Structured literature review of Knowledge Sharing with TheoryMaps

Structured literature review of Knowledge Sharing with TheoryMaps Niels Kijl n.kijl@student.utwente.nl ABSTRACT In the last few years a lot of theories have been developed about why people share their knowledge. However, the links between the causal models of the various theories are fairly unclear. To find this out, a structured literature review is carried out in this article about the causal models of these knowledge sharing theories. After analyzing the selected literature, the causal relations of each theory will be modeled in the online tool TheoryMaps. This tool can transform traditional scientific articles into causal maps. The causal maps of the various theories then can be compared to each other using TheoryMaps to infer contradictions or to find new dependencies between similar variables. On the basis of these results I present an extended KS-model which consists of a joining of all of the studied theories. This extended model gives new insights in the links between the KStheories and may help better understanding why people share their knowledge and how to improve knowledge sharing within an organization. Keywords Knowledge sharing, theories, causal models, motivation factors, constructs 1. INTRODUCTION Knowledge is the foundation of a firm s competitive advantage; the primary driver of a firm s value [7-8, 18]. However, knowledge resides within individuals and does not transform easily into organizational knowledge [4]. If a member leaves an organization a lot of knowledge may be lost, especially if this knowledge was not shared. In this way a firm can lose its competitive advantage. It is also of great importance that the gathered knowledge preserves within the organization. Furthermore knowledge sharing behavior not only exchanges knowledge between individuals but can also lead to knowledge creation by combining knowledge [16]. Extensive knowledge sharing within organizations still appears to be the exception rather than the rule [4], whereas it offers the already mentioned advantages and more. Knowledge sharing not only concerns explicit knowledge but also concerns tacit (implicit) knowledge like expertise or skills. This kind of knowledge is harder to share, because of codifiability difficulties [9]. To better profit from the individual s knowledge it is important for an organization to increase knowledge sharing-behavior. In this research I present a new KS-model by combining different KS-models developed by other researchers. This new extended Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. 12 th Twente Student Conference on IT, January 22, 2010, Enschede, The Netherlands. Copyright 2010, University of Twente, Faculty of Elecrtical Engineering, Mathematics and Computer Science. model may help organizations to improve knowledge sharing behavior and may also help to take advantage of the positive effects knowledge sharing offers. 2. PROBLEM STATEMENT There has been a lot of research conducted on why people share their knowledge, which have led to various KS-theories. However, there is no clear view about the coherence and / or correspondence of these theories. The relations between the causal models which consists in these theories are rather unknown. The lack of this information may prevent (the increase of) knowledge sharing within an organization, because of unclear or unknown dependencies between constructs. It also may lead to the loss of knowledge by member s leaving the organization, in cases where the knowledge was not shared. This problem leads to the following main research questions: - What constructs significantly increase or decrease an individual s knowledge sharing behavior? - What are the mutual dependencies between these constructs? - What are the contradictions between the KS-theories and how can they be explained? The results of this research may help better understanding why people share their knowledge and how to improve knowledge sharing within an organization. The proposed KS-model can be used to stimulate knowledge sharing behavior by optimizing the circumstances. This research is a scientific contribution to the existing body of knowledge. It is not only an analysis of existing knowledge sharing theories, but it will also result in a new extended KSmodel. This new KS-model may underlie to new future scientific research in the field of knowledge sharing. The use of a tool like TheoryMaps in a literature review has not been applied before. This new method can also be scientifically interesting because of a broader application in the future. The results of this research are not only interesting for scientific purposes, but can also be relevant for the social world. The new KS-model can be applied in organizations, e.g. developing knowledge-sharing support tools, optimizing virtual communities or creating an optimal organizational environment. The results can be applied to all kinds of organizations like online communities, commercial firms and government agencies, as knowledge becomes more and more important.[19] 3. RESEARCH METHOD As this project is a literature review, it will start by selecting suitable scientific literature about knowledge sharing. For the selection of the papers I use a structured selection procedure. More information about this procedure can be found in the next section of this paper: Literature selection procedure. After the selection procedure, the literature will be analyzed and modeled in TheoryMaps [13-15]. TheoryMaps is an online tool which can transform traditional scientific articles into causal maps. With TheoryMaps it is possible to compare theories with each other which contain the same variables. TheoryMaps can also infer explanations and predictions based on observations

