A FRAMEWORK FOR KNOWLEDGE MANAGEMENT USING ICT IN HIGHER EDUCATION IN UGANDA

C: AN EMPIRICAL STUDY ON THE RELATIONSHIP BETWEEN SOCIAL CAPITAL AND R&D PERFORMANCE IN HIGHER EDUCATION
MOHD ISKANDAR BIN ILLYAS and ROSE ALINDA ALIAS
Faculty of Computer Science and Information Systems, Universiti Teknologi Malaysia,
81300 UTM Skudai, Johor, Malaysia
E-mail: iskandar@fsksm.utm.my & alinda@utm.my

LEELA DAMODARAN
Information Science Department, Loughborough University,
Leicestershire, LE11 3U, United Kingdom
E-mail: l.damodaran@lboro.ac.uk
Based on the analysis of research groups in higher education in the UK, this paper investigated the relationship between social capital and the performance of those research groups. The study produced a model that considered the different dimensions of social capital and how these dimensions might have an impact on the performance of R&D in the HEI in the UK. The result from the regression analysis shows that trust has the strongest influence towards explaining the R&D performance.
1. Introduction
Social capital is a valuable and inimitable resource that leads to competitive advantage in research and development (R&D) groups. Previous studies have shown that social capital can improve organisational performance through effective networks, higher level of trust, and shared vision and understanding. Although several studies have measured the effects of social capital, there are no studies investigating the impact of social capital on research and development at a group level in Higher Education (HE). The aim of this study was to understand the effects of social capital on R&D performance in higher education. In order to achieve the aim, two objectives were identified, including the examination of different social capital dimensions on R&D performance, and the development of a social capital and R&D performance model.
2. Literature Review
In the following section we define social capital in some depth and then extend the concept to explain how social capital affects innovation in higher education. We look into the relationship between social capital and R&D processes within higher education and how it might affect its performance. We put forward several proposition to support our arguments based upon our conceptual model. Finally, we provide the findings from the analysis of data and outline some recommendation from the findings.
2.1. Social Capital
Social capital is the sum of the actual and potential resources embedded within, available through, and derived from the network of relationships possessed by an individual or social unit (Nahapiet & Ghoshal, 1998). It comprises the assets that may be mobilized through a network. It makes an organization, or any cooperative group, more than a collection of individuals intent on achieving their own private purposes. Its characteristic elements include high levels of trust, robust personal networks and vibrant communities, shared understandings, and a sense of equitable participation in a joint enterprise. These kinds of relationships support collaboration, commitment, ready access to knowledge and talent, and achievement of organizational goals. Social capital requires appropriate organizational investments – providing people space and time to connect, developing trust, effectively communicating aims and beliefs, and offering the equitable opportunities and rewards that invite genuine participation, not mere presence. But even when social capital investments are made solely by individuals who develop ties with one another, many real advantages accrue to the organization as a whole.
Researchers have used the social capital construct to explain different dimensions of human capital that span multiple levels of analysis from organizational learning (Huber, 1991) to a resource-based view of the firm (Barney, 2001). Theoretical advances in this field have forwarded the structural, relational and cognitive dimensions but these have not been equally balanced in empirical studies. Like "physical capital and human capital-tools and training that enhance individual productivity—'social capital' refers to features of social organization, such as networks, norms, and trust, that facilitate coordination and cooperation for mutual benefit (Putnam, 1993)."
Social capital, like other forms of capital, accumulates when used productively. Traditional economic perspectives that focus on short-term self interest and individual transactions ignore the accretion, or growth, opportunities of cooperation (Ostrom, 1990). Closely related to accretion is the self-reinforcing cyclic nature of social relations. Trustful relations tend to be self-reinforcing in the positive direction. Mistrust tends to cycle in the negative direction.
Nahapiet and Ghoshal (1998) present a theoretical model of social capital and propose three dimensions – structural, cognitive, and relational in order to facilitate the various combinations and exchange of resources within firms. However, the model doesn’t consider the deeper cultural aspects, which are intrinsic in developing strong social relationships among members of organizations. Although the relational and cognitive dimensions have some connection with organizational climate, these tend to focus on superficial manifestations of organizational culture.
3. Social Capital and R&D Performance: A Research Model
Investments in human and social capital are widely believed to improve the organizational performance (M. K. Ahuja, Galletta, & Carley, 2003; Annen, 2003; Bosma, Praag, Thurik, & Wit, 2004; Dess & Shaw, 2001; Knack, 2001; Knack & Keefer, 1997; Koka & Prescott, 2002; Lesser & Storck 2001; Tsai, 2003). However, there also has been recognition of the potential risks and pitfalls inherent in utilizing social capital (G. Ahuja, 2000; Brass, Butterfield, & Skaggs, 1998; Shaul M. Gabbay & Leenders, 2001; S. M. Gabbay & Zuckerman, 1998; Hansen, 1999; Leana & Van Buren, 1999; Edwin A. Locke, 1999; Portes, 1998). Nevertheless, the notion of social capital has been moved beyond general or specific literature on concepts as trust and networks to explore concepts which help the development of a dynamic rather than a static concept of social capital (Edelman, Bresnen, Newell, Scarbrough, & Swan, 2004).
Here we advance a model that connects the independent and dependent variables of this construct. The model, shown in Figure 1, suggests that the R&D performance in higher education is influence by the pattern of social capital among the member of the academics.


