Knowledge management enablers, knowledge sharing and research ...

Asian Journal of Technology Innovation, 2013 Vol. 21, No. 2, 251 –276, http://dx.doi.org/10.1080/19761597.2013.866314

Knowledge management enablers, knowledge sharing and research collaboration: a study of knowledge management at research universities in Malaysia ∗

Christine Nya-Ling Tana and Shuhaida Md. Noorb b

a Faculty of Business, Multimedia University, Melaka, Malaysia School of Communication, Universiti Sains Malaysia, Penang, Malaysia

Universities need to be aware of the impact of knowledge management (KM) in order to become world-class academic institutions. This research fills an unexplored gap in regards to the impact of KM enablers (i.e. trust, knowledge self-efficacy, reciprocal benefits, top management support, organizational rewards, organizational culture, KM system infrastructure and KM system quality, openness in communication, and face-to-face (F2F) interactive communication) on knowledge sharing (KS) that supports research collaboration by faculty members. No prior research has focused on the impact of KM enablers that influence research university members to share knowledge. A self-administered questionnaire was employed on members of five research universities in Malaysia to collect data; subsequently, 421 usable responses were analysed using partial least squares path modelling. KS by members was influenced by trust, organizational rewards, organizational culture, KM system quality, openness in communication, and F2F interactive communication; in addition, research collaboration was strongly influenced by KS. The KM–KS –collaboration model shows a KM influence of individual– organizational – technological– communication constructs that encourages KS by members to support research collaboration. Keywords: knowledge management enablers, knowledge sharing, research collaboration, partial least squares, research universities

1. Introduction Research universities are recognized as knowledge-based organizations (Goddard 1998) that revolve around several major knowledge processes: knowledge creation, knowledge dissemination, and learning (Trifonova and Ronchetti 2006). The strategic approach of a university in knowledge management (KM) can lead to a subsequent advancement and growth advantage that merges with knowledge sharing (KS). Research collaboration is the breeding base for new knowledge and makes KS a central focus (Chen, Sandhu, and Jain 2009). KS is imperative to universities (in general) and faculty members (specifically) for career advancement, reputation, and self-empowerment (Patel and Ragsdell 2011). Each research university has members who have worked on projects with extensive knowledge and work experience in research. Research universities support members in KS through research collaboration via KS platforms and help in research work by allowing members to create new theories and ideas, as well as establish new ∗

Corresponding author. Email: [email protected]

# 2013 Korean Society for Innovation Management and Economics (KOSIME)

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C.N.-L. Tan and S.M. Noor

research principles. However, research universities have been unable to understand the long-term valuable impact of KM on collaborative research work that results in the slow implementation of KM initiatives and activities. Members tend to be independent, individualistic, and autonomous by maintaining a distance from KS with others (Koppi et al. 1998). This will dampen and diminish the willingness of KS to achieve institutional goals and objectives established by the university (Kim and Ju 2008). This study identifies and evaluates KM enablers that influence the establishment of KS by faculty members at research universities that support research collaboration. This study proposes a KM– KS – collaboration research model after examining individual KM enablers (i.e. trust, knowledge self-efficacy, and reciprocal benefits), organizational KM enablers (i.e. top management support, organizational rewards, and organizational culture), technological KM enablers (i.e. KM system infrastructure and KM system quality), and communication KM enablers (i.e. openness in communication and F2F interactive communication) that influence KS that supports research collaboration by members at research universities in Malaysia. The individual – organizational – technological – communication constructs from this study provide universities with specific direction to strategize KM initiatives to ensure greater KS and retention of valuable knowledge. This study is useful to understand the role of KM enablers within individual – organizational – technological – communication constructs that create an effective KS environment at research universities. Subsequently, the findings of the study recognize the influence of KS to support research collaborations among academic staff at research universities.

2. Literature review and hypotheses 2.1. The role of KM at research universities Research universities provide knowledge; however, they also encourage knowledge collaboration among existing researchers and educators (Goud, Venugopal, and Anitha 2006). Universities need to be proactive to achieve competitive advantages via collaboration when it comes to KM in order to obtain, create, and share knowledge, particularly in generating and disseminating new knowledge through research activities (Aziz 2006). Researchers and practitioners have stated that KM is a crucial element due to its sustainable and beneficial source of competitiveness. KM is important to research universities due to the massive availability and complexity of knowledge-based resources. The loss of these valuable resources may occur if knowledge assets are not systematically acquired, managed, and controlled (Ismail and Chua 2006). Universities must recognize the true value of intellectual capital and manage intangible assets that can be fully utilized and adapted in a knowledge-based learning society to support the education sector of Malaysia (Metaxiotis and Psarras 2003). Academic institutions find it difficult to grasp the benefits of KM to provide efficient operational practices and enhanced competitiveness (Hijazi and Kelly 2003). Universities lack an in-depth understanding of KM and how it attaches to institutional performance outcomes. KM is usually discussed at universities in terms of technological advances and solutions that often ignore the individual and organizational aspects of KM implementation and programmes. An extensive approach by a university towards KM can lead to a subsequent advancement and growth advantage entailed with KS because research collaboration is the base for new knowledge that makes KS a central focus (Chen et al. 2009). In Malaysia, research university status was designated under the Malaysian Research Assessment Instrument by the Ministry of Higher Education (MOHE) to the Universiti Malaya (UM), Universiti Kebangsaan Malaysia (UKM), Universiti Teknologi Malaysia (UTM), Universiti Putra Malaysia (UPM), and Universiti Sains Malaysia (USM). These universities are recognized

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as research universities in Malaysia for their crucial position in research expansion and commercialization activities due to the relationship of economic growth and R&D activities. The foundation of a university mission is the commitment to intellectual discovery and development; subsequently, research universities are expected to lead in the advancement of innovation at the national level, to develop world-class academic output, and create a conducive environment for research and innovation. The research output reputation of a research university helps it attract and retain high-performance academic staff (nationally and internationally) who assist in continued growth. As stated by Aceto (2005), a university that does not provide facilities for research is not a university. In this research, KM is viewed as a key facility that a research university requires in order to provide a conducive environment for research and innovation.

2.2.

KM enablers

After a review of the literature on KM, this study investigated 10 critical KM enablers: trust, knowledge self-efficacy, reciprocal benefits, top management support, organizational rewards, organizational culture, KM system infrastructure, KM system quality, openness in communication, and F2F interactive communication. These enablers were postulated to influence KS by faculty members in research collaboration at five research universities of Malaysia.

2.2.1.

Trust

Rousseau, Sitkin, Burt, and Camerer (1998) showed that trust was a ‘psychological state comprising the intention to accept vulnerability based upon positive expectations of the intentions of behaviour of another’ (p. 395). Trust is the belief of an individual (i.e. trustor) that the other person (i.e. trustee) is benevolent, reliable, competent, open, and honest when knowledge is shared due to collaboration (Cornelissena, Swetb, Beijaarda, and Bergen 2011). We argue that trust is an important element in the relationships of faculty members that involves knowledge providers and knowledge recipients (Yusof and Suhaimi 2006). Trust works well to encourage KS by facilitating a more proactive and open relationship among faculty members that allows knowledge to be exchanged smoothly. Trust increases sincerity in knowledge exchange and facilitates joint or mutual problems that may arise (McEvily, Peronne, and Zaheer 2003) as a high level of trust eases KS. It is considered to be a first step for effective KS when it comes to the creation and sustentation of KS by members at research universities. Therefore, the following hypothesis is proposed: H1. Trust positively influences KS.