and interventions, can infer contradicting theories, can infer empirical consequences and can manage your bibliographic sources. [14] In the selected literature various knowledge sharing theories are described. These theories consist of cause-and-effect links between variables (also called constructs). TheoryMaps automatically generates a causal map by taking a wizard which consists of the following three steps: 1. Describe theory 2. Add variables 3. Add causal links After the modeling of all the cause-and-effect links, the variables have to be analyzed. It is expected that there are some variables with the same meaning, but which are called different by the authors. It is important to join these variables, so the TheoryMaps tool can treat these variables generically. The effect of every variable on knowledge sharing behavior will be examined. These effects will be presented in a concept matrix. In this way TheoryMaps can compare theories with each other and detect possible contradictions. Furthermore in this way the KS-models can be better combined. After the analysis of the individual theories, the causal mappings will be combined to one big extended KS-model. This may lead to new dependencies between variables which have not appeared in the individual models. The extended model may result in new interesting insights about why people share knowledge. The mutual dependence between the variables will become clear. This structured step-by-step approach ensures that the individual causal models from the literature are clearly entered into Theory Maps. This is of high importance, because this forms the basis of the extended KS-model which will be constructed with TheoryMaps and will give a reliable result. 3.1 Literature research strategy Because of the enormous amount of literature available about knowledge sharing and the limited time available for this research, only a selection of the existing literature about knowledge sharing can be discussed. The selected literature exists of the most cited and suitable scientific papers. The selection procedure of the literature I used is as follows: 1. Start search via Scopus.com / Scholar.google.com with the following keywords: knowledge sharing, knowledge sharing theories, knowledge sharing motivators and knowledge sharing model. 2. Sort the results by cited by descending, so the most cited papers are on top. 3. Read the abstract of the paper and check whether there is a KS-model or KS-theory discussed. 4. Evaluate the paper for suitability for this research: -Is the context of the KS-model similar to the other KS-models? -Is the KS-theory suitable for modeling in TheoryMaps? 5. Select 10 papers with the highest cited by score which are evaluated as suitable for this research. The ten papers selected by this procedure are introduced in the following section. There is also a TheoryMaps-model presentation of each of the KS-theories. 3.2 Literature selection 3.2.1 Social capital in the creation of intellectual capital Nahapiet and Ghoshal [16] have created a hypothesized KSmodel on the basis of the Social Capital Theory. The Social Capital Theory suggests that social capital, the network of relationships possessed by an individual or a social network and the set of resources embedded within it, strongly influence the extent to which interpersonal knowledge sharing occurs. Nahapiet and Ghoshal state that (1) social capital facilitates the creation of new intellectual capital; (2) organizations, as institutional settings, are conducive to the development of high levels of social capital; and (3) it is because of their more dense social capital that firms, within certain limits, have an advantage over markets in creating and sharing intellectual capital. The presented model is a series of hypothesized relationships between different dimensions of social capital and the main mechanisms and processes necessary for the creation of intellectual capital. The TheoryMaps-model can be found in Figure 1. Figure 1: Social capital in the creation of intellectual capital (TheoryMaps) 3.2.2 Behavorial intention formation in KS Bock et al. have developed a KS-model for an integrative understanding of the factors supporting or inhibiting individuals' knowledge-sharing intentions. [4] Their model is based on the Theory of Reasoned Action (TRA) [1]. The components of TRA are three general constructs: behavioral