Fig.1 Social Capital and R&D Performance
3.1. Structural Dimension – Collaborative Network
The studies on organizational social network were perhaps the most studied area among the different dimensions of social capital (Burt, 2000; Coleman, 1990; Granovetter, 1973; Hansen, 1998; Nohria & Eccles, 1992). However, there is a lack of agreement regarding how these dimension influence the organizational outcome. Empirical findings range from the degree of network centrality and its effect on levels of perceived trustworthiness (Tsai & Ghoshal, 1998), new linkage creation (Tsai, 2000), levels of innovation (Tsai, 2001), and levels of intra-organizational knowledge sharing (Tsai, 2002).
Harvey et al. (2002), in their research indicates that high-achieving research groups are aware and adept at leveraging this ‘network advantage’. Similarly, the new production of knowledge conceptualizes knowledge as deriving not merely from individual thought but from collective processes of networking, negotiation, inter-personal communication and influence. The network of contacts sustains the group as a whole. It aids competition, strategic sense-making, collaboration and staff recruitment. It is central to exploiting existing internal and external competencies to respond appropriately to a changing environment by configuring and reconfiguring the internal and external organizational skills, resources and competencies to align with the changing environmental contingencies. Collaborative working is intimately related to networking; it represents ultimately a networking of resources: social, intellectual and infrastructural. Network connectedness is a crucial component of high-achieving research groups (Harvey et al., 2002). Therefore, we posit the following:
Hypotheses 1: The existence of collaborative networks among group members will have a positive effect on their R&D performance.
3.2. Relational Dimension – Trust
The notion of trust within the organisation arises as an important dimension for scholars of social capital (Coleman, 1990; Fukuyama, 1995: Putman, 1995). Several researchers have studied the relationship between trust and performance. However, the outcome of the above research has given varying results, whereby performance of a group or individual may or may not have been affected by the level of trust among the individual or group members. Among the research that established a positive relation between trust and performance are the studies of McAllister (1995) and Barclay (1997), while Dirk’s (1999) study shows that trust doesn’t have any significance on team performance. One of the main reasons for the variations between the results is the type of constructs used to measure the level of trust,. While some of the research used a single dimension (attitude, belief) to measure trust, other researchers explore trust from multidimensional perspectives (Costas, 2003). Hence:
Hypotheses 2: High level of trust among group members will have a positive effect on their R&D performance.
3.3. Cognitive Dimension – Shared Visions
Past studies have found that setting specific goals facilitates overall performance (Edwin A Locke & Latham, 1984). However, McComb et al. (1999) advanced the idea beyond goal setting to suggest that individuals involved in R&D activities need to have a shared understanding of the requirements in innovation activities so that they can have a common foundation or understanding upon which to act. For example, when individuals involved in R&D activities have a common or overlapping understanding of the organisation’s innovation objectives, their responsibilities toward these objectives and the procedures they use to attain these objectives will be more coordinated as they share compatible knowledge. This shared mental framework (Janis A Cannon-Bowers, Salas, & Converse, 1993) facilitates decision-making and coordination so that individuals can conduct their tasks without a continuous process of interpreting and reinterpreting the meanings and expectations of the innovation process (Lynn, Reilly, & Akgun, 2000). Klimstra and Potts (1988) reported that by aligning everyone’s views toward a common framework based on shared expectations and agreement, higher levels of project success occurred. Shared visions affect task performance in the sense that when team members have similar attitudes/ beliefs, “they arrive at compatible interpretations of the environment, which enable them to reach better decisions” (J. A. Cannon-Bowers & Salas, 2001). Therefore, it is hypothesised that:
H3a: The group’s level of shared vision is positively related to their R&D performance
