Knowledge Management Research & Practice
ISSN: (Print) (Online) Journal homepage: https://www.tandfonline.com/loi/tkmr20
Management strategies to mitigate knowledge
hiding behaviours: symmetric and asymmetric
analyses
Dr Muhammad Waseem Bari, Mrs Irum Shahzadi & Dr Muhammad Fayyaz
Sheikh
To cite this article: Dr Muhammad Waseem Bari, Mrs Irum Shahzadi & Dr Muhammad
Fayyaz Sheikh (2023): Management strategies to mitigate knowledge hiding behaviours:
symmetric and asymmetric analyses, Knowledge Management Research & Practice, DOI:
10.1080/14778238.2023.2178344
To link to this article: https://doi.org/10.1080/14778238.2023.2178344
Published online: 16 Feb 2023.
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KNOWLEDGE MANAGEMENT RESEARCH & PRACTICE
https://doi.org/10.1080/14778238.2023.2178344
ORIGINAL ARTICLE
Management strategies to mitigate knowledge hiding behaviours: symmetric
and asymmetric analyses
Dr Muhammad Waseem Bari
, Mrs Irum Shahzadi and Dr Muhammad Fayyaz Sheikh
Lyallpur Business School, Government College University, Faisalabad, Pakistan
ABSTRACT
ARTICLE HISTORY
The study investigates the impact of management strategies (reducing chain of command,
developing informal interaction, implementing incentive policy, easy performance appraisal,
encouraging higher interdependency, and open space workstations) to mitigate the knowl­
edge-hiding behaviours while using the psychological contract as a mediator between man­
agement strategies and knowledge-hiding behaviours. Symmetric (PLS-SEM) and asymmetric
(fsQCA) methods are used to analyse time lag data collected from 457 employees of software
houses. Except for the reducing chain of command, the PLS-SEM results show that all manage­
ment strategies and psychological contract have a significant role in reducing knowledgehiding behaviours. The fsQCA results suggest that all management strategies and psychologi­
cal contract play their role in different causal recipes while influencing the knowledge-hiding
behaviours, however, developing informal interaction, implementing incentive policy, easy
performance appraisal, and psychological contracts have more consistent contributions in
these causal recipes.
Received 15 April 2022
Revised 24 January 2023
Accepted 28 January 2023
1. Introduction
Connelly et al. (2012) initially introduced the concept
of knowledge hiding in the organisational context.
Subsequently, many studies, to control this negative
workplace behaviour, explore the antecedents and
outcomes of knowledge hiding that include innova­
tion (Bogilović et al., 2017b), creativity (Černe et al.,
2017),
individual/organisational
performance
(Connelly et al., 2012), employee silence (Bari, et al.,
2020), psychological contract breach (Bari, Ghaffar
et al., 2020), team performance (Bogilović et al.,
2017; Fong et al., 2018), cronyism, interpersonal rela­
tions (Connelly & Zweig, 2015), altruistic leadership
(Abdillah et al., 2022), dark triad (Pan et al., 2018), and
job security (Serenko & Bontis, 2016).
Knowledge hiding is different from other negative
behaviours in that knowledge hiders adopt straight
denial or sometimes diplomatic behaviour to hide
knowledge from the knowledge seekers (Bernatović
et al., 2021). Therefore, Connelly et al. (2012) classify
knowledge hiding into three distinctive categories
namely, evasive hiding, playing dumb, and rationa­
lised hiding. Each knowledge-hiding behaviour
(KHBs) has certain motives and outcomes. In evasive
hiding, the knowledge holder intends to deceive others
by giving false or partially incorrect information
whereas, in playing dumb, the knowledge holder com­
pletely ignores or puts off others’ requests. On the
other hand, in rationalised hiding, the knowledge
CONTACT Dr Muhammad Waseem Bari
School, Faisalabad, Pakistan
© 2023 The Operational Research Society
KEYWORDS
Management strategies;
knowledge hiding;
psychological contract;
fsQCA; PLS-SEM; software
houses
hider presents enough reasoning for not disclosing
or sharing the requested information (Connelly et al.,
2019, 2012). Considering this, researchers have
emphasised that each category and its antecedents
and outcomes should be examined explicitly. As
a result, several studies have been conducted to exam­
ine the antecedents and outcomes of knowledge hid­
ing, particularly from a negative perspective, however,
very few researchers have proposed empirical solu­
tions to control KHBs (Butt & Ahmad, 2020;
Connelly et al., 2019).
It is noted that the adoption of KHBs depends on
the situation (Connelly & Zweig, 2015; Connelly et al.,
2012), therefore, it is necessary to explore distinctive
ways to control KHBs effectively. In this regard, Butt
and Ahmad (2020) proposed six strategies to mitigate
knowledge hiding (SMKH) in organisations. These
strategies include reducing chain of command
(RCC), developing informal interaction (DII), intro­
ducing and implementing incentive policy (IIP), initi­
ating easy performance appraisal (EPA), encouraging
higher interdependency (EHI), and introducing open
space work stations (OSWS; Butt & Ahmad, 2020).
However, the literature lacks sufficient empirical evi­
dence to validate the effectiveness of these strategies.
Butt and Ahmad (2020) have invited scholars to test
the impact of proposed mitigating strategies on KHBs
in different cultural contexts (Butt & Ahmad, 2020). In
response to this call, this study aims at empirically
testing and validating these strategies in the South
muhammadwaseembari786@hotmail.com
Government College University Faisalabad, Lyallpur Business
2
M. W. BARI ET AL.
Asian context, which has been overlooked by previous
researchers.
Drawing on social exchange theory (SET; Blau,
1964), reciprocal trust between the knowledge holder
and the knowledge seeker is inevitable for knowledge
sharing and diminishing KHBs (Silva & Goncalves,
2016). Therefore, this study proposes that an intact
psychological contract (PC) may enhance the effec­
tiveness of SMKH to decrease KHBs in organisations.
Rousseau (1989) defines the PC as “an individual’s
beliefs about the terms of the exchange agreement
between employee and employer”. Rousseau (1989)
categorised PC into three dimensions namely, transac­
tional PC, relational PC, and a balanced PC. The
application of these three dimensions of PC may per­
form differently in different organisational cultures
(Shaheen et al., 2019). The perceived mutual expecta­
tions between employees and the organisation develop
a PC. The fulfilment/non-fulfilment of these mutual
expectations can influence the performance and beha­
viours of both parties (Bari, et al., 2020; Chaudhry
et al., 2011; Zhang et al., 2018; Vui-Yee Koon, 2022).
The PC is not limited to the relationship between
employer and employee, it also exists between employ­
ees (Schalk & Roe, 2007). An intact PC is a vital factor
in the organisation to reduce negative behaviours
(Bari, et al., 2020). Thus, PC may act as a mediator
between SMKH and KHBs that has not been empiri­
cally examined in previous studies.
Hence, the objectives of this study are two-fold:
first, to explore the impact of SMKH on KHBs
(Playing dump, evasive hiding, and rationalised
knowledge hiding) of employees of an organisation.
Second, to examine how PC mediates the relation
between SMKH and KHBs. To achieve the aforemen­
tioned objectives, this study has two research ques­
tions. First, what is the impact of SMKH on the three
dimensions of KHBs? Second, how does PC mediate
the relationship between SMKH and KHBs? For
empirical investigation, data are collected from the
employees of the software industry in Pakistan. The
rationale behind the selection of the software industry
is the workplace where knowledge workers are regu­
larly involved in creative and innovative activities,
which heavily depend on knowledge. Secondly, pre­
vious studies have confirmed the existence of KHBs in
the software industry of Pakistan (Bari, et al., 2020).
Therefore, it is of great importance to examine the
strategies to mitigate the KHBs in this industry.
This study has at least three important contribu­
tions. First, this is the first study that empirically
investigates the effectiveness of SMKH to control
employees’ KHBs in software houses in a developing
economy context i.e., Pakistan. Second, this study
employs both symmetrical (Partial Least Squares
Structural Equation Modelling; Hair et al., 2016) and
asymmetrical (Fuzzy Set Qualitative Comparative
Analysis; Rasoolimanesh et al., 2021) analyses to con­
firm the existence of KHBs in employees in the pre­
sence or absence of SMKH. Third, this study examines
how PC is supportive of SMKH for diminishing the
KHBs in software houses.
2. Literature review
2.1. Knowledge hiding
Knowledge-hiding practices are found at different
levels in different organisations and countries. For
instance, 46% of Chinese and 76% of Americans are
involved in knowledge-hiding practices in the work­
place (Pan et al., 2018; Peng, 2013; Z. Zhang & Min,
2021). The knowledge hiders have feelings of psycho­
logical ownership regarding the knowledge they
owned, thereby, they show a high tendency to hide
knowledge from others (Peng, 2013). Drawing on SET,
KHBs are reciprocally developed between employees
(Connelly et al., 2012). Knowledge hiders not only
stop sharing their knowledge but also halt colleagues
to develop innovative notions (Černe et al., 2014;
Peng, 2013). Employees’ KHBs impede their creative
skills and performance (Černe et al., 2014). Connelly
and Zweig (2015) report that KHBs impair employees’
social relationships. Drawing on the conservation of
resources theory (Hobfoll, 1989), Han et al. (2020)
find that a competitive psychological climate is an
important determinant of knowledge-hiding beha­
viour to stay competitive as compared to others.