intention (BI), attitude (A), and subjective norm (SN). TRA suggests that a person's behavioral intention depends on the person's attitude about the behavior and subjective norms (BI = A + SN). The presented model has been analyzed by a field survey of 154 managers from 27 different Korean organizations. It appears that attitudes toward and subjective norms with regard to knowledge sharing as well as organizational climate affect individuals' intentions to share knowledge. Also anticipated reciprocal relationships affect individuals' attitudes toward knowledge sharing while both sense of self-worth and organizational climate affect subjective norms. Contrary to common belief, they found extrinsic rewards exert a negative effect on one s knowledge-sharing attitude. 3.2.4 KS behavior in virtual communities Hsu et al. [10] examined the factors that support or hinder one s knowledge sharing behavior in virtual communities from both personal and environmental perspectives. They also integrate the Social Cognitive Theory in their research, which leads to a SCT-based model that includes knowledge sharing self-efficacy and outcome expectations for personal influences, and multidimensional trusts for environmental influences. The research model was evaluated with structural equation modeling, and a confirmatory factor analysis was applied to test if the empirical data conform to the proposed model. Figure 2: Behavorial intention formation in knowledge sharing (Theorymaps) 3.2.3 Understanding KS in virtual communities The model proposed by Chiu et al. [6] is also based on the Social Capital Theory. However, they integrate this theory with the Social Cognitive Theory. This results in a model for investigating the motivations behind people's knowledge sharing in virtual communities. The Social Cognitive Theory [2] defines human behavior as a triadic, dynamic, and reciprocal interaction of personal factors, behavior, and the social network. The proposed model is supported by data collected from 310 members of a professional virtual community. The facets of social capital and the outcome expectations influence individuals' knowledge sharing behavior. Figure 4: Knowledge sharing behavior in VC's: relationship between trust, self-efficacy and outcome expectations (Theory Maps) 3.2.5 Network structure and knowledge transfer Reagans and McEvily [17] have done a research about how different features of informal networks affect knowledge transfer. The research focused on how network structure influences the knowledge transfer process. The results indicate that both social cohesion and network range ease knowledge transfer, over and above the effect for the strength of the tie between two people, but all the effects are significantly positive. Figure 5: Network structure and knowledge transfer Figure 3: Understanding knowledge sharing in virtual communities (TheoryMaps)

3.2.6 Determinants of individual engagement in KS The exploratory research of Cabrera et al. [5] investigates some of the psychological, organizational and system-related variables that may determine individual engagement in intraorganizational knowledge sharing. Results from a survey of 372 employees from a large multinational show that selfefficacy, openness to experience, perceived support from colleagues and supervisors significantly influence the participation in knowledge exchange. Figure 7: Effects of extrinsic and intrinsic motivation on employee knowledge sharing intentions (Theorymaps) Bock and Kim [3] have conducted a research to develop an understanding of the factors affecting the individual's knowledge sharing behavior in the organizational context. The research model includes various constructs based on social exchange theory, self-efficacy, and theory of reasoned action. Research results from the field survey of 467 employees of four large, public organizations show that expected associations and contribution are the major determinants of the individual's attitude toward knowledge sharing. Expected rewards, believed by many as the most important motivating factor for knowledge sharing, are not significantly related to the attitude toward knowledge sharing. Positive knowledge sharing is found to lead to positive intention to share knowledge and to actual knowledge sharing behaviors. Figure 6: Determinants of individual engagement in knowledge sharing (TheoryMaps) By integrating a motivational perspective into the theory of reasoned action (TRA), this study by Lin [12] examines the role of both extrinsic (expected organizational rewards and reciprocal benefits) and intrinsic (knowledge self-efficacy and enjoyment in helping others) motivators in explaining employee knowledge sharing intentions. Based on a survey of 172 employees from 50 large organizations in Taiwan, this study applies the structural equation modeling approach to investigate the research model. The results showed that motivational factors such as reciprocal benefits, knowledge self-efficacy, and enjoyment in helping others were significantly associated with employee knowledge sharing attitudes and intentions. However, expected organizational rewards did not significantly influence employee attitudes and behavior intentions regarding knowledge sharing. Figure 8: Attitudes Knowledge Sharing (TheoryMaps) The study by Kankanhalli et al. [11] formulates and tests a theoretical model to explain electronic knowledge repositories (EKS) usage by knowledge contributors. The model employs social exchange theory to identify cost and benefit factors affecting EKR usage, and social capital theory to account for the moderating influence of contextual factors. The model is validated through a large-scale survey of public sector organizations. Figuur 9: Contributing to Electronic Knowledge Repositories (TheoryMaps)

