chapter: 5 conceptual framework, …shodhganga.inflibnet.ac.in/bitstream/10603/9721/12/12_chapter...

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.

5.3 The Research Model and Proposed Hypothesis

Figure 5.3: The proposed research model

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


team members

Islam, Ahmad & Mahtab, 2010 New testing in the context of


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



PSU

Hypothesis 4: Better reward

system is positively related to

knowledge sharing across cross



McDermott & O‘Dell, 2001;

O‘Dell & Grayson, 1998,

Ruggles, 1998, and Wiig, 1997



PSU

Hypothesis 5: Top management

support is positively related to

Knowledge sharing across the

members of cross functional team.


Hislop, 2003; Oldham, 2003;

Zarraga & Bonache, 2003;

McDermotl & O'Dcll, 2001



PSU

Hypothesis 6a: The higher the job

autonomy, the higher is the

knowledge sharing among cross


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









Hypothesis 6c: The more the job

identity, the more is the knowledge


team members







Hypothesis 6d: The more the job

variety, the more is the knowledge


team members




New in the context of knowledge

sharing

Hypothesis 6e: The more the job

significance, the more is the






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



Chong and Yew, 2011;

Karkoulian, Al-Harake &

Messarra, 2010; Decker, Landaeta

& Kotnour, 2009;


its outcome

Hypothesis 8: Knowledge sharing

is positively related to the team

performance

Cummings 2004; Hansen 2002;

Argote & Ingram 2000


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


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


Knowledge Sharing and its

outcome

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