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JWL
30,1
Impacts of organizational
knowledge sharing practices on
employees’ job satisfaction
2
Mediating roles of learning commitment and
interpersonal adaptability
Received 30 May 2016
Revised 12 November 2016
5 May 2017
25 July 2017
Accepted 31 August 2017
Muhammad Shaukat Malik
Institute of Banking and Finance, Bahauddin Zakariya University,
Multan, Pakistan, and
Maria Kanwal
Institute of Management Sciences, The Women University Multan,
Multan, Pakistan
Abstract
Purpose – The purpose of this paper is to investigate empirically impacts of organizational
knowledge-sharing practices (KSP) on employees’ job satisfaction (JS), interpersonal adaptability (IA)
and learning commitment (LC). Indirect effects of KSP on JS are also confirmed through mediating
factors (LC and IA).
Design/methodology/approach – Self-administered questionnaire was used for data collection.
Knowledge workers from service sector organizations were taken as population of study. Therefore, three
types of institutes (banks, insurance and telecom companies) from services sector of Pakistan were selected
for sampling purpose. A sample size of 435 employees, comprising 145 employees from each type of institute,
was selected. Linear regression analysis and mediation analyses were performed for statistical analysis.
Findings – Organizational support for knowledge sharing fosters learning commitment (LC), and
interpersonal adaptability (IA) among workforce that ultimately grounds employees’ job satisfaction.
Therefore, in our findings, the mediating role of IA is greater than the mediating effect of LC.
Research limitations/implications – This study presents a firm reasoning to decision makers for
implementation of KSP in the organizations. Findings of study offer several subjects for discussion in the field
of KS by academics and research. Present research is limited to test the composite effect of KSP for some
selected employee outcomes only.
Originality/value – This research attempts to provide empirical evidence about impacts of KSP on
employee outcomes. Research work on such issues was lacking in Pakistani context. Therefore, this paper
supplies ample of theoretical base for future research as well as management decision makers to maximize the
benefits of implementing KSP at their organizations.
Keywords Job satisfaction, Interpersonal adaptability, Knowledge sharing practices,
Learning commitment
Paper type Research paper
Journal of Workplace Learning
Vol. 30 No. 1, 2018
pp. 2-17
© Emerald Publishing Limited
1366-5626
DOI 10.1108/JWL-05-2016-0044
1. Introduction
Knowledge is a significant resource for achievement and sustainability of competitive
advantage in businesses (Drucker, 2001). Knowledge management and knowledge sharing
(KS) at workplace have turn out to be a topic of great interest for organizations (Ozlati, 2012).
Organizations are committed to create, expand and apply both quality and quantity of
knowledge within organizational boundaries. This magnitude is even obvious for the firms
which trade in knowledge itself and have a high proportion of qualified staff (Blackler,
1995). Alvesson (1995) referred such organizations as “knowledge-intensive firms”.
Knowledge as a strategic resource empowers individuals and organizations to achieve
several benefits as improved learning, innovation and decision-making. Almahamid et al.
(2010) suggested that KS improves an individual’s competencies and satisfaction. KS is
defined as an exchange of experiences, facts, knowledge and skills all through the
organization (Nonaka and Krogh, 2009). Organization’s ability for the use of knowledge as a
resource is extremely dependent on its individuals (Ipe, 2003). Danish et al. (2014) suggested
KS as an opportunity for employees to learn from each other and promote organizational
learning.
Lin (2006) viewed KS as a source of innovation for organizations. It is a source for
development of new business possibilities and improvement in work processes (Yi, 2009).
Revolutions in business activities and the workplace diversity demonstrate a need for the
use of organizational KSP to improve learning by the staff (Almahamid et al., 2010).
Training and development opportunities improve individual’s self-efficacy levels
(Cabrera and Cabrera, 2005). Abdul Rahman (2011, p.207) established four contributing
factors toward KS “environment and infrastructure, management support, culture and
technology”. Organizational flow of knowledge (Malhotra and Majchrzak, 2004), procedural
veracity or equality among workforce (Bock et al., 2005), development of organizational
citizenship behavior and organizational commitment (Skyrme, 2002) are some important
organizational aspects for support of KS.
