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Knowledge hiding and social exchange theory: a systematic review and meta-analysis

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The literature on the antecedents and consequences of knowledge hiding remains fragmented, limiting its practical applications. Social exchange theory (SET), one of the most widely adopted sociological frameworks, offers unique insights into the dynamics of knowledge hiding. This study synthesizes the application of SET in analyzing the nomological framework of knowledge hiding through a systematic literature review and meta-analysis. A meta-analysis was conducted based on the random-effects model and the meta-analytic structural equation modeling method, incorporating 66 primary studies with a total of 20,603 participants. Additionally, we examined the mediating role of knowledge hiding by linking key antecedents and consequences. Moreover, an exploratory analysis was conducted to investigate the moderating effects of national culture and research methodology, providing evidence to justify the true heterogeneity in the pairwise relationships between knowledge hiding and its antecedents. The research results generally support most pairwise relationships between knowledge hiding and its correlates, which were theoretically developed based on SET. This study is the first attempt to explore the explanatory power of SET in analyzing the knowledge-hiding phenomenon, and whether the establishment of a knowledge exchange loop contributes to a deeper understanding of this dyadic construct.
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Frontiers in Psychology 01 frontiersin.org
Knowledge hiding and social
exchange theory: a systematic
review and meta-analysis
ZijunZhang
1, YoshiTakahashi
1* and RoksanaBinteRezwan
2
1 Graduate School of Humanities and Social Sciences, Hiroshima University, Hiroshima, Japan,
2 Institute for International Strategy, Tokyo International University, Tokyo, Japan
The literature on the antecedents and consequences of knowledge hiding remains
fragmented, limiting its practical applications. Social exchange theory (SET), one
of the most widely adopted sociological frameworks, oers unique insights into
the dynamics of knowledge hiding. This study synthesizes the application of SET in
analyzing the nomological framework of knowledge hiding through a systematic
literature review and meta-analysis. A meta-analysis was conducted based on the
random-eects model and the meta-analytic structural equation modeling method,
incorporating 66 primary studies with a total of 20,603 participants. Additionally,
weexamined the mediating role of knowledge hiding by linking key antecedents
and consequences. Moreover, an exploratory analysis was conducted to investigate
the moderating eects of national culture and research methodology, providing
evidence to justify the true heterogeneity in the pairwise relationships between
knowledge hiding and its antecedents. The research results generally support most
pairwise relationships between knowledge hiding and its correlates, which were
theoretically developed based on SET. This study is the first attempt to explore
the explanatory power of SET in analyzing the knowledge-hiding phenomenon,
and whether the establishment of a knowledge exchange loop contributes to a
deeper understanding of this dyadic construct.
KEYWORDS
social exchange theory, norms of reciprocity, knowledge management, knowledge
hiding, meta-analysis, literature review
1 Introduction
In the knowledge economy, knowledge plays a key role in driving wealth growth and
organizational success (Lin and Hsiao, 2014). Given the salient importance of knowledge,
much of the literature focuses on how to eectively manage the intellectual capital of
employees. is has led to increased investments in knowledge management technologies to
validate knowledge ows and enhance information-sharing within organizations (Mårtensson,
2000). However, despite these advancements, knowledge sharing has not facilitated suciently
to promote productivity and reinforce operational eciency due to the emergence of
knowledge hiding. erefore, it is necessary to determine the mechanisms underlying
knowledge hiding, such as how it comes into existence and how it functions. Knowledge
hiding, as dened as by Connelly etal. (2012, p.65), refers to “an intentional attempt by an
individual to withhold or conceal knowledge that has been requested by one another.
Considering the counterproductive nature of knowledge hiding, it has gradually become a
signicant research concern in recent years, particularly in the elds of knowledge management
and organizational behavior.
e literature on the antecedents and consequences of knowledge hiding is extremely
fragmented. Although previous studies have presented many ways to tackle knowledge hiding
OPEN ACCESS
EDITED BY
Jolita Vveinhardt,
Lithuanian Sports University, Lithuania
REVIEWED BY
Fahri Özsungur,
Mersin University, Türkiye
Ferda Alper Ay,
Cumhuriyet University, Türkiye
*CORRESPONDENCE
Yoshi Takahashi
yoshit@hiroshima-u.ac.jp
RECEIVED 25 October 2024
ACCEPTED 11 December 2024
PUBLISHED 06 January 2025
CITATION
Zhang Z, Takahashi Y and Rezwan RB (2025)
Knowledge hiding and social exchange
theory: a systematic review and
meta-analysis.
Front. Psychol. 15:1516815.
doi: 10.3389/fpsyg.2024.1516815
COPYRIGHT
© 2025 Zhang, Takahashi and Rezwan. This is
an open-access article distributed under the
terms of the Creative Commons Attribution
License (CC BY). The use, distribution or
reproduction in other forums is permitted,
provided the original author(s) and the
copyright owner(s) are credited and that the
original publication in this journal is cited, in
accordance with accepted academic
practice. No use, distribution or reproduction
is permitted which does not comply with
these terms.
TYPE Original Research
PUBLISHED 06 January 2025
DOI 10.3389/fpsyg.2024.1516815
Zhang et al. 10.3389/fpsyg.2024.1516815
Frontiers in Psychology 02 frontiersin.org
from multi-faceted perspectives, their lack of generalizable conclusions
has prevented the ecient transfer of these results from theory to
practice (Arain etal., 2024). Moreover, the implementation of various
theories provides distinctive viewpoints and conicting results in
investigating and rationalizing the knowledge-hiding phenomenon in
professional settings. For example, Černe etal. (2014) suggested that
knowledge hiding exerts a negative inuence on individuals’ creativity
because of the reciprocal distrust loop via the lens of social exchange
(β = 0.21**). However, Zakariya and Bashir (2021) found that
knowledge hiding could promote individuals’ creative performance
because of feelings of envy from a social comparison perspective
(β = 0.435**). Both ndings seem reasonable, but weneed to conrm
the extent to which knowledge hiding can beexamined and explained
through social exchange and social comparison theories. erefore,
this meta-analysis aims to synthesize the empirical ndings
concerning the relationship between knowledge hiding and its
correlates, while also examining the explanatory power of commonly
used theories in rationalizing knowledge hiding. As social exchange
theory (SET) is one of the most popular sociological theories and has
been widely used in analyzing knowledge hiding, weadopt it in this
meta-analysis to develop a nomological framework of
knowledge hiding.
erefore, this study makes three main contributions to the
literature on knowledge hiding and lls the existing research gaps as
follows. First, to address the lack of consensus regarding knowledge
hiding from dierent theoretical backgrounds, we established a
nomological framework containing a wide range of relationships with
knowledge hiding based on SET (Blau, 2017). As knowledge hiding
generally occurs in dyads through social interactions (Connelly etal.,
2012), SET may be eective in explaining the inner nature of
knowledge hiding. Second, this study also explores the intermediatory
role of knowledge hiding via the meta-analytic structural equation
modeling (MASEM) technique, which provides an opportunity to
easily comprehend the dynamic procedures of such knowledge
management failures through social exchange loops (Zhang etal.,
2022). Finally, this study provides in-depth insights into the inuence
of moderators in justifying the variability across primary studies on
the relationships between knowledge hiding and its correlates. By
including national culture (collectivism and power distance) and
research methodology (knowledge intensity and knowledge-hiding
measures) as moderators, weprovide strong explanations for the
variability across studies, when the magnitude of knowledge hiding is
more likely to beinduced or hindered.