2.2.2.

Knowledge self-efficacy

Termed as individual beliefs about the value of shared knowledge to others, knowledge self-efficacy refers to individual success at and willingness to undertake a task. It is partially derived from individual beliefs about competence and ability to share. Knowledge self-efficacy is the judgement of a faculty member in regards to the capability to organize and provide knowledge that is valuable to the faculty/school to achieve specific levels of performance (Lu, Leung, and Koch 2006). Consequently, members need to believe that capabilities and knowledge can enhance the performance of their university (Ba, Stallaert, and Whinston 2001), assist in solving research problems (Constant, Sproull, and Kiesler 1996), and achieve a competitive advantage (Wasko and Faraj 2005).

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A member in the academic world may not share knowledge because they do not believe that sharing their knowledge is valuable since knowledge self-efficacy focuses on knowledge content and a personal assessment of the value of knowledge. In addition, a member may not share knowledge because they think that the likelihood that a person will receive the information is low (Wasko and Faraj 2005). Almahamid, McAdams, and Kalaldeh (2010) believe that the self-efficacy of faculty members will improve by increasing individual knowledge and skills acquisition. Expectancy will be higher if a member believes that new knowledge will increase the value of shared knowledge (Cabrera and Cabrera 2002). A higher self-efficacy results in an increased likelihood that a member will engage and persist in research-related behaviour (Hazzan and Seger 2010). This study anticipates that faculty members who believe that they can contribute towards the sharing of valuable knowledge will further support research collaboration at research universities. Therefore, the following hypothesis is proposed: H2. Knowledge self-efficacy positively influences KS.

2.2.3.

Reciprocal benefits

Several researchers (Lin 2007a,b; Lin, Lee, and Wang 2009) believe that reciprocal benefit was important because it provides an effective drive to facilitate KS by faculty members and achieve long-term cooperation at universities (Bock, Zmud, and Kim 2005; Kankanhalli, Tan, and Wei 2005). Reciprocity can inspire KS when faculty members at research universities (who share their knowledge with others) expect benefits from sharing behaviour by receiving useful knowledge in return (Davenport and Prusak 2000). For that reason, reciprocal knowledge is defined as future knowledge requests met by others (Kankanhalli et al. 2005). The expectation to receive knowledge from a member may be viewed as an incentive to share knowledge (Lin 2007a,b). Lin (2007a,b) confirms that reciprocity behaviour at a faculty/school can provide a sense of mutual indebtedness, lead knowledge contributors to improve their interpersonal relationships and increase expectation for future help from others in ensuring a continually supportive KS. Cho, Gay, Davidson, and Ingraffea (2007) emphasized that the perception that a member will receive knowledge was an important indicator of KM outcomes and that the perception of reciprocity was related to the use of KS mechanisms. Hence, members were more likely to view KS favourably and have a higher tendency to share knowledge at universities if they believe they can obtain reciprocal benefits from others by sharing knowledge. Therefore, the following hypothesis is proposed: H3. Reciprocal benefits positively influence KS.

2.2.4.

Top management support

Top management support is defined as the involvement and participation of top-level management in institutional activities (Jarvenpaa and Ives 1991). Top management support was the degree to which top management understands the importance of KM and the extent to which top management was involved in KS practices at the university. KM involves a process of wide-range organizational change initiatives that surpass strategic management efforts (Eisenhardt and Martin 2000). Support from top management is critical in the growth of KM practices since it encourages the voluntary participation of members to share vital knowledge (Kang, Kim, and Chang 2008). Faculty members need to understand that top management supports KM efforts at their university or


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they may be unconvinced that it was a valid innovation and feel uncomfortable in offering knowledge. The importance of visible top management support for the KS climate at research universities was further emphasized by MacNeil (2004) due to its prominence in the creation and maintenance of a positive KS culture (Lin 2007a,b; Lin and Lee 2006; Lin et al. 2009). Positive support added with personal orientation from the upper management is crucial to create a culture that encourages the unceasing sharing of knowledge (Lin and Lee 2004; Xu and Quaddus 2012). It was believed that KS could not succeed at any academic institutions without the commitment and involvement of top management (Leibowitz 1999). Top management support (e.g. faculty/school deans) who exhibit independent KS behaviour and motivate other influential members to publicly share knowledge acts as a driver for overall research collaboration. It was predicted that high levels of top management support at research universities may encourage the willingness of faculty members to share knowledge. H4. Top management support positively influences KS.

2.2.5. Organizational rewards Organizational rewards range from monetary incentives (such as increased incentive and bonuses) to non-monetary rewards (such as promotion incentive and job security) (Hargadon 1998; Davenport and Prusak 2000) and help to shape the behaviour of faculty members (Cabrera and Bonache 1999). As ascertained by Lin (2007a,b), organizational rewards were useful to encourage staff to share knowledge as they believe that they will receive extrinsic incentives (i.e. salary). As such, it is believed that faculty members will value intangible rewards (such as additional sabbatical leave dedicated to research, financial support for research-related travel, and support for seminars) and financial incentives (Gustad 1960). Past research suggests that reward systems can encourage faculty members to share knowledge when rewards are provided in exchange (Al-Alawi, Al-Marzooqi, and Mohammed 2007). In this study, organizational rewards at research universities are believed to enhance efforts by members and their contributions to university performance (Yu, Kim, and Kim 2004). For example, those that participate in KS will receive organizational rewards for their contributions and can influence institutional commitment by members (Beer and Nohria 2000). In contrast, a lack of rewards will be a barrier in KS (Yao, Kam, and Chan 2007). Apparently, members can develop a greater willingness to share knowledge by offering knowledge to others only if they believe that they can receive expected incentives from the top management of universities. Rewards stimulate KS that allow members to believe that their performance should be commensurate (McDermott and O’Dell 2001). Therefore, this study expects an important relationship between organizational rewards and the sharing of knowledge among academic staff at research universities. Therefore, the following hypothesis is proposed: H5. Organizational rewards positively influence KS.

2.2.6. Organizational culture Organizational culture is a crucial factor for research universities to establish and create a knowledge-friendly culture to achieve effective KS (Hooff and Huysman 2009). The practice of KS should be part of a comprehensive common culture at universities (Yaacob and Hassan 2005). Soliciting feedback, asking questions, providing instructions (or advice) on what needs to be

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done, asking others for help, teamwork requests (in terms of collaborations), soliciting advice, giving advice on what needs to be done (more importantly why it needs to be done), enquiring on what members would do differently, and sharing the know-how and know-why of information should be common cultural activities among faculty members. Cheng (2002) argued that academic institutions should encourage faculty members to work effectively, collaborate, and to share in order to encourage a culture of sharing. Faculty members at universities are expected to voluntarily help each other and share knowledge when colleagues encounter unresolved problems or issues (Lin and Lee 2004). Tuggle and Shaw (2000) stated that the success of research universities depends on the attitude of faculty members in the culture itself; consequently, their ability to share knowledge effectively depends on their willingness to share (Cheng 2002). Research universities should develop a culture that makes faculty members feel good about research collaboration and to share generously (Hariharan and Cellular 2005). It is predicted that the culture of research universities was positively related to the sharing of knowledge among faculty members. Therefore, the following hypothesis is proposed: H6. Organizational culture positively influences KS.