Becerra-Fernandez and Sabherwal (2014) suggested that KS systems support the
communication of explicit and tacit knowledge to other individuals by exchange and
socialization. Hsu (2008) suggested that KSP include socialization in workgroups, IT
systems for communication, training and development and rewards for KS. Socialization
mechanisms include discussion groups that facilitate exchange of knowledge and
experiences among group members (Becerra-Fernandez and Sabherwal, 2014). Mechanisms
that smooth the progress of exchange include letters, manuals, memos and presentations.
Becerra-Fernandez et al. (2004) proposed some employee benefits by knowledge-sharing
practices (KSP). Purpose of present research is to investigate empirically impacts of KSP on
employees’ job satisfaction (JS). Mediating roles of interpersonal adaptability and learning
commitment (LC) are also established in the scope of this research.
2. Literature review
Knowledge management literature conferred “Knowledge” in numerous ways. In the views
of Lee (2009), knowledge is taken as what a person knows. Davenport and Prusak (1998)
discussed knowledge as a mix of experiences, values, background evidence and expert
vision, whereas organizational knowledge tends to be ambiguous in nature and absolutely
attached to the people who keep it (Nonaka, 1994). Data, information and knowledge are not
exchangeable concepts (Wiig, 2012). Knowledge tends to be subjective in nature and is
extremely rigid to be codified (Lee, 2009), as it is difficult to imitate knowledge, so
individuals’ information transfer has ideal significance (Reychav and Weisberg, 2010).
Information has sense, but it is just like a message for which meanings are dependent on the
perceptions of its sender and receiver, whereas knowledge is a mixture of different elements
that range from contextual information, individual experiences to values and insights of a
knowledgeable person (Davenport and Prusak, 1998).
Nonaka and Krogh (2009, p. 638) identified that “knowledge alternates between tacit
knowledge that may give rise to new explicit knowledge and vice versa”. Tacit knowledge is
Organizational
knowledge
sharing
practices
3
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30,1
4
implicit or unarticulated and based on senses, intuition, perceptions or implied rules of
thumb. In contrast, the explicit knowledge is expressed and detained in illustrations and
writings. Organizations must encourage a know-how culture for sharing to promote transfer
of tacit knowledge (Cumberland and Githens, 2012), which is more vital than any technical
expansion (Seng et al., 2002).
KS is an action of making knowledge accessible for others within or outside the institute
(Ipe, 2003). Becerra-Fernandez and Sabherwal (2014, p. 61) defined KS as “the process
through which explicit or tacit knowledge is communicated to other individuals”. AbdulJalal et al. (2013) established KS aptitude as essential for organizational achievement. KS is a
course of action where people mutually exchange their knowledge by “donating” and
“collecting” knowledge (Hooff and Hendrix, 2005). Sharing of codified records in electronic
form, though, saves time but did not improve performance (Haas and Hansen, 2007). Morris
(2001) suggested a drastic shift in corporate culture for execution of KS activities. A general
corporate belief that “information is power” (Hannabuss, 2002) needs to be replaced by
“shared knowledge fabricate power”.
Existing literature identifies three factors as enablers for KS. This includes individual
factors (Lee and Choi, 2003), organizational factors (Lin, 2007) and technology factors
(Taylor and Wright, 2004). Individual and organizational factors considerably control KS
processes. It demands willingness of an individual or group of individuals to contribute in
sharing for mutual benefits and learning (Sharifuddin et al., 2004). Individual’s motivational
behavior either intrinsic or extrinsic is considered as complementary for KS behaviors (Gold
et al., 2001). Wang and Noe (2010) developed a framework of research in KS literature which
included organizational and cultural perspectives, interpersonal and team aspects along
with individual motivational factors. Organizational factors that encourage sharing include
ambiance (Hooff and De Ridder, 2004), procedural integrity or staff equality (Bock et al.,
2005) and organization’s dedication toward sharing (Skyrme, 2002). Koh and Kim (2004)
identified information technology and communication channels as technology factors within
organization. Bartol and Srivastava (2002) considered management support and rewards as
organizational factors, whereas Lin (2006) concluded that management support in contrary
to rewards more effectively motivate employees. Holsapple (2004) suggested that KS
involves organizational members for voluntarily contributing their knowledge toward
organizational memory.
