see Online
Appendix Table A for median values). This is an increase
over the 3.9 complaints per NH found in 2014 (U.S. GAO,
2015). The numbers varied widely, from a mean of 0.4 com-
plaints per NH and 0.4 per 100 residents in Alabama to a
mean of 30.4 complaints per NH (see Figure 1) and 40.2 per
100 residents in Washington State. For complaints that were
substantiated, there was still a considerable gap between the
lowest, also Alabama, with a mean of 0.2 per NH and 0.2 per
100 residents, and the highest, California, with 7.5 per NH
and 8.7 per 100 residents. However, substantiated complaints
per NH and per 100 residents exhibited less than one third of
the variation by state, as measured by the SD.

Percentage of NHs With At Least One Complaint
and One Substantiated Complaint

In our examination of prevalence of complaints, we found
76.7% of NHs in each state received at least one complaint,
ranging from 30% in Alabama to 98.7% in Texas. The state-
by-state variation was slightly wider for the prevalence of
substantiated complaints, ranging from 11.7% in Alabama to
91.3% in California (see Figure 2).

Allegations and Substantiated Complaints in NHs
With At Least One Complaint

As a single complaint can have multiple allegations, we
assessed the mean number of allegations per complaint in
NHs with at least one complaint. We found minimal varia-
tion in the number of allegations per complaint, with a mean
of 2.3 and a range from 1.1 to 4.7. We further considered the
likelihood of a complaint being substantiated among NHs
receiving at least one complaint. In each state, surveyors sub-
stantiated an average of 34.3% of complaints, varying widely
from 12.4% in Rhode Island to a high of 67.6% in Indiana
(see Figure 3). We found no significant relationship between
allegations per complaint and percentages of complaints
substantiated.

Discussion

Consumer complaints are an important tool for the assess-
ment of NH quality (Hansen et al., 2019; Stevenson,
2006). Complaints are the primary mechanism for resi-
dents, family, ombudsman, and others to express their
concerns to regulators, and they have the potential to

Peterson et al. 585

trigger investigations during the months between annual
surveys. If complaints are an indicator of quality, one

would expect that the distribution of complaints from
state to state would be fairly consistent, particularly given

Table 1. State Variation in Frequency and Prevalence of Nursing Home Complaints.

State
Number of

facilities

All facilities

Facilities with at least one
complaint

Average
number

Percentage with at least
one:

Complaints per
facility

Complaints per
100 residents

Substantiated
complaints per Facility

Substantiated complaints
per 100 residents Complaints

Substantiated
complaints

Allegations
per complaint

% complaints
substantiated

Average 14,194 5.2 6.5 1.6 2.1 76.7 51.0 2.3 34.3
SD 5.5 7.0 1.5 1.9 17.6 19.7 0.9 14.1
Minimum 0.4 0.4 0.2 0.2 30.0 11.7 1.1 12.4
Maximum 30.4 40.2 7.5 8.7 98.7 91.3 4.7 67.6
AL 213 0.4 0.4 0.2 0.2 30.0 11.7 2.9 30.2
AR 219 4.0 5.4 1.4 2.0 90.4 62.1 2.5 33.4
AZ 133 3.2 4.5 0.5 0.8 88.0 35.3 2.2 16.7
CA 1,075 14.0 16.1 7.5 8.7 96.3 91.3 1.5 53.2
CO 197 2.6 3.2 1.6 1.9 71.1 53.3 4.1 54.3
CT 216 2.2 2.3 1.4 1.5 75.0 65.3 1.1 64.9
DC 13 2.9 1.5 0.5 0.3 61.5 38.5 3.0 29.2
DE 45 2.9 2.7 1.0 1.0 62.2 44.4 3.0 32.5
FL 672 3.5 3.3 0.9 0.9 84.5 46.3 1.9 25.0
GA 302 2.9 2.9 0.7 0.7 76.8 41.1 2.4 24.6
IA 404 4.4 8.0 1.7 3.3 84.7 55.2 1.9 33.8
ID 63 1.1 2.2 0.6 1.2 46.0 27.0 4.0 46.2
IL 689 8.6 8.7 3.5 3.7 92.6 78.5 2.3 41.9
IN 504 4.8 6.7 3.4 4.7 87.7 80.4 3.3 67.6
KS 283 4.5 8.0 1.6 3.1 84.8 60.8 1.5 39.7
KY 257 3.9 4.6 0.9 1.1 89.9 45.1 1.4 21.8
LA 261 2.5 2.5 0.8 0.8 79.3 47.5 2.5 32.8
MA 397 2.1 2.2 0.4 0.5 71.8 29.2 1.5 20.1
MD 217 13.6 12.7 4.5 4.3 97.2 88.0 1.7 33.2
ME 94 7.5 12.7 2.1 3.6 94.7 73.4 2.0 25.8
MI 414 13.9 16.9 6.6 8.3 96.9 90.1 2.7 47.4
MN 319 1.7 2.6 0.5 0.8 58.6 29.8 1.3 31.7
MO 492 13.9 17.4 2.0 2.6 95.9 64.0 2.3 15.6
MS 178 1.9 2.5 0.7 0.9 70.8 40.4 1.2 34.3
MT 56 1.7 2.8 0.9 1.6 62.5 48.2 3.3 56.8
NC 391 6.7 7.5 1.6 1.8 90.3 53.5 2.8 19.8
ND 63 0.6 0.9 0.2 0.4 38.1 15.9 3.1 30.6
NE 155 5.7 9.6 2.0 3.4 82.6 54.2 1.9 28.0
NH 71 1.5 1.6 0.7 0.7 56.3 43.7 1.2 52.1
NJ 341 1.4 1.2 0.5 0.4 52.8 26.1 2.8 30.7
NM 67 1.6 2.0 1.0 1.2 71.6 61.2 1.1 66.9
NV 43 4.7 4.6 1.2 1.4 90.7 58.1 3.2 24.2
NY 550 4.7 3.3 0.9 0.7 79.6 37.3 1.7 17.6
OH 909 5.0 6.5 1.3 1.7 87.2 53.6 2.1 24.1
OK 295 3.4 5.4 1.1 1.7 81.7 51.9 2.1 30.2
OR 130 3.0 5.7 1.5 2.9 80.0 56.2 1.8 46.5
PA 651 5.9 5.5 2.3 2.2 87.1 65.9 2.9 35.7
RI 83 8.3 8.7 1.2 1.2 95.2 48.2 1.1 12.4
SC 167 1.1 1.2 0.3 0.3 54.5 21.0 1.2 26.5
SD 91 0.7 1.3 0.4 0.6 44.0 23.1 1.6 40.7
TN 292 6.0 7.0 2.1 2.5 91.8 68.8 1.5 35.5
TX 1,146 17.7 23.2 2.4 3.3 98.7 72.5 1.8 13.6
UT 91 1.3 2.3 0.5 1.0 53.8 31.9 4.7 45.5
VA 218 1.3 1.3 0.7 0.7 56.9 38.5 3.4 55.2
VT 31 8.8 13.9 2.1 4.0 93.5 64.5 3.3 22.8
WA 207 30.4 40.2 4.1 5.8 97.6 82.6 1.1 14.8
WI 361 3.7 5.9 1.3 2.1 78.7 52.6 1.2 32.4
WV 102 1.5 1.8 0.4 0.5 59.8 22.5 3.3 23.9
WY 26 3.7 4.7 1.5 1.7 84.6 50.0 2.6 37.0

Note. The sample includes free-standing nursing homes from November 28, 2016 to November 27, 2017. A complaint is defined as having a unique intake identification
number and can consist of multiple allegations. A complaint is considered substantiated if at least one allegation is indicated as substantiated in the CASPER data. CASPER =
Certification and Survey Provider Enhanced Reports.

586 Journal of Applied Gerontology 40(6)

that NH complaint protocols are established federally and
apply equally to all states.

However, we found wide variation by state in the numbers
of total complaints (those recorded at intake), from a low of
0.4 per NH in Alabama to a high of 30.4 per NH in Washington
state. We also found variation in the number of complaints
that were substantiated upon investigation. This variation
was narrower than the variation for total complaints, but it

was still evident, particularly between states with relatively
low numbers of total complaints and those with relatively
high total complaint numbers. Overall this suggests there are
differences among the states in how they interpret and/or
administer federal complaint protocols.

Prior reports have highlighted differences that would
affect the numbers of complaints initially filed and recorded.
For example, Michigan provided consumers with more

Figure 1. U.S. map showing state variation in the numbers of complaints filed per nursing home. Lighter shades represent lower mean
numbers, with the means by state ranging from 0.4 to 30.4.

Figure 2. U.S. map showing state variation in the percentage of nursing homes with at least one substantiated complaint. Lighter shades
represent lower percentages, with the percentages by state ranging from 11.7% to 91.3%.

Peterson et al. 587

complaint filing options and California increased efforts to
enter all complaints into the federal tracking system (U.S.
GAO, 2015). Prior reports also have found differences poten-
tially affecting investigations, including how staff in differ-
ent states determined whether to substantiate a complaint
and differences in resources available to conduct investiga-
tions (U.S. GAO, 2003, 2011). Similarly, the Office of the
Inspector General (2017) reported inadequate investigative
staffing in states that failed to meet standards for investigat-
ing serious complaints within expected timeframes.

Numbers of complaints and substantiated complaints
also may vary if NH quality varies from state to state.
Stevenson (2006) found relationships between complaints
and measures of NH quality, such as nurse staffing and
deficiencies cited on annual surveys. Other factors also
could lead to state differences, such as the availability of
state ombudsmen whose federally defined role includes
advocating for quality improvements in long-term care.
However, it seems unlikely that quality and state ombuds-
men vary to the extent that complaints vary in our data—
comparing, for instance, Oregon, with three complaints per
NH, and Washington, with more than 30, or South Carolina,
with just over one complaint per NH, and neighboring
North Carolina, with nearly seven.

In addition to complaint frequency, we also examined
state variation in the prevalence of complaints and of sub-
stantiated complaints—that is, the likelihood of a state’s NHs
having at least one complaint or at least one substantiated
complaint. We found variation in both, but it was greater for
the percentage of NHs with at least one substantiated com-
plaint. This appeared to be related to total complaint num-
bers, particularly for states with few total complaints per NH.

While a relatively high percentage of NHs in most states had
at least one complaint, the chances of having any substanti-
ated complaints appeared to decrease more for states with the
lowest mean numbers of total complaints (nearly 30% of
states had fewer than two complaints per NH).

The results of our analyses of complaint numbers and
prevalence suggest the outcome of the complaints process is
largely influenced by whether complaints are filed in the first
place. The U.S. GAO (2011) highlighted differences in how
states record and track complaints, noting in a later report
(U.S. GAO, 2015) it had asked CMS to clarify protocols to
states concerning complaint intake, and CMS had done so
with some states on an as-needed basis. The GAO reports
included the complaint frequencies for all states but reviewed
the processes for only a limited number. The results of this
study indicate state variation has persisted, suggesting there
is a need for research to more thoroughly explore the rela-
tionship between numbers of complaints filed and state-level
policies and processes that affect whether and how consum-
ers file complaints and how complaints are recorded.

Further research also is needed into the factors concerning
whether complaints are substantiated, given that states with
higher numbers of complaints (six or more per NH) appear to
have lower substantiation rates than those with lower num-
bers (less than two per NH). We do not know if complaints
were not substantiated because they were deemed to lack
merit or because of other factors, such as survey agency
staffing deficits that hindered states’ responses to the com-
plaints. Walshe and Harrington (2002) found a relationship
between the funding of survey agencies and their ability to
conduct annual regulatory surveys of NHs, with higher fund-
ing associated with more deficiency citations.

Figure 3. U.S. map showing state variation in the percentage of total complaints in the state that were substantiated. Lighter shades
represent lower percentages, with the percentages ranging from 12.4% to 67.6%.

588 Journal of Applied Gerontology 40(6)

The step at which complaints are substantiated or not is
critical because it determines what complaint information is
made public on NH Compare. Research has suggested that
public reporting of NH performance improves care as pro-
viders make changes to appeal to potential residents (Werner
et al., 2010). However, evidence also suggests providers
enhance their profiles partly by changing only care the gov-
ernment routinely measures (Werner et al., 2011). Consumer
complaints represent qualitatively different information that
could be presented as a separate measure to supplement the
commonly used measures of quality. When complaints are
recorded and investigated through a robust and consistent
process they best serve their potential use for more effective
quality assurance.

Considerable attention is needed concerning the factors
influencing whether a complaint is initially filed and
recorded, where variation was greater in our study. This
study details improbable variations ranging from a total
of 94 complaints in Alabama to 6,301 complaints in
Washington, states with nearly the same number of NHs.
An intake process that limits or hinders complaint filings
could keep regulators from learning about quality lapses
between annual surveys, resulting in negligent acts or errors
going unaddressed. It is important to consider, however,
that facilitating complaint filings also may be problematic
if the process is not equitable and brings greater regulatory
scrutiny upon one NH over another.

Given our findings, this study has several limitations.
To our knowledge, no research has been conducted to
directly test the validity of the ACTS data on which this
analysis was based. However, the same individuals who
conduct federally regulated annual survey visits to NHs
are also responsible for investigating complaints, and the
system requests the same data from the states rather than
states generating their own reports. The extensive nature
(e.g., intake, interviews, and on-site visits) of the com-
plaint investigation process, as outlined in the State
Operations Manual (CMS, 2016) substantiates the face
validity of the information contained within the ACTS
data. As a further limitation, we examined 1 year of data,
but it is a recent year, and looking at a single year provides
us with a clearer view, given the management and quality
changes that can occur from year to year in NHs. Also, we
looked only at numbers and percentages of complaints,
not considering types of complaints. Other research has
shown, however, that the majority of complaints fall into
a few fairly broad categories: care or services, resident
rights, and resident neglect and abuse (Hansen et al.,
2019). Furthermore, we did not investigate the relation-
ship of complaints to NH characteristics and indicators of
quality. Future research should investigate the NH-level
factors associated with numbers and prevalence of com-
plaints as well as the relationship between complaints and
deficiency citations and other quality measures.

Conclusion

Our results show that there is considerable variation
between states in the frequency and prevalence of com-
plaints, while the variance in frequency does appear to be
reduced through the investigation process. Given the
potential for complaints to spur investigation of potentially
deficient NH practices, our results suggest more research
is needed to understand state differences in multiple phases
of the complaint process—knowledge of and the ease of
filing a complaint, and the influence of long-term care
ombudsmen and NH lawsuits, as well as investigation
resources. Research also is needed concerning the extent to
which complaints reflect NH quality or the complaint
administration process. Results of these studies could lead
to quality improvement through procedures to ensure the
complaint process gives consumers a voice across the
states, while being fair to providers.

Declaration of Conflicting Interests

The author(s) declared the following potential conflicts of interest
with respect to the research, authorship, and/or publication of this
article: J.R.B. provides consulting services to long-term care pro-
viders, including nursing homes. The other authors have no con-
flicts of interest to disclose.

Funding

The author(s) disclosed receipt of the following financial support
for the research, authorship, and/or publication of this article: This
study was supported by the Patrick and Catherine Weldon
Donaghue Medical Research Foundation. University of South
Florida Institutional Review Board Review Number: Pro00038157

ORCID iDs

Lindsay J. Peterson https://orcid.org/0000-0003-1129-0931

John R. Bowblis https://orcid.org/0000-0003-4776-4182

Dylan J. Jester https://orcid.org/0000-0001-9878-9633

Kathryn Hyer https://orcid.org/0000-0002-6445-3602

Supplemental Material

Supplemental material for this article is available online.

Note

1. For direct care nursing staff, first total hours per resident day
of direct care registered nurses, licensed practical nurses, and
certified nurse aides were calculated. An observation was
considered to have potentially erroneous direct care staff-
ing information if the observation reported staffing levels
that were zero, over 24 hours per resident day, and among
the remaining, four standard deviations above the mean. For
all other staffing variables, potentially erroneous observations
had staffing levels that were four standard deviations above
the mean (excluding facilities with over 24 hours per resident
day of staffing).

Peterson et al. 589

References

Centers for Medicare and Medicaid Services. (2016). State opera-
tions manual. https://www.cms.gov/Regulations-and-Guidance/
Guidance/Manuals/Downloads/som107c05.pdf

Hansen, K. E., Hyer, K., Holup, A. A., Smith, K. M., & Small,
B. J. (2019). Analyses of complaints, investigations of allega-
tion, and deficiency citations in United States nursing homes.
Medical Care Research and Review, 76(6), 736–757. https://
doi.org/10.1177/1077558717744863

Institute of Medicine. (2001). Improving the quality of long-term
care. https://www.nap.edu/download/9611

Office of the Inspector General. (2017). A few states fell short in
timely investigation of the most serious nursing home com-
plaints: 2011-2015 (OIG publication No. OEI-01-16-00330).
Office of the Inspector General. https://oig.hhs.gov/oei/reports/
oei-01-16-00330.asp

Stevenson, D. G. (2005). Nursing home consumer complaints and their
potential role in assessing quality of care. Medical Care, 43(2),
102–111. https://doi.org/10.1097/00005650-200502000-00003

Stevenson, D. G. (2006). Nursing home consumer com-
plaints and quality of care: A national view. Medical Care
Research and Review, 63(3), 347–368. https://doi.org/10.
1177/1077558706287043

Stevenson, D. G. (2018, August 23). The future of nursing home
regulation: Time for a conversation? [Blog post]. https://www.
healthaffairs.org/do/10.1377/hblog20180820.660365/full/

U.S. Government Accountability Office. (1999). Nursing homes:
Complaint investigation processes often inadequate to pro-
tect residents. https://www.gao.gov/products/HEHS-99-80

U.S. Government Accountability Office. (2003). Prevalence of
serious problems, while declining, reinforces importance of
enhanced oversight. https://www.gao.gov/products/GAO-
03-561

U.S. Government Accountability Office. (2011). More reliable
data and consistent guidance would improve CMS oversight of
state complaint investigations. https://www.gao.gov/products/
GAO-11-280

U.S. Government Accountability Office. (2015). CMS should con-
tinue to improve data and oversight. https://www.gao.gov/
products/GAO-16-33

Walshe, K., & Harrington, C. (2002). Regulation of nursing
facilities in the United States: An analysis of resources and
performance of state survey agencies. The Gerontologist,
42(4), 475–486. https://doi.org/10.1093/geront/42.4.475

Werner, R. M., Konetzka, R. T., Stuart, E. A., & Polsky, D.
(2011). Changes in patient sorting to nursing homes under
public reporting: Improved patient matching or provider gam-
ing? Health Services Research, 46(2), 555–571. https://doi.
org/10.1111/j.1475-6773.2010.01205.x

Werner, R. M., Stuart, E., & Polsky, D. (2010). Public reporting
drove quality gains at nursing homes. Health Affairs, 29(9),
1706–1713. https://doi.org/10.1377/hlthaff.2009.0556

Week 1 Assignment/5 Fardapaper-Importance-of-organizational-structure.pdf

Importance of organizational structure for TQM success and customer
satisfaction

Jorge Luis Garcı́a-Alcaraz1 • Francisco Javier Flor Montalvo2 • Cuauhtémoc Sánchez-Ramı́rez3 •
Liliana Avelar-Sosa1 • José Antonio Marmolejo Saucedo4 • Giner Alor-Hernández3

� Springer Science+Business Media, LLC, part of Springer Nature 2019

Abstract
This paper reports a structural equation model to relate three critical success factors for total quality management (TQM)

(i.e. managerial commitment, role of quality department, and quality policies) with customer satisfaction benefits through

six hypotheses, which are statistically tested with information from 398 responses to a survey applied to Mexican

manufacturing industry and using partial least squares technique integrated in WarpPLS v.6 software. The paper also

reports a sensitivity analysis based on conditional probabilities for analyze low and high scenarios. Findings indicate that

managerial commitment is the most important variable to ensure TQM, yet it depends on the role of the quality department

for deploy quality policies and guarantee customer satisfaction. Similarly, sensibility analysis demonstrate that high levels

of managerial commitment always guarantee a high performance in quality departments and good quality policies, thereby

contributing to customer satisfaction. From this perspective, there are statistical evidence to declare that managers and

operators are the main facilitators of TQM success.

Keywords TQM � Critical success factors � Managerial commitment � Quality policies

1 Introduction

Total quality management (TQM) is not a new production

strategy; however, it is highly popular due to the benefits

that still offers. TQM focuses on promoting and working

under a continuous improvement culture where people

acknowledge that there is always opportunities for

improvement in processes and products. Nowadays, TQM

is viewed as a management strategy applicable to different

sectors, such services, industry, government, and education

[1].

Over the years, TQM has evolved and thus moved, from

a concept merely seeking to reduce variation in production

process, to that including process reengineering and total

quality. Deming, Feigenbaum, Crosby, and Juran proposed

a philosophical approach to TQM that focuses first on

human resources, and then, as a consequence, on the pro-

duction process, products, and services [2]. In other words,

quality is viewed therefore from human resources abilities

& Jorge Luis Garcı́a-Alcaraz
[email protected]

Francisco Javier Flor Montalvo

[email protected]

Cuauhtémoc Sánchez-Ramı́rez

[email protected]

Liliana Avelar-Sosa

[email protected]

José Antonio Marmolejo Saucedo

[email protected]

Giner Alor-Hernández

[email protected]

1
Department of Industrial and Manufacturing Engineering,

Autonomous University of Ciudad Juarez, Av. del charro 450

Norte. Col. Partido Romero, Z.C., 32310 Ciudad Juárez,

Chihuahua, Mexico

2
Department of Electric Engineering, University of La Rioja,

Luis de Ulloa 20, 26004 Logroño, La Rioja, Spain

3
Division of Research and Postgraduate Studies, Tecnológico

Nacional de México/I.T. Orizaba, Oriente 9 #852, Col.

Emiliano Zapata, 94320 Orizaba, Veracruz, Mexico

4
Facultad de Ingenierı́a, Universidad Panamericana, Augusto

Rodin 498, 03920 Mexico City, Mexico

123

Wireless Networks
https://doi.org/10.1007/s11276-019-02158-5(0123456789().,-volV)(0123456789().,- volV)

applied to the production process. Although TQM is an old

concept, Fig. 1 depicts the distribution of the number of

papers found in ScienceDirect’s database whose titles

include the words Total Quality Management or TQM from

1995 to May 2019 and it is observer that TQM interest is

increasing in academic and industrial sector.

As a production philosophy, TQM offers many benefits,

extensively reported and discussed in the literature. For

instance, Singh et al. [3] found a relationship between

TQM and organizational performance, whereas Iqbal and

Asrar-ul-Haq [4] discussed the connection between TQM

and employee performance. Then, operators are a key

factor in TQM because they are responsible for applying

the Quality Policies established by top management and

the Quality Department.

TQM benefits can be gained by performing some

important tasks, commonly referred to as critical success

factors (CSFs). CSFs are usually prioritized by top man-

agement departments and comprise a limited number of

characteristics, conditions, or variables that guarantee a

company’s operational performance [5]. In TQM, CSFs

can be related to managerial responsibility, Quality

Department, operators, and production machines and tools,

and they all seek to comply with the company’s Quality

Policies [6].

1.1 Critical success factors for TQM
in the manufacturing industry

The CSFs for TQM are widely studied. Sohal and Ter-

ziovski [7] reported that CSFs for TQM can be associated

with supplier relationships, employee training, and Man-

agerial Commitment, thus highlighting the central role of

the human factor. Seetharaman et al. [8] emphasize on

Managerial Commitment, responsible for create new

knowledge to solve problems, and establish Quality Poli-

cies. Gherbal et al. [9] concluded that there are 15 most

important CSFs for TQM, including top management,

implementation strategy, production process, employee

education, suppliers, resource allocation, and work culture,

among others. Sreedharan et al. [10] concluded that CSF

are Managerial Commitment, the role of Quality Depart-

ment, Quality Policies, employee involvement and recog-

nition are the most important.

Likewise, Salleh et al. [11] ranked the most important

CSFs for TQM as follows: management commitment and

leadership, total customer satisfaction, employee involve-

ment, continuous improvement, employee training, com-

munication, and teamwork. For further information on

CSFs for TQM, readers can consult the work of Talib and

Rahman [12], who conducted a literature review and then

reported nine CSFs as top management, customer focus

and employee training, among others. Similarly, Aletaiby

et al. [13] listed the main CSFs for TQM following a

review of ten previous works, and once more, Managerial

Commitment and Quality Department were identified as the

366

347

327

308

227 225 229

168
162

174 177

197 201 195

162

134

167

199

165

244
238

201
214

258

158

y = 0.8703×2 – 27.018x + 376.62

100

150

200

250

300

350

400

19
95

19
96

19
97

19
98

19
99

20
00

20
01

20
02

20
03

20
04

20
05

20
06

20
07

20
08

20
09

20
10

20
11

20
12

20
13

20
14

20
15

20
16

20
17

20
18

20
19

Pa
pe

rs

Year

Fig. 1 TQM paper published
and years

Wireless Networks

123

basis for TQM policies. Finally, readers can also refer to

the research of Iqbal et al. [14], Khalili et al. [15], and

Singh et al. [3], among others.

1.2 Customer satisfaction and TQM
in the manufacturing industry

TQM brings attractive benefits to companies; however, as

Saumyaranjan [16] claim, TQM must not be carried out in

isolation. For example, it must be supported by total pre-

ventive maintenance (TPM) tools to ensure customer sat-

isfaction. Similarly, Manjot Singh and Anjali [17] point out

that one of the greatest benefits of TQM is communication

because it improves quality along and across the entire

organizational structure and this is ultimately reflected on

greater Customer Satisfaction. In other words, TQM

demands a solid organizational structure, where top man-

agement, the Quality Department, and employees are

properly integrated.

According to Durgesh et al. [18], companies focusing on

customer satisfaction during TQM implementation are able

to manage their resources efficiently and decrease costs,

which have ultimate effects financial income. Nevertheless,

reaching high levels of Customer Satisfaction, as Agus and

Hassan [19] and Singh et al. [3] point out, implies reducing

operational costs and always complying with the products’

technical design specifications, which is what customers

notice first. Additionally, Valmohammadi and Roshanza-

mir [20] suggest that a good indicator of Customer Satis-

faction is to identify the firm’s competitive position and

social image in the market, and Iqbal and Asrar-ul-Haq [4]

recommend using the number of complaints, warranty

expenses, and customer loyalty to determine how satisfied

customers are with the products that they purchase.

1.3 Research problem and goal

Undoubtedly, one of the main goals of TQM is to increase

Customer Satisfaction, but this implies that companies

must perform a specific series of tasks to gain this benefit.

Multiple research works have sought to associate CSFs to

their corresponding benefits. For instance, Singh et al. [3]

found that TQM benefits for both companies and customers

highly depend on aspects such as organizational leadership,

human resources management, and the organizations’

relationships with customers. Likewise, Anil and Satish

[21] developed a second-order SEM that associates TQM

with organization performance, where the main CSFs are

described in terms of human resources. Agus and Hassan

[19] also developed a second-order SEM where TQM

implementation is related to operational benefits and Cus-

tomer Satisfaction by studying CSF such as relationships

with suppliers, continuous improvement, benchmarking,

and quality systems and measures. In turn, Iqbal et al. [14]

associates organizational culture with the best manufac-

turing practices (i.e. just in time and TQM) and operational

and financial indices.

As can be observed, both CSFs for TQM and TQM

benefits are clearly identified; however, the relationship

between three specific CSFs—i.e. Managerial Commit-

ment, Quality Department, and Quality Policies—and

Customer Satisfaction has not yet been clarified because

previous research on TQM usually address overall TQM

performance, where Customer Satisfaction is just one more

variable to be measured. Moreover, the role of Quality

Department has not been thoroughly studied, yet they play

a crucial role in a company’s Quality Policies. To address

such limitations, this research seeks to quantify the direct,

indirect and total relationship between the three CSFs

(Managerial Commitment, Quality Department, and

Quality Policies) and Customer Satisfaction based on

empirical evidence from practitioners experience in

industry and that is the main contribution in this research,

because provides a metric of dependence between those

CSFs for TQM and Customer Satisfaction and reports a

sensitivity analysis based on conditional probabilities that

help managers to know the probability of occurrence for

several scenarios and identify possible risks. Findings are

intended to support managers and decision makers in TQM

to identify crucial tasks to guarantee Customer Satisfaction.

This paper is divided into five sections: introduction,

literature review and hypotheses, materials and methods,

findings, and conclusions.

