微信群中隐性知识传播模型研究
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摘要
微信群已成为隐性知识传播的重要平台,但鲜有研究探讨微信群中隐性知识的传播规律。本文基于复杂网络与传播动力学理论研究了微信群中隐性知识的传播机理,建立了微信群中隐性知识的传播模型,推导出了区分隐性知识在微信群中传播与否的阈值条件,验证了传播阈值始终是一个有限数,并对隐性知识传播过程进行了数值模拟。结果表明,微信群的网络结构对隐性知识传播有重要影响,隐性知识在无标度网络中的传播速度比在均匀网络中更快,传播阈值与最终传播规模更大。研究还表明,只要根据阈值条件将有共享意愿的用户数量与有知识吸收能力的用户数量增加到相应的临界值以上,隐性知识就会在微信群中传播。最后对研究结论和未来研究方向进行了讨论。
Tacit knowledge has become the most important strategic resource for enterprises to obtain sustainable competitive advantage in the era of knowledge economy driven by information technology. Knowledge management theory believes that the transmission of tacit knowledge is an effective way to acquire and innovate knowledge for employees in the enterprise. The construction of knowledge in the organization relies heavily on the propagation of tacit knowledge between demanders and users considering characteristics of organizational cognition. A few new propagation paths begin to emerge with the rapid development of information and communication technology. We Chat group has become an important platform for tacit knowledge transmission in an enterprise and among enterprises. We Chat group is a virtual social platform that uses social network technology to achieve efficient communication and collaboration among employees within the enterprise. It eliminates the obstacles of time and space. We Chat group gathers employees together in a virtual way to communicate and mine the ideas of the group. It also realizes the online communication and dissemination of knowledge in the enterprise. Most of the users in We Chat group are employees within the same enterprise compared with weak online social networks such as Weibo. The users in a We Chat group have a higher sense of community identity and more trust among them. Their communication is more active and direct. A We Chat group gradually forms a cultural circle with a clear topic. Therefore,the knowledge transfer in the We Chat group is more interactive and efficient. The employees are more likely to get work-related tacit knowledge in their We Chat group. Furthermore,employees can understand the social network and knowledge of other employees by observing the topics and commenters of the exchanges in We Chat group. It can pro-mote the recognition and retrieval of knowledge by employees. It also improves the efficiency of knowledge dissemination. In summary,We Chat group has become an important channel for tacit knowledge dissemination within enterprises. However,few studies have explored the law and characteristics of tacit knowledge spreading in We Chat Group. In-depth study of the mechanism of tacit knowledge transmission in We Chat group is of great significance for the realization of knowledge dissemination within the enterprise. In view of this,this study constructs the model of tacit knowledge propagation in We Chat group based on the theory of complex network propagation dynamics. We study the propagation mechanism of tacit knowledge in We Chat group and explores the corresponding promotion strategy of tacit knowledge transmission from the perspective of complex network. We analyze the established dynamic model and the threshold conditions for distinguishing whether a kind of tacit knowledge propagates in a We Chat group are derived. The transmission threshold R0 in this study is identical to the basic reproductive number in infectious disease dynamics. The tacit knowledge transmission threshold indicates that when all users in the We Chat group are users without tacit knowledge,the maximum number of other new users with this tacit knowledge by a knowledge owner in the average time required to forget the knowledge at the initial moment of tacit knowledge propagation. When R0> 1,the tacit knowledge will propagate in the We Chat group; when R0< 1,this tacit knowledge gradually disappears in the We Chat group. This study concludes that the knowledge transmission threshold is always a finite number by analyzing the influence of the degree distribution of the We Chat group on the propagation threshold. The following conclusions can be drawn from our research:( 1) The threshold is always a finite number related to the user's willingness to share knowledge and the ability to absorb knowledge. It is also related to the average and the second moment of the network. It can be seen from the above conclusion that all users in the We Chat group do not need to have tacit knowledge sharing intention or tacit knowledge absorption ability at the same time. Tacit knowledge will spread in the We Chat group as long as the number of tacit knowledge owners with shared willingness and the number of absorbing tacit knowledge recipients at the same time reach the corresponding critical values. Therefore,the administrator can realize the sharing of tacit knowledge in the We Chat group in a shorter time and at a lower cost according to the threshold condition.( 2) Results also show that the network structure of We Chat group has a significant impact on tacitknowledgetransmission. The topology of the online social network composed of users' communication in We Chat group has an important impact on tacit knowledge transmission.First,increasing the ratio of the second order moment to the average degree of the network can increase the threshold. It can lead to that tacit knowledge spreads faster in a scale-free network and the final scale of transmission are larger. The spreading speed is faster in scale-free network than in homogeneous network. The final size and the transmission threshold are larger in scale-free network than in homogeneous network. Therefore,if the difference between the number of communication times in unit time among the users is greater and then the final propagation scale is larger and the propagation speed is faster in the We Chat group.It can be seen from the above conclusion that appropriately changing the number of exchanges in the user's unit time can promote the spread of tacit knowledge in the We Chat group. Managers can select some users with more average communication times per unit time and encourage them to interact with other users. It can increase the difference between the number of exchanges in unit time among the users. That is,the heterogeneity of the network degree distribution is increased. It can increase the final scale of tacit knowledge dissemination in We Chat groups and reduce the time to reach the final scale of knowledge transmission.( 3) It is concluded that the number of knowledge owners in the We Chat group at the initial time of propagation is not a key factor affecting the knowledge transmission in the We Chat group. Therefore,managers do not have to blindly introduce knowledge owners from other companies to promote the knowledge transmission in the We Chat group by increasing the number of knowledge owners.
