Impacts of knowledge sharing: a review and directions for future research

Abstract

Purpose:
Knowledge sharing contributes to the success of an organization in various ways. This paper aims to summarize the findings from past research on knowledge-sharing outcomes in organizations and to suggest promising directions for future research.

Design/methodology/approach:
Design/methodology/approach: We conducted a systematic literature review which consisted of three three main phases: defining a review protocol, conducting the review and reporting the review. The thematic analysis of 61 studies resulted in the development of a framework outlining the impacts of knowledge sharing as well as future research avenues.

Findings:
Previous research has investigated knowledge-sharing outcomes at three levels: the individual, team and organization; specific impacts are summarized for each level. The most commonly studied factors affected by knowledge sharing are creativity, learning and performance. Knowledge sharing is also found to have some beyond-convention work-related impacts, such as those on team climate and employees’ life satisfaction. Research on the outcomes of knowledge sharing is dominated by quantitative studies, as we found only one qualitative study in this review. Based on the discussion of the results, promising avenues for further research were identified and a research agenda was proposed. More research on differential, psychological and negative impacts, as well as interactional and methodological aspects of knowledge-sharing, is suggested.

Originality/value:
To date, no systematic review has been conducted on the impacts of knowledge-sharing. This paper makes an important contribution to knowledge-sharing research, as it consolidates previous research and identifies a number of useful research topics that can be explored to advance the field, as well as to establish the evidence-based importance of knowledge sharing.

Full text

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Impacts of Knowledge Sharing: A review and directions for future
research
Citation
Farhan Ahmad, Muhaimin Karim, (2019) "Impacts of knowledge sharing: a review and
directions for future research", Journal of Workplace Learning, Vol. 31 Issue: 3, pp.207-
230, https://doi.org/10.1108/JWL-07-2018-0096
Final version of this article is available at https://doi.org/10.1108/JWL-07-2018-0096

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Impacts of Knowledge Sharing: A review and directions for future
research
Abstract
Purpose: Knowledge sharing contributes to the success of an organization in various
ways. This paper aims to summarize the findings from past research on knowledge
sharing outcomes in organizations and to suggest promising directions for future research.
Design/methodology/approach: We conducted a systematic literature review that
consisted of three main phases: defining a review protocol, conducting the review, and
reporting the review. The thematic analysis was conducted on 61 studies, based on which
we developed a framework for understanding the impacts of knowledge sharing.
Findings: Previous research has investigated knowledge-sharing outcomes at three levels:
the individual, team, and organization; specific impacts are summarized for each level.
The most commonly studied factors affected by knowledge sharing are creativity,
learning, and performance. Knowledge sharing is also found to have some beyond-
convention work-related impacts, such as those on team climate and employees’ life
satisfaction. Research on the outcomes of knowledge sharing is dominated by quantitative
studies, as we found only one qualitative study in this review. Based on the discussion of
the results, promising avenues for further research were identified and a research agenda
was proposed. More research on differential, psychological, and negative impacts, as well
as interactional and methodological aspects of knowledge sharing is suggested.
Originality/value: To date, no systematic review has been conducted on the impacts of
knowledge sharing. This article makes an important contribution to knowledge sharing
research as it consolidates previous research and identifies a number of useful research
topics that can be explored to advance the field as well as to establish the evidence-based
importance of knowledge sharing.
Keywords: knowledge sharing, knowledge sharing review, knowledge sharing benefits,
knowledge sharing outcomes, knowledge sharing effects, knowledge management,
systematic literature review
1 Introduction
Knowledge sharing is one of the most fundamental activities in organizational operations.
The strategic importance of knowledge is highlighted in knowledge-based view of the
firm (Nickerson and Zenger, 2004). Nevertheless, the mere existence of knowledge
resources does not guarantee success (Hislop, 2013; Hussein et al., 2016). To develop a
sustainable competitive advantage, organizational employees must share and apply
knowledge in practice (Cabrera and Cabrera, 2005; Dalkir, 2017; Nonaka et al., 2000).
Previous research has emphasized the benefits of knowledge sharing in the form of cost
reduction, short product development cycles, increased customer satisfaction and
improved innovation and performance capabilities (Ozer and Vogel, 2015; Wang and
Noe, 2010).
In the last couple of decades, research on different aspects of knowledge sharing
has been increasing. One of the most important purposes of knowledge management is to
systematically influence knowledge exchange, application and creation, thereby creating

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value (Kozhakhmet & Nazri, 2017; Li et al., 2009). Consequently, success of knowledge
management policies in an organization hinges on the knowledge sharing between
employees and its resulting outcomes (Hislop, 2013). Due to the complexity of
knowledge sharing, which is influenced by many individual, organizational, and
contextual factors, a major research focus has been on the identification of factors that
inhibit or support it (Mahnke et al., 2009). Consequently, one of the criticisms of
knowledge-sharing research is that it focuses too much on knowledge-sharing enablers,
such as technology, organizational culture, rewards, and pays comparatively less attention
to the value realization of knowledge sharing (Henttonen et al., 2017).
Since the turn of the decade, interest in knowledge sharing outcomes has surged.
A number of empirical studies have been conducted on the effects of knowledge sharing.
This stream of research has played an important role in establishing the value of
knowledge management, which sceptics once thought of as no more than a passing fad
(e.g. Wilson, 2002). Moreover, it has provided concrete evidence of the benefits that
individuals and organizations can obtain from their involvement and investment in
knowledge sharing. However, much remains to be learned and understood about the value
of knowledge sharing in organizations.
The specific purpose of this paper is to summarize the findings from past research
on knowledge sharing outcomes in organizations and to suggest promising directions for
future research. This paper contributes to our understanding of knowledge sharing
impacts in several ways. First, the field has been growing and, to best of our knowledge,
no systematic review has been conducted on the impacts of knowledge sharing to date.
In contrast, a number of review papers have been published on precursors of knowledge
sharing, providing a strong evidence-based understanding of knowledge sharing
antecedents (e.g., Haq et al., 2016; Ipe, 2003; Wang and Noe, 2010). By consolidating
previous findings, this review will help in building an evidence-based body of research
on knowledge sharing outcomes. Second, existing research seems fragmented.
Knowledge sharing outcomes have been investigated in fields such as information
systems, strategic management, human resource management, and psychology. The
present review synthesizes the current fragmented literature and provides an organizing
framework based on it. Third, as the first review on the theme, it reveals the most
researched topics thus far, which will help in avoiding possible repetitions while directing
attention to areas of inquiry on which research is most needed.
In this review, knowledge sharing is defined as the exchange of task-related
information, advice, and expertise to help others and to collaborate with others to carry
out daily tasks, solve problems and develop new ideas (Ahmad, 2017). The impact of
knowledge sharing refers to work-related implications and changes brought up by
knowledge sharing activities of employees in an organization. We specifically focus on
interpersonal knowledge sharing, that is, knowledge sharing between individuals face-to-
face or via online communication media, such as Skype and e-mail.
2 Methodology
The present literature review followed the guidelines advanced by Kitchenham (2004).
Consequently, the literature review consisted of three main phases: defining a review
protocol, conducting the review and reporting the review. The defined review protocol
was composed of the following elements: (a) inclusions and exclusion criteria, (b) search
strategy, (c) data source, (d) study selection and (e) data extraction (f) data analysis and
synthesis.