The last reviewed paper is of Wasko and Faraj. They apply theories of collective action to examine how individual motivations and social capital influence knowledge contribution in electronic networks. This study reports on the activities of one electronic network supporting a professional legal association. Using archival, network, survey, and content analysis data, we empirically test a model of knowledge contribution. We find that people contribute their knowledge when they perceive that it enhances their professional reputations, when they have the experience to share, and when they are structurally embedded in the network. Surprisingly, contributions occur without regard to expectations of reciprocity from others or high levels of commitment to the network. 3.3 Joining constructs In the table on the next 2 pages the constructs can be found which are mentioned in the studied articles. For every construct there is a definition, which is applicable for every article it is used in. In the table can also be found in which papers the concerning construct has been referenced to and, if applied, the alternative naming of this construct in the article. With this information the extended model can be created and the various KS-models can be compared using TheoryMaps. Not every joining of all single constructs will be discussed, because of the large number of constructs. Besides, the joining of the constructs is obvious. If a construct is described in different articles, but the naming differs, they will be joint together. Therefore TheoryMaps can treat them similar and compare the different KS-models reliably. The table on the next 2 pages will show all the joins. 3.4 Concept matrix In the concept matrix on page 8 can be found in which article which constructs (concepts) are described. The concept matrix shows for every construct what effect it has on knowledge sharing behavior according to the concerning article. The effects have been extracted out of the literature. Four different effects are being distinguished: + : significant positive effect (green) - : negative significant effect (red) O : no significant effect (black)? : unknown individual effect Figure 10: Why should I share? (TheoryMaps)

Construct Definition Paper Anticipated reciprocal relationships Anticipated reciprocal relationships capture employees' desires to maintain ongoing relationships with others, specifically with regard to knowledge provision and reception. [4] [4] Anticipation of value through combining / exchanging intellectual capital Appropriable organization Attitude toward knowledge sharing The anticipation of value through combining and / or exchanging intellectual capital. [16] Organizations created for one purpose may provide a source of valuable resources for other, different purposes. [16] The degree of one's positive feelings about sharing one's knowledge. [4] [16] [16], [4] (Organizational climate) [4], [12], [3] Codification effort The time and effort required to codify and input knowledge [11] into a knowledge repository. [11] Combination capability The capability to combine information or experience. [16] [16] Common knowledge The correspondence of the shared knowledge. [17] Community related outcome expectations The expected outcome as benefit to the community. [6], [10] Conscientiousness Reliable, dependable, industrious, achievement oriented and [5] organized. [5] Conscientiousness "Conscientious individuals are defined as reliable, [5] dependable, industrious, achievement oriented and organized." Ease of knowledge The ease of knowledge transfer. [17] transfer Enjoyment in helping The degree of one s joy to help other people. [11], [12], [20] others Expected associations Employees believe they could improve relationships with [3] other employees by offering their knowledge. [3] Identification Identification is the process whereby individuals see [16], [11], [6] themselves as one with another person or group of people. [16] Intention to share knowledge The degree to which one believes that one will engage in a knowledge sharing. [4] [3] (Motivation to combine / exchange intellectual capital), [4], [12],[16] Intrinsic rewards Intrinsic reward for sharing knowledge. [5] Job autonomy Degree or level of freedom and discretion allowed to an [5] employee over his or her job. Knowledge quality Quality of the shared knowledge. [6], [20] (Helpfulness of contribution) Knowledge sharing behavior Behavior aimed at really sharing your knowledge. [4](New intellectual capital created through combination and exchange), [11] (EKR usage by knowledge contributors), [6] (Quantity of knowledge sharing), [10], [5], [16], [17], [12], [3], [20] (Volume of contribution) Knowledge(sharing) selfefficacy Knowledge self-efficacy relates to the perception of people about what they can do with the knowledge they possess. [11] [11], [10], [5], [12], [20] (Self-rated expertise) Level of IT Usage The invidual s level of IT usage. [3] [3] Network configuration The configuration of the network ties, like the density, [16], [17] (Social cohesion) connectivity, and hierarchy. [16] Network range Range of the network to access resources. [16] (Access to parties for combining / exchanging intellectual capital), [17], [20] (Centrality) Network ties A network tie provides access to resources. [16] [16], [6] (Social interaction ties), [17] (Tie strength)