2 Social exchange theory and
knowledge hiding
SET is one of the most inuential paradigms used in the eld of
knowledge hiding and describes how much eort an individual would
like to dedicate to the sustainment and development of a social
relationship through a benet–cost analysis (Blau, 2017; O'Boyle etal.,
2012). Resource exchanges, such as valuable knowledge exchanges, are
generally processed based on the norms of reciprocity, thus all people
involved in such social interactions would expect rewards to balance
or even exceed their costs. If individuals do not receive the expected
amounts of returns, they intentionally hide their expertise and skills
rather than share them with others to minimize potential costs created
by the exchange relationship. Moreover, O'Boyle etal. (2012) suggested
that individuals engage in the process of reliable exchange not only for
objective goods and services, but also for intangible rewards that yield
socially valued outputs such as status and admiration. erefore, given
that the benets of social exchange are not always tangible and
objective, the applicable scope of SET in justifying the formation and
inuencing mechanisms of knowledge hiding can begreatly extended.
e extant literature reports a series of antecedents and outcomes
of knowledge hiding that have been successfully explained through
the lens of social exchanges from personal, interpersonal, and
organizational perspectives. Although SET has certain explanatory
power in rationalizing the knowledge-hiding phenomenon, it still has
some underlying shortcomings, such as the extent to which it can truly
describe the procedure of knowledge hiding, which would lead to
diculties in theoretical explanations as well as in practical
implementation. Accordingly, in this study, a nomological framework
of knowledge hiding was established based on SET to measure the
reliability and validity of the proposed pairwise relationships, as well
as their theoretical eectiveness (see Figure1).
From the lens of social exchange, wedivided the antecedents of
knowledge hiding into two dierent groups based on the benet–cost
analysis (Blau, 2017). e terms “Benet” and “Cost” represent the
motivating and inhibiting factors that impact on one’s engagement in
the process of social exchange, thereby shaping individuals’ attitudes
toward knowledge hiding. It is expected that antecedents in the
“Benet” category may have a negative association with knowledge
hiding, while variables in the “Cost” category are anticipated to exhibit
a positive relationship with knowledge hiding. Besides, the
consequences of knowledge hiding were also categorized into “positive
reciprocity outcome” (returning kindness to kindness) and “negative
reciprocity outcome” (returning harm to harm) based on their inner
nature. Individuals are likely to respond to the existence of knowledge
hiding with more negative actions and less positive actions in
accordance with the norms of reciprocity, as B’s reaction to A is
contingent on As behavior to B (Gouldner, 1960). Finally, recent meta-
analyses have highlighted the importance of examining moderators in
the eld of knowledge hiding (Arain etal., 2024; Škerlavaj etal., 2023).
erefore, in this study, weincluded moderators from the perspectives
of national culture (collectivism and power distance) and research
methodology (knowledge intensity and knowledge-hiding measures)
to investigate whether moderators could amplify or weaken the
inuences of hypothesized antecedents on knowledge hiding.
3 The nomological framework of
knowledge hiding
3.1 Antecedents of knowledge hiding
3.1.1 Benefits and knowledge hiding
Perceived justice can be summarized as people’s subjective
evaluations of organizational fairness and equity in the resource
allocation process (Gelens etal., 2013). Drawing on SET, high-quality
exchange relationships promote one’s engagement in positive
reciprocity rather than negative reciprocity, representing that
individuals have an obligation to reciprocate others who give
assistance to them in the process of social exchange (Kim etal., 2019).
Jiang et al. (2017) suggested that employees’ perceptions of
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organizational fairness greatly promote their motivation to obey
organizational norms and enhance their trust in organizations. us,
they are more likely to benet from organizational justice in social
transactions and display a resistance toward knowledge-hiding
behaviors based on the norms of reciprocity.
Leader member exchange (LMX) is highly rooted in the norms of
reciprocity; thus, the performance of people with high-quality LMX
is enhanced when eective supervisor support is made available
through eective social exchange processes (Wayne etal., 1997). LMX
also stimulates employees’ willingness to “payback” their supervisors’
advantageous treatments by avoiding deviant behaviors, such as
knowledge hiding, to achieve their shared goals (He etal., 2022).
Similarly, Seers etal. (1995) indicated that team member exchange
(TMX) shares the same core value with LMX, that is, people are
obligated to reciprocate favorable treatments and high-quality
exchange relationships that are executed on behalf of fellow employees.
Extant literature has explored how TMX inuences knowledge hiding.
For example, Tan etal. (2022) suggested that individuals engaged in
eective social exchange relationships with co-workers are more
responsive to their knowledge requests and exhibit less knowledge-
hiding intentions.
Perceived social support (PSS) can beregarded as a cognitive
appraisal of feeling connected and supported by others (Lakey and
Cassady, 1990), which plays a crucial role in shaping one’s attitudinal
and behavioral responses to dierent job-related situations. For
example, Loi etal. (2014) argued that both perceived organizational
support and co-worker support signicantly contribute to
stimulating promotive workplace behaviors. Such arguments can
be well rationalized based on the norms of reciprocity. e
perception of social support makes people feel obligated to return
the advantageous treatments received from others, which
extensively strengthens the interpersonal relationships in the
workplace (Eisenberger etal., 2001). erefore, perceived social
support could prevent people from engaging in knowledge-hiding
behaviors by cultivating good interpersonal climates and tight
social bonds.
Hypothesi s 1: Perceived justice, LMX, TMX, and PSS are negatively
related to knowledge hiding.
3.1.2 Costs and knowledge hiding
Tepper (2000, p.178) dened abusive supervision as “subordinates’
perceptions of the extent to which supervisors engage in the sustained
display of hostile verbal and nonverbal behaviors, excluding physical
contact.” Extant literature conrmed that abusive supervisors would
enable their subordinates to suer from unpleasant working
experiences and lead to unfavorable behavioral responses (Fischer
etal., 2021). Accordingly, Aryee etal. (2007) suggested that abusive
supervision destroys the quality of social exchanges within
organizations, especially between the leader and victim. e
continuous psychological and mental harassment from abusive
supervision stimulates one’s negative reciprocity beliefs in the
workplace because they are forced to experience an annoying social
cost (Lian etal., 2014). To respond to such destructive leadership,
employees would restore their balance of exchanges by retaliating
against the abusive supervisors and violating organizational norms,
such as engaging in workplace deviant behaviors (Lian etal., 2014;
Mitchell and Ambrose, 2007). us, abused subordinates are inclined
to intentionally conceal their valuable expertise and skills from their
supervisors to avoid further exploitation.
FIGURE1
Hypothesized meta-analytic model.
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Relationship conict is dened as the subjective evaluation of
disagreements and incompatibilities among individuals regarding
personal values and social issues beyond work tasks (Jehn etal., 1997),
such as personality dierences and interpersonal tensions.
Relationship conict generally has undesirable eects on personal
aectivity and behavior (Choi etal., 2024; Peng etal., 2021), which
enhances individuals’ willingness to benet themselves over others.
Drawing on SET, social exchanges between two interdependent
parties require them to obey transaction rules that return the favor or
harm to others in the same way as it was received (Kundi etal., 2023;
Mitchell etal., 2012). us, the relationship conict in dyads would
make people generate a feeling that others, such as supervisors or
co-workers, should beaccountable for their potential losses in the
process of exchanges. Moreover, Boz Semerci (2019) also justied the
inuence of relationship conict on knowledge hiding through the
lens of social exchange, as people in relationship conict focus more
on their personal interests by hiding knowledge from others in
the workplace.
Pearson et al. (2000, p. 125) dened workplace incivility as
“mistreatment that may lead to disconnection, breach of relationships,
and erosion of empathy.” Workplace incivility adversely inuences
individuals’ work patterns, eectiveness, and ability to perform daily
tasks (Pearson etal., 2000). erefore, the return on injuries, such as
engaging in counterproductive knowledge-related behaviors, is more
preferred by individuals under the inuence of workplace incivility.
is is because retaliation is a more appropriate response to workplace
mistreatments (Gouldner, 1960; Wu etal., 2014). For example, Anand
etal. (2023) empirically identied that workplace incivility triggers
one’s knowledge-hiding behaviors because of the violation of
reciprocity norms, thus returning harm to harm. Similarly, workplace
ostracism interferes with individuals’ perceptions of organizational
surroundings, which, in turn, exerts a negative inuence on work-
related attitudinal and behavioral outcomes (Wu etal., 2012; Yan etal.,
2014). Based on the norm of reciprocity, poor-quality interpersonal
relations weaken people’s expectations of reciprocity in the social
exchange process (Gouldner, 1960). To balance social costs with
resource loss from workplace exclusion, ostracized individuals are
more likely to exhibit knowledge-hiding behaviors.
