Individuals Factors Affecting Knowledge Sharing and Performance in Higher Education: An Empirical Study of B-Schools

Shachi Pathak¹, Sanjeev Swami², Shalini Nigam³.

¹Research Scholar, Department of Management, Dayalbagh Educational Institute, Dayalbagh, Agra

²Head and Professor, Department of Management, Dayalbagh Educational Institute, Dayalbagh, Agra

³Professor, Department of Management, Dayalbagh Educational Institute, Dayalbagh, Agra

*Corresponding Author E-mail: mspathak31@gmail.com, sswami1853gmail.com, shalinidb@gmail.com.

ABSTRACT:

Purpose – The proposed study examines the effect of employee individual factors on knowledge sharing (KS) process and further shows a way through which top B-Schools of Delhi and NCR (National Capital Region) can perform better in management education.

Design/methodology – The purposive and convenience sampling have been used to conduct the survey. The study derived from a survey conducted on 126 faculty members of top 10 management colleges of Delhi and NCR. SEM (Structural Equation Modeling) has been applied to investigate the effectiveness of the proposed model.

Findings – The outcomes show that individual factors- cooperative behavior, self-efficacy and T-shaped skill significantly affect KS process. The outcomes also revealed that faculty members of the B-Schools of Delhi and NCR willing to share their knowledge with each other to get a better performance in management education.

Research limitation –Upcoming researchers can find out how other factors such as organizational (firm size) and personal traits (age, qualification, and past experience) may have relationships among these factors of KS and processes.

Practical implications – As of a practical point of view, the relationships between KS individual factors, processes, and B-Schools performance can give an idea about how B-schools can encourage KS behavior to maintain their superior performance.

Originality/value – The results of the proposed work offer a hypothetical foundation, and at the same time, can be used to examine relationships between KS individual factors, processes, and B-Schools performance. Since for a managerial viewpoint, present work recognized quite a few individual factors important for successful KS and argued about the inferences of these factors for making strategies that can promote KS in higher educational institutes.

KEYWORDS: knowledge Management, Knowledge sharing, B-Schools, Performance, Individual factors

INTRODUCTION:

Today, a competitive advantage does not mean having physical assets such as facilities, money, and building in an organization. It is an employee, their knowledge and intellectual capital, which are helping organizations to get an ability to be competitive in the market. For any organization, to stay competitive, employees should share and improve their knowledge with each other (Akhavan and Bagheri, 2010; Manesh and Sadeghi, 2015). In recent years, many organizations are using Knowledge Management (KM) as a tool for utilizing the available knowledge within the organization. This knowledge helps to adapt the environment change for innovation, productivity, greater profitability and ultimately to improve upon organization’s performance (Drucker, 1998; Bhatt, 2001; Esmaielpanah and Moghadam, 2013). KM is the most effective solution for the expansion and utilization of an individual's knowledge. With the help of KM, this individual knowledge can be converted into organizational knowledge.

Literature Review:

KM is a process which facilitates organizations to recognize, select, manage, distribute, and relocate the valuable information and skills. This knowledge is the part of the organization's memory and exists within the organization (Turban and Aronson, 2001; Mathew, 2010). Sharing and distribution of knowledge are said to be the most important parts of KM process in the organization. To generate synergy in the organization, It transfers, employee talent to the intellectual capital (Shahlo, 2009; Connelly and Kelloway, 2003; Neyestani, Piran, Nasabi, Nosrati and Maidanipour, 2013). KM, in each system, stands for two levels, such as individual level and organizational level. KM offers a chance to the employees to enhance their expertise and know-how by cooperating through colleagues with mutual knowledge, an individual level. By Increase in efficiency, productivity, quality and innovation, KM results in increased organizational performance, in organizational level (Faraji, 2010; Neyestani, Piran, Nasabi, Nosrati, and Maidanipour, 2013). Nonaka, Krogh, and Voelpel (2006) parted knowledge in two types, implied and explicit knowledge. Implicit knowledge covers individual knowledge and experiences which is based on personal attitude, opinions and ethics. Explicit knowledge can be documented and granted easily to others. Previous studies revealed that an institute may possibly administer its available knowledge simply by converting implied knowledge to explicit knowledge (Hooff and Ridder, 2004).