Tacit knowledge has become the most important strategic resource for enterprises to obtain sustainable competitive advantage in the era of knowledge economy driven by information technology. Knowledge management theory believes that the transmission of tacit knowledge is an effective way to acquire and innovate knowledge for employees in the enterprise. The construction of knowledge in the organization relies heavily on the propagation of tacit knowledge between demanders and users considering characteristics of organizational cognition. A few new propagation paths begin to emerge with the rapid development of information and communication technology. We Chat group has become an important platform for tacit knowledge transmission in an enterprise and among enterprises. We Chat group is a virtual social platform that uses social network technology to achieve efficient communication and collaboration among employees within the enterprise. It eliminates the obstacles of time and space. We Chat group gathers employees together in a virtual way to communicate and mine the ideas of the group. It also realizes the online communication and dissemination of knowledge in the enterprise. Most of the users in We Chat group are employees within the same enterprise compared with weak online social networks such as Weibo. The users in a We Chat group have a higher sense of community identity and more trust among them. Their communication is more active and direct. A We Chat group gradually forms a cultural circle with a clear topic. Therefore,the knowledge transfer in the We Chat group is more interactive and efficient. The employees are more likely to get work-related tacit knowledge in their We Chat group. Furthermore,employees can understand the social network and knowledge of other employees by observing the topics and commenters of the exchanges in We Chat group. It can pro-mote the recognition and retrieval of knowledge by employees. It also improves the efficiency of knowledge dissemination. In summary,We Chat group has become an important channel for tacit knowledge dissemination within enterprises. However,few studies have explored the law and characteristics of tacit knowledge spreading in We Chat Group. In-depth study of the mechanism of tacit knowledge transmission in We Chat group is of great significance for the realization of knowledge dissemination within the enterprise. In view of this,this study constructs the model of tacit knowledge propagation in We Chat group based on the theory of complex network propagation dynamics. We study the propagation mechanism of tacit knowledge in We Chat group and explores the corresponding promotion strategy of tacit knowledge transmission from the perspective of complex network. We analyze the established dynamic model and the threshold conditions for distinguishing whether a kind of tacit knowledge propagates in a We Chat group are derived. The transmission threshold R0 in this study is identical to the basic reproductive number in infectious disease dynamics. The tacit knowledge transmission threshold indicates that when all users in the We Chat group are users without tacit knowledge,the maximum number of other new users with this tacit knowledge by a knowledge owner in the average time required to forget the knowledge at the initial moment of tacit knowledge propagation. When R0> 1,the tacit knowledge will propagate in the We Chat group; when R0< 1,this tacit knowledge gradually disappears in the We Chat group. This study concludes that the knowledge transmission threshold is always a finite number by analyzing the influence of the degree distribution of the We Chat group on the propagation threshold. The following conclusions can be drawn from our research:( 1) The threshold is always a finite number related to the user's willingness to share knowledge and the ability to absorb knowledge. It is also related to the average and the second moment of the network. It can be seen from the above conclusion that all users in the We Chat group do not need to have tacit knowledge sharing intention or tacit knowledge absorption ability at the same time. Tacit knowledge will spread in the We Chat group as long as the number of tacit knowledge owners with shared willingness and the number of absorbing tacit knowledge recipients at the same time reach the corresponding critical values. Therefore,the administrator can realize the sharing of tacit knowledge in the We Chat group in a shorter time and at a lower cost according to the threshold condition.