The optimal feasible knowledge transfer path in a knowledge creation driven team
Introduction
In the context of knowledge-based economy, knowledge matters more than ever. It can be considered as a dominant resource among competitive firms [1]. Strategic alliances, outsourcing, and globalization imply knowledge transfer across organizational, cultural, and national boundaries. However, participants often have insufficient background information of each other and lack a shared language and common interests, which significantly limits their ability to assess and share knowledge [2]. So, transferring knowledge has become an especially important factor in determining the success of alliances [3]. Active knowledge transfer among employees enables a firm to make the best use of its internal knowledge, fostering its survival and prosperity [4], [5]. Compared to the independent management of knowledge transfer projects, managing and integrating inter-organizational knowledge transfer at the firm level have advantages because, in that context, knowledge can be transferred at lower cost and higher quality, compared to independently managed knowledge transfer projects [6].
Knowledge transfer accelerates knowledge flow and drives knowledge creation. Knowledge transfer among connected users is a process of connecting knowledge transmitters with knowledge receivers; the quality and costs of knowledge determine the effect and efficiency of the knowledge transfer. Scholars have focused more on the process of knowledge dissemination or knowledge transfer and their applications in a certain context, rather than on how to make knowledge transfer in team more effectively through mathematic models. Liyanage et al. [7] built a knowledge transfer multi-stage model based on related knowledge communication and translation at different knowledge changing stages. Guechtouli et al. [8] studied knowledge transfer combining an agent-model and social network analysis, identifying the key elements in an organization. Lin et al. [9] proposed a sender–receiver framework for the study of knowledge transfer under asymmetric conditions and incomplete information. Del Giudice et al. [10] estimated knowledge sharing technology and its future trends through data analysis. Sarala et al. [11] suggested that inter-firm linkages between the merging firms influence the level of knowledge transfer in merge and acquisitions by time series data from social media platforms. Ahammad et al. [12] indicated that knowledge transfer and employee retention have positive influence on cross-border acquisition. Georgescu and Popescul [13] found that global innovative networks are different in various regions, some of which include knowledge core connection nodes. Such nodes can disseminate knowledge by building connections anywhere around the world at any time. Li and Liu [14] explored the retweeting behavior patterns to understand online information diffusion. Zhao et al. [15] presented an approach called the knowledge gap based recommendation to bridge the knowledge gap between a researcher’s background knowledge and research target in knowledge transfer. Identifying effective and feasible knowledge transfer paths is time consuming resource intensive at the human level; ineffective knowledge transfer means that resources are wasted. Therefore, how can a team achieve the most effective knowledge transfer? This paper explores the optimal knowledge transfer path in knowledge dissemination with lower costs and higher quality.
Previous research on measuring knowledge transfer mainly used four approaches [16], such as through survey questions [17], archival data [18] or data set [19] to assess relationship, indirect changes and using patent citations. The first is to obtain multiple responses on surveys to measure knowledge transfer in different units. The second approach is to evaluate whether a relationship exists between units based on archival data. A third approach to measure knowledge transfer is to measure changes in this unit associated with the experience of another. For example, Kane et al. [20] showed that personnel mobility resulted in changes in the routines of recipient units. The fourth approach tracks knowledge transfer with patent citations. Knowledge transfer is measured by the recipient’s citing patents [21], [22], [23]. However, the approaches mentioned above had limitations, which were not able to articulate the process of knowledge transfer, including individuals’ absolving, imparting, transferring and recreating knowledge.
The most valuable contributions of this paper can be summarized as follows:
- (1)
Knowledge transfer model in a three-dimensional space. The purposed model is built according to individual’s knowledge potential energy and their relationships, knowledge transfer weighted directed graph was built based on it. The proposed model was put into practice in a knowledge-driven team.
- (2)
The optimal knowledge transfer path solving algorithm. Individuals’ knowledge levels measurement model was built to measure individuals’ knowledge levels in each iteration of knowledge transfer. Evaluation rates and principles are devised to choose the optimal knowledge transfer path.
To the best of our knowledge, our work is the first to find the optimal feasible knowledge transfer path in a team to reallocate human resources effectively. We build a mathematical model by measuring individual’s knowledge level.
Section snippets
Knowledge transfer
Knowledge is intangible, boundaryless and dynamic [24]; it is generally regarded as something (a resource, an object, a potential) to be shared [25], and it changes when new information is introduced. Davenport and Prusak [26] define knowledge as “a fluid mix of framed experience, contextual information, values and expert insight that provides a framework for evaluating and incorporating new experiences and information”. Therefore, knowledge is context-specific. Knowledge transfer is defined as
Model of knowledge transfer in a creative team
Knowledge dissemination, with its autonomy and diffusion, presents a one-to-many style. Correspondingly, knowledge transfer is targeted and presents a one-to-one style. Thus, knowledge transfer can be defined as a microcosmic representation of knowledge dissemination, which is composed of several processes of knowledge transfer. In the paper, the model merely discusses the knowledge transfer in the limited and specific area, where only one variable is under consideration in one specific
Experimental evaluation
To evaluate the knowledge transfer optimal feasible path model in a knowledge creation team, we investigated and interviewed a knowledge creation team and then applied the proposed model with the collected data to find the optimal feasible path, which allocates resources in a more rational way.
Conclusion
One of primary features of the proposed approach is its ability to accurately find an optimal feasible path for knowledge transfer in a team. To find the optimal feasible knowledge transfer path, we had to obtain information about the team members’ knowledge potential energy in three-dimensions, as well as their abilities (absorbing, transferring, imparting and recreating) and mutual connections. The measurement of individual knowledge potential energy in knowledge transfer was a complex and
Acknowledgments
This work was partially supported by the National Natural Science Foundation of China (NSFC, Project No. 71871005, 71571191, 71531001).
Xiangyu Wang is currently a Ph.D. student in the Interdisciplinary Graduate Program in Informatics at the University of Iowa. She received the master degree from the School of Economics and Management at Beihang University in 2017. Her research interests include data mining on social media, predictive modeling and knowledge management.
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Cited by (0)
Xiangyu Wang is currently a Ph.D. student in the Interdisciplinary Graduate Program in Informatics at the University of Iowa. She received the master degree from the School of Economics and Management at Beihang University in 2017. Her research interests include data mining on social media, predictive modeling and knowledge management.
Jun Wang received the Ph.D. degree in management sciences from Northeastern University, China, in 2003. He is currently a professor in the Department of Information Systems, Beihang University, Beijing, China. He is the author or coauthor of more than 30 papers published in international journals. His current research interests include knowledge management, knowledge systems engineering, business intelligence and decision analysis.
Ruilin Zhang is a currently Ph.D. student from Beihang University in Peking, China. Her current research interests focus on knowledge management, knowledge collaboration and innovation and knowledge transfer.