OVERCOMING THE SOCIAL FENCE OF KNOWLEDGE SHARING

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1 OVERCOMING THE SOCIAL FENCE OF KNOWLEDGE SHARING Towards a descriptive model on content contributing behavior Master Thesis cand.merc.(psyk.) Authors: Michael Poulsen Mikkel Daa Schrøder Supervisor: Liana Razmerita Lektor, PhD. Department of International Business Communication Copenhagen Business School, Denmark Date: May 10th 2013 Characters:

2 Resumé: Implementeringen og vedligeholdelsen af videndelingsystemer har længe været stærkt orienteret mod de tekniske aspekter af disse. Samtidigt er opfattelsen af viden som en kilde til konkurrencemæssige fordele blevet mere udbredt blandt professionelle og akademiske eksperter på området. Adgang til kilden af denne konkurrencemæssige fordel er dog afhængig af individet, der besidder den viden, der skal deles. Med videndelingsystemer afhængige af at viden bliver delt og morgendagens organisationer afhængige af denne viden for fuldt ud at udnytte potentialet i deres konkurrencemæssige fordel, er udtrykket "Content Contributing Behaviour" blevet til. Men hvad motiverer medarbejdere til at samarbejde og dele viden? Metode: Kvalitativ data indsamles fra to væsentligt forskellige videnstunge danske konsulenthuse for at kontrastere resultaterne, med henblik på at validere modellen som et deskriptivt framework til at måle faktorer der, hvis overset, kan lede til en situation, hvor ingen medarbejdere deler deres viden - også kaldet et Social Dilemma. Formål: At beskrive faktorer der påvirker individuel videndelingadfærd i brugen af Web 2.0-baserede videndelingsystemer. Empriske fund: Resultaterne ledte frem til konstruktionen af en fem-faktor model "Factors Affecting Content Contributing Behaviour". Disse faktorer inkluderer: 1) Payoff; 2) Efficacy; 3) Organizational Identification; 4) Management Support; 5) Reciprocity. Forskningsimplikationer: Forslag til videre udvikling af modellen inkluderer: Test af modellen i andre brancher og andre videndelingsystemer for at påvise reliabilitet, samt forbedre validitet ved at anvende statistiske målemetoder på faktorerne og deres indbyrdes korrelation. Praktiske implikationer: Ledelsen kan overveje at anvende denne model ved at omdele et tilpasset spørgeskema, baseret på den anvendt i vores undersøgelser (se appendiks), for at vurdere de fem faktorer i modellen med henblik på at afværge eller udbedre risiko for sociale dilemmaer opstår i forbindelse med videndeling.

3 Abstract: The implementation and maintenance of knowledge management systems has long been with a focus on the technical aspects. With knowledge more commonly being accepted as the source of competitive advantage in the organizations of tomorrow and the access to this source being dependent on the graces of the individual in which it resides, it has become a people game. As the knowledge management system is dependent on content being added and the organization dependent on this content to harness and fully utilize the potential of their competitive advantage, the term content contributing behaviour has been born. But what motivates employees to cooperate? This is what we seek to examine within the scope of this thesis. Methodology: Collecting data from two significantly different knowledge-intensive Danish consultancy-firms yields contrasting results, useful for validating the model as a descriptive framework for assessing factors that, if overseen, might lead to the rise of a Social Dilemma and non-cooperative behaviour among employees. The purpose: To describe factors affecting individual knowledge sharing behaviour using web 2.0-based knowledge management systems Empirical finding: Results led to the construction of the five-factor model of Factors Affecting Content Contributing Behaviour. These factors include: 1) Payoff; 2) Efficacy; 3) Organizational Identification; 4) Management Support; 5) Reciprocity. Research implications: Further development of the model; including testing the model in other industries and KMS to prove reliability and enhance validity by applying statistical measures of the factors and items, including their correlation. Practical implications: Management might consider applying the constructed model by distributing a survey based on the one found in the appendix to assess the five factors to ward of a potential deadlock in knowledge sharing. Originality: This thesis develops a descriptive framework taking in to account the people as well as the technical aspect of making Knowledge Management Systems a success. Key words: Knowledge management, social dilemma, social fence, contribution behaviour, motivation.

