The impact of fairness on the performance of crowdsourcing: an empirical analysis of two intermediate crowdsourcing platforms

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Mazzola, Erica and Acur Bakir, Nuran and Piazza, Mariangela and Perronea, Giovanni (2016) The impact of fairness on the performance of crowdsourcing: an empirical analysis of two intermediate crowdsourcing platforms. In: EURAM, 2016-06-01 - 2016-06-04.

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Abstract

Crowdsourcing has been considered a new powerful component of innovation. However, scholars are no closer to understanding how organizations (seekers) can design crowdsourcing challenges that are perceived by the external contributors (solvers) as fair. Building on behavioral agency theory, we aim to examine fairness perceptions’ effects on the behaviors of solvers that are directed at, and benefit, the success of crowdsourcing challenge. Based on a unique database of 1590 challenges gathered from two online crowdsourcing platforms, we show that solvers will perform well in the crowdsourcing contests if they have ability (knowledge, skills etc.), motivation (i.e., rewards etc.) and fair mechanisms (transparent processes and equity award). Our results indicate that reducing the information asymmetry of solvers engaged in the challenge increases the solvers’ perception of procedural and distributive fairness whilst incentivizing their self-selection process. Moreover, posing problems in an ‘open’ way exposes seekers to possible opportunism risks. Thus, seekers utilize safeguarding contractual mechanisms to mitigate these risks and protect the information shared in a challenge. In turn, designing a challenge with strong policies of risk safeguard worsens the benefit that the award guaranteed has in attracting a large pool of participants and a large amount of accepted ideas. Our results not only contribute to crowdsourcing for innovation literature but also to behavioral agency theory.

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