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Managing knowledge sharing: Emergent and engineering approaches
Information & Management 46 (2009) 1–8
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Information & Management journal homepage: www.elsevier.com/locate/im
Managing knowledge sharing: Emergent and engineering approaches Bart van den Hooff *, Marleen Huysman VU University Amsterdam, Faculty of Economics and Business Administration, De Boelelaan 1105, 1081 HV Amsterdam, Netherlands
A R T I C L E I N F O
A B S T R A C T
Article history: Received 5 July 2007 Received in revised form 3 September 2008 Accepted 19 September 2008 Available online 13 November 2008
We wished to determine how the process of knowledge sharing could be managed, seeing that it is a knowledge management dilemma. If knowledge sharing is crucial to an organization’s interests, but is inherently emergent in nature, how can the organization still manage the process? In order to answer this question, a distinction was made between two approaches towards managing knowledge sharing: an emergent approach, focusing on the social dynamics between organizational members and the nature of their daily tasks, and an engineering approach, focusing on management interventions to facilitate knowledge transfer. While the first is central to today’s thinking about knowledge, we used a field study in six organizations to show that both approaches have value in explaining knowledge sharing. Instruments that are part of the engineering approach create conditions for variables in the emergent approach, which in turn also exert a direct influence on knowledge sharing. ß 2008 Elsevier B.V. All rights reserved.
Keywords: Knowledge management Knowledge sharing Social capital Organizational culture ICT infrastructure
1. Introduction Academics and practitioners frequently stress the importance of knowledge as an organizational resource and the consequent importance of managing it. Knowledge is the organization’s intellectual capital, of increasing importance in promoting competitive advantage [20]. For such capital to exist, individual members of the organization must make this knowledge available: share their knowledge with co-workers. An important question that arises is: What factors enable, promote, or hinder sharing of knowledge and what managerial contribution is needed to promoting knowledge sharing? Authors of papers about knowledge management have emphasized that it is personal, subjective, socially determined, primarily tacit, and related to daily practice [21]. As a consequence, the sharing of knowledge cannot be forced, but results from a shared intrinsic motivation to share, gained by the donor being socially embedded. This, we term an emergent approach towards knowledge sharing. Basically, this argues that knowledge sharing is not dependent on management intervention but on the social capital of a group of people. The awareness that knowledge cannot be directed from the outside has pushed the role of the manager to the periphery of KM. Consequently, though knowledge sharing is crucial to an organization, it is inherently emergent in nature.
* Corresponding author. Tel.: +31 20 598 6062; fax: +31 20 598 6005. E-mail addresses: [email protected] (B. van den Hooff), [email protected] (M. Huysman). 0378-7206/$ – see front matter ß 2008 Elsevier B.V. All rights reserved. doi:10.1016/j.im.2008.09.002
A second approach to managing knowledge sharing: the engineering approach assumes that knowledge sharing can be managed: the central assumption is that management can play a role by stimulating and creating an environment for the process. Although empirical research and practical experience have shown that management directly trying to steer knowledge processes is ineffective [17], we argue that such an approach does present useful guidelines on knowledge management but in a more indirect way. Management’s role is not in directly influencing knowledge sharing but in stimulating and creating conditions for this emergent process. 2. Knowledge sharing: emergence and engineering In knowledge management’s early days, knowledge was seen as an object that could be stored, transferred, and retrieved with the aid of IT. Both in practice and academic research, this approach yielded somewhat disappointing results, possibly as it added little to the use of DBMS. The insight that knowledge is not simply an aggregate of information that could be de-coupled from its context was then introduced and attention shifted to considering the tacit dimension: that knowledge is socially embedded in the context where it takes shape and that this creates meaning. Thus knowledge sharing is not stimulated by imposing structures and tools but by rich social interaction and its immersion in practice. Also, knowledge sharing is more than transferring knowledge, but creating it – less exploitation of existing knowledge than generation of new knowledge. Communities (or networks) of practice were considered to be appropriate
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environments for this [24]. Today authors emphasize the importance of practice and social dynamics that result in knowledge sharing, coupled with a diminishing effect of managerial interventions. In considering this, it is useful to make a distinction between two approaches to knowledge sharing: emergent which emphasizes its practice-based and social nature and engineering which incorporates views showing how management may influence processes concerning practice-based and socially determined knowledge. 2.1. Emergent approach This is primarily oriented towards social issues. Learning is a social phenomenon: in the epistemology of practice [8], knowledge is seen as socially constructed and embedded in the social context. Consequently, increasing attention has been given to the personal, subjective, and socially embedded nature of knowledge [2,4,5,22,30]: the process of knowledge sharing should not be seen as merely transferring one person’s knowledge to another but as a shared process of knowledge creation, in which participants make sense of certain events and construct meaning. Central to this approach is the social dynamic between group members; knowledge sharing or creation is primarily determined by the interpersonal and group relationships: how employees are connected in social relations primarily determines to what extent and in what way they can draw upon and contribute knowledge [7,14,26]. The concept of social capital is prominent [1]. It consists of both the network and the assets that may be mobilized through it and is related to the development of intellectual capital created through: (1) combination, the creation of knowledge through incremental change and development of existing knowledge and innovation or double loop learning; and (2) exchange, social interaction, and coactivity. Social capital creates positive conditions for both these processes and, consequently, helps create intellectual capital. In order to analyze this influence on knowledge sharing, three dimensions of social capital have been distinguished [19]: 1. structural, the connections between actors – who and how can they be reached; 2. relational, assets created and leveraged through relationships: trust, norms and sanctions, obligations and expectations, identity and identification; 3. cognitive, resources providing shared representations, interpretations, and systems of meaning among parties – shared language, codes and narratives. Social capital can thus be assumed to affect knowledge contributing and collecting by (1) providing access to people with relevant knowledge or needs and questions; (2) providing a common interest and an atmosphere of mutual trust and appreciation of the value of others’ knowledge; (3) sharing a common ability that helps in understanding other people’s knowledge and as well as correct interpretation and assessment of all knowledge. This leads to the hypotheses:
Hypothesis 3. The level of cognitive social capital positively influences knowledge sharing. The three dimensions of social capital are interrelated [28]. The presence of social interaction ties positively influences mutual trust and obligations (relational social capital) and helps create and share a common set of goals and values (cognitive social capital). This leads to the hypotheses: Hypothesis 4. The level of structural social capital positively influences the level of relational social capital. Hypothesis 5. The level of structural social capital positively influences the level of cognitive social capital. There is also a link between the relational and cognitive dimension of social capital, because shared meanings, goals and values are a basis for mutual trust, as network members work for collective goals and not self-interest. This leads to the hypothesis: Hypothesis 6. The level of cognitive social capital positively influences the level of relational social capital. Although insights from the emergent approach provide insight into the factors influencing knowledge sharing, they provide little aid in deciding how management can increase knowledge sharing. The engineering approach has a more management-oriented perspective. 2.2. Engineering approach This focuses on managing and controlling organizational knowledge in order to provide competitive advantage – on ways to make individual knowledge collective and the management of knowledge as a resource. Knowledge sharing can be managed by providing the context and means to manage knowledge in a top–down fashion. Much literature has concentrated on this area, focusing on the analysis of the role that organizational and technical infrastructures play in facilitating the sharing of knowledge among individuals, e.g. [11,16]. The engineering approach defines ways to manage processes of knowledge sharing, acknowledging that they are inherently emergent in nature. We focused on the roles that both organizational and technical infrastructures were assumed to play in managing knowledge sharing. Our point of departure was the statement that ‘‘Three key infrastructures, technical, structural and cultural, enable maximization of social capital’’ [12]. In other words, these infrastructures do not directly influence knowledge sharing but can help create a context in which such processes are stimulated and facilitated. Organizational infrastructure relates to creating a favorable organizational context for knowledge sharing. A distinction must be made between structural (the extent to which an organization’s structure facilitates knowledge sharing) and cultural infrastructures (establishing a knowledge-friendly culture characterized by a positive orientation towards knowledge and creativity). This leads to the hypotheses: Hypothesis 7. The extent to which the organization’s structure supports knowledge sharing, positively influences the level of (a) structural, (b) relational and (c) cognitive social capital.