4 CHAPTER 1 6! Introduction 6! 1.1 Research Question 8! CHAPTER 2 10! Theory 10! 2.1 Knowledge 11! 2.2 Knowledge management 13! 2.3 Knowledge management systems 16! 2.4 The Social Dilemma of knowledge sharing 17! Social Dilemma 18! Knowledge sharing as a public good dilemma 19! Overcoming the Social Fence 21! 2.5 Motivation 22! 2.6 Factors influencing content contribution behaviour 23! Payoffs 24! Efficacy 26! Organizational Identification 27! Management Support 29! Reciprocity 30! 2.7 Summary of hypotheses 31! CHAPTER 3 33! Research Methodology 33! 3.1. Philosophy of Science 33! Epistemology 34! Ontology 35! The Epistemology and ontology adopted in this thesis 35! 3.2. Research process 36! 3.3. Research approach 37! 3.4.Time Horizon 37! 3.5. Research strategy 38! 3.6. Research design 39! 3.7 Data collection 40! Sampling 44! 3.8. Credibility of research 45! Reliability 45! Validity 45! Generalisability 46! CHAPTER 4 47! Case-descriptions 47! 4.1. Wemind 47! The Organization 48! The Culture 49! Knowledge management in Wemind 49! Yammer 50! 4.2 Rambøll 52! The Organization 52!

5 4.2.2 Knowledge management in Rambøll 53! Ramlink 54! The Reinforcement Project 56! 4.3 Content Contributing Behaviour 56! CHAPTER 5 58! Data Analysis 58! 5.1. Semi-structured interviews 59! Coding process 59! Findings 62! Summary of findings in the interview data 81! 5.2 Online survey 83! Response rate 83! Findings 84! Summary of findings in the survey data 94! CHAPTER 6 96! Discussion 96! 6.1. Factors Affecting Content Contributing Behaviour 97! 6.2 Hypotheses 99! Payoffs 99! Efficacy 101! Organizational Identification 103! Management Support 104! Reciprocity 106! 6.3 Knowledge management in the two case companies 108! Purpose of the knowledge management system 108! Content and daily work procedures 110! 6.4 A Descriptive Model for overcoming the Social Fence? 111! CHAPTER 7 113! Conclusion 113! 7.1 Limitations 116! 7.3 Further research 117! Bibliography 119! Appendix A 121! Appendix B 124! Appendix C 131! Appendix D 132! Appendix E 145!

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7 CHAPTER 1 Introduction Knowledge has become one of the most important resources of our time and extensive attempts to understand how this essential resource can be managed and brought to use has been made by managers and scholars alike. An increasing number of organizations depend on the knowledge and expertise of their employees to set them apart from their competitors. We talk about knowledge workers and knowledge intensive companies, who are all based on the premise that knowledge is their primary competitive advantage (Wasko & Faraj, 2005). Whenever a resource becomes this important to business operations, it is often considered to be too valuable to leave to chance it needs to be managed! Much attention has been given to the leveraging and management of the employees knowledge and experience, so as to allow easy access by other employees resulting in faster and better organizational problem solving. The term knowledge management (KM) is a product of the technological development, which through the emergence of information technology has made the widespread collection and distribution of information possible (Alvesson & Kärreman, 2001). KM has almost become synonymous with information technologies supporting processes of knowledge