Organizational KS is examined by individual’s formal and informal exchange of
knowledge (Bircham, 2003). Dalkir (2005, p. 186) proposed:
A knowledge-sharing culture is one where knowledge sharing is the norm, not the exception,
where people are encouraged to work together, to collaborate and share, and where they are
rewarded for doing so.
Introduction of KS systems requires a consideration for cultural needs and culture-specific
hurdles to the exchange of knowledge in organization (Ardichvili et al., 2006).
It is not possible to arbitrary force employees for KS. Therefore, encouraging members of
groups to share knowledge is a significant and challenging issue (Staples and Webster,
2008). Choi et al. (2008) revealed trust and reward systems, as social enablers more
significantly smooth KS process as compared to technical support. Holsapple (2004)
discussed some influence classes within organizations that effect sharing. Managerial
influences include leadership (responsible for development of trust in KS), coordination
(development of incentives and reward systems that encourage flow of knowledge) and
control (administration of the contents and channels used for KS).
An ideal culture for KS is one where communication and synchronization among groups
are emphasized (Dalkir, 2005). Exchange of job-related ideas and experiences among team
members is termed as KS in teams (Schwarzer, 2014). Organizations smooth the exchange of
information in team members, facilitate problem-solving and encourage team work and
decision-making (Trivellasa et al., 2015). Both individual and group incentives also smooth
the development of cooperative behaviors within workgroups (Siemsen et al., 2007).
Information technology is an important factor for KS (Bhatt, 2001). Conventional
associations, support and intensity for use of information technology drastically shape KS
behaviors (Olatokun and Nneamaka, 2013). Training programs improve an employee’s
knowledge, experiences and skills (Nadeem, 2010). Organizations adapt, innovate and
compete through training and development opportunities offered to its workforce (Salas
et al., 2012).
Existing literature revealed two kinds of KS implications: organizational competitive
advantages and benefits to the employees (Becerra-Fernandez et al., 2004). KSP offer an
agreement for organization’s novelty through socialization, exchange and learning practices
(Lin, 2006). Individuals possess diverse knowledge, expertise and capabilities that deviate
across organization. Effective coordination and guidance for sharing are essential to use this
knowledge for improvement in organizational performance (Almahamid et al., 2010).
Adaptability is discussed in literature in provisions of learning for uncertainty in
interpersonal, cultural, and work stress-related magnitudes. (Ployhart and Bliese, 2006).
Work-related management of emergencies, job stress and uncertain situations is also
discussed in the studies of adaptability by Pulakos et al. (2000). Ployhart and Bliese (2006)
conceptualized eight dimensions of individual’s adaptive performance, which include IA as
a significant aspect of individual’s adaptive performance. IA is an ability to adjust with
interpersonal approach to attain a goal (Hartline and Ferrell, 1996). Paulhus and Martin
(1988) discussed it as flexibility in behaviors to adjust with a new team, colleague or patrons.
A shift from industrialization to service orientation of businesses made it essential for
employees to become adaptive interpersonally. IA is inevitable in project teams as well
(Schneider, 1994).
Employees’ motivation to expand knowledge and their commitment to learn new
comprehensions and skills foster enduring success for the business by improving an
organization’s competitive gains (Tsai et al., 2007). Learning and attainment of skills are
possible only through sharing and deployment of knowledge (Gould, 2009). Personal
motivation to revolutionize and learn is a strong base for organizational learning. So, to
secure competitive advantage, organizations motivate employees to attain and share
knowledge (Senge, 2003). Willingness and ability of individuals to share their experiences
are vital for individual’s learning (Lehesvirta, 2004). Meantime sharing opportunities and
support for learning at workplace also develop employees’ learning (Li et al., 2009).
JS is an expression of individual’s behavior (Schmidt, 2007), tied with an individual’s
contentment from physical as well as psychological and emotional perspectives (Hsu, 2009).
JS is an outcome of employee’s insight and assessment of his job which take influence by
individual’s exclusive needs, ideals and expectations (Sempane et al., 2002).