2.2.7.

KM system infrastructure

KM involves the sharing of information; therefore, technology can be the means for searching, storing updating, retrieving, and accessing information. KM system infrastructure actively facilitate KS by members at research universities (Hansen, Nohria, and Tierney 1999). As posited by McCampbell, Clare, and Gitters (1999), the role of a KM system infrastructure is in its ability to support communication, collaboration, and the search for knowledge and information. Research universities need to implement a KM system to connect members in order to enable interaction and research collaboration (Arthur Andersen 1998) that will increase KS (Yaacob and Hassan 2005). A KM system infrastructure allows members to share their knowledge internally as well as share it across a greater geographical area (Connelly and Kelloway 2003). KM system infrastructure has reduced the economic cost of sharing information and knowledge over various boundaries; in addition, it has also created social conventions on communication that makes it easier to share information and knowledge among diverse groups within a university or across universities (Jarvenpaa and Staples 2001). Therefore, KM system infrastructure allows easy access among members to share knowledge, especially those who are very busy to interact F2F on research-related matters (Connelly and Kelloway 2003). Jarvenpaa and Staples (2001) observe that KM system infrastructure increased the technical and social connectivity at universities through the facilitation of information and KS. Hence, research universities must decide on the most appropriate KM system infrastructure that can be provided as a platform that can consists of networks, computers, storage, web technologies, digital media, databases, system software, software tools, applications, and databases. This study anticipates that a KM system infrastructure at research universities positively influences members to share knowledge. Therefore, the following hypothesis is proposed: H7. KM system infrastructure positively influences KS.

2.2.8.

KM system quality

KM system quality refers to the quality of knowledge provided by the KM system (Lin 2011) that consists of knowledge accuracy, relevance, exchange, reliability, and accessibility that are highly valued


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by individuals at an institution (DeLone and McLean 2003; Nelson, Todd, and Wixom 2005). Kulkarni, Ravindran, and Freeze (2006) proposed that higher learning institutions require a high-quality KM system that is accessible and capable of easily leveraging KM practices by faculty members. To encourage KS beyond a university’s boundary, a KM system should provide appropriate functions with excellent qualities (Hall 2001; Alavi and Tiwana 2002). KM system quality is an enhanced construct that originates from system quality in the information system field (Wu and Wang 2006) that may include availability, ease of use, stability, and response speed. Research universities with greater KM system readiness and a higher KM system quality are more likely to create sustainable growth sources and pursue best KM practices. This study anticipates that a higher KM system quality results in more knowledge that will be shared by members. Therefore, the following hypothesis is proposed: H8. KM system quality positively influences KS.

2.2.9.

Openness in communication

Open communication encourages KS research at universities that nourishes and updates the knowledge of faculty members (Bennet and Bennet 2003). Open communication between members, research teams, departments and faculties/schools is crucial to gain new teaching and research perspectives; create a supportive culture at universities (Samaha 1996; Filipczak 1997); eliminate bureaucracy and secrecy (Ma and Kim 2005); and build effective research teams or research centres (Kotlarsky and Oshri 2005). Open communication at universities is the preferred means to implement KM initiatives and strategies valuable to establish KS for a clear understanding of work requirements, joint achievements, and collaborations among academic staff (Panteli and Sockalingam 2005). It is hypothesized that openness in communication at research universities positively affects faculty members to share knowledge. Therefore, the following hypothesis is proposed: H9. Openness in communication positively influences KS.

2.2.10. F2F interactive communication Nohria and Eccles (1992) illustrate that F2F interactive communication was more important than electronic conversations (such as e-mail) that requires the subsequent use of more F2F communication that would undermine the efficiency towards the sharing of knowledge at universities. Yuen and Majid (2007) found that F2F interactive communication was the preferred form of sharing since it provides instant feedback, helps in seeking clarification, and offers non-verbal clues. Both of these researchers claimed that F2F interactive communication was popular since it allows the accurate conveying of information and encourages fast feedback among involved members. F2F interactive communication incorporated into the working culture of research universities is essential to encourage and increase KS practices. An effective mechanism for members to gain knowledge in a F2F interactive communication was to request help from someone who may already possess essential knowledge or expertise. This request may lead to a conversation that will facilitate the creation of new knowledge on behalf of the member (i.e. knowledge seeker). This implies that conversations can be an effective conduit for KS by members in F2F interaction (Pierce 2002). This context is built through communication and enabled by shared perspectives, language, and common understanding. F2F conversation enables faculty members to learn together when producing collaborative research work (Brown and Isaacs 1996). This study postulates that the level of F2F interactive communication

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by faculty members was positively associated with KS practices at research universities. Therefore, the following hypothesis is proposed: H10. F2F interactive communication positively influences KS.

2.2.11.

KS and research collaboration

KS can only work if a particular university promotes it (Stoddart 2001); subsequently, there is a need to change individual – organizational –technological – communication constructs of the university to effectively encourage KS. Simultaneously, Steyn (2004) stressed that the upper management should emphasize individual (i.e. people) aspects as well as organizational, technological, and communication aspects in order to harness knowledge power at a research university. Yeh (2005) stated that university management should look into policies and practices that help faculty members share and manage knowledge wisely. Hooff and Weenen (2004) supported the notion that KS is composed of mutual communication between individuals that combines the knowledge of sender and receiver whenever members share the knowledge possessed by a member that was converted into a form understood and required by others (Ipe 2003). The KS definition was narrowed down in this study as a process that captures the expertise of members, no matter where they exist; distributing it for the biggest possible mutual benefit of members and research universities (Krogh, Ichijo, and Nonaka 2000). Research collaboration is a method to share collaborative knowledge by faculty members involved in planned or unplanned collaborative research. This collaboration involves informal storytelling, support or advice, sharing methods, materials, and ideas or joint research work where academic staff can share responsibilities (Cornelissena et al. 2011). It is essential to understand the research collaboration needs of a university in order to further define university collaboration policies that support research collaboration among members. The need for collaborative research work includes: (1) extraction of knowledge generated by university-level research work, (2) ensure that research work continues with a specific direction, (3) obtain knowledge from wherever it is, (4) avoid research work duplication and repetition since research work is a continual process, (5) find a perfect direction so that research work can be done efficiently, and (6) obtain original knowledge directly from universities that reduces the time to conduct industrial-level research. Faculty members at research universities need to realize that efficient interactive research collaboration increases individual effectiveness that contributes to the generation of a research capability that is vital to university performance (Kogut and Zander 1996). Kim and Ju (2008) observed that universities tend to produce new knowledge that results from the processing of existing knowledge; however, there was a need for a systematic structure to help members share knowledge and collaborate effectively since efficient collaboration among members increased effectiveness. One of the possible formal instruments to realize the exchange and reuse of knowledge by members was a campus-wide knowledge-base that acquires, organizes, and distributes newly created knowledge for collaboration. This study claims that KS at research universities positively encourages collaborations among faculty members when conducting research. Therefore, the following hypothesis is proposed: H11. KS by faculty members positively influences research collaboration.

3.