( 2) Results also show that the network structure of We Chat group has a significant impact on tacitknowledgetransmission. The topology of the online social network composed of users' communication in We Chat group has an important impact on tacit knowledge transmission.First,increasing the ratio of the second order moment to the average degree of the network can increase the threshold. It can lead to that tacit knowledge spreads faster in a scale-free network and the final scale of transmission are larger. The spreading speed is faster in scale-free network than in homogeneous network. The final size and the transmission threshold are larger in scale-free network than in homogeneous network. Therefore,if the difference between the number of communication times in unit time among the users is greater and then the final propagation scale is larger and the propagation speed is faster in the We Chat group.It can be seen from the above conclusion that appropriately changing the number of exchanges in the user's unit time can promote the spread of tacit knowledge in the We Chat group. Managers can select some users with more average communication times per unit time and encourage them to interact with other users. It can increase the difference between the number of exchanges in unit time among the users. That is,the heterogeneity of the network degree distribution is increased. It can increase the final scale of tacit knowledge dissemination in We Chat groups and reduce the time to reach the final scale of knowledge transmission.( 3) It is concluded that the number of knowledge owners in the We Chat group at the initial time of propagation is not a key factor affecting the knowledge transmission in the We Chat group. Therefore,managers do not have to blindly introduce knowledge owners from other companies to promote the knowledge transmission in the We Chat group by increasing the number of knowledge owners.
引文
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[10]王文平,张兵.动态关系强度下知识网络知识流动的涌现特性[J].管理科学学报,2013,16(2):01-11.Wang Wenping,Zhang Bing. Emergence characteristics of knowledge flow in knowledge networks under dynamic relationship strengths[J]. Journal of Management Sciences inChina,2013,16(2):01-11.
[11]席运江,党延忠,廖开际.组织知识系统的知识超网络模型及应用[J].管理科学学报,2009,12(3):12-21.Xi Yunjiang,Dang Yanzhong,Liao Kaiji. Knowledge super network model and its application in organizational knowledge systems[J]. Journal of Management Sciences in China,2009,12(3):12-21.
[12] Kim H,Park Y. Structural effects of R&D collaboration network on knowledge diffusion performance[J]. Expert Systems with Applications,2009,36(5):8986-8992.
[13] Cowan R,Jonard N,¨Ozman M. Knowledge dynamics in a network industry[J]. Technological Forecasting&Social Change,2004,71(5):469-484.
[14] Cowan R,Jonard N. Network structure and the diffusion of knowledge[J]. Journal of Economic Dynamics&Control,2004,28(8):1557-1575.
[15] Zhu H M,Zhang S T,Jin Z. The effects of online social networks on tacit knowledge transmission[J]. Physica A Statistical Mechanics&Its Applications,2015,441:192-198.
[16]张薇,徐迪.动态知识网络上的知识积累过程模型[J].管理科学学报,2014,17(11):122-128.Zhang Wei,Xu Di. Modeling knowledge accumulation on the dynamic complex network[J]. Journal of Management Sciences in China,2014,17(11):122-128.
[17] Tuomi I. Data is more than knowledge:Implications of the reversed hierarchy for knowledge management and organizational theory[J]. Journal of Management Information Systems,1999,16(3):103-117.
[18] Huo H F,Wang Y Y. Impact of media coverage on the drinking dynamics in the scale-free network[J]. Springerplus,2016,5(1):01-16.
[19] Zhang J P,Jin Z. The analysis of an epidemic model on networks[J]. Applied Mathematics&Computation,2011,217(17):7053-7064.
[20] Kalisky T,Cohen R,Ben-Avraham D,et al. Tomography and stability of complex networks[J]. Lecture Notes in Physics,2007,650:03-34.
[21] Burda Z,Krzywicki A. Uncorrelated random networks.[J].Physical Review E Statistical Nonlinear&Soft Matter Physics,2002,67(2):046118.
[2] Polanyi M. The logic of tacit inference[J]. Philosophy,1966,41(155):1-18.