Hypothesis 1. The level of structural social capital positively influences knowledge sharing.
Hypothesis 8. A knowledge friendly culture positively influences the level of (a) structural, (b) relational and (c) cognitive social capital.
Hypothesis 2. The level of relational social capital positively influences knowledge sharing.
The technical infrastructure includes the use of information and communication technologies (ICT) to aid in the exchange of
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Fig. 1. Theoretical model.
knowledge. Although the contribution of ICT to knowledge management is the subject of much discussion [15,23,27], there is general agreement that ICT can play a supporting role. Different kinds of applications can provide insight into the structural social capital, aid in interaction between people and contribute to a shared identity, norms and values, as well as more understanding of what co-workers are doing. This leads to the hypothesis: Hypothesis 9. The level of ICT support in an organization positively influences the level of (a) structural, (b) relational and (c) cognitive social capital. In the engineering approach, knowledge sharing is assumed to be implemented by providing appropriate means for people to exchange knowledge. So, if management provides good organizational and technical infrastructures, it should have provided a context in which employees share knowledge. The emergent approach assumes that knowledge sharing emerges from factors that are difficult to manage but the engineering approach suggests some ways for managing the process. In Fig. 1, we merged both views.
3. Methods The data used to test our hypotheses were collected by means of an online survey in six different organizations: a cable provider, a mail service provider, an insurance company, a consultancy and both the Dutch national and the international branches of a heavy lifting and transport company. Table 1 provides some demographic information about these organizations. Data was collected between February and April of 2006. The organizations were selected because they are knowledgeintensive organizations and are active in highly competitive markets. Indeed, they all required the sharing of knowledge internally. Sometimes this concerned knowledge about clients, sometimes it was technical or about market developments, but knowledge sharing was a prominent issue. Consequently, answers to the question: How should we manage knowledge sharing? were important for each of the organizations. Within these organizations, all employees were asked (by our contact persons) to fill out the survey. The total number of respondents was 541, with the number per organization ranging from 184 for the mail service company to 28
Table 1 Organizations in study. Organization
Primary process
Nature of work
Response rate
Cable provider
Customer service
97 in 800
Mail service provider
Business development and account management
Insurance company
Damage insurance
Consultancy Transport (NL)
Knowledge management consultancy Heavy lifting and heavy transport
Transport (Int’l)
Heavy lifting and heavy transport
Customer contact. Contact with technical experts in back office. Reporting on use of services. Maintaining relationships with clients. Identifying new commercial opportunities. Marketing and sales. Assessment and acceptance of insurance applications. Controlling reported damages. Relationship and account management. Product development. Consulting on various issues concerning KM. Planning and project management. Sales and contract management. Operations: engineering and inspection. Planning and project management. Sales and contract management. Financial management.
184 in 900
48 in 200
28 in 48 137 in 210
47 in 52
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4 Table 2 Demographics of each organization. Organization
Knowledge sharing
Struct. soc. cap
Relat. soc. cap
Cognit. soc. cap
Org. structure
Org. culture
ICT infrastruct.
Cable
M SD
4.1 0.5
3.7 0.5
3.8 0.5
3.6 0.6
3.0 0.5
3.4 0.6
3.4 0.7
Mail
M SD
4.1 0.4
3.6 0.4
3.9 0.4
3.7 0.5
3.0 0.5
3.5 0.5
3.4 0.6
Insurance
M SD
4.0 0.5
3.7 0.4
3.9 0.5
3.7 0.6
3.0 0.5
3.5 0.6
3.3 0.6
Consultancy
M SD
3.9 0.4
3.7 0.4
3.9 0.4
3.7 0.6
3.1 0.6
3.5 0.5
3.2 0.6
Transport (NL)
M SD
4.1 0.4
3.8 0.4
3.9 0.5
3.7 0.6
3.3 0.5
3.6 0.6
3.7 0.6
Transport (Int)
M SD
4.0 0.4
4.0 0.4
4.0 0.4
3.8 0.5
3.4 0.5
3.8 0.5
3.7 0.6
Total
M SD
4.0 0.4
3.7 0.5
3.9 0.5
3.7 0.6
3.1 0.5
3.6 0.6
3.5 0.7
for the consultancy. The final column shows this. Although the organizations were quite diverse, we decided to aggregate the samples from all six organizations. We had three reasons doing this: The primary aim of the analysis was to obtain a picture of how the engineering and emergent approaches affected knowledge sharing. The sample sizes for three of the organizations were too small to allow us to make separate meaningful statistical analyses. The mean scores of the organizations on the key variables in the theoretical model were relatively comparable, as can be seen in Table 2. 3.1. Measures In the survey, all variables – unless otherwise reported – were measured using a 1–5 point (strongly disagree to strongly agree) Likert-type scale. The knowledge sharing scale was derived from a study on knowledge sharing and communication styles [9]. The scales for organizational structure, organizational culture and ICT infrastructure were slightly adapted versions of the scales used by Gold et al. For relational social capital, a scale was used that integrated items from a social identification scale [10] and a scale for trust [31]. The scales for structural social capital and cognitive social capital were newly designed. Since survey research concerning social capital is very scarce, studies in which the concept was measured in different ways [3,29] were studied for inspiration for new survey items. Also, the definitions of the three dimensions of social capital were used to conceptually validate the items.
Table 3 presents the reliabilities for each of these scales, as well as the correlations between them. Appendix A provides the full wording for the items of each of these scales. Most of the scales are reliable, with Cronbach’s alpha of 0.70 or higher, except for two scales: organizational structure, scoring 0.67 and cognitive social capital with a Cronbach’s alpha of 0.63. However, 0.60 is considered the lowest acceptable value in exploratory research [13], and one of our purposes was to develop survey measures for social capital. Consequently, we judged these alpha values to be acceptable in our study. Some of the correlations shown in Table 3 are high enough to warrant further exploration of the convergent and discriminant validity of the scales, especially because one of our goals was to design appropriate measures of key variables. First, it was important to explore the convergent and discriminant validity of our measures for social capital, since these were all new – at least as measures of social capital. Also, some items used for social capital had similarities to items used for measuring knowledge sharing. Therefore, a principal components analysis with varimax rotation was performed to measure each of the four variables. The results of the analysis are presented in Table 4. This shows that the items loaded quite clearly on the constructs we distinguished, except for the final item of relational social capital, which loaded almost equally on the relational and the cognitive social capital scales. In terms of content, however, this item clearly belonged more to the relational social capital scale, since it is about interpersonal relations and not about ability to explain and understand. Based on these results, we felt that there were no problems about these measures in terms of discriminant validity.
Table 3 Scales: reliabilities and correlations.
1 2 3 4 5 6 7
Knowledge sharing Structural social capital Relational social capital Cognitive social capital Organizational structure Organizational culture ICT infrastructure
1
2
3
4
5
6
7
0.78 0.37*** 0.33*** 0.30*** 0.11* 0.24*** 0.20***
0.76 0.40*** 0.35*** 0.28*** 0.39*** 0.15***
0.70 0.36*** 0.34*** 0.39*** 0.15***
0.63 0.16*** 0.21*** 0.13**
0.67 0.60*** 0.34***
0.77 0.37***
0.86
Table shows Pearson correlation coefficients for all relationships. Cronbach’s alpha shown on diagonals. * p < 0.05. ** p < 0.01. *** p < 0.001.