Methodology

This study used a quantitative, survey-based methodology to collect data, which was crucial when there was a requirement to examine causal relationships among the underlying theoretical


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constructs. The self-administered questionnaires consisted of Internet (electronic) surveys and drop-off surveys that allowed for a quick, inexpensive, and efficient way to administer a large sample (Sekaran and Bougie 2010). 3.1. Sample The records of the MOHE in Malaysia showed that there were 9776 faculty members employed at five research universities in 2012. A total of 1000 members participated in the self-administered questionnaire. Only 37 effective responses were gathered from the first wave survey; however, a second wave survey yielded an additional 384 responses due to more proactive steps taken to encourage participation that included gentle reminders and an extension of the participation period. There were 215 early responses for the Internet survey compared to the drop-off survey that had 216 responses. A total of 431 complete and effective Internet and drop-off responses were available for the data analysis. Ten questionnaires were discarded due to large proportions of the questions not being answered; subsequently, 421 responses were deemed usable. The total response rate of this study was 42.1%. The analysis of the final sample profile showed that the respondents were mainly from USM (117) and UPM (116), representing a ratio of 27.8% and 27.6%, respectively. UKM and UM with 79 (18.8%) and 55 (13.1%) of respondents, and UTM (12.8%). The highest percentages of members were associate professors and senior lecturers (41.1% and 36.6%, respectively); however, the lowest percentages were professors (22.3%). The majority of the respondents, i.e. 132 (31.4%) have been with their current institution for almost 11 – 20 years. As for the remaining respondents, 118 (28.0%) have worked at their current institution between 1 and 5 years, 92 (21.8%) respondents have been with the institutions between 6 and 10 years, and 76 (18.1%) of the respondents have served for more than 21 years. With respect to years of working experience, 143 (34.0%) respondents have the longest experience (i.e. between 11 and 20 years); however, 116 (27.5%) had 21 years or more of experience. As for the rest with 6 – 10 years (24.5%) and 1 – 5 years (13.0%) of working experience. The respondents were 226 males (53.7%) and 193 females (45.8%) with 92.8% of them from Malaysia. The ethnic breakdowns of the 421 respondents were 64.3% (271) Malays, 18.8% (79) Chinese, 9.3% (39) Indians, and 6.2% with ethnicity not indicated. The respondents were mostly educated overseas (75.5%) compared to those who had studied locally (21.6%). A total of 330 (78.3%) respondents had completed their PhD and another 84 (20.0%) had Master’s degree. In terms of specialization, the highest percentages were from the Department of Business Administration and the Department of Engineering (14.0% and 11.6%, respectively). The lowest percentages were respondents from the Department Manufacturing and the Department of Architecture (1.0% and 0.5%, respectively). A further profiling of the respondents indicates that a majority of the respondents, i.e. 119 and 112 respondents (28.9% and 27.2%, respectively) had 2– 5 years and 6 – 10 years of experience in research work; however, only 5 (1.2%) respondents had less than one year of experience in research work. 3.2.

Measurement

Self-administered questionnaires were developed using a 7-point Likert scale (i.e. ranging from 1 ¼ strongly disagree to 7 ¼ strongly agree). The instruments used included a total of 55 items (see Appendix 2) taken from prior valid scales (i.e. 13 items for individual KM enablers, 15 for organizational KM enablers, 10 for technological KM enablers, 8 for communication KM enablers, 5 for KS, and 4 for research collaboration). A pre-test was conducted to confirm that the questions were understood and that no ambiguity existed.

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C.N.-L. Tan and S.M. Noor Data analysis and results

The response analysis involved an assessment of the survey response rate that examined data characteristics such as the demographics and characteristics of the participants. A partial least squares (PLS) tested the hypotheses of the research model after the research model was analysed and interpreted in two stages as recommended by Anderson and Gerbing (1988) and in accordance to the assessment of the reflective measurement model and structural model. A reflective measurement model specified the causal relationship between observed variables (i.e. items) and underlying theoretical constructs; subsequently, a confirmatory factor analysis that used PLS path modelling (i.e. SmartPLS Version 2.0 M3) was performed (Ringle, Wende, and Will 2005).

4.1. Stage one: reflective measurement model assessment The composite reliability values ranged from 0.871 to 0.953 and indicated that all measures show good reliability (i.e. higher than 0.70) without the removal of questions (see Table 1). A composite reliability of 0.70 or greater indicated adequate internal consistency (Gefen, Straub, and Boudreau 2000). The measurements used within this study were within acceptable levels to support the reliability of the constructs. The average variance extracted was 0.629 and 0.833, and above the recommended level of 0.50 (see Table 1). This indicates an adequate convergent validity for the items in each construct. The results show that the measurement model of this study provided adequate internal consistency and convergent validity. Based on the results (see Appendix 1), all square roots of average variance extracted exceeded the off-diagonal elements in the corresponding rows and columns. The bolded elements represent the square roots of the average variance extracted and non-bolded values represent the inter-correlation value between the model constructs. All off-diagonal elements were lower than the square roots of average variance extracted (bolded on the diagonal), which indicates satisfactory discriminant validity. Hence, the result confirmed that the criterion of Fornell and Larker (1981) was met. The measurement model demonstrated adequate convergent validity and discriminant validity.

4.2. Stage two: structural model assessment Figure 1 and Table 2 show the structural model results that indicate the coefficients for each path. Hypotheses, H1, H5, H6, H8 – H10 are supported; however, H2 –H4 and H7 are not supported due to an insignificant influence on KS. H11 was supported with an R2 value of 0.361 and a 36.1% variance in research collaboration that indicates that higher KS results in improved research collaboration. Trust had significant positive effects on KS (b ¼ 0.124, p , 0.10), supporting H1 (i.e. trust positively influences KS). In addition, with respect to H5 (i.e. organizational rewards positively influence KS) with a path coefficient (b ¼ 0.199) and t-statistics 5.262 at p , 0.01 level; H6 (i.e. organizational culture positively influences KS) with a path coefficient, b ¼ 1.925, and t-value of 0.175 at p , 0.05; H8 (i.e. KM system quality positively influences KS) having a path coefficient (b ¼ 2.791) and t-statistics ¼ 0.196 at p , 0.01 level; H9 (i.e. open communication positively influences KS) with a path coefficient between the two constructs at 3.287 with t-statistics 0.243 at p , 0.01 significance level; and H10 (i.e. F2F interactive communication positively influences KS) with a path coefficient of 0.101 with t-value 1.504 at p , 0.01 significance level that positively influenced KS. KS was positively associated research collaboration (path coefficient ¼ 0.601, p , 0.01), providing support for H11.


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Table 1: Descriptive and reliability statistics Model construct

Measurement item

Mean

Std. dev.