Knowledge hiding exists in three forms (Connelly
et al., 2012) namely, playing dumb, evasive hiding, and
rationalised hiding. Being an evasive knowledge hider
an individual intends to deceive others by giving mis­
leading or incorrect information or making
a bewildering promise to answer in the future. When
an individual pretends to be unfamiliar with the
requested knowledge and avoids providing the infor­
mation is referred to as playing dumb behaviour. An
individual who presents enough reasoning for not
disclosing or sharing requested information refers to
as a rationalised knowledge hider (Butt & Ahmad,
2019; Connelly et al., 2012; Demirkasımoğlu, 2016;
Webster et al., 2008). KHBs have several antecedents
and negative consequences presented in the literature
(Bari, Misbah et al., 2020; Butt & Ahmad, 2020;
Demirkasımoğlu, 2016; Fong et al., 2018; Huo et al.,
2016; Xiao & Cooke, 2018), which reflect this destruc­
tive workplace behaviour must be controlled through
effective mitigating strategies.
2.2. Strategies to Mitigate Knowledge Hiding
(SMKH)
Organisations firmly believe in knowledge-sharing
culture among employees, thus, organisations do
KNOWLEDGE MANAGEMENT RESEARCH & PRACTICE
make efforts to control the knowledge-hiding culture.
Scholars have also highlighted the need for the identi­
fication of effective strategies. However, the effective­
ness of the management strategies and practices to
control the knowledge-hiding culture within the orga­
nisations is debatable (Anand & Hassan, 2019; Butt &
Ahmad, 2020; Connelly et al., 2019).
Anand and Hassan (2019) classify the causes of
knowledge hiding into four categories namely, indi­
vidual, organisational, job, and co-worker related.
They suggest various remedies to curtail the conse­
quences of KHBs such as improving employee core
self-evaluation, making employees aware of each
other’s jobs, providing opportunities for socialising
beyond work, 360-degree feedback on hiding acts,
and facilitating a positive work and cross-linking
environment. In addition, Abubakar et al (2019)
also report that fair treatment by organisations
makes their employees more involved in their
work, and develops trust and sharing behaviours in
the workplace. In a qualitative study, Butt and
Ahmad (2020) propose six SMKH within the orga­
nisations to mitigate KHBs. These six strategies and
their potential relationships with KHBs are dis­
cussed in the following lines.
2.3. Reducing chain of command
Complex and bureaucratic structure-based organisa­
tions have more propensity for knowledge hiding.
When a message transmits through a complex hier­
archy, there are higher chances of manipulation of the
original content of the message (Butt & Ahmad, 2020).
Easy access to top management develops mutual
bonding that ultimately diminishes KHBs. Butt and
Ahmad (2020) argue that while transmitting the mes­
sage through many layers, juniors may hide knowl­
edge from senior managers. Open doors for employees
specifically, junior staff develop confidence and trust
among employees and the organisation (Butt, 2021;
Butt & Ahmad, 2020). The confidence and frequent
direct interaction with seniors enhance the productiv­
ity and innovative skills of junior employees. A long
and multi-reporting chain of command also creates
problems such as miscommunication and delays in
decision-making (Crumpton, 2013). A flatter or Semi
flatter organisational structure not only reduces the
cost of administrative/supervisory positions but also
develops direct interaction and trust among workers,
and promotes knowledge sharing culture
(Schneckenberg, 2009; Webster et al., 2008) Hence,
this study proposes that
H1a: Reducing chain of command decreases KHBs.
3
2.4. Developing informal interaction
Literature has underlined the detrimental effects of
knowledge hiding on social relationships (Connelly
& Zweig, 2015), employee creativity (Bogilović et al.,
2017; Černe et al., 2014), and team creativity
(Bogilović et al., 2017). Anand and Hassan (2019)
suggest that providing opportunities for socialisation
beyond work can engender positive work behaviours
and overcome mutual work-related issues. Informal
groups and communities make employees aware of
each other’s jobs and also make them understand the
obstructive effects of knowledge hiding in the work­
place. Informal interaction provides opportunities to
share ideas and experiences that enhance cooperation,
creativity, and confidence (Butt & Ahmad, 2020). The
development of interpersonal trust may curtail unethi­
cal practices i.e., knowledge hiding among employees.
Knowledge hiding impairs interpersonal relationships.
Connelly et al. (2012) explain that personal conflicts
can develop the intention of knowledge hiding while
social interactions can curtail it. An open communica­
tion system (Shaheen et al., 2019), informal interaction
(L. Zhang & Deng, 2014), social get-togethers, and
informal friendship at the workplace (Enwereuzor
et al., 2022) develop trust among workers and moti­
vate them to share their tacit as well as explicit knowl­
edge (Chen et al., 2014). Therefore, the study
hypothesises that
H1b: Developing informal interaction among employ­
ees reduces KHBs.
2.5. Introducing and implementing incentive
policy
A rational reward system encourages employees to
share knowledge and discourages knowledge hiding
simultaneously (Butt & Ahmad, 2020) which increases
workers’ professional commitment to the firm (Butt,
2020). Suppiah and Sandhu (2011) document that
rewards and recognition can motivate workers to
share knowledge. De Almeida et al. (2016) endorse
the relationship between incentives and benefits and
knowledge-sharing behaviours. Offering incentives to
managers for their knowledge-sharing behaviour at
work promotes knowledge-sharing culture and dis­
courages KHBs (Connelly et al., 2019; Miminoshvili
and Černe, 2021). Lee et al. (2014) also support the
positive equation of performance-based compensation
and knowledge-sharing behaviours. The monetary
and non-monetary rewards enhance self-confidence
in employees and motivate them intrinsically to
share their explicit as well as tacit knowledge and skills
4
M. W. BARI ET AL.
with colleagues (Anand & Hassan, 2019). Thus, this
study proposes that
H1c: Introducing and implementing incentive policy
reduces KHBs.
2.6. Initiating easy performance appraisal
Sometimes managers are hesitant to share their
knowledge with colleagues due to fear and the strict
performance criteria of the organisation (Butt &
Ahmad, 2020; Černe et al., 2014). Organizations that
emphasise on prevention of volunteer knowledge
sharing may promote KHBs among managers.
Conversely, organisations that loosely account for
volunteer KHBs while evaluating employee perfor­
mance face fewer KHBs within an organisation (Butt,
2021; Butt & Ahmad, 2020). The strict questions
regarding knowledge transfer during the process of
performance appraisal may annoy the workers and
induce them to hide their tacit knowledge (Connelly
et al., 2019, 2012; Serenko & Bontis, 2016). When
management enforces the incentive-based policy to
share knowledge with subordinates (Butt & Ahmad,
2019), an ambiance of competition with co-workers
starts. Bordia et al. (2006) confirm the negative rela­
tionship between employees’ fear of negative evalua­
tions and their knowledge-sharing intentions. An easy
performance appraisal may work as a proxy for
rewards as it looks unrealistic to offer tangible incen­
tives every time to the workers who adopt knowledgesharing behaviours (Fischer, 2022). Anand and
Hassan (2019) suggest 360 degree feedback as an
effective appraisal tool and strategy to create
a positive culture of knowledge sharing in the organi­
sations. Thus, this study proposes that
H1d: Initiating easy performance appraisal decreases
KHBs.
2.7. Encouraging higher interdependency
Butt and Ahmad (2020) reveal that interdependency at
work discourages KHBs (Bari et al., 2019; Bari, et al.,
2020). Interdependence reciprocates knowledgesharing culture and discourages KHBs (Z. Zhang &
Min, 2021). Bogilović et al (2017) argues that knowl­
edge hiding from co-workers hinders not only
employees’ creativity but also the team and organisa­
tions’ creative capabilities (Hernaus et al., 2017).
Anand and Hassan (2019) favour teamwork and
mutual collaboration of employees across departments
to promote a knowledge-sharing culture. Previous
research describes that task interdependence needs to
be decided first before knowledge integration of the
team (Fong et al., 2018; Kou, 2020). However, new
researchers claim that mutual understanding of the
team members for knowledge integration is more
important than task interdependence decisions (Kou,
2020). Scholars believe that developing an attitude of
interdependence among employees can motivate them
to share knowledge and innovative ideas (Johnson,
2003; Kim et al., 2018). Thus, considering the above
arguments, this study proposes that
H1e: Encouraging higher interdependency among
employees reduces KHBs
2.8. Open space workstation
An open workstation engenders strong interpersonal
relations among colleagues that reduce knowledgehiding. Anand and Hassan (2019) suggest that provid­
ing a conducive work environment for working cre­
ates a sense of connectivity among employees and
restrains them from KHBs. Butt and Ahmad (2020)
state that OSWS promotes knowledge sharing among
employees and discourages KHBs because OSWS pro­
vides convenience in discussions and idea-sharing.