2 Literature review and hypotheses

This research is aimed to associate three CSFs—i.e.

Managerial Commitment (MAC), the role of Quality

Department (QUD), and Quality Policies (QUP)—with

Customer Satisfaction (CUS); all them are considered as

latent variables that are integrate by items or observed

variables. The following subsections discuss the latent

variables and their corresponding observed variables.

2.1 Managerial commitment

Managerial Commitment is pivotal to TQM as the pillar of

lean manufacturing (LM). In the decade of 1990, Cordeiro

and Turner [22] claimed that top management was the

origin of quality, and as such, it had to adopt a long-term

strategic vision, organization’s mission, objectives, and

corporate goals. Unfortunately, according to Pearson et al.

[23], not all managers were ready at that time for such a

commitment. Two years later, Choi and Behling [24]

analyzed the role of top managers in TQM environments

Wireless Networks

123

and concluded that management departments were being

central to TQM implementation. Recently, Psychogios and

Priporas [25] pointed out that some hard TQM concepts

may present limitations to managerial departments, since

they require significant knowledge on statistics and math-

ematical processes; nevertheless, the author declare that, if

well implemented, TQM guarantees both product quality

and income flow. Additionally, Soltani et al. [26] argue that

TQM allows managers to gain control over the production

process if they are highly involved. Finally, according to

RadlovacÌŒki et al. [27] declares that managers play a critical

role in the leadership and ISO certifications.

In this research, Managerial Commitment in TQM

environments is measured as follow [22–30]:

1. Management gives long-term support to production

process improvements.

2. Management clearly conveys the corporate mission

and goals.

3. Management establishes specific quality goals in the

organization.

4. Management sees TQM as a means to increase

economic performance.

5. Management ensures that employees are trained.

2.2 Quality department

TQM demands a solid organizational structure to support

top management and currently, some organizations have

their own Quality Department and corresponding divisions,

such as the Six Sigma department or the quality assurance

department, to name but a few. Authors such as Psychogios

et al. [30] consider Quality Department and middle man-

agement departments as the real core of TQM success,

since they are the link between with top management (who

establishes the policies) and operators (apply the policies).

To Al Rawashdeh [31], middle managers of Quality

Department are the true operational leaders of TQM

implementation. They convey the organization’s Quality

Policies, supervise their compliance, have enough author-

ity to create continuous improvement teams aimed to solve

problems and manage the resources available. Finally, as

Giauque [32] points out, Quality Department must be dri-

vers of operational change within the organization and in

this research, the role of Quality Department is measured

through the following items [30–33]:

1. Quality Department has an organizational structure.

2. Quality Department and top management maintain

communication.

3. Quality Department is autonomous.

4. Quality Department members act as advisers

5. Quality Department creates production process

improvement and quality improvement teams.

6. Quality Department trains employees and evaluates

their performance.

The creation of Quality Department depends on top

management and its commitment to gather the right spe-

cialists from the organization. That is, Quality Department

members must be the link that communicates with opera-

tors [27], designers and creators of employee training

programs, and leaders of implementation projects [34]. In

this sense, our first research hypothesis is proposed as

follows:

H1 Managerial Commitment to TQM has a positive direct
effect on the performance of the Quality Department.

2.3 Quality policies

Top and middle management has to set specific procedures

to implement TQM plans and programs through Quality

Policies [35]. For instance, Sreedharan et al. [10] claim that

manufacturing companies must supervise the quality of

their raw materials using strategies such as acceptance

sampling and statistical quality tools. On the other hand,

Kouaib and Jarboui [36] highlight the importance of

auditing the strategic plans set to enforce TQM, whereas

Iqbal and Asrar-ul-Haq [4] and Dedy et al. [37] argue that

quality is only achieved through employee involvement. In

this research, Quality Policies are assessed through the

following observed variables [31, 34, 38–41]:

1. The company uses quality-focused strategies.

2. The company has an acceptance sampling plan for

received raw materials.

3. The company uses statistical control charts in the

production process.

4. The company implements TPM programs.

5. The production process is audited.

6. The company has and implements an operator self-

inspection program.

7. Work instructions are clearly conveyed to operators.

8. The company works under a zero-defects approach

Quality Policies are the result of multiple efforts from

top managers and the Quality Department, which super-

vises their compliance. To Oakland [40], leadership and

Quality Policies are the backbone of TQM and Valmo-

hammadi and Roshanzamir [20] point out that a company’s

organizational structure must communicate the necessary

quality assurance techniques through employee training

programs and processes and Ugboro and Obeng [38]

studied the role of employee empowerment and top man-

agement leadership in TQM. Following this discussion, the

second and third hypotheses are stated as follows:

Wireless Networks

123

H2 Managerial Commitment to TQM has a positive direct
effect on a company’s Quality Policies.

H3 The Quality Department that gives support to TQM
has a positive direct effect on a company’s Quality

Policies.

2.4 Customer satisfaction

Customer Satisfaction is one of the goals of TQM and

refers to the degree of satisfaction provided by the goods or

services of a company as measured by the number of repeat

customers [3]. Customer Satisfaction must be a priority,

since a lack of it causes product returns and customer

complaints [42] and to know what position in the market a

firm holds, and the social image it projects, the company

must compare itself with its competitors [43]. However,

Customer Satisfaction can also be internally measured by

monitoring a series of factors, such as the number of cus-

tomer complaints, the time that sale assistants spend

solving such complaints, customer loyalty, among others.

In this research, Customer Satisfaction is measured through

the following observed variables [18, 19, 35, 42, 44]:

1. Number of processed customer complaints.

2. The company’s market position.

3. The company’s social image.

4. Time dedicated to customer service.

5. Valid warranty claims.

6. Customer loyalty.

One of the challenges when analyzing Customer Satis-

faction is to find the factors that increase it and Vimal

Kumar and Sharma [45] pointed out that good leadership is

a key element to reach it. Ooi et al. [44] found that quality

plans and programs designed by top managers have posi-

tive effects on Customer Satisfaction if they are customer-

focused and constantly supervised. Finally, Durgesh et al.

[18] claim that when TQM practices are well managed,

customer loyalty increases, while the number of rejected

products and warranty claims decrease, thus contributing to

a high economic margin. Following this discussion, the

fourth hypothesis of this research is proposed below:

H4 Managerial Commitment to TQM has a positive direct
effect on Customer Satisfaction.

Customer Satisfaction does not merely depend on top

management, but also on the Quality Department that

supervise the quality plans and programs from an opera-

tional perspective. To Kumar and Sharma [46], the back-

bone of both TQM and Customer Satisfaction is leadership

from managers, process engineers, and continuous

improvement team members, since they handle the

resources that enable the success of quality programs. Al

Rawashdeh [31] and Chiarini and Vagnoni [47] claim that

middle management (i.e. assistant managers and supervi-

sors) and their leadership are TQM success enablers in the

services industry and the financial industry. In turn, Kiran

[34] argues that Quality Department must work to decrease

customer complaints and minimize warranty expenses,

which in turn increases the customer’s loyalty. Finally,

Durgesh et al. [18] point out that proper quality manage-

ment can increase Customer Satisfaction in the financial

industry. Following this discussion, the fifth hypothesis is

proposed as follows:

H5 The performance of a company’s Quality Department
has a positive direct effect on Customer Satisfaction.

Managerial Commitment and Quality Department set

the operational norms of TQM through Quality Policies

that are clearly conveyed along the entire organizational

structure, especially among operators [48]. Also, Quality

Policies must aim at improving processes and products to

decrease the number of rejected products and increase

customer loyalty [49]. According to Durgesh et al. [18],

rather than implying that companies should dedicate sig-

nificant time to handling customer complaints, Quality

Policies must be focused on collecting opinions for product

improvement. Additionally, as CÌŒater and CÌŒater [50] and

Durgesh et al. [18] point out, measuring customer loyalty

in the manufacturing industry is usually more challenging

than in the services industry. However, in these cases,

Customer Satisfaction measurements must consider cus-

tomer complaints, the company’s image, and its brand.

From this perspective, Allen Broyles et al. [51] suggest that

Quality Policies in the manufacturing industry should be as

much customer-focused as possible in order to maintain a

good social image. Following this discussion, the sixth

hypothesis of this research is formulated below:

H6 Quality Policies for TQM have a positive direct effect
on Customer Satisfaction.

Figure 2 depicts the six research hypotheses.

3 Methodology

3.1 Literature review

As the first step, we conducted a literature review related

TQM using databases such as Springer, Scopus, Science-

Direct, and Emerald, among others. As keywords, we used

the term TQM combined with those of the latent variables

(see Fig. 2). Based on that literature review, a list is created

with the main CSFs for TQM and its benefits. This litera-

ture review represents the rational a validation [52].

Wireless Networks

123

3.2 Survey design and administration

The CSFs and benefits from TQM collected in the literature

review was used to design a survey. We also took the

survey reported in Antony et al. [53] as a reference, yet

modifications were made to make the questionnaire suit-

able to the research geographical and industrial context.

Subsequently, the draft survey was validated by a panel of

judges, composed of five academics and three quality

managers from local firms. Finally, changes were made to

the draft following the judges’ comments. The final version

of the survey comprised three sections: demographic data,

CSFs for TQM and TQM benefits. The second and third

sections of the questionnaire were answered using a five-

point Likert scale, where the lowest value (one) was used

to indicate that a TQM task was not performed, or a TQM

benefit was not obtained. Conversely, the highest value

(five) indicated that a TQM task was always performed, or

a TQM benefit was always obtained.

The questionnaire was applied among Mexican manu-

facturing companies that implement TQM and hold at least

one ISO quality certification. The questionnaire was aimed

at Quality Department managers, managers in general, six

sigma managers, and quality assurance managers, among

others. All the participants must had at least 3 years of

work experience in their current job position and involved

in continuous improvement projects. The questionnaire

was answered in face to face interviews.

3.3 Data capture and screening

The data collected through the questionnaires were cap-

tured using SPSS 24
�
and was screened by identifying the

following information [54]:

• Missing values: questionnaires with more than 10% of
missing values were removed from the analysis,

otherwise they were replaced by the median.

• Extreme values or outliers: items were standardized;
then, absolute values higher than 4 were considered as

outliers and were replaced by the median.

• Unengaged respondents: the standard deviation is
estimated for every questionnaire and if it was lower

than 0.5, the questionnaire was removed from the

analysis.

3.4 Latent variable validation

The latent variables in Fig. 2 were validated with respect to

their own observed variables. The following indices were

estimated to validate each latent variable [55]:

1. Cronbach’s alpha and composite reliability index are

used to test internal validity and composite reliability,

respectively. Only values higher than 0.7 were

accepted.

2. R-Squared (R
2
) and Adjusted R

2
are used to test

parametric predictive validity. Only values higher than

0.2 were accepted.

3. Average variance extracted (AVE) is used to test

convergent validity and values higher that 0.5 are

accepted.

4. Q-Squared (Q
2
) is used to test non-parametric predic-

tive validity. Only values higher than 0 and similar to

their corresponding R
2
values were accepted.

5. Variance Inflation Factors (VIFs) are used as a measure

of collinearity, accepting only values lower than 5.

3.5 The structural equation model (SEM)

The SEM technique is used to validate the relationships

between the latent variables. SEM allows for assessing

variables with different roles and has been employed in

similar TQM studies as for example Iqbal and Asrar-ul-

Haq [4] proposed a SEM to study the relationship between

Fig. 2 Research hypotheses

Wireless Networks

123

TQM and employee performance and Iqbal et al. [14] use

SEM to explore the relationship between TQM, JIT, and

employee performance. Specifically, the SEM is evaluated

using the partial least squares (PLS) method integrated in

software WarpPLS 6
�
recommended for ordinal and non-

normal data.

The model’s efficiency is measured computing six

model fit and quality indices [55]: Average Path Coeffi-

cient (APC), Average R-squared (ARS), Average Adjusted

R-Squared (AARS), Average block VIF (AVIF), Average

Full collinearity VIF (AFVIF), and Tenenhaus (GoF).

APC, ARS, and ARS are associated with a p value that had

to be lower than 0.05 to claim that all the statistical

inferences were made at a 95% confidence level. On the

other hand, AVIF and AFVIF are computed as measures of

collinearity, only accepting values lower than 5 and GoF

index is a goodness of fit measure that indicates how well a

model fits its data and values higher than 0.25 are desirable.

We also measured the direct, indirect, and total effects

between the latent variables. In Fig. 2, the direct effects are

illustrated as arrows connecting two latent variables; they

are expressed in standard deviations and are represented by

a b value as a measure of dependence. For every rela-
tionship, we tested the hypotheses H0: b = 0 versus H1:
b = 0.

Indirect effects occur when two latent variables are

related through a third latent variable, known as the

mediator. For each indirect effect between two latent

variables, we report only the sum of indirect effects though

a b value. On the other hand, total effects are the sum of the
direct and sum of indirect effects in a relationship. Finally,

we also report the effect size (ES) in each relationship as

the percentage of variance in the dependent latent variable

that is explained by the independent latent variable.

3.6 Sensitivity analysis

In PLS technique the latent variables values are standard-

ized, then a probability for each one can be estimated for

high (Z [ 1) or low (Z – 1) level [46]: independently
P(Z – 1) and P(Z [ 1), conjointly P(Zi U Zd) or con-
ditionally P(Zi/Zd).

4 Results

4.1 The sample

The designed questionnaire was administered from April to

May 2019 to Mexican manufacturing industry. Initially,

442 surveys were collected, yet 41 were removed due to

numerous missing values, and 3 were discarded due to

unengaged responses. Therefore, only 398 surveys were

analyzed. Table 1 summarizes the sample’s characteristics

in terms of surveyed industries and length of work expe-

rience. The automotive industry was the most prominent in

the research, accounting for 221 surveys (i.e. 55.52%), and

it was followed by the electrical industry with only 51

surveys. Finally, most of the respondents; that is 276, had

more than 5 years of work experience in quality manage-

ment, which contributes to the reliability of the gathered

data.

Table 2 summarizes the sample’s characteristics in

terms of gender and work positions. The sample comprised

289 male respondents and 109 female respondents, and

most of the respondents pertained to quality or quality

assurance departments.

4.2 Latent variable validation

Table 3 summarizes the latent variable validation indexes.

As can be observed, all the latent variables showed values

higher than the threshold in all the coefficients. We thus

concluded that all latent variables had enough parametric

and non-parametric predictive validity, since the R
2
and

Adjusted R
2
values were higher than 0.2, whereas the Q

2

values were higher than 0 and similar to R
2
. Moreover,

Cronbach’s alpha and composite reliability coefficients

indicated that all the latent variables had internal validity.

Likewise, the AVE values that all the latent variables had

enough were all higher than 0.5, and all the VIF values

were lower than 5, thus confirming convergent validity and

were free from collinearity problems.

Table 1 Length of work
experience versus surveyed

industries

Years Machinery Electrical Automotive Aerospace Electronics Logistics Total

[ 3 and 5 17 18 61 10 15 1 122
C 5 and 10 17 19 85 9 23 1 154
C 10 12 14 75 4 10 7 122

Total 46 51 221 23 48 9 398

Wireless Networks

123

4.3 Structural equation model

The SEM evaluated appears in Fig. 3 and their efficiency

indexes as APC, ARS, and AARS indicate predictive

validity. Likewise, AVIF and AFVIF showed that the

model was free from collinearity problems. Finally, the

GoF indicates that the model fitted the data. The model fit

indexes were:

• Average Path Coefficient (APC) = 0.417, P 0.001
• Average R-Squared (ARS) = 0.627, P 0.001
• Average adjusted R-squared (AARS) = 0.626,

P 0.001
• Average block VIF (AVIF) = 2.720, acceptable if B 5,

ideally B 3.3

• Average Full collinearity VIF (AFVIF) = 3.064,
acceptable if B 5, ideally B 3.3

• Tenenhaus GoF (GoF) = 0.623, small C 0.1, medium
C 0.25, large C 0.36

In Fig. 3 each effect between latent variables is associ-

ated with a b value as a measure of dependence, a p value
as an indicator of statistical significance, and an R

2
value as

a measure of the variance explained.

4.3.1 Direct effects

As Fig. 3 depicts and Table 4 summarizes, all the research

hypotheses were statistically significant. In this sense, the

results of H1 can be interpreted as follows: there is enough

statistical evidence to declare that Managerial Commitment

to TQM has a positive direct effect on Quality Department,

since when the former increases by one standard deviation,

the latter increases by 0.786 units and explains 0.617 of the

variance of Quality Department. The remaining hypothesis

results can be similarly interpreted.

As regards effect sizes, the results demonstrate that

variance in Quality Policies can be explained in 0.571,

being Managerial Commitment responsible for 0.289, and

Quality Department responsible for 0.282. Interestingly, as

regards variance in Customer Satisfaction (i.e. 0.694), the

effect of Quality Policies is larger than those of Manage-

rial Commitment and Quality Department, respectively,

Fig. 3 Validated hypotheses

Table 2 Employee gender
versus work positions

Gender Manager in: Total

General Six sigma Continuous improvement Quality Quality assurance

Female 28 10 4 39 28 109

Male 62 22 17 104 84 289

Total 90 32 21 143 112 398

Table 3 Latent variable coefficients

Indices MAC QUD QUP CUS

R-Squared 0.617 0.571 0.694

Adjusted R-squared 0.616 0.569 0.692

Composite reliability 0.881 0.889 0.934 0.918

Cronbach’s alpha 0.831 0.847 0.92 0.888

AVE 0.597 0.577 0.612 0.692

VIF 3.266 2.916 2.866 3.205

Q-Squared 0.618 0.57 0.695

Wireless Networks

123

thus implying that Quality Policies are the most important

variable to explain the variability of Customer Satisfaction.

4.3.2 Total indirect effects

Table 5 lists the results for the sum of indirect effects and

total effects for each relationship. The sum of indirect

effects are listed in the first two rows—and in this case, we

found that the indirect effect between Managerial Com-

mitment and Customer Satisfaction (b = 0.431) is larger
than the direct effect (b = 0.329). On the other hand, the
total effects are listed in the last three rows and according

to that, Managerial Commitment has the largest effects on

the remaining latent variables, and this confirm its crucial

role in TQM implementation.

4.4 Sensitivity analysis

The results of the sensitivity analysis (see Table 6) indicate

the probability of each latent variable to lie at a high (?) or

low (-) level independently, conjointly (&) or condition-

ally (If) with respect to the other latent variables. For

instance, we found that Managerial Commitment is more

likely to lie at a low level independently (- 0.196) than to

lie at a high level (? 0.161). Moreover, the probability of

Managerial Commitment to lie at a high level in

conjunction with Quality Department is much lower than

expected (&= 0.106). However, high levels of Managerial

Commitment can be associated with high levels of Quality

Department performance (If = 656). Such results indicate

that top management must remain engaged to TQM to

guarantee that its subordinates are equally committed.

Additionally, it seems that high levels of Managerial

Commitment cannot be associated with low levels of

Quality Department performance; that is Quality Depart-

ment always responds to Managerial Commitment. Finally,

the results indicate that low levels in Managerial Com-

mitment imply risks of having poor level in Quality

Department (If = 0.688).

5 Conclusions and industrial implications

According to the SEM results, Managerial Commitment is

the most important variable in the TQM implementation

process. All its effects are statistically significant, larger

and have greater explanatory power, if compared to those

of the other latent variables. In other words, managers must

provide the necessary support to their subordinate depart-

ments to ensure the successful implementation of quality

projects and the long-term compliance with corporate

goals. Moreover, managers must promote both horizontal

Table 4 Hypothesis validation
results

Hi Independent L. V. Dependent L. V. b value/ES p value Conclusion

H1 Managerial commitment Quality department 0.786/0.617 0.001 Accept
H2 Managerial commitment Quality policies 0.405/0.289 0.001 Accept
H3 Quality department Quality policies 0.396/0.282 0.001 Accept
H4 Managerial commitment Customer satisfaction 0.329/0.250 0.001 Accept
H5 Quality department Customer satisfaction 0.167/0.121 0.001 Accept
H6 Quality policies Customer satisfaction 0.419/0.323 0.001 Accept

Table 5 Indirect effects and
total effects

Managerial commitment Quality department Quality policies

SIE
Quality policies 0.311 (p 0.001)

ES = 0.222
SIE

Customer satisfaction 0.431 (p 0.001)
ES = 0.327

0.166 (p 0.001)
ES = 0.120

TE
Quality department 0.786 (p 0.001)

ES = 0.617
TE
Quality policies 0.716 (p 0.001)

ES = 0.511

0.396 (p 0.001)
ES = 0.282

TE
Customer satisfaction 0.759 (p 0.001)

ES = 0.577

0.332 (p 0.001)
ES = 0.241

0.419 (p 0.001)
ES = 0.323

SIE, total indirect effect; TE, total effect

Wireless Networks

123

and vertical communication with other departments,

including operators.

All managerial actions should be driven by the need to

increase Customer Satisfaction. In this sense, we found that

the direct effect between these two latent variables is much

smaller (0.329) than the indirect effect, which occurs

through Quality Department and Quality Policies. This

implies that managerial actions are more effective for

Customer Satisfaction when Quality Department are

engaged, and Quality Policies are clearly stated and fol-

lowed. Additionally, the SEM results indicate that Quality

Policies contribute to the ability of Quality Department to

increase Customer Satisfaction, since the total effects of

this relationship are significantly higher than the direct

effects (i.e. 0.167 vs. 0.332). In other words, high Man-

agerial Commitment and an efficient Quality Department

are not enough in TQM environments—Quality Policies

must be clearly stated and properly followed in order to

keep customers satisfied. In conclusion, according to the b
coefficients estimated in the SEM analysis, the critical

sequence of tasks for TQM implementation is as follows:

Managerial Commitment ? Quality Depart-
ment ? Quality Policies ? Customer Satisfaction.

As regards the sensitivity analysis, the following con-

clusions are proposed:

1. High levels in Managerial Commitment favor high

levels of Quality Department performance (If = 0.656),

Quality Policies (If = 0.484), and Customer Satisfac-

tion (If = 0.531), thus indicating that managerial

leadership and engagement are central to TQM

success. Conversely, high levels in Managerial

Commitment cannot be associated with low levels in

Quality Department performance (If = 0.000), Quality

Policies (If = 0.000), or Customer Satisfaction (If =

0.016), thus concluding that subordinates and cus-

tomers will always respond positively to managerial

efforts, such as training, communication, and goal

setting.

2. Low levels in Managerial Commitment cannot be

related to high levels in Quality Department perfor-

mance (If = 0.013), Quality Policies, or Customer

Satisfaction (If = 0.000), thereby confirming once

more that TQM success is highly reliant on managerial

efforts. Additionally, low levels in Managerial Com-

mitment imply risks of TQM failure, since little

management commitment leads to low levels in

Quality Department (If = 0.688), Quality Policies

(If = 0.610), and Customer Satisfaction (If = 0.558).

3. High levels of Quality Department performance are

more likely to lead to both successful Quality Policies

(If = 0.459) and greater Customer Satisfaction (If =

0.514), which is the ultimate goal of TQM. Also, high

levels of Quality Department performance cannot be

associated with low levels in Quality Policies (If =

0.014) and Customer Satisfaction (If = 0.014). Such

results imply that customers will always respond

positively if TQM policies are properly conveyed.

4. Low levels in Quality Department performance do not

lead to high levels in either Quality Policies (If =

0.000) or Customer Satisfaction (If = 0.003), thereby

confirming the important role of Quality Department as

quality enforcers and TQM success enablers. Likewise,

low levels in Quality Department performance are a

Table 6 Sensitivity analysis

Dependent latent variable (to) Independent latent variable (from)

Managerial commitment Quality department Quality policies

Probability ? 0.161 – 0.196 ? 0.186 – 0.196 ? 0.188 – 0.188

Quality department 1 0.186 & = 0.106 & = 0.003

If = 0.656 If = 0.013

2 0.196 & = 0.000 & = 0.133

If = 0.000 If = 0.688

Quality policies 1 0.188 & = 0.078 & = 0.000 & = 0.085 & = 0.000

If = 0.484 If = 0.000 If = 0.459 If = 0.000

2 0.188 & = 0.000 & = 0.118 & = 0.000 & = 0.126

If = 0.000 If = 0.610 If = 0.014 If = 0.641

Customer satisfaction 1 0.193 & = 0.085 & = 0.000 & = 0.095 & = 0.003 & = 0.103 & = 0.000

If = 0.531 If = 0.000 If = 0.514 If = 0.013 If = 0.547 If = 0.000

2 0.163 & = .0003 & = 0.108 & = 0.003 & = 0.108 & = 0.000 & = 0.106

If = 0.016 If = 0.558 If = 0.014 If = 0.551 If = 0.000 If = 0.560

Wireless Networks

123

source of risk for companies, entail low levels of both

Quality Policies (If = 0.641) and Customer Satisfac-

tion (If = 0.551), and ultimately compromise the

success of TQM.

5. We found that high levels of Quality Policies compli-

ance are always associated with greater Customer

Satisfaction (If = 0.547) but never with lower levels

(If = 0.000). Such results indicate that quality policies

such as audits and statistical process techniques

guarantee TQM success, customer retention, and thus

customer loyalty.

6. Finally, low levels of Quality Policies compliance

cannot be associated with greater Customer Satisfac-

tion (If = 0.000), but rather with lower satisfaction

(If = 0.560), which compromises the success of TQM.

6 Future work

As its name suggests, TQM must integrate all the resources

of a company to attain product quality as expected by

customers. This research merely explores the impact of

three CSFs for TQM on Customer Satisfaction; thus, as

further research, we recommend extending the search to

other factors such as human resources, educational pro-

cesses, and technological capacity. Additionally, we sug-

gest developing a second-order SEM to offer a holistic

view of the problem.

References

1. Aich, S., Muduli, K., Onik, M. M. H., & Kim, H. C. (2018). A

novel approach to identify the best practices of quality manage-

ment in SMES based on critical success factors using interpretive

structural modeling (ISM). International Journal of Engineering

and Technology, 7(3), 130–133. https://doi.org/10.14419/ijet.

v7i3.29.18540.

2. Jayashree, M., & Mohammed Faisal, A. (2017). Development of

a conceptual model for implementation of total quality manage-

ment (TQM) and human resource management (HRM): A liter-

ature review. International Journal of Applied Business and

Economic Research, 15(21), 205–213.

3. Singh, V., Kumar, A., & Singh, T. (2018). Impact of TQM on

organisational performance: The case of Indian manufacturing

and service industry. Operations Research Perspectives, 5,

199–217. https://doi.org/10.1016/j.orp.2018.07.004.

4. Iqbal, A., & Asrar-ul-Haq, M. (2018). Establishing relationship

between TQM practices and employee performance: The medi-

ating role of change readiness. International Journal of Produc-

tion Economics, 203, 62–68. https://doi.org/10.1016/j.ijpe.2018.

05.034.

5. Jusoh, A., Mardani, A., Omar, R., SÌŒtreimikien_e, D., Khalifah, Z.,
& Sharifara, A. (2018). Application of MCDM approach to

evaluate the critical success factors of total quality management

in the hospitality industry. Journal of Business Economics and

Management, 19(2), 399–416. https://doi.org/10.3846/jbem.2018.

5538.

6. Kumar, V., & Sharma, R. R. K. (2017). An empirical investiga-

tion of critical success factors influencing the successful TQM

implementation for firms with different strategic orientation. In-

ternational Journal of Quality and Reliability Management,

34(9), 1530–1550. https://doi.org/10.1108/IJQRM-09-2016-0157.

7. Sohal, A. S., & Terziovski, M. (2000). TQM in Australian

manufacturing: Factors critical to success. International Journal

of Quality and Reliability Management, 17(2), 158–167. https://

doi.org/10.1108/02656710010304564.

8. Seetharaman, A., Sreenivasan, J., & Boon, L. P. (2006). Critical

success factors of total quality management. Quality and Quan-

tity, 40(5), 675–695. https://doi.org/10.1007/s11135-005-1097-2.

9. Gherbal, N., Shibani, A., Saidani, M., & Sagoo, A. (2012).

Critical success factors of implementing total quality manage-

ment in Libyan Organisations. In Paper presented at the inter-

national conference on industrial engineering and operations

management, Istanbul, Turkey, July 3–6.

10. Sreedharan, R. V., Sunder, V. M., & Raju, R. (2018). Critical

success factors of TQM, six sigma, lean and lean six sigma: A

literature review and key findings. Benchmarking, 25(9),

3479–3504. https://doi.org/10.1108/BIJ-08-2017-0223.

