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Mesfin Tsegaye

DBA Graduate - 2018

Thesis title

Leveraging a talent-driven collaborative electronic network of practice in a large MNE

Supervisor(s)

Vincent Mangematin
Purpose: The purpose of this paper is to examine the ‘value opportunities’ arising from the use of knowledge collaboration networks among globally distributed talents in a large MNE. The key question is to identify those areas where data visualization (social network analysis) could provide step changes in support of Knowledge Collaboration among talents and improving the Human Resource (HR) department’s practice of talent assessments and performance management to support knowledge collaboration and enhance the knowledge-sharing culture of the organisation through the digital knowledge-sharing platform SIGN.Design/Methodology/Approach: The present study is conducted on an organisation’s knowledge-sharing platform, the SIGN network, which has 10 unique forums defined on the basis of areas of expertise. The research conducts a retrospective social network analysis (SNA) in connection with HR data analytics, covering a study of data for 10 Forums using UCINET® for the year 2015. Through SNA, the collaboration patterns, density of networks and their spread across age, job-group and geography, and the results of individual performance factor (IPF) and current estimate potential (CEP) of talents, were assessed for individuals, as well as whole networks. Overall, in this study, the SNA of the SIGN network and its individual forums, with respect to the talents and top performers, as well as their well-connectedness and network performance, were analysed at the individual level (Ego behaviours), as well as at the network level (forum and entire SIGN network), in order to test several hypotheses. A survey was also conducted to determine the relationship between participation and the perception of the knowledge-sharing organizational culture within the SIGN network. Findings: Analysis showed that no relationship could be found between the knowledge-sharing behaviours of talents or their network effectiveness with their individual performance or talent assessments results. In addition, the analysis presented that Vertical boundary (seniority) and Demographic boundary (Age group) of individual participants has a certain correlation with their well-connectedness and network effectiveness within the network. These same boundary-spanning behaviours were also seen to be influenced by the performance (IPF values) and talents (CEP values) of the individuals. Moreover, the ENoP of the SIGN network was not found to be significantly influencing the knowledge-sharing culture of the organization, since no difference could be seen between individuals participating and not participating in the ENoP. However, the research identified differences in knowledge-sharing culture between eastern and western countries, with a low extent of interactions from west to east verses a high-level of interaction from east to west. Research Implications/Limitations: The research is significant in linking knowledge collaboration behaviours within a knowledge-sharing platform with demographic profile, individual performance, talent assessments, as well as organizational boundaries. Lastly, it provides insights into the significance of knowledge-sharing culture perception with collaboration and participation behaviours within a social network. The study suffers from certain limitations as the researcher had to work within the restrictive environment of not being allowed to link survey data with the forum matrix data of the individual participants, due to compliance with the anonymity and data privacy regulations of the company. As a result, the impact of organizational culture on boundary spanning and knowledge collaboration behaviours could not be linked directly with Egos but only on the forum level. Moreover, further correlation studies were lacking between participation and areas where value generation takes place within the network, including the impact of such digital social platforms on the quality of knowledge contribution, resilience and engagement q