Introduction

For many companies, knowledge is viewed as a competitive advantage. Many companies have tried to organize their knowledge into knowledge bases with varying success. The reasons for the success or failure of any given project are numerous. Many organizations fail to understand the full spectrum of determinants for knowledge management and knowledge sharing across the enterprise at the start, during, and even at the end of a failed implementation attempt. This paper conducts a literature review of knowledge management enablers, processes, theories of reuse, sharing propensities, social media impacts, and individual motivations for knowledge transfer and management.

Knowledge management

Since the start of the new millennium, the rise of social media technologies fostering new methods for collaboration and (organizational) virtual communities within the enterprise has resulted in an increase in employee-generated content. The internet, as a platform for hosting these technologies, has become an inexpensive medium that allows billions of people worldwide to exchange information and knowledge. Most of these people interact with social networks regularly and this interaction modality has carried over into the workplace. According to McKinsey, over 70% of companies are using social technologies and 90% of those report some business benefit from them (Chui, Manyika et al. 2012). As firms seek competitive advantages, they often turn to these technologies in the hope that these efforts will result in a tangible benefit.

“Two-thirds of the value creation opportunity afforded by social technologies lies in improving communications and collaboration within and across enterprises. By adopting these technologies, it is estimated that the productivity of knowledge workers could be improved by 20 to 25 percent. However, realizing such gains will require significant transformations in management practices and organizational behavior”.

(Chui, Manyika et al. 2012)

Knowledge is recognized as a strategic resource and is considered to be a sustainable competitive advantage (Drucker 2001 as cited by Yu, Lu et al. (2010). In this context of this paper knowledge management is the “combination of management systems, organizational mechanisms, information, and communication technologies through which an organization fosters and focuses individual and group behaviors in terms of assimilation and generation, transfer and sharing, capitalization and reuse of knowledge, in tacit or explicit form, that is useful to the organization” (Lin, Hung et al. 2009).

Choi and Lee (2003) assert that, “Exchanging knowledge among different members is a prerequisite for knowledge creation. Collaborative culture fosters this type of exchange by reducing fear and increasing openness to other members”. Considering this definition, a generation of new knowledge and a collaborative culture for transfer and sharing are requirements for knowledge management. An organization that fosters and focuses behaviors for assimilation would be found in a collaborative community; although other community types may also accomplish this, collaboration between “organizational members also tightens individual differences. It can help people develop a shared understanding about an organization’s external and internal environments through supportive and reflective communication” (Choi and Lee 2003). Reuse of knowledge is often attempted via a “knowledge pool”, however the inherent difficulties in knowledge reuse are not obvious, likely because knowledge reuse is seen as a “unitary phenomenon – pretty much the same regardless of who does it, how, and why” (Markus 2001).

Hara (2009) defines communities of practice to be: “Collaborative, informal networks that support professional practitioners in their efforts to develop shared understandings and engage in work-relevant knowledge building”. This is consistent with the assumptions and definitions used by Choi and Lee (2003) in their empirical study of knowledge enablers and organizational performance. Communities of Practice provide environments for fostering informal learning (Hara 2009). A community of practice “develops around a certain activity/profession, such as legal practice, medical practice, collaborative efforts of information technology professionals, librarianship, or teaching and instruction. In fact, a shared professional identity is the glue that binds the members of a community together” (Wenger et al., 2002 as cited by Hara (2009); which for this paper will serve as the working definition against which to conduct the review; whether those communities are virtual or face-to-face.

Lastly, it is necessary to address the topic of information and communication technologies. It should be noted that “only explicit knowledge is the province of information technology, including the communication systems by which people informally share their observations and the more formal repositories in which structured knowledge is stored for later reuse”. “One of the key themes in knowledge management today is the role of information technology (IT) in the transfer of knowledge between those who have it and those who don’t” (Markus 2001). Given the premise that IT only addresses explicit knowledge, codified or otherwise, she addresses the distinction of explicit knowledge as a form of knowledge that is declared and tacit knowledge to be that which is not.

