Peer-Reviewed Journal Details
Mandatory Fields
Whelan, E,Teigland, R
Information and Organization
Transactive memory systems as a collective filter for mitigating information overload in digitally enabled organizational groups
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Optional Fields
Information overload Collective behavior Transactive memory systems Web technologies ICT Social networks Social network analysis KNOWLEDGE COMMUNICATION PERFORMANCE TECHNOLOGY BOUNDARY EXPLORATION NETWORKS MODEL
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 stud, 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. (C) 2013 Elsevier Ltd. All rights reserved.
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