Treating the crowd fairly: Increasing the solvers’ self-selection in idea innovation contests

Abstract

The success of idea crowdsourcing contests depends on the wideness of the number of solvers that voluntarily self-select to solve the problem broadcast by the seeker and previous research has started to highlight the role of fairness in the self-selection process of solvers. This study aims at deepening the understanding concerning how fairness can influence the solvers’ self-selection. By applying a netnographic research design, we identify possible unexplored facets of fairness in the crowdsourcing context, i.e., prize award, award guaranteed, and non-blind contest. Theoretically, we drew from the organizational justice and fairness literature to develop hypotheses about how the three fairness elements affect solvers’ participation in idea crowdsourcing contests. Then, to empirically test the hypotheses, we performed an econometric analysis building on a distinctive dataset of 1067 contests, broadcast on the 99designs crowdsourcing platform. We found that the three fairness factors which emerged from the netnography have a positive impact on the self-selection of solvers. The results of this study offer important contributions to previous literature and provide several implications for organizations and contest organizers in the idea crowdsourcing context.