Previous empirical studies

This paper relies heavily on two key studies regarding knowledge management enablement and knowledge reuse for the following reason. The study in knowledge management enablers by Choi and Lee (2003) has been widely accepted, having been cited 633 times since publication, approximately 40 times per year and an h-index value of 42 and 4,868 citing articles; while the study by Markus (2001) is similarly well accepted, being cited approximately 21 times per year and a citation analysis h-index of 39, given 5,473 articles which leverage her study.

Knowledge Enablement

According to Lee and Choi (2003), investigating a direct relationship between knowledge processes and organization performance is risky because of the many factors which can influence an organization’s performance; therefore trying to trace the causality of any one factor to a knowledge process is meaningless. However, they suggest that intermediate outcomes such as knowledge satisfaction or organizational creativity reflect different aspects of an organization’s performance and could therefore be used as correlation that knowledge enablers ultimately create business value, even if the linkage is not directly measurable. Additionally, they impute that since knowledge management is inherent a cross-functional and multi-faceted discipline, theories about modeling knowledge management and future research would be better served by applying a system thinking paradigm to the problem, noting that “Systems thinking theory considers problems in their entirety. This theory is better able to describe complex and dynamic characteristics of knowledge management in a systematic fashion” (Lee and Choi 2003).

Overall, there is limited holistic study of the topic that organizational performance is directly linked to knowledge management. Most empirical studies examine specific aspects of knowledge management, while holding the presumption that knowledge sharing and transfer across the organization leads to organizational performance improvement. Few seek to examine the connection as a system. A notable exception is the study by Choi and Lee (2003), who grouped previous studies of knowledge management factors into four categories:

1. Relationships between knowledge enablers
2. Relationships between knowledge enablers and process
3. Relationships between knowledge process and organizational performance
4. Relationships between knowledge enablers, processes, and organizational performance

In the first category, the focus is on the examination of the effect of knowledge enablers. Studies in this category largely investigated knowledge enablers such as knowledge management methods in view of organizational structure and culture. In this context, “enablers” are environmental aspects, such as organizational culture, structure, people, and IT technologies deployed within the organization. This category would be helpful to managers seeking to understand a given aspect of their knowledge management project’s prospective success or failure; but will not give any insights as to how to adjust things to foster project success. The study by Lin, Hung et al. (2009) to explore which factors foster information sharing – clearly a key aspect of any knowledge management program – seeks to develop an integrated model to explain the relationships between key factors: context, personal perceptions of knowledge sharing, knowledge sharing behavior, and community loyalty. They report that trust significantly influences sharing behavior; whereas concepts of reciprocity do not. Yu, Lu et al. (2010) suggest that there are three key factors of a sharing culture: fairness, identification, and openness which “means that enjoying helping ad usefulness/relevancy thereafter promote knowledge sharing behavior”.

The second category supposes that enablers such as industry characteristics influence knowledge transfer and cite a study of the effect of organizational knowledge which was codified into the manufacturing process on the transfer time of that knowledge. Other cited studies examined such aspects as weak organizational ties affecting transfer times within a multi-unit organization and knowledge stickiness affected by the characteristics of the knowledge transferred (Szulanski 1996), the source of the knowledge, the recipient of the knowledge, and the context in which the transfer takes place (Hansen 1999).

The focus of the third category is to examine the relationships between knowledge management strategies (such as know-how) and organizational performance, as exemplified by return on assets (ROA) or return on sales (ROS). In the fourth category, enablers, processes, and performance are examined – (Lee and Choi 2003) make the point that for knowledge satisfaction, “socialization is suitable for broad an process-oriented tasks, externalization for focused and content-oriented tasks, combination for broad and content-oriented tasks, and internalization for focused- and process-oriented tasks; [the net of which is that] combination and externalization affect knowledge satisfaction; [while for organizational effectiveness] infrastructure and process capabilities contribute to the achievement of the organizational effectiveness”.