An institute plays an important part in KM Process. KS, an important part of KM process, helps the organization to convert individual knowledge to organizational knowledge, reduce learning time and efforts, create innovation and ultimately leads to improved organizational performance. The organization provides the suitable KS culture to their employees to achieve predefined objectives. According to Hogel, Parboteeah and Munson (2003), KS, a social communication culture, includes the transfer of individual knowledge, skills, and talent within the organization. Knowledge, acquired by an employee with time and experience, exists in people's minds cannot be shared without his readiness. This knowledge is implied in nature and cannot be coded into documents, even if many efforts are made. Energetic involvement of employees having this knowledge is necessary to access this type of knowledge.

A country depends on its education system for economic, social and cultural development so as India. Today, India secures 2^ndrank in terms of student enrollment and has largest higher education system in terms of the number of institutions, in the world. Trained and experienced manpower is able to create knowledge and apply their specialized skills for the development of the country. Researchers suggest that KM is important for business organizations, but equally important for knowledge-based institutions where the invention of new knowledge, delivery, and application of knowledge take place, such as universities or colleges.

Moreover, KM is playing an important role in creating a supportive-innovation-work environment. Today's educational environment deals with so many changes. Consequently, colleges or universities need to apply different methods of creating and implementing knowledge to get educational goals and maximum required asset (Mohammadi Rad, 2006). Teachers play a vital role in knowledge creation, so comprise as a major factor of educational institutions and universities. Performance of colleges and universities is affected by KS and cooperation among faculty members, including increased technical and research productivity. However, according to Cheng (2008), there is no direct approach to determine the performance of knowledge sharing in knowledge institutions. While some studies addressing on determining the performance of knowledge sharing of business organizations. There is a strong need for further empirical research along these lines in regard to identify the major factors and measure the performance of knowledge sharing of B-Schools of India. Therefore, to fill this gap, the proposed study is an attempt to identify factors that can affect KS among faculty members toward sharing knowledge in B-Schools of India appeared to be important and ultimately the performance of the colleges. The study also builds up a research model that connects individual factors as KS enablers, process and performance of B-schools. Based on the survey conducted on 126 faculty members of top ten B-schools in Delhi and NCR, SEM (Structural Equation Modeling) has been applied to investigate the effectiveness of the proposed research model. Moreover, the results of proposed work offer a hypothetical foundation, and at the same time, can be used to examine relationships between KS individual factors, processes, and B-Schools performance for higher educational institutes as an output. Since for a managerial viewpoint, proposed work recognized quite a few individual factors important for successful of KS and argued about the inferences of these factors for making strategies that can promote KS in higher educational institutes.

RESEARCH MODEL AND HYPOTHESES:

Figure 1: framework for KS and Institute Performance

Source: Developed by researcher

Figure I explain the constructed framework of strategic assessment practices shown above. This framework is based on the method suggested by Rajagopalan et al. (1993). The research construction suggested in proposed work consists of three parts: inputs, processes, and output. These enablers or inputs are the means of developing individual and organizational learning which helps in developing behavior of KS among employees within the organization. KS enablers include individual factors which are an effect caused by employee motivators (Lin and Lee, 2006).

Here “KS processes” means, how employees are sharing their work-related knowledge, information, skill, and experience with their colleagues. The process of KS includes both employee readiness to discuss (knowledge donating) and consulting with the colleague to gain knowledge from them (knowledge collecting). The “output” part shows the effect of KS to enhance the performance of an organization.

Previous studies revealed the presence of different employee influencing factors of KS such as individual factors (Taylor and Wright, 2004). Many of the authors agreed upon sharing knowledge with others depends on characteristics of an individual. These characteristics may include an individual’s beliefs, values, experience, and motivation. The employee feels motivated and willing to share knowledge when experience of KS activities are able to help others and worth his efforts (Wasko and Faraj, 2005). In the nutshell, employees can be encouraged to share knowledge with others in expectation of individual benefit.

Davenport and Prusak (2000); Hsu and Chan (2014); Saifi, Dillon, and McQueen (2016) defined KS as one providing knowledge to others and receiving knowledge from others. Similarly, according to Ipe (2003), making knowledge accessible to others within the organization is called as KS. These definitions indicate that the behavior of KS includes collecting and donating knowledge together. Whereas Lin (2007) suggested that KS is a culture of interacting with others socially to exchange one's knowledge, experience, and skills within the organization.