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2.1 Inclusion/exclusion criteria
The inclusion and exclusion criteria aims to identify studies that provide direct evidence
about the research question (Kitchenham, 2004). In this study, inclusion and exclusion
criteria consist of four aspects. First, we include all such studies in our review that
investigate the anecdotal role of knowledge sharing, whether positive or negative, toward
other factors in organizations. Second, because research on knowledge sharing is
interdisciplinary, the literature review is not limited to a specific discipline. Third, this
review paper focuses on interpersonal knowledge sharing, which means the unit of
analysis in this paper is the impacts of individual-level knowledge sharing. It is not viable
to analyze knowledge sharing across teams, departments, subsidiaries, organizations and
industries in one review paper. This criterion also excludes studies on knowledge transfer
that has been mostly used to describe the “movement of knowledge between different
units, divisions, or organizations rather than individuals”(Wang & Noe, 2010, p.117).
Fourth, only empirical studies are included in the literature review. Moreover, editorials
and book reviews are excluded, as they do not include original research.
2.2 Search strategy
We used eight search terms; impact of knowledge sharing, benefits of knowledge sharing,
role of knowledge sharing, effects of knowledge sharing, influence of knowledge sharing,
knowledge sharing consequences, knowledge sharing outcomes, and knowledge sharing
implications to find published papers studying the impacts of knowledge sharing. We also
made a more focused search by adding commonly known impacts of knowledge sharing,
such as performance, innovation, learning and creativity, into the search terms to retrieve
studies that could have been missed in the first round.
2.3 Data source
The search terms were used to collect related studies from EBSCOhost, a database that
provides access to publications in a variety of fields. Moreover, it allows using complex
search strings and filters, which makes it easy to apply complex selection criteria.
Therefore, it is considered a suitable choice for systematic literature reviews (e.g., Wang
and Noe, 2010). To ensure inclusion of all relevant studies into our literature review
analysis, we also searched for relevant studies in major digital libraries, such as Science
Direct, Wiley, Springer, Sage and Google Scholar. We did not use any time period
restriction and included studies published in English only.
2.4 Study selection
The initial search generated a result of 2,061 articles. We read the title and abstract of
each article. We removed all duplicates, which considerably reduced the sample size.
Then, we applied the selection criteria: the study must be empirical, published in a peer-
reviewed journal and focused on knowledge sharing within organizations. Consequently,
105 articles were retained. We found 22 more articles after a more focused search on
knowledge sharing impacts as described in the search strategy section (Section 2.2).
Overall, we had 127 articles for full text review. After thorough reading of the articles,
we removed another 78 mainly due to irrelevance to our topic of interest or lack of quality.
Reading the studies and their references, we found 12 more articles relevant to our
objective. In the end, our final sample was composed of 61 studies. The literature

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selection process is described in the Figure 1.
Figure 1. Study selection process.
2.5 Data extraction
A data extraction form was created to retrieve information on demographics, research
design and knowledge sharing impacts. Both authors divided the articles among
themselves and read each article, one by one. The extracted information was stored in an
Excel spreadsheet.
2.6 Data analysis and synthesis
For data synthetization and analysis, we divided the data into two categories. The first
category contained information regarding demographic and methodological attributes. It
was quantitatively analyzed producing descriptive results, presented in Section 3.1. The
second category contained text extracted directly from previous studies about the nature
of the impacts analyzed, explanation of the impacts and key points of the study. As
suggested by Zahedi et al. (2016), the thematic analysis technique, developed by Braun
and Clarke (2006), was used to systematically analyze the data in the second category.
The six-step process of thematic analysis is outlined below.
 Familiarization with the data: Initially, familiarization with the data was developed by
reading the papers selected for review. To delve into the data further, we utilized the
‘repeated reading’ approach to search for meanings and patterns (Braun and Clarke,
2006). To remove any ambiguity, the extracted data was connected to its source paper
to develop contextual understanding helpful in data interpretation.
 Generating initial codes: While identifying the key points in the extracted data,
appropriate codes were assigned. The coding process was research question driven,
i.e. we developed codes capturing different aspects of the impacts of knowledge
sharing such as type of impact, nature of impact and level of impact. The studies were
very elaborative in terms of outlining and defining the knowledge sharing impacts
under investigation, which made it easier to assign relevant codes.
 Generating themes: After the completion of coding process, all codes were reviewed
and collated to generate potential themes relevant to the research question. For
example, codes, problem solving and work efficiency, were clustered under theme
performance. As suggested by Braun and Clarke (2005), a visual representation of
codes elaborating connections among codes and potential themes was created.
Clustering helped to create main themes.