8 6 Introduction creation, sharing and capturing in organizations (Von Krogh, 2012). Many attempts to manage knowledge through knowledge management systems (KMS) have failed in what Kirchner, Razmerita & Sudzina (2009) describe as the first phase of KM, which primarily focused on this institutionalization of knowledge through the use of technology. These initial attempts to manage knowledge by creating extensive databases have in many cases failed because they fail to take into account the multifaceted nature of knowledge, which assumes different forms as tacit, explicit, individual and collective. A new people-centred paradigm of KM seems to be emerging in which technology is aimed at supporting human interaction instead of being focused on harvesting knowledge from the individuals. This new paradigm is labelled KM 2.0 and focuses on adopting the advances of Web 2.0 technologies seen in the public sphere (Kirchner et al., 2009). A rapid development in the public use of these new Web2.0 based platforms has been seen in recent years. In March 2013 Facebook had 1.11 billion monthly active users worldwide, which proves that these tools are more than a simple fad (Facebook.com, 2013). Web 2.0-tools and platforms have fundamentally changed the way in which people communicate and interact, allowing any individual to easily publish and share information with a far larger group of people than ever before (O Reilly, 2005). Information is no longer controlled and communicated by a limited number of people, but rather by any individual with access to a computer. Considering the complex nature of knowledge and the huge popularity of Web2.0 platform this way of sharing and communicating information has not surprisingly appealed to management and scholars dealing with KM. This has paved the way for concepts like Enterprise 2.0 and Social Business which describe the use of Web 2.0-tools such as Social Networks Services (SNS) in organizational settings, and advocate their ability to enable organizations to realize the potential of knowledge management (McAfee, 2006; Greenberg, 2011). Within the scope of this thesis we make no attempts to answer whether the full potential of knowledge management is met through the use of Web 2.0-tools, however, we do seek to further understand one critical element fundamental to the use of these system for KM. Any KMS is without values and effect if no knowledge is to be found in it. The same can be said for Web 2.0-based KMS, however, the task of

9 7 ensuring content is considerably different, as it relies on the contributions of the individual employee rather than being a centrally controlled process. Fundamental to the use of a Web 2.0-based KMS is the content contributions by the employees. 1.1 Research Question With the increased interested on the use of Web 2.0-tools for knowledge management, our thesis aims at giving support to this agenda by exploring the fundamental issue of the content contribution behaviour of employees. Prior research indicates that the building and sustaining of a successful KMS is dependent on the employees motivation and willingness to add content, thereby contributing to the collective pole of organizational knowledge (Cabrera & Cabrera, 2002; Mark, Polak, Mccoy & Galletta, 2008; Cress, Kimmerle & Hesse, 2006; Wasko & Faraj, 2005; Ardichivili, Page & Wentling, 2003). It is this motivation and the factors affecting it, which we seek to elaborate on within the scope of this thesis. By adopting the notion that knowledge sharing is fundamentally a Social Dilemma, we seek to explore which factors motivate employees to overcome this Social Dilemma and share knowledge by adding contents to a KMS. This leaves us with the following research question: What factors affect individual knowledge sharing behaviour when using web 2.0 based knowledge management systems? By identifying factors motivating or hindering knowledge sharing behaviour related to the use of a Web 2.0-based KMS, it would be possible for organizations and managers to pave the way for a more successful use of these vital tools. The aim of this thesis is therefore to gain an in-depth understanding of factors affecting knowledge sharing behaviour, which is why a qualitative research methodology has been chosen. By building on the works of Cabrera & Cabrera (2002) and by studying the content contributing behaviour of two Danish knowledge intensive organizations, we seek to construct a descriptive model capable of giving guidance to managers as how to ensure a successful Web 2.0-based knowledge management system. In our attempt to uncover factors affecting the individual employee s content contributing behaviour we have developed hypotheses indicative of the expected effect of a series of factors, identified through initial data collection and literature review. The predefined factors

10 8 Introduction are primarily based on the works of Cabrera & Cabrera (2002). Testing these hypotheses in the two case companies will enable us to discuss further the descriptive power of the identified factors and whether other factors may affect contributing behaviour. Chapter 2 will give a thorough introduction to the theoretical foundation of this thesis. The fundamental challenge of knowledge management as a Social Dilemma will be presented. Following this the constructed descriptive model of Factors Affecting Content Contribution Behaviour will be presented along with the five factors and accompanying hypotheses, which will be tested though the analysis. In Chapter 3 the methodology of the thesis will be presented including a presentation of the research strategy and the data collecting methods applied. Chapter 4 contains a description of the two case companies: Wemind and Rambøll, where the data for this thesis has been collected. Chapter 5 contains the analysis of the data collected through semistructured interviews and surveys. The analysis is aimed at examining the model presented in chapter 2. In chapter 6 the findings of the analysis will be discussed along with the overall construction of the model. Chapter 7 will provide the conclusion and an answer to the above research question. This chapter will also present the limitations of this thesis and suggestions for further research.