KS plays an important role in knowledge-based organizations, therefore, is an essential component of KM. Every organization wants an effective utilization of its knowledge resources such as its knowledgeable employees, which makes KS highly crucial for the organization (Cabrera and Cabrera, 2005; Stenius, Hankonen, Ravaja and Haukkala, 2016). Although, sharing knowledge with others or organization for an individual employee may create a situation of a dilemma as it is not a usual behavior in an organization (Milne, 2007; Riege, 2005). This emphasizes to identify and understand the factors which an individual consider sharing or otherwise, to refuse to share knowledge with others within and outside the organization. Figure II explains the research model for the present study.

Figure 2: The research model

Source: Developed by researcher

Individual Factors of KS:

The proposed work has emphasized on individual factors that support or hinder KS behavior within an organization. Some factors that may influence individual for KS activities are identified: Cooperative behavior, Knowledge self-efficacy, and T-Shaped skill. People feel happy or enjoyable when they help or cooperate with others to get their goals (Wasko and Faraj, 2000). Workers share their knowledge, experience, and expertise for social purpose because they like to help others. Previous studies say that an employee who likes to cooperate with others may be more positive toward KS in both terms donating and collecting (He and Wei, 2009; Hsu and Lin, 2008; Shin, Ishman and Sanders, 2007; Wasko and Faraj, 2005; Jarvenpaa and Staples, 2001). Thus the following hypothesis is proposed:

H1. Cooperative behavior positively influences faculty member readiness to (a) collect and (b) donate knowledge.

Knowledge self-efficacy is an individual’s judgment about his capabilities to attain a particular level of performance (Lin, 2007; Bandura, 1986). This belief about their capability motivates an individual to share knowledge with co-workers and develops the confidence to achieve specific performance level (Wasko and Faraj, 2005). It also helps people to believe that knowledge shared by them can support in solving job-related difficulties, reduce efforts and time to ultimately improve work efficiency (Luthans, 2003). Knowledge workers believing in their capability to contribute towards organization better performance by sharing their valuable knowledge have positive behavior to both give and accept knowledge. Thus, the following hypothesis is proposed:

H2. Knowledge self-efficacy influences positively faculty member readiness to (a) collect and (b) donate knowledge.

According to Leonard-Barton (1995), T-shaped skill is both deep and broad as the shape of ‘T’. People with T-Shaped skill can bring to light to certain knowledge areas and their numerous applications in some specific products. Employees possessing T-shaped skill can create new knowledge with the different combinations of numerous knowledge assets. Unifying both theoretical and practical knowledge of a particular knowledge area, these people detect their interconnection with other branches of knowledge (Johannessen, Olsen and Olaisen, 1999; Madhavan and Grover, 1998). Therefore, when we talk about the higher education system which leads to innovation and strategic planning to improve on performance T-shaped skill people create the competence. Hamdi, Silong, Omar, and Rasdi (2016) revealed with their research that T-shaped skill has a positive impact on innovation speed. The shared knowledge, skill, and expert team are essential for innovation (Moorman and Miner, 1997) T-shaped skill may support individual experts to have a harmonious discussion with other people to give and collect new skill (Madhavan and Grover, 1998). Thus, the following hypothesis is proposed:

H3: T -Shaped skill positively influences faculty member readiness to (a) donate and (b) collect knowledge.

KS and Performance of Higher Educational Institutes

Khalil and Shea (2012), today, in most of the academic institutions, it is very rare to share knowledge possessed by faculty with colleagues. This analysis required answers to two questions about the perception of faculty for factors influencing KS and its effectiveness may have on the performance of institutes. Keramati and Azadeh (2007) said that storing knowledge is not new in universities, but sharing it with colleagues and students is new. Previous studies conducted in Asia revealed higher education have similar barriers of KS as of the business environment (Khosravi and Ahmad, 2013). KS in an organization can develop innovation and performance (Rhodes et al., 2008)

There is yet no KS framework which discovers the important aspects of KS in B-schools of India and can assist them in KS. The proposed work is an attempt to design a framework, which can guide faculty of B-schools to promote KS activities and improve upon the performance of Indian B-schools. KS in research and colleges is, therefore, an important research area that needs more attention, especially in India because no study has been performed yet in this regard. This proposed work examines the effects of KS on institutional performance. Thus the following hypothesis is proposed:

H4: faculty member readiness to donate knowledge influences positively Institutional Performance

H5. Faculty member readiness to collect knowledge influences positively Institutional Performance

Methods

Sampling and data collection

A self-constructed questionnaire was first analyzed to check for the content and wording of the questions by three professors to make sure that the questionnaire was not having any problem. Then 30 teaching faculty, including assistant professors, associate professors and professors from ten top business schools in Delhi and NCR examined the revised questionnaire for the purpose of the pilot study to further validate the survey instrument. The participants were invited to evaluate the questionnaire for meaningfulness, relevance, and clarity. The results of the pilot survey showed adequate values of Cronbach alpha (greater than 0.7).