Further, OSWS may help reduce competition among
employees, improve collaboration, a friendly work
environment, and effective communication among
managers/ employees, and motivate employees to
avoid knowledge-hiding practices. Peng (2013)
explains that organisation-based psychological owner­
ship helps reduce self-perception of knowledge posses­
sion, as OSWS discourages a knowledge-hiding
culture. Scholars recommend that organisations
should provide an open workplace with resources
and facilities, and remove barriers during communi­
cation among workers to enhance the collaboration
and knowledge-sharing culture (Evans, 2012). The
knowledge-sharing behaviour in the open workplace
also depends on the nature of workplace interdepen­
dencies (Perumal & Sreekumaran Nair, 2022). When
goals and resources are independent in the open work­
place, workers perceive non-competition and avoid
knowledge hiding. On the other hand, when goals
and resources are interdependent, colleagues perceive
competition and adopt KHBs. Given the above discus­
sion, the study hypothesises that:
H1f: Open space workstation reduces KHBs
2.9. Psychological contract (mediator)
A PC is an informal and unwritten set of expectations
between an employee and his employer that outlines
an employment relationship. Rousseau (1989) defines
the PC as an “individual’s beliefs about the terms of
KNOWLEDGE MANAGEMENT RESEARCH & PRACTICE
the exchange agreement between employee and
employer”. The PC has three perspectives namely,
transactional PC, relational PC, and balanced PC
(Rousseau, 1989). In the organisational context, PC
is considered an important individual mechanism
that engenders the expected employment relationship
and helps in setting the future behaviour of employees
(Dai & Wang, 2016; Kingshott, 2006). An individual’s
PC with the organisation depends on the individual’s
expectations, which are implicit and perceptual
(Rousseau, 1996), and likely to be revised over time
(Guest, 2004). Mutual trust regulates reciprocal obli­
gations under PC (Dai & Wang, 2016) that further
develops positive behaviour of employees (Dai &
Wang, 2016; Neill & Adya, 2007). Dai and Wang
(2016) report that a strong PC promotes a knowledgesharing culture (He & Li, 2011). Contrary, a PC breach
develops counterproductive work behaviours in
employees (Ng et al., 2014; Khalid et al., 2021).
When an employee receives fair treatment and justi­
fied benefits from the organisation against his/her
performance, he/she performs more positively
(Bashir et al., 2021). Therefore, this study proposes
that PC as a mediator enhances the impact of SMKH
to decrease the KHBs. Hence, the following hypoth­
eses are formulated.
H2a. PC mediates the relationship between RCC and
KHBs.
H2b. PC mediates the relationship between DII among
employees and KHBs.
Figure 1. Conceptual framework.
5
H2c. PC mediates the relationship between IIP and
KHBs.
H2d. PC mediates the relationship between EPA and
KHBs.
H2e. PC mediates the relationship between EHI
among employees and KHBs.
H2f. PC mediates the relationship between OSWS and
KHBs.
Figure 1 explains the positions of the variables and
the direction of the hypotheses.
3. Methods
3.1. Population and data collection procedure
The sample consists of randomly selected employees
working in IT firms in Pakistan. IT sector is pre­
sumed to be a place of innovation and knowledge
creation. It is also embedded with a dynamic busi­
ness environment therefore quite suitable for testing
the hypotheses developed in this study. According to
Pakistan Software Export Board (PSEB), currently,
more than 4,641 (PSEB, 2020) IT-based registered
companies are providing IT-related business solu­
tions and services. Considering the knowledge crea­
tion and sharing perspective, this study filters 337
firms that are providing business-related new soft­
ware development services in different cities of
6
M. W. BARI ET AL.
Figure 2. Post-analysis model.
Pakistan (PSEB 2021). Given the difficult circum­
stances due to COVID-19, an online questionnaire
in English language is designed in “google forms”.
A cover letter is attached with the questionnaire,
which asks employees to take permission from
their leadership before filling it out and be as
rational as possible because this survey is based on
their perception of a particular phenomenon not on
right or wrong answers. The cover letter also
describes what was attached to this survey form
bearing the following instructions/information.
First, it was asked the employees before filling out
the survey form, got permission from your leader­
ship, and confirmed it on the provided place of the
form. Second, the respondents were asked to provide
answers as rationale as possible because this survey
is just their perception about a particular phenom­
enon, not a right or wrong answer. Third, all data
will be kept secret, no individual identification will
be disclosed to anyone and these responses will be
used only for research and analysis purposes.
One thousand email addresses of the employees
from the IT firms’ websites are collected. Three times
lag approach is adopted while collecting the data. Each
time lag has 45 days gap. In the first wave, 781 ran­
domly selected employees are approached and sent
a web link of the survey form with questions about 6
SMKH. Out of 781, 642 employees filled the survey
form (attrition rate 17.79%). In wave 2 after 45 days,
642 employees are asked questions about knowledge
hiding. Out of 642 employees, 561 filled out the survey
form (attrition rate 12.05%). In the third wave, again
with 45 days gap, questions about PC and employees’
demographics are asked from 561 employees. Out of
561, 457 employees completed the survey form (attri­
tion rate 18.53%).
The demographics of respondents are as follows.
Gender: out of 457 employees, 332 are male and 125
are female. Education: out of 457 employees, 251 have
master’s degrees, 112 have bachelor’s degrees, and 94
employees have higher secondary school degrees and
some IT-related diplomas. Experience: 125 employees
have more than 10 years of experience, 205 employees
have 5–10 years of experience, and 127 employees
have 0–5 years of experience. Position and Role: 61
employees are project managers and they have some
strategic level roles, 127 are senior team leaders and
have project development roles, 105 are supervisors
and have operational roles, and 164 employees are at
the input stage.
3.2. Measurement
All items are measured on a five-point Likert scale
ranging from 1 (strongly disagree) to 5 (strongly
agree).
3.2.1. Strategies to Mitigate Knowledge Hiding
(SMKH)
This study uses six SMKH developed by Butt and
Ahmad (2020). The items used to measure these
KNOWLEDGE MANAGEMENT RESEARCH & PRACTICE
strategies are also adopted from Butt and Ahmad
(2020). The following lines describe the items of
SMKH.
Reducing Chain of Command (5 items), sample
item is “I can easily approach top management of
my organization to discuss any innovative business
idea”, Developing Informal Interactions (5 Items),
sample item is “Informal social interactions
improve my level of productivity and creativity”.
Implementing Incentive Policy (3 items), sample
item is “I am rewarded for my participation in
knowledge sharing activities”. Easy Performance
Appraisal (3 items), sample item is “My perfor­
mance is evaluated against my knowledge-sharing
behavior”. Encourage Higher Interdependency (4
items), sample item is “I prefer to work in teams
to get work-related information from team mem­
bers”. Open Space Work Stations (4 items), sample
item is “Open space work station improves sociali­
zation at the workplace”. Cronbach’s Alpha values
of these six SMKH are 0.873, 0.892, 0.731, 0.724,
0.758, and 0.879 respectively.
3.2.2. Knowledge hiding
The authors adopt 12 items KHBs (Evasive hiding,
playing dumb, and rationalised hiding) scale devel­
oped by Connelly et al. (2012). Respondents are
asked to specify the extent to which they involve in
any of mentioned behaviours. Sample items for eva­
sive hiding, playing dumb, and rationalised hiding are
“agree to help him/her but never really intend to”, “I
pretend that I do not know the information”
(α = 0.00), “I explain that I would like to tell him/her
but was not supposed to” respectively. Cronbach’s
Alpha value is 0.919.
3.2.3. Psychological contract
PC is measured with 11 items scale developed by
Rousseau and Tijoriwala (1999). A sample item is “I
do this job just for the money”. Cronbach’s Alpha
value is 0.892.
3.3. Statistical tools, results, and findings
Numerous traditional approaches such as Partial
Least Squares Structural Equation Modelling (PLSSEM) and regression have been applied to examine
the relationships of KHBs with their different ante­
cedents and consequences. However, the small R2
value in PLS-SEM and regression models can be
misleading about the ability of different measures
to sustainably explain the variance in the exogen­
ous constructs (Kaya et al., 2020; Wu et al., 2014).
To account for such potential difficulties, we aug­
ment our PLS-SEM approach with a fuzzy set qua­
litative comparative analysis (fsQCA). While the
PLS-SEM approach provides general tendencies of
7
the constructs towards the outcomes, fsQCA
exposes the presence of different realities regarding
the observed variables (Kaya et al., 2020). The
fsQCA relies on configuration theory, which helps
evaluate the holistic interactions among non-linear
and disordered variables. The fsQCA approach
facilitates the outcomes and predictor constructs
to be on a fuzzy scale (continuous) not on
a merely dichotomous scale (binary), as in other
QCA-based approaches (Kaya et al., 2020;
Kourouthanassis et al., 2017). Further, the PLSSEM approach provides symmetric analyses as it
calculates average effects, whereas, the scope of
the fsQCA approach can be extended to an asym­
metric analysis (Kaya et al., 2020; Rasoolimanesh
et al., 2021). In sum, the fsQCA may extend our
understanding of the research questions under
investigation in an innovative way in addition to
the PLS-SEM approach. The following sections
describe symmetric and asymmetric approaches
we adopt and their findings.