Knowledge Reuse

Knowledge reuse was empirically studied by Markus (2001). In that study, she leverages the work of Nancy Dixon (Dixon, N.M, Common Knowledge: How Companies Thrive by Sharing What They Know. Boston: Harvard Business School Press, 2000.) who identified five different knowledge transfer scenarios: serial transfer, near transfer, far transfer, strategic transfer, and expert transfer. Additionally, she outlines key concepts in knowledge reuse as follows:

First, the process of capturing, packaging, distributing, and reusing the knowledge. Capturing knowledge is the step of documenting the information objects. It should be noted that this is a gross simplification of the necessary preparation activities, as the roles of the people involved will demand certain strategies for indexing and recall. Packaging, as defined by

Markus includes the actions of culling, polishing, structuring, formatting, tagging, and indexing the documentation against (at least) one classification scheme. Distribution can be either a push style publication or a more active style – facilitation – of helping organizations understand the methodologies for using knowledge management tools or the need to adopt newly codified best practices. Note that Szulanski (1996) wrote about the “stickiness” of best practices and the difficulties experienced by organizations in implementing best practices organization-wide. Reusing knowledge involves the recall and recognition of the information as pertinent to the question at hand.

Increasingly, information technologies are capable of automatically categorizing, classifying, tagging, filing, reformatting, and indexing information objects across a variety of formats. These technologies can help relieve both the tedium of tagging and classifying as well as improve the precision with which an information object is classified against one or more schemas. Technologies such as XML allow for multiple formatting outputs and the collection & aggregation of metadata for the object, against which handling and filing rules can be run for distribution and reuse purposes.

Second, Markus identifies three major roles in the reuse process: a knowledge producer, who is the creator of the knowledge; the knowledge intermediary who sanitizes and prepares the knowledge for indexing, distribution, etc.; and the knowledge consumer who retrieves the information and uses it in some fashion. These roles can potentially be fulfilled by the same person at different times.

Finally, Markus outlines a typology of knowledge reuse scenarios. It is here where the distinction across these situations that the breadth of the reuse spectrum becomes clear and some of the reasons for which knowledge management projects fail can be intuited. People working together, such as a team of developers, referred to as shared work producers, generally need to keep track of current status and track items which need to be addressed; people doing similar work in different settings, such as medical practitioners, referred to as shared work practitioners, seek to acquire new knowledge generated by others to solve a problem or get advice on a problem; novices seeking advice from experts who need expert advice to solve an arcane problem; or knowledge miners who seek to answer new questions via an analysis of existing data. It is at this point where the correspondence to Koll’s observations regarding knowledge repositories and information retrieval can be drawn. From Markus we have four process steps, three roles, and four scenarios while Koll (2000) observed that information retrieval questions could reasonably be grouped into 11 categories:

• a known needle in a known haystack;
• a known needle in an unknown haystack;
• an unknown needle in an unknown haystack;
• any needle in a haystack;
• the sharpest needle in a haystack;
• most of the sharpest needles in a haystack;
• all the needles in a haystack;
• affirmation of no needles in the haystack;
• thinks like needles in any haystack;
• let me know whenever a new needle shows up;
• where are the haystacks?
• needles, haystacks – whatever.

The nature of the knowledge reuse scenario will imply the implementation selection of a schema, which leads to specific culling and indexing practices. These practices may be inconsistent with the expectations of a user not originally considered in the implementation planning of the repository. Markus (2001) notes that this documenting for dissimilar others can sometimes “mean the deliberate withholding of information or providing inaccurate information”. Hiding behaviors can be predicted by distrust (Connelly, Zweig, et al. 2012), although Grudin (as cited by Markus (2001) notes that “social, political, and motivational concerns may prevent the explicit statement of the real reasons underlying design choices”.

Knowledge Sharing

The advent of internet and associated collaboration technologies such as social media have given rise to ‘virtual communities of practice’. It is not obvious why people would choose to share their expertise and knowledge with strangers. Chang and Chuang (2011)explore the critical factors of such behavior and “combined the theories of social capital and individual motivation to investigate the factors influencing sharing behavior in a virtual community, applying a participant involvement concept to analyze the moderating effects of individual motivation on knowledge sharing behavior”.

Why people share

As mentioned above, knowledge is described as a competitive advantage in a knowledge-intensive economy. Sharing knowledge so that the recipient may perform their job or task better is assumed to increase overall organizational performance. Knowledge sharing, then, is at the “core of continuous improvement processes and is quintessential in terms of transforming an individual’s process improvements into actual learning” (Yu, Lu et al.). Knowledge creation is directed towards more informal knowledge sharing activities within communities of practice (Yu, Lu et al. 2010). Moreover, knowledge that is embedded within a community is conceptualized as the ‘‘social practice of knowing”, which includes the routines and commonly shared languages of a community (Wasko &Faraj, 2005 as cited by Yu, Lu et al. (2010).