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 Reviewing themes: All of the themes were defined and common characteristics in the
themes were outlined, which led to development of higher-level themes composed of
many sub-themes. For example, the level of impact was a common thread connecting
different themes, which led to the development of main themes, for example,
individual-level impact, composed of individual performance, individual learning and
creativity and individual psychological effects. Overall, this process resulted in the
identification of knowledge sharing impacts explored in previous studies and potential
research gaps needing further investigation.
 Producing the written analysis: Our analysis reveals knowledge sharing impacts at the
individual, team and organizational level, which is presented in Section 3.2.
3 Findings
3.1 Descriptive findings
The systematic search and analysis of the papers show that the number of empirical
studies investigating knowledge sharing impacts and outcomes has increased over time.
Figure 2 depicts the growth in literature on knowledge sharing impacts. Overall, 76
percent of the studies were published after 2010, which shows that the impacts of
knowledge sharing have attracted the most attention in the last seven years.
The studies were published in peer-reviewed journals in different fields. As shown
in Figure 3, most of the studies were published in organizational management, around 50
percent, followed by knowledge management and psychology. We found one qualitative
and 60 quantitative studies. To ensure that the absence of qualitative studies in our sample
was not due to our search strategy, we ran our search query one more time including the
terms interviews, qualitative study, case study, and observations. Nevertheless, we did
not find any new relevant studies. We determined the research methodology according to
what was stated in the paper.
0
1
2
3
4
5
6
7
8
9
10
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2003 2004 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017
Figure 2. Growth of the empirical studies on knowledge sharing
impacts in 2002–2017

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Figure 3. Distribution of studies based on types of fields
3.2 Impacts of knowledge sharing
This section reports the findings regarding keys impacts of knowledge sharing found in
the empirical research. Table 1 provides an overview of the key effects of knowledge
sharing.
3.2.1 Individual-level impact
At the individual level, knowledge sharing has three types of impact. It influences
individual performance, learning, and creativity, and has psychological effects.
Individual performance
The empirical evidence suggests a positive effect of knowledge sharing on employee
performance. The most common finding is that the utilization of collective know-how
and expert opinion enabled by knowledge sharing enhances efficiency in task
accomplishment, problem solving and decision making, which leads to improved
employee performance (Kang et al., 2008; Masa’deh et al., 2016; Reychav & Weisberg,
2009; Zhu, 2016).
Nevertheless, the notion that knowledge sharing enhances performance is not
ubiquitous. Previous research shows that many contextual factors can influence
performance outcomes of knowledge sharing. For example, an abusive supervision style
and lack of management support can reduce the positive impact of knowledge sharing on
employee performance (Kim & Yun, 2015; Ozer & Vogel, 2015; Park et al. 2015). Hostile
behavior limits self-regulation resources, impairing employees’ knowledge absorption
and utilization capacity (Tepper, 2007). Beyond contextual conditions, personal
characteristics, such as level of education (Henttonen, 2016), self-efficacy (Kim & Yun,
2015) and personal aspiration, i.e. setting difficult goals for oneself, (Quigley et al. 2007)
affect whether and to what extent employees experience improvement in performance
due to knowledge sharing.
Chow (2012) examined the knowledge sharing–performance relationship in the
context of individuals’ network position and concluded that the performance benefits of
knowledge sharing do not vary with network position of knowledge sharing participants.

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Table 1. Key effects of knowledge sharing in empirical studies
Impact
level
Impact
type
Key effects
Nature of
effect
Empirical studies (examples)
Individual
Performance
Task accomplishment efficiency
Problem solving efficiency
Positive
Positive
Masa’deh, Obeidat, & Tarhini, 2016
Kang, Kim, & Chang, 2008
Learning and
creativity
Innovative work behavior
Creative fluency and originality
Knowledge creation
Realized absorptive capacity
Positive
Positive
Positive
Positive
Radaelli et al., 2014
Carmeli, Gelbard, & Palmon, 2013
Park, Song & Lim, 2014
Kang & Lee, 2017
Psychological
effects
Job satisfaction
Life satisfaction
Intention to leave
Positive
Positive
Negative
Zhu, 2016
Jian & Hu, 2016
Reychav & Weisberg, 2009
Team
Performance
Work efficiency
Sales performance
Problem solving
Positive
Positive
Positive
Liu et al, 2011
Song et al., 2015
Cummings, 2004
Creativity
Creative solution and novel ideas
Absorptive capacity
Positive
Positive
Cheung et al., 2016
Lee, Lee, & Park, 2014
Climate
Trust
Socialization
Attitude toward diversity
Positive
Positive
Positive
Alsharo, Gregg & Ramirez, 2017
Radaelli et al., 2014
Lauring & Selmer, 2011
Organization
Performance
Financial performance (revenue,
return on investment)
New product performance
Positive
Positive
Collins & Smith, 2006; Wang &
Wang, 2012
McCurtain et al., 2010
Learning and
innovation
Ideation capability
Organizational absorptive
capacity
Entrepreneurial orientation and
corporate entrepreneurship
Positive
Positive
Positive
Saenz et al., 2012
Kumar & Rose, 2012; Liao et al.,
2007
De Clercq et al., 2015; Mustafa et al.,
2016
Business process
efficiency
Strategic alignment of business
processes
Project management capability
Process improvement (error
prevention, standardization &
documentation)
Organizational effectiveness
Positive
Positive
Positive
Positive
Pai, 2006; Kearns & Lederer, 2003
Li, Shiue, & Chen, 2016
Law & Ngai, 2008
Noor et al., 2015
This is against expectations, as network theory suggests that central positions are
advantageous in terms of time, range, access and referral and, therefore, central

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individuals should enjoy better performance outcomes than peripheral individuals (Burt,
1992).
Individual learning and creativity
When employees engage in knowledge sharing, they elaborate and externalize their
knowledge (Ahmad and Widén, 2018). Many studies have found that knowledge sharing
enhances innovative work behavior (Hu and Zhao, 2016), knowledge creation (Hu et al.,
2009; Iqbal et al., 2015; Ma et al., 2013; Park et al., 2014), creative fluency (frequency of
ideas) and creative originality (highly original ideas) (Carmeli et al., 2014), leading to
effective learning and creativity (Mura et al., 2016: Zhu, 2016). As engagement in mutual
discussion and exchange of ideas improve one’s capability to make sense of things,
knowledge sharing has also been found to enhance employees’ absorptive capacity (e.g.
Kang & Lee, 2017).
Some studies show that reciprocity and individual diversity are extremely critical
in the relationship between knowledge sharing, and learning and creativity (Radaelli et
al., 2014). One-sided knowledge sharing means exiguous discussion and feedback, which
is not sufficient for creativity that flourishes with interactive dialogue. Similarly,
homogeneity among knowledge sharing participants is detrimental to learning and
creativity. Employees exhibit more creativity when exposed to a range of perspectives
and out-of-the-box thinking enabled by individuals with dissimilar rather than similar
backgrounds (Huang et al., 2014). Overall, reciprocity and individual diversity are the
only contextual factors whose role in knowledge sharing and learning relationship have
been empirically investigated.
Individual psychological effects
In our review, only three studies examine the relationship between knowledge sharing
and psychological aspects. As a major source of personal professional development,
knowledge sharing enhances autonomy, skills utilization, and self-fulfillment. Based on
a study of R&D engineers, Zhu (2016) indicates that active knowledge sharing improves
job satisfaction among employees. While drawing on social exchange and social
determination theory, Jiang and Hu (2016) showed that knowledge sharing enhances
employees’ life satisfaction as it fosters quality relationships, buffers work-related stress
and ameliorates work–life conflict. Another psychological aspect closely related to work
and life satisfaction is intention to leave. Reychav and Weisberg (2009) showed that
employees’ intention to change jobs decreases with tacit and increases with explicit
knowledge sharing. Nevertheless, if explicit knowledge sharing is rewarded monetarily,
intention to leave the organization diminishes. As compared to tacit, explicit knowledge
sharing contributes less to personal development. Therefore, employees engaging in
extensive explicit knowledge sharing find it difficult to recognize learning opportunities
and hence consider alternative job options.
3.2.2 Team-level impact
Knowledge sharing also has team-level consequences. Review of the literature shows that
knowledge sharing influences team performance, creativity, and climate.
Team performance
Teams are essential elements of modern organizational work arrangements. Therefore,