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Table 4 Factor loadings for knowledge sharing and social capital items. Knowledge sharing I like to be kept fully informed of what my colleagues know. When I need certain knowledge, I ask my colleagues about it. I regularly inform my colleagues of what I am working on. When I have learned something new, I make sure my colleagues learn about it too. I share information that I acquired, with my colleagues. I ask my colleagues about their skills when I want to learn particular skills. I consider it important that my colleagues are aware of what I am working on. When a colleague is good at something, I ask him/her to teach me. My colleagues know what knowledge I need. I know what knowledge could be relevant to which colleague. When a customer client has a question, I know which colleague or department will be able to help. Within my department, I know who has knowledge that is relevant to me at their disposal. Outside my department, I know who has knowledge that is relevant to me at their disposal. My colleagues know what knowledge I have at my disposal. I am regularly in contact with colleagues who have knowledge at their disposal that is relevant to me. My colleagues and I speak the same ‘technical’ language. Sometimes I do not understand my colleagues when they tell me something about their work. (R) Often I only need ‘half a word’ when I am talking about work with my colleagues. Sometimes I have difficulty formulating what I know in so that my colleagues can understand. (R) I feel connected to my colleagues. I view this organization as a group I belong to. I can rely on my colleagues when I need support in my work. I completely trust the skills of my colleagues. When I tell someone what I know, I can count on it that he or she will tell me what he or she knows. Eigenvalue Percentage of explained variance
There was also a rather high correlation between the measures for organizational structure and culture, and these scales contained items that looked somewhat similar. Again, a principal components analysis with varimax rotation was performed for the items in these measures; this yielded the results presented in Table 5. From this analysis, four components emerged – two containing different items from the organizational structure scale, and two containing different items from the organizational culture scale. Thus there was discriminant validity in these measures because the structure and culture items did not cross-load, but we found four instead of two components, raising questions about the convergent validity of the measures. These components, however, did not constitute sufficiently homogenous scales when compared with the original measures (Cronbach’s alpha 0.44 and 0.61 for the structure components, and 0.71 and 0.65 for the culture
Structural social capital
Relational social capital
Cognitive social capital
0.45 0.53 0.69 0.69 0.65 0.61 0.62 0.63 0.15 0.10 0.02 0.22 0.05 0.10 0.20 0.11 0.09 0.11 0.05 0.12 0.09 0.09 0.08 0.21
0.02 0.15 0.05 0.18 0.17 0.14 0.02 0.16 0.47 0.61 0.66 0.66 0.73 0.61 0.50 0.18 0.11 0.21 0.04 0.15 0.09 0.17 0.05 0.14
0.07 0.15 0.15 0.00 0.07 0.10 0.18 0.02 0.17 0.02 0.07 0.16 0.04 0.12 0.35 0.32 0.16 0.01 0.04 0.75 0.76 0.62 0.60 0.42
0.22 0.04 0.15 0.22 0.19 0.07 0.02 0.03 0.17 0.15 0.15 0.02 0.05 0.23 0.09 0.63 0.61 0.55 0.70 0.12 0.08 0.20 0.16 0.36
5.50 22.93
2.04 8.48
1.69 7.03
1.50 6.25
components). Also, they did not constitute homogenous scales in terms of face validity (there was no logical ‘‘split’’ between the components for structure and culture). Therefore, we chose to keep these measures intact – i.e., one measure for structure and one for culture, acknowledging that both measures were multidimensional, but valid measurements in terms of discriminant validity. 4. Results In order to determine the relative influence of the two approaches on knowledge sharing, structural equation modeling was applied, using AMOS, which provides, SEM and analysis of covariance structures, or causal modeling. This package allows the testing of a set of regression equations simultaneously, providing both parametric statistics for each equation and indices that
Table 5 Factor loadings for organizational structure and culture items. Culture 1 The structure of our organization impedes interaction and knowledge sharing. (reverse coded) The structure of our organization promotes collective behavior over individual behavior. The structure of our organization facilitates the development of new ideas/processes/products, i.e. knowledge creation. This organization uses a standardised reward system for knowledge sharing. The structure of our organization facilitates the exchange of knowledge across functional formal boundaries, like departments. The staff in this organization are accessible. The management of our organization expects everyone to actively contribute to the registration and transmission of knowledge. Staff are encouraged to innovate, to investigate and to experiment. On-the-job training and learning are highly appreciated in this organization. In this organization staff are encouraged to ask others for help whenever necessary. Interaction between different departments is encouraged in this organization. The goals and vision of this organization are clearly communicated to the employees. The management of this organization stresses the importance of knowledge to the success of the organization. Eigenvalue Percentage of explained variance
Culture 2
Structure 1
Structure 2
0.03 0.01 0.21
0.26 0.02 0.29
0.11 0.69 0.59
0.73 0.27 0.06
0.17 0.35
0.17 0.11
0.72 0.47
0.18 0.38
0.33 0.06
0.00 0.64
0.02 0.08
0.73 0.23
0.54 0.77 0.72 0.63 0.19 0.22
0.35 0.17 0.04 0.21 0.76 0.73
0.29 0.03 0.13 0.22 0.04 0.30
0.11 0.00 0.10 0.31 0.04 0.03
4.14 31.83
1.17 9.02
1.04 8.05
1.04 8.01
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Fig. 2. Final tested model. x2 = 17.41 (d.f. = 7, p = 0.01), ratio x2/d.f. = 2.48, AGFI = 0.96, TLI = 0.96, RMSEA = 0.05. Exogenous variables are allowed to covary. Figures near arrows denote standardized regression weights, figures in italics denote proportion of variance explained.
indicate the fit of the model to the original data. Models can thus be adjusted in terms of adding or deleting relationships. The strength and significance of the different hypothesized relationships were therefore tested using AMOS. The model produced mixed results for model fit: x2 value was significant (13.9, d.f. = 3, p < 0.003), and the ratio of the Chi square to the degrees of freedom was 4.6. Since the Chi square value is sensitive to sample size and non-normality, the ratio of x2 to degrees of freedom is preferred as a fit statistic [6,18]. Although different critical values are considered valid for this statistic, values below 3.0 are generally assumed to indicate sufficient fit. The AGFI was above the critical value of 0.90 at 0.93; the Tucker-Lewis Index should be close to 1 and it scored 0.91, so both indices indicated a sufficient fit. On the other hand, the Root Mean Square Error of Approximation was 0.082, whereas a value of 0.05 would indicate a close fit, and a value of 0.08 or lower still indicated a reasonable error of approximation. These fit statistics led to a mixed conclusion: not all criteria were met, but in itself this is an insufficient reason to reject the model. However, it also contained a number of relationships that were not significant at the 0.05 level: Organizational structure was not found to influence structural social capital and cognitive social capital, rejecting Hypotheses 7a and c. ICT infrastructure was not found to influence relational social capital and cognitive social capital, rejecting Hypotheses 9b and c. We decided that these non-significant relationships must be removed from the model, leading to the model that is presented in Fig. 2. This model had a satisfactory fit: although the x2 value (17.4, d.f. = 7) was significant (p < 0.05), the ratio of x2 to d.f. was 2.5 (below 3). AGFI scores were 0.96, well above 0.90, and TLI scores were 0.96, which is sufficiently close to 1. The RMSEA value of 0.052 fell just short of a close fit (0.05), but it was well within the range of a satisfactory error of approximation (0.08). All relationships in this model were significant at p < 0.05. Finally, the model explained 20% of the variance in knowledge sharing, 28% of the variance in relational social capital, 16% of the variance in structural social capital and 13% of the variance in cognitive social capital. The model showed that all three dimensions of social capital positively influenced the degree to which knowledge was shared in
the organization, providing strong support for the emergent approach. These results provided support for Hypotheses 1, 2 and 3. Also, the different dimensions of social capital were mutually related, supporting Hypotheses 4, 5 and 6. Thus, the assumptions based on the emergent approach were supported. Hypotheses derived from the engineering approach were partly supported by these results. Although Hypotheses 7a and c were rejected, Hypothesis 7b was supported: organizational structure was found positively to influence the level of relational social capital. Similarly, ICT infrastructure was found to positively influence the level of structural social capital, providing support for Hypothesis 9a, although Hypotheses 9b and c were rejected. Finally, organizational culture was found to be an important variable from the engineering approach: a knowledge-friendly culture was found to positively influence the level of social capital on each dimension (structural, relational and cognitive), providing support for Hypotheses 8a, b, and c. 5. Discussion Both the engineering and the emergent approach had value in explaining organizational knowledge sharing, in line with our theoretical arguments. Knowledge sharing is an emergent process, influenced by the social dynamics between individuals. This does not mean, however, that management does not have a role in knowledge sharing: engineering knowledge management is needed, as it creates the conditions in which emergent variables exist. By providing organizational and technical infrastructures, management can facilitate, stimulate, and influence the emergence of social capital, which in turn influences knowledge sharing. All the engineering variables were found to have a distinct influence on social capital. Organizational culture was a crucial factor, as it was found to influence all three dimensions of social capital. Establishing and communicating a knowledge-friendly culture, establishing a clear vision and objectives, and clear values related to knowledge, was effective in promoting the social dynamics that were beneficial to knowledge sharing. Such a culture leads to more insight into where relevant knowledge is located, more active interaction between members of the organization, a higher mutual understanding, and an atmosphere of social identification, trust, and reciprocity. Supporting the creation and maintenance of this culture in a top–down fashion seemed possible, based on our results.