11. Salleh, N. M., Zakuan, N., Ariff, M. S. M., Bahari, A. Z., Chin, T.

A., Sulaiman, Z., et al. (2018). Critical success factors of total

quality management implementation in higher education institu-

tion: UTM case study. In AIP conference proceedings, 2018 (Vol.

2044). https://doi.org/10.1063/1.5080060.

12. Talib, F., & Rahman, Z. (2010). Studying the impact of total

quality management in service industries. International Journal

of Productivity and Quality Management, 6(2), 249–268. https://

doi.org/10.1504/IJPQM.2010.034408.

13. Aletaiby, A., Kulatunga, U., & Pathirage, C. (2017). Key success

factors of total quality management and employees performance

in Iraqi oil industry. In Paper presented at the 13th international

postgraduate research conference (IPGRC), University of Sal-

ford, UK.

14. Iqbal, T., Huq, F., & Bhutta, M. K. S. (2018). Agile manufac-

turing relationship building with TQM, JIT, and firm perfor-

mance: An exploratory study in apparel export industry of

Pakistan. International Journal of Production Economics, 203,

24–37. https://doi.org/10.1016/j.ijpe.2018.05.033.

15. Khalili, A., Ismail, M. Y., Karim, A. N. M., & Che Daud, M. R.

(2017). Critical success factors for soft TQM and lean manu-

facturing linkage. Jordan Journal of Mechanical and Industrial

Engineering, 11(2), 129–140.

16. Saumyaranjan, S. (2018). An empirical exploration of TQM,

TPM and their integration from Indian manufacturing industry.

Journal of Manufacturing Technology Management, 29(7), 1188.

https://doi.org/10.1108/jmtm-03-2018-0075.

17. Manjot, B., & Anjali, A. (2018). Assessing relationship between

quality management systems and business performance and its

mediators: SEM approach. International Journal of Quality and

Reliability Management, 35(8), 1490. https://doi.org/10.1108/

IJQRM-05-2017-0091.

18. Durgesh, P., Maddulety, K., & Plavini, P. (2017). Investigating

the influence of TQM, service quality and market orientation on

customer satisfaction and loyalty in the Indian banking sector.

International Journal of Quality and Reliability Management,

34(3), 362. https://doi.org/10.1108/IJQRM-04-2015-0057.

19. Agus, A., & Hassan, Z. F. (2011). Enhancing production per-

formance and customer performance through total quality man-

agement (TQM): Strategies for competitive advantage. Procedia:

Social and Behavioral Sciences, 24, 1650–1662. https://doi.org/

10.1016/j.sbspro.2011.09.019.

Wireless Networks

123

20. Valmohammadi, C., & Roshanzamir, S. (2015). The guidelines of

improvement: Relations among organizational culture, TQM and

performance. International Journal of Production Economics,

164, 167–178. https://doi.org/10.1016/j.ijpe.2014.12.028.

21. Anil, A. P., & Satish, K. P. (2016). Investigating the relationship

between TQM practices and firm’s performance: A conceptual

framework for Indian Organizations. Procedia Technology, 24,

554–561. https://doi.org/10.1016/j.protcy.2016.05.103.

22. Cordeiro, W. P., & Turner, R. H. (1995). 20/30 Hindsight:

Managers must commit to TQM. Interfaces, 25(3), 104–112.

https://doi.org/10.1287/inte.25.3.104.

23. Pearson, J. M., McCahon, C. S., & Hightower, R. T. (1995). Total

quality management. Are information systems managers ready?

Information and Management, 29(5), 251–263. https://doi.org/10.

1016/0378-7206(95)00028-0.

24. Choi, T. Y., & Behling, O. C. (1997). Top managers and TQM

success: One more look after all these years. Academy of Man-

agement Executive, 11(1), 37–47. https://doi.org/10.5465/AME.

1997.9707100658.

25. Psychogios, A. G., & Priporas, C.-V. (2007). Understanding total

quality management in context. Qualitative Report, 12(1), 40–66.

26. Soltani, E., Singh, A., Liao, Y.-Y., & Wang, W.-Y. (2010). The

rhetoric and reality of ‘process control’ in organisational envi-

ronments with a TQM orientation: The managers’ view. Total

Quality Management and Business Excellence, 21(1), 67–77.

https://doi.org/10.1080/14783360903492637.

27. Radlovački, V., Beker, I., Majstorović, V., Pečujlija, M., Stani-

vuković, D., & Kamberović, B. (2011). Quality managers’ esti-

mates of quality management principles application in certified

organisations in transitional conditions: Is Serbia close to TQM?

Strojniski Vestnik/Journal of Mechanical Engineering, 57(11),

851–861. https://doi.org/10.5545/sv-jme.2010.204.

28. Nwabueze, U. (2001). The implementation of TQM for the NHS

manager. Total Quality Management, 12(5), 657–675. https://doi.

org/10.1080/09544120120060132.

29. Taylor, W. A., & Wright, G. H. (2003). The impact of senior

managers’ commitment on the success of TQM programmes: An

empirical study. International Journal of Manpower, 24(5),

535–550. https://doi.org/10.1108/01437720310491071.

30. Psychogios, A. G., Wilkinson, A., & Szamosi, L. T. (2009).

Getting to the heart of the debate: TQM and middle manager

autonomy. Total Quality Management and Business Excellence,

20(4), 445–466. https://doi.org/10.1080/14783360902781949.

31. Al Rawashdeh, F. M. (2014). Assessment of the middle admin-

istrative leadership’s awareness of the implementation of the

concept of total quality management (TQM) in Commercial

Banks operating in Jordan. Arab Economic and Business Journal,

9(1), 81–92. https://doi.org/10.1016/j.aebj.2014.05.001.

32. Giauque, D. (2015). Attitudes toward organizational change

among public middle managers. Public Personnel Management,

44(1), 70–98. https://doi.org/10.1177/0091026014556512.

33. Lodgaard, E., Ingvaldsen, J. A., Gamme, I., & Aschehoug, S.

(2016). Barriers to lean implementation: Perceptions of top

managers, middle managers and workers. Procedia CIRP, 57,

595–600. https://doi.org/10.1016/j.procir.2016.11.103.

34. Kiran, D. R. (2017). Chapter 4: Leadership and TQM. In D.

R. Kiran (Ed.), Total quality management (pp. 39–55). Oxford:

Butterworth-Heinemann.

35. Moitra, T. (2019). From employees to customers: impact of HRM

on TQM. HCM Sales, Marketing and Alliance Excellence

Essentials, 18(4), 18–21.

36. Kouaib, A., & Jarboui, A. (2014). External audit quality and

ownership structure: Interaction and impact on earnings man-

agement of industrial and commercial Tunisian sectors. Journal

of Economics Finance and Administrative Science, 19(37),

78–89. https://doi.org/10.1016/j.jefas.2014.10.001.

37. Dedy, A. N., Zakuan, N., Omain, S. Z., Rahim, K. A., Ariff, M.

S. M., Sulaiman, Z., et al. An analysis of the impact of total

quality management on employee performance with mediating

role of process innovation. In IOP conference series: Materials

science and engineering, 2016 (1 ed., Vol. 131). https://doi.org/

10.1088/1757-899X/131/1/012017.

38. Ugboro, I. O., & Obeng, K. (2000). Top management leadership,

employee empowerment, job satisfaction, and customer satis-

faction in TQM organizations: An empirical study. Journal of

Quality Management, 5(2), 247–272. https://doi.org/10.1016/

S1084-8568(01)00023-2.

39. Sá, P. M. E., & Kanji, G. K. (2003). Leadership for excellence in

the Portuguese municipalities: Critical success factors, measure-

ments and improvement strategies. Total Quality Management

and Business Excellence, 14(2), 131–139. https://doi.org/10.

1080/1478336032000051313.

40. Oakland, J. (2011). Leadership and policy deployment: The

backbone of TQM. Total Quality Management and Business

Excellence, 22(5), 517–534. https://doi.org/10.1080/14783363.

2011.579407.

41. Dubey, R., Gunasekaran, A., & Samar Ali, S. (2015). Exploring

the relationship between leadership, operational practices, insti-

tutional pressures and environmental performance: A framework

for green supply chain. International Journal of Production

Economics, 160, 120–132. https://doi.org/10.1016/j.ijpe.2014.10.

001.

42. Topalović, S. (2015). The implementation of total quality man-

agement in order to improve production performance and

enhancing the level of customer satisfaction. Procedia Technol-

ogy, 19, 1016–1022. https://doi.org/10.1016/j.protcy.2015.02.

145.

43. Mehralian, G., Nazari, J. A., Zarei, L., & Rasekh, H. R. (2016).

The effects of corporate social responsibility on organizational

performance in the Iranian pharmaceutical industry: The medi-

ating role of TQM. Journal of Cleaner Production, 135, 689–698.

https://doi.org/10.1016/j.jclepro.2016.06.116.

44. Ooi, K. B., Lin, B., Boon, I. T., & Loong Chong, A. (2011). Are

TQM practices supporting customer satisfaction and service

quality? Journal of Services Marketing, 25(6), 410. https://doi.

org/10.1108/08876041111161005.

45. Kumar, V., & Sharma, R. R. K. (2018). Leadership styles and

their relationship with TQM focus for Indian firms: An empirical

investigation. International Journal of Productivity and Perfor-

mance Management, 67(6), 1063–1088. https://doi.org/10.1108/

IJPPM-03-2017-0071.

46. Kumar, V., & Sharma, R. R. K. (2017). Relating management

problem-solving styles of leaders to TQM focus: An empirical

study. The TQM Journal, 29(2), 218–239. https://doi.org/10.

1108/TQM-01-2016-0002.

47. Chiarini, A., & Vagnoni, E. (2017). TQM implementation for the

healthcare sector: The relevance of leadership and possible cau-

ses of lack of leadership. Leadership in Health Services, 30(3),

210–216. https://doi.org/10.1108/LHS-02-2017-0004.

48. Bouranta, N., Psomas, E., Suárez-Barraza, M. F., & Jaca, C.

(2019). The key factors of total quality management in the ser-

vice sector: a cross-cultural study. Benchmarking: An Interna-

tional Journal, 26(3), 893. https://doi.org/10.1108/BIJ-09-2017-

0240.

49. Arsić, M., Nikolić, D., Živković, Ž., Urošević, S., & Mihajlović,

I. (2012). The effect of TQM on employee loyalty in transition

economy, Serbia. Total Quality Management and Business

Excellence, 23(5/6), 719–729. https://doi.org/10.1080/14783363.

2012.669930.

50. CÌŒater, B., & CÌŒater, T. (2009). Relationship-value-based ante-

cedents of customer satisfaction and loyalty in manufacturing.

Wireless Networks

123

Journal of Business and Industrial Marketing, 24(8), 585–597.

https://doi.org/10.1108/08858620910999457.

51. Allen Broyles, S., Ross, R. H., Davis, D., & Leingpibul, T.

(2011). Customers’ comparative loyalty to retail and manufac-

turer brands. Journal of Product and Brand Management, 20(3),

205–215. https://doi.org/10.1108/10610421111134932.

52. Parsazadeh, N., Ali, R., Rezaei, M., & Tehrani, S. Z. (2018). The

construction and validation of a usability evaluation survey for

mobile learning environments. Studies in Educational Evalua-

tion, 58, 97–111. https://doi.org/10.1016/j.stueduc.2018.06.002.

53. Antony, J., Leung, K., Knowles, G., & Gosh, S. (2002). Critical

success factors of TQM implementation in Hong Kong industries.

International Journal of Quality and Reliability Management,

19(5), 551–566. https://doi.org/10.1108/02656710210427520.

54. Ekrot, B., Kock, A., & Gemünden, H. G. (2016). Retaining

project management competence: Antecedents and consequences.

International Journal of Project Management, 34(2), 145–157.

https://doi.org/10.1016/j.ijproman.2015.10.010.

55. Kock, N. (2018). WarpPLS 6.0 user manual. Laredo, TX:

ScriptWarp Systems.

Publisher’s Note Springer Nature remains neutral with regard to
jurisdictional claims in published maps and institutional affiliations.

Jorge Luis Garcı́a-Alcaraz is a
full time researcher at Autono-

mous University of Ciudad

Juárez. He received a M.Sc. in

Industrial Engineering from the

Instituto Tecnológico de Colima

(Mexico), a Ph.D. in Industrial

Engineering from Instituto Tec-

nológico de Ciudad Juárez

(Mexico), a Ph.D. in Innovation

in Product Engineering and

Industrial Process from Univer-

sity of La Rioja (Spain). His

main research areas are Multi-

criteria decision making applied

to lean manufacturing, production process modeling and statistical

inference. He is founding member of the Mexican Society of Oper-

ation Research and active member in the Mexican Academy of

Industrial Engineering.

Francisco Javier Flor Montalvo
has a Ph.D. in Ph.D. in Innova-

tion in Product Engineering and

Industrial Process from Univer-

sity of La Rioja (Spain). Cur-

rently his research area is aimed

to life cycle assessment analysis

in the winery sector. He has

several paper published in jour-

nals indexed in the Journal

Citation Reports. Currently, he

is manager at is own company,

focus to cork production.

Cuauhtémoc Sánchez-Ramı́rez is
a full-time researcher of the

Division of Research and Post-

graduate Studies of the Orizaba

Technology Institute. He

received a Ph.D. in Industrial

Engineering from COMIMSA,

center of research of National

Council of Science and Tech-

nology of Mexico (CON-

ACYT). His research projects

have been granted by CON-

ACYT and PRODEP. Dr. Sán-

chez is member founding of

Industrial Process Optimization

Network (ROPRIN) and member of the National Researcher System

by CONACYT level 1. His research interests are modeling and

simulation of logistics process and supply chain from a system

dynamics approach.

Liliana Avelar-Sosa has a bach-
elor’s degree in Electronic

Engineering, a master’s degree

in industrial sciences, a Ph.D. in

Engineering Sciences. She is a

National Researcher level 1

recognized by National Council

of Science and Technology in

Mexico, expert in logistics and

supply chain aspects applied to

manufacturing companies. She

has experience in maquiladora

industry and, currently she has

published papers in JCR jour-

nals as a first author, she has

attended international and national congress and conferences. Cur-

rently she is an APICS member in the El Paso/Juarez chapter.

José Antonio Marmolejo Sau-
cedo is a Professor at Panamer-
ican University, Mexico. His

research is on operations

research, large-scale optimiza-

tion techniques, computational

techniques and analytical meth-

ods for planning, operations,

and control of electric energy

and logistic systems. He

received his Doctorate in Oper-

ations Research (Hons) at

National Autonomous Univer-

sity of Mexico. At present, He

has the second highest country-

wide distinction granted by the Mexican National System of Research

Scientists for scientific merit (SNI Fellow, Level 2). He is a member

of the Network for Decision Support and Intelligent Optimization of

Complex and Large Scale Systems and Mexican Society for Opera-

tions Research. He has co-authored research articles in science cita-

tion index journals, conference proceedings, presentations, books, and

book chapters.

Wireless Networks

123

Giner Alor-Hernández is a full-
time researcher of the Division

of Research and Postgraduate

Studies in Orizaba’s technolog-

ical institute: Tecnológico de

Orizaba. He received a MSc and

a Ph.D. in Computer Science

from the Center for Research

and Advanced Studies of the

National Polytechnic Institute

(CINVESTAV), Mexico. He

has led 10 Mexican research

projects granted by CONACYT,

DGEST, and PROMEP. He is

author/coauthor of around 130

journal and conference papers on computer science. Also, he has been

a committee program member of around 30 international conferences

sponsored by IEEE, ACM, and Springer.

Wireless Networks

123

  • Importance of organizational structure for TQM success and customer satisfaction
    • Abstract
    • Introduction
      • Critical success factors for TQM in the manufacturing industry
      • Customer satisfaction and TQM in the manufacturing industry
      • Research problem and goal
    • Literature review and hypotheses
      • Managerial commitment
      • Quality department
      • Quality policies
      • Customer satisfaction
    • Methodology
      • Literature review
      • Survey design and administration
      • Data capture and screening
      • Latent variable validation
      • The structural equation model (SEM)
      • Sensitivity analysis
    • Results
      • The sample
      • Latent variable validation
      • Structural equation model
        • Direct effects
        • Total indirect effects
      • Sensitivity analysis
    • Conclusions and industrial implications
    • Future work
    • References

Week 1 Assignment/6 Organizing Management Structure to Manage Customer.pdf

Journal of Business Research 137 (2021) 116–127

Available online 19 August 2021
0148-2963/© 2021 Elsevier Inc. All rights reserved.

Do they see the signs? Organizational response behavior to customer
complaint messages☆

Sergej von Janda *, Andreas Polthier, Sabine Kuester
University of Mannheim, 68131 Mannheim, Germany

A R T I C L E I N F O

Keywords:
Customer complaint management
Signaling theory
Attention-based view
Customer integration
New product development
Organizational learning

A B S T R A C T

Companies often view customer complaints as a nuisance, but such complaints can be of high value to firms.
Building on signaling theory and the attention-based view of the firm, this study examines organizational
response behavior to customer complaint messages that differ in their value signaled to the firm. In a field
experiment, we manipulate whether a complaint message contains ideas for product improvement (versus does
not), and whether a long-term customer-firm relationship is indicated (versus first-time relationship). Our results
show that companies are less likely to respond to complaints that convey improvement ideas and that are voiced
by long-term customers. Companies also exhibit longer response times to complaints that signal improvement
ideas. Further, the findings reveal that lower response rates to customer complaints are associated with lower
levels of customer satisfaction. This research contributes to the literature on customer complaint management
and customer integration by providing implications for managers seeking to utilize complaints as a valuable
source of ideas for new product development.

1. Introduction

Customer complaints are a widespread phenomenon, especially in
business-to-consumer industries (Homburg & Fürst, 2005) and are
becoming more common as a result of the proliferation of digital
complaint channels such as e-mail and social media (Causon, 2015).
Some companies view complaints solely as issues that need to be cor-
rected, while others engage in defense mechanisms when faced with
complaints (Homburg & Fürst, 2007). At the same time, research has
shown that companies have the opportunity to learn from customer
complaints in the long term, for example, by improving their internal
processes (Yilmaz, Varnali, & Kasnakoglu, 2016).

In this study, we define customer complaints as behavioral customer
responses to perceived dissatisfaction with a product experience, which
includes negative communications to the responsible company (Bearden
& Teel, 1983; Blodgett, Hill, & Tax, 1997; Knox & van Oest, 2014).
While some customer complaints are not legitimate or are simply a
means to vent anger or to obtain monetary benefits (Daunt & Harris,
2012), others are constructive and include suggestions for improvement
(Christiansen, Gasparin, Varnes, & Augustin, 2016; Jang & Chung,
2015). As such, complaints constitute an easily accessible and

inexpensive source of ideas for new product development (NPD) (Xiao,
Zhang, & Cervone, 2018). For example, a customer of Tesla, the U.S. car
manufacturer, complained to the CEO, Elon Musk, on Twitter that the
steering wheel would be in the way when getting out of the car and
would wear out quickly as a result. In this complaint, the customer
suggested a software update that moves the driver’s seat backward and
raises the steering wheel once the car is parked. Musk responded by
announcing the integration of this proposed feature in a software update
that was released a few days later.

This example shows how embracing a customer complaint can lead
to ideas for product improvements that benefit both customers and the
firm itself. Yet, the literature on complaint management lacks an un-
derstanding of the extent to which companies leverage customer com-
plaints to enable organizational learning in general (Homburg & Fürst,
2007; Yilmaz et al., 2016) and NPD specifically. The previous literature
has predominantly focused on the impact of the strategic management of
customer complaints, which we refer to as customer complaint man-
agement (CCM), on customer satisfaction and loyalty (Gelbrich &
Roschk, 2011; Homburg & Fürst, 2005). In contrast, we explore the
potential of CCM for organizational learning and focus on the value of
customer complaints for NPD, as reflected in organizational response

☆ This work was supported by the University of Mannheim’s Graduate School of Economic and Social Sciences.
* Corresponding author.

E-mail addresses: [email protected] (S. von Janda), [email protected] (A. Polthier), [email protected] (S. Kuester).

Contents lists available at ScienceDirect

Journal of Business Research

journal homepage: www.elsevier.com/locate/jbusres

https://doi.org/10.1016/j.jbusres.2021.08.017
Received 3 August 2021; Accepted 6 August 2021

Journal of Business Research 137 (2021) 116–127

117

behavior.
In so doing, our study addresses two gaps in the literature. First,

research in the CCM domain lacks an understanding of the value of
customer complaints for organizational learning. The use of customer
complaint information for NPD can be seen as a form of customer
integration within the realm of open innovation (Poetz & Schreier,
2012). While the open innovation literature emphasizes the positive
impact of customer integration in NPD for innovation success (Chang &
Taylor, 2016; Ho-Dac, 2020), research on customer complaint integra-
tion as a form of open innovation—that is, the use of customer complaint
information for NPD purposes—is lacking. Interestingly, dissatisfaction
with existing solutions is positively related to the quality of customers’
ideas in open innovation projects (Lüthje, 2004; Schuhmacher &
Kuester, 2012). Given this positive association and the continuous and
relatively inexpensive availability of information from customer com-
plaints (Xiao et al., 2018), examining organizational responses to
customer complaints in an NPD context is worthwhile.

Second, the prior literature has not considered the potential impact
of complaint message characteristics on organizational responses to
customer complaints. Building on signaling theory and the attention-
based view of the firm (ABV), we posit that customer complaint mes-
sages contain signaling cues, which are small signal components indi-
cating differences in the value of complaints for companies. We propose
that different signaling cues in complaint messages can lead to diverging
levels of organizational attention to complaints. Specifically, we
examine whether companies react differently to complaints that indicate
a product improvement idea (versus do not) and that indicate a long-
term customer-firm relationship (versus first-time relationship). One
the one hand, companies may react positively to constructive complaint
messages which provide improvement ideas. On the other hand, com-
panies are not necessarily open to systematic learning from constructive
complaints (Homburg & Fürst, 2007). Addressing this interesting ten-
sion, our study examines whether companies recognize the potential of
complaints that indicate an improvement idea for organizational
learning and react in a differentiated manner.

Moreover, complaints can contain signaling cues about the length of
the business relationship between the complainant and a company. In
this regard, first-time customers can be distinguished from long-term
customers. Although it seems reasonable to assume that companies
should prioritize complaints from loyal long-term customers, evidence
has shown that these customers initially have lower expectations
regarding firms’ response times but are ultimately more demanding
regarding the resolution of their problems (Hogreve, Bilstein, & Mandl,
2017). While research has investigated customer loyalty as a function of
CCM performance (for a meta-analysis, see Gelbrich & Roschk, 2011),
there is paucity of research that explores the length of a business rela-
tionship as an antecedent to organizational response behavior. There-
fore, we examine whether organizations handle complaints sent by first-
time versus long-term customers differently.

To address these identified research gaps, we conduct a natural field
experiment with 125 companies from the fast-moving consumer goods
(FMCG) and consumer durables industries—industries where customer
complaints are commonplace (European Commission, 2018). We send a
generic complaint message to the companies in our sample, and
manipulate (a) whether the customer complaint message indicates ideas
for product improvement (versus does not), and (b) whether the com-
plaining customer indicates a long-term business relationship with the
company concerned (versus first-time customer relationship). Our
experimental field research design enables us to study actual organiza-
tional response behavior to different types of customer complaints. As
such, we can control for several potential confounding factors, including
company characteristics and the effect of the timing and wording of a
complaint message.

The insights of our study are highly relevant for companies that need
to prioritize complaint messages in line with organizational objectives,
especially as we show that customer satisfaction at the firm level

correlates with firms’ CCM strategy. Specifically, our results imply
benchmarks in response rate, time, and quality to customer complaints.
For example, response times of more than two days are above average
and can lead to negative customer perceptions. Our results also shed
light on the relative importance of different dimensions of firms’
response behavior for customer satisfaction. In particular, we reveal that
the response rate, which captures whether a company responds to a
complaint, is a crucial predictor of customer satisfaction, whereas
response time is not as critical within certain bounds. We offer several
suggestions for companies aiming to leverage their CCM with a focus on
extracting value from complaints as a promising but often overlooked
source of ideas for NPD. Our study also yields actionable insights for
complaining customers. In terms of their expectation management, we
demonstrate that long-term customers indicating an improvement idea
when issuing a complaint cannot expect better chances of receiving a
(fast) response as compared to first-time customers not providing
improvement ideas. Thus, from the customer’s perspective, the extra
effort involved in writing a complaint with an improvement idea does
not seem to pay off in terms of securing the company’s attention.

2. Theoretical foundation

We draw on signaling theory (Akerlof, 1970; Spence, 1973) and the
ABV (Ocasio, 1997) to study interactions between customers and com-
panies in complaint situations. Signaling theory deals with interactions
under information asymmetry, in which the more informed party (the
sender) has exclusive information about an underlying quality that is of
interest to the less informed party (the receiver) (Connelly, Certo,
Ireland, & Reutzel, 2011; Lee, Chen, & Hartmann, 2016).

A signal is a compact indication or information, which a sender
transmits to a receiver in order to reduce the information deficit on the
part of the receiver (Xia et al., 2016). A sender chooses to transmit a
signal to a receiver to elicit some kind of action, which the receiver
would not have initiated without the signal (Connelly et al., 2011;
Kuester, Konya-Baumbach, & Schuhmacher, 2018). For example, a
sender can use a signal to draw the receiver’s attention to a particular
quality of herself. According to the ABV (Bouquet & Birkinshaw, 2008;
Ocasio, 1997; Rhee & Leonardi, 2018), attention is defined as the
“noticing, encoding, interpreting, and focusing of time and effort by
organizational decision-makers on […] issues […] and answers” (Ocasio,
1997, p. 189). Generally speaking, the ABV proposes that firm behavior
can be explained by the distribution of attention of decision-makers
when responding to external or internal challenges (Ocasio, 1997). A
key principle of the ABV is the concept of situated attention, meaning
that an individual’s focus of attention is strongly dependent on the
situational context and can be triggered by external situational stimuli,
such as signals (Joseph & Wilson, 2018; Ocasio, 1997). Hence, signaling
theory and the ABV (Bouquet & Birkinshaw, 2008; Ocasio, 1997; Rhee &
Leonardi, 2018) are closely connected (for a recent combined applica-
tion of these theories, see Bianchi, Murtinu, & Scalera, 2019).

According to signaling theory, a signal must be costly and observable
in order to effectively attract the receiver’s attention and to evoke the
intended reaction (Connelly et al., 2011). For example, a job applicant
who successfully completed a study program at a prestigious university
can use this degree as a quality signal, as the degree is observable and
the applicant had to exert effort (i.e., incurred costs) to obtain the de-
gree. If sending a signal does not entail costs for the sender, the receiver
will consider the signal as not credible as it could be transmitted by any
sender, independently of the sender’s qualities, making false signaling
attractive. In addition, if a signal is unobservable for the receiver, the
receiver simply does not become aware of the information intended to
elicit a reaction. Hence, observability is a central attribute of an effective
signal. Environmental conditions can lead to variations in the effec-
tiveness of signals. For example, a noisy environment can reduce signal
observability by causing problems in signal transmission (Plummer
et al., 2016). Similarly, the effectiveness of an individual signal

S. von Janda et al.

Journal of Business Research 137 (2021) 116–127

118

decreases when other conflicting signals are present in the receiver’s
environment (Biswas & Biswas, 2004). In such a noisy environment, the
receiver’s focus of attention will not be allocated to the actual signal
transmitted, leading to decreased signaling effectiveness.

As indicated above, we define a signal as information transmitted
from a sender to a receiver to reduce the latter’s information deficit.
Hence, the sender, or signaler, is an insider who possesses information
the receiver, or outsider, is lacking. Applied to the complaint context,
the complaining customers are senders, who possess information about
themselves and possibly about problems with a product and potential
improvement opportunities. As explained by Connelly et al. (2011), this
information provides the complainant “with a privileged perspective
regarding the underlying quality of some aspect of the individual [or]
product” (Connelly et al., 2011, p. 44). For example, a customer who has
purchased and used a particular product has privileged information
about the product quality and functionality in use (i.e., aspects of the
product). If the use experience was not satisfactory, the customer can
decide to communicate this information to the company issuing a
complaint. Customers also have exclusive information about their rela-
tionship with the firm, for example, their relationship duration. Cus-
tomers can reveal this information when complaining with the intention
to evoke a particular reaction on the part of the company.