3. Research model and hypothesis
Present research is an attempt to empirically investigate proposed relations among KS and
employee benefits by Becerra-Fernandez et al. (2004) in the context of service sector of
Pakistan. At first, impacts of KSP (independent variable) are hypothesized for JS, LC and IA
as dependent variables. Second, IA and LC are proposed as mediating variables that mediate
impacts of KSP on JS. Hypothesized research model of the study is illustrated in Figure 1.
Organizational
knowledge
sharing
practices
5
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30,1
M1*
Learning
Commitment-(LC)
H4
6
H2
Organizational
Knowledge Sharing
Practices--(KSP)
Job
Satisfaction-((JS)
H1
H3
H5
M2*
Interpersonal
Adaptability-(IA)
Figure 1.
Hypothesized
research model
Notes: Black line represents direct paths, whereas green dotted line represents the indirect paths;
M1* = indirect effect of KSP on JS through LC (H4); M2* = indirect effect of KSP on JS through
IA (H5)
Ambition and fascination to share knowledge are associated with employee’s JS (DeVries
et al., 2006). Organizational support for the fulfillment of employees’ socioemotional
requirements creates positive job attitudes and satisfaction (Cullen et al., 2014). Management
support for exchange of ideas among workers promotes employees’ performance
(Fernandez, 2008). Strength of the social relations is crucial for KS in teams (Burke, 2011).
Positive relations among team work and JS as well as organizational commitment have been
identified by Karia and Asaari (2006), whereas informative training opportunities also play
considerable role in employee’s overall JS (Schmidt, 2007). Training and learning
opportunities improve workers’ level of satisfaction (Lowry et al., 2002). Cross and
Cummings (2004) identified strong relations between KS potentials and individual’s
outcomes in knowledge-centered businesses. Teh and Sun (2012) discovered positive
relations among JS and KS opportunities. Therefore, for present study, first hypothesis is
derived as:
H1. Organizational KSP are positively related to JS of employees.
Socialization practice in organizations helps employees to obtain knowledge and develop
skills (Becerra-Fernandez and Sabherwal, 2014). Almahamid et al. (2010) discovered positive
relations among KSP and LC. Hegazy and Ghorab (2014) found positive associations
between KS and individual’s learning. Employees’ LCs in the construct of JS take much
influence from interpersonal relationships (Tsai et al., 2007), whereas “team learning
depends on each member’s individual ability to acquire knowledge, skills, and abilities as
well as his or her ability to collectively share that information with teammates” (Day et al.,
2004, p. 870). Exchange of knowledge among individuals positively contributes to both
individual and organizational learning (Andrews and Delahaye, 2000). Thus, second
hypothesis of this study is derived as:
H2. Organizational KSP and employees’ LC are positively related.
IA depends on individual’s willingness to interact with each other, and KS possibilities
available to them (Burke et al., 2006). Pulakos et al. (2006) suggested that KS smooths the
progress of individual’s adaptability. Innovation-oriented organizations share knowledge
about successes and failures across disciplines, which allows for faster innovation (Krogh
et al., 2001). Findings of Tuominen et al. (2004) indicated strong relations between
organizational adaptability and innovativeness. When employees get opportunities to
interact with others in the organization, they anticipate change, persistently expand
knowledge and consequently become more adaptive (Becerra-Fernandez et al., 2004). Hence,
third hypothesis of present study is:
H3. Organizational KSP significantly impact employees’ IA.
Assessment of literature in this study illustrates that KSP have positive relations with
employees’ LC and IA same as for JS. At the same time, employees’ LC and IA are positively
related with JS. Based on above findings, this study proposed a mediating role of employees’
IA and LC in fourth and fifth hypotheses as:
H4. Employees’ LC mediates the relationship between KSP and JS.
H5. Employees’ IA mediates the relationship between KSP and JS.
4. Research methodology
This research was based on deductive method. To validate the associations among
variables, a positivistic approach and quantitative research strategy were used.
4.1 Population and study sample
Service sector plays very important role in development of overall economy of a country,
and Pakistan is no exemption from this scenario. So, focus for the study was service sector
of Pakistan. Therefore, for industry selection, it was primarily concerned with two criteria:
first, the industry which considers knowledge management practices as imperative; second,
the industry which had developed proper information technology-based infrastructures for
sharing of knowledge among workforce (Kim and Lee, 2006).