3.4. Symmetric analysis
In this study, PLS-SEM is applied for symmetric
modelling. The constructs’ reliability and validity
are measured with different indicators such as item
loadings, Cronbach’s alpha, composite reliability
(CR), rho_A, average variance extracted (AVE), con­
vergent validity, and discriminant validity. The
retained factor loadings of all constructs are above
0.700 and significant (see, Table 1, and outer values
of Figure 1; Hair et al., 2014). The convergent valid­
ity of this model is assessed with the estimations of
Cronbach’s alpha, CR, rho_A, and AVE. Table 1
presents values of Cronbach’s alpha, CR, rho_A,
and AVE that are above the recommended standards
of 0.70, 0.70, .0.70, and 0.5, respectively (Hair et al.,
2016). Fornell and Larcker’s criterion (Fornell &
Larcker, 1981) and HTMT ratios (Hair et al., 2016)
are used to confirm the discriminant validity. In
Table 2, the square root of AVE for every variable
is greater than its correlation coefficients with the
other variables. In short, the results confirm the
convergent validity and discriminant validity of the
model.
In Table 3, DII (β = −0.146, ρ = 0.045), IIP
(β = −0.152, ρ = 0.028), EPA (β = −0.165, ρ = 0.034),
and EHI (β = −0.184, ρ = 0.017) have a negative impact
on KHBs while RCC (β = −0.027, ρ = 0.637) and
OSWS (β = −0.29, ρ = 0.600) have no impact on
KHBs. Therefore, H1b, H1c, H1d, H1e are accepted
and H1a and H1f are rejected. Table 4, this study uses
bootstrapping approach with a simulation of 5,000
samples with replacement, PC significantly mediates
the impact of DII (β = −0.203, ρ = 2.878), IIP
(β = −0.189, ρ = 2.174), EPA (β = −0.182, ρ = 2.162),
8
M. W. BARI ET AL.
Table 1. Model measurement.
Variables
Reducing Chain of Command
Developing Informal Interaction
Implementing Incentive Policy
Easy Performance Appraisal
Encouraging Higher Interdependency
Open Space Workstation
KHBs
Psychological Contract
Items
RCC-1
RCC-2
RCC-3
RCC-4
DII-1
DII-2
DII-3
DII-4
DII-5
IIP-1
IIP-2
IIP-3
EPA-1
EPA-2
EPA-3
EHI-1
EHI-2
EHI-3
EHI-4
OSWS-1
OSWS-2
OSWS-3
OSWS-4
KHBs-1
KHBs-2
KHBs-3
KHBs-4
KHBs-5
KHBs-6
KHBs-7
KHBs-8
KHBs-9
KHBs-10
KHBs-11
KHBs-12
PC-1
PC-2
PC-3
PC-4
PC-5
PC-6
PC-7
PC-8
PC-9
PC-10
PC-11
Factor
Loadings
0.811
0.861
0.883
0.847
0.825
0.859
0.850
0.804
0.837
0.801
0.759
0.858
0.815
0.786
0.800
0.718
0.817
0.679
0.796
0.876
0.802
0.883
0.862
0.800
0.793
0.735
0.783
0.765
0.731
0.751
0.806
0.716
0.732
0.784
0.697
0.829
0.834
0.802
0.796
0.821
0.814
0.806
0.797
0.824
0.799
0.820
Cronbach’s Alpha
0.873
rho_A
0.875
CR
0.913
AVE
0.634
0.892
0.894
0.920
0.697
0.731
0.737
0.848
0.651
0.724
0.738
0.842
0.640
0.758
0.779
0.840
0.569
0.879
0.888
0.917
0.634
0.919
0.905
0.928
0.560
0.892
0.889
0.855
0.661
Note: CR = Composite Reliability, AVE = Average Variance Extracted
Table 2. Discriminant validity.
Fornell-Larcker Criterion
Constructs
DII
EHI
EPA
IIP
KHBs
OSWS
PC
RCC
Heterotrait-Monotrait Ratio (HTMT)
DII
EHI
EPA
IIP
KHBs OSWS
PC
RCC
0.835
0.091
0.755
0.174
0.370
0.800
0.525
0.212
0.414
0.807
−0.169 −0.111 −0.192 −0.187
0.748
0.251
0.237
0.501
0.332 −0.156 0.857
0.513
0.267
0.514
0.570 −0.183 0.465 0.813
0.247
0.233
0.474
0.255 −0.147 0.649 0.427 0.851
DII
EHI
EPA
0.118
0.204
0.646
0.176
0.275
0.555
0.284
0.455
0.279
0.137
0.259
0.295
0.249
0.536
0.206
0.623
0.606
0.586
IIP
KHBs
OSWS
PC
0.219
0.398 0.161
0.678 0.180
0.316 0.161
0.504
0.730
0.467
RCC
Note: RCC = Reducing Chain of Command, DII = Developing Informal Interaction, IIP = Implementing Incentive Policy, EPA = Easy Performance Appraisal,
EHI = Encouraging Higher Interdependency, OSWS = Open Space Work Station, KHBs = KHBss, PC = Psychological Contract
EHI (β = −0.218, ρ = 3.141), and OSWS (β = −0.141,
ρ = 1.969) on KHBs. However, PC fails to mediate
thesignificant effect of RCC on KHBs (β = −0.056,
ρ = 0.669). Thus, H2b, H2c, H2d, H2e, and H2f are
accepted based on empirical support, and H2a is
rejected due to lack of evidence. Figure 2 explains the
post analyses results.
3.5. Asymmetric analysis
The fsQCA is an asymmetric approach, which is based
on fuzzy logic and fuzzy sets. It has multiple signifi­
cances. First, beta and correlation coefficients in the
regression analysis are not sufficient to explain the
relationship between two or more constructs.
However, fuzzy sets are very effective in this scenario
KNOWLEDGE MANAGEMENT RESEARCH & PRACTICE
9
Table 3. Direct relationship.
Structural Paths
RCC→KHBs
DII→ KHBs
IIP→ KHBs
EPA→ KHBs
EHI → KHBs
OSWS → KHBs
Path co-efficient
(t-value)
−0.027, (0.472)
−0.146, (2.694)
−0.165, (3.062)
−0.152, (2.865)
−0.184, (3.711)
−0.029, (0.525)
Confidence interval (95%)
(−0.133–0.090)
(−0.289 − 0.014)
(−0.387 − 0.064)
(−0.314 − 0.038)
(−0.431–0.109)
(−0.133 0.078)
f2 Effect
size
0.001
0.026
0.023
0.032
0.038
0.001
p-Value
0.637
0.045
0.028
0.034
0.017
0.600
Results
H1a, Rejected
H1b, Accepted
H1c, Accepted
H1d, Accepted
H1e, Accepted
H1f, Rejected
Note: Note: RCC = Reducing Chain of Command, DII = Developing Informal Interaction, IIP = Implementing Incentive Policy, EPA = Easy Performance
Appraisal, EHI = Encouraging Higher Interdependency, OSWS = Open Space Work Station, KHBs = KHBs, PC = Psychological Contract
Table 4. Mediation effect.
Paths
RCC→PC→KHBs
DII→ PC→KHBs
IIP→ PC→KHBs
EPA→ PC→KHBs
EHI → PC→KHBs
OSWS → PC→KHBs
Indirect Effects
−0.056
−0.203
−0.189
−0.182
−0.218
−0.141
T Statistics
0.669
2.878
2.174
2.162
3.141
1.969
Confidence Interval (95%)
−0.213
0.110
−0.339 − 0.047
−0.304 − 0.036
−0.281 − 0.029
−0.411 − 0.059
−0.260 − 0.025
p-Value
0.502
0.020
0.021
0.024
0.013
0.048
Results
H2a, Rejected
H2b, Accepted
H2c, Accepted
H2d, Accepted
H2e, Accepted
H2f, Accepted
Note: Note: RCC = Reducing Chain of Command, DII = Developing Informal Interaction, IIP = Implementing Incentive Policy, EPA = Easy Performance
Appraisal, EHI = Encouraging Higher Interdependency, OSWS = Open Space Work Stations, KHBs = KHBs, PC = Psychological Contract
(Kaya et al., 2020; Olya & Altinay, 2016; Wu et al.,
2014) because fsQCA provides manifold solutions that
can provide the same output. Second, the effectiveness
of symmetric methods (i.e., multiple regression analy­
sis, MRA, and structural equation modelling, SEM)
while testing a model with several endogenous con­
structs that are highly correlated is questionable due to
collinearity (Olya & Altinay, 2016; Rasoolimanesh
et al., 2021). Therefore, while applying MRA and
SEM models, the big size of a sample is not helpful
to control the impact of confounding factors such as
education, experience, gender, age, etc. (Woodside,
2013).
Third, practically, an outcome of a construct
depends on the joint impact of multiple antecedents
which is known as an algorithm in an asymmetric
approach (Kaya et al., 2020; Woodside, 2013). In
symmetric methods, high values of an endogenous
construct (say A) are enough for forecasting the
occurrence of high values of an endogenous con­
struct (say B), however, a high score of (A) does not
confirm the occurrence of a high score of (B). On
the other hand, in asymmetric association, high
scores of an endogenous construct (A) are impor­
tant and enough for predicting the existence of
a high score construct (B; Kaya et al., 2020).
Fourth, the asymmetric method is conditional on
both logic (i.e., negative and positive; Woodside,
2013), because dependence on one logic can be
inappropriate.