11 CHAPTER 2 Theory This chapter contains an outline of the relevant literature, which provides the basis for the constructed model describing content contribution behaviour. Later in this chapter the model will be presented, and each of the five factors will be explored in depth which will result in five hypotheses, which are the subjects of the data analysis. The constructed model has been developed from the synthesis of literature and the emergent themes from the interview data collected. The methodological approach will be further described in chapter 3. In the following sections we will present important theoretical concepts relevant to the constructed model. Initially we will give a brief introduction to the concept of knowledge and knowledge management (KM). Next we will describe how sharing knowledge in a Knowledge Management System (KMS) can be perceived as a Social Dilemma, and how this affects the content contributing behaviours of individual employees. The last part of this chapter is dedicated to the presentation of the

12 10 Theory constructed five-factor model of Factors Affecting Content Contributing Behaviour and the five hypotheses. 2.1 Knowledge The concept of knowledge is difficult to define even though considerable attempts have been made even as far back as the philosophers of Ancient Greece. Needless to say a definition of knowledge lies beyond the scope of this thesis. But an understanding of some of the dominant perspectives is needed to support the analysis and the discussions of knowledge management and individual knowledge sharing behaviours. Different perspectives of knowledge will therefore be introduced in the following paragraphs. Within some branches of KM knowledge and information have been used as synonyms, and the focus has been on how to extract, store and access knowledge (Tsoukas & Vladimirou, 2001; Jensen, Mønsted & Olsen, 2005). Nonaka & Takeuchi s SECI model describes knowledge as an entity (a product) going through a circular process of transformation changing between tacit and explicit through codification and internalization (Jensen et al., 2005). This model is based on Michael Polanyi s concept of tacit knowledge, which is the notion that the individual knows more than can be articulated. The SECI model is an attempt to conceptualize how to leverage tacit knowledge within an organization by externalizing it for others to adopt and use (ibid.). This focus on knowledge as an entity, which through codification can be disconnected from human actors, has met considerable criticism. Alvesson & Kärreman (2001) credit this perception of knowledge to the misleading Cartesian distinction between knowing subjects and knowable objects, which they believe to have no merit in reality. One chief objection is that not all tacit knowledge can be externalized, and even when it is, it needs to be understood in relation to the tacit knowledge that sets the context for the now externalized knowledge (Gourlay, 2006). Additionally, this view of knowledge is criticized for not including the process of human judgment (Tsoukas & Vladimirou, 2001; Davenport & Prusak, 1998 in Jensen et al. 2004). This has led to a process-based view of knowledge, based on the distinction between data, information and knowledge. Information being the contextualization of otherwise disconnected but

13 11 ordered data, and knowledge being the evaluation and judgment of this contextual arrangement, connecting it to other sets of information. Knowledge is in this perspective defined as the capability or process of generating contextual meaning from otherwise meaningless items and events (Jensen et al., 2005). Related to this discussion of whether knowledge can be considered a separate entity or a process of interpretation and judgment is the discussion of whether knowledge lies within the individual or in the interaction with the context. A traditional fear, also motivating knowledge sharing activities, is that knowledge can be lost when individuals who possess this knowledge leave an organization (Jensen et al. 2005). This view is, however, challenged by others, who argue that organizational knowledge is more than the knowledge of the individuals combined and that knowledge also lies in the routines and norms of organizations (Tsoukas & Vladimirou, 2001). In this perspective knowledge is both individual and collective, as the process of knowing - interpreting information - is tied to the contextual setting and is thereby influenced by others. As is evident from the above section a definition of knowledge is a highly abstract and theoretical discussion, which according to Alvesson & Kärreman (2001) at best leaves the concept of knowledge vague and so wide-ranging that it tends to be empty covering everything and nothing at the same time. In relation to organizational management attempts have been made to focus more on the practical ramification of knowledge theories (Tsoukas & Vladimirou, 2001; Mathews, 2012). Akbar (2003 in Mathews, 2012) states that knowledge should be defined and understood in terms of its application and in relation to its context and utility rather than being treated as an abstract concept. As will become clear through the analysis of Rambøll and Wemind s KMS, a unified abstract definition of knowledge will not support a deeper understanding of the two cases or their employees knowledge sharing behaviours. As will be clarified in the following paragraphs, the two organizations represent different perspectives of knowledge in this way supporting Akbar s statement that knowledge should be defined within its context and utility (2003 in Mathews, 2012).