Loading

CRa

AVEb

Trust

TR1 TR2 TR3 TR4 TR5 KE1 KE2 KE3 KE4 RB1 RB2 RB3 RB4 TM1 TM2 TM3 TM4 OR1 OR2 OR3 OR4 OC1 OC2 OC3 OC4 OC5 OC6 OC7 KI1 KI2 KI3 KI4 KI5 KQ1 KQ2 KQ3 KQ4 KQ5 OP1 OP2 OP3 OP4 FC1 FC2 FC3 FC4 KS1 KS2 KS3 KS4 KS5

5.549 5.515 5.470 5.249 5.571 5.843 5.838 5.708 4.828 5.582 5.613 4.910 5.069 5.387 5.347 4.971 5.230 3.599 3.800 3.952 3.401 5.392 5.466 5.530 5.140 5.078 5.057 5.309 4.855 4.945 5.095 5.135 4.897 4.922 4.841 4.618 4.641 4.774 5.321 5.399 5.520 5.855 4.869 5.686 5.330 5.321 4.800 4.774 4.891 4.950 5.150

1.169 1.158 1.166 1.204 1.085 0.897 0.896 1.011 1.206 1.147 1.117 1.353 1.308 1.357 1.341 1.375 1.358 1.612 1.602 1.567 1.563 1.289 1.252 1.323 1.312 1.340 1.355 1.361 1.354 1.341 1.287 1.281 1.312 1.243 1.223 1.266 1.168 1.249 1.203 1.061 1.143 1.067 1.289 1.258 1.099 1.106 1.348 1.361 1.442 1.368 1.263

0.886 0.925 0.853 0.881 0.875 0.861 0.871 0.847 0.611 0.833 0.839 0.729 0.767 0.880 0.932 0.911 0.922 0.915 0.933 0.901 0.901 0.809 0.830 0.810 0.803 0.844 0.835 0.818 0.903 0.926 0.909 0.880 0.861 0.828 0.881 0.911 0.864 0.895 0.841 0.878 0.877 0.831 0.816 0.730 0.882 0.875 0.858 0.880 0.888 0.908 0.848

0.947

0.782

0.953

0.803

0.871

0.629

0.952

0.831

0.952

0.833

0.917

0.734

0.943

0.768

0.878

0.648

0.936

0.675

0.897

0.686

0.943

0.768


Reciprocal benefits


Organizational rewards

Organizational culture


KM quality


F2F communication

KS

(Continued)

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Table 1: Continued Model construct

Measurement item

Mean

Std. dev.

Loading

CRa

AVEb

Research collaboration

RC1 RC2 RC3 RC4

5.468 5.266 4.834 5.211

1.212 1.291 1.248 1.258

0.817 0.744 0.804 0.868

0.884

0.655

Note: aComposite reliability (CR) ¼ (square of the summation of the factor loadings)/{(square of the summation of the factor loadings) + (square of the summation of the error variances)}. bAverage variance extracted (AVE) ¼ (summation of the square of the factor loadings)/{(summation of the square of the factor loadings) + (summation of the error variances)}.

Figure 1: Results of the PLS analysis. Note: ∗ p , 0.10, ∗∗ p , 0.05, ∗∗∗ p , 0.01.

Table 2:

PLS structural model results

Hypothesis

Relationship

H1 H2 H3 H4 H5 H6 H7 H8 H9 H10 H11

Trust KS Knowledge self-efficacy KS Reciprocal benefits KS Top management support KS Organizational rewards KS Organizational culture KS KM system infrastructure KS KM system quality KS Openness in communication KS F2F interactive communication KS KS research collaboration

Path coefficient (b)

SE

t-Value

0.124 20.027 20.052 0.033 0.199 0.175 0.011 0.196 0.243 0.101 0.601

0.083 0.050 0.069 0.068 0.038 0.091 0.072 0.070 0.074 0.067 0.041

1.493∗ 0.543 0.748 0.489 5.262∗∗∗ 1.925∗∗ 0.148 2.791∗∗∗ 3.287∗∗∗ 1.504∗ 14.816∗∗∗

Hypothesis testing Supported Not supported Not supported Not supported Supported Supported Not supported Supported Supported Supported Supported

Notes: b, regression weight; SE, standard error; t-values were computed through bootstrapping procedure with 421 cases and 1000 samples. ∗ p , 0.10. ∗∗ p , 0.05. ∗∗∗ p , 0.01.

This study did not support H2 (i.e. knowledge self-efficacy positively influences KS), H3 (i.e. reciprocal benefits positively influence KS), H4 (i.e. top management support positively influences KS), and H7 (i.e. KM system infrastructure positively influences KS). The path coefficients and t-statistics for these hypotheses were not statistically significant.

Asian Journal of Technology Innovation 5.

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Discussion

The outcome of this study supports the hypothesized relationships proposed in the research model. The results suggest the relationship between independent variables of trust, knowledge self-efficacy, reciprocal benefits, top management support, organizational rewards, organizational culture, KM system infrastructure, KM system quality, openness in communication, F2F interactive communication, and KS among academic staff (i.e. professors, associate professors, and senior lecturers) at five research universities in Malaysia (i.e. UM, UKM, UTM, UPM, and USM) using a PLS technique to test the 10 hypotheses. This study also examines the extent KS influences KS by faculty members in support of research collaboration. The results are consistent with previous studies (Ridings, Gefen, and Arinze 2002; Yusof and Suhaimi 2006) in that trust positively influences KS. Faculty members are more likely to participate in KS practices based on the competence and goodwill of the faculty members due to the exchange of useful knowledge (Young and Tseng 2008). Trust is asserted in this study as a critical KM enabler and a fundamental approach to encourage members at research universities to participate in KS practices and to further facilitate cooperative action among researchers (Ridings et al. 2002; Wideń-Wulff 2004; Wasko and Faraj 2005). The results indicate that universities should build a trustworthy environment where academic staff feel comfortable and secure when sharing knowledge. This study discovered that knowledge self-efficacy was not a significant influencer of KS by faculty members at research universities. This finding contradicts prior studies (Kankanhalli et al. 2005; Endres, Endres, Chowdhury, and Alam 2007) that indicated that knowledge self-efficacy was an essential KM enabler to encourage KS. One possible explanation for its non-significant relationship with KS originates from the discovery of Rejeski, Katula, Rejeski, Rowley, and Sipe (2005), whose investigation found that an individual will only participate in volitional behaviours (i.e. behaviours under the control of the individual) due to self-relevance that could provide personal implications and importance for a particular individual. KS can be a semivolitional behaviour at universities because faculty members do not have a high confidence in the ability to provide useful knowledge since they view knowledge as unbeneficial to their institution and did not feel the need to share the knowledge with others. Members with low knowledge self-efficacy in expressing their ideas, applying their knowledge, or answering questions from others may still have the determination to share knowledge if others are perceived as willing to share. From the social exchange theory perspective, the cost (i.e. the time and effort required to contribute knowledge) and benefit (i.e. organizational rewards) provided by the university should be at least equal in order to inspire members to accomplish sharing practices (Kankanhalli et al. 2005). One of the findings of this study suggests that the degree of reciprocal benefits among members has no effect on KS. This finding was consistent with that of Quinn, Anderson, and Finkelstein (1996), who argued that there was a need for members to have some self-motivation to share knowledge. Some members do not expect reciprocal benefits from sharing because they do not believe in these benefits or yet to have any encounter of the benefits given. Even if the members could anticipate the expected benefits of their contributions, the expected question of ‘What’s in it for me?’ was often not clear to those who lack self-motivation in terms of a commitment to share knowledge. Based on the research findings, the result of this study indicates that the top management support construct was not a KS predictor. This result contradicted previous studies that indicated that top management support was a vital element to foster KS (Davenport, De Long, and Beers 1998; Lin 2007a,b; Lin et al. 2009). Based on Yu et al. (2004), the finding indicated that top management support may be an antecedent of other KM enablers such as organizational rewards or