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many studies have analyzed the impact of knowledge sharing on team performance
(Henttonen, 2013; Huang, 2009; Srivastava et al., 2006). According to Liu, Keller, and
Shih (2011), knowledge sharing between team members develops a transactive memory
system that enhances work efficiency. Their study of R&D project teams showed that
those teams that engage regularly in knowledge sharing are better equipped to deal with
project-related challenges and obstacles and consequently perform better. In the Korean
context, a study by Song and colleagues (2015) shows a positive relationship between
teams’ sales performance and knowledge sharing intensity. Cummings (2004) showed
that structural diversity on a team enriches knowledge sharing by exposing the team to
different sources of information and know-how, which consequently generates better
performance measured in terms of effective problem solving. Nevertheless, Haas and
Hansen (2007), in their study on management teams, note that lack of effort to adjust the
complexity of knowledge according to the expert status of the individuals involved can
reduce the positive impact of knowledge sharing on team performance. As knowledge
customization improves understanding, it ensures knowledge application in novel ways
(Choi, Lee, & Yoo, 2010).
Team creativity
Teams in organizations perform many heuristic tasks without readily identifiable paths to
task accomplishment (Kessel et al., 2012). Knowledge sharing among team members has
been found to be an important element of team creativity and learning. Previous research
shows that knowledge sharing improves teams’ idea generation and absorptive capacity,
which spur team creativity. In a longitudinal study, Cheung and colleagues (2016) showed
that novel ideas emanate from knowledge sharing because it enables discussion of the
feasibility of creative solutions. Furthermore, Lee (2014) noted that knowledge sharing
between team members builds a mental model of who knows what, known as absorptive
capacity, which is a critical component in team creativity.
In previous research, two contextual conditions, project complexity and instability of the
environment, have been found to play an important role in the knowledge sharing and
creativity relationship. A study by Wang et al., (2012) shows that for teams operating in
dynamic environments, sharing task-centric knowledge, relevant to immediate problems
and work-related issues, is more important for the development of the team’s capability
to come up with creative solutions and ideas than sharing human-centric knowledge,
relevant to interpersonal issues and team objectives. Overall, empirical research on the
impacts of knowledge sharing on team creativity is very limited.
Team climate
Knowledge sharing influences social climate in teams. Knowledge sharing is known to
induce interaction and reciprocation, providing a platform for team socialization
(Radaelli et al., 2014) and instilling trust among team members (Alsharo, Gregg and
Ramirez, 2017). This also has consequences for team climate. The findings of a study by
Flinchbaugh et al. (2016) confirm that intensive knowledge sharing between team
members develops a positive perception of overall team collaboration climate,
characterized by enhanced service quality and satisfaction.
Knowledge sharing has also been found to develop a positive attitude towards
diversity in heterogeneous teams. In a Danish study, Lauring and Selmer (2011) found
that knowledge sharing drives interaction between employees, which promotes openness
to linguistic, visual and informational diversity. In other words, knowledge sharing builds

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a positive diversity climate in teams and in departments.
3.2.3 Organizational-level impact
Organizational-level implications of knowledge sharing is the most studied topic in the
literature on knowledge sharing impacts. At the organizational level, knowledge sharing
between employees influences organizational performance, learning and business process
efficiency.
Organizational performance
Many studies have investigated the impact of knowledge sharing on organizational
performance (Gomes et al., 2017; McCurtain et al., 2016, Noor et al., 2015; Oyemomi et
al., 2016; Rezaei et al., 2017; Wang & Wang, 2012).
Knowledge sharing improves organizational (financial) performance in terms of
profitability, market share, return on investment and sales growth (Collins & Smith, 2006;
Gomes et al., 2017; Rezaei et al., 2017). Wang and Wang (2012) confirmed that tacit
knowledge sharing enhances both financial as well as operational performance; however,
explicit knowledge sharing contributes to financial performance only. Nevertheless,
knowledge sharing must be in alignment with internal organizational processes to achieve
positive performance outcomes (Oyemomi et al., 2016).
Most knowledge-sharing research has been conducted in the context of middle-
and low-level management. Although rarely researched, top-management knowledge
sharing is very critical for organizational performance. McCurtain and colleagues (2010)
investigated the performance outcomes of knowledge sharing between top-management
employees. They showed that an organization’s new product performance is a direct
function of knowledge sharing in top management. As organizational top management
has a holistic view of industrial dynamics and organizational capabilities, knowledge
sharing in upper management can result in timely interventions leading to high
organizational performance in the market.
Organizational learning and innovation
Previous empirical research shows that knowledge sharing among employees supports
organizational innovation and ideation capability, absorptive capacity, and
entrepreneurial orientation (Kumar and Rose (2012).
Lin (2007) showed that knowledge sharing, whether receiving it or providing it,
is valuable as it erodes knowledge stickiness and sets in motion knowledge combination
and reorientation processes, and leads to sustained organizational innovativeness. Wang
and Wang (2012) further confirm that knowledge sharing enhances not only the quality
but also the pace of learning and innovation in organizations.
Knowledge sharing is critical to the development of organizational absorptive
capacity as it supports a continuous leveraging of existing knowledge to build innovative
new knowledge (Khan et al., 2015; Liao et al., 2007; Iqbal et al., 2015; Yang, 2007.
According to Wang et al., (2016), knowledge sharing improves organizational learning
capability and thus supports knowledge embeddedness in routines and procedures and
exploitation of knowledge in relationships with stakeholders.
Knowledge sharing develops entrepreneurial orientation in the organization. De
Clercq and colleagues (2015) found a positive relationship between knowledge sharing
and organizational entrepreneurship in SMEs. Intensive knowledge sharing enables