Social Capital Theory (SCT) may provide some insight as to why people will choose to share at all. Communities can provide large amounts of insights, knowledge, and expertise networking (see the discussions regarding Markus’ study above) but “…there is no guarantee that they will share their knowledge without expecting a return. Individuals contribute knowledge only if their expected benefits overweigh the costs. In addition, spontaneous knowledge sharing behavior can be regarded as organizational citizenship behavior” (Chang and Chuang 2011).

Social Capital

Some of this sharing behavior could be attributed to the aggregation of social capital. As Coleman (1988) notes,

“Social capital is defined by its function. It is not a single entity but a variety of different entities, with two elements in common: they all consist of some aspect of social structures, and they facilitate certain actions of actors-whether persons or corporate actors-within the structure. Like other forms of capital, social capital is productive, making possible the achievement of certain ends that in its absence would not be possible. Like physical capital and human capital, social capital is not completely fungible but may be specific to certain activities. A given form of social capital that is valuable in facilitating certain actions may be useless or even harmful for others”.

(Coleman 1988)

Nahapiet and Ghoshal (1998) suggest that social capital theory has at its core the concept that networks of relationships constitute a valuable resource for the conduct of social affairs, affording networked members with “the collectivity- owned capital, a ‘credential’ which entitles them to credit, in the various senses of the word” (Bourdieu, 1986: 249 as cited by Nahapiet and Ghoshal (1998). Additional resources are afforded through the contacts or connections networks bring, such as inclusion within a virtual community. Community members can gain privileged access to information and to opportunities. Finally, significant social capital in the form of social status or reputation can be derived from membership in specific networks, particularly private networks (Coleman 1988).

However, “…social capital is productive, making possible the achievement of certain ends that in its absence would not be possible… a group within which there is extensive trustworthiness and extensive trust is able to accomplish much more than a comparable group [lacking] that trustworthiness and trust” (Coleman 1988). Social capital is intangible as it exists within the relationships between people. This does not fully explain the motivation to share, merely the benefits that accrue from belonging to a social group whereby the norm is to share information. It could be asserted that people would expect to obtain some benefit (social capital, which could be later used to influence a group behavior or outcome) but this does not seem to always be the case “…recent empirical studies of behavioral economists and psychologists reveal by contrast that people base their trust decisions on motivations unrelated to the utility maximization” (Berg et al., 1995, cited in Andrei and Zait (2014). Virtual communities provide a mechanism and reinforcing function to foster the behavioral norm to share information.

Virtual communities

Virtual communities are an online form of a community of practice. They provide the user the ability to interact, to share information, and to establish a networking relationship (Hara 2009). Knowledge is accrued by integrating information, experience, and theory. Tacit knowledge can only be shared face-to-face or other interpersonal means; explicit knowledge can be delivered by technology or encoded into organizational processes (Markus 2001). “Individuals will participate in a virtual community to share or exchange knowledge if the personal perceived benefit outweighs the perceived loss of valuable knowledge” (Chang and Chuang 2011).

“Virtual communities are developed around affinities, shared interests, professional disciplines, common practice, and values. They are formed and evolve through the participation and interaction of participants. A virtual community is totally different from a conventional one; participants interact with each other via a communication system instead of face to face. In addition, communities do not have concrete reward systems to foster knowledge sharing; therefore, individual motivation is necessary to sustain participation” .

(Chang and Chuang 2011)

A virtual community is a social network and a community of practice (or community of interest) … participants in a virtual community have common interests and characteristics that lead them to interact with each other regularly via cyberspace. A key item that is lacking from a virtual community [of practice] as compared to a face-to-face community of practice is the cultural knowledge typically found in a community of practice (Hara 2009). This may be an issue for younger members of an online community compared to experienced members and professionals who have already internalized cultural knowledge as they gathered real-world experience.

Trust in Sharing

A similarity exists between consumer perceptions and judgments regarding companies and how they judge individuals; that “people are hard-wired to instantly detect intentions of others (warmth) and secondly to assess the ability they seem to possess of carrying out those intentions (competence)” (Andrei and Zait 2014). In other words, consumers judge a brand’s ‘warmth’ before they judge the brand’s ability to execute (competence). “In this vein, we can ascribe various reasons to individual behaviors in sharing knowledge and information as a form of acquiring social capital, which could be potentially leveraged later for some gain” (Andrei and Zait 2014).