Perceived competition refers to people’s perception that they need
to outperform others within their organizations in terms of rewards,
recognition, and status, along with a sense of hostility (Chaker etal.,
2021). Continuous pressure from competition causes people to
generate greater workplace tension and view colleagues as primary
rivals. When people perceive competition around them, they are easily
displaying withholding practices toward valuable resources to ensure
their competitiveness (Dedman and Lennox, 2009). From the social
exchange perspective, individuals expect to be well rewarded
equivalently from the social exchange when they incur certain amount
costs (Chen etal., 2009). us, people would exhibit a tendency to
break reciprocal relationships and hold on to their knowledge and
skills when they feel threatened by adverse competition (Oubrich
etal., 2021).
Psychological entitlement can besummarized as a stable feeling
that one deserves more or is entitled to more privileges than one’s
peers (Campbell etal., 2004). When the requirements of inated self-
importance are not satised with the proclaimed rewards,
psychologically entitled individuals generally assert that they are
treated unfairly, as they do not get what they are seeking (Harvey and
Harris, 2010). In this case, entitled people are more likely to violate
conventional social norms and engage in vengeful behaviors toward
the so-called “inequity” at work. For example, Khalid et al. (2020)
empirically identied that entitled employees believe that
organizations violate the norms of reciprocity and mistreat them in
social transactions. e hatred on organizations and fellow employees
would stimulate knowledge-hiding behaviors as retaliation.
Hypothesi s 2: Abusive supervision, relationship conict, workplace
incivility, workplace ostracism, perceived competition, and
psychological entitlement are positively related to
knowledge hiding.
3.2 Outcomes of knowledge hiding
3.2.1 Knowledge hiding and positive reciprocity
outcomes
Gurteen (1998) stated that creativity and innovation represent the
procedures of creating and implementing knowledge, through which
the commercial value of knowledge is realized instead of it being
conned to a laboratory. In the knowledge economy era, the speed of
knowledge updates and the eectiveness of knowledge ows are
important motivators for promoting creative and innovative
performance (CIP) (Park et al., 2014; Wang and Wang, 2012).
However, the existence of knowledge hiding within organizations
destroys one’s intrinsic motivation to pursue further creativity and
innovation because of triggering a reciprocal distrust loop (Černe
et al., 2014). erefore, knowledge hiding prevents people from
obtaining the expertise required to create new ideas, because it
damages social exchange relationships and hinders the
information ows.
Task performance is another important outcome of knowledge
hiding concerning job-related obligations and responsibilities (Singh,
2019). In accordance with SET, employees would like to shape their
social relationships based on their own experiences acquired from
workplace exchanges (Blau, 2017). e poor experiences from social
transactions motivate people to breach the norms of reciprocity by
acting against perceived unfavorable treatments, thus keeping a
balance between giving and taking in functional systems (Chen etal.,
2009). From the empirical perspective, Singh (2019) demonstrated
that knowledge seekers adversely respond to knowledge hiders by
exhibiting a non-cooperative attitude as a retaliation for their
territoriality of knowledge. Besides, Moin etal. (2024) conrmed this
nding as well, indicating that knowledge hiding hinders individuals’
capability to perform well on job-related aairs due to knowledge
seekers’ reluctancy to return good for evil.
Extra-role behavior (ERB) refers to discretionary activities that
go beyond job descriptions but contribute to enhancing
organizational eectiveness (Leung, 2008), such as employees’ voice.
Extra-role behavior is considerably suppressed because the
prevalence of knowledge hiding fosters the trust crisis of exchange
rules within organizations (Hameed et al., 2023). Intentionally
concealing knowledge has been viewed as a form of anti-social
behavior (Connelly and Zweig, 2015) and individuals are compelled
to respond to knowledge hiding with lower prosocial motivation to
protect their personal interests. From the social exchange perspective,
individuals would like to reciprocate negative treatments with
Zhang et al. 10.3389/fpsyg.2024.1516815
Frontiers in Psychology 05 frontiersin.org
something of same value, thus, people are inclined to disengage from
ERB until a balanced knowledge exchange is achieved (Cropanzano
etal., 2017).
Hypothesis 3: Knowledge hiding is negatively related to CIP, task
performance, and ERB.
3.2.2 Knowledge hiding and negative reciprocity
outcomes
Turnover intention refers to employees’ desire to leave their
current job and look forward to nding a better one (Ahmad Sau
et al., 2023). Drawing on SET, the maintenance of a workplace
relationship depends on interdependent parties involved in the social
exchange process perceiving the relationship as benecial and
favorable (Blau, 2017; Cropanzano et al., 2017). In other words,
employees generally behave in a similar manner to the way they are
being treated within organizations. As instructed by Serenko and
Bontis (2016), knowledge hiding generally acts as a motivator of
turnover intention, resulting in signicant tangible and intangible
costs because of the loss of relational and human capital us,
employees’ knowledge requests are not fullled, and they
simultaneously experience a sense of exclusion, which increases the
possibility of their departure from the organization.
Finally, this study investigates how knowledge hiding inuences
workplace deviance. Workplace deviance can besummarized as a
subjective violation of organizational norms, thereby damaging
people’s overall well-being and lowering organizational performance
(Robinson and Bennett, 1995). As instructed by Ahmad etal. (2023),
individuals prefer to maximize the benets with the least cost in the
process of social exchanges. If potential risks outweigh promised
rewards, people easily exhibit deviant behaviors to avoid further loss.
Several empirical studies (Arain et al., 2022; Singh, 2019) have
identied that knowledge hiding adversely destroys mutual trust and
reciprocal relationships among colleagues and individuals, thus
exhibiting higher levels of workplace deviance.
Hypothesis 4: Knowledge hiding is positively related to turnover
intentions and workplace deviance.
3.3 Moderators
e rst moderators were based on the cultural environment in
which the primary studies were conducted. As noted by Xiong etal.
(2021), cultural variations may inuence the ideas and practices of
knowledge-related behaviors. For example, collectivism encourages
individuals to obey organizational obligations, work for shared goals,
and even make personal sacrices for the collective benets (House
etal., 2004); whereas individualists generally behave in accordance
with their personal values and attitudes rather than widely accepted
social norms, along with an emphasis on personal interests (Xiong
et al., 2021). In the case of knowledge hiding, personal values
developed from social culture tend to greatly regulate individuals’
communication styles and work behaviors, especially how they deal
with knowledge hiding (Boz Semerci, 2019). us, Benet, as well as
Cost, may exert signicantly dierentiated eects on knowledge
hiding when comparing collectivism and individualism. erefore,
weexplored the following question:
Research question 1: Does collectivism culture moderate the
relationships between Benet and knowledge hiding relationships
and Cost and knowledge hiding?
Power distance is dened as the extent to which people anticipate
and concur with the unequal distribution of power within an
organization or society (Hofstede, 2001). In a higher power distance
culture, individuals are more likely to advocate an internal hierarchy
and hold on to the belief that they should not challenge or question
the decisions of those in charge (De Luque and Sommer, 2000; Tyler
etal., 2000). By contrast, Shao etal. (2013) indicated that, within a
lower power distance culture, there is a notable inclination toward
power equality and procedural justice when it comes to any existing
unfair treatment. erefore, weassumed that people’s attitudes toward
knowledge hiding would not only beshaped by external environments,
but also by their power cognition, such as tolerance of power
inequality. As such, the eects of Benet and Cost on knowledge
hiding may vary across various levels of power distance culture.
erefore, weexplored the following question.
Research question 2: Does power distance culture moderate the
relationships between Benet and knowledge hiding and Cost and
knowledge hiding?