3.4. Cognitive Dimension – Shared Language
Language is the means by which individuals engage in communication. It provides a frame of reference for interpreting the environment and its mastery is typically indicated by an individual’s level of expertise (Wasko & Faraj, 2005, p. 41). Nahapiet and Ghoshal (1998) argue that to the extent that people share a common language, this facilitates their ability to gain access to people and their information. To the extent that their language and codes are different, this keeps people apart and restricts their access. Since R&D work often involves novel tasks that are inherently ambiguous and complex (Janz, Colquitt, & Noe, 1997), individuals who have a shared mental model with others can share ideas and information more efficiently and effectively (Levesque, Wilson, & Wholey, 2001). As a result of having greater shared expectations and understanding, improved coordination, communication, and better R&D performance occurred(Klimstra & Potts, 1988; Pan & Scarbrough, 1999; Rouse, Cannon-Bowers, & Salas, 1992). Hence, group members who shared the same codes and language with each other will demonstrate higher levels of R&D performance. These observations suggest the following:
H3b: The group’s level of shared codes and language is positively related to their R&D performance.
3.5. Cultural Dimension – Cooperative Culture
According to goal interdependence theory (Deutsch, 1949), cooperatively structured situations create perceptions of shared fate and promote supportive behaviour, whereby each group member looks out for the interests of the others. In addition, insights and lessons learned by one member are shared so that all can benefit vicariously from others’ experiences. Therefore, it is hypothesized that:
H4a: The group’s cooperative culture is positively related to their R&D performance.
3.6. Cultural Dimension – Competitive Culture
Typically, people placed in competitive structures tend to keep valuable information proprietary - rather than share information and experience. Moreover, rather than supporting each other, people placed in competitive reward structures may be motivated to impair the progress of others in an effort to gain positive advantage (Beersma et al., 2003). This discussion suggests the following proposition:
H4b: The group’s competitive culture is negatively related to their R&D performance.
3.7. R&D Performance – Research Assessment Exercise (RAE)
The UK higher education system has developed a range of statistics in an attempt to measure its performance. Nevertheless, the UK Research Assessment Exercise (RAE) is widely accepted as the most rigorous measure of research output available in the UK. Therefore, the data source of the academic researcher submission for the 2001 UK RAE was used as the indicators of R&D performance (dependent variable) in this study.
4. Research Methodology
The research design followed a mixed method approach. This involved a set of semi-structured interviews that informed the formulation of appropriate research hypotheses, and a questionnaire survey, that facilitated the statistical testing of these hypotheses. A total of six academic researchers from Loughborough University participated in the semi-structured interviews. During the interviews participants were asked a set of 13 questions about the effects of structural, relational, cognitive and cultural dimensions of social capital on the performance of their research groups within the university. The results of the interviews revealed that six main factors affected group performance. These were: the presence of structural dimensions (collaborative networks), relational dimensions (trust), cognitive dimensions (shared vision and shared codes and language), and cultural dimensions (cooperative and competitive culture).
4.1. Questionnaire Development
The primary aim of the pilot study questionnaire was to develop a reliable instrument for organizational social capital and the R&D performance that could be tested effectively in the main study. The principal construct of organizational social capital was developed comprising four sub-constructs: structural dimension, relational dimension, cognitive dimension and cultural dimension. An outline of the questionnaire is shown in Table 1. Each of the four sub-constructs of organizational social capital were developed using 7 items: collaboration cosmopolitan pattern, collaboration strategies, trust, shared vision, shared language, internal forces of cooperation and internal forces of competition.