Thus, employees serving in telecom, banking and insurance companies operating all
over Pakistan were taken as population of this study. A reason to select these organizations
from service sector was knowledge orientation of the tasks performed in such organizations
along with the use of up-to-date IT-based infrastructure, which obligates employees for
sharing of knowledge with each other.
Stratified convenience sampling method was used to make the research purposeful and
to get it completed within limited time span. Therefore, the criterion adopted to select
sample subjects for this study was that respondents must be holding at least first-line
managerial position in the organization.
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sharing
practices
7
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30,1
Primary data for the study were collected with the help of survey method. Practically, 540
questionnaires were floated in banks, insurance and telecom companies, out of which 450
responses were received, and among these, 15 questionnaires were incomplete and so rejected
from study analysis. Therefore, a sample of 435 knowledge workers was engaged for study,
taking 145 respondents from each stratum. Response rate for the study is 81.00 per cent.
8
4.2 Data collection and instrumentation
Primary data were collected with the help of a structured questionnaire and assembled
based on prior tested and validated instruments in the published literature. Minor
adjustments incorporated, so that prior measures suit in the present study context.
Participants of the study were requested to rate every item on a five-point Likert scale
ranging from (1) as strongly disagree SD to (5) as strongly agree SA.
Organizational KSP was measured by seven items from Hsu (2008). The composite
reliability of items as reported by Hsu (2008) for this measure is 0.91, whereas reliability
tested by Cronbach’s alpha for this research was 0.80. Sample items are “My company offers
incentives to encourage knowledge sharing” and “My company offers a variety of training
and development programs”.
Learning commitment: Five items to measure LC were taken from Tsai et al. (2007)
reporting reliability as 0.94. Its reliability for present study was 0.75. Sample statements are
“I am willing to spend extra time taking part in the internal and external training courses
provided by the firm” and “To me, being able to learn constantly is very important”.
Interpersonal adaptability: Individual adaptability measure constructed by Ployhart and
Bliese (2006) was used to measure IA. Cronbach’s alpha reported reliability of this measure
was 0.80 for this research. Sample items from this measure include the statements as “I am
an open- minded person in dealing with others” and “My insights help me to work
effectively with others”.
Job satisfaction: The measures for JS were used from a shortened version of Minnesota
Satisfaction Questionnaire (Weiss et al., 1967). This consists of 20 items with reliability as 0.90.
Teh and Sun (2012) used 7 out of these 20 items and come up with composite reliability value as
0.912 and convergent validity value as 0.598. Reliability of these items for our study was 0.80.
This measure includes the statements as “the chance to make use of my abilities and
skills at job” and “the chance to be ‘somebody’ in the community”.
4.3 Data analysis
Statistical Package for Social Sciences (SPSS) version 21 was used for statistical analysis of
primary data. Descriptive statistics, correlation coefficients and construct reliability were
calculated to check measures of study. To test hypothesis, both simple linear regression
analysis and mediation analysis were performed.
5. Results
5.1 Sample descriptions
Demographic analysis performed by cross-tabulation is detailed in Table I, sample
consisting of 435 respondents involving 145 employees from each type of selected institute
comprising 73 per cent male and 27 per cent female respondents. Respondents from an age
group of 20-30 years were 45 per cent of total sample, and only 15.4 per cent respondents
belong to an age group of 41-50 years.
Maximum number of respondents (40.7 per cent) was from less than five years job
experience group. In sample group, 35.2 per cent male and 15.6 per cent female respondents
Variables
Category
Male (%)
Females (%)
Total (%)
Age group
20-30
31-40
41-50
126 (28.97)
127 (29.19)
64 (14.71)
317 (72.87)
106 (24.37)
105 (24.14)
106 (24.37)
317 (72.87)
126 (28.97)
127 (29.19)
64 (14.71)
317 (72.87)
116 (26.67)
108 (24.83)
93 (21.38)
317 (72.87)
69 (15.86)
46 (10.57)
3 (0.69)
118 (27.13)
39 (8.97)
40(9.20)
39 (8.97)
118 (27.13)
68 (15.63)
46 (10.58)
4 (0.92)
118 (27.13)
61 (14.02)
51 (11.72)
6 (1.38)
118 (27.13)
195 (44.83)
173 (39.77)
67 (15.40)
435 (100)
145 (33.33)
145 (33.33)
145 (33.33)
435 (100)
194 (44.60)
173 (39.77)
68 (15.63)
435(100)
177 (40.69)
159 (36.55)
99 (22.76)
435 (100)
Institution type
Managerial position
Job experience
Total
Bank
Insurance
Telecom
Total
First-line manager
Middle manager
Top manager
Total
<5
5-10
>10
Total
Organizational
knowledge
sharing
practices
9
Table I.