14 12 Theory 2.2 Knowledge management Regardless of the lack of a clear and unified understanding of knowledge, knowledge management has been of increasing interest to scholars and managers alike. A search on Ebscohost.com, one of the world s leading research databases, reveals this increased interest in the topic of KM. The number of articles containing the key word Knowledge Management has increased dramatically by 82% of the 7,165 articles which have been written within the last ten years (Ebscohost, 2013). Within management, knowledge management has also taken up a central position as an important instrument for organizations to secure a competitive advantage by leveraging and utilizing their employees unique knowledge and capabilities (Von Krogh, 2012). In 2000 KPMG released a study showing that 81% of the leading organizations in Europe and the U.S. were considering adopting some kind of knowledge management system. 79% of these organizations did so to gain a competitive advantage (Cabrera & Cabrera, 2002). More recently the importance of organizational knowledge management was emphasized in The Forbes Magazine with the statement that Fortune 500 companies lose roughly $31.5 billion a year by failing to share knowledge (Quast, 2012). The actual concept of knowledge management and the idea that knowledge can be managed is far from as old as the concepts of knowledge and management themselves. KM owes much to the rapid development of information technologies which enables fast and cost efficient sharing of information across time and geographic locations (Alvesson & Kärreman, 2001). With this rapid development of communication technologies the distribution of organizational knowledge has become more feasible than ever before. The KPMG report showed that approximately 22% of knowledge management projects where likely to be led by IT-departments compared to only 5% by Human Resources (Cabrera & Cabrera, 2002). Despite this apparent technology focused approach by managers it is widely acknowledged that there is more to knowledge management than technology. KM is not simply a matter of building large electronic libraries but rather a matter of connecting people so they can think together (McDermott, 1999 in Alvesson & Kärreman, 2001). This is supported by Kirchner et al. (2009) arguing for a transition from KM 1.0 to KM 2.0 based of web 2.0 technologies. Technology is in this

15 13 perspective considered something that supports a wide people based approach to knowledge management (Alvesson & Kärreman, 2001). There is, however, still little agreement on how knowledge management should be approached. In her work Dixon (2000) concluded that the approach adopted by one organization often bore little resemblance to that of other organizations and that each organization swore to their own approach. One significant differentiation lies in the weighing of knowledge versus management (Alvesson & Kärreman, 2001). Put bluntly, the more management, the less knowledge to manage, and the more knowledge matters, the less space there is for management to make a difference - Alvesson & Kärreman, 2001: 996 To gain a better understanding of different approaches to knowledge management we look to Alvesson & Kärremans (2001) framework which aims to clarify the relationship between knowledge and different management approaches by identifying four different approaches to knowledge management; Community, Normative Control, Extended Library and Enacted Blueprints based on the dimensions "Modes of managerial intervention" and the "Medium of interaction". Modes of managerial intervention Co-ordination Control Medium of interaction Social Technostructural Community (sharing of ideas) Extended Library (Information Exchange) Normative Control (prescribed interactions) Enacted Blueprints (templates for action) Figure 1: A typology of knowledge management approaches (Alvesson & Kärreman, 2001)

