Human-Computer Interaction Institute
School of Computer Science, Carnegie Mellon University
Impression Formation in Online Peer Production
Technology increasingly enables new forms of distributed and ad-hoc online
collaboration, among them a form of large-scale, loosely coordinated, mostly voluntary work known as peer production. Some peer production environments now connect social media functionality directly to collaborative work artifacts, which provides participants with detailed information about other contributors’ work history, interests and interactions.
Activity visibility provides informational signals individuals can use to make inferences about important characteristics of the people they interact with. However, the impact of the increased variety of information about collaborators and potential colleagues in a peer production setting is not well known.
Understanding how people form impressions of other contributors can inform the design of peer production environments. This thesis investigates the process and outcomes of using activity traces for interpersonal impression formation in online peer production.
I describe two interview studies with users of a social media enabled site supporting open source software development. The first study investigates what signals people use to form impressions about others' expertise and attitudes, and the second study identifies how they used this information to make decisions about work contribution acceptance. My results show that observers use cues that they see as reliable and easy to verify to draw conclusions about not only workers' abilities but also their underlying personal characteristics.
Finally, I present an experiment investigating how the visual presentation of activity history influences impressions of contributors and evaluation of work. In the study I vary the amount of detail and quality of work shown in an activity history and measure the influence on impressions and evaluation of work products. I find that greater detail enhances valence and persistence of initial impressions and bias towards an unknown worker as well as effort expended to correct the worker's output on the task.
My thesis advances our understanding of when and how social networking information and activity traces influence the process of making sense of unknown contributors' inherent qualities, and how this relates to work-related decision-making in peer production. The thesis also informs design principles for showcasing individuals' activity history in collaborative production sites.