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Furthermore, organizational structure was found to positively influence the level of relational social capital; the extent to which a structure is characterized by clear roles and responsibilities for knowledge sharing and reduced structural barriers to it, leads to more trust, identification, and reciprocity between employees. It might seem that a greater influence of organizational structure on social capital would result in positive influence on structural social capital – a more transparent structure leading to more insight into the location of knowledge and how to contact relevant people. However, clarity of roles and responsibilities and less formal divisions in the organization may lead to a more ‘‘informal’’ climate, where trust, identification and reciprocity exist. Finally, an effective ICT infrastructure positively influences the level of structural social capital. Since its role in knowledge sharing often lies in facilitating interaction through personal yellow pages, knowledge maps, etc., this relationship makes sense. In a case study within a multinational ICT consultancy company, it was found that the use of a KM system influenced all three dimensions of social capital [25]. Our study, however, indicated that the effect of technology on knowledge sharing via social capital was more complex. We found that ICT only influenced the structural dimension: helping by showing where knowledge was located and improving organizational connectivity. The structural dimension influenced both the relational and cognitive dimension: technology seems to only have an indirect influence on levels of trust, reciprocity, shared norms, and narratives. On the whole, this means that engineering and emergent approaches do not oppose each other but interact. Moreover, our study provided a more dynamic perspective of social capital, since it showed that the three dimensions do not have an equal contribution to knowledge sharing and are not equally influenced by engineering variables. As for practical implications, our results indicated that management could indirectly promote knowledge sharing through the creation of an environment that fostered social capital by creating an organizational structure that showed who was responsible for which knowledge activities and that had little formal barriers to interaction between different parts of the organization; establishing a knowledge-friendly culture with openness, innovativeness, a willingness to share, etc.; establishing and maintaining an IT infrastructure that efficiently and effectively helped organizational members to learn what is relevant knowledge, where it is located, and how to contact those possessing or needing it. Basically, managing knowledge sharing is difficult – the direct influence of management measures may be limited, since it is primarily social group interactions that stimulate knowledge sharing. However, indirectly, engineering knowledge sharing is possible. As most research, our study had some shortcomings. First, the data from the different organizations were merged into a single sample, because some separate samples were relatively small (the consultancy firm, the insurance company, and the international branch of the transport organization); this meant that no separate models could be created for any of the organizations. Although our primary aim was to provide a generalizable picture of knowledge sharing, and not an organization-specific one, this was still a serious limitation. Organizational differences were ignored in our analysis. Still, the analyses start to build a robust model of knowledge sharing – following a replication logic where a theory is tested through the process of replicating the results of a case study in consequent studies [32].
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Another limitation is that our sample consisted of organizations based in Holland. However, only the consultancy was a purely Dutch organization: the cable provider was the Dutch subsidiary of an American company, the mail provider had merged with an Australian organization, the transport organization was a multinational and the insurance company was part of an Italian organization. Overall, the alpha values on the scales used in our study were satisfactory, and we feel that the items used had conceptual validity also. Two scales, however, do need attention: cognitive social capital and organizational structure had alpha values that were only acceptable when considered in terms of exploratory research. Furthermore, the organizational structure and culture scales turned out to be multidimensional: both contained two underlying constructs. In conclusion, our results indicated that managing knowledge sharing was a daunting task, and that there were limits to the influence that management had on the process; however, they also indicated that managers who realize that knowledge sharing is a process of emergence as well as engineering can still be effective in managing the organization’s intellectual capital through thoughtful design and maintenance of organizational infrastructures.
Appendix A. Items in scales Knowledge sharing I like to be kept fully informed of what my colleagues know. When I need certain knowledge, I ask my colleagues about it. I regularly inform my colleagues of what I am working on. When I have learned something new, I make sure my colleagues learn about it too. I share information that I acquired, with my colleagues. I ask my colleagues about their skills when I want to learn particular skills. I consider it important that my colleagues are aware of what I am working on. When a colleague is good at something, I ask him/her to teach me. Organizational structure The structure of our organization impedes interaction and knowledge sharing. (reverse coded) The structure of our organization promotes collective behaviour over individual behavior. The structure of our organization facilitates the development of new ideas/ processes/products, i.e. knowledge creation. This organization uses a standardised reward system for knowledge sharing. The structure of our organization facilitates the exchange of knowledge across functional formal boundaries, like departments. The staff in this organization are approachable. Organizational culture The management of our organization expects everyone to actively contribute to the registration and transmission of knowledge. Staff are encouraged to innovate, to investigate and to experiment. On-the-job training and learning are highly appreciated in this organization. In this organization staff are encouraged to ask others for help whenever necessary. Interaction between different departments is encouraged in this organization. The goals and vision of this organization are clearly communicated to the employees. The management of this organization stresses the importance of knowledge to the success of the organization. ICT infrastructure The IT facilities within this organization provide a positive contribution to my productivity and effectiveness. Our IT facilities make it easier to cooperate with others within our organization. Our IT facilities make it easier to cooperate with others outside our organization. The IT facilities within this organization provide a positive contribution to the development of my knowledge. The IT facilities within this organization provide important support for knowledge sharing. IT makes it is easier for me to get in contact with employees who have knowledge that is important to me. IT makes it is easier for me to have knowledge that is relevant to me at my disposal.
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Structural social capital My colleagues know what knowledge I need. I know what knowledge could be relevant to which colleague. When a customer client has a question, I know which colleague or department will be able to help. Within my department, I know who has knowledge that is relevant to me at their disposal. Outside my department, I know who has knowledge that is relevant to me at their disposal. My colleagues know what knowledge I have at my disposal. I am regularly in contact with colleagues who have knowledge at their disposal that is relevant to me. Cognitive social capital My colleagues and I speak the same ‘technical’ language. Sometimes I do not understand my colleagues when they tell me something about their work. (reverse coded) Often I only need ‘half a word’ when I am talking about work with my colleagues. Sometimes I have difficulty formulating what I know in such a way that my colleagues can understand. (reverse coded) Relational social capital I feel connected to my colleagues. I view this organization as a group I belong to. I can rely on my colleagues when I need support in my work. I completely trust the skills of my colleagues. When I tell someone what I know, I can count on it that he or she will tell me what he or she knows.
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Bart van den Hooff is an associate professor of Knowledge and Organization at the Knowledge, Information and Networks research group, Faculty of Economics and Business Administration, VU University Amsterdam. His research interests include knowledge management, communities and networks of practice and online networks.
Marleen Huysman is a full professor of Knowledge and Organization at the Faculty of Economics and Business Administration, VU University Amsterdam. She leads the Knowledge, Information and Networks (KIN) research group, studying knowledge and information intensive processes in organizational (online) networks.