For a complaint to constitute an effective signal, it must be costly and
observable (Connelly et al., 2011). Sending a complaint message about a
product to a firm is costly for a customer, as the customer needs to (a)
purchase the product, (b) gain experiences with the product, (c) cogni-
tively evaluate these experiences, and (d) formulate and send the
complaint message. Hence, effort is involved in sending a complaint
message, and even more effort is required to think about an improve-
ment idea and communicate it in the complaint. Moreover, the outcome
of sending a complaint message is uncertain for the customer, further
increasing the costs.

A complaint message also fulfills the second criterion of signal
observability. The receiving company can easily observe written
complaint messages. For example, when customers send complaints
electronically, these signals are undistorted by environmental in-
fluences. Put differently, the receiver faces exactly the message that the
sender intended to convey. Thus, a complaint message can be considered
as a signal, costly for the sender and observable for the receiver.

Taking the vantage point of the complaining customer in the position
of a signaler, or insider, stands in contrast to prior research in marketing,
which has typically considered the company as the more informed party
sending information and the customer as the less informed party
receiving information (e.g., Kuester et al., 2018). In our study, the
company is the outsider receiving information which is potentially
beneficial for organizational decision-making. Potential benefits accrue
from (a) ideas for product improvement and development and (b) a
more effective prioritization of complaint messages based on customer
value, as we outline in chapter 3.

3. Hypotheses

Since most companies face a substantial number of customer com-
plaints, customer service employees need to prioritize complaints and
focus their attention in terms of the effort invested in responding
(Ahghari & Balcioĝlu, 2009). From a profitability perspective, com-
panies should assign more attention to complaint messages sent by more
valuable customers compared to messages from less valuable customers
(Kumar, Ramani, & Bohling, 2004). Customer value depends on both the
customer’s financial (e.g., profitability) and non-financial contribution
(e.g., intellectual capital) to the firm (Hogan, Lemon, & Libai, 2003). In
terms of financial contributions, some studies show that long-term
customers are particularly valuable to a firm because it is more cost-
effective to retain a current customer than to acquire a new one
(Reichheld, 2003). Further, loyal customers have higher lifetime values
(Zeithaml, Rust, & Lemon, 2001). Yet, other research shows that

customer loyalty might not always go hand in hand with higher
customer value (Reinartz & Kumar, 2000). In terms of non-financial
contributions, innovative customers who offer suggestions for
improvement are considered particularly valuable due to their potential
intellectual contribution to the firm (Eisingerich, Auh, & Merlo, 2014).
The existing literature indicates that customer loyalty and innovative-
ness are often correlated, for example, because loyal members of a brand
community are especially likely to offer constructive criticism in the
form of suggestions for improvement (Hur, Ahn, & Kim, 2011).

While customers differ in their value for companies and customer
complaints should be prioritized differently depending on the value of
the complaining customer (Ahghari & Balcioĝlu, 2009), the company
receiving a complaint possesses limited information about the com-
plainant’s value at the time of a complaint interaction. In a CCM context,
the text of a complaint message is often the only basis from which to
judge customer value. Signaling cues related to the complaining cus-
tomer’s value for the receiving firm can provide particularly relevant
information and thereby direct receivers’ situated attention. For
example, customers may provide an idea for the improvement of the
product that caused a complaint. Further, customers may mention the
length of their business relationship with the firm in the complaint
message. These two types of signaling cues relate to both non-financial
and financial customer value as they provide information about
customer innovativeness and loyalty.

We hypothesize that companies use these signaling cues to judge a
complainant’s value, focus their attention on the most valuable com-
plainants, and adapt their response behavior accordingly. Further, we
hypothesize that adequate response behavior to a complaint has a pos-
itive effect on organizational outcomes, such as customer satisfaction
(Gelbrich & Roschk, 2011; Homburg & Fürst, 2005). A firm that does not
manage a customer complaint adequately has a higher risk of losing the
customer when compared to providing an adequate reaction (Gus-
tafsson, 2009; Lapidus & Pinkerton, 1995). We deem the response
behavior to be adequate when a company responds to a complaint
(Strauss & Hill, 2001), does so quickly (Istanbulluoglu, 2017), and does
so with a high-quality message (Tax, Brown, & Chandrashekaran, 1998).

These three dimensions of organizational response behavior reflect
the effort invested in responding to customer complaints, and all three
have been found relevant for customers in past research (Martin &
Smart, 1988). First, the variable response rate captures whether a
company responds to a complaint message. Responding to a complaint
(versus ignoring it) can be considered as a hygiene criterion that a firm
must fulfill to enable post-complaint customer satisfaction (Strauss &
Hill, 2001). Second, we study the response time to a customer complaint
(Davidow, 2003), as it reflects the firm-internal complaint prioritization.
Third, we consider the quality of a response as another indicator of the
effort invested in responding to a complaint (Boshoff, 1999).

Our conceptual model is illustrated in Fig. 1. We propose that the
signaling cues of customers’ innovativeness and business relationship
length both affect organizational response behavior. Organizational
response behavior, represented by the response rate, response time, and
response quality, is related to overall customer satisfaction. We present a
discussion of our hypotheses in the following.

3.1. The value of innovative customers

As previous research has shown, a significant part of customer value
lies in the customer’s innovativeness (Roehrich, 2004), for example,
from innovative customers’ strong tendency to adopt new products (Im,
Bayus, & Mason, 2003) and their relatively high purchase intention and
brand attitude (Bartels & Reinders, 2011). Customers can proactively
signal their innovativeness by offering specific suggestions to a company
about product improvements and/or new product ideas (Eisingerich
et al., 2014). Such behavior holds value for companies for several rea-
sons. First, when customers voice their ideas for product improvement,
this indicates their high involvement with a company’s products and

S. von Janda et al.

Journal of Business Research 137 (2021) 116–127

119

makes them attractive, brand-loyal customers (Leckie, Nyadzayo, &
Johnson, 2016). Second, customers who provide constructive feedback
manifest a form of customer citizenship behavior, so that these cus-
tomers are also likely to spread positive word-of-mouth and help fellow
customers (Groth, 2005). Third, ideas voiced voluntarily by customers
can be considered as a cost-effective input for NPD (Xiao et al., 2018).
Usually, companies incur no direct costs for the reception of customer
complaint messages other than those related to maintaining customer
communication channels. Fourth, examples from practice show that
customers’ ideas can be successfully integrated into NPD efforts, leading
to future growth opportunities (van Doorn et al., 2010).

Based on this line of argument, innovative customers should be more
valuable to a company than non-innovative customers. Innovative cus-
tomers are likely to have improvement ideas that they can point out in
their complaints. In line with signaling theory and the ABV, we hy-
pothesize that companies will value signaling cues of innovativeness
sent by complaining customers and focus their attention on greater
effort in responding to such customers than to less innovative customers.
Taking the facets of organizational response behavior into account, we
hypothesize that:

H1: Companies respond to customer complaint messages that carry a
signaling cue for a product improvement idea (a) more frequently,
(b) faster, and (c) with higher quality than they do for complaint
messages without such a signaling cue.

3.2. The value of long-term customers

On the one hand, the literature reports empirical evidence for the
high value of loyal customers for companies. For example, prior research
has shown that long-term customers provide value to companies in a
direct financial way (Zeithaml, Rust, & Lemon, 2001). Repeat purchases
of a product over an extended period indicate brand loyalty, which is
positively related to market share and relative price margin (Chaudhuri
& Holbrook, 2001). The creation of affectionate ties between customer
and firm over the course of a long-term relationship leads to high

loyalty, which comes with increased purchase intentions (Yim, Tse, &
Chan, 2008). Loyal long-term customers are also less likely to switch to a
competing product than first-time customers, implying a higher ex-
pected lifetime value of long-term customers (Reichheld & Earl Sasser
Jr., 1990; Verhoef & Lemon, 2013). This valuation should be reflected in
companies’ efforts to satisfy and retain these customers.

On the other hand, there is also evidence in the literature that long-
time customers are in fact not more valuable to firms than first-time
customers. For non-contractual settings, such as consumer goods,
Reinartz and Kumar (2000) find that long-time customers exhibit a
lower willingness to pay than first-time customers, which reduces the
value of long-term customers for firms over time. The authors attribute
this finding to fierce competition, low switching costs, and a high fre-
quency of impulse buying in the consumer goods sector (Reinartz &
Kumar, 2000). In their study on the antecedents of marketing service
use, Grayson and Ambler (1999) even demonstrate a negative impact of
long-term relationships on service use. The authors identify higher ex-
pectations of long-term customers and an increased likelihood of
opportunistic behavior as drivers behind the so-called dark side effect of
long-term customer-firm relationships (Grayson & Ambler, 1999).

Our literature review shows that no clear conclusion can be drawn as
to whether long-term business relationships with customers are more or
less valuable to companies overall. However, it seems reasonable that
complaints signaling higher customer value should generally lead to
heightened attention by the company and be reflected in more intensive
efforts by companies to satisfy and retain these customers (Thakur &
Workman, 2016). We expect these efforts to be also reflected in
complaint response behavior. At the same time, we acknowledge the
contrasting views in the literature on the value of long-term customers
for companies and the possible implications for complaint management.
Thus, we formulate two competing hypotheses:

H2: Companies respond to customer complaint messages sent in long-
term customer relationships (a) more frequently, (b) faster, and (c)
with higher quality than they do for complaint messages sent in first-
time customer relationships.

Fig. 1. Conceptual Model.

S. von Janda et al.

Journal of Business Research 137 (2021) 116–127

120

H2alt: Companies respond to customer complaint messages sent in
long-term customer relationships (a) less frequently, (b) slower, and
(c) with lower quality than they do for complaint messages sent in
first-time customer relationships.

3.3. Organizational complaint response behavior and customer
satisfaction

The first and most fundamental characteristic of a firm’s response
behavior that is related to customer complaints is whether the company
responds at all, which we refer to as response rate. When a dissatisfied
customer voices a complaint to a firm, they expect, at the very least, a
reply in acknowledgment of their complaint (Strauss & Hill, 2001). If a
company does not respond or responds very slowly, this behavior is
likely to reduce the perceived justice on the complainant’s part (Urueña
& Hidalgo, 2016). Often, firms implement an automated reply system to
confirm the receipt of complaint messages. However, this sort of
response does not seem to be sufficient, as research shows that com-
plainants do not differentiate between an automated reply and no reply
at all (Mattila, Andreau, Hanks, & Kim, 2013). Both forms of reply lead
to negative emotions and negative behavioral intentions on the com-
plainant’s part (Mattila et al., 2013). Overall, responding to a customer
complaint (versus not responding) can be seen as a fundamental signal
of a firm’s appreciation for the complainant (Stevens, Spaid, Breazeale,
& Esmark Jones, 2018).

Therefore, we hypothesize that a personalized reply to a customer
complaint is a necessary type of response that a company needs to send
to be able to re-establish customer satisfaction after a complaint. The
practice of not replying to customer complaints in a personalized way, or
not replying at all, will diminish customer satisfaction at the firm level.
Our expectations are supported by Fornell and Wernerfelt’s conceptual
work (1987, 1988), who demonstrate a positive relationship between
professional complaint management and overall firm success, particu-
larly in competitive industries. Thus:

H3: Companies responding to customer complaints achieve higher
levels of customer satisfaction than companies that do not respond to
customer complaints.

Timeliness (Davidow, 2003) is the second important aspect of
organizational response behavior to customer complaints. Customers
who take the time to voice a complaint typically expect a reply without a
long delay, as a signal of the company’s appreciation. Especially in an
online context, complainants expect a reply within a short timeframe
(Istanbulluoglu, 2017), and they interpret a delayed reply as a lack of
responsiveness to customer concerns. In fact, research shows that quick
responses to customer complaints increase customer satisfaction levels
and repurchase intention at the individual customer level (Istanbulluo-
glu, 2017; Smith, Bolton, & Wagner, 1999). In a similar vein, Gloor,
Fronzetti Colladon, Giacomelli, Saran, and Grippa (2017) provide evi-
dence that an increase in a firm’s response time to a customer’s e-mail
leads to reduced net promoter scores on the customer group level. Thus,
we hypothesize:

H4: The length of a company’s response time to a customer complaint
is negatively related to customer satisfaction with the firm.

The quality of the company’s response message is the third aspect of
organizational response behavior related to complaints. We conceptu-
alize response quality as a combination of hard and soft quality in-
dicators. Hard quality indicators refer to objectively observable hygiene
factors, such as addressing the complainant by name (Strauss & Hill,
2001). Soft quality indicators are more subjective and include, for
example, politeness or empathy (Gelbrich & Roschk, 2011; Tax et al.,
1998). The prior literature indicates that the fulfillment of quality in-
dicators via e-mail responses to customers results in higher customer

satisfaction with company replies and higher perceptions of company
concern (Liao, 2007; Strauss & Hill, 2001; Tax et al., 1998).

In a social media context, Javornik, Filieri, and Gumann (2020)
demonstrate that customers perceive message characteristics, such as
the response style of companies’ public replies to complaints, as
signaling cues of how the complaint is handled. We argue that it is the
combination of hard and soft factors of response quality that distin-
guishes a high-quality response from a low-quality response. Building on
these insights, we expect that companies can increase the overall satis-
faction of their customers by replying to complaints in a formally and
substantially adequate way because customers perceive such behavior
as a signal of professionalism and respect (Estelami, 2000; Hogreve,
Bilstein, & Hoerner, 2019). Thus:

H5: The quality of a company’s response to a customer complaint is
positively related to customer satisfaction with the firm.

Additionally, we consider three control variables: industry (FMCG
versus consumer durables), firm size, and firm age, as they may impact
the way a firm responds to complaints.

4. Method

To test our hypotheses, we conducted a field experiment, focusing on
companies in the business-to-consumer industry. This industry context is
particularly relevant when studying company reactions to customer
complaints for several reasons. First, companies in these industries
typically have a large and diversified customer base, hence they tend to
receive a sizable number of complaints. Second, products are mostly
non-high-tech, thus enabling customers to provide feasible improve-
ment ideas in their complaints as opposed to more advanced products
(Füller, Jawecki, & Mühlbacher, 2007). Third, research has shown that
some firms in these industries do leverage customer complaints as a
source of information for NPD (Christiansen et al., 2016; Merlo, Eisin-
gerich, Auh, & Levstek, 2018). Despite their high levels of external
validity (Berkowitz & Donnerstein, 1982), field experiments are rarely
applied in CCM research. However, we consider a field experiment to be
particularly well suited for exploring how organizations respond to
customer complaints because this method enables us to observe actual
company behavior directly. Further, because we focus on organizations’
response behavior (Homburg & Fürst, 2005; Yilmaz et al., 2016), we
chose the firm level as the unit of analysis.

4.1. Data collection and sample

To study organizational response behavior to customer complaints,
we sent a generic e-mail complaint message to 125 European consumer
goods companies (45.6% consumer durables companies and 54.4%
FMCG firms). We selected companies that represent a broad range of
product categories such as food, clothing, and household products. Our
sample is sufficiently diverse in terms of size (annual revenue < USD 1
bn: 9.6%, USD 1–50 bn: 75.2%, USD 51–100 bn: 12.8%, and > USD 100
bn: 2.4%) and age (firm age less than 50 years: 17.6%, 51–100 years:
32.0%, 101–150 years: 33.6%, 151–200 years: 14.4%, and > 200 years:
2.4%).

4.2. Manipulations

We randomly assigned the companies in our sample to one of four
treatment conditions. These treatment conditions were comprised of
four different e-mail complaints that were in line with our full-factorial 2
(signaled improvement idea: no versus yes) × 2 (signaled duration of
business relationship: first-time customer versus long-term customer)
between-subjects design. We created our manipulations by adapting the
phrasing of a generic baseline complaint message (see Appendix A). By
making our stimuli generic, and therefore, applicable across industries

S. von Janda et al.

Journal of Business Research 137 (2021) 116–127

121

and product categories, we intended to minimize a bias from adapting
the message format and/or content to the industry or individual firm.
For the condition signaling the improvement idea, we indicated an idea
for product improvement. This signaling cue was missing in the other
condition. For the second manipulation, the complaint carried infor-
mation about the length of the business relationship with the
complainant, identifying the complainant either as a first-time customer
or a long-term customer.

We pre-tested our complaint messages with innovation researchers
and managers (n = 22). In particular, we tested whether participants
noticed the difference in the indicated business relationship length, and
whether an improvement idea was signaled in the complaint. The t-tests
from our manipulation checks indicated significant differences in par-
ticipants’ perception (p < .05) in the expected direction for both
manipulated dimensions of the complaint messages.

In our main study, the randomization of our complaint messages
yielded almost identical cell sizes for the four treatment conditions,
ranging from 30 to 32. All messages were sent out between 09:00 and
10:00 a.m. on weekdays, except Monday, to avoid bias from the po-
tential abundance of complaint messages to be processed after a
weekend.

4.3. Measures

Data for our dependent variables were collected both directly,
regarding organizational response behavior, and from secondary data
sources, regarding customer satisfaction and our control variables. The
variable response rate was captured as a binary variable, with a value of 1
when we received a response to the complaint message within 10
working days, and a value of 0 when no response or only an automated
response was received. Typically, after more than ten working days,
customers will perceive even a personalized reply as unacceptable
(Boshoff, 1997). However, because only two companies replied after
more than ten days, relaxing this threshold does not substantially
change the results of our study.

To measure response time, we apply a rather conservative assumption
of a 5-day week (Monday–Friday). We assume that the customer service
operating hours are from 09:00 to 05:00 p.m., and use the number of
working hours that passed between sending the initial complaint and
receiving the reply. We do so to avoid a potential distortion of the
response time data caused by companies not replying on the day that
they received the complaint and by companies answering after a
weekend. We log-transform the response time variable for our analyses
as the response time pattern is highly non-normal and includes several
outliers.

As measures of the objective hard indicators of response quality, we
use three binary variables, mostly adapted from Strauss and Hill (2001).
Specifically, we capture whether a response message addressed the
complainant by name, whether it was signed using an employee’s name
(rather than simply the company name), and whether the e-mail was
formatted well (consistent font size and style). In addition, we assess the
subjective soft quality indicators using a 7-point Likert scale that was
adapted from previous research. We considered an apology (Davidow,
2003; Liao, 2007; Smith et al., 1999), perceived empathy (Davidow,
2003; Miller, Craighead, & Karwan, 2000; Simon, 2013), and politeness
(Borah, Prakhya, & Sharma, 2019; Liao, 2007; Tax et al., 1998) as soft
quality indicators. Two independent researchers evaluated and coded
the company replies to our complaints based on these quality indicators
to ensure the reliability of our data (see Appendix B for the scales applied
to evaluate the company replies). The inter-rater agreement was high for
the overall evaluation of soft response quality as an equally weighted
average of the three factors (88% within a scale point difference of one
on the Likert scale). The remaining cases were settled by discussion until
a consensus was achieved. Subsequently, we used the average rating for
our analysis.

Customer satisfaction data were collected from publicly available

secondary sources: the American Customer Satisfaction Index (ACSI)
and the aggregator of net promoter score (NPS) ratings (customergauge.
com). For our analyses, we use a combination of z-transformed scores
from both sources unless otherwise stated. This approach achieves a
reasonable sample size for the analyses, given that companies safeguard
internal information about customer satisfaction. The approach rests on
the assumption that the NPS and ACSI are comparable measures of
customer satisfaction at the firm level (East, Romaniuk, & Lomax, 2011).

We include several control variables in our analysis. Industry is
assessed using a binary variable (FMCG versus consumer durables)
based on publicly available industry classifications. Firm size is measured
by a firm’s annual revenue, as reported in annual reports. Firm age is
measured in years and reflects how long the firm has existed based on
publicly available company information. We control for these three
variables in all analyses pertaining to both the organizational response
behavior variables and customer satisfaction.

5. Analysis and results

Our data analysis shows an overall response rate of 80%; that is, 100
of 125 companies in our field experiment replied to our complaint
messages. Table 1 shows the descriptive statistics for our main variables
and their correlations.

To test the hypotheses H1a, H2a, and H2a_alt, we employ a logistic
regression because the variable response rate is binary. We test H1b, H2b,
and H2b_alt using an ANCOVA and use a Mann–Whitney U test as a
robustness check, owing to the non-normal nature of the variable
response time. An ANCOVA is also used to test H1c, H2c, H2c_alt, and H3.
We test H4 and H5 using ordinary least squares regression, as the inde-
pendent and dependent variables of interest are continuous.

Surprisingly, our analysis reveals that companies reply to complaint
messages which carry a signaling cue related to an improvement idea
significantly less often than they do for complaints without such a cue
(βImprovement Idea = -1.212, p < .05). We also find that complaint mes-
sages sent by long-term customers have a lower chance of receiving a
reply than complaint messages sent by first-time customers do (βLong-
term = -0.982, p < .05). The coefficients for both manipulations remain
essentially unchanged when the control variables are excluded, indi-
cating that no suppression effects are present. Hence, we do not find
support for H1a and H2a because the data contradict our hypotheses,
while we are able to support H2a_alt. Note that while there is a significant
and positive effect of the control variable firm age on the response rate in
both models, we do not find industry specific effects controlling for the
industry affiliation (i.e., FMCG versus consumer durables) of the com-
panies in our sample (Table 2). Fig. 2 shows the patterns of the response
rates for the four treatment conditions.

Further, the results of the ANCOVA show no significant impact on
response time depending on whether the complaint message signals an
improvement idea (F(1,95) = 2.590, p > .1) or the duration of the
business relationship (F(1,95) = 0.075, p > .1). However, we do discern
a tendency toward faster responses in the conditions without an
improvement idea signaling cue, which is reflected in their p-values
being slightly above 0.1 (and below 0.1 when excluding the control
variables). As such, and because of the skewed distribution of the
response times, even after the log-transformation, we also use the
nonparametric Mann–Whitney U test as a robustness check. This test is
used to compare the distributions of a dependent variable for two cat-
egories of an independent variable. After applying this test, we find that
the distribution of the response times differs between the conditions for
a signaled versus not-signaled idea for improvement (U = 1538.5, p <
.05). In particular, the mean rank for messages without a signaled
improvement idea is 45.01, whereas it is 57.78 for messages with such a
signaling cue. This insight supports the tendency toward slower re-
sponses in the conditions where the message contains a signaling cue of
an improvement idea. Overall, we find no support for neither H2b or
H2b_alt, and some counterevidence for H1b.

S. von Janda et al.

Journal of Business Research 137 (2021) 116–127

122

To analyze the impact of our manipulations on response quality, we
consider the structure of the hard and soft indicators of response quality
in our sample. For the three binary components of hard response quality,
we find almost no variance in the sample: 66% of the firms fulfill all
three criteria and 97% adhere to at least two of the hygiene criteria.
Therefore, the following analyses related to response quality only focus
on the three continuous soft indicators that exhibit high variance. We
find no support for H1c, which posits that complaint messages that signal
an improvement idea are associated with higher-quality replies (F(1,95)
= 0.217, p > .1). Similarly, the result that long-term customers do not
receive higher-quality responses than first-time customers (F(1,95) =
0.142, p > .1) does not support H2c or H2c_alt.

Regarding the outcome variable in our research model, we analyze
the effects of the three variables of organizational complaint response

behavior on overall customer satisfaction. For the impact of response
rate on customer satisfaction, we find a significant positive relationship
between the two variables (F(1,54) = 7.775, p < .01), which provides
support for H3. With a partial η2 of 0.126, the response rate explains
more of the variance in customer satisfaction than any of the control
variables. The relationship between response time and customer satis-
faction is less clear. Although the sign of the coefficient points in the
expected direction, the negative association between response time and
customer satisfaction is non-significant (βLog Response Time = -0.119, p >
.1). Hence, we do not find clear support for H4. Finally, our regression of
customer satisfaction on soft response quality shows no significant
impact of response quality on customer satisfaction (βResponse Quality =
0.007, p > .1), and provides no evidence for H5.

6. Discussion

Customer complaints remain an inevitable challenge for companies,
and it is up to companies to leverage these expressions of dissatisfaction
as effectively as possible (Yilmaz et al., 2016). We apply signaling theory
and the ABV to examine how companies process different forms of
customer complaints with varying value to the firm. We examine com-
panies’ reactions to these complaints in terms of three typical perfor-
mance aspects of CCM: response rate, response time, and response
quality.

6.1. Signaling of improvement ideas in complaint messages and response
behavior

We generate counterintuitive results regarding the effects of
improvement ideas being signaled in a complaint message on firms’
response rates and response times. We find that the signaling of an

Table 1
Descriptive Statistics and Correlations.

Variable n Mean Std. Dev. 1 2 3 4 5

1) Response time (hours) 100 10.91 15.1 1
2) Response quality (soft) a) 100 3.38 0.95 -0.14 1
3) Customer satisfaction b) 59 0.08 0.92 -0.10 0.12 1
4) Firm age (years) 125 101.79 51.09 0.10 0.26** 0.36** 1
5) Firm revenue (USD bn) 125 22.74 30.95 0.05 -0.01 0.10 -0.03 1

Note: ** p < .01; a) measured with Likert-scale; b) values were standardized

Table 2
Results of Binary Logistic Regression Analysis for Response Rate.

Response Rate Response Rate
Model 1: Improvement
idea signaled

Model 2: Long-term
customer

Intercept 0.534 (0.695) 0.327 (0.669)
Main Effects
Improvement idea

signaled (yes = 1)
− 1.212* (0.505)

Long-term customer (yes
= 1)

-0.982* (0.49)

Controls
Industry (durables = 1) 0.813 (0.517) 0.916 (0.512)
Firm age 0.011 (0.005)* 0.012 (0.005)*
Firm size 0.007 (0.009) 0.007 (0.009)

Note: * p < .05 (two-tailed tests); standard errors in parentheses;
Nagelkerke R2Model 1 = 0.173; Nagelkerke R

2
Model 2 = 0.150

Fig. 2. Overview of Response Rates Across Treatment Conditions.

S. von Janda et al.

Journal of Business Research 137 (2021) 116–127

123

improvement idea in a complaint message decreases firms’ response
rates and tends to increase firms’ response times. In short, customers who
complain constructively by signaling improvement ideas that are
potentially valuable to firms can expect poorer customer service in terms
of response rate and time when compared to customers who complain
without mentioning such ideas. This finding contrasts with our predic-
tion derived from signaling theory and the ABV that higher value
conveyed in the customer complaint via the signaling of an improve-
ment idea should focus companies’ attention and increase efforts in CCM
activities. We suggest several potential explanations for our findings.

First, organizational inertia theory could help to explain our results.
This theory builds on the assumption that formal organizations operate
in a reliable and stable way and are, thus, unable to allocate organiza-
tional resources flexibly, for example, to different CCM activities
(Hannan & Freeman, 1984; Zhou & Wu, 2010). Gilbert (2005) refers to
this stability in processes as resource rigidity and as a “failure to change
resource investment patterns” (p. 741). Companies tend to continue
using standard operating procedures that have been established over
time. Accordingly, companies may have standard ways of dealing with
customer complaints, such as standardized responses or response tem-
plates (Istanbulluoglu, 2017). These standardized procedures might lead
to higher response rates and lower response times for “classical”
complaint messages that do not convey improvement suggestions when
compared to “unusual” complaints that include such suggestions.

Second, an alternative explanation for our findings could be legal
issues arising from integrating improvement ideas from customers in
NPD. Despite widely practiced open innovation approaches, the quali-
tative data that we garnered in the interactions with companies in-
dicates that some companies fear intellectual property issues when
leveraging customer ideas that are clearly not part of specified
contractual relationships. For example, one of the companies in our
sample explicitly stated that only product ideas from firm-employed
developers are considered, as external ideas cannot be integrated due
to intellectual property reasons. This alternative explanation for our
findings is supported by the prior literature (Schaarschmidt & Kilian,
2014). For example, Gassmann, Kausch, and Enkel (2010) advise com-
panies to establish written contracts with customers before integrating
their ideas in NPD to any extent. In a similar vein, Bartl, Füller, Mühl-
bacher, and Ernst (2012) show that managers consider intellectual
property issues to be one of the main disadvantages of virtual customer
integration. Assuming that customer service employees are aware of
such legal issues, their nonresponse or tendency to provide slower re-
sponses to customer complaints that convey improvement ideas might
not indicate a lack of attention, but simply reflect a lack of knowledge
about how to appropriately react to such complaint messages. In our
sample, the number of companies that directly asked for the offered
product improvement ideas is higher than the number of companies that
explicitly mention an unwillingness to integrate customer ideas due to
legal issues or exclusive internal NPD processes. This insight underscores
our argument about organizational inertia rather than attributing our
findings to concerns over intellectual property rights.