3.5.1. Calibration
The fsQCA is based on the logic of set membership,
therefore, data should be converted into fuzzy sets
scaling from one (full membership) to zero (full nonmembership; Afonso et al., 2018; Ragin, 2009). This
study uses standardised latent variables calculated
through PLS-SEM (SmartPLS.3) for all constructs
cases (conditions & outcomes; Hair et al., 2016;
Ragin, 2009). The calibration procedure needs to
define 3 anchors: full non-membership, crossover
point, and full membership (Afonso et al., 2018;
Ragin, 2009; Silva & Goncalves, 2016). Therefore, in
this paper the rating of 3 is full membership, zero is the
crossover point, and −3 is full non-membership.
3.5.2. Necessary conditions
Necessary conditions play an important role in fsQCA
to complete the analysis of sufficient conditions
(Afonso et al., 2018; Schneider & Wagemann, 2010).
This paper investigates the two endogenous con­
structs, psychological contract, and knowledge hiding
in the PLS-SEM model as outcome conditions. While
conducting the fsQCA, six antecedent conditions for
the outcome PC (RCC, DII, IIP, EPA, EHI, and
OSWS) and seven antecedent conditions for the out­
come KHBs (RCC, DII, IIP, EPA, EHI, OSWS, and
PC) are considered. To determine whether any of the
six or seven conditions are necessary for PC or KHBs
respectively, this study measures whether the condi­
tion is consistently present or absent in all cases where
the outcome is present or absent (Kaya et al., 2020;
Rihoux & Ragin, 2008). Hence, PC or KHBs is attain­
able if the condition in question happens. The level to
which the cases follow this rule depicts “consistency”.
As per scholars’ recommendation, a relevant consis­
tency value above the threshold of 0.8 or 0.9 indicates
a condition is “almost always necessary” or “neces­
sary” respectively (Afonso et al., 2018; Ragin, 2009).
Table 5 shows the fsQCA test results on the necessity
of the conditions concerning both the outcomes PC
10
M. W. BARI ET AL.
Table 5. Analysis of necessary conditions.
S. No.
Conditions
Consistency
Outcome = PC, Consistency Cut = 0.9, Coverage Cut = 0.80
1
DII+IIP
0.945
2
DII+EPA
0.959
3
DII+OSWS
0.940
4
EPA+OSWS
0.951
Outcome = ~PC, Consistency Cut = 0.9, Coverage Cut = 0.80
1
~DII+~IIP
0.942
2
~DII+~OSWS
0.965
3
~IIP+~EPA
0.955
4
~IIP+~OSWS
0.962
Relevance of Necessity
Coverage
0.824
0.793
0.804
0.809
0.823
0.803
0.806
0.813
0.820
0.819
0.818
0.808
0.844
0.849
0.846
0.841
0.792
0.783
0.810
0.802
Outcome = KHBs, Consistency Cut = 0.9, Coverage Cut = 0.80
1
~DII+~PC
0.910
2
~IIP+~PC
0.905
Outcome = ~KHBs, Consistency Cut = 0.9, Coverage Cut = 0.80
No combination of conditions meets the criteria of the necessary condition
Note: RCC = Reducing Chain of Command, DII = Developing Informal Interaction, IIP = Implementing Incentive Policy,
EPA = Easy Performance Appraisal, EHI = Encouraging Higher Interdependency, OSWS = Open Space Work Station,
Knowledge Hiding Behaviours= KHBs, PC = Psychological Contract
and KHBs. The results present that no necessary con­
ditions are identified for PC and KHBs
3.5.3. Sufficient conditions
The truth table provides the base for the analyses of
sufficient conditions (Ragin, 2009). The fsQCA algo­
rithm is used to develop the truth tables for both out­
comes, PC, and KHBs. This study uses a frequency
threshold of 10 observations to eliminate comparatively
less significant configurations (Rihoux & Ragin, 2008).
It helps reduce truth tables for finding meaningful con­
figurations. Scholars suggest that post-frequency
restriction, a minimum of 80% of the cases should
stay in the sample set (Ragin, 2009). The frequency
threshold confirms that 83% and 88% of the cases are
part of the analyses for KHBs and PC respectively. To
confirm which configurations are sufficient for attain­
ing the outcomes, this study uses 0.80 (Ragin, 2009;
Rasoolimanesh et al., 2021) as the threshold for consis­
tency and 0.5 threshold for the proportional reduction
in inconsistency (PRI) to control the occurrence of
simultaneous subset associations of attribute
Table 6. Analysis of sufficiency.
S. No.
Conditions
Consistency
Outcome = PC, Consistency Cut = 0.9, PRI. Cut = 0.5, n. Cut = 10
1
RCC*IIP*EPA*EHI
0.976
2
RCC*IIP*EPA*OSWS
0.973
3
RCC*~DII*~IIP*EPA*~EHI
0.967
4
~DII*~IIP*EPA*EHI*~OSWS
0.963
5
~RCC*~DII*IIP*EPA*~EHI*~OSWS
0.975
6
RCC*~DII*~IIP*~EPA*EHI*OSWS
0.974
Solution 0.925 0.844 0.669
Outcome = ~PC, Consistency Cut = 0.9, PRI. Cut = 0.5, n. Cut = 10
1
~RCC*~EPA*~EHI*~OSWS
0.965
2
~DII*~IIP*~EPA*~OSWS
0.974
3
~IIP*~EPA*~EHI*~OSWS
0.971
4
~RCC*~DII*~IIP*~EHI*~OSWS
0.979
Solution
0.955
Coverage
Unique Coverage
Proportional Reduction Inefficiency (PRI)
0.673
0.694
0.547
0.575
0.533
0.544
0.014
0.021
0.012
0.021
0.013
0.027
0.872
0.863
0.571
0.474
0.510
0.592
0.665
0.879
0.662
0.630
0.789
0.041
0.701
0.008
0.023
0.826
0.879
0.856
0.883
0.826
Outcome = KHBs, Consistency Cut = 0.9, PRI. Cut = 0.5, n. Cut = 10
1
~RCC*~DII*~IIP*~EPA*~OSWS*~PC
0.900
2
~RCC*~DII*~IIP*~EHI*~OSWS*~PC
0.901
3
~RCC*~DII*~EPA*~EHI*~OSWS*~PC
0.904
4
~RCC*~IIP*~EPA*~EHI*~OSWS*~PC
0.908
5
~DII*~IIP*~EPA*~EHI*~OSWS*~PC
0.906
Solution
0.895
0.599
0.575
0.576
0.574
0.586
0.678
0.041
0.017
0.018
0.016
0.028
0.652
0.640
0.653
0.653
0.662
0.675
Outcome = ~KHBs, Consistency Cut = 0.9, PRI. Cut = 0.5, n. Cut = 10
1
RCC*~DII*~IIP*~EPA*EHI*~PC
0.943
2
RCC*~DII*~IIP*EPA*~OSWS*~PC
0.953
3
RCC*~DII*IIP*EPA*OSWS*~PC
0.957
4
RCC*DII*IIP*EPA*EHI*PC
0.964
5
RCC*DII*IIP*EPA*OSWS*PC
0.954
6
~DII*~IIP*EPA*EHI*~OSWS*~PC
0.929
7
~RCC*~DII*IIP*EPA*~EHI*~OSWS*~PC
0.941
8
~RCC*DII*IIP*~EPA*~EHI*~OSWS*~PC
0.942
9
RCC*DII*~IIP*~EPA*~EHI*~OSWS*~PC
0.942
Solution
0.859
0.522
0.506
0.531
0.620
0.633
0.528
0.494
0.522
0.492
0.793
0.014
0.001
0.007
0.005
0.010
0.008
0.003
0.014
0.006
0.600
0.643
0.675
0.757
0.718
0.538
0.564
0.575
0.506
0.496
Note: RCC = Reducing Chain of Command, DII = Developing Informal Interaction, IIP = Implementing Incentive Policy, EPA = Easy Performance Appraisal,
EHI = Encouraging Higher Interdependency, OSWS = Open Space Work Station, Knowledge Hiding Behaviours= KHBs, PC = Psychological Contract
KNOWLEDGE MANAGEMENT RESEARCH & PRACTICE
combinations in both the outcomes and their negations
as well (Afonso et al., 2018; Schneider & Wagemann,
2012, p. 242). The fsQCA software offers three types of
solutions (i.e., parsimonious, complex, and intermedi­
ate; Ragin, 2009). Table 6 reports the complex solutions
for both outcomes (PC and KHBs). The coverage and
consistency values for each solution and their specific
configurations are above the thresholds.
3.5.4. Causal recipes for the presence of the
outcomes
The complex solution for the presence of a PC has six
configurations (Table 6). For instance, configuration 1
depicts that effectively reducing the chain of com­
mand, effective implementation of incentive policy,
easy performance appraisal, and higher interdepen­
dency among employees (RCC*IIP*EPA*EHI) pro­
motes psychological contract. Similarly, the complex
solution for the presence of KHBs has five configura­
tions (Table 6). The results depict these five config­
urations are negation-based combinations of different
causal conditions. For instance, the first configuration
explains that combining negation of reducing the
chain of command, developing informal interaction,
implementation of incentive policy, easy performance
appraisal, open space workstation, and PC
(~RCC*~DII*~IIP*~EPA*~OSWS*~PC) promotes
KHBs at the workplace.