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organizations to develop knowledge that can be used to create new ideas, engage in
experimentation, compare alternative decisions and build innovations. Mustafa,
Lundmark, and Ramos (2016) also confirmed a positive relationship between knowledge
sharing and organizations’ entrepreneurial activities.
Business process efficiency
A desired outcome of knowledge sharing is the improvement of internal organizational
practices and processes, which are considered essential for long-term organizational
survival (Harmon and Trends, 2010). Therefore, previous research has empirically
examined the influence of knowledge sharing on strategic alignment, project
management capability and process development.
Pai (2006) found that knowledge sharing not only improves the quality of IT
strategic planning processes, but also leads to the alignment of information system and
business strategies, thus leading to increased efficiency in organizational operations.
Kearns and Lederer (2003) also confirmed these findings in a study on knowledge-sharing
behavior of chief information officers (CIOs) and chief executive officers (CEOs).
Knowledge-sharing activities of CIOs and CEOs create alignment in IT and business
planning processes and contribute to the development of process refinement and
implementation efficiency.
Knowledge has also been found to improve organizations’ project management
competence. For example in the context of software projects, knowledge sharing reduces
cycle time reduction (Li et al., 2016; Sáenz et al., 2012) and optimizes deployment of
information systems (Shao et al., 2012).
Some studies have elaborated on the relationship between knowledge sharing and
business process development (Chang et al., 2012). Law and Ngai (2008) noted that
knowledge sharing supports process standardization, process simplification, coordination
of activities and responsiveness in service offerings. Noor, Hajar, and Idris (2015)
investigated knowledge sharing impact in the NGO context and found that knowledge
sharing improves internal effectiveness by instilling clarity in project processes and
activities. Overall, knowledge sharing improves business process efficiency in a number
of ways.
4 Emerging issues and future research directions
The previous section discussed the impacts of knowledge sharing as investigated in
previous research. While identifying the research gaps, the following discussion outlines
emerging issues and future research directions with a particular focus on interactional,
negative, differential, psychological, and methodological concerns in knowledge sharing
impact research.
4.1 Knowledge-sharing impacts from the interaction and process perspective
Knowledge sharing is a complex process that encompasses more than the simple
communication of knowledge (Ahmad, 2017). The characteristics of individuals, teams,
and organizations can shape the commitment, conditions and environment of knowledge-
sharing processes and, consequently, their outcomes. A few studies have explored the
role of external factors in knowledge-sharing outcomes. Nevertheless, the interactive
potential of individual, team, and organizational characteristics remains to be explored.

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4.1.1 Individual dispositions
Dispositional characteristics represent the uniqueness of individuals and explain the way
they tend to think and behave (Kalat, 2013). In the performance of work, individual traits
are activated as suggested in the trait activation theory (Tett et al., 2013). Because
productive knowledge sharing requires some creative conflict and tension (Skilton and
Dooley, 2010), variance in individuals’ agreeableness traits can influence the quality of
knowledge sharing outcomes, such as creativity, learning and problem solving. Similarly,
activation of highly neurotic traits during complex problem-solving discussion can result
in fear and anxiety, lowering individuals’ capacity to articulate and comprehend
knowledge during knowledge sharing, thereby impeding the achievement of desired
outcomes.
Another important individual disposition is sense of coherence. Having a high
sense of coherence means an individual can make sense of things around him/her, show
strong resilience, focus on the positive aspects of a situation and make appropriate
decisions (Antonovysky, 1993; Nielsen, Matthiesen & Einarsen, 2008), all of which play
an integral role in the success of knowledge sharing interactions and their outcomes. This
is an interesting area that has not been investigated before.
The dispositional perspective could be particularly useful in explaining nuances
in a direct relationship between knowledge sharing and individual cognitive capacity
suggested in previous studies (e.g. Carmeli, 2013). For example, employees with positive
dispositions will focus on positive aspects and hence will use self-evaluations and cross-
validation triggered through knowledge sharing as opportunity for further learning.
Nevertheless, the relationship may reverse if knowledge sharing participants possess
highly negative personal dispositions such as negative effectivity. Therefore, future
research should pay close attention to individual dispositional characteristics in the
investigation of knowledge sharing impacts.
4.1.2 Team characteristics
In our literature review, only one study (Wang et al., 2012) investigated the role of team
characteristics, team climate stability, in knowledge sharing outcomes. Teams, like
individuals, possess certain characteristics that are important in the knowledge sharing–
outcome transformation process.
Work interdependence
Work interdependence is considered a defining characteristic of teams and has been
widely studied in the teamwork literature (Campion et al., 1996; Somech et al., 2009),
but not in relation to knowledge sharing outcomes. The performance of teams
characterized by high task interdependence relies on the equal and mutual contribution of
team members (Campion et al., 1996), which means that not only the amount but also the
diversity of the knowledge being shared will matter for performance outcomes.
Moreover, the performance and creativity outcomes of knowledge sharing can vary with
teams’ work interdependence levels. Teams with high task interdependence can better
utilize knowledge sharing, as the achievement of their goals is dependent on the collection
and integration of diverse ideas. This may not be the case in low-task-interdependence
teams in which individuals may not clearly identify the relevance and benefits of the
knowledge being shared and hence lean on their own expertise for decision-making or
problem solving. Consequently, knowledge sharing may not result in enhanced creativity.