After the pilot study, this evaluated questionnaire was used to collect the responses. Total 126 responses are recorded after pre-testing and by eliminating incomplete responses. Table I lists the characteristics of respondents, including, gender, age, education level, total working experience, and designation.

Table1: Characterstics of respondents

Characteristic

Category

Frequency

Designation

Professors

Associate Professors

Assistant Professors

20.6%

32.5%

46.8%

Education Level

Doctorate

Post Graduate

Post Doctorate

77.8%

18.3%

3.9%

Gender

Male

Female

44.5%

55.6%

Age

31-35

36-40

41-45

46-50

Above 50

18.2%

34.9%

30.9%

8.7%

7.1%

Working Experience

(Years)

Below 9

10-19

19-29

30-39

40 and above 40

42.1%

26.9%

15.1%

8.7%

7.1%

Source: Developed from the responces collected by researcher

METHODOLOGY:

The purposive and convenience sampling have been used to conduct the survey. The proposed study develops a model explaining the individual factors affecting KS process of sharing knowledge with others to get a better performance of B–schools of Delhi and NCR using a self-structured questionnaire.

Measures

The measures used in this research were taken from some previous studies and edited as per the requirement of KS context. A five-point Likert-type scale was used to measure all the items (ranging from 1= strongly disagree to 5 =strongly agree). Cooperative behavior was considered having four items derive from Lin (2007) and Wasko and Faraj (2000), focusing individual opinion of joy achieved from KS. Knowledge self-efficacy having four item scale were driven from Lin (2007) and Spreitzer (1995). These four items are examining the individual's opinion about their ability for KS which can be important to the organization. T-Shaped skill having four items derived from Tan and Zhao (2003). Three items of knowledge donating, measuring the degree of readiness to give expertise to others, derived from Van den Hooff and Van Weenen (2004). Four items scale of Knowledge collecting, analyzing the joint viewpoint regarding enhancing learning with other staff, was taken from Van den Hooff and Van Weenen (2004). Finally, the performance of the institutes was measured using six items developed according to Indian education system with the help of government website MHRD (Ministry of Human Resource Development).

DATA ANALYSIS AND RESULTS:

Measurement model

Confirmatory Factor Analysis (CFA) has been applied to examine the proposed model, to evaluate measurement model. For the purpose of applying CFA, Amos software was used. First, the measurement model should be evaluated and then further re-specifying it to get the best fit model (Segars and Grover, 1998). In the first evaluation level of the model, twenty items were left after removing six items, as shown in Table II. The reliability of items ranged from 0.53 to 0.83, this lies within the acceptable range of 0.50 to 0.90 (Hair, Anderson, Tatham, and Black, 1992).

Table 2: CFA Results for Measurement Model

Items	Item Reliability	Composite Reliability	AVE
CB1	0.71	0.80	0.55
CB2	0.72
CB3	0.79
CB4	0.81
SE1	0.67	0.77	0.63
SE2	0.79	0.77	0.63
TS1	0.73	0.71	0.51
TS2	0.83
TS3	0.81
KSc1	0.56	0.81	0.57
KSc2	0.53	0.81	0.57
KSd1	0.61	0.79	0.52
KSd2	0.64	0.79	0.52
PER1	0.79	0.89	0.68
PER2	0.71
PER3	0.60
PER4	0.75
PER5	0.69
PER6	0.68
PER7	0.71

Source: Developed from the collected responces by researcher

The composite reliability acceptable value is above 0.60 (Bagozzi and Yi, 1988) and in the table, it ranges from 0.71 to 0.89. The benchmark for average variance extracted (AVE) value must exceed 0.50; in the table, all the items have AVE values exceeded the threshold value.

As the reliability values exceeded the threshold values, the measurement scale items were supposed to be demonstrating convergent reliability. The values of variance taken out by scale items were more than any square of the correlation between items shown in Table III, which means scale items were empirically distinct. Finally, it also showed that the test of measurement model was adequate.