In the knowledge economy era, knowledge hiding has been
examined dierently across industries, and industry dierences
inuence the way in which it develops and functions. Accordingly, this
meta-analysis also examined the moderating role of knowledge
intensity in the industries from which data were collected. From a
knowledge-based perspective, knowledge intensity can beexplained
as the extent to which an organization relies on knowledge to develop
competitive advantages, maintain survival, and achieve commercial
value (Andreeva and Kianto, 2011). Knowledge has also been found
to have a positive relationship with the knowledge creation and
sharing processes, especially in knowledge-intensive industries
(Andreeva and Kianto, 2011). erefore, weassumed that knowledge-
intensive organizations would behave dierently when they notice the
eects of Benet and Cost on knowledge hiding compared with less
knowledge-intensive organizations. en, we examined the
following question:
Research question 3: Does knowledge intensity moderate the
relationships between Benet and knowledge hiding and Cost and
knowledge hiding?
In extant literature, dierent scales have been adopted to measure
knowledge hiding. For example, Peng (2012), Serenko and Bontis
(2016), and Rhee and Choi (2017) developed their own scales to
evaluate the magnitude of the knowledge-hiding phenomenon, with
an emphasis on its deceptive nature. Further, Connelly etal. (2012)
provided the most typical measurement tool for knowledge hiding by
rst introducing three dimensions: evasive hiding, playing dumb, and
rationalized hiding, which exhibited a more holistic depiction of
knowledge hiding. Unlike other scales, Connelly etal.s (2012) scale
justies the non-deceptive nature of knowledge hiding, indicating that
it is not always negative and counterproductive (Oergelt etal., 2019).
Given the distinctiveness of knowledge-hiding scales, weassume that
the variations across studies may originate from the adoption of
Zhang et al. 10.3389/fpsyg.2024.1516815
Frontiers in Psychology 06 frontiersin.org
dierent scales to measure knowledge hiding. Hence, in this study, the
moderating role of knowledge-hiding measures was examined and put
forward the following research question was proposed:
Research question 4: Does the way in which knowledge hiding is
measured moderate the relationships between Benet and
knowledge hiding and Cost and knowledge hiding?
4 Research methodology
4.1 Literature search and inclusion criteria
To identify relevant studies for the meta-analysis, weconducted a
thorough search on the correlates of knowledge hiding from seven
online academic sources: Google Scholar, Web of Science, ProQuest,
ScienceDirect, Elsevier, Sage, and Taylor & Francis Online. Search
items included a combination of “knowledge hiding,” “hide
knowledge,” “knowledge hoarding,” “knowledge withholding,
“withhold knowledge,” “social exchange theory,” and “norm of
reciprocity.” Moreover, wealso consulted the reference lists of previous
systematic literature reviews and meta-analyses in the eld of
knowledge hiding. Finally, this online search was supplemented by an
additional examination of theses and dissertations, to include all
relevant literature to the maximum extent possible. Figure2 shows a
owchart of the literature searching and screening processes.
For inclusion in the meta-analysis, each primary study should
meet ve established criteria: (1) Cronbach’s alpha and at least one
bivariate correlation between knowledge hiding and its correlates were
provided; (2) the study was written in English; (3) eld samples of
FIGURE2
Flowchart of literature searching and screening.
Zhang et al. 10.3389/fpsyg.2024.1516815
Frontiers in Psychology 07 frontiersin.org
employed respondents were used; (4) if a study included two
independent samples, the data from these two samples were coded
independently; (5) meta-analytic analyses were conducted only for
pairwise relationships that were examined by at least two empirical
studies. erefore, our preliminary search identied 1,216 primary
studies that matched the keywords from seven online sources. Of
these, 139 studies were selected aer excluding papers that were not
relevant, not in English, not quantitative research, or used in
experimental samples. In the second round of the literature screening,
we excluded studies that did not provide the required statistical
information or were not developed based on SET. Finally, 66 primary
studies (60 journal articles and 6 theses/dissertations) from 2014 to
2024, comprising 20,603 participants, were included in the current
meta-analysis.
Two of the authors independently coded each primary study
included in the meta-analysis. e coding procedure was initially
tested with a sample of 10 studies and their preliminary results were
shared to ensure accuracy. e two authors carefully double-checked
the statistical values from primary studies, such as inter-correlations,
number of respondents, and reliability coecients. e initial inter-
rater agreement of data coding was 91%. If the two authors could not
reach an agreement, a third author was asked to help them decide.
Finally, all discrepancies were resolved, and a 100% consensus
was reached.
4.2 Meta-analytic model
Schmidt and Hunter's (2015) random-eects model was
introduced to correct sampling and measurement errors for all
observed correlations by incorporating two key values: sample size
and Cronbach’s alpha. e random-eects model allows population
parameters to vary across studies owing to variable respondents,
interventions, and other types of unexplained heterogeneity
(Borenstein etal., 2010; Higgins etal., 2009). In this meta-analysis,
wereported the number of primary studies (K) that included the
relationships between knowledge hiding and its correlates, and the
corresponding total sample size (N). Additionally, we reported
sample-size weighted mean uncorrected correlation (
r
) and estimated
true score correlation corrected for measurement and sampling errors
(
ρ
), as well as their respective standard deviations (
SD
and
SD
ρ
).
Moreover, 95% condence intervals and 80% credibility intervals were
computed to assess the statistical signicance of true-score correlations
and the potential moderation eects in the meta-analysis (Miao etal.,
2016). Finally, wecalculated the percentage of observed variance
attributed to statistical artifacts (Var%), such as sampling and
measurement errors (Schmidt and Hunter, 2015).
is study also examined the potential mediating eects of
knowledge hiding in the relationships between its antecedents and
outcomes through MASEM, which incorporates the advantages of
meta-analytic techniques and the structural equation modeling (SEM)
framework. e MASEM technique is a two-stage quantitative
research method that summarizes pooled eect sizes and their
standard deviations from this current meta-analysis and previous
relevant meta-analytic studies to develop average correlation matrices
that can befurther adopted to t the path analysis in SEM to detect
the validity of the proposed hypotheses (Cheung, 2014; Cheung and
Cheung, 2016). e MASEM can eectively boost sample sizes by
accumulating dierent samples, which enhances the interpretive
power of the structural model and the precise assessment of estimates,
rather than either technique alone.
Finally, to rationalize observed heterogeneity in the current meta-
analysis, we conducted a moderation analysis to assess whether
national culture, organizational context, and knowledge-hiding
measures could foster or attenuate the inuence of the proposed
predictors on knowledge hiding. Following the research protocol of
Nguyen etal. (2019), weonly examined the moderating eects when
a moderator was accompanied by at least two primary studies on each
side. In the eld of knowledge management, collectivism and power
distance are two cultural indices used to evaluate how national
cultures aect individuals’ social cognition and behaviors (Boz
Semerci, 2019; Chiu et al., 2018). In terms of the cultural context,
wecoded the national information of each primary study and assigned
values of collectivism and power distance to each country based on
the global scores provided by House etal. (2004). Wethen used the
mean value to code these countries into higher or lower subgroups of
collectivism and power distance. Similarly, weconducted a thorough
investigation of the included studies to obtain pertinent details
regarding knowledge intensity and knowledge-hiding measures. If
there was a lack of information regarding the role of moderators, the
primary study was coded as “NA” and subsequently excluded from the
moderation analysis.
5 Results
5.1 Publication bias
Publication bias is dened as “an editorial predilection for
publishing particular ndings, e.g., positive results, which leads to the
failure of authors to submit negative ndings for publication
(ornton and Lee, 2000, p.207). Some statistical methods were also
adopted to identify whether publication bias could disturb the
synthesized results as follows. Egger’s value was chosen to examine the
existence of publication bias (Egger et al., 1997); all p-values for
Egger’s tests were greater than 0.05, indicating no true publication bias
for pairwise relationships between knowledge hiding and its correlates.