16 14 Theory Extended Library The extended library is focused on information exchange and relies heavily on technology - databases and advanced search mechanisms. A central unit is responsible for gathering organizational knowledge making it actionable for all within the organization. The motivation for choosing this more bureaucratic approach is that it may allow for faster and better work in the organization, and it may provide a more consistent use of knowledge across the organization (Alvesson & Kärreman, 2001). Community In a community approach to knowledge management, management plays a limited role as this approach is based on the organizational environment and the members shared values and beliefs. This approach is mainly focused on the sharing of ideas and an interest in utilizing tacit knowledge. This approach is enabled through human and technological systems which make room for people to think together and for an environment which encourages knowledge sharing and the adoption of new ideas. This approach to KM closely relates to KM 2.0 with its dependence on social technologies (Kirchner et al., 2009). Opposed to bureaucratic management based on control and hierarchy, the Community approach is based on social relations and knowledge sharing and is motivated by a sense of altruism (Alvesson & Kärreman, 2001). Normative Control Management can attempt to stimulate knowledge sharing by cultivating the organizational culture related to the Community approach. The discrepancies between the community based approach to KM and the bureaucratic nature of organizational management can lead to this hybrid. Here management aims to establish shared values and beliefs by encouraging social relations between organizational members and strengthen the feeling of organizational identity. One way to achieve this would be to downplay sub-organizational boundaries within the organization (Alvesson & Kärreman, 2001). Enacted Blueprints Finally the enacted blueprint approach focuses on guiding and controlling work performed by employees by gathering and sharing narrowly specified knowledge. This approach attempts to engineer and control individuals on a behavioural level rather than on a normative level by focusing on values and ideas. This form of knowledge

17 15 management, much like assembly lines, provides templates and guidelines that produce the wanted action, regardless of the nature of the employee s values and thoughts (Alvesson & Kärreman, 2001). 2.3 Knowledge management systems The development of information and communication technologies plays a central role in knowledge management today. As was stated earlier individual and organizational knowledge has become crucial to the competitive advantage of the organizations, making effective knowledge management systems (KMS) more needed than ever before. Fast and cost-efficient distribution of information became possible with the Internet which connected organizational members in widespread geographical locations. More than simply easing the distribution of knowledge the technological development has significantly changed the way humans interact with each other. We are now able to interact with a far larger number of people as well as create and share content ourselves. This change from content being controlled by a few individuals towards being created and contributed by a wide population is a part of the Web 2.0 development (O Reilly, 2005). According to O'Reilly (2005) Web 2.0 is a list of characteristics on how internet-based platforms can create a dynamic experience for the user. In the same way as Web 2.0 has changed the way people in general interact online, the technology has helped reinvent knowledge management as it facilitates interactions rather than simply facilitating distribution of information (Razmerita & Kirchner, 2011). Kirchner el al. (2009) sees a potential for the use of these new knowledge management tools as they produce more relevant virtual content in the organization. New knowledge management tools enable knowledge-intensive organizations to better capitalize, share and reuse knowledge and thus to be more efficient, more flexible and more innovative - Razmerita & Kirchner, 2011: 175) The use of web 2.0 technologies within knowledge management in many ways supports the previously presented perspective, that technology is only a medium for knowledge management, and that it should increasingly take into account the interactions between people. The new tools also support the technological needs of

18 16 Theory the community based approach to knowledge management, as it enables more free interaction in which values and ideas can shape the interaction, and thereby the knowledge that is being shared. Even though web 2.0 technologies are increasingly popular, many organizations still struggle to fully implement them in their knowledge management strategies. This is most likely due to the previously mentioned discrepancies between the freedom of use that these tools require and the bureaucratic nature of management. To account for the fact that not all organizations are capable of fully adopting the KM 2.0 approach, we therefore in this thesis adopt the slightly broader definition of KMS by Sherif, Hoffman & Thomas (2006) as: "...a class of information systems applied to managing organizational knowledge... They are IT-based systems developed to support and enhance the organizational processes of knowledge creation, storage/retrieval, transfer and application". The same authors found that the three common uses of IT in Knowledge Management have been: (1) Storage of lessons learned; (2) access to expertise in the organization; and (3) creation of knowledge networks; the first being closely related to the Extended Libraries of Alvesson & Kärreman (2001) and the latter primarily to the Community approach. Regardless of how the KMS is defined and how the degree to which web 2.0 tools are used, a crucial factor of the success of the system is the contribution of content by those who possess the knowledge. Not uncommonly people are willing to use a KMS for acquiring information, but reluctant to enter their own information into it (Cabrera & Cabrera, 2002; Wasko & Faraj, 2005, Ardichvili, et al., 2003; Cress et al., 2006). In the following sections we will further explore why organizations may experience a lack of content contribution. 2.4 The Social Dilemma of knowledge sharing With an understanding of what modern technology can contribute to the field of knowledge management, and a deeper understanding of the concept of knowledge and how it might be managed, we now need to understand better the behavioural aspects linked to knowledge sharing. As already established the principal of knowledge management is the attempt to leverage employees individual knowledge, so that it is accessible and beneficial for others within the organization. Fundamental to obtaining this goal is the individual