Table 3: Results of Variance Extracted

	CB	SE	TS	KSc	KSd	PER
CB	0.55
SE	0.46	0.63
TS	0.33	0.42	0.51
KSc	0.41	0.34	0.50	0.57
KSd	0.49	0.44	0.48	0.39	0.52
PER	0.27	0.21	0.38	0.44	0.48	0.68

Source: Developed from the responces collected by researcher

Table IV shows the measurement model’s fitness measures. According to Bentler and Bonett (1980), x²/d.f suppose to be lower or equal to 3, i.e.; NFI and CFI values > 0.9. The RMSEA value should be < 0.08, recommended by Hair et al., (1992). Moreover, according to Scott (1994); Seyal, Rahman, and Rahim (2002), the threshold value of GFI and AGFI is greater than 0.8. For the study, the proposed model presented a suitable fit, as all measures fell within acceptable ranges.

Table: 4 Overall Fit Measures

Fit Index	Recommended Values	Results	Authors
x²/d.f	< 3	2.1	Bentler and Bonett (1980)
NFI	> 0.9	0.95	Bentler and Bonett (1980)
CFI	> 0.9	0.98	Bentler and Bonett (1980)
RMSEA	<0.08	0.06	Hair et al. (1992)
GFI	>0.8	0.96	Seyal et al. (2002)
AGIF	>0.8	0.92	Scott (1994)

Source: Developed from the responces collected by researcher

Testing the Structural Model

By testing, the research hypothesized relationship amongst variables the structural model can be tested (Figure: III). The results revealed that cooperative behavior has a significant relationship with knowledge collecting (H1a, path coefficient 0.43, p<0.01) and knowledge donating (H1b, path coefficient 0.31, p<0.01).

Individual Factors of KS:

Figure 3. Result of analysis of structural model

Source: Developed from the responces collected by researcher

Self-efficacy has a significant relation with knowledge collecting (H2a, path coefficient 0.42, p<0.01) and knowledge donating (H2b, path coefficient 0.38, p<0.01). Moreover, T-shaped skill has a significant relationship with knowledge collecting (H3a, the path coefficient.23, p<0.01) and knowledge donating (H3b, path coefficient 0.20, p<0.01). Similarly, knowledge collecting and knowledge donating have a significant relationship with performance (H4, path coefficient 0.45, p<0.01) and (H5, path coefficient 0.30, p<0.01) respectively. The results are shown in Table V.

CONCLUSION:

The present study has proposed a structural model of the individual factors affecting KS process in top Indian management colleges of Delhi and NCR, all the proposed among the variables in the model was significantly supportive. Firstly, being constant with individual factors such as cooperative behavior (He and Wei, 2009) and self-efficacy (Lin, 2007), both the factors have a strong effect on teaching staff ready to share their knowledge with others.

Table: 5 Structural Model Results

Hypothesis	Hypothesized path	Path coefficient	Results
H1a	CB->KSc	0.43*	Supported
H1b	CB->KSd	0.31*	Supported
H2a	SE->KSc	0.42*	Supported
H2b	SE->KSd	0.38*	Supported
H3a	TS->KSc	0.23*	Supported
H3b	TS->KSd	0.20*	Supported
H4	KSc->PER	0.45*	Supported
H5	KSd->PER	0.30*	Supported

*p<0.01

Source: Developed from the responces collected by researcher

This implies that faculty member, who believes in their ability and feel good while sharing knowledge with their organization or other faculty members has a strong readiness to collect and donate expertise. Moreover, T-shaped skill has a moderate positive effect on teaching faculty members readiness to collect and donate knowledge with other faculty members. Additionally, the results showed that faculty members' readiness to collect and donate their knowledge with others significantly improve the performance of the management colleges.

REFERENCES:

1. Akhavan, P., and Bagheri, N. (2010). Knowledge Management (2nd ed.). Atinegar Publications.

2. Bagozzi, R. P., and Yi, Y. (1988). On the evaluation of structural equation models. Journal of the Academy of Marketing Science , 74–94.

3. Bandura, A. (1989). Social Foundations of Thought and Action: A Social Cognitive Theory. Englewood Cliffs, NJ: Prentice-Hall.

4. Bentler, P. M., and Bonett, D. G. (1980). Significance tests and goodness of fit in the analysis of covariance structures. Psychological Bulletin , 588–606.

