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CHAPTER: 5
CONCEPTUAL FRAMEWORK, THEORETICAL CONSTRUCTS AND
RESEARCH HYPOTHESES
5.1 Conceptual Framework
As is evident in the present literature, there is not only a dearth of knowledge
sharing studies in India, but an absence of empirical research that investigated the role of
environmental and individual factors on knowledge sharing simultaneously. Most of the
literature of knowledge management argued for the role of organizational structure, climate,
information and communication technology in the extent of knowledge sharing. The main
emphasis of this research is to bridge the gap in literature to explain how organizational as well
as individual factors enhance extent of knowledge sharing in cross functional teams. The
research has been based on the theoretical underpinning of theory of reasoned action and social
exchange theory.
The conceptual framework as proposed in figure 5.1 depicts predictors in the form
of organizational, job and organizational characteristics. Knowledge sharing mediates the
relationship of these characteristics to team performance. Other than this, the relationship of
knowledge sharing with team performance gets moderated by mutual trust.
Figure 5.1 Conceptual framework
5.2 Theoretical Constructs
Independent variables have been divided into three broad categories,
organizational characteristics, job characteristics and individual characteristic. The selection of
variables under each category has been made, as per the identified research gaps. Organizational
characteristics constitute, organization structure, learning culture, employee training, better
rewards and top management support as theoretical constructs. Job characteristics are job
autonomy, job variety, feedback, job identity and job significance (Hackman & Oldham, 1975).
Under individual characteristic, researchers included single construct i.e. emotional intelligence.
Coming sections include our research model and research propositions.
5.2.1 Organizational Structure
Organizational structure consists of two components. One is formalization in the
organization and the other is the degree of decentralization or centralization in decision-making
(Fuchs, 2004). Formalization indicates the extent to which the rights and duties of the members
of the organization are determined and the extent to which these are written down in rules,
procedures, and instructions (Willem, 2006; Schminke et al., 2000). Organization that is less
formal in its structure leads to greater extent of organizational socialization because it provides
better communication with partners and employees (Kanter, 1983). It creates greater flexibility
and openness, which is conducive for organizational socialization. The greater flexibility helps to
lower the obstacles during communication flow in the organization (Islam, Ahmad & Mahtab,
2010).
Decentralization is the delegation of decision making authority throughout the
organization. Decentralization creates an environment that increases communication and
commitment among the employees in the organization. The central idea of decentralization is to
provide greater opportunities for participation in decision-making and for the better interactions
among the employees. Greater participation in decision-making also destroys the boundary
between those who make decision and those that are affected by the decision, facilitating easy
interaction and socialization (Islam, Ahmad & Mahtab, 2010). Centralized decision-making
drives the knowledge sharing process ineffective, especially when complex knowledge is
involved (Willem, 2006). Centralization and especially hierarchy have a negative effect on
knowledge sharing between units in organizations because of the control embedded in
centralized systems (Tsai, 2002). Top-down directives can reinforce an environment of fear,
distrust, and internal competition reducing collaboration and integrative actions (Senge, 1997)
Flexible organizations‘ structure advances knowledge sharing by encouraging horizontal
communication (Chkravarthy, Zaheer & Zaheer, 1999; Hansen, 1999; Bhatt, 2001).
Organizations can develop proper structures to leverage this knowledge sharing between
departments (Teece, 1998). The problem of designing an organization that optimizes knowledge
sharing remains unsolved, but several studies have shed light on the issue and reveal insight into
the relevant influencing factors (Easterby-Smith & Lyles, 2003). One important facilitator of
knowledge sharing between departments is the coordination that exists between departments
(Grant, 1996).
5.2.2 Organizational Learning Culture
The creation of a knowledge-sharing culture is thought to be one of the most
important knowledge-sharing antecedents (Davenport & Prusak, 1998; Lilleoere & Hansen,
2011). Thus, one key challenge may be to facilitate effective knowledge sharing in the
organization by ensuring an adaptive or learning culture that supports knowledge sharing
(Nielsen, 2006). A learning culture in an organization promotes knowledge sharing among
organization members who believe that learning by sharing knowledge would improve work
performance (Goodman & Darr, 1998; Hargadon, 1998; Kostova, 1999; Ruggles, 1998; O‘Dell
& Grayson, 1998; Tsai & Ghoshal, 1998).