3.5.5. Causal recipes for the negation of the
outcomes
The fsQCA provides additional analyses about the
inverse of the outcome to explain which configuration
can consistently lead to the negation of the outcome
(Schneider & Wagemann, 2012). As compared to PLSSEM, the fsQCA may provide different results from
the configurations leading to the outcome (presence)
to the configurations leading to the negation of the
outcome (absence; Afonso et al., 2018; Rihoux &
Ragin, 2008). The present study also investigates
which conditions consistently lead to ~PC and
~KHBs. Table 6, the results of ~PC depict the four
configurations that have met the threshold of consis­
tency as all values are greater than 0.9. These results
show four configurations are consistently associated
with ~psychological contract. Similarly, this study
examines which conditions consistently lead to
~KHBs at the frequency threshold 10, consistency
0.9, and PRI 0.5. Table 6, The fsQCA generates nine
configurations with different combinations to negate
the KHBs. For instance, in the first configuration,
presence of reduced chain of command and higher
interdependency among employees, and in absence
of informal interactions, Incentive Policy, Easy
Performance Appraisal, and PC decrease knowledge
hiding (RCC* ~DII*~IIP* ~EPA *EHI *~PC).
11
4. Discussion
The objective of this study is to evaluate the impact of
different management strategies to mitigate knowl­
edge hiding in the workplace, and the absence of
these strategies, how KHBs develop. Besides, how psy­
chological contact mediates the impact of these man­
agement strategies on KHBs. This study uses both
symmetric (PLS-SEM) and asymmetric (fsQCA)
approaches to investigate the phenomenon. The
results of this study are based on the data provided
by 684 employees working in IT firms in Pakistan. The
final results from the PLS-SEM show that four ante­
cedents (DII, IIP, EPA, and EHI) have a significant
negative impact on KHBs, however, two antecedents
(RCC and OSWS) have no significant impact on
KHBs. PC mediates the negative impact of five ante­
cedents (DII, IIP, EPA, EHI, OSWS) on KHBs. On the
other hand, fsQCA results present a detailed under­
standing of how these six antecedent conditions sup­
port psychological contract. For instance, EPA
consistently play role in all six configurations to
develop PC (also in line with PLS-SEM results).
Similarly, RCC also consistently appears in four out
of six configurations and supports PC. In contrast,
Table 6 shows four antecedent conditions in combina­
tion impact PC negatively. For example, ~OSWS con­
sistently appears in all four conditions and confirms
that the absence of OSWS is a problem in the devel­
opment of the PC.
Table 6, considering the KHBs as an outcome,
fsQCA provides five recipes based on different ante­
cedent combinations. From these five combinations of
antecedents, ~PC and ~OSWS consistently appears in
all five recipes and confirm that the absence of PC and
OSWS promotes KHBs. Similarly, Table 6 presents
nine recipes based on different combinations of ante­
cedents that negate KHBs. In these nine recipes, the
antecedents, DII, IIP, EPA, EHI, and PC are consis­
tently absent/present to negate KHBs. The results of
the net effect present a R2 value of 52% for a PC while
the outcome of the combinatory effects presents an
overall solution coverage of 84% for PC presence and
78% for ~PC outcome condition. Similarly, the R2
value for KHBs is 46% while the analysis of the com­
bined recipe effects shows an overall solution coverage
of 67% for KHBs’ existence and 79% for the negation
of KHBs.
The results of this study are in line with previous
studies and indicate that the management strategies
(RCC, DII, IIP, EPA, HI, and OSWS) can play an
effective and significant role to decrease the KHBs
(Butt, 2020, 2020; Butt & Ahmad, 2019). PC acts as
a bridge to develop trust among workers and promote
knowledge-sharing culture (Bari, Misbah et al., 2020;
Connelly & Kevin Kelloway, 2003; Khoreva &
Wechtler, 2020). Although, all recommended
12
M. W. BARI ET AL.
management strategies (RCC, DII, IIP, EPA, EHI, and
OSWS) and PC with different combined recipes
(Table 6) play their role to change the KHBs of the
workers, however, DII, IIP, EPA, EHI, and PC con­
sistently play their role (presence/absence) in different
combinations to decrease knowledge hiding practices.
This study has conducted analyses on the responses
collected from the employees working in software
houses in Pakistan. Different software houses have
different organisational structures, places, and sizes.
Therefore, the occurrence of the phenomenon (knowl­
edge hiding) and application of the SMKH may be
different in different organisations. For instance, the
workplace of some software houses may be small and
they already have installed OSWS. Similarly, flat or
semi-flat organisational structures do not need to
focus on RCC. On the other hand, for the software
houses that already have applied the job interdepen­
dence approach, the chances of knowledge hiding may
be less as compared to other ones.
4.1. Theoretical contribution
The theoretical framework of this study is based on
SET (Blau, 1964) and it is hypothesised that certain
management strategies (RCC, DII, IIP, EPA, EHI,
OSWS; Butt & Ahmad, 2020)can help to control
KHBs of workers at the workplace. In addition, PC
can strengthen these management strategies to control
KHBs. To confirm the application of SET, this study
employs two methods PLS-SEM and fsQCA. The
results of both methods (PLS-SEM & fsQCA) con­
firms the effectiveness and significance of all proposed
SMKH (except RCC which is not significant through
PLS-SEM) to decrease the KHBs. Moreover, the said
methods also confirm the significant role of PC to
increase the efficacy of SMKH and decreasing the
KHBs. These results support Blau’ theory of social
exchange (Blau, 1964). The fsQCA approach further
elaborates the results by highlighting which individual
strategy and combinations of SMKH (Table 6) consis­
tently support SET and required to control the KHBs.
PLS-SEM based results indicate that OSWS strategy
has no direct significant effect on KHBs, however, as
a mediator, PC enhances its significance to control the
KHBs, and supports the application of SET.
should promote social interaction-based activities i.e.,
social get gathers, and open-place communication dis­
cussions. Second, managers should arrange knowl­
edge-based activities and provide awareness about
the outcomes of knowledge sharing and KHBs.
Third, although all proposed SMKH are effective to
control KHBs, however, PC is imperative. Without
mutual trust, it is very difficult for managers to moti­
vate employees to knowledge sharing. For instance,
OSWS has no direct significant effect on KHBs but
PC significantly mediates this relationship. Therefore,
managers should promote a trusting culture in the
workplace. Fourth, to build trust among employees,
job interdependence is an effective strategy (Fong
et al., 2018). Fifth, an appraisal strategy based on
reward against knowledge sharing can help control
KHBs.
4.3. Limitations and directions for future studies
This study has certain limitations and future
research directions. First, the objective of this
study is to investigate the effectiveness of SMKH
proposed by (Butt, 2020; Butt & Ahmad, 2020) in
their exploratory studies. Although the empirical
investigation in this study has proved the signifi­
cance of the SMKH, in the future, further manage­
ment strategies and practices should be explored for
the effective controlling of KHBs. Second, the appli­
cation of these SMKH is checked in one industry
(IT), and the responses of other industries may be
different. Therefore, in the future, industry-based
comparative studies are recommended. Third, dif­
ferent cultures and soft issues of human resources
can impact the significance of the SMKH differently.
Thus, in the future, cross-culture/border studies are
recommended. Fourth, the time lag method is used
for data collection which may limit the generalisa­
bility of this study. Therefore, a longitudinal study
can be conducted for the long-term application of
these strategies (Butt, 2020). Fifth, this study uses
PC as a mediator, in the future, other variables
(workplace democracy, organisational justice) as
a mediator and moderators (job interdependence)
can be checked.
Disclosure statement
4.2. Managerial implications
The outcomes have significant managerial workplace
implications because they enhance the stakeholders’
understanding of which management strategies/mea­
sures can motivate employees to avoid KHBs and
adopt knowledge-sharing behaviour. First, to promote
a knowledge-sharing culture, workplace managers
No potential conflict of interest was reported by the
author(s).
ORCID
Dr Muhammad Waseem Bari
0003-2329-3857
http://orcid.org/0000-
KNOWLEDGE MANAGEMENT RESEARCH & PRACTICE
References
Abdillah, M. R., Wu, W., & Anita, R. (2022). Can altruistic
leadership prevent knowledge-hiding behaviour? Testing
dual mediation mechanisms. Knowledge Management
Research and Practice, 20(3), 352–366. https://doi.org/
10.1080/14778238.2020.1776171
Afonso, C., Silva, G. M., Gonçalves, H. M., & Duarte, M.
(2018). The role of motivations and involvement in wine
tourists’ intention to return: SEM and fsQCA findings.
Journal of Business Research, 89(1), 313–321. https://doi.
org/10.1016/j.jbusres.2017.11.042
Anand, P., & Hassan, Y. (2019). Knowledge hiding in orga­
nizations: Everything that managers need to know.
Development and Learning in Organizations, 33(6),
12–15. https://doi.org/10.1108/DLO-12-2018-0158
Anser, M. K., Ali, M., Usman, M., Rana, M. L. T., & Yousaf,
Z. (2021). Ethical leadership and knowledge hiding: An
intervening and interactional analysis. The Service
Industries Journal, 41(5–6), 307–329.
