Copyright © (2009) by Association for Computing Machinery, Inc. Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distrubuted for profit or commercial advantage. To copy otherwise, to republish, to post on servers, or to redistribute to lists, requires prior specific permission and/or a fee (http://portal.acm.org/citation.cfm?id=1518701.1518713).
Contemporary work increasingly involves interacting with strangers in technology-mediated environments. In this context, we come to rely on digital artifacts to infer characteristics of other people. This paper reports the results of a study conducted in a global company that used expertise search as a vehicle for exploring how people interpret a range of information available in online profiles in evaluating whom to interact with for expertise. Using signaling theory as a conceptual framework, we describe how certain ‘signals’ in various social software are hard to fake, and are thus more reliable indicators of expertise. Multi-level regression analysis revealed that participation in social software, social connection information, and self-described expertise in the corporate directory were significantly helpful in the decision to contact someone for expertise. Qualitative analysis provided further insights regarding the interpretations people form of others’ expertise from digital artifacts. We conclude with suggestions on differentiating various types of information available within online profiles and implications for the design of expertise locator/recommender systems.
By: N. Sadat Shami; Kate Ehrlich; Geri Gay; Jeffrey T. Hancock
Published in: Proceedings of the 27th international conference on Human factors in computing systems,New York, , ACM. , p.69-78 in 2009
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