5.2.3 Employee training
Employee training helps members of organizations realize the importance of
sharing their knowledge in learning processes through which employees are involved in a multi-
faceted set of learning activities (O‘Dell & Grayson, 1998; Liedtka & Haskins, 1997). Employee
training helps employee adapt themselves to the new environment quickly and facilitates the
creation and dissemination of new knowledge for maintaining a continuous learning cycle for
better performance (Kang, Kim, & Chang, 2008).
5.2.4 Rewards
Appropriate reward systems aligned clearly with the creation and dissemination of
knowledge into organization would also promote employees‘ knowledge sharing (O‘Dell &
Grayson, 1998; Ruggles, 1998; McDermott & O‘Dell, 2001; Wiig, 1997). Ruggles (1998)
contended that the establishment of the reward systems in an organization facilitated knowledge
sharing among organizational members because they can expect positive reinforcements for
sharing their knowledge with co-workers. Finally, support from the top management is critical in
the growth of knowledge sharing as it attracts voluntary participation from employees in
initiating and disseminating important knowledge (O‘Dell & Grayson, 1998; Chkravarthy,
Zaheer, & Zaheer, 1999). Bartol and Srivastava (2002) proposed that rewarding individual,
teams and team/work units for knowledge is one of the potential approaches to encouraging
knowledge sharing.
5.2.5 Top management support
Kang, Kim & Chang (2008) examined the impact of knowledge sharing on
individual work performance. They have also indicated that four exogenous variables namely,
employee training, reward systems, support from the top management, and openness in
communication are perceived to have a positive influence on employees‘ knowledge sharing,
which in turn improved individual work performance. Perceived trustworthiness between
individuals involved in knowledge sharing has also positively influenced both knowledge
sharing and individual work performance.
Gagne (2009) presented a model of knowledge-sharing motivation based on a
combination of the theory of planned behavior (TPB) and self-determination theory (SDT), along
with a review of research supporting the model and suggestions for future research and
methodologies to study knowledge sharing behavior. He also suggested inclusion of five
important human resource management (HRM) practices, including staffing, job design,
performance and compensation systems, managerial styles, and training for design. Bock et al
(2005) developed an integrative understanding of the factors supporting or inhibiting individuals'
knowledge-sharing intentions. They developed a theoretical framework in which the theory of
reasoned action (TRA) was augmented with extrinsic motivators, social-psychological forces and
organizational climate factors that were believed to influence individuals' knowledge sharing
intentions.
5.2.6 Job Design
Foss, Minbaeva, Pedersen, and Reinholt (2009) argued that job design matters to
knowledge sharing for motivational reasons. Management can design jobs to influence variables
such as autonomy, task identity, and the degree of feedback the employee receives. These job
characteristics impact employee motivation to share knowledge, albeit in different ways, and
eventually affect knowledge-sharing behavior (Foss, Minbaeva, Pedersen, & Reinholt, 2009).
They mentioned that the links between job design and knowledge sharing practices have
received little attention in the literature.
Hackman and Oldham (1975) pointed out, jobs with motivating characteristics
able to inspire a sense of accomplishment in employees and a high level of intrinsic job
motivation, which will satisfy an individual employee's higher order needs (e.g., self-esteem and
self-actualization) leads to good job performance (Krishnan et. al., 2010). Hackman and
Oldham‘s (1975 & 1980), job characteristics model explained five core job characteristics that
may influence employee attitudes and work outcomes. The dimensions are as follows:1) job
variety (the extent to which an employee can use different skills in doing his/her work); 2) job
identity (the extent to which an employee can complete the whole or identifiable piece of work);
3) job significance (the extent of the significant impact of the job on others); 4) job autonomy
(the extent of freedom, independence, and discretion of an employee to plan his/her work pace
and method); and 5) job feedback (the extent to which an employee knows his/her own job
performance from the job itself, colleagues, supervisors, or customers). These five core job
dimensions were expected to foster three important psychological states in employees
(meaningfulness of the job, experienced responsibility for the job results, and awareness of the
actual effects of their work), which, in turn, resulted in various personal and work outcomes
including intrinsic work motivation (Millette & Gagne, 2008), job satisfaction (Menguc &
Bhuian, 2004; Schjoedt, 2009), lower absenteeism (Rentsch & Steel, 1998), job performance and
productivity (DeVaro & Brookshire, 2007).