14
Hierarchal diversity
Hierarchal diversity in teams is also an important characteristic. Strong hierarchical
differences can generate congruent behavior and dictate whose opinions are actualized
(Cantimur et al., 2016), which means that even extensive knowledge sharing may not
necessarily result in the best decisions. When power dynamics come into action,
convergence on opinions and assessment of alternative views are nuanced (LePine et al.,
1997), thereby influencing knowledge sharing outcomes. This proposition is very
plausible in high power-distance cultures. Cross-cultural management research shows
that in high power-distance contexts, subordinates show less strength in defending their
arguments and are more accommodative of their superiors’ views out of respect (Hofstede
et al., 2010). Although previous research has analyzed the role of diversity, for example
structural diversity, in knowledge sharing outcomes (e.g. Cummings, 2004), future
research should pay attention to hierarchical diversity on teams and how it can influence
knowledge sharing outcomes.
4.1.3 Organizational structural characteristics
More research is also needed to help us to understand the role of organizational
characteristics in knowledge sharing outcomes. Organizational structure and
environmental turbulence can influence knowledge sharing outcomes. Previous research
shows that organizational survival in highly turbulent environments depends on persistent
knowledge sharing enabling responsive decision-making (Keszey, 2018). However,
organizational structures, in which decision-making power rests with a few individuals,
experience a sharing–acting gap (Zheng, Yang, and Mclean, 2010), thereby undermining
the benefits of knowledge sharing. We already know, as shown by Wang et al. (2012),
that environmental dynamism influences the outcomes of knowledge sharing. Therefore,
the relationship between organizational structure and knowledge sharing outcomes is
intuitive and requires more empirical exploration in future research.
4.2 Negative impacts of knowledge sharing
Our review shows that previous research has mostly found positive impacts of knowledge
sharing. Nevertheless, as pointed out by Mahnke et al. (2009), knowledge sharing is not
always good. Along with benefits, it involves costs for the parties involved.
4.2.1 Repeated collaborations
Previous research has shown that knowledge sharing is mostly a voluntary but demanding
activity that requires time, commitment of cognitive resources, and engagement (Ahmad,
2017; Cabrera and Cabrera, 2005). Individuals can end up sharing extensive amounts of
knowledge, even for minor tasks, due to pervasive knowledge sharing norms. Too much
knowledge sharing can lead to redundancy and cognitive costs (Foss et al., 2010).
Research on mental models on teams has questioned the potential benefits of knowledge
sharing, particularly when individuals engage in repeated collaborations (e.g. Haas and
Hansen, 2005; Mohammed and Dumville, 2001; Skilton and Dooley, 2010). Extensive
knowledge sharing in repeated collaborations can stagnate creative abrasion and team
creativity over time by establishing rigid mental models, i.e. accepting views without
overt discussion, evaluation and criticism due to pre-established trust acquired in past

15
collaborations. Therefore, team members’ collaboration history and repetition should be
considered in future research on the impacts of knowledge sharing.
4.2.2 Cognitive stress
There is also evidence that knowledge sharing among highly differentiated individuals
may not generate positive performance outcomes (Dahlin et al., 2005; Huang et al., 2014).
When disparities between individuals’ cognitive mental models and professional
expertise are great, development of even a basic level of understanding can result in
cognitive exhaustion. This can result in negative health outcomes in the form of stress.
Social psychology literature shows that work engagements with diverse individuals can
trigger stress, as individuals try to comprehend differences, and solve communication
problems and misunderstandings. As knowledge sharing is an interaction-intensive
activity, it can lead to stress and burnout, particularly when it is enforced by
management’s expectations and evaluations. The relationship between stress and work
behaviors has been established in previous research(Colligan and Higgins, 2006; Henle
and Blanchard, 2008); however, stress implications of knowledge sharing, which is an
important and cognitive-intensive activity, have not been empirically analyzed thus far.
4.2.3 Time cost
Most interpersonal knowledge sharing in organizations takes place informally (Dalkir,
2017), which is beneficial, as it allows dealing with unanticipated problems.
Nevertheless, knowledge sharing can also drain the time and resources available for other
work activities, leading to work overload (Szulanski, 1996; Wang and Noe, 2010). When
additional benefits are marginal, time invested on knowledge sharing beyond a certain
point can inflict performance penalty. In addition, this situation is further aggravated by
the perception of knowledge sharing as an extra-role activity (Cheng and Coyte, 2014).
The time waste consequence of knowledge sharing has been recognized (Ahmad, 2017;
Haas and Hansen, 2007); nevertheless, it requires further empirical investigation.
4.2.3 Workplace politics
Individuals engage in knowledge sharing with good intentions - a conception widely held
in knowledge sharing research. Organizational politics theory suggests that political
motivations, which may not necessarily be good, can influence work behavior (Chang et
al., 2009; Miller, Rutherford, and Kolodinsky, 2008). Employees are known to
strategically adjust their behavior to maximize their self-interest (Vigoda, 2002).
Knowledge sharing has not been studied through the lens of workplace politics theory,
even though knowledge is known as a source of power. It is highly plausible that
employees can intentionally share knowledge that may not be beneficial for the
accomplishment of a task. They can hold back their personal expertise for professional
gains while pretending to be active knowledge sharers. Similarly, some individuals can
engage in free-riding knowledge sharing by trying to benefit from the expertise of others
while making little contributions themselves, leading to a public good dilemma (Cabrera
and Cabrera, 2005). Such a type of knowledge sharing can intensify workplace politics
and result in negative outcomes, such as negative feelings among employees, a tense work
environment and hampered performance. Future research should explore the negative
impacts of knowledge sharing, for example on employee relationships and work climate,
using the organizational politics theory.