“Corroborating the aforementioned idea that trust is incorporated in every commercial transaction (Arrow, 1972; cited in Andrei 2014), with the recent results of behaviorists stating that spontaneous emotions, coming from intuition, influence the decision to trust to a greater extent than rational considerations (Dunning et al., 2012; again cited in Andrei 2014), we are tempted to believe that: (1) consumer propensity to establish relationships with companies (commercial transactions) would depend on trust, and (2) the positive emotions towards company would influence decision to trust it to a greater extent than rational evaluations”.

(Andrei and Zait 2014)

Customers may choose to form their own community of interest for a given brand or participate in a corporate-sponsored community. Such communities foster information-sharing, vendor evaluation and comparison, even application development. Such communities are intended to engender trust with the brand and foster repeat purchases. While not a direct correlation to improved organizational performance, organizations hope to have increased satisfactory usage of their products, leading to increased sales and profits (Tanner Jr and Shipp 2005).

Fostering the determinants of success within an organization

Organizations have special facilities for creating and sharing tacit knowledge; as well as organizing principles by which expertise is communicated and structured. Additionally, organizations have the ability to encourage and encode into the culture various communication mechanisms, such as communities of practice and communities of interest (Nahapiet and Ghoshal 1998). They attempt to address the gap with Social Capital Theory (SCT). Organizations have the ability to encode tacit knowledge into the form of a best practice, thus creating a superior process to alternative processes. However, this process may be lacking in the contextual descriptions that allow newcomers to rapidly understand and internalize the real-world knowledge that has been so encoded (Hara 2009). (See Virtual communities, above).

Embedded / Encoded knowledge

Organizations can be very effective with their knowledge management processes when the facilitate internal knowledge transfer while minimizing or blocking external knowledge from leaking out. Embedding knowledge into the “subnetworks” that involve people minimizes the probability of external knowledge transfer because the context of the knowledge as transferred will be different and therefore less likely to stick (See Szulanski). As pointed out in the virtual community discussions previously, selection, socialization, and training make people more similar within a firm. Therefore, the subnetworks within a firm are more likely to be mutually compatible and foster knowledge transfer and translation within the firm than transplanting that knowledge into an external subnetwork (Argote and Ingram 2000). This makes the competitive advantage more likely to be a long-term advantage and one not easily replicated.

“Best practices are a replication of an internal practice that is performed in a superior way to internal alternated practices and known alternatives outside the company. The practice refers to the organization routine use of knowledge and often has tacit component embedded (Szulanski (1996) as cited by Doumit). The widespread terminology of “best” practice however can easily lead to confusion; it is not so much a universal practice, but rather a localized one related to a specific context”.

(Doumit, Huet et al. 2013)

Companies rely on experienced users to rationalize, document, and disseminate best practices. To generate and validate this type of corporate knowledge, a network of colleagues is used to develop the practice and follow a standard verification and validation process. Although best practices are quite obviously a core aspect of corporate knowledge, creating them is time-consuming and their adoption across projects, departments, and even the entire organization can be erratic. (Szulanski 1996, Doumit, Huet, et al. 2013).

Conclusion

This paper has reviewed literature across the spectrum of knowledge management, best practice creation, communities of practice, theories of knowledge reuse, and various empirical studies in an attempt to outline the necessary organizational factors which influence the success or failure of adoption of various knowledge management techniques, processes, and technologies. Knowledge management is not a unified phenomenon but rather a set of varied actors, processes, influences, and motivations. Studying knowledge management at a microscopic level of detail does not facilitate macroscopic understanding. This paper agrees with the position established by Choi and Lee (2003) that knowledge management is cross-functional and multi-disciplinary; therefore a systems theory-based approach to considering the problems and issues is necessary. Many factors can influence a firm’s success or failure, therefore any effort to associate cause and effect to knowledge is risky. Traditional measures of success or failure, such as sales, profit, Return on Investment (ROI), Share of Market, etc. may not be the appropriate measures against which to measure the degree to which knowledge management has influenced an organization. Factors such as development agility, new product introduction rates, customer satisfaction rates, employee turnover versus ‘knowledge leakage’ are opportunities for further study. This review did not include discussion of the effects of trust factors in weak ties and partner/technology alliances, which also should be measures to consider in organizational ‘success’. Today’s high tech, rapidly changing, knowledge-intensive environments are no longer the purview of single organizations but of entire ecosystems of original equipment manufacturers (OEMs), value-added resellers (VARs), service partners, and integration partners. For pure services ecosystems such as cloud-based SaaS (Software-as-a-Service) providers, such integrations are key to their success. Documentation of the system interconnects and allowing customers to extend these integration points has become a new value proposition in the buy/build decision process. Such ecosystems would provide a rich body of knowledge-intensive environments to study.