5.2 Heterogeneity test
At the beginning of a meta-analysis, it is necessary to conduct a
test of heterogeneity, which reects the extent to which heterogeneity
may inuence the overall conclusions from the collection of primary
studies (Higgins and ompson, 2002). Two typical methods were
used to examine true heterogeneity in the current meta-analysis. e
Q-test was rst introduced in 1954. If the p-value of the Q-test was
signicant at the 95% condence level, werejected the homogeneous
null hypothesis that all selected studies are identical, indicating that
there is a true heterogeneity across the included studies (Lin, 2020).
However, the Q value is highly inuenced by the number of included
studies, as it exhibits poor statistical power to identify the existence of
heterogeneity with a small number of primary studies (Huedo-Medina
etal., 2006). erefore, wecannot rely on only one specic method to
assess true heterogeneity in the meta-analysis. I-squared is also used
to compensate for the shortcomings of the Q-test, which is not
Zhang et al. 10.3389/fpsyg.2024.1516815
Frontiers in Psychology 08 frontiersin.org
sensitive to the number of included studies. Table1 shows the results
of the heterogeneity test for each relationship in the meta-analysis.
Most Q-values for pairwise relationships, except for TMX, were
signicant at the 95% condence level (p < 0.05), indicating the
presence of true heterogeneity between studies. e same results were
found using I-squared. In conclusion, because of the presence of true
heterogeneity in the hypothesized relationships, the random-eects
model was the most appropriate statistical technique for current meta-
analysis. It exhibits a higher acceptance of between-study variance
rather than xed-eects model, assuming that “the true population
eect size could vary from study to study” (Montazemi and Qahri-
Saremi, 2015, p.218).
5.3 Main eects between knowledge
hiding and its correlates
Table 2 shows the overall meta-analytic results for the
hypothesized relationships between knowledge hiding and its
correlates (10 relationships between knowledge hiding and its
antecedents and 5 relationships between knowledge hiding and its
outcomes). Cohen (1992) proposed specic values for Pearson’s R,
which serve as thresholds for determining the magnitude of the eect
size. To classify the eects as small, medium, and large, Cohen (1992)
suggested using R-values of 0.10, 0.30, and 0.50. From the perspective
of antecedents, workplace incivility (ρ = 0.572, k = 7), is strongly and
positively associated with knowledge hiding. Abusive supervision (
ρ
=0.453, k = 8), relationship conict (ρ = 0.405, k= 8), and workplace
ostracism (ρ = 0.417, k = 4) are moderately and positively associated
with overall knowledge hiding. Conversely, TMX (ρ = 0.590, k = 2)
is strongly and negatively related to knowledge hiding, while LMX
(ρ = 0.310, k = 8) and perceived justice (ρ = 0.364, k = 6) are
moderately and negatively associated with knowledge hiding. PSS
(ρ = 0.269, k = 9) is weakly and negatively related to knowledge
hiding. However, perceived competition and psychological entitlement
do not show signicant correlations with knowledge hiding. us,
wecan conclude that Hypothesis 1 is fully supported, and Hypothesis
2 is partially supported.
From the perspective of outcomes, knowledge hiding is negatively
associated with CIP (ρ = 0.398, k = 12), task performance
(ρ = 0.465, k = 6), and ERB (ρ = 0.235, k = 4), indicating that
Hypothesis 3 is fully supported. Among these three negative
relationships, knowledge hiding is moderately related to CIP and task
performance, but knowledge hiding is weakly associated with ERB. By
contrast, knowledge hiding is moderately and positively related to
workplace deviance (ρ = 0.493, k = 6). Finally, there is no bivariate
relationship between knowledge hiding and turnover intentions,
indicating that the Hypothesis 4 is partially supported.
5.4 Mediation analysis
In addition to investigating knowledge hiding as an antecedent or
outcome of a specic factor that wehypothesized, this meta-analysis
also examines the intermediatory role of knowledge hiding using
MASEM techniques. Weadopted three criteria to choose appropriate
antecedents and outcomes to beincluded in the mediation analysis:
(1) the constructs should have a signicant relationship with
knowledge hiding; (2) to construct correlation matrices as the data
input for mediation analysis, pooled eect sizes (ρ) should befound
in previous meta-analyses or can becalculated from independent
primary studies; and (3) an antecedent or outcome needs to
beexamined in at least four studies (Škerlavaj etal., 2023). Accordingly,
weincluded four antecedents (abusive supervision, perceived justice,
workplace incivility, PSS, and LMX) and four outcomes (CIP, task
performance, workplace deviance, and ERB) in the mediation analysis.
Before running the MASEM, weassessed the goodness of t
between the measurement model and alternative models. As the
relationships between knowledge hiding and its correlates were
developed based on the SET, we simultaneously included four
antecedents and one outcome in the mediation analysis. To examine
the mediation eect, wecompared a partial mediation model with a
total eect model and full mediation model (Miao etal., 2016). When
CIP was regarded as the criterion variable in the measurement model,
the total eect model (χ
2
= 1027.483, df = 6, p = 0.000, root mean
square error of approximation (RMSEA) = 0.300, comparative t
index (CFI) = 0.494, Tucker-Lewis index (TLI) = 0.072, standardized
root mean square residual (SRMR) = 0.275) and full mediation model
(χ
2
= 818.442, df = 5, p = 0.000, RMSEA = 0.294, CFI = 0.597,
TLI = 0.113, SRMR = 0.156) did not show a good model t compared
TABLE1 Heterogeneity test.
Pairwise
relationship
KQ-statistics p-value
2
I
Abusive
supervision > KH
8 117.058 0.000 94.020
Perceived
competition > KH
3 80.962 0.000 97.530
Relationship
conict > KH
8 70.256 0.000 90.036
Perceived
justice > KH
6 64.603 0.000 92.260
Workplace
incivility > KH
7 77.115 0.000 92.219
Workplace
ostracism > KH
4 28.544 0.000 89.490
LMX > KH 5 41.703 0.000 90.408
TMX > KH 2 0.004 0.951 0
PSS > KH 9 29.766 0.000 73.124
Psychological
entitlement > KH
3 135.036 0.000 98.519
KH > CIP 12 137.030 0.000 91.973
KH > Workplace
deviance
6 112.043 0.000 95.537
KH > task
performance
6 52.788 0.000 90.528
KH > turnover
intentions
5 142.876 0.000 97.200
KH > ERB 4 13.253 0.006 77.364
K: independent samples; LMX: leader-member exchange; TMX: team-member exchange,
PSS: perceived social support; CIP: creative and innovative perfor mance; ERB: extra-role
behavior; Q-statistics and
2
I
: statistical indices for heterogeneity.
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Frontiers in Psychology 09 frontiersin.org
with the partial mediation model (χ
2
= 0.000, CFI = 1.000, TLI = 1.000,
SRMR = 0.000). When task performance, workplace deviance, and
ERB were included in the mediation analysis with the four antecedents
remaining constant, similar results were found for the partial
mediation model (χ
2
= 0.000, CFI = 1.000, TLI = 1.000,
SRMR = 0.000). us, wechose the partial mediation model for the
mediation analysis.
Table3 and Figure3 present the results of the mediation analyses.
Knowledge hiding can act as a mediator in most indirect relationships.
Specically, it plays a mediating role in the relationship between
abusive supervision on the one hand, and CIP (
β
= 0.034, [0.049,
0.021]), task performance (
β
= 0.056, [0.078, 0.036]), ERB (
β
= 0.012, [0.020, 0.005]), and workplace deviance (
β
=0.046,
[0.029, 0.064]) on the other. Similarly, the direct eects of workplace
incivility on CIP (
β
= 0.120), task performance (
β
= 0.197), ERB
(
β
= 0.040), and workplace deviance (
β
=0.160) are also mediated by
knowledge hiding. e indirect estimates of knowledge hiding in the
relationship between perceived justice, LMX, and the proposed
outcomes (CIP, task performance, ERB and workplace deviance) are
all signicant. However, the underlying mechanisms between PSS and
CIP, task performance, ERB, and workplace deviance could not
beelucidated through knowledge hiding.