19 17 employee s willingness and motivation to contribute knowledge (Cabrera & Cabrera, 2002; Cress, et al., 2006; Wasko & Faraj, 2005; Ardichivili, et. al, 2003). Within the scope of this thesis knowledge contribution is considered synonymous with the contribution of content to a KMS. The broad definition of knowledge contribution is chosen as it allows for a broader understanding of what knowledge is, and how it can be transferred and shared. The question worth asking is why should an employee choose to add content? That knowledge sharing is of value seems obvious at an organizational level, but the value might not be equally clear at the individual level (Cabrera & Cabrera, 2002). The individual no doubt benefits from gaining access to the knowledge of others, but could be argued to be better off not contributing and instead freeride on the contribution of others (Kerr, 1983 in Cress el al., 2006). Freeriding is a strategy that benefits the individual employee, but were all employees to follow this strategy, the consequence would be that no content would be obtained at all, as everybody would be looking to freeride on the contributions of others. Given that this is the nature of knowledge exchange in an organizational setting, we argue that knowledge management could be described as what is called a Social Dilemma (Cabrera & Cabrera, 2002). Dawes & Messick (2000: 111) defines a Social Dilemma in the following way: A Social Dilemma is a situation in which each member of a group has a clear and unambiguous incentive to make a choice that when made by all members provides poorer outcomes for all than they would have received if none had made the choice By applying the concept of Social Dilemma, we will in this thesis seek to gain an understanding of which factors affect the individual employee s motivation for contributing content to a knowledge-sharing platform Social Dilemma Before entering into further details of the separate factors we need to establish a basic understanding of the concept of Social Dilemmas or problems of social cooperation. The concept is not new and has been studied within different scientific fields for

20 18 Theory almost 50 years (Olson, 1965). The economist Mancur Olson (1965) argued that the existing assumption that an individual sharing a common interest or objective with a group, would logically act towards accomplishing this common objective, had little if any merit. From his economic perspective he argued that rational, self-interested individuals will not act to achieve their common or group interests (Olson, 1965, p. 2). Simply put he argued that a rational individual will not voluntarily bear the cost of achieving a group s objective even if this objective was shared by the individual, unless a separate incentive, other than the achievement of the group objective, was offered in return for bearing the costs or burden related to achieving the group objective. Typically, Social Dilemmas can be categorized into two different types of dilemmas; Tragedy of the Commons or Public Good Dilemmas (Cabrera & Cabrera, 2002; Cress et al., 2006). Tragedy of the commons is a resource dilemma in which a limited availability of resources demands that members within a group must not attempt to maximize individual payoffs, i.e. hoard resources for themselves, as this will result in collective damage when resources deplete (Cabrera & Cabrera, 2002). Typical examples of this are fishing and pollution of the atmosphere. A public good dilemma occurs when a shared resource from which all members of a group may benefit, regardless of whether or not they personally contribute to its provision, and whose availability does not diminish with use (Olson, 1965). Typical examples of this are public parks and public television, which can be enjoyed by all, whether or not the taxes that support these facilities are paid by the individual. When we look at this definition of a public good dilemma and the nature of knowledge management it is clear that knowledge management can be considered a public goods dilemma (Wasko & Faraj, 2000; Cabrera & Cabrera, 2002; Cress et al., 2006). In the following sections we will elaborate on the public good dilemma of KM Knowledge sharing as a public good dilemma When considering knowledge sharing through the use of a computer-based KMS as a public good dilemma, an individual is faced with two choices of action: The individual can either contribute by adding content; or defect by not contributing to the collective

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