Sample
demographics
hold the first-line managerial position, whereas 10.1 per cent male and only 0.9 per cent
female respondents have top management positions.
5.2 Construct reliability and correlations
As shown in Table II, Cronbach’s alpha for all constructs ranges from 0.75 to 0.81, which is
greater than the threshold level of 0.60 set by Kaiser (1974). This establishes reliability of the
construct items used to measure variables of study. Pearson correlations in Table II present
that no bivariate correlation exists among variables (maximum correlation is 0.557).
Moreover, as in a self-report investigation survey, the reliability of construct’s relations is
susceptible to the flaw of “common method variance”. To check such variance, Harman’s
one-factor analysis was conducted. Our analysis confirmed that common method bias is not
a main concern in this study, as only 45 per cent variance was explained by one factor which
is less than the 50 per cent cutoff point (Mat Roni, 2014).
5.3 Statistical analysis and hypothesis testing
Linear regression analysis was executed three times to test first three hypotheses of study.
Findings of model summary, relationship coefficients and regression analysis of variance
are presented in Table III. KSP served as predictor for employee’s JS, LC and IA.
Model 1 demonstrates a reasonable coefficient of determination. Variance in JS is
substantially explained by KSP (R2 = 0.300). Path coefficient (0.496) for KSP and JS is
statistically significant (p < 0.01). These findings support first hypothesis of study.
Construct
KSP
LC
IA
JS
KSP
LC
1
0.393**
0.498**
0.548**
1
0.545**
0.308**
Notes: N = 435; **p < 0.01
IA
1
0.557**
JS
Mean
SD
Cronbach’s alpha
1
3.978
4.231
4.205
4.113
0.631
0.607
0.535
0.572
0.80
0.75
0.80
0.81
Table II.
Correlation matrix,
descriptive statistics
and reliability
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Model 2 shows that variance in learning commitment is less explained by KSP (R2 = 0.154).
Therefore, path coefficient (0.378) for KSP and LC is statistically significant (p < 0.01)
supporting second hypothesis of research.
Model 3 provides a coefficient of determination for variance in interpersonal adaptability
as explained by KSP (R2 = 0.248). Path coefficient (0.423) is statistically significant (p < 0.01)
substantiating third hypothesis of research.
Regression analysis was conducted separately for three types of institutes presented in
Table IV. Findings from telecom suggest that path coefficients for JS (0.594), LC (0.387) and
IA (0.422) are statistically significant (p < 0.01). Coefficient of determination for JS (R2 =
0.404) is more than the coefficient of determination calculated for service sector as one cluster
in Table III (R2 = 0.300). Variance in LC by KSP (R2 = 0.167) is marginally increased in
comparison to (R2 = 0.154) in Table III. Variance in IA of telecom employees is significantly
explained by KSP (R2 = 0.303) improved in comparison to (R2 = 0.248) in Table III.
Findings from bank data in Table IV also confirm that path coefficients for JS (0.375), LC
(0.258) and IA (0.405) are statistically significant (p < 0.01). Variance in JS is partially
explained by KSP (R2 = 0.160) in comparison to (R2 = 0.300) in Table III for service sector.
Variance in LC (R2 = 0.068) is decreased in comparison to (R2 = 0.154) in Table III. Variance
in IA is (R2 = 0.168) decreased in comparison to (R2 = 0.248) in Table III. Regression analysis
for insurance companies in Table IV presents that path coefficients for JS (0.505), LC (0.434)
and IA (0.418) are statistically significant (p < 0.01). Variance in JS (R2 = 0.345) is more than
the one calculated for service sector (R2 = 0.300) in Table III. Variance in LC (R2 = 0.216) is
also increased from (R2 = 0.154) in Table III. Variance in IA by KSP (R2 = 0.273) is greater
than (R2 = 0.248) in Table III for service sector.