Third, customer service employees’ incentives might shed light on
our results. Typically, these employees are paid a base salary and an
additional variable component that depends on individual performance
metrics (Pazy & Ganzach, 2009), such as the number of customer mes-
sages replied to per hour, or the average response time. Conversely, the
number of NPD ideas from complaints that are collected and potentially
shared with the R&D department might not be used as a performance
indicator in most firms. From the perspective of a customer service
employee, it may be easier to reply to a standard customer complaint
than to a constructive and innovative complaint message. Thus, there
might be a lack of incentives for employees to deal with such messages
because they require more time and mental effort, which increases
employees’ average response time and decreases the number of mes-
sages they can handle in a given period.

Fourth, the lack of functional integration between the customer

service department and the innovation/R&D department can further
shed light on our findings. The lack of cross-functional collaboration
between customer service and NPD can stem from barriers such as cul-
tural thought worlds and language (Griffin & Hauser, 1996). Further,
the so-called not-invented-here syndrome (Antons & Piller, 2015) might
cause a lack of interest in customer ideas among R&D employees, which
would inhibit the information flow between the customer service em-
ployees that process complaint messages and the R&D employees. The
limited organizational influence of customer service departments (For-
nell, 1981) and the lack of standard operating procedures, with respect
to forwarding customer ideas, might result in employees postponing the
customer messages or not responding at all.

6.2. Length of the business relationship and response behavior

The effect of the length of the customer–firm relationship stated in a
complaint message on the response rate is in line with our alternative
hypothesis (H2alt). We find that business relationship length is negatively
related to response rate, which concurs with research on the dark side of
long-term customer relationships (Grayson & Ambler, 1999; Reinartz &
Kumar, 2000). If companies do not perceive long-term customers in
their industry as particularly valuable, there is no need to focus attention
on such customers and prioritize their complaints.

An additional possible explanation for our results is that, owing to
higher loyalty levels (Dick & Basu, 1994), companies focus only limited
attention to long-term customers who complain because they are less
likely to defect; in short, firms assume a certain level of customer inertia
(Zeelenberg & Pieters, 2004). When a customer and a firm have a long-
term business relationship, this implies a certain amount of accrued
relational capital (Hogreve et al., 2017). Hence, a slow response to a
complaint by a loyal customer might not necessarily lead to defection as
long as some relational capital remains. Conversely, a late reply to a
first-time customer complaint could lead to the instant loss of the
customer since no goodwill has been established in a prior relationship.
This expectation could then contribute to a decrease in the response rate
to complaints by long-term customers. At the same time, firms might
also see an opportunity to turn complaining first-time customers (who
are costly to acquire) into long-term customers by providing excellent
customer service when handling the complaint. Complaining first-time
customers would, in this sense, be perceived as a particularly attrac-
tive customer segment that requires superior attention. This notion
follows that of classical customer relationship marketing, which states
that it is expensive to acquire a new customer.

6.3. Organizational complaint response behavior and customer
satisfaction

Our results for the effect of organizational complaint response
behavior on overall customer satisfaction show that customers are
affected most by whether they receive any response to their complaint at
all. Our results indicate that the mere existence of a response to a
complaint message increases customer satisfaction significantly,
whereas response time and response quality aspects only incrementally
contribute to customer satisfaction. It is important to note that the
companies in our sample performed well in general, in terms of response
time, with a mean of less than 11 h, which is less than 2 business days.
Hence, if a company responds to a customer complaint via e-mail, they
most often respond quickly, which might explain the non-significant
impact of response time on customer satisfaction. Regarding the rela-
tionship between response quality and customer satisfaction, our null
findings might be driven by the fact that almost all companies performed
very well in terms of the “hard” quality indicators (personalization and
adequate formatting). Strauss and Hill (2001) show that a fulfillment of
these aspects of response quality is key to customer satisfaction, whereas
soft quality indicators are less important. Despite the apparent changes
in the online environment over the past two decades, this finding seems

S. von Janda et al.

Journal of Business Research 137 (2021) 116–127

124

to remain valid.

7. Theoretical contributions

Our field experiment generates several theoretical contributions.
First, it contributes to the CCM literature by providing insights into
companies’ response behavior to customer complaints for the purpose of
NPD (Christiansen et al., 2016). While the extant literature has mostly
focused on the customer response path of CCM, we specifically consider
the organizational learning path as proposed by Yilmaz et al. (2016).
Although the clear majority of firms in our sample optimize the
customer response path to customer complaints with fast and high-
quality responses, our observations in the field indicate that com-
panies are not well-prepared to leverage innovative complaint messages
as opportunities for organizational learning. This finding is surprising in
light of Yilmaz et al.’s (2016) recommendation to focus on organiza-
tional learning from complaints as it is positively related to both short-
and long-term performance. To shed some light on these findings, we
discuss potential barriers to the integration of complainants’ ideas for
product innovation purposes, as reflected in the organizational response
behavior to customer complaints that signal constructive improvement
ideas.

Second, our study adds to the customer relationship management
literature by revealing that companies’ complaint response behavior
does not always follow classical customer relationship paradigms.
Typically, research in the customer relationship marketing domain has
suggested that companies should focus on customer retention as it is
significantly less expensive than customer acquisition (Reichheld,
2003), and is the decisive predictor of a firm’s market share (Rust &
Zahorik, 1993). We provide evidence that innovative long-term cus-
tomers, who potentially provide direct and indirect financial benefits to
companies, face a poorer complaint management service than other
customer groups when sending complaint messages to these firms.
Especially the observations with regard to innovative customers contrast
with established recommendations to focus resources on high-value
customers (Kumar et al., 2004).

Third, our study contributes to the literature on open innovation by
taking a new perspective on customer integration in NPD. In contrast to
classical approaches to open innovation, where customers are actively
recruited for integration into NPD processes (Kristensson, Gustafsson, &
Archer, 2004; Nishikawa, Schreier, & Ogawa, 2013), we investigate an
approach which sees customers self-selected as potential innovation
contributors by complaining to a company. Given the low costs associ-
ated with this more passive form of customer integration and the crea-
tivity of dissatisfied customers (Schuhmacher & Kuester, 2012), this
approach is worth considering. This perspective is novel in the field of
customer integration in innovation and provides a fresh perspective for
research and theory development on customer integration.

Finally, this study makes a valuable theoretical contribution by
integrating signaling theory and the ABV. As signals are a means to draw
the receiver’s attention to a quality of the sender, we perceive these two
theoretical frameworks as complementary. Such a combined application
of these theories can be useful to shed light on various other types of
interactions between (organizational) customers and companies but also
on company-to-company exchanges.

8. Managerial implications

With a focus on organizational learning, this study provides several
managerially relevant insights into CCM. We demonstrate that managers
responsible for ensuring customer satisfaction should be aware of the
importance of replying to complaint messages, and doing so in an
adequate and timely manner. Managers in the field of consumer goods
should consider that replying to complaints after two business days is
above average and might lead to negative customer perceptions.

Moreover, our findings indicate that companies tend to take longer to

reply to complaint messages that signal an improvement idea than they
do for messages that lack such a signaling cue. However, complainants
who provide improvement ideas should be considered as more valuable
to a firm than those who do not offer constructive suggestions. Thus, a
high level of post-complaint satisfaction among the former group should
be of particular importance to companies. The extant literature has
shown that a quick response time is positively associated with post-
complaint satisfaction at both the individual customer level (Davidow,
2003) and the customer group level (Gloor et al., 2017).

Note that inviting complainants to join idea crowdsourcing events
and platforms is an approach that has been pursued by only a few
companies in our sample. Yet, prior research has shown that dissatis-
faction with existing solutions is positively related to the quality of
customers’ ideas for new products (Schuhmacher & Kuester, 2012).
Hence, complainants have relevant ideas for NPD, yet these might be
missed if NPD integration is disregarded. When using customer com-
plaints in NPD, companies keep full control over the innovation process
and can select relevant information, which can constitute an important
advantage compared to, for example, the lead user method (Ho-Dac,
2020).

Finally, we suggest that managers should critically examine their
customer service employees’ performance criteria. For example, if an
employee’s variable salary is based solely on productivity criteria such
as the number of e-mail messages replied to during a certain time frame,
such remuneration may constrain the process of learning from com-
plaints in the long-term for the purpose of product improvements.

9. Limitations and future research opportunities

To the best of our knowledge, this study is the first to conduct a field
experiment in the context of CCM using companies as experimental
subjects. We view field experiments as a promising approach when
studying interactions between customers and companies as they enable
objective observations that are not affected by the biases typically pre-
sent in other research methods. We, thus, recommend this methodology
for future studies of organizational phenomena. While we garnered
fruitful insights in this field experiment, our findings are subject to
several limitations. Our study sample is drawn from the industry of fast-
moving and durable consumer goods, which is characterized by both a
high number of firms and customers who typically have relatively weak
ties. Hence, our findings cannot be easily generalized to other types of
industries, such as service industries with more personal customer-firm
relationships.

The fact that our study design is based on interactions with real
companies was accompanied by some particular challenges in data
collection. First, we had to invest considerable effort in the data
collection for our field experiment, as the complaint messages had to be
adapted to each company and then sent out individually. In doing so, we
were able to ensure that the complaint messages were perceived as
realistic, stimulating reactions which can be regarded as externally
valid. Second, the benefits of collecting highly reliable data in our field
experiment came along with careful ethical considerations, as the
companies in our sample were unaware that they were part of an
experiment when we contacted them initially. We ensured to meet the
ethical research standards for data collection, processing, and analysis in
field experiments established in the literature (e.g., Bertrand & Mullai-
nathan, 2004). For example, we sent out only one complaint message
per firm to keep the economic costs for companies in our sample at a
minimum. We debriefed all companies two weeks after the last e-mail
contact, informing them that they had been part of an experiment and
giving them the opportunity to oppose to the use of the interaction data
in our study. We also offered all companies an executive summary of our
findings after completing the project.

Finally, while we provide several possible explanations for our re-
sults, we cannot draw conclusions regarding their relative importance. A
survey of managers dealing with and responding to customer complaints

S. von Janda et al.

Journal of Business Research 137 (2021) 116–127

125

would be useful to scrutinize the underlying processes and reasons to
explain our findings in more detail. In particular, the role of an orga-
nizational culture that is open to learning from failures (Sarangee,
Woolley, Schmidt, & Long, 2014) might be prominent in explaining
organizational response behavior to customer complaints.

Declaration of Competing Interest

The authors declare that they have no known competing financial
interests or personal relationships that could have appeared to influence
the work reported in this paper.

Appendix A. : Examples of complaint messages

Treatment 1 (signaling cues: no improvement idea, first-time customer)

Dear Sir or Madam,
After using your [product], I unfortunately have to inform you that I am dissatisfied. This is the first time I have purchased a product from your

company, and I would appreciate a response to this message to discuss my complaint in more detail.
Yours sincerely,
[Name]

Treatment 4 (signaling cues: Improvement idea, long-time customer)

Dear Sir or Madam,
After using your [product], I unfortunately have to inform you that I am dissatisfied. I have specific suggestions for how you could improve your

product. As a long-term customer of your company, I would appreciate a response to this message to discuss my complaint in more detail. Yours
sincerely,

[Name]

Appendix B:. Soft response quality assessment

Apologya (Davidow, 2003; Liao, 2007; Smith et al., 1999), α = 0.98

Indicate the extent that you agree with the following statements (scale from 1 = strongly disagree to 7 = strongly agree).
The customer service representative (CSR) made an apology for what happened.
The CSR apologized for the inconvenience the problem has brought.
The CSR expressed regret for the mistake the company has made.
Perceived Empathya (Davidow, 2003; Miller et al., 2000; Simon, 2013), α = 0.93
Indicate the extent that you agree with the following statements (scale from 1 = strongly disagree to 7 = strongly agree).
The complaint caused the CSR to have feelings of concern for the complainant.
The CSR really understood the complainant’s feelings.
The CSR tried to adapt the complainant’s perspective.
The CSR put themselves in the complainant’s shoes.
The CSR seemed to personally care about the complainant a great deal.
Politenessa (Borah et al., 2019; Liao, 2007; Tax et al., 1998), α = 0.93
Indicate the extent that you agree with the following statements (scale from 1 = strongly disagree to 7 = strongly agree).
The CSR was friendly to the complainant.
The CSR was polite to the complainant.
The CSR showed respect to the complainant.
Note: a7-point Likert scale (1 = strongly disagree and 7 = strongly agree).

References

Ahghari, M., & Balcioĝlu, B. (2009). Benefits of cross-training in a skill-based routing
contact center with priority queues and impatient customers. IIE Transactions, 41(6),
524–536.

Akerlof, G. A. (1970). The Market for “Lemons”: Quality Uncertainty and the Market
Mechanism. The Quarterly Journal of Economics, 84(3), 488–500.

Antons, D., & Piller, F. T. (2015). Opening the Black Box of “Not Invented Here”:
Attitudes, Decision Biases, and Behavioral Consequences. Academy of Management
Perspectives, 29(2), 193–217.

Bartels, J., & Reinders, M. J. (2011). Consumer innovativeness and its correlates: A
propositional inventory for future research. Journal of Business Research, 64(6),
601–609.

Bartl, M., Füller, J., Mühlbacher, H., & Ernst, H. (2012). A Manager’s Perspective on
Virtual Customer Integration for New Product Development. Journal of Product
Innovation Management, 29(6), 1031–1046.

Bearden, W. O., & Teel, J. E. (1983). Selected Determinants of Consumer Satisfaction and
Complaint Reports. Journal of Marketing Research, 20(1), 21.

Berkowitz, L., & Donnerstein, E. (1982). External validity is more than skin deep: Some
answers to criticisms of laboratory experiments. American Psychologist, 37(3),
245–257.

Bertrand, M., & Mullainathan, S. (2004). Are Emily and Greg More Employable Than
Lakisha and Jamal? A Field Experiment on Labor Market Discrimination. American
Economic Review, 94(4), 991–1013.

Bianchi, M., Murtinu, S., & Scalera, V. G. (2019). R&D Subsidies as Dual Signals in
Technological Collaborations. Research Policy, 48(9), Article 103821.

Biswas, D., & Biswas, A. (2004). The diagnostic role of signals in the context of perceived
risks in online shopping: Do signals matter more on the Web? Journal of Interactive
Marketing, 18(3), 30–45.

Blodgett, J. G., Hill, D. J., & Tax, S. S. (1997). The effects of distributive, procedural, and
interactional justice on postcomplaint behavior. Journal of Retailing, 73(2), 185–210.

Borah, S. B., Prakhya, S., & Sharma, A. (2019). Leveraging service recovery strategies to
reduce customer churn in an emerging market. Journal of the Academy of Marketing
Science, 10(3), 22.

Boshoff, C. (1997). An experimental study of service recovery options. International
Journal of Service Industry Management, 8(2), 110–130.

Boshoff, C. (1999). Recovsat. Journal of Service Research, 1(3), 236–249.
Bouquet, C., & Birkinshaw, J. (2008). Weight Versus Voice: How Foreign Subsidiaries

Gain Attention From Corporate Headquarters. Academy of Management Journal, 51
(3), 577–601.

Causon, J. (2015). Customer complaints made via social media on the rise. Retrieved
from https://www.theguardian.com/media-network/2015/may/21/customer-com
plaints-social-media-rise.

Chang, W., & Taylor, S. A. (2016). The Effectiveness of Customer Participation in New
Product Development: A Meta-Analysis. Journal of Marketing, 80(1), 47–64.

Chaudhuri, A., & Holbrook, M. B. (2001). The Chain of Effects from Brand Trust and
Brand Affect to Brand Performance: The Role of Brand Loyalty. Journal of Marketing,
65(2), 81–93.

S. von Janda et al.

Journal of Business Research 137 (2021) 116–127

126

Christiansen, J. K., Gasparin, M., Varnes, C., & Augustin, I. (2016). How Complaining
Customers Make Companies Listen and Influence Product Development. International
Journal of Innovation Management, 20(01), 1650001.

Connelly, B. L., Certo, S. T., Ireland, R. D., & Reutzel, C. R. (2011). Signaling Theory: A
Review and Assessment. Journal of Management, 37(1), 39–67.

Daunt, K. L., & Harris, L. C. (2012). Exploring the forms of dysfunctional customer
behaviour: A study of differences in service scape and customer disaffection with
service. Journal of Marketing Management, 28(1–2), 129–153.

Davidow, M. (2003). Organizational Responses to Customer Complaints: What Works
and What Doesn’t. Journal of Service Research, 5(3), 225–250.

Dick, A. S., & Basu, K. (1994). Customer Loyalty: Toward an Integrated Conceptual
Framework. Journal of the Academy of Marketing Science, 22(2), 99–113.

East, R., Romaniuk, J., & Lomax, W. (2011). The NPS and the ACSI: A Critique and An
Alternative metric. International Journal of Market Research, 53(3), 327–346.

Eisingerich, A. B., Auh, S., & Merlo, O. (2014). Acta Non Verba? The Role of Customer
Participation and Word of Mouth in the Relationship Between Service Firms’
Customer Satisfaction and Sales Performance. Journal of Service Research, 17(1),
40–53.

Estelami, H. (2000). Competitive and Procedural Determinants of Delight and
Disappointment in Consumer Complaint Outcomes. Journal of Service Research, 2(3),
285–300.

Commission, E. (2018). Consumer complaints statistics: Complaints data 2006–2018.
Retrieved from https://ec.europa.eu/info/policies/consumers/consumer-
protection/evidence-based-consumer-policy/consumer-complaints-statistics_en. Last
accessed: 01/28/2021.

Fornell, C. (1981). Increasing the Organizational Influence of Corporate Consumer
Affairs Departments. Journal of Consumer Affairs, 15(2), 191–213.

Fornell, C., & Wernerfelt, B. (1987). Defensive Marketing Strategy by Customer
Complaint Management: A Theoretical Analysis. Journal of Marketing Research, 24
(4), 337–346.

Fornell, C., & Wernerfelt, B. (1988). A Model for Customer Complaint Management.
Marketing Science, 7(3), 287–298.

Füller, J., Jawecki, G., & Mühlbacher, H. (2007). Innovation creation by online
basketball communities. Journal of Business Research, 60(1), 60–71.

Gassmann, O., Kausch, C., & Enkel, E. (2010). Negative side effects of customer
integration. International Journal of Technology Management, 50(1), 43.

Gelbrich, K., & Roschk, H. (2011). A Meta-Analysis of Organizational Complaint
Handling and Customer Responses. Journal of Service Research, 14(1), 24–43.

Gilbert, C. G. (2005). Unbundling the Structure of Inertia: Resource Versus Routine
Rigidity. Academy of Management Journal, 48(5), 741–763.

Gloor, P., Fronzetti Colladon, A., Giacomelli, G., Saran, T., & Grippa, F. (2017). The
impact of virtual mirroring on customer satisfaction. Journal of Business Research, 75,
67–76.

Grayson, K., & Ambler, T. (1999). The Dark Side of Long-Term Relationships in
Marketing Services. Journal of Marketing Research, 36(1), 132–141.

Griffin, A., & Hauser, J. R. (1996). Integrating R&D and Marketing: A Review and
Analysis of the Literature. Journal of Product Innovation Management, 13(3), 191–215.

Groth, M. (2005). Customers as Good Soldiers: Examining Citizenship Behaviors in
Internet Service Deliveries. Journal of Management, 31(1), 7–27.

Gustafsson, A. (2009). Customer satisfaction with service recovery. Journal of Business
Research, 62(11), 1220–1222.

Hannan, M. T., & Freeman, J. (1984). Structural Inertia and Organizational Change.
American Sociological Review, 49(2), 149–164.

Ho-Dac, N. N. (2020). The value of online user generated content in product
development. Journal of Business Research, 112, 136–146.

Hogan, J. E., Lemon, K. N., & Libai, B. (2003). What Is the True Value of a Lost Customer?
Journal of Service Research, 5(3), 196–208.

Hogreve, J., Bilstein, N., & Hoerner, K. (2019). Service Recovery on Stage: Effects of
Social Media Recovery on Virtually Present Others. Journal of Service Research, 22(4),
421–439.

Hogreve, J., Bilstein, N., & Mandl, L. (2017). Unveiling the recovery time zone of
tolerance: When time matters in service recovery. Journal of the Academy of
Marketing Science, 45(6), 866–883.

Homburg, C., & Fürst, A. (2005). How Organizational Complaint Handling Drives
Customer Loyalty: An Analysis of the Mechanistic and the Organic Approach. Journal
of Marketing, 69(3), 95–114.

Homburg, C., & Fürst, A. (2007). See no evil, hear no evil, speak no evil: A study of
defensive organizational behavior towards customer complaints. Journal of the
Academy of Marketing Science, 35(4), 523–536.

Hur, W.-M., Ahn, K.-H., & Kim, M. (2011). Building brand loyalty through managing
brand community commitment. Management Decision, 49(7), 1194–1213.

Im, S., Bayus, B. L., & Mason, C. H. (2003). An Empirical Study of Innate Consumer
Innovativeness, Personal Characteristics, and New-Product Adoption Behavior.
Journal of the Academy of Marketing Science, 31(1), 61–73.

Istanbulluoglu, D. (2017). Complaint handling on social media: The impact of multiple
response times on consumer satisfaction. Computers in Human Behavior, 74, 72–82.

Jang, S., & Chung, J. (2015). How Do Interaction Activities among Customers and
between Customers and Firms Influence Market Performance and Continuous
Product Innovation? An Empirical Investigation of the Mobile Application Market.
Journal of Product Innovation Management, 32(2), 183–191.

Javornik, A., Filieri, R., & Gumann, R. (2020). “Don’t Forget that Others Are Watching,
Too!” The Effect of Conversational Human Voice and Reply Length on Observers’
Perceptions of Complaint Handling in Social Media. Journal of Interactive Marketing,
50, 100–119.

Joseph, J., & Wilson, A. J. (2018). The growth of the firm: An attention-based view.
Strategic Management Journal, 39(6), 1779–1800.

Knox, G., & van Oest, R. (2014). Customer Complaints and Recovery Effectiveness: A
Customer Base Approach. Journal of Marketing, 78(5), 42–57.

Kristensson, P., Gustafsson, A., & Archer, T. (2004). Harnessing the creative potential
among users. Journal of Product Innovation Management, 21(1), 4–14.

Kuester, S., Konya-Baumbach, E., & Schuhmacher, M. C. (2018). Get the show on the
road: Go-to-market strategies for e-innovations of start-ups. Journal of Business
Research, 83, 65–81.

Kumar, V., Ramani, G., & Bohling, T. (2004). Customer lifetime value approaches and
best practice applications. Journal of Interactive Marketing, 18(3), 60–72.

Lapidus, R. S., & Pinkerton, L. (1995). Customer complaint situations: An equity theory
perspective. Psychology and Marketing, 12(2), 105–122.

Leckie, C., Nyadzayo, M. W., & Johnson, L. W. (2016). Antecedents of consumer brand
engagement and brand loyalty. Journal of Marketing Management, 32(5–6), 558–578.

Lee, R. P., Chen, Q., & Hartmann, N. N. (2016). Enhancing Stock Market Return with
New Product Preannouncements: The Role of Information Quality and
Innovativeness. Journal of Product Innovation Management, 33(4), 455–471.

Liao, H. (2007). Do it right this time: The role of employee service recovery performance
in customer-perceived justice and customer loyalty after service failures. The Journal
of applied psychology, 92(2), 475–489.

Lüthje, C. (2004). Characteristics of innovating users in a consumer goods field.
Technovation, 24(9), 683–695.

Martin, C. L., & Smart, D. T. (1988). Relationship correspondence: Similarities and
differences in business response to complimentary versus complaining consumers.
Journal of Business Research, 17(2), 155–173.

Mattila, A. S., Andreau, L., Hanks, L., & Kim, E. E. (2013). The impact of cyberostracism
on online complaint handling. International Journal of Retail & Distribution
Management, 41(1), 45–60.

Merlo, O., Eisingerich, A., Auh, S., & Levstek, J. (2018). The benefits and implementation
of performance transparency: The why and how of letting your customers ‘see
through’ your business. Business Horizons, 61(1), 73–84.

Miller, J. L., Craighead, C. W., & Karwan, K. R. (2000). Service recovery: A framework
and empirical investigation. Journal of Operations Management, 18(4), 387–400.

Nishikawa, H., Schreier, M., & Ogawa, S. (2013). User-generated versus designer-
generated products: A performance assessment at Muji. International Journal of
Research in Marketing, 30(2), 160–167.

Ocasio, W. (1997). Towards an attention-based view of the firm. Strategic Management
Journal, 18(1), 187–206.

Pazy, A., & Ganzach, Y. (2009). Pay Contingency and the Effects of Perceived
Organizational and Supervisor Support on Performance and Commitment. Journal of
Management, 35(4), 1007–1025.

Plummer, L. A., Allison, T. H., & Connelly, B. L. (2016). Better Together? Signaling
Interactions in New Venture Pursuit of Initial External Capital. Academy of
Management Journal, 59(5), 1585–1604.

Poetz, M. K., & Schreier, M. (2012). The Value of Crowdsourcing: Can Users Really
Compete with Professionals in Generating New Product Ideas? Journal of Product
Innovation Management, 29(2), 245–256.

Reichheld, F. F. (2003). The one number you need to grow. Harvard Business Review, 81
(12), 46–54, 124.

Reichheld, F. F., & Earl Sasser Jr., W. (1990). Zero Defection: Quality Comes to Services.
Harvard Business Review, 68(5), 105–111.

Reinartz, W. J., & Kumar, V. (2000). On the Profitability of Long-Life Customers in a
Noncontractual Setting: An Empirical Investigation and Implications for Marketing.
Journal of Marketing, 64(4), 17–35.

Rhee, L., & Leonardi, P. M. (2018). Which pathway to good ideas? An attention-based
view of innovation in social networks. Strategic Management Journal, 39(4),
1188–1215.

Roehrich, G. (2004). Consumer innovativeness. Journal of Business Research, 57(6),
671–677.

Rust, R. T., & Zahorik, A. J. (1993). Customer satisfaction, customer retention, and
market share. Journal of Retailing, 69(2), 193–215.

Sarangee, K. R., Woolley, J. L., Schmidt, J. B., & Long, E. (2014). De-escalation
Mechanisms in High-technology Product Innovation. Journal of Product Innovation
Management, 31(5), 1023–1038.

Schaarschmidt, M., & Kilian, T. (2014). Impediments to customer integration into the
innovation process: A case study in the telecommunications industry. European
Management Journal, 32(2), 350–361.

Schuhmacher, M. C., & Kuester, S. (2012). Identification of Lead User Characteristics
Driving the Quality of Service Innovation Ideas. Creativity and Innovation
Management, 21(4), 427–442.

Simon, F. (2013). The influence of empathy in complaint handling: Evidence of
gratitudinal and transactional routes to loyalty. Journal of Retailing and Consumer
Services, 20(6), 599–608.

Smith, A. K., Bolton, R. N., & Wagner, J. (1999). A Model of Customer Satisfaction with
Service Encounters Involving Failure and Recovery. Journal of Marketing Research, 36
(3), 356.

Spence, M. (1973). Job market signaling. Journal of Economics, 87(3), 355–374.
Stevens, J. L., Spaid, B. I., Breazeale, M., & Esmark Jones, C. L. (2018). Timeliness,

transparency, and trust: A framework for managing online customer complaints.
Business Horizons, 61(3), 375–384.

Strauss, J., & Hill, D. J. (2001). Consumer complaints by e-mail: An exploratory
investigation of corporate responses and customer reactions. Journal of Interactive
Marketing, 15(1), 63–73.

Tax, S. S., Brown, S. W., & Chandrashekaran, M. (1998). Customer Evaluations of Service
Complaint Experiences: Implications for Relationship Marketing. Journal of
Marketing, 62(2), 60.

S. von Janda et al.

Journal of Business Research 137 (2021) 116–127

127

Thakur, R., & Workman, L. (2016). Customer portfolio management (CPM) for improved
customer relationship management (CRM): Are your customers platinum, gold,
silver, or bronze? Journal of Business Research, 69(10), 4095–4102.

Urueña, A., & Hidalgo, A. (2016). Successful loyalty in e-complaints: FsQCA and
structural equation modeling analyses. Journal of Business Research, 69(4),
1384–1389.

van Doorn, J., Lemon, K. N., Mittal, V., Nass, S., Pick, D., Pirner, P., & Verhoef, P. C.
(2010). Customer Engagement Behavior: Theoretical Foundations and Research
Directions. Journal of Service Research, 13(3), 253–266.