5.5 Moderation analysis
To examine the source of heterogeneity, a subgroup analysis was
adopted to investigate how potential moderators inuenced
knowledge hiding. As instructed by Nguyen etal. (2019), wecalculated
the between-group variance (Q-statistics) to identify whether
moderators could make signicant dierences in specic
pairwise relationships.
Regarding national culture, as shown in Table4, wefound that
the eect size of the abusive supervision-knowledge hiding
relationship was larger in low collectivist cultures (ρ = 0.588) than in
high ones (ρ = 0.310). In addition, this study also identied the
moderating role of power distance on the relationship between
abusive supervision and knowledge hiding at the 90% condence
level, in which the eect size was larger in lower power distance
cultures (ρ = 0.505) than in their higher counterparts (ρ = 0.275).
Similarly, power distance also moderated the relationship between
LMX and knowledge hiding at the 95% condence level. e
inhibiting eect of LMX on knowledge hiding was greater in lower
power distance cultures (ρ = 0.454) than in higher power distance
cultures (ρ = 0.282).
From a knowledge-based perspective, the importance of
industrial-level knowledge intensity has also been considered in
knowledge management research because dependence on knowledge
in productive activities is the main source of competitive advantage
(Autio etal., 2000). As OECD (2006) suggested, knowledge-intensive
industries include research and development institutions, information
technology and communication services, legal services, nancing,
advertising, and market-related services, among others. erefore,
we assume that knowledge intensity moderates the relationships
between knowledge hiding and its antecedents. In this case, the
relationship between relationship conict and knowledge hiding was
moderated by knowledge intensity. Interpersonal conict in more
knowledge-intensive industries (ρ = 0.498) could have a greater
TABLE2 Meta-analytic results of antecedents and consequences of overall knowledge hiding.
Antecedents of
KH
N K
r
SD ρ
ρ
SD CILCIUCVLCV
U%Var
Abusive supervision 2,384 8 0.409 0.183 0.453 0.215 0.267 0.638 0.148 0.757 5.980
Perceived competition 968 3 0.410 0.311 0.502 0.351 0.380 1.380 0.159 1.160 2.470
Relationship conict 2,637 8 0.367 0.143 0.405 0.160 0.264 0.546 0.179 0.631 9.964
Perceived justice 1,316 6 0.319 0.217 0.364 0.240 0.626 0.102 0.718 0.010 7.740
Workplace incivility 2,085 7 0.511 0.148 0.572 0.166 0.412 0.732 0.333 0.812 7.781
Workplace ostracism 1,056 4 0.35 0.139 0.417 0.193 0.092 0.742 0.100 0.733 10.510
LMX 3,129 8 0.262 0.155 0.310 0.162 0.453 0.167 0.540 0.080 9.592
TMX 830 2 0.535 0.023 0.590 0.00 0.612 0.569 0.59 0.59 26008.702
PSS 1,825 9 0.233 0.136 0.269 0.125 0.381 0.157 0.443 0.095 26.876
Psychological
entitlement
1,152 3 0.187 0.400 0.202 0.451 0.927 1.330 0.648 1.050 1.481
Outcomes of KH
CIP 4,413 12 0.355 0.157 0.398 0.174 0.514 0.283 0.636 0.161 8.027
Workplace deviance 2,026 6 0.447 0.191 0.493 0.225 0.251 0.735 0.160 0.826 4.463
Task performance 1,859 6 0.420 0.144 0.465 0.161 0.643 0.286 0.703 0.226 9.472
Turnover intention 1,616 5 0.056 0.332 0.061 0.367 0.402 0.524 0.502 0.624 2.800
ERB 1,126 4 0.199 0.135 0.235 0.121 0.455 0.016 0.434 0.037 22.636
K: number of primary studies included in the meta-analysis; N: total numbers of respondents;
r
: sample-size weighted mean uncorrected correlation;
: standard deviation of sample-size
weighted mean uncorrected correlation; ρ: estimated true score correlation corrected for measurement and sampling errors; ρ
SD : standard deviation of estimated true score correlat ion
corrected for measurement and sampling er rors; CL: condence intervals; CV: credibility intervals; Var%: p ercentage of observed variance attributed to statistical artifacts; LMX: leader-
member exchange; TMX: team-member exchange; PSS: perceived s ocial supp or t; CIP: creative and innovative performance; ERB: extra-role behavior.
Zhang et al. 10.3389/fpsyg.2024.1516815
Frontiers in Psychology 10 frontiersin.org
stimulating inuence on employees’ knowledge-hiding behaviors than
in less knowledge-intensive industries (ρ = 0.297).
Finally, weinvestigated the moderating role of knowledge-hiding
measures. As knowledge hiding has generally been measured using
the scale developed by Connelly etal. (2012) in recent studies, it is
worth exploring whether dierent measures of knowledge hiding
could aect the research ndings. e results indicated that most
pairwise relationships between knowledge hiding and its correlates
did not vary signicantly across dierent knowledge-hiding measures,
except for the relationships between relationship conict and
knowledge hiding, and between PSS and knowledge hiding. Among a
few exceptions, the scale developed by Connelly etal. (2012) did not
show greater statistical power in measuring knowledge hiding than
other measures.
6 Discussion
First, this meta-analysis provides a quantitative summary of the
relationships between knowledge hiding and its correlates from a
social exchange perspective based on literature from 2014 to 2024. Ten
antecedents were categorized into two subgroups: “Benet” and
“Cost,” in accordance with the norm of reciprocity, which is regarded
as the main tenet of SET (Gouldner, 1960). Considering the
antecedents of knowledge hiding, perceived justice, LMX, TMX, and
PSS showed negative relationships with knowledge hiding, while
abusive supervision, relationship conict, workplace incivility, and
workplace ostracism showed positive relationships with knowledge
hiding. Among these antecedents, the magnitude of eect sizes for
“Cost” is generally greater than that for “Benet” in predicting
knowledge hiding. is is consistent with the ndings of a previous
meta-analysis in the eld of knowledge hiding (Arain etal., 2024),
which indicated that negative events generally have a greater impact
on individuals’ negative behavior than positive ones (Chiaburu etal.,
2013). In addition, based on SET and the norm of reciprocity, the
consequences of knowledge hiding were divided into positive and
negative reciprocity outcomes. Similar results were identied:
knowledge hiding exerted a stronger inuence on negative reciprocity
outcomes, such as workplace deviance, than on positive reciprocity
outcomes, such as CIP, task performance, and ERB.
Second, this study examined the intermediary role of knowledge
hiding, which highlighted the reciprocal loop of knowledge hiding
from the social exchange perspective. As expected, the results
indicated that knowledge hiding connected the relationships between
abusive supervision, perceived justice, workplace incivility, LMX, and
all selected outcomes (CIP, task performance, ERB and workplace
deviance). Unfortunately, knowledge hiding could not mediate the
relationship between PSS and the four selected outcomes. is can
beexplained by two reasons: (1) there might exist a methodological
limitation in the mediation analysis, which collected pooled eect
sizes from published meta-analyses and caused the loss of important
information; (2) the indirect relationships between PSS, CIP, task
performance, ERB, and workplace deviance via knowledge hiding
might not berobust enough, as previous studies exhibited conicting
results on the relationship between PSS and knowledge hiding. For
example, Batistič and Poell (2022) indicated that perceived
organizational support (a form of PSS) was signicantly related to
knowledge hiding, whereas Alnaimi and Rjoub (2021) argued that
there was no signicant relationship between perceived organizational
support and knowledge hiding.
ird, the moderating roles of collectivism and power distance
were also demonstrated regarding the relationships between
knowledge hiding and its correlates, suggesting that cultural
dierences also aect peoples knowledge-related behaviors by shaping
their shared goals and power cognition. By investigating of
TABLE3 Mediation analysis.