5. Bhatt, G. D. (2001). Knowledge management in organizations: examining the interaction between technologies, techniques and people. Journal of knowledge management , 5 (1), 68-75.

6. Cabrera, E., and Cabrera, Á. (2005). Fostering knowledge sharing through people management practices. The International Journal of Human Resource Management , 720-735.

7. Cheng, M. Y. (2008). Knowledge Sharing in Academic Institutions: A study of Multimedia University Malaysia. Electronic Journal of Knowledge Management , 313-324.

8. Connelly, C. E., and Kelloway, E. K. (2003). Predictors of employees’ perceptions of knowledge sharing culture. Leadership and Organization Development Journal , 294-305.

9. Davenport, T., and Prusak, L. (2000). Working Knowledge: How Organizations Manage What They Know. Boston, MA: Harvard Business School Press.

10. Drucker, P. F. (1998). The Coming of the New Organization. Harvard Business Review , 66 (1), pp. 230-245.

11. Esmaielpanah, F., and Moghadam, S. K. (2013). Identification of factors influencing on faculty members attitudes toward knowledge sharing. Advances in Environmental Biology , 3131-3136.

12. Faraji, A. (2010). Knowledge management-based Model for improving Productivity in Operational units of Petroleum Ministry. The second international conference on Knowledge Management . Tehran Razi, Iran: Razi international conference center, Jan 30-31.

13. Hair, J. F., Anderson, R. E., Tatham, R. L., and Black, W. C. (1992). Multivariate data analysis with reading. New York: MacMillan.

14. Hamdi, S., Silong, A. D., Omar, Z. B., and Rasdi, R. M. (2016). Impact of T-shaped skill and top management support on innovation speed; the moderating role of technology uncertainty. Cogent Business and Management , 1-13.

15. He, W., and Wei, K.-K. (2009). What drives continued knowledge sharing? An investigation of knowledge-contribution and -seeking beliefs. Decision Support Systems , 826-838.

16. Hogel, M., Parboteeah, C. L., and Munson, K. P. (2003). Team-level antecedents of individuals’ knowledge networks. Decision Sciences , 741-70.

17. Hooff, B. v., and Ridder, J. A. (2004). Knowledge sharing in context: The Influence of Organizational Commitment, Communication Climate and CMC Use on Knowledge Sharing. Journal of Knowledge Management , 117-130.

18. Hsu, C.-L., and Lin, J.-C. (2008). Acceptance of blog usage: The roles of technology acceptance, social influence and knowledge sharing motivation. Information and Management , 65-74.

19. Hsu, M., and Chan, C. (2014). Examining interpersonal trust as a facilitator and uncertainty as an inhibitor of intra-organisational knowledge sharing. Information Systems Journal , 119-142.

20. Ipe, M. (2003). Knowledge Sharing on Organizations: A Conceptual Framework. Human Resource Development Review , 337-359.

21. Jarvenpaa, S., and Staples, D. (2001). Exploring Perceptions of Organizational Ownership of Information and Expertise. Journal of Management Information Systems , 151–183.

22. Johannessen, J. A., Olsen, B., and Olaisen, J. (1999). Aspects of innovation theory based on knowledge-management. International Journal of Information Management , 121–139.

23. Keramati, A., and Azadeh, M. A. (2007). Exploring the Effects of Top Management's Commitment on Knowledge Management Success in Academic: A Case Study. Proceedings of World Academy of Science, Engineering and Technology, (pp. 292-297). Vienna, Austria.

24. Khalil, O. E., and Shea, T. (2012). Knowledge Sharing Barriers and Effectiveness at a Higher Education Institution . International Journal of Knowledge Management , 43-64.

25. Khosravi, A. (2013). Knowledge Sharing Framework For Research Supervision. International Journal of Scientific Knowledge , 10-23.

26. Leonard-Barton, D. (1995). Wellsprings of Knowledge: Building and Sustaining the Sources of Innovation. Harvard Business School Press.

27. Lin, H. F., and Lee, G. G. (2006). Effects of socio-technical factors on organizational intention to encourage knowledge sharing. Management Decision , 74-88.

28. Lin, H.‐F. (2007). Knowledge sharing and firm innovation capability: an empirical study. International Journal of Manpower , 315 – 332.

29. Luthans, F. (2003). Positive organizational behavior: developing and managing psychological strengths. Academy of Management Executive , 57-75.

