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Whelan E.,Business Information Systems Group | Teigland R.,Stockholm School of Economics | Vaast E.,McGill University | Butler B.,University of Maryland University College
Information and Organization | Year: 2016

The study of social networks has attracted much interest from the IS community in recent years, driven mainly by the accessibility of trace data that remain as a by-product of interactions conducted through technology-enabled platforms. Despite its rapidly growing influence, we have some concerns about the current trajectory of social network research in the IS field. Our purpose in this commentary piece is to accentuate for the new generation of social network researchers, who are au fait with mathematical techniques for analyzing massive digital datasets, how the combination of quantitative and qualitative approaches can enrich our understanding of networks. First we highlight how the social network perspective has contributed to our understanding of IS phenomena. Next we review mixed methods research in IS social network research. An agenda for future IS social network research is then presented where we suggest how qualitative approaches can best complement trace data in addressing focal social network questions. We conclude by discussing the challenges of conducting mixed method studies of digitally enabled social networks. © 2016.Elsevier Ltd. All rights reserved.

Whelan E.,Business Information Systems Group | Teigland R.,Stockholm School of Economics
Information and Organization | Year: 2013

With the increasing processing power and plummeting costs of information and communication technologies, the ability of employees to ubiquitously access and disseminate information grows. However, emerging research shows that individuals are struggling to process information as fast as it arrives. The problem of information overload is a significant one for contemporary knowledge-intensive organizations because it can adversely affect productivity, decision making, and employee morale. To combat this problem, organizations often invest in technical solutions such as business intelligence software or semantic technologies. While such technical approaches can certainly aid in reducing information overload, less attention has been directed at understanding how collective behavior, and in particular transactive memory systems, might enhance the ability of organizations to cope with information overload. In this study, we ask whether (and, if so, how do) transactive memory systems act as a collective filter to enable organizational groups to mitigate the potential for information overload. We used social network analysis and interview evidence from the R&D departments of two high-technology firms in the life science industry and found that individuals spontaneously organized without any centralized control to create a collective filter. For example, we found that one set of individuals specialized in filtering external information into the group while another set specialized in filtering that information for internal use. We conclude by discussing the theoretical and practical implications of our findings. © 2013 Elsevier Ltd.

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