Path Estimate SE CLLCL
U
AS-KH-CIP 0.034 0.007 0.049 0.021
AS-KH-Task
performance
0.056 0.011 0.078 0.036
AS-KH-ERB 0.012 0.004 0.020 0.005
AS-KH-
Workplace
deviance
0.046 0.009 0.029 0.064
Perceived justice-
KH-CIP
0.034 0.007 0.021 0.048
Perceived justice-
KH-task
performance
0.055 0.010 0.036 0.076
Perceived justice-
KH-ERB
0.011 0.004 0.005 0.019
Perceived justice-
KH-workplace
deviance
0.045 0.009 0.063 0.029
Incivility-KH-
CIP
0.120 0.011 0.142 0.098
Incivility-KH-
task performance
0.197 0.014 0.225 0.171
Incivility-KH-
ERB
0.040 0.011 0.062 0.019
Incivility-KH-
workplace
deviance
0.160 0.013 0.136 0.186
PSS-KH-CIP 0.005 0.006 0.017 0.007
PSS-KH-Task
performance
0.008 0.010 0.027 0.011
PSS-KH-ERB 0.002 0.002 0.006 0.002
PSS-KH-
workplace
deviance
0.006 0.008 0.009 0.022
LMX-KH-CIP 0.014 0.006 0.002 0.058
LMX-KH-task
performance
0.024 0.010 0.004 0.044
LMX-KH-ERB 0.005 0.002 0.001 0.010
LMX-KH-
workplace
deviance
0.019 0.008 0.036 0.003
AS: abusive supervision; KH: knowledge hiding; CIP: creative and innovative performance;
ERB: extra-role behavior; PSS: perceived social support; LMX: leader-member exchange; SE:
standard error; CL: condence intervals.
Zhang et al. 10.3389/fpsyg.2024.1516815
Frontiers in Psychology 11 frontiersin.org
collectivism and power distance, wefound that the primary studies
included in this meta-analysis were generally conducted in Asian
countries, especially those characterized by higher levels of
collectivism and power distance. is is consistent with the ndings
of Oliveira et al. (2021) regarding the regional distribution of
knowledge-hiding research in recent years, with publications from
non-eastern perspectives increasing slowly.
Finally, in addition to substantive moderators, this study also
examined whether the research methodology makes a dierence.
Knowledge intensity and knowledge-hiding measures were explored
as the methodological moderators. As for the research context,
knowledge intensity moderated the relationship between relationship
conict and knowledge hiding, indicating that the relative importance
of knowledge in dierent industries also inuenced the severity of
knowledge hiding. To examine the distinctiveness of Connelly etal.’s
(2012) scale, wecategorized studies on knowledge hiding into two
subgroups: those using Connelly etal.s (2012) scale and those using
other scales. ere were signicant dierences between the two
subgroups in the relationships between relationship conict, PSS, and
knowledge hiding. However, the other measures exhibited stronger
statistical power than the scale of Connelly etal. (2012). is result
can bejustied through the inclusion of rationalized hiding, which
justies the non-deceptive nature of knowledge hiding. e existence
of positive aspects in knowledge hiding would make the values based
on Connelly etal.’s (2012) scale less negative than those obtained from
the other measures.
7 Theoretical contributions and
practical implications
is meta-analysis contributes to the understanding of knowledge
hiding in several ways. First, as an extension of the recent meta-
analyses in the eld of knowledge hiding (Arain etal., 2024; Škerlavaj
etal., 2023), it established a new nomological framework of knowledge
hiding, which successfully synthesized the reciprocal loop of this
phenomenon in organizations from the social exchange perspective.
By identifying knowledge hiding as a social exchange-based construct,
the underlying mechanisms of knowledge hiding can beexplored
through individuals’ commitment to workplace fairness and
adherence to reciprocal norms (O'Boyle etal., 2012; Xiao and Cooke,
2019). In terms of antecedents, dyadic relationships, such as horizontal
interactions between co-workers and vertical interactions between
supervisors and their subordinates, are critical to knowledge hiding.
Its dynamics are also highly associated with the quality of personal
social networks, and their position at both ends of the knowledge
exchange spectrum also determines their attitudes toward knowledge
hiding. Specically, perceptions of reciprocal relationships contribute
to the development of benecial social exchanges that prevent people
from engaging in knowledge-hiding behaviors, whereas perceptions
of imbalanced social exchanges motivate people to violate social
norms and prioritize personal interests, which increases the possibility
of knowledge hiding.
Further, in terms of consequences, knowledge hiding due to
failure in social exchanges weakens the internal solidity of
relationships, groups, and organizations (Lawler, 2001), leading to
negative reciprocation of undesirable behaviors in the workplace. In
addition, the mediation analysis of knowledge hiding ensures the
causalities between knowledge hiding and its antecedents and
consequences, which provides a more comprehensive picture of the
knowledge hiding loop between knowledge seekers and hiders. In
conclusion, this study reveals the extent to which SET could truly
depict and interpret the knowledge-hiding phenomenon.
In response to the appeal of Xiao and Cooke (2019), cultural-
related constructs also matter signicantly as boundary conditions in
shaping people’s knowledge-hiding behaviors by adopting a cross-
cultural perspective (Batistič and Poell, 2022). Concerning the
moderating roles of cultural dimensions, our ndings highlight the
importance of integrating the knowledge-hiding literature with
current cultural theories, such as Hofstede’s cultural framework (Xiao,
2024), which contributes to the establishment of important theoretical
implications and eectively extends the generalizability of current
meta-analytic ndings on knowledge hiding across nations and
cultures. Specically, the collectivism and power distance in this meta-
analysis could exert additional inuences on knowledge hiding
beyond the identied antecedents by shaping their cognitive appraisals
of shared values and power distribution. Cultural-specic
interpretations of knowledge hiding can help us explore how
knowledge hiding is conceptualized and implemented in diverse
cultures, and this investigation of cultural dierences contributes to
FIGURE3
Mediation model.
Zhang et al. 10.3389/fpsyg.2024.1516815
Frontiers in Psychology 12 frontiersin.org
TABLE4 Subgroup analysis.
Variable k N ρCILCIUZ-value Q-statistics p-value
Antecedents
Abusive supervision
Lower collectivism 3 829 0.588 0.511 0.655 12.001*** 13.780 0.000***
Higher collectivism 5 1,555 0.310 0.168 0.439 4.155***
Lower power
distance
5 1,382 0.505 0.363 0.624 6.217*** 3.589 0.058+
Higher power
distance
3 1,002 0.275 0.059 0.466 2.477*
Lower knowledge
intensity
1 241 NA NA NA NA / /
Higher knowledge
intensity
4 1,141 0.499 0.315 0.647 4.840***
Connelly measures 5 1,573 0.460 0.350 0.558 7.358*** 0.323 0.570
Other measures 3 811 0.361 0.009 0.645 1.913+
Relationship conflict
Lower collectivism 4 1,322 0.425 0.307 0.530 6.519*** 0.593 0.441
High collectivism 4 1,315 0.345 0.164 0.504 3.636***
Lower power
distance
3 788 0.385 0.152 0.577 3.145** 0.000 0.991
Higher power
distance
5 1,849 0.386 0.262 0.497 5.762***
Lower knowledge
intensity
3 1,231 0.297 0.196 0.391 5.595*** 15.026 0.000***
Higher knowledge
intensity
4 1,168 0.498 0.454 0.540 18.599***
Connelly measure 6 2,019 0.342 0.227 0.447 5.582*** 7.156 0.007**
Other measures 2 618 0.507 0.446 0.563 13.816***
Workplace incivility
Lower collectivism 2 670 0.530 0.229 0.738 3.245** 0.329 0.566
Higher collectivism 3 861 0.466 0.344 0.538 7.744***
Lower power
distance
2 753 0.405 0.334 0.471 10.246*** 1.774 0.183
Higher power
distance
3 778 0.542 0.344 0.693 4.804***
Lower knowledge
intensity
2 694 0.574 0.289 0.766 3.590*** 0.195 0.659
Higher knowledge
intensity
3 1,078 0.503 0.276 0.677 4.021***
Connelly measures 6 1,862 0.526 0.400 0.632 7.119*** / /
Other measures 1 223 NA NA NA NA
LMX
Lower collectivism 1 323 NA NA NA NA / /
Higher collectivism 5 2,290 0.325 0.468 0.166 3.894***
Lower power
distance
2 653 0.454 0.513 0.390 12.447*** 4.195 0.041*
(Continued)
Zhang et al. 10.3389/fpsyg.2024.1516815
Frontiers in Psychology 13 frontiersin.org
understanding the nuances of knowledge-hiding mechanisms among
various social contexts. In addition, the moderating role of knowledge
intensity suggests that knowledge hiding is a complex social
phenomenon that uctuates across industries, and knowledge
concentration inuences the way in which people react to knowledge
hiding. Finally, this study indicates the unique characteristics and
dierential validity of Connelly etal.’s (2012) scale relative to others by
highlighting the double-edged aspects of knowledge hiding. By
identifying related yet dierent dimensions of knowledge hiding, a
more comprehensive and practical measurement tool is introduced to
knowledge-hiding research.