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organizational culture instead of a KM enabler because top management plays a crucial role to provide incentives to members and promote a KS culture at research universities. Consistent with expectations, the result of this study demonstrated that the organizational rewards construct was an important KM enabler and has a strong positive influence to predict KS. A university reward system can enhance the effort and involvement by members in KS (Yu et al. 2004). For example, members that share knowledge will receive organizational rewards for contributions and can influence the commitment of other members (Beer and Nohria 2000); however, Yao et al. (2007) state that the lack of rewards and incentives would be a barrier for KS by members. Furthering the aim to investigate KS as a consequence of organizational culture, it was hypothesized that organizational culture will positively influence academic staff to share their knowledge at research universities. This indicates that the establishment and communication of a knowledge-friendly and conducive organizational culture will positively influence members to engage in effective KS activities (Syed-Ikhsan and Rowland 2004; Bock et al. 2005; Sondergaard, Kerr, and Clegg 2007). An organizational culture that shares knowledge acts as a catalyst to stimulate, facilitate, and inspire KS practices. Thus, research universities must create a KS environment that promotes successful KS by initializing and realizing more KS activities. The study found that KM system infrastructure has a non-significant relationship with KS members at research universities. This finding might be because knowledge is embedded in the minds of copious members within a university as well as in university work practices, values, and systems (DeTienne and Jackson 2001). The codification, creation, and dissemination of institutional knowledge among members do not require the building of sophisticated KM system infrastructure to enable, access, and leverage KS-related activities at research universities (Lin 2011). This paper demonstrates that KM system quality was a significant facilitator of KS practices among members. Higher KM system quality increases the usefulness, accessibility, and capability of the KM system that allows for the easy leveraging of KS practices through the enhancement of fit between KM system output and the knowledge requirements by members (Kulkarni et al. 2006). The KM system could result in a faster task performance and more mature KM practices in the KM system at research universities provided accurate, relevant, up-to-date, reliable, and easy to access knowledge. Subsequently, universities with a higher KM system quality were more likely to create sources of sustainable growth in KS. The finding posited that openness in communication has a significant and positive influence on KS by faculty members at research universities. Consistent with expectations, the results show that openness in the communication climate by members enhances the awareness of effective communication and the willingness of members to share knowledge with each other. Given this significance, it reveals that members were willing to exchange their ideas, opinions, and knowledge through seminars, workshops, and other small group meetings, even if those ideas and knowledge contradict popular opinions. Open communication among members is an evolutionary concept that describes the steady but effective development (in terms of willingness) of academic staff to share knowledge (Lin 2011). This showing of enthusiasm and willingness of faculty members to communicate is crucial since they are aware of the benefits that come with sharing. Past studies showed that openness in communication acts as a major facilitator to establish a learning culture (Marquardt and Reynolds 1994) and helps eliminate resistance barriers to KS activities (Kim 2003). This study proves that successful KS by members will not exist at research universities without open communication. F2F interactive communication was a positive significant influencer of KS. The findings suggest that a high level of F2F interactive communication among faculty members was vital


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in promoting KS by members at research universities (Skryme 1997; Davenport and Prusak 2000). For example, the willingness of members to share knowledge with others that they like will be higher; however, the enthusiasm of academic staff to share knowledge with those they dislike will be lower. The result provides support that there is a positive and strong significant relationship between KS and research collaboration among academic staff at research universities. The extent of KS by members has a significant impact on research collaboration. This finding was consistent with the concept postulated by Janowicz and Noorderhaven (2002) that mentioned that KS practices were essential to overcome the research limitations faced by faculty members in order to develop a commonly acceptable sustainable long-term research strategy among members at research universities. 6.

Implications, limitations, and future research

From a theoretical perspective, this study provided a KM – KS –collaboration research model that can be applied to other higher learning institutions that would like to deepen their understating of KM enablers. This study extended the current research of KM enablers (i.e. the individual – organizational – technical – communication constructs) through an investigation of their influence on KS. This association reflects the necessity to understand if these four KM enablers enhance KS within the research university context. However, the findings also demonstrate that not all KM enablers (i.e. knowledge self-efficacy, reciprocal benefits, top management support, and KM system infrastructure) were significant KS determinants in this research setting. These enablers might not be new in the field of KM; however, KM enablers that affect KS members at research universities have not been researched. There has been no past research to discover the influence of KS to support research collaboration at research universities in Malaysia. From a managerial perspective, the findings of this study confirm that trust (i.e. individual KM enablers) was associated with the KS of faculty members. Trust has been asserted as a critical determinant to further the success of KS (Levin, Cross, Abrams, and Lesser 2002; McEvily et al. 2003; Langfred 2004). Research universities should create an environment that strengthens trust among faculty members and allows them to work together, be interested in different viewpoints, value the experiences of others, voice opinions, experiment, take risks, initiate involvement in team discussions; and the inspired to share knowledge (Brink 2003). This consequently enforces a decree to members to actively share knowledge (von Krogh 1998). This atmosphere of trust in higher learning institutions is an important prerequisite in the KM field as members are allowed to share knowledge with others (Pan and Scarbrough 1998). The intention of a faculty member to share knowledge will increase after they can trust the expertise and skills of others; subsequently, they will have a higher value of the benevolence of others to determine how knowledge will be shared (Abrams, Cross, Lesser, and Levin 2003). Prior research suggests the importance of increased confidence by members when sharing useful knowledge with others. Knowledge self-efficacy is an important antecedent to KS member practices. Efforts should be made by management to foster and enhance positive reciprocal relationships among academic staff in order to create and maintain KS at research universities. These institutions should emphasize the self-efficacy of faculty members through useful feedback that improves KS endeavours. Highly self-efficacious faculty members can be recruited by selecting individuals who are proactive, have high cognitive aptitude, strong self-esteem, and are intrinsically motivated (Parker 1998). Management can enhance knowledge self-efficacy perceptions among knowledgeable members through indications that KS would significantly support research collaboration. A study by Bryant (2005) suggested that universities could enrich KS by increasing the self-efficacy of members through training, role modelling, and positive communication. In

