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The Role of Social Influence and Network Churn in Beliefs about Electronic Medical Record Technology

  • Yuan, Christina T.1
  • Kane, Gerald C.2
  • Fletcher, Jason M.3
  • Nembhard, Ingrid M.4
  • 1 Armstrong Institute for Patient Safety and Quality, Johns Hopkins School of Medicine, Baltimore, MD, 21202
  • 2 Carroll School of Management, Boston College, Chestnut Hill, MA, 02467
  • 3 Robert M. La Follette School of Public Affairs, University of Wisconsin-Madison, 1225 Observatory Drive, Madison, WI, 53706
  • 4 The Wharton School, University of Pennsylvania, Philadelphia, PA 19104
Published Article
Journal of Social Structure
Exeley Inc.
Publication Date
Jan 01, 2019
DOI: 10.21307/joss-2019-005


The successful implementation of technology often hinges on individual beliefs about the innovation being introduced. Little is known about how social networks shape these beliefs. In this study, we examine: (1) whether individual beliefs about technology are influenced by the beliefs of their peers within their social networks (network content); and (2) whether changes in the composition of the social network over time (network churn) moderates the effect of peer beliefs on individual beliefs. We offer and test hypotheses about these relationships using longitudinal social network survey data from hospital staff collected 2 – 4 months before (N = 256) and 3 – 5 months after (N = 284) the implementation of a new electronic medical record (EMR) system at a large, academic hospital. Our findings suggest that peer beliefs about new technology significantly and negatively affect individual beliefs about technology in the early stages of EMR implementation. We also find that the effect of peer beliefs on individual beliefs is stronger in more stable social networks (i.e., social networks that experience few tie deletions over time) and weaker in less stable social networks (i.e., social networks that experience many tie deletions over time). Our study examines social influence in a novel context – the implementation of EMR systems in the hospital setting – and extends network theory by conceptualizing network churn as a moderating variable that may amplify or dampen the effect of networks.

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