Verhoef, P. C., & Lemon, K. N. (2013). Successful customer value management: Key
lessons and emerging trends. European Management Journal, 31(1), 1–15.

Xia, J., Dawley, D. D., Jiang, H., Ma, R., & Boal, K. B. (2016). Resolving a dilemma of
signaling bankrupt-firm emergence: A dynamic integrative view. Strategic
Management Journal, 37(8), 1754–1764.

Xiao, Y., Zhang, H., & Cervone, D. (2018). Social Functions of Anger: A Competitive
Mediation Model of New Product Reviews. Journal of Product Innovation Management,
35(3), 367–388.

Yilmaz, C., Varnali, K., & Kasnakoglu, B. T. (2016). How do firms benefit from customer
complaints? Journal of Business Research, 69(2), 944–955.

Yim, C. K., Tse, D. K., & Chan, K. W. (2008). Strengthening Customer Loyalty through
Intimacy and Passion: Roles of Customer-Firm Affection and Customer-Staff
Relationships in Services. Journal of Marketing Research, 45(6), 741–756.

Zeelenberg, M., & Pieters, R. (2004). Beyond valence in customer dissatisfaction. Journal
of Business Research, 57(4), 445–455.

Zeithaml, V. A., Rust, R. T., & Lemon, K. N. (2001). The Customer Pyramid: Creating and
Serving Profitable Customers. California Management Review, 43(4), 118–142.

Zhou, K. Z., & Wu, F. (2010). Technological capability, strategic flexibility, and product
innovation. Strategic Management Journal, 31(1), 547–561.

Sergej von Janda is Assistant Professor of Marketing at the University of Mannheim. Sergej
received his M.Sc. and Ph.D. in Marketing from the University of Mannheim. His research
focuses on International Marketing & Innovation, Digital Marketing, and Customer Rela-
tionship Management with publications in journals such as the Journal of International
Marketing, the International Journal of Innovation Management, the Journal of Cleaner
Production, and the Journal of Research-Technology Management. Sergej worked as a
visiting researcher at the Indian Institute of Management in Bangalore (India) in 2016 and
at the University of Texas at Austin (USA) in 2019.

Andreas Polthier is a Postdoctoral Researcher at the University of Mannheim. Andreas
received his Ph.D. in Marketing from the University of Mannheim in 2021. His research has
been presented at leading conferences in the area of marketing and innovation, including
the American Marketing Association (AMA) Winter Academic Conference, the European
Marketing Academy Conference (EMAC), and the Innovation and Product Development
Management Conference (IPDMC). He was a doctoral fellow at the 2019 Product Devel-
opment and Management Association (PDMA) Doctoral Consortium and the 2020 EMAC
Doctoral Colloquium, Marketing Strategy – Intermediate/Advanced Track.

Sabine Kuester is Professor of Marketing and Director of the Institute for Market- Oriented
Management at the University of Mannheim. Previously, Sabine held various positions at
ESSEC Graduate School of Management in France, at Leonard N. Stern School of Business,
New York University in the U.S., and at Vienna University of Economics and Business
Administration in Austria. Sabine received her B.Sc. and M.Sc. in Economics and Business
from the University of Cologne in Germany and her Ph.D. in Marketing from London
Business School in England. Her research has been published in journals such as the
Journal of Marketing, the International Journal of Research in Marketing, the Journal of
International Marketing, the Journal of Product Innovation Management, and the Journal
of Business Research.

S. von Janda et al.

  • Do they see the signs? Organizational response behavior to customer complaint messages
    • 1 Introduction
    • 2 Theoretical foundation
    • 3 Hypotheses
      • 3.1 The value of innovative customers
      • 3.2 The value of long-term customers
      • 3.3 Organizational complaint response behavior and customer satisfaction
    • 4 Method
      • 4.1 Data collection and sample
      • 4.2 Manipulations
      • 4.3 Measures
    • 5 Analysis and results
    • 6 Discussion
      • 6.1 Signaling of improvement ideas in complaint messages and response behavior
      • 6.2 Length of the business relationship and response behavior
      • 6.3 Organizational complaint response behavior and customer satisfaction
    • 7 Theoretical contributions
    • 8 Managerial implications
    • 9 Limitations and future research opportunities
    • Declaration of Competing Interest
    • Appendix A : Examples of complaint messages
      • Treatment 1 (signaling cues: no improvement idea, first-time customer)
      • Treatment 4 (signaling cues: Improvement idea, long-time customer)
    • Appendix B: Soft response quality assessment
    • References

Week 1 Assignment/7 Matching as Service Provision of Sharing Economy.pdf

Technological Forecasting & Social Change 171 (2021) 120901

Available online 28 June 2021
0040-1625/© 2021 Elsevier Inc. All rights reserved.

Matching as Service Provision of Sharing Economy Platforms: An
Information Processing Perspective

Ke Rong a, Hui Sun b, Dun Li c, Di Zhou d, *

a Institute of Economics, Tsinghua University, Hai Dian District, Beijing, P.R. China 100084
b Sheffield University Management School, The University of Sheffield, Conduit Road, Sheffield, S10 1FL, United Kingdom
c Management School, Guizhou University, P.R. China
d Institute of Economics, Tsinghua University, Hai Dian District, Beijing, P.R. China 100084

A R T I C L E I N F O

Key Words:
Sharing Economy
Information processing theory
matching
service management

A B S T R A C T

The purpose of this paper is to explore how the sharing economy (SE) manages its matching service through the
development of specific information processing mechanisms to achieve enhanced service performance. Two
game theory models are established to reveal the underline mechanism of how SE platforms design and improve
their service operations through matching the information-processing capacities (IPC) with the changing
information-processing requirements (IPR). The findings show that under different combinations of IPR and IPC,
SE falls into four kinds of match zones. The study further identified that pricing strategy and trust building are
two initial strategies to increase SE platforms’ IPR, which starts up service operation. And supply chain structure
change and information technology are essential to improve firms’ IPC, which increases service operation effi-
ciency. The findings are further demonstrated and verified through a longitudinal case study. This research
provides guidance for practitioners to identify the position of a SE platform through evaluating the company’s
information processing capabilities and suggests what information processing strategies should be deployed and
implemented for specific supply chains. This is one of the first studies theoretically explains and empirically
demonstrates how specific information processing mechanisms can be gainfully aligned with different statuses of
SE platforms. Doing so, it reinforces the conceptual foundation that SE firms should align and leverage on in-
formation capability management to achieve operation effectiveness and efficiency in service management.

1. INTRODUCTION

It has been witnessed that the sharing economy (SE) is booming and
sweeping across the globe in just a matter of years (Munoz & Cohen
2017). SE platforms have been increasingly adopted by companies in
various service sectors of the economy, such as transportation, accom-
modation, finance, and labour market. Nowadays, seven out of the top
ten largest unicorn companies across the world provide sharing service
(Forbes 2018). Given the rapid growth and high value of SE firms like
Airbnb, Lyft, and Uber, hundreds of technology ventures emerged in
recent years to develop similar business models. However, only a frac-
tion has reached a substantial network size, while many already went
out of business. Thus, the explosion in SE start-ups and the subsequent
high failure rate capture the interests of academics, professionals, and
the public (Kumar et al. 2018; Govindan et al. 2020).

Sharing economy is defined as “the monetization of underutilized assets

that are owned by service providers (firms or individuals) through short-term
rental” (Kumar et al. 2018, p.148). The underlying proposition of SE
firms is that they may add value by allowing owners of resources to
make their idle personal assets (e.g. rooms or cars) available to those in
need (e.g. holidaymakers or passengers). Matching supply and demand
is the heart of the sharing economy. SE platforms provides the techno-
logical infrastructure to establish connections between platform users
for exchanging, interacting, communicating, and participating in the
network. The platform’s overarching purpose is to be matchmakers so
that there is exchange of goods and services between peer groups (Evans
& Schmalensee 2016). Effective matching strategy can enhance the
liquidity of SE platform, provide opportunity for platform to develop. As
direct alternatives to traditional suppliers (e.g. hotels, taxi companies),
the resource optimization offered by SE firms has become possible with
recent technological advances in search, rating, and matching
algorithms.

* Corresponding author: Tel: +86 18811556933
E-mail addresses: [email protected] (K. Rong), [email protected] (H. Sun), [email protected] (D. Li), [email protected] (D. Zhou).

Contents lists available at ScienceDirect

Technological Forecasting & Social Change

journal homepage: www.elsevier.com/locate/techfore

https://doi.org/10.1016/j.techfore.2021.120901
Received 18 January 2021; Received in revised form 15 May 2021; Accepted 19 May 2021

Technological Forecasting & Social Change 171 (2021) 120901

2

SE, as a context, may bring new insights into firms’ information
capabilities and service supply chain management. Traditional service
supply chain is developed as complement to production-based supply
chain (Anderson & Morrice 2000; Ellram et al. 2004). Baltacioglu et al.
(2007) define a service supply chain as “a network of suppliers, service
providers, consumers, and other supporting units that performs the
functions of transactions of resources required to produce services;
transformation of these resources into supporting and core services; and
the delivery of these services to customers.” SE supply chain is different
from a traditional service supply chain. In a traditional service supply
chain, service providers integrate various functional services into ser-
vice solutions and deliver value to customers in exchange for own eco-
nomic benefits. While the SE creates value for multiple parties in the
chain (Wirtz et al. 2019). The sharing economy has evolved from the
development of Internet-based platforms whose functions are to mediate
transactions by matching supply and demand and addressing informa-
tion asymmetry from both service suppliers and customers (Armstrong
2006; Kumar et al. 2018). So, driven by the information technology,
sharing platform may achieve value co-creation among service sup-
pliers, platforms, and customers through offering strong support for
resource integration (Breidbach & Brodie 2017). The essential condition
under which to successfully establish an SE supply chain is to achieve
decent matching of huge numbers of participated users on a platform.
Also, the value of a platform for any user on one side depends on the
number of users on the other side (Eisenmann et al. 2006). This dynamic
matching process poses higher uncertainties compared to a traditional
service supply chain. As the production, distribution, and consumption
of sharing services are simultaneous processes, which means there is a
need for a SE platform to develop a more flexible and responsive supply
chain than a traditional one. The flexibility and responsiveness can be
moderated through the development of information processing capa-
bilities (Sutherland et al. 2018). The platform integrates a wide range of
advanced information technologies, such as big data, and cloud
computing (Kenney and Zysman 2016) and constantly optimizes the
matchmaking between supply and demand, which benefits the users no
longer limited by space or time. Utilizing the benefits of information
technology, SE platform provides various resources which can be
accessed on-demand. By leveraging network effects via an abundant
reserve of service providers, SE platform can generate value through
lower technical and search costs (Akbar 2019). This research is therefore
focused on understanding the inner complexity of SE and contributing to
literature with an investigation into the development of information
processing capability in service management specifically in SE context
and attempts to explore the following research questions:

How do SE firms achieve and improve matching through specific
information-processing mechanisms to enhance service performance?

This study draws on information processing theory (IPT) (Galbraith
1973) to address the limited focus of current research in understanding
how SE businesses achieve and develop better services through man-
aging their information management capabilities in a dynamic envi-
ronment. Accordingly, the underlying theoretical framework
investigates the role of the digital pricing mechanism and online trust
building mechanism to increase the information-processing require-
ment. Meanwhile, the role of IT development and supply chain structure
change in achieving different matches between information-processing
needs and capacities in an increasingly dynamic business environment
of a SE supply chain has been explored.

The remainder of the paper is structured as follows. Section 2 pre-
sents the theoretic background on SE and information processing
perspective. In section 3, two game theory models are established to
reveal the underline mechanism of how a SE platform modifies its supply
chain information processing capacities to match the changing
information-processing requirements. In section 4, findings of the study
are presented. Section 5 further demonstrates and verifies these findings

through a longitudinal case study in representative SE. Section 6 pre-
sents the discussion with existing research. Section 7 presents the con-
clusions and contributions of this paper, with future research directions
recommended.

2. THEORETICAL BACKGROUND

2.1. Sharing Economy

In traditional economy, due to the propensity of risk aversion of
average person and high access uncertainty caused by information
asymmetry, it is almost impossible to form a stable sharing market.
Rapidly developing digital platforms make it possible to coordinate
peer-to-peer sharing market and B2C (business-to-customer) sharing
market (Gansky 2010; Belk 2014; Goudin 2016). As Breidbach & Brodie
(2017) asserts, an Internet-based platform can offer strong support for
resource integration, thus allowing it to achieve value co-creation be-
tween three actors in SE: service supplier, service platform, and
customer. The separation of usage rights and property rights with a
relatively low transaction cost in a multi-sided market increases the
willingness of collaborative consumption in economy. In this process,
Internet-based platforms mediate transactions by matching supplies and
demands (Armstrong 2006) and reducing asymmetric information from
both service suppliers and customers, thus reducing transaction costs,
such as searching (Koopman et al. 2014; Kumar et al. 2018). The
matching is the core SE service operation provided by SE platforms.

After the access uncertainties from both sides have been solved, there
can be a stable sharing market that allows more people to access the
services and goods with improved usage efficiency. In addition, the
development of IT-mediated technologies (such as cloud computing,
digital positioning, Internet of things, and prediction algorithm) may
increase the service efficiency, and further decrease transaction costs
and information asymmetry. As an example, it has been witnessed that,
with the application of digital positioning, not only has it enabled
sharing bike platforms to locate and manage millions of sharing bikes
any time, but it has also allowed customers to easily find available bikes
using this function with the platform apps on their mobile. Thus, the
digital positioning system lowers the searching cost for both the plat-
form and bike users and improves the usage efficiency of the bikes.
Taking advantage of advanced information processing capability, SE
platform may optimize the filtering, evaluation and searching process
and achieve efficient matchmaking, lower transaction costs, and
removes the need for institutional bureaucratic overhead (Carroll and
Bellotti 2015).

Two factors are seen as essential for achieving better SE
performance:

1) Achieving service operation effectiveness through the involvement
and matching of more people on SE platforms (Rong et al. 2017;
Horton & Zeckhauser 2016).

2) Improving service operation efficiency (operational and financial
efficiency) by reducing transaction costs, encouraging service in-
novations, and increasing the rates of utilization of goods and ser-
vices (Sundararajan 2013; Bostman & Rogers 2010).

Currently, SE plays an important role in expanding and fostering
business innovation. Yet, extant understanding of factors contributing to
SE platforms’ growth and success remains unclear and fragmented
across multiple research disciplines such as marketing, entrepreneur-
ship, economics, technology management, and strategy (Tiwana et al.
2010).

2.2. Information Processing Theory

Information flow is the foundation of an effective SE supply chain
and can reduce uncertainty (Ellram et al. 2004). Information across SE

K. Rong et al.

Technological Forecasting & Social Change 171 (2021) 120901

3

includes suppliers’ and customers’ data, search algorithms (i.e. data
analytics), and information on market and politics. Platforms gather and
use such information to increase their capacity in matching the multiple
sides of the market and providing efficient, effective services to supply
chain participants.

IPT provides a grounded base to understand how a SE platform de-
velops and manages its services. This theory posits that supply chains are
information-processing networks that are intrinsically programmed to
mitigate environmental uncertainties through increasing their capabil-
ities in gathering, processing, and acting on information from the
environment (Tushman & Nadler 1978; Daft & Weick 1984). IPT theory
reveals the process of improving organizations’ ability to cope with
various uncertainties (i.e., supply and demand uncertainties) (Tushman
& Nadler 1978). Uncertainty creates information-processing re-
quirements (IPR), meaning that SE firms should build up enough in-
formation processing capacity (IPC) to meet the IPR created by these
uncertainties and subsequently achieve the desired outcomes (Tushman
& Nadler 1978). The focal point of this perspective is based on the
assumption that the performance of organizational systems is dependent
on how well matched an organization’s IPR is with its IPC (Galbraith
1973).

IPT originated in organization theory (Galbraith 1973; Tushman &
Nadler 1978) and has more recently been applied in operations and
supply chain management studies (Busse et al. 2017; Jia et al. 2020).
This theory has significant implications for understanding SE for three
reasons. First, SE is genetically based on IT-enabled platforms embedded
in the supply chain. Second, although previous studies have identified a
number of different perspectives to manage SE business, such as value
co-creation and social exchange theory (Acquier et al. 2017; Kim et al.
2015; Wu et al. 2014). Few of these studies look at SE from the infor-
mation management perspective. Third, prior research has acknowl-
edged the important role of information management in SE (Wu et al.
2014), but the research is highly descriptive, and theoretical explana-
tions to understand how SE firms forms its information management
mechanism to face various demand and supply uncertainties are largely
under-represented.

2.2.1. Uncertainties faced by SE platform
From a managerial perspective, supply chain management is pre-

dominantly defined by a persistent endeavour to reduce information
asymmetry, uncertainty, and complexity (Bode & Wagner 2015; Fros-
tenson & Prenkert 2015; Serdarasan 2013; Thomé et al. 2016). SE sys-
tems are complex in nature due to the involvement of various
stakeholders and relationships among stakeholders (Bode & Wagner
2015; Serdarasan 2013). These new technology-driven marketplaces do
not merely enable or facilitate new processes of production, delivery in
organizations, but fundamentally redefine industry size, scope, and
participation. They increase the pool of potential service providers and
sellers by leveraging networked technology to change how market
participants engage in a specific transaction. SE has developed from
platform-based two-sided markets where the goal is to get both parties
to interact through the platform. However, the challenge for the plat-
forms also lies in how to create conditions that generate favourable
network effects that lead to gaining a critical mass and effectively
matching the highly dynamic supply and demand requirements. From
the customer side, they engage with SE as they may gain access to the
same service at a lower cost, without the burden of ownership and at
their convenience (Möhlmann 2015; Tussyadiah 2016). From the supply
side, service providers join SE to supplement their low paying or
part-time jobs, or as a stopgap between jobs. If the service providers are
dissatisfied with the compensation or the pricing mechanism set by the
platform, there will not be enough suppliers to meet customer demand.
Nevertheless, the significant rise in demand may also lower the avail-
ability of service providers and increase waiting times for customers,
which results in customers being inconvenienced. So, a necessary con-
dition for SE is to effectively match a critical mass of users, including

suppliers and customers (Evans & Schmalensee 2010; Rochet & Tirole
2003). If an SE can only solve limited cross-side uncertainty and
matching an inadequate number of suppliers and customers, the plat-
forms’ market potential is likely to be too small to generate sufficient
network effects, which may prevent the platform from growing or
reaching its full potential (Bass 1969). Therefore, in SE service opera-
tion, huge uncertainty lies in the difficulty to matchmake the demand
and supply due to frequent fluctuations on the amounts of demand and
supply over time caused by highly dynamic the environment (Lou et al.
2004).

The highly dynamic nature of matchmaking may carry huge uncer-
tainty and demand a higher information processing requirement for a
platform’s growth, but also bring opportunities for the platform to
develop. The increase of the uncertainty means that if platforms can
maximize the amount of platform users and matchmake more effec-
tively, they may achieve more market potential and better service out-
comes comparing to the competitors.

2.2.2. Strategies to increase the information processing requirement
According to IPT, uncertainties include task characteristics and inter-

dependence which determine information processing requirements for
SE platforms (Tushman & Nadler 1978; Jia et al. 2020).

The uncertainty of task characteristics can be reflected by the task
predictability (Tushman & Nadler 1978). The predictability of match-
making is mainly reflected by pricing mechanism, which plays a crucial
role (Caillaud & Jullien 2003; Rochet & Tirole 2006). Just like
price-sensitive customers on the demand side, crowdsourced suppliers
are also sensitive to their rewards for providing services. A proper
pricing mechanism is the crucial factor in building an audience. The
service platform in SE charges a fee for facilitating connections between
suppliers and customers. SE service can be effective only when all the
stakeholders among SE can obtain surplus or achieve expected value
from this symbiotic practice (Jacobsen 2006; Mirata 2004). Thus, the
service charge for service provider and price for customers are the two
key controls for the SE platform to coordinate supply and demand.
Various studies have explored the dynamic pricing policy to best match
supply and demand in time and also in space (Bimpikis et al. 2019;
(Chen and Sheldon, 2016) 6).

Task inter-dependence in SE can be reflected by the coordination
between platform users which determines users’ willingness to use the
SE platform. The SE firms acts as the mediator between provider and
consumer retain considerable control over each peer-to-peer transaction
not only in terms of payment, but also in terms of search, matching, and
feedback. Trust has been understood as a mechanism, which eases
concerns of opportunism and thereby enables cooperation and effec-
tively coordinate the joint activities (Dyer & Singh 1998; Zaheer et al.
1998; Gulati & Nickerson 2008). Building trust is often viewed as a
necessary condition in reaching critical mass within this market poten-
tial (Davis 1989; Rogers 2003; Venkatesh et al. 2003). The concept of
sharing goods and services via the Internet is based on the fundamental
mechanism of strangers interacting with each other in the digital sphere,
which means SE platforms have to pay special attention to technologies
and digital mechanisms that foster trust-building and risk aversion
among platform users (Santana & Parigi 2015). Such methods include
the use of rankings and reviews (Orlikowski & Scott 2014), as well as
technologies and data-driven systems that facilitate transactions, track
goods and their usage, and provide guidance in case of conflicts (Hartl
et al. 2016). Therefore, as in any (online or offline) business setting, the
presence of trust is a major precondition for successful transactions in
SE.

2.2.3. Mechanisms for improving the information processing capacity
The information processing capability can be derived from a number

of mechanisms to increase IPC in order to match high IPR (Bensaou &
Venkatraman 1995). Following Bensaou and Venkatraman (1995), such
mechanism can include organisational structural design, coordination

K. Rong et al.

Technological Forecasting & Social Change 171 (2021) 120901

4

and control, and information technology. Accordingly, In the context of
SE, two critical strategies are identified: supply chain structure change
and the development of IT (Tushman & Nadler 1978).

Tushman and Nadler (1978) note that the organismic-mechanistic
nature of the organisational structure affects the information process-
ing capacity of an organisation. A highly coordinated structure may
enable companies to cope with more uncertainties. Facing a dynamic
environment, an organization will need to increase the IPC through
identifying, utilizing, and assimilating both internal and external in-
formation to facilitate the entire service production and delivery activ-
ities. SE firms may change the supply chain structure to reduce
transaction costs and have reliable, efficient, and cooperative responses
towards the uncertainties (Rogers et al., 1993)(Rogers et al. 1993 which
guarantees improved service to both service providers and customers.
Doing so, SE firms may develop and renew firm-specific competences to
better respond to shifts in the environment Collis 1994 (Collis, 1994)
Teece et al. 1997(Teece et al., 1997)). Such structure change can be
outsourcing part of the function in the organization or collaborating
with other partners. To enhance the IPC, the sharing firm will not only
share knowledge with its supply chain partners in an effective and
efficient manner but will also be involved in inter-firm technology
integration between sharing partners.

Furthermore, information processing capacity is also derived from
information technology mechanisms. IT is a crucial enabler, or struc-
tural mechanism, to process large amounts of information easily, that is,
to increase a SE firm’s IPC by extending the existing information sys-
tems’ technical competency (BenArieh & Pollatscheck 2002; Bensaou &
Venkatraman 1996). The adoption and diffusion of various advanced
digital techniques such as big data, new algorithms, and cloud
computing (Kenney & Zysman 2016) enable SE platform to increase the
speed of decision-making. The application of advanced business ana-
lytics to extract valuable insights from information flows has always
been an integral and vital ingredient in efficient matching between
suppliers and customers, and even in promoting service innovation.

In summary, empirical studies on SE have so far focused on particular
aspects of the phenomenon, such as the drivers of SE (Bucher et al. 2016;
Hellwig et al. 2015), the development of two-sided markets in SE which
are up-stream service suppliers and down-stream customers markets
(Kumar et al. 2018), sustainability (Cohen & Muñoz 2016), and the
governance of end customers (Hartl et al. 2016). However, at the nu-
cleus of this burgeoning concept is the service management enabled by
digital technologies. Fuelled by information technology, sharing activ-
ities facilitate interactions between platform users and support value
co-creation from the untapped potential of the possessions that are
under-utilized by the owners (Belk 2014; Hamari et al. 2015). In many
such sharing enterprises, SE system is predicated on efficient and scal-
able technology, which brings large networks of people together and
matches them to the goods or services they need (May et al. 2017;
Botsman & Rogers 2010). A few recent studies have investigated the
roles of mediating technologies (May et al. 2017; Lee et al. 2015) in SE,
but they focus on technology as a stand-alone resource. In some dis-
cussions this technology is an ‘algorithm’ (Lustig et al. 2016;
(Möhlmann and Lior, 2017), while in others it is a ‘platform’ (Cheng
et al. 2018; Scholz 2014).

Indeed, companies may enjoy the benefits brought by information
technology (IT), however, investment in IT does not guarantee a stron-
ger organizational performance. The value of IT can be enhanced when
it is embedded into supply chain management (Powell and Dent-Mi-
callef, 1997)(Powell & Dent-Micallef 1997). In other words, the market
successes and failures of SE businesses are often closely associated with
not only IT itself, but also how SE firms deploy IT capabilities in their
service operations (Frenken 2017). Still, research on how information
processing capabilities of SE platform are developed and managed
throughout the development of SE supply chain is largely missing
(Sutherland & Jarrahi 2018).

3. RESEARCH METHOD AND MODEL ANALYSIS

To address the research question and theoretical gap, this research
establishes two game theory models to illustrate how a SE platform
chooses specific information processing mechanisms by improving the
IPR and IPC to enhance SE firm’s performance. Specifically, price
mechanism and trust building have been identified as two strategies to
effectively increase the IPR. While, IT and supply chain structure change
have been identified to increase the IPC. This paper tests and validates
how these four constructs (1) price mechanism, (2) trust building, (3)
supply chain structure change, (4) IT capability are adjusted to enable
and improve SE services. A case study of a leading ride sharing company
in China (Didi Chuxing) is provided to demonstrate the findings from the
game theory and further elaborate the findings.

3.1. Model 1: Improve IPR in a Low IPC Environment

The efficient and effective matching of supply side and demand side
is the foundation on which to create a marketplace. The platforms
matchmake the demand and supply sides using data-driven algorithms
to drive better buyer-seller matches. During this key matching process,
both inter- and intra-organizational collaborations are important. Model
1 explains why SE platform with low IPR and low IPC is not effective in
the long term and what SE platform should do to improve IPR in a low
IPC environment in order to cultivate a large-scale sharing market.

3.1.1. Sharing Economy with Low IPR and Low IPC
Most of the newly established SE platforms in a new industry or small

sharing company which target the niche market have low IPR and IPC.
This type of SE platforms has limited users and low capacity to enable
the matching service.

In the initial SE scenario, consumers are divided into two groups:
owners and non-owners. Owners have the ownership of the good and
could choose to share the untapped capacity with non-owners; while
non-owners do not own the good but could choose to pay a certain
sharing price to get partial usage right of the good.

This model supposes the average utility gained from using the
product each time is u for both owner and non-owner. The owner has
two choices when he/she is not using the good: a) to put it aside and he/
she will not receive any extra profit; or b) to share it with the non-owner
by charging a certain sharing price p, thus the extra earnings of the
owner will be p. The non-owner also has two choices: a) to refuse to pay
the sharing price and decide not to use the good; or b) to pay the sharing
price (p) to the owner and thus get the utility (u) from using the good. It
is defined that the extra cost has to be covered by both the owner and the
non-owner in the sharing market: the access cost (ca > 0). Access cost is
a kind of cost generated before the match takes place. For example, there
will be a searching cost for both sides before they are matched. SE
platform will charge a certain fee to match the supply and demand. The
cost to establish the platform is assumed to be c and then a proportional
fee (αp, 0 < α < 1) is charged by the platform from the sharing price.
The fee will be finally paid in equal amounts by the owner and non-
owner if the sharing agreement is reached. The sharing process is
demonstrated in Figure 1.

So, the payoff for the owner and the non-owner will be
(2 − α)p/2 − ca and u − (2 +α)p/2 − ca respectively if a match is ach-
ieved; − ca for both parties if a match is not achieved; and 0 for both
parties if they do not access the sharing market. The payoff matrix is
shown in Table 1.

