Making sense of strangers' expertise from signals in digital artifacts

  • Authors:

  • N. Sadat Shami;Kate Ehrlich;Geri Gay;Jeffrey T. Hancock

  • Affiliations:

  • IBM TJ Watson Research Center, Cambridge, MA, USA;IBM TJ Watson Research Center, Cambridge, MA, USA;Cornell University Information Science Program, Ithaca, NY, USA;Cornell University Information Science Program, Ithaca, NY, USA

  • Venue:
  • Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
  • Year:
  • 2009

Quantified Score

Hi-index 0.01

Visualization

Abstract

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.