5.4 Mediation analysis
Mediation analysis was performed to confirm the mediating effects of IA and LC among
the dependency relations of JS and KSP. “Simple mediation model” (Model Number 4) in
Table III.
Regression analysis
for services sector
(bank þ insurance þ
telecom)
Constant
KSP
R2
F-value
Model 1
JS
Model 2
LC
Model 3
IA
2.139**
0.496**
0.300
185.553**
2.728**
0.378**
0.154
79.001**
2.524**
0.423**
0.248
143.105**
Notes: N = 435; **p < 0.01
JS
Telecom
LC
IA
JS
Banks
LC
IA
Insurance companies
JS
LC
IA
Constant 1.658** 2.782** 2.600** 2.663** 3.162** 3.162** 2.143** 2.452** 2.505**
KSP
0.594** 0.387** 0.422** 0.375** 0.258** 0.405** 0.505** 0.434** 0.418**
0.404
0.167
0.303
0.160
0.068
0.168
0.345
0.216
0.273
R2
F-value 96.863** 28.626** 62.163** 27.717** 10.365** 28.934** 75.455** 39.367** 53.755**
Table IV.
Separate regression
analysis for each unit Note: **p < 0.01
PROCESS bootstrap approach introduced by Hayes (2013) was used for analysis.
Present study proposed two mediating variables: LC (M1) and IA (M2). Mediation
model in PROCESS bootstrap was applied two times independently for each mediating
variable.
Findings of mediation analysis are presented in Table V. “Total effect model” with LC
(M1) confirms that KSP positively predicts JS (coefficient = 0.457, p = <0.01, R2 = 0.310) and
positive significant relation of LC with JS (coefficient = 0.103, p = <0.05). Total effect of KSP
on JS is (0.496), whereas direct effect of KSP (0.457) and indirect effect with M1 is (0.039). It is
a small effect size with respect to power of mediation discussed by Kenny (2016). Confidence
interval for an indirect effect (BootLLCI: 0.006, BootULCI: 0.084) confirms the significance of
effect, as it does not include 0 (Kenny, 2016). These findings support fourth hypothesis of
this study.
“Total effect model” with IA (M2) as presented in Table V confirms positive significant
relation of IA with JS (coefficient = 0.404, p = <0.01) and that KSP predicts JS (coefficient =
0.326, p = <0.01, R2 = 0.407). Total effect of KSP on JS is (0.496), whereas direct effect of KSP
(0.326) and indirect effect with M2 is (0.171). It is a medium size significant effect, as
confidence interval (BootLLCI: 0.117, BootULCI: 0.242) does not include 0 (Kenny, 2016).
These findings validate fifth hypothesis of this study.
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11
6. Discussion
Emphasis of present research was to evaluate the impacts of organization’s KSP on its
workforce, whereas another vital aspect proposed in this research was to check the
interrelations of employee’s outcome variables. Therefore, an attempt has been made to
check the mediating effects of IA and LC among the relation of KSP and JS. However, such
researches have been formerly reported, but the proposed mediation effects are not
investigated in previous studies. Almahamid et al. (2010) empirically substantiated relations
among KSP, LC, JS and all kinds of employee adaptability in manufacturing companies at
Jordan. Hegazy and Ghorab (2014) in a study on UAE university academic and
administrative staff discovered positive associations among KS by means of a corporate
Constant
LC (M1)
IA (M2)
KSP
R
R2
F-value
Total effect
Direct effect
Indirect effect
Partially standardized indirect effect
Completely standardized indirect effect
Ratio of indirect-to-total effect
Ratio of indirect-to-direct effect
R2 mediation effect size
Notes: *p < 0.05; **p < 0.01
JS-(M1)
JS-(M2)
1.858**
0.103*
0.457**
0.557
0.310**
97.084**
0.496**
0.457**
1.119**
0.404**
0.326**
0.638
0.407**
148.449**
0.496**
0.326**
0.039
0.068
0.043
0.079
0.085
0.085
BootLLCI BootULCI
0.006
0.084
0.008
0.141
0.005
0.092
0.012
0.166
0.012
0.199
0.039
0.164
0.171
0.298
0.188
0.344
0.524
0.203
BootLLCI BootULCI
0.117
0.242
0.206
0.402
0.131
0.259
0.242
0.478
0.318
0.917
0.136
0.284
Table V.