Table 1 .Outline of the Questionnaire

Construct Sub-construct References Items Total
Personal Information 12 12
Structural Collaboration Cosmopolitan Pattern (Bozeman & Corley, 2004)
6 18
Collaboration Strategies (Bozeman & Corley, 2004; Melin, 2000)
12
Relational Trust (Huff & Kelley, 2003; Politis, 2003; Zaheer, McEvily, & Perrone, 1998)
10 10
Cognitive Shared Vision (Tsai & Ghoshal, 1998)
2 8
Shared Language New 6
Cultural Internal Forces of Cooperation (Jashapara, 2003; Mintzberg, 1991)
5 10
Internal Forces of Competition (Jashapara, 2003; Mintzberg, 1991)
5
Social Desirability Scale (Ballard, 1992)
13 13
2001 RAE Rating 1 1
Total 72
4.2. Sampling
The data source of the academic researcher submission for the 2001 UK Research Assessment Exercise (RAE) was used as an effective sampling frame in this study. The RAE was a UK-wide undertaking, whereby each publicly funded university and higher education college in the UK was invited to submit information about their research activity for assessment. Information provided was assessed and quality ratings were awarded for all subjects in which research was submitted. These data were collected by the Higher Education Funding Council for England (HEFCE) on behalf of the four UK funding bodies. The information largely relates to a five-year period, running from the last RAE in 1996. The data can be searched on-line or sections downloaded from http://www.hero.ac.uk/rae/index.htm. The theoretical sample size for this study were determined from the formula given by Tull and Hawkins (1993) for a stratified sample:

Given these parameters, the estimated stratified sample size for this study is:


The stratified random sample using the 2001 RAE submission data for the pilot and main study was determined as shown in Table 2.
Table 2. Stratified Sample of Pilot and Main Study

RAE Rating No. of RAE 2001 Submission Pilot Study Sample Main Study Sample
5* 11767 58 582
5 21850 108 1080
4 14879 74 736
3a 7528 37 372
3b 3174 16 157
2 1368 7 68
1 119 1 6
Total 60685 300 3000
4.3. Main Survey
The data for the pilot and main study were collected using an online questionnaire. The main reason for choosing the online method of data collection is it offers some advantages over the traditional postal approach from an efficiency and cost effectiveness viewpoint. One of the major assumptions for the data collection process is that most of the academic researchers in the UK have access to the Internet in order to participate in this research. The development of the online questionnaire was done using the HTML editor and the layout was design to be as simple as possible to avoid any technical complications to access the questionnaire. The questionnaire for this research can be access at http://www-staff.lboro.ac.uk/~lsmii/start.htm.
The data collected from the online questionnaire survey were used to test the hypotheses. The questionnaire included items related to the six main factors affecting group performance. Each item was measured through a 7-point scale, which 1 represents Strongly Disagree and 7 represents Strongly Agree. The questionnaire was piloted and then distributed to a sample population of 3000 academic researchers from various UK HE institutions. A total of 311 (response rate = 10.37%) researchers provided input to the questionnaire. To ensure that the research instrument measured the right elements consistently, each construct was also tested for reliability and validity. The reliability of the construct was confirmed by computing the value of Cronbach’s alpha. Table 3 shows the results of the reliability and validity analysis for each of the constructs. The results demonstrated that the measurement of the research constructs were reliable (the value of Cronbach’s alpha ranging from 0.72 to 0.94) and hence, suitable for further validation testing. The evidence of construct validity, which consists of convergent, discriminant and nomological validity, were provided using different correlational analyses and multi-method matrix. Factor analysis was also used to test the construct validity of each dimension. All the constructs pass the convergent validity test, but the collaboration strategies construct failed the discriminant and nomological test. With this failure in mind, the collaboration strategies construct was excluded from further hypotheses testing and does not appear in the following section.
Table 3 Reliability and Validity Test Results

Dimensions Constructs Reliability Validity
Alpha Cronbach Convergent Discriminant Nomological
Structural Collaboration Strategies 0.72  Fail Fail
Relational Trust 0.94   
Cognitive Shared Vision 0.89   
Shared Language 0.79   
Cultural Cooperative Culture 0.85   
Competitive Culture 0.80   
5. Results
This section presents the analysis on the relationships between the constructs. It tries to achieve the objective of the research: 1) To test the research hypothesis and 2) To evaluate the proposed model of social capital and R&D performance.
The hypotheses proposed were based on the relationship between the independent variables and the dependent variable as identified and discussed earlier. The hypotheses are evaluated by doing an analysis of correlation coefficients between each of the independent variables and the dependent variables. The results of the correlation between each of the independent variables represented by Trust, Shared Vision, Shared Codes and Language, Cooperative Culture and Competitive Culture and the dependent variables represented by the outcome of the 2001 RAE (RAE) of the test are shown in Table 4.
Table 4 Correlation Matrix of the Research Model