Figure 5.2 Dimensions to measure job characteristics
5.2.7 Emotional Intelligence
Emotional intelligence (EI) has become of widespread interest to psychological
research in recent years. Salovey and Mayer (1990) first introduced the term emotional
intelligence. They defined EI as a subset of social intelligence and involving "the ability to
monitor one's own feelings and emotions, to discriminate among them, and use this information
to guide one's thinking and actions" (p. 189). It is also defined as the ability to recognize and
regulate emotions in one and others (Spector, 2005). Salovey and Mayer's model was soon
followed by a plethora of alternative conceptualizations of EI (Bar-On, 1997; Cooper & Sawaf,
1997; Goleman, 1995; Wessinger, 1998).
EI has generally been defined as the ability to perceive, understand, and manage
one's emotions (Salovey, Hsee & Mayer, 1993; Salovey & Mayer, 1990). In the spirit of Charles
Darwin (1872) viewed the emotional system as necessary for survival and as providing an
important signaling system within and across species; Salovey and Mayer (1990) emphasized the
functionality of feelings and described a set of competencies that might underlie the adaptive use
of affectively charged information. EI has its roots in the concept of social intelligence first
identified by Thorndike in 1920. Thorndike (1920) defined social intelligence as ―the ability to
understand and manage men and women, boys and girls—to act wisely in human relations‖ (p.
228). Following Thorndike‘s ideas, Gardner (1993) included interpersonal and intrapersonal
intelligences in his theory of multiple intelligences. According to Gardner, social intelligence is
one among seven intelligence domains, comprises an individual‘s interpersonal and intrapersonal
intelligences. Intrapersonal intelligence relates to one‘s ability to deal with oneself and to
―symbolize complex and highly differentiated sets of feelings‖ within the self. Interpersonal
intelligence relates to one‘s ability to deal with others and to ―notice and make distinctions
among other individuals and, in particular, among their moods, temperaments, motivations and
intentions‖. EI can be viewed as a combination of the intrapersonal and interpersonal intelligence
of an individual (Law, Wong and Song, 2004).
Emotional intelligence is involved in the capacity to perceive emotions, assimilate
emotion related feelings, understand the information of those emotions, and manage them. EI
involves the adaptive use of emotions (Salovey & Mayer, 1990; Schutte, Malouff, Hall,
Haggerty, Cooper and Goldenl, 1998) with a strong focus on the interaction between emotions
and cognition (Mayer, Salovey, & Caruso, 2004). Perception of emotion, understanding
emotions, using emotion in cognitive processes, and managing emotions are all aspects of
emotional intelligence (Mayer & Salovey, 1997, Wing et. al. 2006).
Goleman (1995) proposed that cognitive skill 'can help you get a job' in a
company, but emotional skill helps you grow in the job once you are hired. He also suggested
that for professional competence at the work place one has to be more positive, approachable,
warm, empathetic and optimistic. Goleman (1998) concluded that emotional intelligence matters
twice with technical and analytic skills for star performances. The higher people move up in the
company, the more crucial emotional intelligence becomes.
According to Singh (2010), EI behavior addresses the basic issues for bringing
workplace effectiveness and helps to attain higher levels of organizational growth and
excellence. This essentially aids in the process of developing efficiency at the workplace and
development and enhancement of human capital. While organizations can put the tools in place,
there is no guarantee that employees are going to use them, or use them effectively, so there is
still a human aspect for knowledge sharing (Smith, 2003; Kharabsheh, 2007). Studies of
individual-level knowledge sharing have been conducted in information systems (Wasko &
Faraj, 2005), organizational behavior (Bordia, Irmer, & Abusah, 2006), strategic management
(Reagans & McEvily, 2003), psychology (Lin, 2007a). Focussing on the field of organizational
behavior then some authors found a positive relationship between personality traits and KS
(Teh, Yong, Chong & Yew, 2011; Cho, Li & Su, 2007) and some research has explored linkage
between motivational factors and KS (Foss & Pedersen, 2002; Cho, Li & Su, 2007; Galia, 2008).