[3] Nonaka I. A dynamic theory of organizational knowledge creation.[J]. Organization Science,1994,5(1):14-37.
[4]王亚洲,林健.人力资源管理实践、知识管理导向与企业绩效[J].科研管理,2014,35(2):136-144.Wang Yazhou,Lin Jian. Human resource management practices,knowledge management orientation and firm performance[J]. Science Research Management,2014,35(2):136-144.
[5]赵玉林.机理与应对:微信条件下群体性事件的扎根分析[J].情报杂志,2018(3).Zhao Yulin. Mechanism and countermeasures:We Chat group events analyzed by grounded theory[J]. Journal of Intelligence,2018(3).
[6]卞娜.大学生微信群的人际传播研究——以北京某高校为例[J].中国社会科学院研究生院学报,2017(3):138-144.Bian Na. College students’ interpersonal communication through We Chat groups:A case study in a Beijing-based university[J]. Journal of Graduate School of Chinese Academy of Social Sciences,2017(3):138-144.
[7]蒋建国.微信群:议题、身份与控制[J].探索与争鸣,2015,31(11):108-112.Jiang Jianguo. We Chat group:Issues,identity and control[J]. Exploration and Free Views,2015,31(11):108-112.
[8]巴志超,李纲,王晓等.微信群内部的会话网络结构及关键节点测度研究[J].图书情报工作,2017,61(20):111-119.Ba Zhichao,Li Gang,Wang Xiao,et al. Research on session network structure and key node measure in We Chat group[J]. Library and Information Service,2017,61(20):111-119.
[9]王芳,翟羽佳.微信群社会结构及其演化:基于文本挖掘的案例分析[J].情报学报,2016,35(6):617-629.Wang Fang,Zhai Yujia. Social structure and evolvement of We Chat groups:A case study based on text mining[J]. Journal of the China Society for Scientific and Technical Information,2016,35(6):617-629.
[10]王文平,张兵.动态关系强度下知识网络知识流动的涌现特性[J].管理科学学报,2013,16(2):01-11.Wang Wenping,Zhang Bing. Emergence characteristics of knowledge flow in knowledge networks under dynamic relationship strengths[J]. Journal of Management Sciences inChina,2013,16(2):01-11.
[11]席运江,党延忠,廖开际.组织知识系统的知识超网络模型及应用[J].管理科学学报,2009,12(3):12-21.Xi Yunjiang,Dang Yanzhong,Liao Kaiji. Knowledge super network model and its application in organizational knowledge systems[J]. Journal of Management Sciences in China,2009,12(3):12-21.
[12] Kim H,Park Y. Structural effects of R&D collaboration network on knowledge diffusion performance[J]. Expert Systems with Applications,2009,36(5):8986-8992.
[13] Cowan R,Jonard N,¨Ozman M. Knowledge dynamics in a network industry[J]. Technological Forecasting&Social Change,2004,71(5):469-484.
[14] Cowan R,Jonard N. Network structure and the diffusion of knowledge[J]. Journal of Economic Dynamics&Control,2004,28(8):1557-1575.
[15] Zhu H M,Zhang S T,Jin Z. The effects of online social networks on tacit knowledge transmission[J]. Physica A Statistical Mechanics&Its Applications,2015,441:192-198.
[16]张薇,徐迪.动态知识网络上的知识积累过程模型[J].管理科学学报,2014,17(11):122-128.Zhang Wei,Xu Di. Modeling knowledge accumulation on the dynamic complex network[J]. Journal of Management Sciences in China,2014,17(11):122-128.
[17] Tuomi I. Data is more than knowledge:Implications of the reversed hierarchy for knowledge management and organizational theory[J]. Journal of Management Information Systems,1999,16(3):103-117.
[18] Huo H F,Wang Y Y. Impact of media coverage on the drinking dynamics in the scale-free network[J]. Springerplus,2016,5(1):01-16.
[19] Zhang J P,Jin Z. The analysis of an epidemic model on networks[J]. Applied Mathematics&Computation,2011,217(17):7053-7064.
[20] Kalisky T,Cohen R,Ben-Avraham D,et al. Tomography and stability of complex networks[J]. Lecture Notes in Physics,2007,650:03-34.
[21] Burda Z,Krzywicki A. Uncorrelated random networks.[J].Physical Review E Statistical Nonlinear&Soft Matter Physics,2002,67(2):046118.