If the final business volume is n, then the necessary conditions for the
match to be achieved are that (2 − α)p/2 ≥ ca, u ≥ (2 +α)p/2 + ca and
αpn − c > 0. Under this condition, both the owner and the non-owner
would benefit from the match if they access the market, and the plat-
form could make a profit. If the necessary conditions are satisfied, two
pure strategy Nash equilibriums can then be easily calculated: (Share,
Access) and (Not Share, Not Access). Here the mixed strategy Nash

K. Rong et al.

Technological Forecasting & Social Change 171 (2021) 120901

5

equilibrium is not considered.
However, some owners may not even know where they can share

their idle resource to the non-owner. In this case, sharing market will not
be effective, and the match may be inefficient. Even if the match hap-
pens incidentally, because the trust level between owner and non-owner
is low, the owners will prefer to charge high service fee to prevent the
non-owners from defaulting, which makes the match less effective. In
this kind of sharing market, the risk averse nature of consumers would
finally lead to the equilibrium (Not Share, Not Access). This result can be
easily obtained via a simple analysis. If the owner chooses (Share) and
assumes that the non-owner deviates from the strategy (Access), then
the loss of the owner will be − − (2 − α)p/2. If the owner chooses (Not
Share) and the non-owner deviates from the strategy (Not Access), the
loss for the owner is only 0. A risk averse owner would no doubt choose
(Not Share) as the strategy. Meanwhile, a risk averse non-owner would
also choose (Not Access) as the strategy since the deviation of the owner
would incur more loss with strategy (Access) ( − [(2 + α)p /2 − u] > 0).

So, for the SE platform which has consistently low IPR and IPC, the
sharing mechanism is not effective, and the final equilibrium would tend
to be (Not Share, Not Access).

3.1.2. Sharing Economy with High IPR and Low IPC
Since the sharing market with continuous low IPR and low IPC

cannot be effective, SE platform will try to cultivate a large sharing
market and increase the matchmaking rate in order to improve the IPR.
So, SE platform could handle more uncertainties caused by the various
kinds of demands from the consumers and thus gradually promote the
development of the sharing market. Based on the existing matching
technology, SE platform may have two strategies to engage more users,
thus increasing the IPR: 1) to lower the sharing price; or 2) to build trust
among service providers, customers, and the platform.

As an enterprise which seeks to maximize the profits, the SE platform
would try to attract more users in both supply and demand sides in order
to increase the business volume. Obviously, as the matching re-
quirements rise, the uncertainty of the IPR for matching task would also
increase. So, the SE firm with high IPR may experience strong growth.
To improve the effectiveness of the matching mechanism, the platform
may offer a relatively low price to attract users and meantime guarantee
suppliers’ benefits; in addition, the trusting relationships between the
owners and the non-owners need to be established.

The matching process of SE with high IPR and low IPC is shown in
Figure 2. The platform needs to design a certain mechanism to solve the
problem caused by the risk averse. Facing the substantial matching
uncertainty brought from the large scale of the market, the platform
would develop a high-tech matching system by signalling both supply
and demand sides to build a trusting relationship. Consumers will
receive a message containing probabilities: the platform will inform the
owner that the non-owner will access the sharing market with a prob-
ability of pa, then the probability of (Not Access) will be 1 − pa; Mean-
while, the platform will inform the non-owner that the owner will share
the good with a probability of ps, then the probability of (Not Share) will
be 1 − ps.

The higher the rate of accurate matches, the higher the signals would
be. And the higher the signals are, the higher the cost and the larger the
business volume would be. So, the cost and the business volume are
based on the level of signals. And they are defined as c(pa, ps) and
n(pa, ps) with ∂c(pa, ps)/∂pa, ∂c(pa, ps)/∂ps, ∂n(pa, ps)/∂pa, ∂n(pa,
ps)/∂ps > 0. Assuming there is only one platform in the market, then

payoff for the platform will be αpn(pa, ps) − c(pa, ps) if the match is
achieved and − − c(pa, ps) if the match is not achieved. For the owners
and non-owners in high IPR and low IPC sharing market, although the
payoffs in the payoff matrix are the same as the one in low IPR and IPC
scenario, users’ decision making will be influenced by the additional
signals received from the platform in response to the access. Now the
payoff matrix after the signals are received is shown in Table 2.

The necessary conditions for a potential sharing market become
αpn(pa, ps) − c(pa, ps) > 0, (2 − α)p/2 ≥ ca > 0 and u − (2 +
α)p/2 ≥ ca > 0. Based on the payoff matrix, the expected payoff for the
owner in the strategy of (Share) and (Not Share) will respectively be:

E(Share) = pa[(2 − α)p / 2 − ca] − (1 − pa)ca

E(Not Share) = 0

If E(Share) ≥ E(Not Share), the owner will choose (Share) and
pa ≥ 2ca/[(2 − α)p]. Since (2 − α)p/2 ≥ ca > 0, it is known that
2ca/[(2 − α)p] ≤ 1. Then for an effective sharing market, the platform
should make sure that 2ca/[(2 − α)p] ≤ pa ≤ 1.

Similarly, the expected payoff for the non-owner in the strategy of
(Access) and (Not Access) will respectively be:

E(Access) = ps[u − (2 + α)p / 2 − ca] − (1 − ps)ca

E(Not Access) = 0

If E(Access) ≥ E(Not Access), the non-owner will choose (Access) and
ps ≥ 2ca/[2u − (2 + α)p]. Since u − (2 + α)p/2 ≥ ca > 0, it is known that
2ca/[2u − (2 + α)p] ≤ 1. Then for an effective sharing market, the
platform should make sure that 2ca/[2u − (2 + α)p] ≤ ps ≤ 1.

So, the sufficient conditions for an effective sharing market are:
αpn(pa, ps) − c(pa, ps) ≥ 0, 2ca/[(2 − α)p] ≤ pa ≤ 1 and 2ca/[2u − (2 +
α)p] ≤ ps ≤ 1. Based on these conditions, the platform’s final problem
becomes:

max
pa, ps

αpn(pa, ps) − c(pa, ps)

Figure 1. Matching process for sharing economy with low IPR and low IPC.

Table 1
The payoff matrix for sharing economy with low IPR and low IPC

Non-owner
Access Not Access

Owner Share ((2 − α)p/2 − ca, u − (2 + α)p/2 − ca ) ( − ca , 0)
Not Share (0, − ca ) (0, 0)

Figure 2. Matching process of sharing economy with high IPR and low IPC

K. Rong et al.

Technological Forecasting & Social Change 171 (2021) 120901

6

s.t. 2ca/[(2 − α)p]< pa ≤ 1

2ca/[2u − (2 + α)p]< ps ≤ 1

Here, n(pa, ps) is assumed as an increasing concave function:

∂n(pa, ps)
/

∂pa > 0, ∂n(pa, ps)
/

∂ps > 0 and ∂2n(pa, ps)
/

∂p2a
< 0, ∂2n(pa, ps)

/
∂p2s < 0

and c(pa, ps) is an increasing convex function:

∂c(pa, ps)
/

∂pa > 0, ∂c(pa, ps)
/

∂ps > 0 and ∂2c(pa, ps)
/

∂p2a
> 0, ∂2c(pa, ps)

/
∂p2s > 0

Then, the solution (p*a, p
*
s ) would be either a lower bound corner

solution or an inner solution, though it depends on the format of
n(pa, ps) and c(pa, ps).

As a result, this research finds that under the sufficient conditions, an
effective sharing market could be built when the SE platform adopts
appropriate strategies. With the help of the signals, the SE platform
supports the whole matching process by providing a relatively low price
and facilitating the trust building between the owners and non-owners.
Thus, the effectiveness of matching can improve, and the sharing market
may be gradually incubated into a large market with a great number of
users. Hence, to improve IPR, the SE platform needs to transmit signals
to both sides to build up a trusting relationship and charge a relatively
low price to attract more users to access the platform. In turn, with a
high IPR, the match in the sharing market would be more effective.

3.2. Model 2: Improve IPC in a High IPR Environment

By improving IPR, the SE platform may achieve market development
through effective matching service. However, due to the possibility of
access conflict, the matching process can be inefficient. Here, “access
conflict” refers to a situation that a number of non-owners access the
sharing service simultaneously and some of the non-owners are unable
to access the sharing service due to the limited number of idle resources
on the market. Model 2 explains why a SE platform with high IPR and
low IPC may still be not efficient. So, the SE platform will try to improve
IPC in a high IPR environment to achieve an enhanced firm performance
and better service outcome.

An efficient sharing market means an efficient matching mechanism
between supply and demand. There are two strategies who company
may adopt to improve the IPC: 1) to invest in developing advanced in-
formation technology such as artificial intelligence and prediction al-
gorithms; and 2) to change the supply structure which means more
efficiently manage the service providers (i.e., contracting with special-
ized service providers). By predicting the spatial and temporal distri-
bution of the demand for matching, the platform could schedule the
allocation of idle resources owned by the companies according to the
anticipated distribution requirements in advance.

3.2.1. Sharing Economy with High IPR And High IPC
As has been shown, in the sharing market with high IPR and low IPC,

both supply and demand side may experience access conflicts. For

example, in the sharing ride market, non-owners may find difficult to get
access to sharing vehicles in the morning rush hour. Because the demand
of the sharing services exceeds the supplied vehicles. Owners may also
find that it is hard to receive orders when their cars are in the suburb
area due to the limited non-owners who requests the riding services.
Obviously, access cost is not constant; it is highly dependent on the
quantity of supply and demand. The more imbalance the supply-demand
structure has, the larger the access cost would be. So, the access conflict
would significantly increase the access cost and make the whole
matching system inefficient.

A potential way to solve the problem is to invite business partners
into the platform to provide more available resources. Unlike private
owners, supply chain partners could supply more idle resources and
increase supply chain responsiveness. In the meantime, the platform
develops high levels of artificial intelligence and prediction algorithms
to predict the spatial and temporal distribution of the matching demand
more accurately in advance. If the supply chain partners could arrange
their resources according to the forecast with high rates of accuracy, the
access cost would be reduced significantly and the whole matching
system would be more efficient. Figure 3 shows how an SE with high IPR
and high IPC works.

In this model, the access cost ca(s, q) depends on the supply chain
structure s and matching technology innovation q. If a larger s presents a
more rational supply chain structure, then following the logic above,
∂ca(s, q)/∂s < 0 and ∂ca(s,q)/∂q < 0. For the platform, the cost c(pa, ps, s,
q) depends not only the signals, but s and q. Hence:

∂c(pa, ps, s, q)
/

∂s > 0, ∂2c(pa, ps, s, q)
/

∂s2 > 0;

and ∂c(pa, ps, s, q)/∂q > 0, ∂2c(pa, ps, s, q)/∂q2 > 0.
Also, the number of the consumers n(pa, ps, s, q) also depends on s

and q:

∂n(pa, ps, s, q)
/

∂s > 0, ∂2n(pa, ps, s, q)
/

∂s2 < 0;

and ∂n(pa, ps, s, q)/∂q > 0, ∂2n(pa, ps, s, q)/∂q2 < 0.
So, using the same mechanism design as before, the payoff matrix is

shown in Table 3.
It could then achieve the requisite conditions for an efficient and

effective sharing market: αpn(pa, ps, s, q) − c(pa, ps, s, q) ≥ 0,
2ca(s, q)/[(2 − α)p] ≤ pa ≤ 1 and 2ca(s, q)/[2u − (2 + α)p] ≤ ps ≤ 1.
Based on these conditions, the platform’s final problem becomes:

max
pa, ps

αpn(pa, ps, s, q) − c(pa, ps, s, q)

s.t. 2ca(s, q)/[(2 − α)p]<pa ≤ 1

2ca(s, q)/[2u − (2 + α)p]<ps ≤ 1

Again, the assumption that could guarantee the solution (p*a, p
*
s ) is

either a lower bound corner solution, or an inner solution. Since a larger
s and q would lead to a smaller ca(s, q), then it would be easier for the
platform to convince the consumers to access the platform with rela-
tively lower signals. Model 2 has proven that SE platforms need to
improve IPC by introducing new supply chain partners and developing

Table 2
The payoff matrix for sharing economy with high IPR and low IPC

Non-
owner
pa 1 − pa

Access Not
Access

Owner ps Share ((2 − α)p/2 − ca , u − (2 + α)p/
2 − ca )

( − ca ,
0)

1 − ps Not Share (0, − ca ) (0, 0)

Figure 3. Matching process of sharing economy with high IPR and high IPC

K. Rong et al.

Technological Forecasting & Social Change 171 (2021) 120901

7

new prediction algorithms to promote matching efficiency.

4. FINDINGS

This paper applies the IPT theory to demonstrate how SE firms in-
crease the IPC and enable the essential matching service through pricing
mechanisms and trust building. while the IPC needs to be adjusted
accordingly to match with the IPR to make matching more efficiently.
An improved IPC could be achieved through supply chain structure
change and IT.

Under different combinations of IPR and IPC, SE firms would fall into
four kinds of zones. Figure 4 represents different matches status of IPR
and IPC in SE.

Zone I represents SE firms who have high IPC to deal with high IPR.
They may have a good market incubation and are highly active in ser-
vice innovation and supply chain management. So, these companies
may achieve optimal matching which leads to better service outcome
and long-term sustainable business development. In a highly uncertain
environment, if a SE platform does not make any efforts to maintain its
service on a reasonable level, the service level may decrease. Conse-
quently, the customers may turn to other companies which have higher
responsiveness. So, to remain competitive, a SE company should invest
in its IPC development to match the high information requirements (i.e.
massive platform users’ requirements).

Zone II represents SE firms with high IPR but insufficient IPC to deal
with the uncertainties, which leads to less information and suboptimal
decision-making (Tushman & Nadler 1978). Furthermore, the low IPC
rate restricts the capability to share the information with service pro-
viders and customers. The lower transparency of the supply chain may
increase the information asymmetry and transaction costs, which in turn
will lower the matching rate. When the IPR are high, a SE platform
should increase its responsiveness to avoid the negative effects of high
uncertainty on the user service level. Otherwise, user retention will
reduce due to the low service level, low product availability, long lead

times, and low responsiveness. Uncertainty causes the overall supply
chain service level to decrease. For sample, the insufficient IPC was ever
one of the bottlenecks during Didi Chuxing’s (a leading ride sharing
company in China) development. On one side, the users (car owners and
customers) on the vehicle SE platform were increasing significantly, and
on the other side, the inadequate server processing capability and
matching algorithm led to slow matching and the crash of platform
operation system. Customers found it difficult to find a sharing car
through the Didi Chuxing’s platform in rush hours or in poor weather.
Although there is enough supply and demand on the market, which
leads to high IPR, the lack of IPC makes the platform to be incapable to
effectively match two sides and in turn lower the service quality.

Zone III represents SE firms with lower-level market needs and lower
information process capacity. In SE context, it is typical when SE firms in
their early stage of business development falls into Zone III scenario.
However, if the SE companies keeps staying in this zone and not taking
any actions to change, they will not be able to make effective matching
to generate healthy profits for business growth. Gradually, SE firms in
this zone will be washed out in the severe market competition. This may
explain the high failure rate of SE business following their start-ups.

Zone IV represents the case where more IPC has been created than is
required, which may lead to redundancy and unnecessary costs in terms
of time, effort, and control (Tushman & Nadler 1978). However, in the
long term, if sufficient matching could be developed to balance the in-
vestment on IPC, the company may achieve a positive outcome. The
introduction of autonomous car sharing service could be a representa-
tive example of sharing company in zone IV. The initial investments on
autonomous car technology and business model development are costly.
However, if the SE firms could take advantage of this technology, it
might have great market potential with more users gaining access to the
autonomous cars sharing service.

Different matches between IPR and IPC will lead to different orga-
nization performance when SE platforms deliver the service. A mismatch
will either lower the matching rate or increase the costs of matching. In a
mismatch status, it is expected that no SE firms can be viable in the long
term, as market forces will naturally demand drastic changes or drive
them out of business.

Based on the four match zones of SE, this research establishes two
game theory models to illustrate how SE platforms achieve different
levels of matching from zone III to II (achieve sharing market incubation
by improving matching effectiveness) and zone II to I (improve the IPR
and IPC to achieve enhanced matching efficiency). The findings are
shown in Table 4.

This research revealed that the initial sharing market is usually
formed in zone III with low IPR and low IPC. To gain business devel-
opment, the SE companies need to increase matching effectiveness
which means increase the number of matching services. Based on
existing technology, SE platform should increase the number of users
(improve IPR) and thus gradually move to zone II through two key
mechanisms: offering low prices and building trusting relationships. A
relatively low price could attract more users into SE platform and thus
trigger the requirements and in turn improve the business. In the
meantime, the platform could transmit signals to both the service/

Table 3
The payoff matrix for sharing economy with high IPR and high IPC

Non-
owner
pa 1 − pa

Access Not
Access

Owner ps Share ((2 − α)p/2 − ca(s,q), u − (2 + α)
p/2 − ca(s, q))

( − ca(s,
q), 0)

1 − ps Not
Share

(0, − ca(s, q)) (0, 0)

Figure 4. Match zones of information processing capabilities in the
sharing economy.

Table 4
Findings from the game theory models

IPR IPC Matching
Objective

Mechanism

Zone III →
Zone II

Low ↑
High

Low Effectiveness Low Price

Trust Relationship
Zone II →

Zone I
High Low ↑

High
Efficiency Technology

Innovation
Supply chain structure
change

K. Rong et al.

Technological Forecasting & Social Change 171 (2021) 120901

8

product providers and customers to promote trust between the two
sides. If a stable trust relationship is established, both sides will be
loyalty to platform and the effective SE matching could then be gradu-
ally generated. During this process, the IPR of the platform increases and
the status of SE platform switches from zone III to zone II.

This research also found that after the effective matching is achieved
through improving IPR, SE platforms need to upscale IPC and thus
switch to zone I by improving the matching efficiency. There are also
two key mechanisms: implementing information technology innovation
and supply chain structure change. To better meet demand, which may
occur anytime and anywhere, one strategy to improve IPC is to enhance
the platform’s information technology. With the help of artificial intel-
ligence and prediction algorithms, the platform could predict the
sharing demand distribution more precisely in order to allocate the idle
resources in advance. Cooperation with new partners to take advantage
of partners’ resources and capabilities is another strategy to increase the
IPC. Dedicated SE platforms may enjoy better capacity utilization and
more efficiently match demand for productive resources through
increased supply chain responsiveness. The improvement of IPC could
finally nudge SE status to switch from zone II to zone I.

5. CASE STUDY

The mixed-methods approach provided a more holistic understand-
ing than a single qualitative or quantitative approach could have done
on how a SE platform constantly manages its matching process through
adjusting the IPR and IPC and eventually achieve high business perfor-
mance. The perspectives were generally complementary rather than
contradictory. The game theory models revealed that pricing strategy
and trust building are two initial strategies to increase SE platforms’ IPR
and supply chain structure change and information technology are
essential to improve firms’ IPC. The case study provided corroboration
of the findings of modelling and led to more explicit and generalisable
conclusions concerning the relationship between the performance of SE
firm and different matches of IPR and IPC. Overall, combining the
modelling and case offered a deeper insight into the subject matter and
greater confidence in the results.

In this session, Didi Chuxing (Didi) is used as a case to further explain
and illustrate how a SE platform constantly manages its matching pro-
cess through adjusting the IPR and IPC and eventually achieve high
business performance.

Founded in 2012, Didi is China’s largest ride-sharing service com-
pany. Within five years, it had covered over 400 cities across the country
serving 550 million users and recruited 30 million registered drivers. In
three years, Didi rapidly developed its business and dominated ride
sharing market. By 2015, over 1.4 billion rides were delivered by Didi,
comparing to its competitor Uber Technologies (a US ride-hailing pro-
vider) who reached just one billion rides in its six years development in
China. In 2016, Didi acquired Uber’s ride sharing business in China. In
December 2017, Didi’s company was valued at $56 billion (CNN 2017).
As the most successful ride-sharing platform in China, Didi provides an
informative case and best example for researchers to analyse how a SE
platform firm can choose specific information processing mechanisms to
optimize its matching process and eventually achieve enhanced service
performance.

The data was collected between September 2013 and October 2018.
This research is longitudinal in the sense and the data collection
involved observation of the change of information processing mecha-
nism and different levels of matchmaking. Insights are gained from the
Didi’s historical and evolutionary business development. Special atten-
tion has been put on the changes in Didi’s operational practices within
the company and also the collaboration between its partners. Such
changes have been interpreted from IPT perspective. An interview
protocol was updated after each interview (see Appendix A). A total
number of 18 semi structured interviews were conducted in three
rounds. Interviewees included Didi’s management team and chief

operation officers such as Didi’s president, managers, and engineers.
The details of the interviews conducted was shown in Table 5. Archival
data were also collected from the company’s website, meeting notes and
company’s annual reports. The research was validated according to
Yin’s (2008) four tests, as shown in Appendix B.

Didi provides a clear example of how a SE platform guarantees and
improves its matching operation facing various uncertainties to achieve
optimal service performance. Acting as an open and sharing-based
intermediary platform, Didi’s platform the connects passengers and
car providers to initiate a new mobile-based transportation mode. A
customer submits his/her car-sharing service request using Didi’s
application on mobile device. Didi then uses mapping data and intelli-
gent dispatching technology to allocate the best service provider (i.e.,
private car owners) based on the providers’ location, reputation, or
response speed to service requests. Once a car is dispatched, the con-
sumer can then check driver and vehicle details, as well as track the
route and arrival time on the build-in map with Didi’s app. When the
journey is completed, the consumer pays for the trip via a third-party
online payment platform. Both customer and service provider can rate
the other and give anonymous feedback about the trip should they so
wish. Didi’s main revenue is commission from consumer’s payment for
the ride. The payment from customer is automatically divided between
Didi and the service provider of a predefined proportion. Figure 5 shows
Didi’s supply chain structure in early stage. The arrow indicates the
direction of the information flow.

At the early stage of the sharing-ride market, Didi, as well as its
competitors in China ploughed a large amount of investment into two-
fold advertising: how drivers can make higher profits joining this busi-
ness model than working full-time as traditional taxi drivers, and how
passengers can save travel costs due to the low pricing strategy. Didi sent
all kinds of vouchers to customers and offered huge subsidies to drivers
to encourage them to use the service. The reason for Didi to do so was to
capture ride-sharing users to prefer its app instead of the competitors’.
Only when enough service providers and customers are attracted to the
platform, then could Didi start matchmaking and facilitating the service
production and delivery.

Before SE riding market was formed, ‘taxi would be the choice when
customer wanted to get a ride. Since Passengers did not think private car
drivers can provide a safe trip and drivers did not trust passengers would
pay for the service neither because they do not know each other.
Building trust is essential to establishing SE. As one of the early estab-
lished sharing-ride platforms in China, Didi faced the challenge of un-
derstanding customers’ requirements, travelling patterns, and feedbacks
on their car-sharing experience. Didi launched rating system in its app
and constructed a two-way scoring mechanism to build trust on its
platform.

Very quickly, through building trust and its low-price strategy, Didi
won a large sharing-ride market in major cities in China. Later in 2015,

Table 5
Interview list

Location Interviewee Details Interview
Date

Interviewee
Code

Headquarter of Didi
(Beijing)

Project Manager Jan, 2015 A

Tsinghua University
(Beijing)

Vice President of Public
Policy

Jan, 2016 B

Tsinghua University
(Beijing)

Operation Manager Jan, 2016 C

Headquarter of Didi
(Beijing)

Chief Economist Jan, 2017 D

Headquarter of Didi
(Beijing)

Chief Development
Officer (CDO)

Jan, 2017 E

Tsinghua University
(Beijing)

Manager of Public
Policy

Nov, 2017 F

Tsinghua University
(Beijing)

Product Manager and
Engineer

Dec, 2018 G

K. Rong et al.

Technological Forecasting & Social Change 171 (2021) 120901

9

after the merge with a Chinese competitor Kuaidi and the acquisition of
Uber China, Didi virtually monopolized the country’s sharing-ride
market. As customers and drivers were getting used to car-sharing,
they started to demand better service. Passengers expected more car
availability and shorter waiting time, while drivers expected enhanced
responsiveness from the platform that directs them to match with the
passengers more efficiently. The development of digital positioning,
mobile payment, and 4G network provided Didi a rare opportunity to
better meet these needs. However, the matching algorithm was still
challenged. The rapid increase in the number of Didi’s customers made
the matching system more and more complicated. The whole system
often crashed due to the huge amount usage at the same time especially
at peak hour when there was a high demand. The whole operation
system got overcrowded and become very slow. The drivers may miss
the order because what they received is the delayed order. Didi’s
customer services received high volume of complaint but have no clear
procedure to deal with. In addition, Didi’s original organizational
structure was simple and did not have the dedicated team to manage and
prompt the enormous volume of drivers to meet customers’ demands.
Therefore, resources from IT department had to be allocated to manage
the drivers at that time. The situation became even complicated for Didi
when the government started to regulate the sharing-ride market. On 21
December 2016, local authority in Beijing announced a policy that all
private cars drivers accessing Didi’s platform need to have local
household registration and local license (NetEase 2016). This policy
largely restricted the availability of the drivers since previously anyone
with a car could register as Didi’s driver. At the meantime, a series of
security incidents including two crime cases involving serious insulting
and injury happened between the drivers and customers using Didi’s
platform (Xinhuanet 2018). The incidents further jeopardized the
company’s operation and reputation. Didi actively participated in inci-
dent/complaint investigation and prepared effective incentive/-
compensation scheme. In respond to the crisis, Didi took a significant
change in its way to manage the suppliers. Didi started working with
professional car leasing companies and using their cars to deliver ser-
vice. Unlike the private cars, cars from the leasing companies can be
better deployed and managed to meet peak demands and security
requirements.

Didi fully cooperated with car leasing companies and gave them full
access to Didi’s users management system. Spare resources freed from
driver management could be put into developing own customer service
team and Didi gave the staff greater authority to handle emergencies and
complaints.

Meanwhile, to solve the problem of low efficiency in matching, Didi
has developed high level artificial intelligence and a prediction algo-
rithm to better allocate the cars. In December 2012, Didi updated its App
and launched a “call waiting service” to enhance the matching process
and the call success rate improved by almost 40% comparing to using its
previous APP version (Baike 2019). The collaboration with leasing
companies changes Didi’s original supply chain structure. Together with
the development of the prediction algorithm, Didi increased respon-
siveness, improved the efficiency of its matching service and enhanced
the platform user experience. Table 6 shows Didi’s matching strategies
and specific practices in different development periods.

Figure 7 summarizes how Didi has constantly adapted its informa-
tion processing capabilities to achieve commercial success.

To summarize, under different combinations of IPR and IPC, SE
platform may have different performance and need to adopt specific
strategies to promote business development. In the initial stage, Didi’s
IPR and IPC were both low (Zone III) and Didi adopted low-price
strategy and heavily subsidized both the service providers and the
consumers. At the same time, Didi developed different kinds of functions
in its platform, such as the rating system and the real-time locator
display to build trust between the private car owners and the passengers.
All these strategies are to attract more users to the platform and enable
effective matching service. During the process, an effective sharing-ride
market was gradually incubated, and the market quickly grew into a
large scale (Gradually move to Zone II). However, the increase in market
scale was accompanied by higher IPR which requires high IPC to match
with. To better address the issues of low matching efficiency, Didi
invested heavily in IT innovation to develop a world-leading prediction
algorithm. As a result, Didi successfully improved the IPC of the platform
which enable Didi to precisely match the needs of customers. Further-
more, through collaboration with car leasing companies, Didi achieved
better car supply management (Move to Zone I).

6. DISCUSSION

While the use of IT in SE has been found to deliver supply chain
performance benefits in general (Rai et al. 2006; Subramani 2004),
acknowledgment of contingency in the relationship between the nature
of the applications’ IT mechanism and type of sharing supply chain has
been missing. Our research introduces IPT perspective and theoretically
explains and empirically demonstrates how and why service matching
can be gainfully aligned with specific information processing mecha-
nisms and achieve different service levels in SE. Previous studies have
proved that the performance of single organizational systems may
depend on the fit of an organization’s IPR and IPC (Galbraith 1973).
Previous research has highlighted the importance of fit between these
two (e.g., Busse et al. 2017; Tushman & Nadler 1978). Mismatch is
associated with lower organisational performance. For example, when
the information processing capacity is not sufficient to handle re-
quirements, it will lead to the firm’s organisational inefficiencies (Bergh

Figure 5. Didi’s original supply chain

Figure 6. shows Didi’s current supply chain structure.

