درک مسائل کلیدی در طراحی و بکارگیری شبکه های جریان دانش : رویکرد الگوبرداری مدیریتی مبتنی بر بهینه سازی

There is an increasing recognition that knowledge can be an organization's source of competitive advantage. Hence, knowledge management (KM) has been extensively researched. Prior knowledge management research has recognized the importance of making individual knowledge available throughout the organization. Most KM research, however, has thus far focused on a technology-based KM strategy with relatively little discussion on how knowledge can be effectively shared using organizational social relationships. This paper focuses on how knowledge-intensive organizations can design and use “knowledge flow networks (KFNs)” in order to facilitate knowledge sharing. Designing and using KFNs to maximize knowledge sharing is a complex problem. We formulate a mixed integer programming model (MIP), and present a heuristic in order to facilitate systematic analysis and understanding of effective KFNs. We consider organizations that support multiple skills and have workers with varying levels of competence who are connected through IT-facilitated organizational social relationships. Our results, based on computational experiments, provide several interesting insights and intelligence into the design of an effective KFN. First, our results highlight that average workers play a vital bridging role in knowledge sharing. Second, social networking concepts of ties and cohesiveness are used to better understand the dynamics of knowledge sharing. The importance of indirect relationships between expert workers and the network effects due to indirect relationships are illustrated. For effective KM, we also illustrate how organizations can reduce the total number of ties required in a multi-skill environment. In our model extensions, we study the impact of worker turnover and knowledge depreciation on the design and use of effective KFNs. Managerial implications of these results are discussed. The model and solution procedure proposed in this paper can serve as a managerial benchmarking framework for effective management of KFNs.

There is a growing recognition that employees' knowledge is an organization's most valuable asset, particularly in knowledge-intensive environments such as consulting, research, and IT service delivery [16], [18] and [19]. Such organizations consider knowledge creation [43] and knowledge application [23] to be vital to organizational performance. Knowledge management research has recognized the concept of “a knowledge-creating company” [43]. The concept of “knowledge reservoirs” has also been proposed as a source of long-term competitive advantage [2]. Prior research has recognized that “making personal knowledge available to others is the central activity of the knowledge-creating company. It takes place continuously and at all levels of the organization” [43]. Hence, firms are increasingly investing in Knowledge Management (KM) projects expecting to improve employees' knowledge levels [22]. Most KM research has thus far focused on information technologies [13] and [15], with relatively little discussion on how knowledge can be shared effectively among employees using organizational social relationships [39]. In practice, however, organizations are finding that employees often prefer to consult their peers and colleagues (organizational social relationships) in order to acquire knowledge, rather than access electronic knowledge bases [13]. Recognizing the importance of using organizational social relationships to transfer knowledge, an increasing number of Chief Knowledge Officers (CKOs) are moving from a technological-based KM strategy to a socialization-based KM strategy [42]. Such a strategy uses organizational information flow networks (IFNs) to facilitate knowledge sharing [42]. Such IFNs use ties (or information flow connections) between individuals in order to transfer knowledge. Recent research [18] has illustrated that the structure of IFNs and associated knowledge sharing behavior significantly impact organizational performance and employees' knowledge level. Knowledge management research has only recently begun to focus on designing effective “knowledge flow networks” (KFNs) [25], [36] and [53]. In this paper, we use the term KFNs to refer to organizational IFNs that facilitate knowledge sharing. KFNs can be studied from two perspectives. First, organizations are increasingly interested in using existing KFNs effectively for KM [25] and [53]. This is the use perspective. Second, organizations are also interested in designing effective KFNs as a part of organization design initiatives [36]. Leading consulting organizations such as IBM recognize the importance of optimizing social networks in the context of KM. For example, “IBM Global Business Services — offers a social network analysis (SNA) service designed to help reveal a multitude of underlying personnel issues, such as where collaboration falls apart, where talent and expertise could be better used, where decision-making gets bogged down, and where opportunities for innovation are being lost…..While SNA can pinpoint problems and improvement opportunities, social network optimization (SNO) provides decision support for what to do next” (http://domino.watson.ibm.com/odis/odis.nsf/pages/board.13.html). Understanding effective KFNs allows organizations to compare their existing KFNs with effective KFNs. This, in turn, provides managerial guidance on what to do next. Hence, design and use of effective KFNs is an important problem, with significant real-world interest. However, academic research on this problem is limited. We focus on the following research question: how should knowledge‐intensive organizations design and use their KFNs in order to maximize employees' knowledge level (over a planning horizon) through sharing under different organizational environments? In order to answer this question, we examine organizations that support multiple skills and have workers with varying levels of knowledge in these skills. These workers are connected to each other through IT-facilitated organizational social relationships. Designing and using KFNs to maximize knowledge sharing is a complex problem. We formulate a Mixed Integer Programming Model (MIP), and present a heuristic in order to facilitate systematic analysis and understanding of the above research question. In summary, the model and solution procedure proposed in this paper can serve as a managerial benchmarking framework for effective management of KFNs. Our computational results provide several interesting insights and intelligence into the design of an effective KFN. First, our results highlight the important role of average workers. In contrast to the common practice of encouraging knowledge sharing between experts and novices, we find that most knowledge sharing happens between average and expert workers, followed by knowledge sharing between average and novice workers. Second, the value of a direct tie is significantly enhanced by indirect ties. Cohesive groups of experts allow less competent workers to access more experts through indirect ties. Such cohesive groups are less important for lower skilled worker groups. Third, we examine the impact of number of skills supported by an organization on an effective KFN. Organizations supporting multiple skills need to create direct ties between workers with complementary skill sets, particularly between experts. Such ties tend to be used extensively, which reduces the number of direct ties needed as the number of skills supported by the organization increases. In our model extensions, we study the impact of worker turnover and knowledge depreciation on the design and use of effective KFNs. We find that for effective KM, organizations need to compensate for high worker turnover and high knowledge depreciation by encouraging the creation of more direct ties and creating more cohesive groups of workers. The rest of this paper is organized as follows. Section 2 discusses related literature that serves as the foundation for our research. The mathematical model of KFNs and solution procedure to solve this model is discussed in 3 and 4, respectively. Design of the simulation-based experiments is described in Section 5. Computational results are the focus of Section 6. Section 7 discusses the performance of the proposed Heuristic. Model extensions are presented in Section 8. 9, 10 and 11 focus on managerial implications and conclusions, limitations and future research, and conclusions respectively.

“Knowledge intensive service providers are highly dependent on human workers who possess specialized knowledge and skills.” [36]. Such companies are increasingly interested organizational design for knowledge work [36]. While there is extensive prior research on using technology for storing and retrieving knowledge, the concept of knowledge sharing using organizational social relationships is relatively new. Organizational design to facilitate knowledge sharing is an under researched topic. This research aims to facilitate the design of effective KFNs. The model and results presented in this paper facilitate managerial benchmarking. Managers can use this model to understand important factors to consider when designing and using KFNs. Identifying such factors and their interrelationships allows managers to compare (benchmark) their organizations with effective KFNs and facilitates organizational change [40]. The model and solution procedure proposed in this paper can be used either as a starting point for organizational design or as a means of benchmarking existing organizations that create or apply knowledge.