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Abstract
Effective workgroups engage in team boundary spanning, that is, using communication ties as conduits to critical external resources. The proliferation of enterprise social media (ESM) and the associated increase in visibility of people, content, and interactions, has resulted in a widespread assumption that unlimited visibility improves boundary spanning. Consequently, the ESM literature has generally ignored the sentry functions of teams and failed to examine the possible strategic nature of visibility choices by ESM groups. Using log and content data from 655 ESM-based workgroups at a multinational enterprise, we contribute a deeper understanding of the distinct ways that ESM visibility—bounded or unbounded—is leveraged strategically to evoke diverse network structures, which in turn have implications for distinct boundary-spanning activities. Practically, these findings show that ESM present a unique opportunity for workgroups to simultaneously sustain multiple virtual spaces—with varying levels of visibility—through which they can manage their diverse boundary-spanning goals.
Notes
1. Please note that the actual terms used to describe these three team boundary-spanning activities by various authors differ.
3. Mean comparisons showed that visible groups displayed significantly larger group sizes (F = 38.330; p = 0.000) and significantly greater content creation (F = 6.510; p = 0.011) than invisible groups.
4. To normalize our dependent variable by two variables, we first created an “engagement” variable (by dividing the # of total content creation activities in a group by the size of the group) and then divided the number of boundary-spanning activities—representation, coordination, or information search—by the newly created engagement variable. We then rounded up the normalized variable to the nearest integer to retain a count variable.
5. The analysis results indicate that our main variables of interest are significantly zero-inflated, thereby offering further statistical support that the zero-inflated Poisson regression is the appropriate method of analysis.
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Notes on contributors
Wietske Van Osch
Wietske van Osch ([email protected]; corresponding author) is an assistant professor in the Department of Media and Information at Michigan State University. She received her Ph.D. in economics (information systems) from the University of Amsterdam’s Business School. Her research work has appeared in the Journal of Management Information Systems, Journal of Information Technology, Information and Management, and leading conferences, including the International Conference on Information Systems. She has received funding from the National Science Foundation and industry for her research on social media. Current research projects involve extensive industry collaborations with several companies, including Steelcase and Leo Burnett.
Charles W. Steinfield
Charles W. Steinfield ([email protected]) is a professor and former chair of the Department of Media and Information at Michigan State University. He received his Ph.D. in communication theory and research from the Annenberg School for Communication at the University of Southern California. He studies the social and organizational impacts of new information and communication technologies. He is an award-winning author, and editor of 7 books and anthologies and more than 130 articles published in scholarly venues, including Information Systems Research, Journal of Management Information Systems, MIS Quarterly, Organization Science, and others.