Correlation Coefficients
RAE Trust Shared Vision Shared Language Cooperative Culture Competitive Culture
RAE 1
Trust .776** 1
Shared Vision .590** .666** 1
Shared Language .505** .586** .584** 1
Cooperative Culture .680** .728** .696** .599** 1
Competitive Culture -.405** -.493** -.521** -.495** -.495** 1
** - Signif. LE .01

The associations between constructs were investigated by examining the correlation coefficients between the constructs. The analysis supported all the hypotheses proposed in the study. The results showed that five out of the six dimensions of social capital had an effect on the performance of research groups. These were trust (H2), shared vision (H3a), shared codes and language (H3b), cooperative culture (H4a), and competitive culture (H4b).
The second research objectives, which tries to evaluate the proposed model of social capital and R&D performance in Section 3 was tested using the multiple regression analysis technique. This analysis shows how well a set of variables (the different dimension of social capital) is able to predict a particular outcome (R&D performance). The results of regressing the five independent variables against R&D performance (RAE) can be seen in Table 5. The indicator that is important here is the value of R Square. It indicates how much of the variance in the dependent variable is explained by the model (Pallant, 2005). Table 5 shows that the value of R square is 0.632, indicates that the model (Trust, Shared Vision, Shared Codes and Language, Internal Forces of Cooperation and Internal Forces of Competition) explains 63.2 per cent of the variance in R&D performance.
Table 5 Research Model Summaries
Model R R Square Adjusted R Square Std. Error of the Estimate
.795 .632 .625 .906

The next step is to investigate which of the variables from the model contributes more to the prediction of the dependent variable (R&D performance – RAE). The results in Table 6 show that the highest beta coefficient is 0.585 (p<.0005), which is for Trust. This means that this variable makes the strongest unique contribution to explaining the R&D performance (RAE), when the variance explained by all other variables in the model is controlled for. The beta value for Internal Forces of Cooperation was slightly lower (.224) (p<.0005), indicating that it may also contribute to the prediction of the R&D performance but at a lower level.
Table 6 Coefficients for the Variables
Model Unstandardised Coefficients Standardised Coefficients
B Std. Error Beta
Constant .125 .440
Trust .071 .007 .585
Cooperative Culture .052 .014 .224
Shared Vision .013 .013 .053
Competitive Culture .007 .011 .028
Shared Language .004 .021 .011
6. Conclusions
In conclusion, this study provides important empirical evidence on the relationship between social capital and R&D performance in higher education in the UK. Furthermore, the study reveals the variables that have a significant positive relationship with the performance of R&D are trust, shared vision, shared codes and language and internal forces of cooperation. The internal forces of competition however, have a significant but negative influence on the R&D performance.
The study also produced a model that considered the different dimensions of social capital and how these dimensions might have an impact on the performance of R&D in the HEI in the UK. The result from the regression analysis shows that trust, an element of relational dimension of social capital, has the strongest influence towards explaining the R&D performance. The study also reveals that the proposed social capital model, which consists of relational dimension (Trust), cognitive dimension (Shared Vision and Shared Codes and Language) and the Internal Forces of Cooperation and Competition explains 63.2 per cent of the variance in R&D performance.
The study makes a substantial methodological contribution for investigating social capital by offering a validated tool for measuring key constructs related to social capital in organisations. Most of the constructs have demonstrated a high level of reliability and validity in their development. Therefore, the methodological implications of this research are twofold. Firstly, by carefully defining the constructs of the different dimensions of social capital, it helps the researcher to design the instrument in a systematic way and therefore helps to develop more valid and grounded indicators. Secondly, since most of the constructs to measure the social capital were carefully selected and adopted from the previous studies, it helps the researcher to compare and evaluate its suitability and reliability to this research. The results show that all of the constructs adopted are well suited to the HE environment and this provides the evidence of construct reliability across different sector.
The findings also give rise to a range of recommendations to enhance the performance of R&D within research groups. These include provision of social space for discussion and informal communication, nurturing of a culture conducive of trust and cooperation, knowledge sharing, shared vision and shared codes and language. At the strategic level, consideration should be given to developing and implementing initiatives, based upon the findings reported in this report, that can be carried out by the university to improve research group performance. Ways of countering the negative effects of a competitive culture that might weaken R&D performance within research groups should also be explored.
Acknowledgments
This research was funded by the Ministry of Science, Technology and Innovation (MOSTI), Malaysia and Universiti Teknologi Malaysia.
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