Karkoulian, Al-Harake and Messarra (2010) found a positive relationship between organizational
commitment and KS via Emotional Intelligence but he mentioned that there is lack of a
systematic review till date and study has been conducted in terms of personality determinants of
emotional intelligence, and how this relates to the individual‘s knowledge sharing.
In a knowledge environment, a knowledge citizen is focused on personal
development, motivation and connectedness and has a high degree of self-commitment, work–
life integration, individual competence building, is open to transfer of tacit knowledge and is
generally an empowered individual. Goleman's (1995) definition of motivation applies to a
person's own inner fire or drive as opposed to the inspirational effect a person has on others – a
passion to work for reasons that go beyond money or status and the propensity to pursue goals
with energy and persistence. Clearly, he is describing what we know as knowledge citizens
(Emmerling & Goleman 2003; Sutton, 2006).
Knowledge provides context for people, ideas and experience and, therefore,
transferred knowledge must be internalized before it can be used (Sutton, 2006). In addition,
knowledge management will have different meanings in different contexts. For example,
knowledge management provides social capital for a knowledge worker community with social
networks that encourage leadership, membership, trust, value and a knowledge-sharing attitude
and behaviour. Knowledge management is about creative capital when it refers to our diversity
of skills, emotional intelligence, knowledge creation and innovation (Sutton, 2006). Knowledge
sharing is strongly dependent on the setting, various personal beliefs, and the actions and
practices among the individuals involved (Lilleoere & Hansen, 2011).
For the success of an organization, knowledge sharing is perceived to be very
crucial (Chow et al., 2000; Davenport & Prusak, 1998; Nevis et al., 1995; Drucker, 1993). Thus,
we should understand the various factors that influence knowledge sharing behaviors
(Mooradian et al, 2006). However, it is important to change employees‘ behaviors and attitudes,
in order to willingly share knowledge (Moffett et al., 2003; Lee & Choi, 2003; Jones et al.,
2006). Given the above, we can expect emotional intelligence to play a key role. Decker,
Landaeta and Kotnour (2009) have mentioned that an individual‘s personality can be an
important factor for knowledge sharing. Thus, if we know more about the relationship between
personality and knowledge sharing, we will be able to better handle questions about knowledge
sharing and encourage it. In this research, we focus on the personality determinants of emotional
intelligence and how it relates to the individual‘s knowledge sharing.
5.2.8 Mutual trust
The importance of trust as a driver of knowledge sharing has been most widely
recognized (Adler 2001; Andrews & Delahaye 2000; Ciborra & Andreu 2001; De Cremer,
Snyder, & Dewitte 2001; McEvily, Perrone, & Zaheer 2003; Newell & Swan 2000).
Interpersonal trust facilitates effective knowledge-creation through removing knowledge-sharing
barriers in an organization (Cross, Rice, & Parker, 2001; Holste & Fields, 2010; Tsai & Ghoshal,
1998). Nonaka (1994) suggested that trust builds a healthy atmosphere for knowledge sharing
and acts as a moderator. New employees initially may lose confidence due to lack of
interpersonal trust which can be improved through finding interpersonal similarities and joint
problem solving techniques (Moreland, 2006; Renzl, 2008; Wang, Shieh, & Wang, 2008). Thus,
interpersonal trust enables employees to mingle easily in similar networks on and off the job,
which can boost knowledge-sharing activities. Recently, a study found that that trust
strengthened the relationship between the knowledge seeker and the knowledge source in the
IBM Institute for Knowledge-Based Organizations (Levin, Cross, Abrams, & Lesser, 2002).
Dietz and Hartog (2005) concluded in their overview of the most-quoted
definitions of trust, the possible forms that trust can take are: trust as a belief, as a decision, and
as an action. They have adopted the view of Mayer et al. (1995) and Gabbay and Leenders
(2003) and define trust as ―a set of beliefs about the other party (trustee), which lead one (trustor)
to assume that the trustee‘s actions will have positive consequences for the trustor‘s self‖. As
Leenders, Gabbay and Engelen (2006) have discussed in the introduction, trust is frequently
argued to be important to knowledge sharing. Many authors believe that when there are trust-
relationships, people are more willing to provide useful knowledge. Also, when trust exists,
people are more willing to listen and absorb each other‘s knowledge (Andrews & Delahay, 2000;
Levin, 1999; Mayer et al., 1995; Tsai & Ghoshal, 1998). Therefore, from the literature, we can
expect trust to have a positive influence on knowledge sharing. However, they had strong doubts
about the importance of trust as a motivator of knowledge sharing. Obviously, low levels of trust
(or even the existence of mistrust) will tend to thwart knowledge sharing in any team. But in
most new product development (NPD) team‘s reasonable levels of trust will generally exist.