Bibliography

Andrei, A. G. and A. Zait (2014). Branding insights: an interdisciplinary journey from perception to action. Strategica: Management, Finance, and Ethics. C. Bratianu, A. Zbuchea, F. Pinzaru and E. M. Vatamanescu. Bucharest, Tritonic Publ House: 593-604.

Argote, L. and P. Ingram (2000). “Knowledge transfer: A basis for competitive advantage in firms.” Organizational Behavior and Human Decision Processes 82(1): 150-169.

Chang, H. H. and S.-S. Chuang (2011). “Social capital and individual motivations on knowledge sharing: Participant involvement as a moderator.” Information & Management 48(1): 9-18.

Choi, B. and H. Lee (2003). “An empirical investigation of KM styles and their effect on corporate performance.” Information & Management 40(5): 403-417.

Chui, M., J. Manyika, J. Bughin, R. Dobbs, C. Roxburgh, H. Sarrazin, G. Sands and M. Westergren (2012). “The social economy: Unlocking value and productivity through social technologies.” McKinsey Global Institute: 1-184.

Coleman, J. S. (1988). “SOCIAL CAPITAL IN THE CREATION OF HUMAN-CAPITAL.” American Journal of Sociology 94: S95-S120.

Connelly, C. E., D. Zweig, J. Webster and J. P. Trougakos (2012). “Knowledge hiding in organizations.” Journal of Organizational Behavior 33(1): 64-88.

Doumit, N., G. Huet and C. Fortin (2013). The Role of Enterprise Social Media in the Development of Aerospace Industry Best Practices. Product Lifecycle Management for Society. A. Bernard, L. Rivest and D. Dutta. 409: 356-364.

Hansen, M. T. (1999). “The Search-Transfer Problem: The Role of Weak Ties in Sharing Knowledge across Organization Subunits.” Administrative Science Quarterly 44(1): 82-111.

Hara, N. (2009). Communities of Practice Fostering Peer-to-Peer Learning and Informal Knowledge Sharing in the Work Place. Berlin, Berlin, Heidelberg : Springer Berlin Heidelberg.

Koll, M. (2000). “Track 3: Information Retrieval.” Bulletin of the American Society for Information Science and Technology 26(2): 16-18.

Lee, H. and B. Choi (2003). “Knowledge management enablers, processes, and organizational performance: An integrative view and empirical examination.” Journal of Management Information Systems 20(1): 179-228.

Lin, M., S. Hung and C. Chen (2009). “Fostering the determinants of knowledge sharing in professional virtual communities.” Computers in Human Behavior 25(4): 929-939.

Markus, M. L. (2001). “Toward a theory of knowledge reuse: Types of knowledge reuse situations and factors in reuse success.” Journal of Management Information Systems 18(1): 57-93.

Nahapiet, J. and S. Ghoshal (1998). “Social capital, intellectual capital, and the organizational advantage.” Academy of Management Review 23(2): 242-266.

Szulanski, G. (1996). “Exploring internal stickiness: Impediments to the transfer of best practice within the firm.” Strategic Management Journal 17(S2): 27-43.

Tanner Jr, J. F. and S. Shipp (2005). “Sales technology within the salesperson’s relationships: A research agenda.” Industrial Marketing Management 34(4): 305-312.

Yu, T., L. Lu and T. Liu (2010). “Exploring factors that influence knowledge sharing behavior via weblogs.” Computers in Human Behavior 26(1): 32-41.

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