Bari, M. W., Abrar, M., Shaheen, S., Bashir, M., &
Fanchen, M. (2019). Knowledge hiding behaviors and
team creativity: The contingent role of perceived mastery
motivational climate. SAGE Open, 9(3), 1–15. https://doi.
org/10.1177/2158244019876297
Bari, M. W., Ghaffar, M., & Ahmad, B. (2020). Knowledgehiding behaviors and employees’ silence: Mediating role
of psychological contract breach. Journal of Knowledge
Management, 1(1), 1–37. https://doi.org/10.1108/JKM02-2020-0149
Bari, M. W., Misbah, G., & Bashir, A. (2020). Knowledgehiding behaviors and employees’ silence: Mediating role
of psychological contract breach. Journal of Knowledge
Management, 24(9), 2171–2194. doi:10.1108/JKM-022020-0149.
Bashir, H., Ahmad, B., Bari, M. W., & Khan, Q. U. A. (2021).
The impact of organizational practices on formation and
development of psychological contract: Expatriates’ percep­
tion-based view. International Journal of Emerging Markets,
1–17. https://doi.org/10.1108/IJOEM-10-2020-1187
Bernatović, I., Slavec Gomezel, A., & Černe, M. (2021).
Mapping the knowledge-hiding field and its future pro­
spects: A bibliometric co-citation, co-word, and coupling
analysis. Knowledge Management Research & Practice, 20
(3), 394–409.
Blau, P. M. (1964). Social exchange theory. Sage
International. http://www.tut.ee/public/m/martmurdvee/EconPsy/5/6._EconPsy_-_Social_exchange.pdf
Bogilović, S., Černe, M., & Škerlavaj, M. (2017b). Hiding
behind a mask? Cultural intelligence, knowledge hiding,
and individual and team creativity. European Journal of
Work and Organizational Psychology, 26(5), 710–723.
https://doi.org/10.1080/1359432X.2017.1337747
Bordia, P., Irmer, B. E., & Abusah, D. (2006). Differences in
sharing knowledge interpersonally and via databases: The
role of evaluation apprehension and perceived benefits.
European Journal of Work and Organizational
Psychology, 15(3), 262–280. https://doi.org/10.1080/
13594320500417784
Butt, A. S. (2020). Mitigating knowledge hiding in firms: An
exploratory study. Baltic Journal of Management, 15(4),
631–645. https://doi.org/10.1108/BJM-01-2020-0016
Butt, A. S. (2021). Top-down knowledge hiding in buying
and supplying firms: Causes and some suggestions.
International Journal of Services and Operations
Management, 38(2), 299–307. https://doi.org/10.1504/
IJSOM.2021.113030
13
Butt, A. S., & Ahmad, A. B. (2019). Are there any antece­
dents of top-down knowledge hiding in firms? Evidence
from the United Arab Emirates. Journal of Knowledge
Management, 23(8), 1605–1627. https://doi.org/10.1108/
JKM-04-2019-0204
Butt, A. S., & Ahmad, A. B. (2020). Strategies to mitigate
knowledge hiding behavior: Building theories from mul­
tiple case studies. Management Decision, 59(6), 1291–
1311. doi:10.1108/MD-01-2020-0038.
Černe, M., Hernaus, T., Dysvik, A., & Škerlavaj, M. (2017).
The role of multilevel synergistic interplay among team
mastery climate, knowledge hiding, and job characteris­
tics in stimulating innovative work behavior. Human
Resource Management Journal, 27(2), 281–299. https://
doi.org/10.1111/1748-8583.12132
Černe, M., Nerstad, C. G. L., Dysvik, A., & Škerlavaj, M.
(2014). What goes around comes around: Knowledge
hiding, perceived motivational climate, and creativity.
Academy of Management Journal, 57(1), 172–192.
https://doi.org/10.5465/amj.2012.0122
Chaudhry, A., Coyle-Shapiro, J. A. M., & Wayne, S. J.
(2011). A longitudinal study of the impact of organiza­
tional change on transactional, relational, and balanced
psychological contracts. Journal of Leadership and
Organizational Studies, 18(2), 247–259. https://doi.org/
10.1177/1548051810385942
Chen, Y. H., Lin, T. P., & Yen, D. C. (2014). How to facilitate
inter-organizational knowledge sharing: The impact of
trust. Information and Management, 51(5), 568–578.
http://dx.doi.org/10.1016/j.im.2014.03.007
Connelly, C. E., Černe, M., Dysvik, A., & Škerlavaj, M.
(2019). Understanding knowledge hiding in
organizations. Journal of Organizational Behavior, 40(7),
779–782. https://doi.org/10.1002/job.2407
Connelly, C. E., & Kevin Kelloway, E. (2003). Predictors of
employees’ perceptions of knowledge sharing cultures.
Leadership & Organization Development Journal, 24(5),
294–301. https://doi.org/10.1108/01437730310485815
Connelly, C. E., & Zweig, D. (2015). How perpetrators and
targets construe knowledge hiding in organizations.
European Journal of Work and Organizational
Psychology, 24(3), 479–489. https://doi.org/10.1080/
1359432X.2014.931325
Connelly, C. E., Zweig, D., Webster, J., & Trougakos, J. P.
(2012). Knowledge hiding in organizations. Journal of
Organizational Behavior, 33(1), 64–88. https://doi.org/
10.1002/job
Crumpton, M. A. (2013). Is the chain of command working
for you? The Bottom Line: Managing Library Finances, 26
(3), 88–91. https://doi.org/10.1108/BL-07-2013-0017
Dai, L., & Wang, L. (2016). Psychological contract, recipro­
cal preference and knowledge sharing willingness. Open
Journal of Social Sciences, 4(8), 60–76. https://doi.org/10.
4236/jss.2016.48008
de Almeida, F. C., Lesca, H., & Canton, A. W. P. (2016).
Intrinsic motivation for knowledge sharing –
Competitive intelligence process in a telecom company.
Journal of Knowledge Management, 20(6), 1282–1301.
https://doi.org/10.1108/JKM-02-2016-0083
Demirkasımoğlu, N. (2016). Knowledge hiding in academia:
Is personality a key factor? International Journal of Higher
Education, 5(1), 128–140. https://doi.org/10.5430/ijhe.
v5n1p128
Enwereuzor, I. K., Ugwu, L. E., & Ugwu, L. I. (2022).
Unlocking the mask: How respectful engagement
enhances tacit knowledge sharing among organizational
14
M. W. BARI ET AL.
members. International Journal of Manpower, 1–20.
https://doi.org/10.1108/IJM-04-2021-0246
Evans, N. (2012). Destroying collaboration and knowledge
sharing in the workplace: A reverse brainstorming
approach. Knowledge Management Research & Practice,
10(2), 175–187. https://doi.org/10.1057/kmrp.2011.43
Fischer, C. (2022). Incentives can’t buy me knowledge: The
missing effects of appreciation and aligned performance
appraisals on knowledge sharing of public employees.
Review of Public Personnel Administration, 42(2),
368–389. https://doi.org/10.1177/0734371X20986839
Fong, P. S. W., Men, C., Luo, J., & Jia, R. (2018). Knowledge
hiding and team creativity: The contingent role of task
interdependence. Management Decision, 56(2), 329–343.
https://doi.org/10.1108/MD-11-2016-0778
Fornell, C., & Larcker, D. F. (1981). Structural equation
models with unobservable variables and measurement
error: Algebra and statistics. Journal of Marketing
Research, 18(3), 382–388. https://doi.org/10.1177/
002224378101800313
Guest, D. E. (2004). The psychology of the employment
relationship: An analysis based on the psychological
contract. Applied Psychology, 53(4), 541–555. https://doi.
org/10.1111/j.1464-0597.2004.00187.x
Hair, J. F., Jr, Hult, G. T. M., Ringle, C., & Sarstedt, M.
(2016). A primer on partial least squares structural equa­
tion modeling (PLS-SEM) (2nd) ed.).
Hair, J. F., Jr, Sarstedt, M., Hopkins, L., &
Kuppelwieser, V. G. (2014). Partial least squares struc­
tural equation modeling (PLS-SEM).An emerging tool in
business research. European Business Review, 26(2),
106–121. https://doi.org/10.1108/EBR-10-2013-0128
Han, M. S., Masood, K., Cudjoe, D., & Wang, Y. (2020).
Knowledge hiding as the dark side of competitive psy­
chological climate. Leadership and Organization
Development Journal, 42(2), 195–207. doi:10.1108/
LODJ-03-2020-0090.
He, M. R., & Li, Y. J. (2011). An empirical study on the
influence of psychological contract types on the tacit
knowledge sharing willingness. Journal of Management,
8(1), 56–60.
Hernaus, T., Dysvik, A., & Behaviour, O. (2017). The role of
multilevel synergistic interplay among team mastery cli­
mate, knowledge hiding, and job characteristics in stimu­
lating innovative work behavior. 27(2), 281–299. https://
doi.org/10.1111/1748-8583.12132
Hobfoll, S. E. (1989). Conservation of resources: A new
attempt at conceptualizing stress. American Psychologist,
44(3), 513–520. https://doi.org/10.1037/0003-066X.44.3.
513
Huo, W., Cai, Z., Luo, J., Men, C., & Jia, R. (2016).
Antecedents and intervention mechanisms: A multilevel study of R&D team’s knowledge hiding behavior.