16
Overall, a critical perspective is needed to comprehend the potential drawbacks
of knowledge sharing. Research on the negative outcomes of knowledge sharing can
enrich our understanding of its net impacts, particularly of when and how drawbacks
outweigh benefits.
4.3. Differential impacts of knowledge sharing
Knowledge sharing represents a combination of some form of knowledge and a sharing
activity or mechanism. Characteristics of knowledge and channel used for knowledge
sharing can result in varying outcomes (Dalkir, 2017).
4.3.1. Knowledge types
The type of knowledge shared for a specific task can influence the achievement of
individual and organizational goals. Previous studies on knowledge sharing impacts have
mostly conceptualized knowledge sharing as a whole. Research on the differential
impacts of sharing various types of knowledge is not only scarce but also contradictory.
For example, Kessel et al. (2012) noted a positive impact of explicit knowledge sharing
on innovativeness, while Reychav et al. (2012) found a negative one. Reychav et al.
(2012) also showed that explicit knowledge sharing enhances employees’ intention to
leave the organization, whereas tacit knowledge sharing reduces it. Future research
should further explore the differential impacts of different forms of knowledge sharing,
as different forms of knowledge can have different outcomes.
4.3.2. Knowledge relevance
Explicit knowledge sharing in the form of instructions and protocol can be useful to
perform standard tasks, such as software testing and maintenance and problem solving.
Sharing tacit knowledge can help comprehend complex problems and develop new
solutions (Reychav and Weisberg, 2009). However, for time efficiency and rapid
responsiveness, sharing explicit knowledge could be more useful than sharing tacit
knowledge. For example, the development of a new solution through tacit knowledge
sharing will be less efficient and productive compared to sharing an already existing
solution that has been designed to solve a similar problem. This shows that, for certain
tasks, relevance rather than the nature of the knowledge could be more critical for the
efficient accomplishment of tasks. Future research should pay close attention to what is
being shared during knowledge sharing and how it feeds into outcomes.
4.3.3 Knowledge sending vs. receiving
While some studies conceptualize knowledge sharing as encompassing both sending and
receiving, others do not make this difference and focus only on knowledge flow,
regardless of direction (e.g. Kim and Yun, 2015; Law and Ngai, 2008). It would be
difficult to differentiate between sending and receiving knowledge in interactive
discussions; nevertheless, one’s usual status as sender or receiver in the organization can
influence knowledge sharing outcomes at the individual level. For example, continuous
knowledge sending can be helpful in establishing one’s status as an expert, but continuous
receiving can be more useful in enhancing learning potential and absorptive capacity. It
would be interesting to explore how individuals’ general role during knowledge sharing,

17
for example in team discussions, can lead to differential individual- and team-level
outcomes.
4.3.4 Knowledge sharing media
Interpersonal knowledge sharing occurs face-to-face and via communication
technologies such as e-mail and Skype. In our review, we did not find any studies
exploring the potential variation in the outcomes of knowledge sharing through different
channels. This is an important question, as technology-mediated knowledge sharing has
become a common characteristic of today’s workplaces. Ubiquitous access and flexibility
offered by virtual communication channels enhance individuals’ knowledge-processing
capability (Barley et al., 2011) and control, leading to better knowledge-sharing
outcomes. Nevertheless, technology-mediated knowledge sharing has its disadvantages.
For example, Ahmad (2017) found that diversity-driven misunderstandings increase in
technology-mediated knowledge sharing. Consideration of the synchronous or
asynchronous nature of knowledge-sharing outcomes is also important. Real-time
interactive discussion allows capitalizing on dialectical and contextualization cues and
helps in the verbalization of complex cognitive thoughts, which is important for
knowledge sharing quality (Ahmad, 2017). Previous research has shown that email, one
of the most commonly used online tools for knowledge sharing, is a source of distraction
from work due to its asynchrony, which allows people to send and receive e-mail anytime
(Barley et al., 2011). Technology has inherent differential characteristics that make
technology-mediated communication experiences different from face-to-face ones
(Alsharo, 2017). Future research should explore how technology-mediated knowledge
sharing contrasts with face-to-face knowledge sharing in terms of outcomes and
achievement of individual, team and organizational goals.
4.4. Psychological impacts of knowledge sharing
Work practices have psychological effects (Lee et al., 2010), and knowledge sharing is
no exception to this. Indeed, it is plausible that emotional consequences of individual
knowledge sharing will be more blatant than those of many other work behaviors due to
the crucial importance of knowledge sharing for performance and career advancement.
In our review, we found only three studies that explored the psychological impacts of
knowledge sharing—life satisfaction, job satisfaction and turnover intention. On the one
hand, this confirms that knowledge sharing has psychological impacts and, on the other,
it exposes the lack of research in this area and signals future research directions.
Future research should examine the impact of knowledge sharing on organization-
based self-esteem, i.e. the belief about one’s organizational worthiness (Gardner and
Pierce, 2016). Knowledge sharing is a mechanism to contribute to organizational success.
As individuals put their expertise into action through knowledge sharing, they
consciously or unconsciously evaluate organizational dependence on their professional
expertise (Mukahi, 2016). Therefore, an impact of knowledge sharing on organization-
based self-esteem is highly likely. Nevertheless, this relationship could have certain
nuances in that individuals may weigh knowledge contribution more than acquisition on
their personal assessment of organizational worthiness.
Knowledge sharing could significantly influence stress. This relationship has been
discussed under the negative impacts of knowledge sharing. Nevertheless, some other
related psychological aspects, such as job security, person–job fit and job autonomy
provide a fertile ground for future research on knowledge-sharing impacts.

18
4.5 Methodological issues
4.5.1 Qualitative research
Knowledge sharing research has been mostly quantitative thus far. In one of the most
comprehensive reviews of antecedents of knowledge sharing, Wang and Noe (2010)
found only a small number of qualitative studies. In our review, we found only one
qualitative study on knowledge sharing outcomes, that of Oyemomi et al. (2016).
This lack of qualitative research is an important gap in research on knowledge-
sharing outcomes. The potential reason for this gap is the sensitive nature of knowledge
and temporal aspects of ‘impact’, which makes it difficult to secure permission to openly
discuss knowledge sharing activities and to analyze their impacts that usually unfold over
time. Moreover, knowledge sharing is a collaborative process, which means a valid
assessment of its benefits require access to all relevant parties involved in the knowledge
sharing process. Nevertheless, with all these challenges comes along the opportunity to
address the depictions of “realities that cannot be reduced to a few variables”(Rynes &
Gephart, 2004, 455). Due to lack of qualitative research, we are largely unware of
emergent processes that entail a transformation of knowledge sharing into potential
outputs. For example, how knowledge-sharing interactions evolve during a problem-
solving episode, what types of linguistic patterns are depicted, and how language and
communication related aspects of knowledge sharing influence potential outcomes, are
such questions that have not been addressed so far (Ahmad, 2018). Why certain
knowledge sharing interactions fail and generate required output while others do not, is
still largely unknown. A limited understanding of procedural, contextual and experiential
aspects of the knowledge sharing process can be clearly attributed to a lack of qualitative
research. Investigation into knowledge sharing interactions through observations and
interviews can help understand how hidden, nevertheless important, elements of
knowledge-sharing discussion, such as conflict resolution, advocacy and
convergence/divergence, define knowledge sharing outcomes.
4.5.2 Context sensitive scale
Knowledge sharing is often measured through standard questionnaires in the form of
willingness, attitude, and frequency of knowledge sharing. Although research
generalizability is enhanced, standard measures face the risk of incongruence with
knowledge sharing contexts and practices (DeVellis, 2016). Objectivity of measures can
be improved by developing more situation-specific ones, as done by Hu et al. (2009).
Refining focus by attending to nuances, such as timeliness of knowledge sharing, facets
or types of knowledge and the quality of it, can help develop good knowledge sharing
measures that provide a better understanding of its outcomes.
4.5.3 Longitudinal research design
Longitudinal research designs are needed to understand the dynamic processes of
knowledge sharing impacts. Cross-sectional designs can show direct relationships;
nevertheless, causal direction and development of events can be better analyzed through
repeated data collection (Flinchbaugh et al, 2016). An important benefit of longitudinal
research designs would be the possibility to analyze the reciprocal impacts of knowledge
sharing on its antecedents. Those factors that influence knowledge sharing over time are
themselves influenced by it and, consequently, influence knowledge sharing again,