These teams are inhabited by professionals, each an expert at his job; there is generally little
reason to believe that one will not do his job or cannot be trusted with particular knowledge. In
addition, the complex nature of many modern products demands that members of NPD teams
work together and share knowledge – refraining from sharing knowledge will impede the
performance of the team as a whole as vital knowledge will either not be present at required
locations or can simply not be created. As a result, we believe that trust is highly overrated as a
main driver of knowledge sharing. In order to test our suspicions regarding the limited value of
trust for knowledge sharing in NPD teams, they have empirically tested the hypothesis that trust
does in fact explain knowledge sharing and found a significant relationship between trust and
knowledge sharing amongst the members of NPD team.
Willem and Buelens (2007) suggested that three types of organization-specific
coordination mechanisms directly influence knowledge sharing between departments and
organizations are also characterized by members‘ social identification and trust, which in the
absence of power games are assumed to create a knowledge-sharing context. According to them
the combination of power games and informal coordination seems to be remarkably beneficial
for knowledge sharing and furthermore, compared with other public sector organizations,
government institutions have organizational characteristics that are less beneficial for knowledge
sharing. Thus, a strong positive relationship was found between trust and knowledge sharing for
all types of teams (Staples & Webster, 2008).
Kang, Kim, and Chang (2008) have emphasized on the significant mediating role
of mutual trust in the relationship between knowledge sharing and work performance. They have
suggested that organizations must find ways of improving interpersonal trust relationships in an
organization using various mechanisms, such as guaranteeing employee participation in decision
making process, improving fairness in performance appraisals and promotion, and instituting a
pay-for-performance system.
5.2.9 Team performance
Srivastava and Bartol (2006) examined the intervening roles of knowledge
sharing and team efficacy in the relationship between empowering leadership and team
performance. Their results showed that empowering leadership was positively related to both
knowledge sharing and team efficacy, which, in turn, were both positively related to
performance. Staples and Webster (2008) said that the sharing of knowledge within teams is
critical to team functioning. Choi, Lee and Yoo (2010) explored the precise role of TMS
(transactive memory system), a specialized division of cognitive labor among team members that
relates to encoding, storage, and retrieval of knowledge as an important factor that affects a
team‘s performance on knowledge sharing and knowledge application.
The above model has arrived from our research framework (figure 5.1) which
shows an overall picture of; and direction for subsequent data analysis. Variables in the
analytical model are drawn from an extensive review of literature on knowledge management
and knowledge sharing. The model suggests that 12 exogenous variables influence the level of
knowledge sharing, which in turn improves team performance. The 12 variables have been
categorized into three broad dimensions- organizational, job and individual characteristics. The
degree to which knowledge sharing affects team performance is also moderated by the mutual
trust among members of cross functional teams in the course of sharing their knowledge. Based
on the relationship of the variables shown in the above mentioned research model, the following
research hypotheses have been developed.