Contents lists available at ScienceDirect
Information & Management journal homepage: www.elsevier.com/locate/im
Managing knowledge sharing: Emergent and engineering approaches Bart van den Hooff *, Marleen Huysman VU University Amsterdam, Faculty of Economics and Business Administration, De Boelelaan 1105, 1081 HV Amsterdam, Netherlands
A R T I C L E I N F O
A B S T R A C T
Article history: Received 5 July 2007 Received in revised form 3 September 2008 Accepted 19 September 2008 Available online 13 November 2008
We wished to determine how the process of knowledge sharing could be managed, seeing that it is a knowledge management dilemma. If knowledge sharing is crucial to an organization’s interests, but is inherently emergent in nature, how can the organization still manage the process? In order to answer this question, a distinction was made between two approaches towards managing knowledge sharing: an emergent approach, focusing on the social dynamics between organizational members and the nature of their daily tasks, and an engineering approach, focusing on management interventions to facilitate knowledge transfer. While the first is central to today’s thinking about knowledge, we used a field study in six organizations to show that both approaches have value in explaining knowledge sharing. Instruments that are part of the engineering approach create conditions for variables in the emergent approach, which in turn also exert a direct influence on knowledge sharing. ß 2008 Elsevier B.V. All rights reserved.
Keywords: Knowledge management Knowledge sharing Social capital Organizational culture ICT infrastructure
1. Introduction Academics and practitioners frequently stress the importance of knowledge as an organizational resource and the consequent importance of managing it. Knowledge is the organization’s intellectual capital, of increasing importance in promoting competitive advantage [20]. For such capital to exist, individual members of the organization must make this knowledge available: share their knowledge with co-workers. An important question that arises is: What factors enable, promote, or hinder sharing of knowledge and what managerial contribution is needed to promoting knowledge sharing? Authors of papers about knowledge management have emphasized that it is personal, subjective, socially determined, primarily tacit, and related to daily practice [21]. As a consequence, the sharing of knowledge cannot be forced, but results from a shared intrinsic motivation to share, gained by the donor being socially embedded. This, we term an emergent approach towards knowledge sharing. Basically, this argues that knowledge sharing is not dependent on management intervention but on the social capital of a group of people. The awareness that knowledge cannot be directed from the outside has pushed the role of the manager to the periphery of KM. Consequently, though knowledge sharing is crucial to an organization, it is inherently emergent in nature.
* Corresponding author. Tel.: +31 20 598 6062; fax: +31 20 598 6005. E-mail addresses: [email protected] (B. van den Hooff), [email protected] (M. Huysman). 0378-7206/$ – see front matter ß 2008 Elsevier B.V. All rights reserved. doi:10.1016/j.im.2008.09.002
A second approach to managing knowledge sharing: the engineering approach assumes that knowledge sharing can be managed: the central assumption is that management can play a role by stimulating and creating an environment for the process. Although empirical research and practical experience have shown that management directly trying to steer knowledge processes is ineffective [17], we argue that such an approach does present useful guidelines on knowledge management but in a more indirect way. Management’s role is not in directly influencing knowledge sharing but in stimulating and creating conditions for this emergent process. 2. Knowledge sharing: emergence and engineering In knowledge management’s early days, knowledge was seen as an object that could be stored, transferred, and retrieved with the aid of IT. Both in practice and academic research, this approach yielded somewhat disappointing results, possibly as it added little to the use of DBMS. The insight that knowledge is not simply an aggregate of information that could be de-coupled from its context was then introduced and attention shifted to considering the tacit dimension: that knowledge is socially embedded in the context where it takes shape and that this creates meaning. Thus knowledge sharing is not stimulated by imposing structures and tools but by rich social interaction and its immersion in practice. Also, knowledge sharing is more than transferring knowledge, but creating it – less exploitation of existing knowledge than generation of new knowledge. Communities (or networks) of practice were considered to be appropriate
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environments for this [24]. Today authors emphasize the importance of practice and social dynamics that result in knowledge sharing, coupled with a diminishing effect of managerial interventions. In considering this, it is useful to make a distinction between two approaches to knowledge sharing: emergent which emphasizes its practice-based and social nature and engineering which incorporates views showing how management may influence processes concerning practice-based and socially determined knowledge. 2.1. Emergent approach This is primarily oriented towards social issues. Learning is a social phenomenon: in the epistemology of practice [8], knowledge is seen as socially constructed and embedded in the social context. Consequently, increasing attention has been given to the personal, subjective, and socially embedded nature of knowledge [2,4,5,22,30]: the process of knowledge sharing should not be seen as merely transferring one person’s knowledge to another but as a shared process of knowledge creation, in which participants make sense of certain events and construct meaning. Central to this approach is the social dynamic between group members; knowledge sharing or creation is primarily determined by the interpersonal and group relationships: how employees are connected in social relations primarily determines to what extent and in what way they can draw upon and contribute knowledge [7,14,26]. The concept of social capital is prominent [1]. It consists of both the network and the assets that may be mobilized through it and is related to the development of intellectual capital created through: (1) combination, the creation of knowledge through incremental change and development of existing knowledge and innovation or double loop learning; and (2) exchange, social interaction, and coactivity. Social capital creates positive conditions for both these processes and, consequently, helps create intellectual capital. In order to analyze this influence on knowledge sharing, three dimensions of social capital have been distinguished [19]: 1. structural, the connections between actors – who and how can they be reached; 2. relational, assets created and leveraged through relationships: trust, norms and sanctions, obligations and expectations, identity and identification; 3. cognitive, resources providing shared representations, interpretations, and systems of meaning among parties – shared language, codes and narratives. Social capital can thus be assumed to affect knowledge contributing and collecting by (1) providing access to people with relevant knowledge or needs and questions; (2) providing a common interest and an atmosphere of mutual trust and appreciation of the value of others’ knowledge; (3) sharing a common ability that helps in understanding other people’s knowledge and as well as correct interpretation and assessment of all knowledge. This leads to the hypotheses:
Hypothesis 3. The level of cognitive social capital positively influences knowledge sharing. The three dimensions of social capital are interrelated [28]. The presence of social interaction ties positively influences mutual trust and obligations (relational social capital) and helps create and share a common set of goals and values (cognitive social capital). This leads to the hypotheses: Hypothesis 4. The level of structural social capital positively influences the level of relational social capital. Hypothesis 5. The level of structural social capital positively influences the level of cognitive social capital. There is also a link between the relational and cognitive dimension of social capital, because shared meanings, goals and values are a basis for mutual trust, as network members work for collective goals and not self-interest. This leads to the hypothesis: Hypothesis 6. The level of cognitive social capital positively influences the level of relational social capital. Although insights from the emergent approach provide insight into the factors influencing knowledge sharing, they provide little aid in deciding how management can increase knowledge sharing. The engineering approach has a more management-oriented perspective. 2.2. Engineering approach This focuses on managing and controlling organizational knowledge in order to provide competitive advantage – on ways to make individual knowledge collective and the management of knowledge as a resource. Knowledge sharing can be managed by providing the context and means to manage knowledge in a top–down fashion. Much literature has concentrated on this area, focusing on the analysis of the role that organizational and technical infrastructures play in facilitating the sharing of knowledge among individuals, e.g. [11,16]. The engineering approach defines ways to manage processes of knowledge sharing, acknowledging that they are inherently emergent in nature. We focused on the roles that both organizational and technical infrastructures were assumed to play in managing knowledge sharing. Our point of departure was the statement that ‘‘Three key infrastructures, technical, structural and cultural, enable maximization of social capital’’ [12]. In other words, these infrastructures do not directly influence knowledge sharing but can help create a context in which such processes are stimulated and facilitated. Organizational infrastructure relates to creating a favorable organizational context for knowledge sharing. A distinction must be made between structural (the extent to which an organization’s structure facilitates knowledge sharing) and cultural infrastructures (establishing a knowledge-friendly culture characterized by a positive orientation towards knowledge and creativity). This leads to the hypotheses: Hypothesis 7. The extent to which the organization’s structure supports knowledge sharing, positively influences the level of (a) structural, (b) relational and (c) cognitive social capital.