19
triggering a feedback loop. For example, impression management is a strong motivation
for sharing knowledge(Gagné, 2009). Previous research has shown that individuals share
knowledge to attain expert status and positive supervisor appraisals. However, if
knowledge sharing results are not in alignment with motivations, individuals may
withdraw from sharing activities. The same reciprocal relationship exists between
organizational socialization and knowledge sharing. To unravel such dynamics,
longitudinal designs with repeated surveys, observations or interviews may be of great
value.
4.5.4. Multilevel analysis
A multilevel analysis of knowledge sharing outcomes is required in future studies.
Knowledge sharing is a multilevel phenomenon that operates across boundaries and is
nested within different layers of the organization. Multilevel analysis of the antecedents
of knowledge sharing has been conducted before (Quigley et al., 2007); nevertheless, the
multilevel impacts of knowledge sharing remain to be explored. It is possible that
knowledge sharing can improve individual but not team performance or vice versa.
Furthermore, employees can be tactical in knowledge sharing as they can focus on
exchanging expertise and know-how to the extent that is helpful in achieving their
personal rather than team goals. Multilevel analysis can be helpful in exploring the
conditions and mechanisms necessary to relay and realize the value of knowledge sharing
from one level to another.
5. Conclusion and implications
Knowledge sharing is an integral part of knowledge management, which, in turn, plays
an important role in the efficient accomplishment of organizational goals. In this paper,
we conducted a systematic literature review to develop a comprehensive understanding
of knowledge sharing outcomes. Based on our literature review, we propose a theoretical
framework (Figure 4) for research pertaining to knowledge sharing impacts. It offers an
overview of the current state of the field and identifies emerging theoretical and
methodological issues as discussed in the previous sections.
Overall, we can summarize four important findings from this review. First, our
review shows that the impact of knowledge sharing is holistic and can be broken down
into three categories: individual, team, and organizational. It shows that not only the
organization but also individuals gain from engaging in knowledge sharing. Second, the
most commonly studied factors influenced by knowledge sharing are creativity, learning,
and performance. Third, knowledge sharing has some beyond convention work related
impacts. For example, it contributes positively to team climate. As an interaction-
intensive activity, it enhances socialization, builds trust, encourages reciprocity and helps
in the realization and appreciation of diversity. Moreover, it improves job and life
satisfaction, although the evidence is limited and needs further investigation. Fourth,
research on knowledge sharing impacts is dominated by quantitative studies, as we found
only one qualitative study in this review. Overall, this review shows that knowledge
sharing is an important organizational activity and its potential impacts cut across all
organizational levels.

20
Figure 4. A framework for knowledge sharing impact research.
Although previous research has offered considerable evidence on the benefits that
can be accrued from knowledge sharing, more work needs to be done. We identify five
research areas that need to be explored in future research. First, research on knowledge
sharing impacts should adopt an interaction and process perspective. In particular, we
emphasize the importance of individual-, team-, and organizational-level characteristics.
Such characteristics represent the unique traits and, thus, can determine how and what is
achieved through knowledge sharing. Second, we propose that a critical perspective be
taken to broaden our understanding of the net impacts of knowledge sharing, with a
particular focus on its drawbacks. This review shows that previous research has tended to
focus on the positive aspects of knowledge sharing. Although beneficial overall,
knowledge sharing can have unintended impacts as well. For example, as a cognitive-
intensive activity, knowledge sharing can lead to stress, particularly when diversity
among individuals is high and convergence is difficult to achieve. Third, the differential
impacts of knowledge sharing should be further investigated. Knowledge sharing has
many constituents; it involves different types of knowledge as well as interaction media.
Tacit and explicit knowledge sharing, technology-mediated and face-to-face knowledge
sharing and knowledge sending and receiving are inherently different and can result in
varying impacts. Fourth, we suggest that the psychological effects of knowledge sharing
should be further explored. Knowledge sharing is a social behavior, thus intuitively
connected with psychological and social consequences. Fifth, methodological
improvements are suggested to better understand the impacts of knowledge sharing.
Qualitative research is needed to understand processes such as knowledge sharing
interaction, which conditions the influence of knowledge sharing. Moreover, longitudinal
research design, which is a natural choice for developing an in-depth understanding of
effect processes, needs to be operationalized in future research.

21
There are some practical implications that we can draw from consistent findings
in the existing literature on knowledge sharing impacts. First, knowledge sharing
activities have positive psychological impacts. Employees experience not only high job
satisfaction and strong commitment to the organization, but also life satisfaction.
Consequently, knowledge sharing should be systematically embedded into organizations’
employee well-being program. Moreover, organizations should design a knowledge
management evaluation system such that, in addition to performance and innovation,
effectiveness of knowledge sharing should be evaluated in terms of employee experiences
and emotions related to knowledge sharing activities. Second, research has shown that
knowledge sharing is beneficial at all levels - individual, team and organization.
Consequently, organizations can adjust their strategies aimed at motivating employees to
engage in knowledge sharing. For example, in highly individualistic cultures, individual
level benefits can be advertised to encourage knowledge sharing activities in
organizations. Third, many previous studies have established that knowledge sharing
strongly promotes corporate entrepreneurship. Consequently, organizations, which aim
to leverage current assets to develop new businesses and enter into new markets, should
strongly promote knowledge sharing.
This article makes an important contribution to knowledge sharing research. This
review consolidates previous research and identifies a number of useful research topics
that can be explored to advance the field as well as establish the evidence-based
significance of knowledge sharing. Moreover, it is a timely contribution, as it responds to
recent calls for more research on knowledge sharing impacts (Henttonen et al., 2016).
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