30. Madhavan, R., and Grover, R. (1998). From embedded knowledge to embodied knowledge: New product development as knowledge management. Journal of Marketing , 1-12.

31. Manesh, F. B., and Sadeghi, R. (2015). The relationship between knowledge management elements and organizational learning among faculty members. International journal of new trends in education and literature , 1 (6), 1-15.

32. Mathew, V. (2010). Service Delivery Through Knowledge Management In Higher Education. Journal of Knowledge Management Practice .

33. Milne, P. (2007). Motivation, incentives and organisational culture. Journal of Knowledge Management , 28-38.

34. Mohammadi Rad, S. (2006). The structure of knowledge management with organizational learning among Iranian Social Security Organization employees. Tabriz branch: MA thesis. Islamic Azad University.

35. Moorman, C., and Miner, A. S. (1997). The impact of organizational memory on new product performance and creativity. Journal of Marketing Research , 91-106.

36. Neyestani, M., Piran, M., Nasabi, N. A., Nosrati, K., and Maidanipour, K. (2013). The Necessity of Organizational Commitment for Knowledge Sharing Case Study: Shiraz University of Medical Sciences. Journal of Sociological Research , 162-171.

37. Nonaka, I., and G. v. (2006). Organizational Knowledge Creation Theory: Evolutionary Paths and Future Advances. Organizational Studies , 1179-1208.

38. Rajagopalan, N., Datta, D. K., and Rasheed, A. M. (1993). Strategic decision processes: critical review and future directions. Journal of Management , 349-384.

39. Rhodes, J., Hung, R., Lok, P., Lien, B. Y., and Wu, C. M. (2008). Factors influencing organizational knowledge transfer: implication for corporate performance . Journal of Knowledge Management , 84-100.

40. Riege, A. (2005). Three-dozen knowledge-sharing barriers managers must consider. Journal of Knowledge Management , 18-35.

41. Saifi, S. A., Dillon, S., and McQueen, R. (2016). The relationship between face to face social networks and knowledge sharing: an exploratory study of manufacturing firms. Journal of Knowledge Management , 308-326.

42. Scott, J. E. (1994). The measurement of information systems effectiveness: Evaluating a measuring instrument. In Proceedings of the fifteenth international conference on information system (pp. 111–128). British Columbia: Vancouver.

43. Segars, A. H., and Grover, V. (1998). Strategic Information Systems Planning Success: An Investigation of the Construct and Its Measurement. MIS Quarterly , 139-164.

44. Seyal, A. H., Rahman, M. N., and Rahim, M. M. (2002). Determinants of academic use of the Internet: A structural equation model. Behaviour and Information Technology , 71–86.

45. Shahlo, V. E. (2009). Steps in implementing knowledge management and organizational barriers to its establishment. The Second National Conference on Knowledge Management. Tehran Razi: International Conference Center, Tehran Razi, 10 and 11 February.

46. Shin, S. K., Ishman, M., and Sanders, G. L. (2007). An empirical investigation of socio-cultural factors of information sharing in China. Information and Management , 165-174.

47. Spreitzer, G. M. (1995). An empirical examination of a comprehernsive model of psychological empowerment in the workplace. American Journal of Community Psychology , 601-629.

48. Stenius, M., Hankonen, N., Ravaja, N., and Haukkala, A. (2016). Why share expertise? A closer look at the quality of motivation to share or withhold knowledge. Journal of Knowledge Management , 181-198.

49. Tan, H., and Zhao, B. (2003). Individual- and perceived contextual-level antecedents of individual technical information inquiry in organizations. The Journal of Psychology , 597-621.

50. Taylor, W. A., and Wright, G. H. (2004). Organizational readiness for successful knowledge sharing: challenges for public sector managers. Information Resources Management Journal , 22-37.

51. Turban, E., and Aroson, J. (2001). Decision Support Systems (Sixth Edition ed.). Prentice-Hall Inc.

52. Wasko, M. M., and Faraj, S. (2005). Why should I share? Examining social capital and knowledge contribution in electronic networks of practices. MIS Quarterly , 35-57.

53. Wasko, M., and Faraj, S. (2000). It is what one does: why people participate and help others in electronic communities of practice. Journal of Strategic Information Systems , 155-173.

Received on 17.01.2018 Modified on 20.02.2018

Int. J. Ad. Social Sciences. 2018; 6(1):65-71.

DOI: 10.5958/2454-2679.2018.00005.1