Hypothesis 1. The level of structural social capital positively influences knowledge sharing.
Hypothesis 8. A knowledge friendly culture positively influences the level of (a) structural, (b) relational and (c) cognitive social capital.
Hypothesis 2. The level of relational social capital positively influences knowledge sharing.
The technical infrastructure includes the use of information and communication technologies (ICT) to aid in the exchange of
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Fig. 1. Theoretical model.
knowledge. Although the contribution of ICT to knowledge management is the subject of much discussion [15,23,27], there is general agreement that ICT can play a supporting role. Different kinds of applications can provide insight into the structural social capital, aid in interaction between people and contribute to a shared identity, norms and values, as well as more understanding of what co-workers are doing. This leads to the hypothesis: Hypothesis 9. The level of ICT support in an organization positively influences the level of (a) structural, (b) relational and (c) cognitive social capital. In the engineering approach, knowledge sharing is assumed to be implemented by providing appropriate means for people to exchange knowledge. So, if management provides good organizational and technical infrastructures, it should have provided a context in which employees share knowledge. The emergent approach assumes that knowledge sharing emerges from factors that are difficult to manage but the engineering approach suggests some ways for managing the process. In Fig. 1, we merged both views.
3. Methods The data used to test our hypotheses were collected by means of an online survey in six different organizations: a cable provider, a mail service provider, an insurance company, a consultancy and both the Dutch national and the international branches of a heavy lifting and transport company. Table 1 provides some demographic information about these organizations. Data was collected between February and April of 2006. The organizations were selected because they are knowledgeintensive organizations and are active in highly competitive markets. Indeed, they all required the sharing of knowledge internally. Sometimes this concerned knowledge about clients, sometimes it was technical or about market developments, but knowledge sharing was a prominent issue. Consequently, answers to the question: How should we manage knowledge sharing? were important for each of the organizations. Within these organizations, all employees were asked (by our contact persons) to fill out the survey. The total number of respondents was 541, with the number per organization ranging from 184 for the mail service company to 28
Table 1 Organizations in study. Organization
Primary process
Nature of work
Response rate
Cable provider
Customer service
97 in 800
Mail service provider
Business development and account management
Insurance company
Damage insurance
Consultancy Transport (NL)
Knowledge management consultancy Heavy lifting and heavy transport
Transport (Int’l)
Heavy lifting and heavy transport
Customer contact. Contact with technical experts in back office. Reporting on use of services. Maintaining relationships with clients. Identifying new commercial opportunities. Marketing and sales. Assessment and acceptance of insurance applications. Controlling reported damages. Relationship and account management. Product development. Consulting on various issues concerning KM. Planning and project management. Sales and contract management. Operations: engineering and inspection. Planning and project management. Sales and contract management. Financial management.
184 in 900
48 in 200
28 in 48 137 in 210
47 in 52
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4 Table 2 Demographics of each organization. Organization
Knowledge sharing
Struct. soc. cap
Relat. soc. cap
Cognit. soc. cap
Org. structure
Org. culture
ICT infrastruct.
Cable
M SD
4.1 0.5
3.7 0.5
3.8 0.5
3.6 0.6
3.0 0.5
3.4 0.6
3.4 0.7
M SD
4.1 0.4
3.6 0.4
3.9 0.4
3.7 0.5
3.0 0.5
3.5 0.5
3.4 0.6
Insurance
M SD
4.0 0.5
3.7 0.4
3.9 0.5
3.7 0.6
3.0 0.5
3.5 0.6
3.3 0.6
Consultancy
M SD
3.9 0.4
3.7 0.4
3.9 0.4
3.7 0.6
3.1 0.6
3.5 0.5
3.2 0.6
Transport (NL)
M SD
4.1 0.4
3.8 0.4
3.9 0.5
3.7 0.6
3.3 0.5
3.6 0.6
3.7 0.6
Transport (Int)
M SD
4.0 0.4
4.0 0.4
4.0 0.4
3.8 0.5
3.4 0.5
3.8 0.5
3.7 0.6
Total
M SD
4.0 0.4
3.7 0.5
3.9 0.5
3.7 0.6
3.1 0.5
3.6 0.6
3.5 0.7
for the consultancy. The final column shows this. Although the organizations were quite diverse, we decided to aggregate the samples from all six organizations. We had three reasons doing this: The primary aim of the analysis was to obtain a picture of how the engineering and emergent approaches affected knowledge sharing. The sample sizes for three of the organizations were too small to allow us to make separate meaningful statistical analyses. The mean scores of the organizations on the key variables in the theoretical model were relatively comparable, as can be seen in Table 2. 3.1. Measures In the survey, all variables – unless otherwise reported – were measured using a 1–5 point (strongly disagree to strongly agree) Likert-type scale. The knowledge sharing scale was derived from a study on knowledge sharing and communication styles [9]. The scales for organizational structure, organizational culture and ICT infrastructure were slightly adapted versions of the scales used by Gold et al. For relational social capital, a scale was used that integrated items from a social identification scale [10] and a scale for trust [31]. The scales for structural social capital and cognitive social capital were newly designed. Since survey research concerning social capital is very scarce, studies in which the concept was measured in different ways [3,29] were studied for inspiration for new survey items. Also, the definitions of the three dimensions of social capital were used to conceptually validate the items.
Table 3 presents the reliabilities for each of these scales, as well as the correlations between them. Appendix A provides the full wording for the items of each of these scales. Most of the scales are reliable, with Cronbach’s alpha of 0.70 or higher, except for two scales: organizational structure, scoring 0.67 and cognitive social capital with a Cronbach’s alpha of 0.63. However, 0.60 is considered the lowest acceptable value in exploratory research [13], and one of our purposes was to develop survey measures for social capital. Consequently, we judged these alpha values to be acceptable in our study. Some of the correlations shown in Table 3 are high enough to warrant further exploration of the convergent and discriminant validity of the scales, especially because one of our goals was to design appropriate measures of key variables. First, it was important to explore the convergent and discriminant validity of our measures for social capital, since these were all new – at least as measures of social capital. Also, some items used for social capital had similarities to items used for measuring knowledge sharing. Therefore, a principal components analysis with varimax rotation was performed to measure each of the four variables. The results of the analysis are presented in Table 4. This shows that the items loaded quite clearly on the constructs we distinguished, except for the final item of relational social capital, which loaded almost equally on the relational and the cognitive social capital scales. In terms of content, however, this item clearly belonged more to the relational social capital scale, since it is about interpersonal relations and not about ability to explain and understand. Based on these results, we felt that there were no problems about these measures in terms of discriminant validity.
Table 3 Scales: reliabilities and correlations.
1 2 3 4 5 6 7
Knowledge sharing Structural social capital Relational social capital Cognitive social capital Organizational structure Organizational culture ICT infrastructure
1
2
3
4
5
6
7
0.78 0.37*** 0.33*** 0.30*** 0.11* 0.24*** 0.20***
0.76 0.40*** 0.35*** 0.28*** 0.39*** 0.15***
0.70 0.36*** 0.34*** 0.39*** 0.15***
0.63 0.16*** 0.21*** 0.13**
0.67 0.60*** 0.34***
0.77 0.37***
0.86
Table shows Pearson correlation coefficients for all relationships. Cronbach’s alpha shown on diagonals. * p < 0.05. ** p < 0.01. *** p < 0.001.
