The Impact of Fairness on the Performance of Crowdsourcing: An Empirical Analysis of Two Intermediate Crowdsourcing Platforms

Mazzola, E., Piazza, M., Acur, N. and Perrone, G. (2016) The Impact of Fairness on the Performance of Crowdsourcing: An Empirical Analysis of Two Intermediate Crowdsourcing Platforms. EURAM Conference, Paris, France, 1-4 June 2016. (Unpublished)

Abstract

This research aims to investigate mechanisms available to the seeker to encourage participation of solvers to a challenge. We hypothesize that specific crowdsourcing mechanisms, reducing the information asymmetry of solvers on the challenge, increase solvers’ perception of procedural and distributive fairness and incentive their self-selection process. Moreover, posing problem in an ‘open’ manner exposes seekers to possible opportunism risks. Thus, seekers utilize many safeguard contractual mechanisms to mitigate these risks and protect the information shared in a challenge. By using data from two intermediate crowdsourcing pltaforms, we provide that safeguard mechanisms may have a drawback effect; since they increase information asymmetry of participants on the execution system and the rewarding schema of a challenge, they decrease solvers’ fairness and make solvers less commit. Combining previous research from the field of tournament-based crowdsourcing together with behavioural agency theory literature, this study offers important contributions, both to the theory and the practice.

• Item Type: Conference or Workshop Item
• Status: Unpublished
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Acur, Professor Nuran
• Authors: Mazzola, E., Piazza, M., Acur, N., and Perrone, G.
• College/School: College of Social Sciences > Adam Smith Business School > Management