K. Rong et al.

Technological Forecasting & Social Change 171 (2021) 120901

10

1998).The findings of this research extend this by identifying four zones
of information processing capabilities, which represent, respectively,
four statues of SE; a specific SE status is assigned as a result of a different
match between IPR and IPC and, in turn, different matching strategies
are required to enhance the service performance. Doing so, it reinforces
the conceptual foundation that SE firms should align and leverage on
information capability management to achieve operation effectiveness
and efficiency in service management.

7. CONCLUSION AND CONTRIBUTIONS

Drawing on information processing theory, this study explores how
the sharing economy (SE) manages and improves its matching service
through the development of specific information processing mecha-
nisms via a series of game theory models and a case study. The findings
show that under different combinations of IPR and IPC, SE falls into four
kinds of match zones. The study further identified that pricing strategy
and trust building are two initial strategies to increase SE platforms’ IPR,
which starts ups service operation. And supply chain structure change
and information technology are essential to improve SE firms’ IPC,
which increases service operation efficiency.

Currently, SE plays an important role in expanding and fostering
business innovation. This study makes a number of theoretical

contributions. First, this research directly contributes towards infor-
mation processing theory. Prior studies argue that adoption of the in-
formation capability enables SE firms to achieve better performance
(Rai et al. 2006; May et al. 2017). While, this research is one of the first
studies introduces IPT perspective and theoretically explains and
empirically demonstrates the performance of SE businesses are often
closely associated with not only IT itself, but also how SE firms align
specific information processing mechanisms with different statuses of SE
platforms.

Second, this research empirically brings new insights to SE supply
chain management. Current understanding of factors that contribute to
SE growth and success remains poor and fragmented across multiple
research disciplines, such as marketing, economics, and technology
management (Tiwana et al. 2010). The literature is mainly focused on
the growth of SE, such as the value co-creation process. However,
without identifying the chief problems platforms are facing, sustainable
profitability might not be achieved. This research advances current
understanding of SE by putting together fragmented constructs (i.e.,
uncertainties, information technology, price, trust, supply chain man-
agement). Through the inputs from literature and practice, this study
identifies critical uncertainties SE platforms are facing, and propose
ways to act accordingly. This study highlights four strategies to make
matching effectively and efficiently. Price and trust building are

Table 6
Didi’s matching strategies and specific practices in different stages

Mechanisms Strategies Specific Practices Quotations
Improve IPR to achieve

matching
effectiveness
(From 2015 to 2016)

Pricing Mechanism Supply side: encouraged drivers to take more orders
through giving extra monetary rewards.
Customer side: provided substantial subsidies to
attract customers to use Didi’s service.

“burned huge amount of money every day.” (Interviewee C)
“sent all kinds of vouchers to customers and offered huge subsidies to drivers
to encourage them to use Didi’s service.” (Interviewee C)

Trust Relationship Launched rating system in its app and constructed a
two-way scoring mechanism to build trust on Didi’s
platform.

“Passengers did not think private car drivers can provide a safe trip and
drivers did not trust passengers would pay for the service neither because
they do not know each other. We encouraged many to register with our
platform and write down their travel experience. We also invited celebrities
with trustworthy reputation to support Didi’s idea.” (Interviewee B)
“Building such rating system is to cultivate the trust between Didi and users.
Specifically, Didi could get the feedbacks from both driver and customer
regarding their service experience and achieve service quality
improvement.” (Interviewee B)

Improve IPC to increase
the matching
efficiency
(From 2016 to now)

Information
Technology
Innovation

Developing the advanced artificial intelligence and
the travel prediction algorithm to better allocate the
cars.

“With the help of advanced digital technology, Didi was enabled to create an
advanced stable SE, which results in competitive advantages. However, the
digital platform can occasionally occur glitches, especially with all the
matching uncertainty and huge number of inputs at peak hours.
Improvement in algorithm is always a must-do and our priority.”
(Interviewee A)
“Didi receives more than 30 million daily orders from users. hence, we
invested heavily in developing the advanced algorithm to improve the ratio
of matchmaking. Also, using the huge amount data generated daily, Didi
developed a solid travel prediction system, which means Didi is now able to
predict customers’ daily travel patterns in cities and allocate the car
resources accordingly. Advanced algorithm, is considered as a basis of
providing match-making service, indicating the level of Didi’s information
processing capacity.” (Interviewee G)

Supply Chain
Management

Collaborated with professional car leasing companies
for better managing the drivers and cars and develop
own customer services team

“It is hard for Didi, as a platform company, to manage so many drivers and
cars, we, therefore, started to work with professional car leasing companies
who had more experiences in managing the drivers. We signed contract with
leasing companies. Information of the car and drivers are shared between
us. Leasing companies are in charge of the car maintenance, driver selection,
staff training and dealing with drivers’ complaint. In this way, we can free
our resources to focus on our service innovation and information technology
improvement and these leasing companies may help us to better understand
and manage our drivers which eventually improved our service quality.”
(Interviewee F)
“Since Didi let car leasing companies have direct access to its system, Didi
can have the spare resources to develop our own customer service team and
give the staff greater authority to handle emergencies and complaints. Didi
only retains half of the 16,000 outsourced customer service operators, and
this number continues to reduce.” (Interviewee F)

K. Rong et al.

Technological Forecasting & Social Change 171 (2021) 120901

11

identified as highly relevant to the increase of IPR, which may effec-
tively form the matching and enable the service production and delivery
through customer and supplier development. And the improvement of
IPC requires the adoption of supply chain structure change and IT so that
they can reduce the transaction costs, increase responsiveness, enjoy
better capacity utilization and better matching demand for productive
resources.

For SE supply chain and operations management practitioners, the
study demonstrates the importance of adopting and implementing
different information processing mechanism to face different SE status.
This paper presents guidance for managers to identify the position of a
company through evaluating the company’s IPC and IPR. Specifically,
the research provided managers working in SE firms with four SE
development status based on the level of IPC and IPR. By doing so, this
research provides a basis for understanding which information pro-
cessing strategy should be deployed and implemented for specific SE
status. The paper thereby offers a framework by which SE management
can analyse investment decisions with regard to the deployment of in-
formation processing capabilities. Within the overall evidence-set,
managers are provided with the implications of adoption of specific
strategies typically required for the management of information pro-
cessing capabilities.

This research also brings some implications for government. To
foster the culture and capacity for sharing, the government plays an
important role in facilitating, regulating, and monitoring the SE com-
panies’ development. Adequate funding may be provided for start-ups. A
supportive legal environment should be established to reduce trans-
actional and social risks, such as information asymmetry, misuse of

consumers’ right and uncertainty in ex-post handling (Kim 2019). The
government may encourage the partnerships with a range of stake-
holders, and timely and transparency provision of information as well.

For future research, in our study only one longitudinal case study has
been conducted and is restricted in transportation industry. It is rec-
ommended cases from other industries could be studied to identify the
patterns in different settings which may either seek to increase the in-
ternal validity of our findings or generate new insights.

Authorship Statement

Hui Sun: Conceptualization, Methodology, Writing- Original draft
preparation, Formal analysis, Validation, Visualization, Writing-
Reviewing and Editing

Ke Rong: Data curation, Supervision, Resources, Project
administration.

Di Zhou: Methodology, Writing- Original draft preparation, Inves-
tigation, Formal analysis, Visualization, Validation, Writing- Reviewing
and Editing.

Dun Li: Visualization, Validation, Writing- Reviewing and Editing

Acknowledgement

This research is supported by the National Natural Science Founda-
tion of China (Grant no. 71872098; 71834006), Major Program of Na-
tional Social Science Foundation, (Grant No. 20&ZD075;
No.18ZDA149), Major Research Projects of Philosophy and Social Sci-
ences of the Ministry of Education (No.17JZD018).

Appendix A. Interview Question Guidelines

Description: These guidelines specify the questions that will be asked during our interviews with DiDi. The information collected in the interviews
is designed to target the strategies to achieve matchmaking effectiveness and efficiency.

decimal(%1) General information
decimal%1 Please introduce your position and responsibility in the company.
decimal%1 Please tell us your views on the sharing ride industry.
decimal%1 Please introduce the history of your company and the strategies that you believe have been critical to its current success.
decimal%1 Could you please describle your company’s business model?

Figure 7. Didi’s matching strategies

K. Rong et al.

Technological Forecasting & Social Change 171 (2021) 120901

12

decimal%1 How did your company evlove over time?
decimal%1 What is the orgnazational structuer of your company?

decimal(%1) Questions about the strategies to achieve matchmaking effectiveness
decimal%1 What platform strategy did your company adopt to start the sharing business?
decimal%1 Did you meet any barriers during company’s development? Please be specify with examples. How did you solve them?
decimal%1 How did you attract the users to your platform?
decimal%1 Please describe the price war between your company and other sharing ride companies? What was your company pricing strategy? How

do you position your company’s pricing strategy when compared with others?
decimal%1 Please evaluate the effectiveness of the low pricing strategy, for example, the effectiveness on the consumer loyalty.
decimal%1 Please describe how your company obtain the financing to support your pricing strategy.
decimal%1 How do you build trust on the platform and what remediation strategies that your company adopted after security incidents.
decimal%1 Please describe the business logic for your company’s acquisitions and mergers.

(3) Questions about the strategies to increase matchmaking efficiency.
4 Please describe the strategies that your company adopted to solve the mismatch between supply and demand.
5 What is the IT infrustructrue in your company?
6 Please descibe the influece of the government regulation on your company’s operation, for example, Beijing’s policy to restrict private

car drivers.
7 How do you work with leasing companies?
8 Please evaluate the role of your IT department and the leasing companies.

Appendix B

Test Application in this study

Construct validity • Multiple sources of evidence including semi-structured interviews, secondary data;
• A chain of evidence: multiple informants in focal companies, and multiple informants at suppliers/partners;
• Review of findings by an uninvolved senior academic;
• The senior managers of each focal company review the draft within case analysis with feedbacks.

Internal validity • Structured data coding and analysis based on a chain of evidence.
External validity • Thick descriptive data;

• Participate in focal company’s training sessions.
Reliability • Use case study protocol to guide field research and analysis;

• Develop case study database including recordings, transcripts, field notes, internal documents, news coverage and etc;
• Iterative discussion with an uninvolved senior academic.

Reliability and validity in case research (Source: Yin, 2008)

REFERENCES

Acquier, A., Valiorgue, B., Daudigeos, T., 2017. Sharing the shared value: A transaction
cost perspective on strategic CSR policies in global value chains. Journal of Business
Ethics 144, 139–152.

Akbar, P., 2019. Guiding Empirical Generalization in Research on Access-based Services.
Journal of Business Research 100, 16–26.

Anderson, E.G. Jr & Morrice, D.J. (2000), “A simulation game for teaching service-
oriented supplychain management: does information sharing help managers with
service capacity decisions”, Production and Operations Management, Vol. 9 No. 1,
pp. 40-55.

Armstrong, M., 2006. Competition in two-sided markets. RAND Journal of Economics 37
(3), 668–691.

Baike, 2019. Didi Chuxing available at. https://baike.baidu.com/item/%E6%BB%B4%
E6%BB%B4%E5%87%BA%E8%A1%8C/18596106?fromtitle=%E6%BB%B4%E6%
BB%B4%E6%89%93%E8%BD%A6&fromid=5517472. accessed 07 January 2020.

Baltacioglu, T., Ada, E., Kaplan, M., Yurt, O., Kaplan, Y.C., 2007. A new framework for
servicesupply chains. Service Industries Journal 27 (2), 105–124.

Bass, F.M., 1969. A new product growth for model consumer durables. Management
Science 15 (5), 215–227.

Belk, R., 2014. You are what you can access: Sharing and collaborative consumption
online. Journal of Business Research 67 (8), 1595–1600.

Ben-Arieh, D., Pollatscheck, M.A., 2002. Analysis of Information Flow in Hierarchical
Organizations. International Journal of Production Research 40 (15), 3561–3573.

Bensaou, M., Venkatraman, N., 1995. Configurations of interorganizational
relationships: A comparison between U.S. and Japanese automakers. Management
Science 41 (9).

Bensaou, M., Venkatraman, N., 1996. Inter-organizational relationships and information
technology: A conceptual synthesis and a research framework. European Journal of
Information Systems 5 (2), 84–91.

Bimpikis, K., Ehsani, S., Mostagir, M., 2019. “Designing Dynamic Contests”,
Forthcoming,. Operations Research 67 (2).

Bode, C., Wagner, S.M., 2015. Structural drivers of upstream supply chain complexity
and the frequency of supply chain disruptions. Journal of Operations Management
36, 215–228.

Botsman, R., Rogers, R., 2010. What’s Mine Is Yours: The Rise of Collaborative
Consumption. HarperBusiness, New York, NY.

Breidbach, C.F., Brodie, R.J., 2017. Engagement platforms in the sharing economy:
Conceptual foundations and research directions. Journal of Service Theory and
Practice 27 (4), 761–777.

Bucher, E., Fieseler, C., Lutz, C., 2016. What’s mine is yours (for a nominal fee) –
Exploring the spectrum of utilitarian to altruistic motives for Internet-mediated
sharing”. Computers in Human Behavior 62, 316–326.

Busse, C., Meinlschmidt, J., Foerstl, K., 2017. Managing information processing needs in
global supply chains: A prerequisite to sustainable supply chain management.
Journal of Supply Chain Management 53 (1), 87–113.

Caillaud, B., Jullien, B., 2003. Chicken & egg: Competition among intermediation service
providers. RAND Journal of Economics 34 (2), 309–328.

Carroll, J.M., Bellotti, V., 2015. Preface to the special issue on peer-to-peer exchange and
the sharing economy: analysis, designs, and implications. Interaction Design and
Architecture(s) 24, 5–13.

Chen, M.K., Sheldon, M., 2016. Dynamic Pricing in a Labor Market: Surge Pricing and
Flexible Work on the Uber Platform. Mimeo, UCLA, p. 455.

Cheng, X., Fu, S.X, Vreede, G., 2018. A Mixed Method Investigation of Sharing Economy
Driven Car-Hailing Services: Online and Offline Perspectives. International Journal
of Information Management 41 (August), 57–64.

CNN (2017), “China’s Didi said to be worth $56B after raising more cash”, available at:
https://money.cnn.com/2017/12/20/technology/didi-most-valuable-startup/inde
x.html (accessed 07 January 2020).

Cohen, B., Muñoz, P., 2016. Sharing cities and sustainable consumption and production:
towards an integrated framework. Journal of Cleaner Production 134 (A), 87–97.

Collis, D.J., 1994. Research Note: How Valuable are Organisational Capabilities?
Strategic Management Journal 15 (2), 143–152.

Daft, R., Weick, K., 1984. Toward a model of organizations as interpretation systems, 9.
Academy of Management Review, pp. 284–295.

K. Rong et al.

Technological Forecasting & Social Change 171 (2021) 120901

13

Davis, F.D., 1989. Perceived usefulness, perceived ease of use, and user acceptance of
information technology. MIS Quarterly 13 (3), 319–340.

Dyer, J.H., Singh, H., 1998. The relational view: Cooperative strategy and sources of
interorganizational competitive advantage. Academy of Management Review 23,
660–679.

Eisenmann, T., Parker, G., Van Alstyne, M., 2006. Strategies for two-sided markets.
Harvard Business Review 84 (10), 92–101.

Ellram, L.M., Tate, W.L., Billington, C., 2004. Understanding and managing the services
supply chain. Journal of Supply Chain Management: A Global Review of Purchasing
and Supply 40 (4), 17–32.

Evans, D.S., Schmalensee, R., 2010. Failure to Launch: Critical Mass in Platform
Businesses. Review of Network Economics 9, 1–26.

Evans, D.S., Schmalensee, R., 2016. Matchmaking: The new economics of multisided
platforms. Harvard Business Review Press, Brighton, MA.

Frenken, K., 2017. Political economies and environmental futures for the sharing
economy. Philosophical Transactions of the Royal Society A: Mathematical, Physical
and Engineering Sciences 375, 2095.

Forbes, 2018. Top 10 Companies Behind The 2018 Midas List: Exits And Unicorns That
Helped Their Investors The Most available at. https://www.forbes.
com/sites/gradsoflife/2019/01/08/youth-opportunity-a-global-priority/#65a9
50fb3cb8/. accessed 29 January 2019.

Frostenson, M., Prenkert, F., 2015. Sustainable supply chain management when focal
firms are complex: a network perspective. Journal of Cleaner Production 107, 85–94.

Galbraith, J., 1973. Designing Complex Organizations. Addison-Wesley, Reading: MA.
Gansky, L., 2010. The Mesh: Why the Future of Business Is Sharing. Penguin, New York.
Goudin, P., 2016. The cost of non-Europe in the sharing economy: Economic, social and

legal challenges and opportunities. European Parliament. January, available at.
http://www.europarl.europa.eu/RegData/etudes/STUD/2016/558777/EPRS_STU
(2016)558777_EN.pdf/. accessed 29 January 2019.

Govindan, K., Shankar, K.M., Kannan, D., 2020. Achieving sustainable development
goals through identifying and analyzing barriers to industrial sharing economy: A
framework development. International Journal of Production Economics 227,
107575.

Gulati, R., Nickerson, J.A., 2008. Interoganizational trust, governance choice, and
exchange performance. Organization Science 19, 688–708.

Hamari, J., Sjöklint, M., Ukkonen, A., 2015. The sharing economy: Why people
participate in collaborative consumption. Journal of the Association for Information
Science and Technology 67 (9), 2047–2059.

Hartl, B., Hofmann, E., Kirchler, E., 2016. Do we need rules for what’s mine is yours?
Governance in collaborative consumption. Journal of Business Research 69 (8),
2756–2763.

Hellwig, K., Morhart, F., Girardin, F., Hauser, M., 2015. Exploring different types of
sharing: A proposed segmentation of the market for “sharing” businesses. Psychology
& Marketing 32 (9), 891–906.

Horton, J.J., Zeckhauser, R.J., 2016. Owning, Using and Renting: Some Simple
Economics of the ‘Sharing Economy. National Bureau of Economic Research,
Cambridge. NBER Working Paper SeriesFebruary 2016.

Jacobsen, N.B., 2006. Industrial symbiosis in Kalundborg, Denmark: a quantitative
assessment of economic and environmental aspects. Journal of Industrial Ecology 10
(1-2), 239–255.

Jia, F., Blome, C., Sun, H., Yang, Y., Zhi, B., 2020. Towards an integrated conceptual
framework of supply chain finance: An information processing perspective.
International Journal of Production Economics 219, 18–30.

Kenney, M., Zysman, J., 2016. The Rise of the Platform Economy. Science and
Technology 32 (3), 61–69.

Kim, J., Yoon, Y., Zo, H., 2015. Why People Participate in the Sharing Economy: A Social
Exchange Perspective. In: The annual Pacific Asia Conference on.Information
Systems (PACIS). 2015 Proceedings, p. 76.

Kim, M.J., 2019. Benefits and Concerns of the Sharing Economy: Economic Analysis and
Policy Implications. KDI Journal of Economic Policy 41 (1), 15–41.

Koopman, R., Wang, Z., Wei, S.J., 2014. Tracing value-added and double counting in
gross exports. American Economic Review 104 (2), 459–494.

Kumar, V., Lahiri, A., Dogan, O., 2018. A strategic framework for a profitable business
model in the sharing economy. Industrial Marketing Management 69, 147–160.

Lee, M.K., Kusbit, D., Metsky, E., Dabbish, L., 2015. Working with Machines: The Impact
of Algorithmic and Data-Driven Management on Human Workers. In: Proceedings of
the ACM CHI’15 Conference on Human Factors in Computing Systems, 1,
pp. 1603–1612.

Lou, H., Kulkarni, M.A., Singh, A., Huang, Y., 2004. A game theory based approach for
emergy analysis of industrial ecosystem under uncertainty. Clean Technol Environ.
Policy 6, 156–161.

Lustig, C., Katie, P., Bonnie, N., Lilly, I., M, K.L., Dawn, N., Christian, S, 2016.
Algorithmic Authority: The Ethics, Politics, and Economics of Algorithms That
Interpret, Decide, and Manage. In: Proceedings of the 2016 CHI Conference
Extended Abstracts on Human Factors in Computing Systems, 2016. New York, NY,
USA, pp. 1057–1062.

May, S., Königsson, M., Holmstrom, J., 2017. Unlocking the sharing economy:
Investigating the barriers for the sharing economy in a city context. First Monday 22
(2).

Mirata, M., 2004. Experiences from early stages of a national industrial symbiosis
programme in the UK: Determinants and collaboration challenges. Journal of
Cleaner Production 12 (8), 967–983.

Möhlmann, M., 2015. Collaborative consumption: determinants of satisfaction and the
likelihood of using a sharing economy option again. Journal of Consumer Behaviour
14 (3), 193–207.

Möhlmann, M., Lior, Z., 2017. Hands on the Wheel: Navigating Algorithmic Management
and Uber Drivers’ Autonomy. In: Proceedings of the International Conference on
Information Systems (ICIS 2017), December 10-13. Seoul, South Korea.

Munoz, P., Cohen, B., 2017. Mapping out the sharing economy: A configurational
approach to sharing business modeling. Technological Forecasting and Social
Change 125, 21–37.

NetEase, 2016. Beijing’s “Detailed Rules for the Management of Online Car-hailing
Service” was Officially Promulgated available at. http://news.163.com/16/1221/16
/C8QRM2T0000187VE.html/. accessed 29 January 2019.

Orlikowski, W.J., Scott, S.V., 2014. What Happens When Evaluation Goes Online?
Exploring Apparatuses of Valuation in the Travel Sector”. Organization Science 25
(3), 868–891.

Powell, T.C., Dent-Micallef, A., 1997. Information technology as competitive advantage:
the role of human, business, and technology resources. Strategic Management
Journal 18 (5), 375–405.

Rai, A., Patnayakuni, R., Seth, N., 2006. Firm Performance Impacts of Digitially Enabled
Supply Chain Integration Capabilities. MIS Quarterly 30 (2), 225–246.

Rochet, J.C., Tirole, J., 2003. Platform Competition in Two-Sided Markets. Journal of the
European Economic Association 1, 990–1029.

Rochet, J.C., Tirole, J., 2006. Two-sided markets: A progress report. RAND Journal of
Economics 37, 645–667.

Rogers, E.M., 2003. Diffusion of innovations, 5th ed. Free Press, New York, NY.
Rogers, D.S., Daugherty, P.J., Stank, T.P., 1993. Enhancing Service Responsiveness: The

Strategic Potential of EDI. Logistics Information Management 6 (3), 27–32.
Rong, K., Shi, Y.J., Shang, T.J., Chen, Y.T., Hao, H., 2017. Organizing Business

Ecosystems in Emerging Electric Vehicle Industry: Structure, Mechanism, and
Integrated Configuration. Energy Policy 107, 234–247.

Santana, J., Parigi, P., 2015. Risk Aversion and Engagement in the Sharing Economy.
Games 6, 560–573.

Serdarasan, S., 2013. A review of supply chain complexity drivers. Computers &
Industrial Engineering 66 (3), 533–540.

Scholz, T., 2014. Platform Cooperativism vs. the Sharing Economy. Big Data & Civic
Engagement 47.

Subramani, M., 2004. How Do Suppliers Benefit from Information Technology Use in
Supply Chain Relationships? MIS Quarterly 28 (1), 45–73.

Sundararajan, A., 2013. From Zipcar to the Sharing Economy. Harvard Business Review
(1).

Sutherland, W., Jarrahi, M.H., 2018. The Sharing Economy and Digital Platforms: A
Review and Research Agenda. International Journal of Information Management 43.

Teece, D.J., Pisano, G., Shuen, A., 1997. Dynamic Capabilities and Strategic
Management. Strategic Management Journal 18 (7), 509–533.

Tiwana, A., Konsynski, B., Bush, A.A., 2010. Platform Evolution: Coevolution of Platform
Architecture, Governance, and Environmental Dynamics. Information Systems
Research 21 (4), 675–687.

Thomé, A.M.T., Scavarda, L.F., Scavarda, A., Thomé, F.E.S.D.S, 2016. Similarities and
contrasts of complexity, uncertainty, risks, and resilience in supply chains and
temporary multi-organization projects. International Journal of Project Management
34 (7), 1328–1346. Issue.

Tushman, M.L., Nadler, D.A., 1978. Information processing as an integrating concept in
organizational design, 3. Academy of management review, pp. 613–624.

Tussyadiah, I.P., 2016. Factors of satisfaction and intention to use peer-to-peer
accommodation. International Journal of Hospitality Management 55, 70–80.

Venkatesh, V., Morris, M.G., Davis, G.B., Davis, F.D., 2003. User acceptance of
information technology: Toward a unified view. MIS quarterly 27 (3), 425–478.

Wirtz, J., So, K.K.F., Mody, M.A., Liu, S.Q., Chun, H.H., 2019. Platforms in the Peer-to-
Peer Sharing Economy. Journal of Service Management 30 (4), 452–483.

Wu, L., Chuang, C.H., Hsu, C.H., 2014. Information sharing and collaborative behaviors
in enabling supply chain performance: A social exchange perspective. International
Journal of Production Economics 148, 122–132.

Xinhuanet, 2018. The Stewardess was Killed by a Didi Driver in Zhengzhou and the
Police Arrested the Driver available at. http://www.xinhuanet.
com/legal/2018-05/11/c_1122815196.htm/(Accessed. at 24 January 2019.

Yin, R., 2008. Case Study Research: Design and Methods, 4th ed. Sage, London.
Zaheer, A., McEvily, B., Perrone, V., 1998. Does trust matter? Exploring the effects of

interorganizational and interpersonal trust on performance. Organization Science 9,
141–159.

Ke Rong is an associate professor in the Institute of Economics, School of Social Science at
Tsinghua University in China. Ke has received the PhD degree from University of Cam-
bridge and obtained Bachelor degree in Tsinghua University. Before joining Tsinghua, he
was a senior lecturer in the University of Exeter and Bournemouth University in UK and a
visiting scholar in the Harvard Business School. His current research interests focus on
Business/Innovation Ecosystems, Platform Economy and Sharing Economy. He has pub-
lished over 30 refereed leading journal articles including Journal of International Business
Studies, International Journal of Production Economics, Journal of International Man-
agement, Group and Organization Management, Technological Forecasting & Social
Change and Expert System with Applications. He also served as the editorial board
member in leading journal- Long Range Planning.

K. Rong et al.

Technological Forecasting & Social Change 171 (2021) 120901

14

Hui Sun is a PhD researcher in University of Sheffield, School of Management in UK. She
has extensive research and professional experience in the operation management and
supply chain management. Her research spans the implementation of sustainable business
systems, transition to the circular economy and risk management from an operations and
supply chain perspective. She has published in leading journals including International
Journal of Operations & Production Management, European Journal of Operational
Research and International Journal of Production Economics.

Dun Li (Ph.D., University of Liverpool) is an Associate Professor in Supply Chain Man-
agement at the Management School, Guizhou University. His work has been published in

many academic journals including International Journal of Operations & Production
Management, Production Planning Control. His current research interests focus on the
interactions between sharing economy and service operations management and also
supply chain risk management.

Di Zhou is a PhD candidate in the Institute of Economics, School of Social Science at
Tsinghua University in China. Di has obtained Master degree and Bachelor degree in
Central University of Finance and Economics in China. Di is also a visiting scholar in the
Harvard Business School. His current research interests focus on Business/Innovation
Ecosystems, Platform Economy and Sharing Economy.

K. Rong et al.

  • Matching as Service Provision of Sharing Economy Platforms: An Information Processing Perspective
    • 1 INTRODUCTION
    • 2 THEORETICAL BACKGROUND
      • 2.1 Sharing Economy
      • 2.2 Information Processing Theory
        • 2.2.1 Uncertainties faced by SE platform
        • 2.2.2 Strategies to increase the information processing requirement
        • 2.2.3 Mechanisms for improving the information processing capacity
    • 3 RESEARCH METHOD AND MODEL ANALYSIS
      • 3.1 Model 1: Improve IPR in a Low IPC Environment
        • 3.1.1 Sharing Economy with Low IPR and Low IPC
        • 3.1.2 Sharing Economy with High IPR and Low IPC
      • 3.2 Model 2: Improve IPC in a High IPR Environment
        • 3.2.1 Sharing Economy with High IPR And High IPC
    • 4 FINDINGS
    • 5 CASE STUDY
    • 6 DISCUSSION
    • 7 CONCLUSION AND CONTRIBUTIONS
    • Authorship Statement
    • Acknowledgement
    • Appendix A. Interview Question Guidelines
    • Appendix B
    • REFERENCES
error: Content is protected !!