is study makes several practical contributions. Based on the
nomological framework of knowledge hiding, the meta-analytic
ndings suggest that negative events have stronger power in fostering
knowledge hiding than positive events. erefore, top executives
should becautious about implementing managerial interventions to
deal with destructive leadership and workplace mistreatment from
supervisors and co-workers to inhibit the occurrence of knowledge
hiding, especially in knowledge-intensive industries, which would
contribute to mitigating the detrimental consequences on employees’
attitudes, behaviors, and job performance. In particular, leaders
should beprovided with leadership development programs to improve
their abilities in drawing up eective knowledge management
practices to address the knowledge-hiding phenomenon in
organizations. For example, leaders should take time and make eorts
to cultivate a supportive climate to maintain workplace equality and
develop positive reciprocal relationships between co-workers; they
should also be aware of exploitative relationships with their
subordinates and take actions to improve open communication and
mutual understanding, which facilitate eective vertical social
exchange in the future. Simultaneously, employees in organizations
should also beconcerned about establishing good social networks
with their co-workers and avoid engaging in vicious competition.
Finally, under dierent cultural backgrounds, organizations need to
adapt to local conditions and timely adjust measures to cope with
knowledge-hiding behaviors.
8 Limitations and further research
directions
e rst limitation of this meta-analysis is the small number of
primary studies included for each pairwise relationship, which may
have inuenced the reliability of the meta-analytic ndings because of
sampling errors. We anticipated this issue in the process of data
coding, as there were only 12 years of knowledge-hiding research from
which to collect data. As instructed by Lim (2021), the precision of the
meta-analytic eect sizes can beenhanced by increasing the number
of primary studies included, which indicates the importance of
integrating a larger number of studies in future meta-analytic
investigations. In addition, the lack of samples within moderator
subgroups inuenced the accuracy of the moderation analysis, as
Q-statistics exhibits poor statistical power in identifying the true
heterogeneity with a small number of primary studies (Huedo-Medina
TABLE4 (Continued)
Variable k N ρCILCIUZ-value Q-statistics p-value
Higher power
distance
4 1,960 0.282 0.434 0.116 3.267**
Lower knowledge
intensity
2 546 0.354 0.496 0.193 4.157*** 1.239 0.266
Higher knowledge
intensity
4 2,149 0.227 0.382 0.061 2.661**
Connelly measures 4 1,062 0.240 0.392 0.074 2.814** 0.622 0.430
Other measures 4 2,067 0.339 0.509 0.143 3.317**
PSS
Lower collectivism 1 199 NA NA NA NA / /
Higher collectivism 5 941 0.283 0.415 0.139 3.773***
Lower power
distance
3 596 0.218 0.362 0.065 2.767** 2.025 0.155
Higher power
distance
3 544 0.361 0.480 0.228 5.080***
Lower knowledge
intensity
1 199 NA NA NA NA / /
Higher knowledge
intensity
4 851 0.208 0.340 0.068 2.895**
Connelly measure 6 1,281 0.190 0.274 0.103 4.236*** 4.530 0.033*
Other measures 3 544 0.361 0.480 0.228 5.080***
K: number of primary studies included in the subgroup analysis; N: tota l numbers of respondents; ρ: sample-size weighted and reliability corrected population correlation; CL: condence
intervals; Q-statistics and p-values: signicance of heterogeneity. +p < 0.1.
*p < 0.05; **p< 0.01; ***p < 0.001.
Zhang et al. 10.3389/fpsyg.2024.1516815
Frontiers in Psychology 14 frontiersin.org
etal., 2006; Miao etal., 2016). is can explain why some moderators
are insignicant and meaningless regarding certain relationships
between knowledge hiding and its antecedents.
Second, this study did not distinguish between studies conducted
at individual, team, and organizational levels. Most primary studies
included therein were conducted at the individual level, with only a
few exceptions that focused on team and organizational levels.
erefore, it is necessary to examine knowledge hiding at higher
levels, such as the team and organizational levels. Further
investigations to explore the knowledge-hiding phenomenon from a
multilevel perspective are thus essential. e multilevel investigation
of knowledge hiding has the potential to reveal the “black box” of
operative mechanisms of knowledge hiding at higher levels, enhancing
the generalizability of previous research ndings in the eld of
knowledge hiding (Huo etal., 2016).
ird, this study developed a nomological framework of
knowledge hiding from the social exchange perspective, which
theoretically demonstrated the explanatory power and substantial
value of SET in the eld of knowledge hiding (Anand etal., 2022).
However, weshould also recognize that SET is not perfect enough by
exhibiting a limited predictive validity in explaining some specic
relationships between knowledge hiding and its correlates. Given that
the conicting result was found regarding the relationship between
knowledge hiding and creativity from the lens of social comparison
(Zakariya and Bashir, 2021), weshould highlight the deciency of
SET. In essence, SET mainly relies on the benet–cost analysis to
decide whether to engage in or disengage from a social relationship
(Ahmad etal., 2023). It seems plausible but oversimplies the complex
dynamics inherent in human relationships to some extents because of
disregarding the signicance of emotions and subjective initiatives,
especially irrational ones (Redmond, 2015). As such, weneed to
further investigate some new theoretical perspectives to compensate
for the limitations of SET in justifying knowledge hiding phenomenon.
is entails exploring theories rooted in social cognition or
psychology. Some scholars suggested that cognition-related theory
provides a more holistic overview of the quality of social exchange by
considering the importance of self-ecacy (Liao etal., 2010) .”
Finally, the primary studies included in this meta-analysis mainly
focused on the composite form of knowledge hiding and ignored the
necessity of investigating its specic facets. Some scholars have called
for further examination of whether the relationships between knowledge
hiding and its correlates vary across the three sub-dimensions: evasive
hiding, playing dumb, and rationalized hiding (Wang etal., 2022). It is
thus necessary to further examine and compare the predictive validity
of these sub-dimensions of knowledge hiding in analyzing certain
outcomes when considering their dierential levels of deceptiveness.
Data availability statement
e original contributions presented in the study are included in
the article/supplementary material, further inquiries can bedirected
to the corresponding author.
Author contributions
ZZ: Conceptualization, Data curation, Formal analysis,
Funding acquisition, Investigation, Methodology, Project
administration, Resources, Soware, Validation, Visualization,
Writing – original dra. YT: Conceptualization, Funding
acquisition, Methodology, Supervision, Validation, Writing
review & editing. RR: Data curation, Investigation, Methodology,
Writing– review & editing.
Funding
e author(s) declare that nancial support was received for the
research, authorship, and/or publication of this article. is work was
supported by JST SPRING, Grant Number JPMJSP2132.
Conflict of interest
e authors declare that the research was conducted in the
absence of any commercial or nancial relationships that could
beconstrued as a potential conict of interest.
Generative AI statement
e authors declare that no Gen AI was used in the creation of
this manuscript.
Publisher’s note
All claims expressed in this article are solely those of the authors
and do not necessarily represent those of their aliated organizations,
or those of the publisher, the editors and the reviewers. Any product
that may beevaluated in this article, or claim that may bemade by its
manufacturer, is not guaranteed or endorsed by the publisher.
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