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Table 4 Factor loadings for knowledge sharing and social capital items. Knowledge sharing I like to be kept fully informed of what my colleagues know. When I need certain knowledge, I ask my colleagues about it. I regularly inform my colleagues of what I am working on. When I have learned something new, I make sure my colleagues learn about it too. I share information that I acquired, with my colleagues. I ask my colleagues about their skills when I want to learn particular skills. I consider it important that my colleagues are aware of what I am working on. When a colleague is good at something, I ask him/her to teach me. My colleagues know what knowledge I need. I know what knowledge could be relevant to which colleague. When a customer client has a question, I know which colleague or department will be able to help. Within my department, I know who has knowledge that is relevant to me at their disposal. Outside my department, I know who has knowledge that is relevant to me at their disposal. My colleagues know what knowledge I have at my disposal. I am regularly in contact with colleagues who have knowledge at their disposal that is relevant to me. My colleagues and I speak the same ‘technical’ language. Sometimes I do not understand my colleagues when they tell me something about their work. (R) Often I only need ‘half a word’ when I am talking about work with my colleagues. Sometimes I have difficulty formulating what I know in so that my colleagues can understand. (R) I feel connected to my colleagues. I view this organization as a group I belong to. I can rely on my colleagues when I need support in my work. I completely trust the skills of my colleagues. When I tell someone what I know, I can count on it that he or she will tell me what he or she knows. Eigenvalue Percentage of explained variance
There was also a rather high correlation between the measures for organizational structure and culture, and these scales contained items that looked somewhat similar. Again, a principal components analysis with varimax rotation was performed for the items in these measures; this yielded the results presented in Table 5. From this analysis, four components emerged – two containing different items from the organizational structure scale, and two containing different items from the organizational culture scale. Thus there was discriminant validity in these measures because the structure and culture items did not cross-load, but we found four instead of two components, raising questions about the convergent validity of the measures. These components, however, did not constitute sufficiently homogenous scales when compared with the original measures (Cronbach’s alpha 0.44 and 0.61 for the structure components, and 0.71 and 0.65 for the culture
Structural social capital
Relational social capital
Cognitive social capital
0.45 0.53 0.69 0.69 0.65 0.61 0.62 0.63 0.15 0.10 0.02 0.22 0.05 0.10 0.20 0.11 0.09 0.11 0.05 0.12 0.09 0.09 0.08 0.21
0.02 0.15 0.05 0.18 0.17 0.14 0.02 0.16 0.47 0.61 0.66 0.66 0.73 0.61 0.50 0.18 0.11 0.21 0.04 0.15 0.09 0.17 0.05 0.14
0.07 0.15 0.15 0.00 0.07 0.10 0.18 0.02 0.17 0.02 0.07 0.16 0.04 0.12 0.35 0.32 0.16 0.01 0.04 0.75 0.76 0.62 0.60 0.42
0.22 0.04 0.15 0.22 0.19 0.07 0.02 0.03 0.17 0.15 0.15 0.02 0.05 0.23 0.09 0.63 0.61 0.55 0.70 0.12 0.08 0.20 0.16 0.36
5.50 22.93
2.04 8.48
1.69 7.03
1.50 6.25
components). Also, they did not constitute homogenous scales in terms of face validity (there was no logical ‘‘split’’ between the components for structure and culture). Therefore, we chose to keep these measures intact – i.e., one measure for structure and one for culture, acknowledging that both measures were multidimensional, but valid measurements in terms of discriminant validity. 4. Results In order to determine the relative influence of the two approaches on knowledge sharing, structural equation modeling was applied, using AMOS, which provides, SEM and analysis of covariance structures, or causal modeling. This package allows the testing of a set of regression equations simultaneously, providing both parametric statistics for each equation and indices that
Table 5 Factor loadings for organizational structure and culture items. Culture 1 The structure of our organization impedes interaction and knowledge sharing. (reverse coded) The structure of our organization promotes collective behavior over individual behavior. The structure of our organization facilitates the development of new ideas/processes/products, i.e. knowledge creation. This organization uses a standardised reward system for knowledge sharing. The structure of our organization facilitates the exchange of knowledge across functional formal boundaries, like departments. The staff in this organization are accessible. The management of our organization expects everyone to actively contribute to the registration and transmission of knowledge. Staff are encouraged to innovate, to investigate and to experiment. On-the-job training and learning are highly appreciated in this organization. In this organization staff are encouraged to ask others for help whenever necessary. Interaction between different departments is encouraged in this organization. The goals and vision of this organization are clearly communicated to the employees. The management of this organization stresses the importance of knowledge to the success of the organization. Eigenvalue Percentage of explained variance
Culture 2
Structure 1
Structure 2
0.03 0.01 0.21
0.26 0.02 0.29
0.11 0.69 0.59
0.73 0.27 0.06
0.17 0.35
0.17 0.11
0.72 0.47
0.18 0.38
0.33 0.06
0.00 0.64
0.02 0.08
0.73 0.23
0.54 0.77 0.72 0.63 0.19 0.22
0.35 0.17 0.04 0.21 0.76 0.73
0.29 0.03 0.13 0.22 0.04 0.30
0.11 0.00 0.10 0.31 0.04 0.03
4.14 31.83
1.17 9.02
1.04 8.05
1.04 8.01
6
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Fig. 2. Final tested model. x2 = 17.41 (d.f. = 7, p = 0.01), ratio x2/d.f. = 2.48, AGFI = 0.96, TLI = 0.96, RMSEA = 0.05. Exogenous variables are allowed to covary. Figures near arrows denote standardized regression weights, figures in italics denote proportion of variance explained.
indicate the fit of the model to the original data. Models can thus be adjusted in terms of adding or deleting relationships. The strength and significance of the different hypothesized relationships were therefore tested using AMOS. The model produced mixed results for model fit: x2 value was significant (13.9, d.f. = 3, p < 0.003), and the ratio of the Chi square to the degrees of freedom was 4.6. Since the Chi square value is sensitive to sample size and non-normality, the ratio of x2 to degrees of freedom is preferred as a fit statistic [6,18]. Although different critical values are considered valid for this statistic, values below 3.0 are generally assumed to indicate sufficient fit. The AGFI was above the critical value of 0.90 at 0.93; the Tucker-Lewis Index should be close to 1 and it scored 0.91, so both indices indicated a sufficient fit. On the other hand, the Root Mean Square Error of Approximation was 0.082, whereas a value of 0.05 would indicate a close fit, and a value of 0.08 or lower still indicated a reasonable error of approximation. These fit statistics led to a mixed conclusion: not all criteria were met, but in itself this is an insufficient reason to reject the model. However, it also contained a number of relationships that were not significant at the 0.05 level: Organizational structure was not found to influence structural social capital and cognitive social capital, rejecting Hypotheses 7a and c. ICT infrastructure was not found to influence relational social capital and cognitive social capital, rejecting Hypotheses 9b and c. We decided that these non-significant relationships must be removed from the model, leading to the model that is presented in Fig. 2. This model had a satisfactory fit: although the x2 value (17.4, d.f. = 7) was significant (p < 0.05), the ratio of x2 to d.f. was 2.5 (below 3). AGFI scores were 0.96, well above 0.90, and TLI scores were 0.96, which is sufficiently close to 1. The RMSEA value of 0.052 fell just short of a close fit (0.05), but it was well within the range of a satisfactory error of approximation (0.08). All relationships in this model were significant at p < 0.05. Finally, the model explained 20% of the variance in knowledge sharing, 28% of the variance in relational social capital, 16% of the variance in structural social capital and 13% of the variance in cognitive social capital. The model showed that all three dimensions of social capital positively influenced the degree to which knowledge was shared in
the organization, providing strong support for the emergent approach. These results provided support for Hypotheses 1, 2 and 3. Also, the different dimensions of social capital were mutually related, supporting Hypotheses 4, 5 and 6. Thus, the assumptions based on the emergent approach were supported. Hypotheses derived from the engineering approach were partly supported by these results. Although Hypotheses 7a and c were rejected, Hypothesis 7b was supported: organizational structure was found positively to influence the level of relational social capital. Similarly, ICT infrastructure was found to positively influence the level of structural social capital, providing support for Hypothesis 9a, although Hypotheses 9b and c were rejected. Finally, organizational culture was found to be an important variable from the engineering approach: a knowledge-friendly culture was found to positively influence the level of social capital on each dimension (structural, relational and cognitive), providing support for Hypotheses 8a, b, and c. 5. Discussion Both the engineering and the emergent approach had value in explaining organizational knowledge sharing, in line with our theoretical arguments. Knowledge sharing is an emergent process, influenced by the social dynamics between individuals. This does not mean, however, that management does not have a role in knowledge sharing: engineering knowledge management is needed, as it creates the conditions in which emergent variables exist. By providing organizational and technical infrastructures, management can facilitate, stimulate, and influence the emergence of social capital, which in turn influences knowledge sharing. All the engineering variables were found to have a distinct influence on social capital. Organizational culture was a crucial factor, as it was found to influence all three dimensions of social capital. Establishing and communicating a knowledge-friendly culture, establishing a clear vision and objectives, and clear values related to knowledge, was effective in promoting the social dynamics that were beneficial to knowledge sharing. Such a culture leads to more insight into where relevant knowledge is located, more active interaction between members of the organization, a higher mutual understanding, and an atmosphere of social identification, trust, and reciprocity. Supporting the creation and maintenance of this culture in a top–down fashion seemed possible, based on our results.