Journal of Knowledge Management, 20(5), 880–897.
https://doi.org/10.1108/JKM-11-2015-0451
Johnson, D. W. (2003). Social interdependence:
Interrelationships among theory, research, and practice.
American Psychologist, 58(11), 934. https://doi.org/10.
1037/0003-066X.58.11.934
Kaya, B., Abubakar, A. M., Behravesh, E., Yildiz, H., &
Mert, I. S. (2020). Antecedents of innovative perfor­
mance: Findings from PLS-SEM and fuzzy sets (fsQCA).
Journal of Business Research, 114(April), 278–289. https://
doi.org/10.1016/j.jbusres.2020.04.016
Khoreva, V., & Wechtler, H. (2020). Exploring the conse­
quences of knowledge hiding: An agency theory
perspective. Journal of Managerial Psychology, 35(2),
71–84. https://doi.org/10.1108/JMP-11-2018-0514
Kim, B., Kim, E., Kim, Y., & Cho, J. Y. (2018). Where to find
innovative ideas: Interdependence-building mechanisms
and boundary-spanning exploration. Knowledge
Management Research & Practice, 16(3), 376–387.
https://doi.org/10.1080/14778238.2018.1493367
Kingshott, R. P. J. (2006). The impact of psychological con­
tracts upon trust and commitment within supplier –
Buyer relationships: A social exchange view. Industrial
Marketing Management, 35(6), 724–739. doi:10.1016/j.
indmarman.2005.06.006.
Koon, V. Y. (2022). The role of organizational compassion
in knowledge hiding and thriving at work. Knowledge
Management Research & Practice, 20(3), 486–501.
Kou, C. Y. (2020). Subjective interdependencies in knowl­
edge integration. Knowledge Management Research and
Practice, 18(4), 394–404. https://doi.org/10.1080/
14778238.2019.1678413
Kourouthanassis, P. E., Mikalef, P., Pappas, I. O., &
Kostagiolas, P. (2017). Explaining travellers online infor­
mation satisfaction: A complexity theory approach on
information needs, barriers, sources and personal
characteristics. Information & Management, 54(6),
814–824. https://doi.org/10.1016/j.im.2017.03.004
Lee, H., Reid, E., & Kim, W. G. (2014). Understanding
knowledge sharing in online travel communities:
Antecedents and the moderating effects of interaction
modes. Journal of Hospitality & Tourism Research, 38
(2), 222–242. https://doi.org/10.1177/1096348012451454
Miminoshvili, M., & Černe, M. (2021). Workplace inclu­
sion–exclusion and knowledge-hiding behavior of min­
ority members. Knowledge Management Research &
Practice, 20(3), 422–435.
Neill, B. S. O., & Adya, M. (2007). Knowledge sharing and
the psychological contract stages of employment. 22(4),
411–436. https://doi.org/10.1108/02683940710745969
Ng, T. W. H., Feldman, D. C., & Butts, M. M. (2014).
Psychological contract breaches and employee voice
behaviour: The moderating effects of changes in social
relationships. European Journal of Work and
Organizational Psychology, 23(4), 537–553. https://doi.
org/10.1080/1359432X.2013.766394
Olya, H. G. T., & Altinay, L. (2016). Asymmetric modeling
of intention to purchase tourism weather insurance and
loyalty. Journal of Business Research, 69(8), 2791–2800.
https://doi.org/10.1016/j.jbusres.2015.11.015
Pan, W., Zhang, Q., Teo, T. S. H., & Lim, V. K. G. (2018).
The dark triad and knowledge hiding. International
Journal of Information Management, 42(2), 36–48.
https://doi.org/10.1016/j.ijinfomgt.2018.05.008
Peng, H. (2013). Why and when do people hide knowledge?
Journal of Knowledge Management, 17(3), 398–415.
https://doi.org/10.1108/JKM-12-2012-0380
Perumal, S., & Sreekumaran Nair, S. (2022). Impact of views
about knowledge and workplace relationships on tacit
knowledge sharing. Knowledge Management Research
and Practice, 20(3), 410–421. https://doi.org/10.1080/
14778238.2021.1947756
PSEB. (2020). Company Directory. Pakistan Software Export
Board. https://pseb.org.pk/app/company_directory.php
Ragin, C. C. (2009). Redesigning social inquiry Fuzzy sets and
beyond. University of Chicago Press.
Rasoolimanesh, S. M., Ringle, C. M., Sarstedt, M., & Olya, H.
(2021). The combined use of symmetric and asymmetric
approaches: Partial least squares-structural equation
modeling and fuzzy-set qualitative comparative analysis.
KNOWLEDGE MANAGEMENT RESEARCH & PRACTICE
International Journal of Contemporary Hospitality
Management, 33(5), 1571–1592. https://doi.org/10.1108/
IJCHM-10-2020-1164
Rihoux, B., & Ragin, C. C. (2008). Configurational compara­
tive methods: Qualitative comparative analysis (QCA) and
related techniques. Sage Publications.
Rousseau, D. M. (1989). Psychological and implied con­
tracts in organizations. Employee Responsibilities and
Rights Journal, 2(2), 121–139. https://doi.org/10.1007/
BF01384942
Rousseau, D. M. (1996). Psychological contract measures.
Carnegie Mellon University.
Rousseau, D. M., & Tijoriwala, S. A. (1999). What’s a good
reason to change? Motivated reasoning and social
accounts in promoting organizational change. Journal of
Applied Psychology, 84(4), 514–528.
Schalk, R., & Roe, R. E. (2007). Towards a dynamic model of
the psychological contract. Journal for the Theory of Social
Behaviour, 37(2), 167–182. https://doi.org/10.1111/j.
1468-5914.2007.00330.x
Schneckenberg, D. (2009). Web 2.0 and the empowerment of
the knowledge worker. Journal of Knowledge Management,
13(6), 509–520. https://doi.org/10.1108/
13673270910997150
Schneider, C. Q., & Wagemann, C. (2010). Standards of
good practice in qualitative comparative analysis (QCA)
and fuzzy-sets. Comparative Sociology, 9(3), 397–418.
https://doi.org/10.1163/156913210X12493538729793
Schneider, C. Q., & Wagemann, C. (2012). Set-theoretic
methods for the social sciences: A guide to qualitative
comparative analysis. Cambridge University Press.
Serenko, A., & Bontis, N. (2016). Understanding counter­
productive knowledge behavior: Antecedents and conse­
quences of intra-organizational knowledge hiding.
Journal of Knowledge Management, 20(6), 1199–1224.
https://doi.org/10.1108/JKM-05-2016-0203
Shaheen, S., Bari, M. W., Hameed, F., & Anwar, M. M.
(2019). Organizational cronyism as an antecedent of
ingratiation: Mediating role of relational psychological
contract. Frontiers in Psychology, 10(1), 1–20. https://
doi.org/10.3389/fpsyg.2019.01609
Silva, G. M., & Goncalves, H. M. (2016). Causal recipes for
customer loyalty to travel agencies: Differences between
15
online and offline customers. Journal of Business
Research, 69(11), 5512–5518. https://doi.org/10.1016/j.
jbusres.2016.04.163
Suppiah, V., & Sandhu, M. S. (2011). Organisational cul­
ture’s influence on tacit knowledge-sharing behaviour.
Journal of Knowledge Management, 15(3), 462–477.
doi:10.1108/13673271111137439.
Webster, J., Brown, G., Zweig, D., Connelly, C. E., Brodt, S.,
& Sitkin, S. (2008). Beyond knowledge sharing:
Withholding knowledge at work. Research in Personnel
and Human Resources Management, 27(8), 1–37. https://
doi.org/10.1016/S0742-7301(08)
Woodside, A. G. (2013). Moving beyond multiple regression
analysis to algorithms: Calling for adoption of a paradigm
shift from symmetric to asymmetric thinking in data
analysis and crafting theory. Journal of Business Re, 66
(4), 463–472. https://doi.org/10.1016/j.jbusres.2012.12.
021
Wu, P.-L., Yeh, -S.-S., Woodside, A. G., & Woodside, A. G.
(2014). Applying complexity theory to deepen service
dominant logic: Configural analysis of customer
experience-and-outcome assessments of professional ser­
vices for personal transformations. Journal of Business
Research, 67(8), 1647–1670. https://doi.org/10.1016/j.
jbusres.2014.03.012
Xiao, M., & Cooke, F. L. (2018). Why and when knowledge
hiding in the workplace is harmful: A review of the
literature and directions for future research in the
Chinese context. Asia Pacific Journal of Human
Resources, 57(4), 470–502. https://doi.org/10.1111/17447941.12198
Zhang, L., & Deng, Y. (2014). Guanxi with supervisor and
counterproductive work behavior: The mediating role of
job satisfaction. Journal of Business Ethics, 134(3),
413–427. https://doi.org/10.1007/s10551-014-2438-7
Zhang, Z., & Min, M. (2021). Organizational rewards and
knowledge hiding: Task attributes as contingencies.
Management Decision, 59(10), 2385–2404. https://doi.
org/10.1108/MD-02-2020-0150
Zhang, Y., Ren, T., & Li, X. (2018). Psychological con­
tract and employee attitudes. Chinese Management
Studies, 13(1), 26–50. https://doi.org/10.1108/cms-062017-0171