CiteSeerX — Version 0: REVEALING KNOWLEDGE NETWORKS FROM COMPUTER MEDIATED COMMUNICATION IN ORGANIZATIONS

REVEALING KNOWLEDGE NETWORKS FROM COMPUTER MEDIATED COMMUNICATION IN ORGANIZATIONS

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TITLE	REVEALING KNOWLEDGE NETWORKS FROM COMPUTER MEDIATED COMMUNICATION IN ORGANIZATIONS	SVM HeaderParse 0.2
ABSTRACT	In today’s knowledge driven economy, knowledge is considered to be the key factor in defining the success of an organization. We have learned that knowledge is residing in the informal network of the organization. Hence, to improve performance, it is the informal knowledge network that should be examined and developed. For this purpose, social network analysis is increasingly applied in business contexts. This is, however, a new domain, which is still in development. This paper aims to aid in this development by researching how representative knowledge networks can be revealed in organizations. While surveying is a common first option to capture an organizational network, this technique may not always be suitable. Communication sources (e.g. e-mail) may provide an alternative, however, we do not know to what extent these sources can represent the actual knowledge network. This paper examines a Dutch IT services organization. Here, a web-survey among the employees baselines the knowledge network, which is compared to 3 communication networks from the same organization, captured by means of e-mail, telephone and SMS (Short Message Service) communication (also known as text messaging or texting). A comparison is made by means of correlating the network matrices and by comparing essential network properties. Findings show that only the e-mail network is significantly representative for the baselined knowledge network. This exercise is exploratory in nature as only one organization is examined, but comprehensive with regard to the richness of data that is available for examination. From our findings we gain insight in the extent to which networks, captured from e-mail, telephone and SMS archives can represent an organizational knowledge network.	SVM HeaderParse 0.2
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