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Furthermore, organizational structure was found to positively influence the level of relational social capital; the extent to which a structure is characterized by clear roles and responsibilities for knowledge sharing and reduced structural barriers to it, leads to more trust, identification, and reciprocity between employees. It might seem that a greater influence of organizational structure on social capital would result in positive influence on structural social capital – a more transparent structure leading to more insight into the location of knowledge and how to contact relevant people. However, clarity of roles and responsibilities and less formal divisions in the organization may lead to a more ‘‘informal’’ climate, where trust, identification and reciprocity exist. Finally, an effective ICT infrastructure positively influences the level of structural social capital. Since its role in knowledge sharing often lies in facilitating interaction through personal yellow pages, knowledge maps, etc., this relationship makes sense. In a case study within a multinational ICT consultancy company, it was found that the use of a KM system influenced all three dimensions of social capital [25]. Our study, however, indicated that the effect of technology on knowledge sharing via social capital was more complex. We found that ICT only influenced the structural dimension: helping by showing where knowledge was located and improving organizational connectivity. The structural dimension influenced both the relational and cognitive dimension: technology seems to only have an indirect influence on levels of trust, reciprocity, shared norms, and narratives. On the whole, this means that engineering and emergent approaches do not oppose each other but interact. Moreover, our study provided a more dynamic perspective of social capital, since it showed that the three dimensions do not have an equal contribution to knowledge sharing and are not equally influenced by engineering variables. As for practical implications, our results indicated that management could indirectly promote knowledge sharing through the creation of an environment that fostered social capital by creating an organizational structure that showed who was responsible for which knowledge activities and that had little formal barriers to interaction between different parts of the organization; establishing a knowledge-friendly culture with openness, innovativeness, a willingness to share, etc.; establishing and maintaining an IT infrastructure that efficiently and effectively helped organizational members to learn what is relevant knowledge, where it is located, and how to contact those possessing or needing it. Basically, managing knowledge sharing is difficult – the direct influence of management measures may be limited, since it is primarily social group interactions that stimulate knowledge sharing. However, indirectly, engineering knowledge sharing is possible. As most research, our study had some shortcomings. First, the data from the different organizations were merged into a single sample, because some separate samples were relatively small (the consultancy firm, the insurance company, and the international branch of the transport organization); this meant that no separate models could be created for any of the organizations. Although our primary aim was to provide a generalizable picture of knowledge sharing, and not an organization-specific one, this was still a serious limitation. Organizational differences were ignored in our analysis. Still, the analyses start to build a robust model of knowledge sharing – following a replication logic where a theory is tested through the process of replicating the results of a case study in consequent studies [32].
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Another limitation is that our sample consisted of organizations based in Holland. However, only the consultancy was a purely Dutch organization: the cable provider was the Dutch subsidiary of an American company, the mail provider had merged with an Australian organization, the transport organization was a multinational and the insurance company was part of an Italian organization. Overall, the alpha values on the scales used in our study were satisfactory, and we feel that the items used had conceptual validity also. Two scales, however, do need attention: cognitive social capital and organizational structure had alpha values that were only acceptable when considered in terms of exploratory research. Furthermore, the organizational structure and culture scales turned out to be multidimensional: both contained two underlying constructs. In conclusion, our results indicated that managing knowledge sharing was a daunting task, and that there were limits to the influence that management had on the process; however, they also indicated that managers who realize that knowledge sharing is a process of emergence as well as engineering can still be effective in managing the organization’s intellectual capital through thoughtful design and maintenance of organizational infrastructures.
Appendix A. Items in scales Knowledge sharing I like to be kept fully informed of what my colleagues know. When I need certain knowledge, I ask my colleagues about it. I regularly inform my colleagues of what I am working on. When I have learned something new, I make sure my colleagues learn about it too. I share information that I acquired, with my colleagues. I ask my colleagues about their skills when I want to learn particular skills. I consider it important that my colleagues are aware of what I am working on. When a colleague is good at something, I ask him/her to teach me. Organizational structure The structure of our organization impedes interaction and knowledge sharing. (reverse coded) The structure of our organization promotes collective behaviour over individual behavior. The structure of our organization facilitates the development of new ideas/ processes/products, i.e. knowledge creation. This organization uses a standardised reward system for knowledge sharing. The structure of our organization facilitates the exchange of knowledge across functional formal boundaries, like departments. The staff in this organization are approachable. Organizational culture The management of our organization expects everyone to actively contribute to the registration and transmission of knowledge. Staff are encouraged to innovate, to investigate and to experiment. On-the-job training and learning are highly appreciated in this organization. In this organization staff are encouraged to ask others for help whenever necessary. Interaction between different departments is encouraged in this organization. The goals and vision of this organization are clearly communicated to the employees. The management of this organization stresses the importance of knowledge to the success of the organization. ICT infrastructure The IT facilities within this organization provide a positive contribution to my productivity and effectiveness. Our IT facilities make it easier to cooperate with others within our organization. Our IT facilities make it easier to cooperate with others outside our organization. The IT facilities within this organization provide a positive contribution to the development of my knowledge. The IT facilities within this organization provide important support for knowledge sharing. IT makes it is easier for me to get in contact with employees who have knowledge that is important to me. IT makes it is easier for me to have knowledge that is relevant to me at my disposal.
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Structural social capital My colleagues know what knowledge I need. I know what knowledge could be relevant to which colleague. When a customer client has a question, I know which colleague or department will be able to help. Within my department, I know who has knowledge that is relevant to me at their disposal. Outside my department, I know who has knowledge that is relevant to me at their disposal. My colleagues know what knowledge I have at my disposal. I am regularly in contact with colleagues who have knowledge at their disposal that is relevant to me. Cognitive social capital My colleagues and I speak the same ‘technical’ language. Sometimes I do not understand my colleagues when they tell me something about their work. (reverse coded) Often I only need ‘half a word’ when I am talking about work with my colleagues. Sometimes I have difficulty formulating what I know in such a way that my colleagues can understand. (reverse coded) Relational social capital I feel connected to my colleagues. I view this organization as a group I belong to. I can rely on my colleagues when I need support in my work. I completely trust the skills of my colleagues. When I tell someone what I know, I can count on it that he or she will tell me what he or she knows.
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Bart van den Hooff is an associate professor of Knowledge and Organization at the Knowledge, Information and Networks research group, Faculty of Economics and Business Administration, VU University Amsterdam. His research interests include knowledge management, communities and networks of practice and online networks.
Marleen Huysman is a full professor of Knowledge and Organization at the Faculty of Economics and Business Administration, VU University Amsterdam. She leads the Knowledge, Information and Networks (KIN) research group, studying knowledge and information intensive processes in organizational (online) networks.