Mediation analysis
(direct and indirect
effects of mediators)
JWL
30,1
12
portal and individual’s learning and adaptability. Hussain et al. (2016) identified impacts of
KS behavior of teams on service novelty and performance in Malaysia. Therefore, no
empirical evidence is available from service sector to check such relations. Moreover, this
study is first of its kind in the context of service sector of Pakistan. Present study engaged
employees of banking, insurance and telecom companies serving in various cities of Punjab
province of Pakistan for collection of primary data. Proposed impacts of KSP on JS, LC and
IA were empirically tested.
Findings of this empirical investigation proved the proposed employee benefits by
Becerra-Fernandez et al. (2004) and determine that KSP in organizations positively impact
employees’ outcomes, including IA, JS and LC. Logical relations among KSP and employee
outcomes confirmed in the study are also in line with the empirical findings of previous
studies (Almahamid et al., 2010; Hegazy and Ghorab, 2014).
Major outcomes embrace that KS practices are implemented in all three types of
institutes under investigation from service sector of Pakistan. KS supported by
organizations has significant positive effect on JS directly as well as indirectly through LC
and IA as mediators. Research findings from mediation analysis proved the study
hypothesis; IA cultivates among workforce because of organizational focus toward KS
which in turn improves JS. Same as for LC which results by organizational support for KS
and improves the satisfaction level on the job. Mediation analysis results indicate that IA
has a medium size mediation effect (indirect effect = 0.171), whereas LC has a small size
mediation effect (indirect effect = 0.039).
6.1 Academic and practical implications of the study
In Pakistan, there has been little research in the field of knowledge management and KS.
Furthermore, no empirical evidence is available from service sector to check impacts of
KSP and workforce benefits. This study is first of its nature that discusses impacts
of organizational KSP on employees’ IA, LC and JS in the context of service sector of
Pakistan.
This study contributes to the literature from a theoretical standpoint, as scope of this
research embraces an investigation about mediating roles of LC and IA. Han et al. (2016)
suggested that KS is a subject in the domain of professional development and workplace
learning. Findings of this research also support the need for KSP for workforce learning,
IA and JS. Hence, the outcomes serve as a pathway for scholastic persons to advance
research on KS issues in relations with employee outcomes. The strategy and findings of
this study offer several subjects of discussion for academics as well as research and
practice.
Practically, this study presents a firm reasoning to decision makers for implementation
of KSP in the organizations, as it empirically proves significant positive relation among
KSP, JS and IA of service sector employees. KS is critical for effective performance in
knowledge-intensive organizations, particularly in service sector all over the world.
Presence of positive relations between KSP and IA presents likelihood that employing
extroverted individuals intensifies the benefits of KSP; therefore, the study offers a superior
decision-making support for organizations in their staffing and recruitment activities.
6.2 Study limitations and directions for future research
This study extends the theoretical observations of Becerra-Fernandez et al. (2004) through
an empirical investigation in the context of service sector of Pakistan.
Scope of this research was limited with regard to some design aspects. These limitations
provide directions for future studies in this field. Outcomes of this research are limited to
investigate only three types of institutes (bank, insurance and telecom) among service sector
of Pakistan. A comparative study among these three or some other service sector institutes
to check the role of KSP in JS of employees is a future possibility for research.
Second, this study is limited to test the effects of KSP for just three kinds of employee
outcomes, i.e. LC, IA and JS. In future, additional impacts of KS approaches need to be
investigated empirically.
Finally, a composite impact for all KSP on employee outcomes is measured in present
study which instigates a strong need to check impacts of each type of KS activity separately
in relation to employee outcomes.
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Corresponding author
Maria Kanwal can be contacted at: maria.kanwal@wum.edu.pk
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