Formalization creates an environment of control and reduces flexibility in
knowledge sharing. Hence, formalization is ineffective to reach integration from a knowledge
sharing point of view (Willem, 2006; Van den Bosch, Volberda, & de Boer, 1999). Hence we
propose,
H1a: Formalization is negatively related to the knowledge sharing across cross functional team
members
Centralization refers to the extent to which the decision-making power is
concentrated at the top management level in the organization (Alexander & Bauerschmidt, 1987;
Hage & Aiken, 1967). Although centralization achieves integration and coordination among
units in the organization, it is not considered to be positively related to knowledge sharing
(Willem, 2006). Thus, we propose that,
H1b: Centralization is negatively related to the knowledge sharing across cross functional team
members
De Long and Fahey (2000) considered that in the creation, sharing and use of
knowledge, organizational culture plays a fundamental role. An open and trusting culture
sustained by high band-width communication, egalitarianism, fairness and support with strong
norms for knowledge sharing (Cabrera & Cabrera, 2005).Hence, we propose that,
H2: Learning culture is positively related to the extent of knowledge sharing across cross
functional team members
Cross training will facilitate knowledge sharing among employees from different
areas by increasing interactions, creating a common language, building social ties and any
training that emphasizes cooperation and builds relationships among employees to increase
knowledge sharing behaviors (Cabrera & Cabrera, 2005). Hence, we propose that,
H3: Formal training is positively related to knowledge sharing across cross functional team
members
Appropriate reward systems aligned clearly with the creation and dissemination of
knowledge into organization would also promote employees‘ knowledge sharing (O‘Dell and
Grayson, 1998, Ruggles, 1998, McDermott and O‘Dell, 2001 and Wiig, 1997). Bartol and
Srivastava (2002) suggested that rewards are important for most mechanisms of knowledge
sharing. Thus, we propose that,
H4: Better rewards are positively related to knowledge sharing across cross functional team
members
Numerous articles have alluded to the importance of support, from the
organization, supervisor or peers, for encouraging knowledge-sharing behaviors (Hislop, 2003;
McDermott & O‘Dell, 2001; Oldham, 2003; Zarraga & Bonache, 2003). Oldham (2003) includes
supervisor and co-worker support as critical work context antecedents of creative idea
formulation and sharing. Thus, we propose that,
H5: Top management support is positively related to Knowledge sharing across the members of
cross functional team
Job/Work design is an important tool for fostering knowledge flows by leveraging
social networks (Cabrera & Cabrera, 2005). For instance, rather than designing stable,
individualized jobs with concrete tasks, work can be conceptualized as a sequence of
assignments where employees work closely with other employees on a series of projects
(Cabrera & Cabrera, 2005). Such designs encourage lateral linkages across functions,
geographical locations, business units and companies (Mohrman, 2003; Cabrera & Cabrera,
2005). For employees, the opportunity to work closely with others and knowledge sharing could
be enhanced by designing work around teams especially when rewards are based on team results
(Cabrera & Cabrera, 2005). Hence, we propose that,
H6: The Job Characteristics (a. autonomy, b. feedback, c. task identity, d. task variety and e. task
significance) are positively related to the extent of knowledge sharing
Employees with high emotional intelligence tend towards outcomes that benefit
others as well as themselves (Scott-Ladd & Chan, 2004). Decker, Landaeta and Kotnour (2009)
have suggested that there are remarkable relationships between emotional intelligence factors
and the use of specific methods to transfer knowledge within and across projects. Hence, we
propose that,
H7: Higher Emotional Intelligence leads to more knowledge sharing across cross functional
team members
The literature on knowledge management suggests that knowledge sharing and
knowledge application will have a positive impact on team performance. Past research has
clearly shown that knowledge sharing has a positive impact on team performance in many
different contexts (Argote & Ingram 2000; Cummings, 2004; Hansen, 2002). Thus, we propose
that,
H8: Knowledge sharing is positively related to the team performance
Empirical evidence supports the positive impact of trust on knowledge sharing in
a variety of situations, including teams (Butler, 1999; Connelly & Kelloway, 2003; Akgun et al.,
2005; Arthur & Kim, 2005; Chowdhury, 2005; Muthusamy & White, 2005). Nonaka (1994)
suggested that trust constructs a healthy environment for knowledge sharing and acts as a
moderator. Hence, we propose that,
H9: Mutual trust moderates the effect of knowledge sharing on team performance
Apart from this, the extent of knowledge sharing mediates the effect of
(organizational, task and individual) characteristics on team performance. Hence it‘s important to
find out that,
H10a: The knowledge sharing behavior mediates the effect of organizational characteristics on
team performance
H10b: The knowledge sharing behavior mediates the effect of job characteristics on team
performance
H10c: The knowledge sharing behavior mediates the effect of emotional intelligence on team
performance
Table 5.1 Summary of hypothesis and supporting literature
Hypothesis Key Supporting Literature Prior testing in the context of
Knowledge Sharing
Hypothesis 1a: Formalization is
negatively related to the knowledge
sharing among cross functional
team members
Islam, Ahmad & Mahtab,
2010; Kanter, 1983
New testing in the context of
Knowledge Sharing in CFTs and
its outcome
Hypothesis 1b: Centralization is
negatively related to the knowledge
sharing among cross functional
team members
Islam, Ahmad & Mahtab, 2010 New testing in the context of
Knowledge Sharing in CFTs and
its outcome
Hypothesis 2: Organizational
learning culture is positively related
to knowledge sharing across cross
functional team members.