Norm A norm is perceived social pressure to perform a behavior, in this context the behavior to share knowledge. [16], [4], [11] (Pro-sharing norms),[6] (Norm of reciprocity) Obligation A commitment or duty to take some action in the future. [16] [16], [5] (Organizational commitment), [20] (Commitment) Opennes to experience One s openness to experience. [5] Organizational reward Extrinsic reward for sharing knowledge. [4] (Anticipated extrinsic rewards), [11], [12], [5] (Extrensic reward), [3] (Expected rewards) Perceived support Perceived support from colleagues and supervisors. [5] [5] Personal outcome The expected outcome as benefit to the person. [10] expectations Reciprocal benefits Personal and organization-related benefits. [11] (Reciprocity), [12], [20] (Reciprocity) Reputation The expected reputation by sharing knowledge. [20] Sense of self-worth The extent to which employees see themselves as providing value to their organizations through their knowledge sharing. [4] [4], [3] (Expected contribution) Shared language and Members of an organization having the same language and [16], [6] code code. Shared narratives Members of an organization having the same myths, stories, [16] and metaphors. Shared vision A shared vision embodies the collective goals and [6], [5] (Agreeableness) aspirations of the members of an organization. [6] System availability The availability of the knowledge management system. [5] System quality The quality of the knowledge management system. [5] Tenure in field The term during which some position is held. [20] Trust Table 1: join of constructs with definitions The belief that the results of somebody's intended action will be appropriate from our point of view. [16] [16], [11] (Generalized trust), [10] (Splitted into economy-, information- and identification-based trust), [6]

Nahapi et & Ghosh al [16] Boc k et al. [4] Anticipated reciprocal relationships + Anticipation of value through combining / exchaning intellectual capital Appropriable organization + + + Kankanh alli et al. [11] Chiu et al. [6] Hsu et al. [10] Reaga nd & McEvil y [17] Cabre ra et al. [5] Attitude toward knowledge sharing + + + Codification effort? Combination capability + Common knowledge + Community related outcome expectations + ᴑ Ease of knowledge transfer + Enjoyment in helping others + + + Expected associations + Identification +? + Intention to share knowledge + + + + Knowledge quality + + Knowledge self-efficacy + + + + ᴑ Knowledge sharing behavior Level of IT Usage Network configuration + + Network range + + + Network tie + + + Norm + +? + Obligation + ᴑ - Opennes to experience + Organizational reward - + ᴑ ᴑ ᴑ Perceived support + Personal outcome expectations + Reciprocal benefits? + - Sense of self-worth + + ᴑ Shared language and code + ᴑ Shared narrative + Shared vision - ᴑ Trust +? + Table 2: concept matrix Lin [12] Boc k & Kim [3] ᴑ Wask o & Faraj [20]