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addition, university management should provide strategies such as training programmes and support mechanisms to increase the self-efficacy of members so that members believe that they can share knowledge with others. Faculty members need to believe that KS can increase the scope and depth of relations with others since there was a high tendency to share knowledge with others. As for study limitations, Arnold and Bianchi (2001) suggested that different cultural and social contexts may influence the findings of this study. Therefore, caution about generalizing the results of this study is required as they reflect the perspective of faculty members (primarily professors, associate professors, and senior lectures) at five research universities of Malaysia. It was predicted that knowledge self-efficacy, reciprocal benefits, top management support, and KM system infrastructure from individual-organizational-technological constructs would encourage the pleasure of KS by faculty members; however, the results of this study are contrary to those found in the previous literature. Consequently, future research may further explore specific types of knowledge self-efficacy, reciprocal benefits, top management support, and KM system infrastructure that could encourage KS at higher learning institutions. Future research may focus on knowledge creation self-efficacy for the current research purpose, which refers to the belief by a learner and their ability to articulate ideas and experiences, synthesize knowledge from different sources, and learn from others (Chen, Chen, and Kinshuk 2009). Reciprocal benefits had three types (Wu and Wang 2006): (1) balanced reciprocity, (2) generalized reciprocity, and (3) negative reciprocity. Balanced reciprocity is where faculty members contribute and expect something of equal value back, generalized reciprocity is when members expect more than what they contribute and negative reciprocity is if members feel they receive less than what they contribute. A faculty member will tend to contribute more when they have a higher level of selfefficacy to reciprocate due to the belief that they will receive more than what they can contribute. 7. Conclusion There could be additional constructs to those incorporated in the KM – KS – collaboration research model; however, this study included pertinent constructs found in various studies that were further delineated to develop a theoretical justification. This study adds to the KM literature through an extension of the research on KM enablers that includes the constructs of: trust, knowledge self-efficacy, reciprocal benefits (i.e. individual KM enablers), top management support, organizational rewards, organizational culture (i.e. organizational KM enablers), KM system infrastructure, KM system quality (i.e. technological KM enablers), openness in communication, and F2F interactive communication (i.e. communication technological KM enablers) on KS. It also examined the influence of KS in support of research collaboration. The results revealed that individual KM enablers (i.e. trust), organizational KM enablers (i.e. organizational rewards, organizational culture), technological KM enablers (i.e. KM system quality), and communication KM enablers (i.e. openness in communication, F2F interactive communication) were imperative to build a significant KS relationship. Higher learning institutions should emphasize trust, provide rewards, developed a culture of sharing, equip their departments with a quality KM system, encourage openness in communication, and provide regular F2F interactive communication among members to elicit and encourage KS. This study reinforced the understanding of KM enablers, KS, and research collaboration within the context of faculty members at research universities. This study has extended this understanding to include the association of trust, knowledge self-efficacy, reciprocal benefits, top management support, organizational rewards, organizational culture, KM system infrastructure, KM system quality, openness in communication, and F2F interactive communication with KS as well as between KS with research collaboration as a key variable in the study of KM.


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FC1 FC2 FC3 FC4 KE1 KE2 KE3 KE4 KI1 KI2 KI3 KI4 KI5 KQ1 KQ2 KQ3 KQ4 KQ5 KS1 KS2 KS3 KS4 KS5 OC1 OC2 OC3 OC4 OC5 OC6 OC7 OP1 OP2 OP3

Loadings and cross loadings

0.816 0.730 0.882 0.875 0.432 0.393 0.419 0.261 0.382 0.369 0.408 0.399 0.389 0.372 0.429 0.461 0.496 0.465 0.507 0.494 0.505 0.541 0.524 0.445 0.464 0.483 0.484 0.525 0.540 0.569 0.617 0.600 0.558

0.338 0.433 0.428 0.408 0.861 0.871 0.847 0.611 0.241 0.217 0.275 0.266 0.259 0.233 0.268 0.264 0.237 0.233 0.303 0.300 0.311 0.363 0.406 0.445 0.465 0.439 0.365 0.358 0.349 0.363 0.481 0.535 0.528

0.444 0.260 0.341 0.350 0.218 0.210 0.270 0.178 0.903 0.926 0.909 0.880 0.861 0.670 0.690 0.654 0.623 0.706 0.424 0.442 0.449 0.447 0.432 0.380 0.370 0.429 0.481 0.432 0.531 0.520 0.367 0.369 0.346

0.513 0.296 0.412 0.422 0.213 0.193 0.254 0.236 0.678 0.693 0.680 0.676 0.677 0.828 0.881 0.911 0.864 0.895 0.512 0.526 0.513 0.513 0.485 0.373 0.335 0.382 0.493 0.428 0.519 0.488 0.460 0.415 0.372

KM F2F interactive Knowledge KM system system communication self-efficacy infrastructure quality

Appendix 1.

0.596 0.358 0.474 0.461 0.295 0.248 0.402 0.240 0.501 0.448 0.424 0.380 0.471 0.411 0.460 0.577 0.523 0.548 0.858 0.880 0.888 0.908 0.848 0.446 0.409 0.414 0.586 0.575 0.569 0.566 0.537 0.565 0.535

KS 0.545 0.444 0.518 0.514 0.420 0.399 0.453 0.211 0.543 0.478 0.484 0.436 0.527 0.411 0.452 0.484 0.474 0.514 0.546 0.524 0.558 0.584 0.563 0.809 0.830 0.810 0.803 0.844 0.835 0.818 0.559 0.593 0.544

0.547 0.501 0.595 0.584 0.600 0.560 0.547 0.246 0.355 0.336 0.386 0.370 0.352 0.367 0.399 0.426 0.418 0.385 0.502 0.497 0.487 0.575 0.589 0.554 0.562 0.537 0.501 0.503 0.505 0.520 0.841 0.878 0.877

0.371 0.044 0.186 0.152 20.012 0.007 0.123 0.216 0.335 0.293 0.226 0.236 0.365 0.311 0.322 0.370 0.383 0.367 0.365 0.401 0.382 0.373 0.304 0.175 0.095 0.165 0.322 0.312 0.299 0.274 0.193 0.210 0.134

0.412 0.363 0.455 0.450 0.531 0.489 0.548 0.349 0.342 0.338 0.360 0.342 0.361 0.327 0.339 0.412 0.384 0.347 0.391 0.446 0.408 0.443 0.441 0.434 0.433 0.420 0.507 0.452 0.450 0.457 0.591 0.574 0.607

0.579 0.469 0.566 0.557 0.399 0.347 0.441 0.256 0.399 0.373 0.372 0.368 0.408 0.363 0.456 0.507 0.507 0.458 0.481 0.541 0.539 0.567 0.502 0.419 0.425 0.418 0.489 0.512 0.516 0.536 0.536 0.523 0.494

0.522 0.394 0.495 0.481 0.406 0.328 0.384 0.168 0.464 0.407 0.408 0.373 0.476 0.381 0.404 0.444 0.424 0.475 0.499 0.505 0.516 0.524 0.504 0.614 0.626 0.650 0.711 0.615 0.620 0.742 0.549 0.515 0.518

0.510 0.505 0.588 0.539 0.527 0.459 0.540 0.158 0.341 0.338 0.401 0.325 0.358 0.320 0.383 0.403 0.413 0.370 0.458 0.490 0.494 0.565 0.551 0.534 0.573 0.538 0.593 0.578 0.574 0.577 0.636 0.581 0.613

Top Organizational Openness in Organizational Reciprocal Research management culture communication rewards benefits collaboration support Trust

272 C.N.-L. Tan and S.M. Noor

0.523 0.185 0.242 0.281 0.201 0.481 0.501 0.287 0.337 0.540 0.408 0.532 0.620 0.504 0.548 0.533 0.526 0.550 0.589 0.547 0.576 0.587

0.607 0.065 0.132 0.143 0.026 0.641 0.633 0.307 0.335 0.477 0.333 0.316 0.384 0.458 0.401 0.336 0.328 0.527 0.547 0.463 0.383 0.507

0.277 0.291 0.319 0.330 0.256 0.282 0.297 0.325 0.332 0.337 0.246 0.371 0.402 0.385 0.399 0.516 0.432 0.332 0.354 0.333 0.358 0.362

0.297 0.344 0.389 0.384 0.347 0.319 0.335 0.350 0.314 0.395 0.295 0.507 0.464 0.382 0.403 0.528 0.452 0.342 0.399 0.344 0.431 0.388

0.415 0.357 0.396 0.414 0.346 0.394 0.391 0.362 0.394 0.441 0.323 0.530 0.586 0.472 0.514 0.583 0.541 0.483 0.561 0.439 0.562 0.522

Note: Bold values are loadings for items above the recommended value of 0.5.