Cabrera & Cabrera, 2005; De
Long & Fahey, 2000; Goodman &
Darr, 1998, Hargadon, 1998,
Kostova, 1999, Ruggles, 1998
Previously tested in the context of
Knowledge Sharing on employees of
PSU
Hypothesis 3: Formal and regular
employee training is positively
related to knowledge sharing across
cross functional team members
Kang, Kim & Chang, 2008;
Cabrera & Cabrera, 2005; O‘Dell
& Grayson, 1998; Liedtka &
Haskins, 1997
Previously tested in the context of
Knowledge Sharing on employees of
PSU
Hypothesis 4: Better reward
system is positively related to
knowledge sharing across cross
functional team members
Kang, Kim & Chang, 2008;
McDermott & O‘Dell, 2001;
O‘Dell & Grayson, 1998,
Ruggles, 1998, and Wiig, 1997
Previously tested in the context of
Knowledge Sharing on employees of
PSU
Hypothesis 5: Top management
support is positively related to
Knowledge sharing across the
members of cross functional team.
Kang, Kim & Chang, 2008;
Hislop, 2003; Oldham, 2003;
Zarraga & Bonache, 2003;
McDermotl & O'Dcll, 2001
Previously tested in the context of
Knowledge Sharing on employees of
PSU
Hypothesis 6a: The higher the job
autonomy, the higher is the
knowledge sharing among cross
functional team members
Foss, Minbaeva, Pedersen, &
Reinholt, 2009; Cabrera &
Cabrera, 2005; Mohrman, 2003
Previously tested with an intervening
variable (motivation) in the context
of knowledge sharing
Hypothesis 6b: The more the job
feedback, the more is the
knowledge sharing among cross
functional team members
Foss, Minbaeva, Pedersen, &
Reinholt, 2009; Cabrera &
Cabrera, 2005; Mohrman, 2003
Previously tested with an intervening
variable (motivation) in the context
of knowledge sharing
Hypothesis 6c: The more the job
identity, the more is the knowledge
sharing among cross functional
team members
Foss, Minbaeva, Pedersen, &
Reinholt, 2009; Cabrera &
Cabrera, 2005; Mohrman, 2003
Previously tested with an intervening
variable (motivation) in the context
of knowledge sharing
Hypothesis 6d: The more the job
variety, the more is the knowledge
sharing among cross functional
team members
Foss, Minbaeva, Pedersen, &
Reinholt, 2009; Cabrera &
Cabrera, 2005; Mohrman, 2003
New in the context of knowledge
sharing
Hypothesis 6e: The more the job
significance, the more is the
knowledge sharing among cross
functional team members
Foss, Minbaeva, Pedersen, &
Reinholt, 2009; Cabrera &
Cabrera, 2005; Mohrman, 2003
New in the context of knowledge
sharing
Hypothesis 7: Emotional Baruch and Lin, 2012; Teh, Yong, New testing in the context of
intelligence is positively related to
knowledge sharing among cross
functional team members
Chong and Yew, 2011;
Karkoulian, Al-Harake &
Messarra, 2010; Decker, Landaeta
& Kotnour, 2009;
Knowledge Sharing in CFTs and
its outcome
Hypothesis 8: Knowledge sharing
is positively related to the team
performance
Cummings 2004; Hansen 2002;
Argote & Ingram 2000
Previously tested in the context of
Knowledge Sharing on virtual teams
Hypothesis 9: Mutual trust
moderates the effect of knowledge
sharing on team performance
Akgun et al., 2005; Arthur &
Kim, 2005; Chowdhury, 2005;
Muthusamy & White, 2005;
Connelly & Kelloway, 2003;
Butler, 1999; Nonaka, 1994
New testing in the context of
Knowledge Sharing and its
outcome
Hypothesis 10: Knowledge sharing
mediates the effect of, a)
organizational characteristics, b)
job characteristics, c) individual
characteristic on team performance
Choi, Lee & Yoo, 2010; Kang,
Kim & Chang, 2008; Srivastava
& Bartol, 2006
New testing in the context of
Knowledge Sharing and its
outcome