4. RESULTS 4.1 Joining The joining of the analyzed KS-theories into one extended model can be found on page 10. The joining is based on the analysis of the ten selected papers. Every non-contradictive link of all of the TheoryMaps-models of these papers between two constructs can be found in the new extended KS-model. The positive links between two constructs are marked in green, negative links are marked in red and non-significant links are marked in black. Finally contradictive links are marked in orange. The contradictions are specified in the following section. The extended model consists of a lot more constructs than the individual KS-models. This is also the power of this model, it shows far more dependencies between constructs, and is therefore more complete. However, this model is only theoretically based and should be examined in practice. 4.2 Contradictions The first contradiction which showed up is the link between the constructs trust and knowledge sharing behavior. According to Chiu et al. [6] there is a significant positive link between these constructs, whereas Hsu et al. [10] state that there is no significant link between the constructs. In spite of the fact that both researches are conducted in virtual communities, the results don t match. This may prove that the findings cannot be generalized to all types of virtual communities; there might be differences between them. This could also be applied to the next contradiction, the link between community related outcome expectations and knowledge sharing behavior. Unlike Chiu et al. [6], who state that there is positive link, Hsu et al. [10] have found a nonsignificant link between these construct. The third contradiction which appeared is the direct link between knowledge self-efficacy and knowledge sharing behavior. The link between these two constructs is found to be positive by Cabrera et al. [5], Kankanhalli et al. [11] and Hsu et al. [10]. However, Wasko & Faraj [20] say that the link is nonsignificant. It seems that in most cases, there is a positive link between these constructs, but this cannot be stated without further research. The most remarkable contradiction is the one between organizational reward and knowledge sharing behavior. Bock et al. [3] have found support for a negative link between these constructs, as Lin [12] says that the link is non-significant. On the other hand Kankanhalli et al. [11] have found a positive link. A possibility is that this link between these constructs cannot be generalized, but it is clear that it needs further research to evaluate this.

Figure 11: Extended KS-model

5. CONCLUSION / FURTHER WORK After having finished the literature review and analysis of the different KS-models I can conclude that the theories proposed in the literature are mainly in accordance with each other. Only a small number of links between constructs have contradictions, as already described. These contradictions need further research to find out what kind of link is applicable in what situation. The extended KS-model shows clearly what constructs significantly indirectly or directly increase or decrease knowledge sharing behavior. It also shows the mutual dependencies between the constructs. The model may give better understanding in why or under what circumstances people share their knowledge. The model can be useful for organizations to optimize the environment for knowledge sharing behavior. The proposed KS-model also needs further research; the model needs to be tested in practice under different circumstances and in different organizations. Only then the value and usability can be determined. 5.1 Proof of concept The use of TheoryMaps during a literature review offers some big advantages: TheoryMaps makes it easy to transform traditional scientific articles into causal maps. TheoryMaps generates clear models, directly useful in scientific papers. TheoryMaps manages the bibliographic sources practically TheoryMaps can compare theories to each other. Because TheoryMaps is not yet completely finished, the tool also has some limitations: Variables (constructs) have to be joint manually. Combination of theories have to be done manually. TheoryMaps automatically compares the theories to all the other theories in the repository. TheoryMaps doesn t show what the contradiction is. With some optimizations TheoryMaps can become a very valuable tool by conducting a literature review. 5.2 Limitations In this research there is no distinction made between the extent a construct influences knowledge sharing behavior. To construct a differentiated model, this distinction should be made. Furthermore no distinction is made between different types of knowledge, like explicit and tacit knowledge. It is assumed that there is no difference between knowledge sharing behavior of different types of knowledge. Moreover the various KS-theories are not tested under the same circumstances, e.g. one is investigated in a virtual community whereas another is investigated in a commercial firm. This may lead to differences / errors. 6. REFERENCES [1] Ajzen I, F. M., Understanding Attitudes and Predicting Social Behavior. Englewood Cliffs, NJ: Prentice Hall, 1980. [2] Bandura, A., "Social cognitive theory," Annals of child development, vol. 6, pp. 1-60, 1989. [3] Bock, G. W. and Kim, Y. G., "Breaking the Myths of Rewards: An Exploratory Study of Attitudes About Knowledge Sharing," Information Resources Management Journal, 2002. [4] Bock, G. 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