OP4 OR1 OR2 OR3 OR4 RB1 RB2 RB3 RB4 RC1 RC2 RC3 RC4 TM1 TM2 TM3 TM4 TR1 TR2 TR3 TR4 TR5

0.467 0.230 0.303 0.333 0.214 0.502 0.490 0.372 0.381 0.551 0.398 0.402 0.534 0.719 0.728 0.728 0.742 0.613 0.650 0.580 0.609 0.606 0.831 0.106 0.188 0.217 0.095 0.666 0.693 0.380 0.421 0.559 0.408 0.390 0.512 0.580 0.577 0.499 0.527 0.613 0.679 0.607 0.602 0.626

0.012 0.915 0.933 0.901 0.901 0.092 0.099 0.345 0.368 0.248 0.187 0.382 0.285 0.182 0.201 0.367 0.270 0.148 0.197 0.085 0.222 0.161

0.576 0.226 0.281 0.348 0.155 0.833 0.839 0.729 0.767 0.454 0.397 0.335 0.465 0.541 0.514 0.520 0.519 0.551 0.623 0.541 0.534 0.576

0.403 0.281 0.359 0.367 0.262 0.427 0.450 0.348 0.378 0.817 0.744 0.804 0.868 0.445 0.477 0.534 0.502 0.546 0.546 0.551 0.508 0.520

0.448 0.205 0.286 0.352 0.181 0.518 0.506 0.370 0.418 0.469 0.359 0.390 0.511 0.880 0.932 0.911 0.922 0.613 0.639 0.597 0.680 0.609

0.600 0.121 0.209 0.230 0.112 0.604 0.617 0.368 0.429 0.544 0.406 0.408 0.575 0.641 0.695 0.617 0.645 0.886 0.925 0.853 0.881 0.875

Asian Journal of Technology Innovation 273

I have the expertise required to provide valuable knowledge to academics in my faculty/school It does make a difference when I share my knowledge with other academics in my faculty/school I can provide more valuable knowledge than most of the academics in my faculty/school I strengthen ties between them and myself when I share my knowledge with academics in my faculty/school I expand the scope of my association when I share my knowledge with other academics in my faculty/school I expect to receive knowledge in return when I share my knowledge with academics in my faculty/school I believe that my future requests for knowledge will be answered when I share my knowledge with academics in my faculty/school In my faculty/school, top management thinks that encouraging KS among academics is beneficial In my faculty/school, top management always supports academics to share our knowledge with each other In my faculty/school, top management provides most of the necessary help to enable academics to share knowledge In my faculty/school, top management is keen to see that academics are happy to share knowledge with each other I will receive a higher salary in return for sharing my knowledge

KE2 KE3 KE4 RB1

Organizational rewards


Reciprocal benefit

OR2 OR3 OR4

OR1

TM4

TM3

TM2

TM1

RB4

RB3

I will receive increased promotion opportunities in return for sharing my knowledge I will receive increased job security in return for sharing my knowledge I will receive a higher bonus in return for sharing my knowledge

I trust the expertise of academics in my faculty/school When I face difficulties, I am willing to ask the academics in my faculty/school for help I believe that the academics in my faculty/school are honest I believe that academics in my faculty/school are knowledgeable in their area I am confident in my ability to provide knowledge that other academics in my faculty/school consider valuable

TR2 TR3 TR4 TR5 KE1

RB2

I trust my faculty/school academics in general

TR1

Trust


Items

Constructs and items used in the research model

Constructs

Appendix 2.

Developed based on Lin (2007a)

Developed based on Lin et al. (2009) and Lin (2007b)

Developed based on Lin et al. (2009) and Lin (2007a)

Developed based on Lin et al. (2009) and Lin (2007a, 2007b)

Developed based on Kim and Ju (2008) and Choi, Kang, and Lee (2008)

References


KM system quality


Organizational culture

KQ2 KQ3 KQ4 KQ5

KQ1

KI5

KI4

KI3

KI2

KI1

OC7

OC6

OC5

OC4

OC3

OC2

OC1

The knowledge provided by the KM system at my institution is accurate The knowledge provided by the KM system at my institution is always up-to-date The operation of the KM system at my institution is dependable The KM system at my institution makes knowledge easy to access

In my faculty/school, the management expects academics to actively contribute to the registration of knowledge In my faculty/school, the management expects academics to actively contribute to the transmission of knowledge In my faculty/school, the management stresses the importance of knowledge to the success of the institution Management expects academics to actively contribute to the registration of knowledge at my faculty/school Management expects academics to actively contribute to the transmission of knowledge at my faculty/school Management stresses the importance of knowledge to the success of the institution at my faculty/school Management expects academics to actively contribute to the registration of knowledge at my faculty/school My institution uses a KM system that allows academics in my faculty/school to collaborate with each other My institution uses a KM system that allows academics in my faculty/school to communicate with each other My institution uses a KM system that allows academics in my faculty/school to search necessary knowledge My institution uses a KM system that allows academics in my faculty/school to access necessary knowledge My institution uses a KM system that allows academics in my faculty/school to store specific types of knowledge that includes explicit knowledge (e.g. documents) and tacit knowledge (e.g. personal/experience-based knowledge) The knowledge provided by the KM system at my institution is relevant to my research work

(Continued)

Developed based on Lin (2011) and DeLone and McLean (2003)

Developed based on Lin (2011) and Lee and Choi (2003)

Developed based on Hooff and Huysman (2009)

Asian Journal of Technology Innovation 275

Research collaboration

KS

F2F interactive communication

RC3 RC4

RC2

RC1

KS5

KS4

KS2 KS3

FC4 KS1

FC2 FC3

FC1

OP4

OP2 OP3

If I have options, I prefer to work with other academics in my faculty/school than to working independently The academics in my faculty/school were satisfied with current levels of collaboration There is a willingness to collaborate across departments and research centres at my faculty/school

Language is not a problem when communicating with other academics Teamwork discussion among academics on research-related matters takes place through F2F meetings Research collaboration among academics takes place through F2F meetings Academics share research reports and documents that include publication materials/ documents and research project reports Academics share research project’s guidelines, methodologies, and models Academics share research knowledge gained from conferences, workshops, and seminars Academics share know-how from research experiences such as securing research grants/funds Academics share know-where and know-whom of conferences, workshops and seminars at the request of others I prefer to work collaboratively with other academics in my faculty/school rather than work alone

Open communication among academics at my faculty/school is helpful when it comes to research-related activities/tasks I interact with academics at my faculty/school in exchange of research knowledge I will not hesitate to ask academics at my faculty/school to share knowledge with me if I need it I am actively willing to share my knowledge with academics at my faculty/school when they ask There is a high level of F2F interaction among academics


OP1

Items

Continued

Constructs

Appendix 2.

Developed based on Kim and Ju (2008) and Lee and Choi (2003)

Developed based on Yang and Chen (2007)

Developed based on Al-Alawi et al. (2007)

Developed based on Kim and Ju (2008)

References
