Multiple facets of factors were examined to be drivers for crowdsourcing intention. However, there is limited research that has studied whether this factors-intention link is uniform for all solvers or not in detail. In fact, the present studies have identified three different segments that are internally consistent and stable. The comparison between the results of two different solutions, single-class and prediction-oriented-segmentation, confirms the existence of unobserved solver segments. The three established segments are “Self-leading solvers”, “External-driving solvers” and “Dual-driving solvers”. These results point the way for factors-based segmentation in intention initiatives and reflect the importance of a multidimensional conceptualization of factors, comprising motivation, perceived sponsor’s and platform’s support components. The paper expands and deepens the application of the heterogeneity theory in the study of crowdsourcing usage behavior and offers implications for organizers to recognize the solvers more clearly and get directions for more valid strategies.
Liang, Xiaobei; Jiang, Jiang; and Huang, LiXia, "Heterogeneity Based Solvers’ Segmentation In Crowdsourcing" (2016). ICEB 2016 Proceedings. 81.