Identifying and determining crowdsourcing service strategies: An empirical study on a crowdsourcing platform in China

<p style='text-indent:20px;'>The crowdsourcing platforms, as mediators and service providers, play a critical role in crowdsourcing initiatives. The service quality of a platform has a direct impact on solver satisfaction, and ultimately affects the platform's continuous operation. Service quality can be measured by service quality attributes (SQAs). Thus, identifying and quantifying SQAs are crucial to enhance solver satisfaction. Besides, choosing pertinent strategies and determining priorities for the SQAs are another core issue. To address these issues, this study proposes a novel decision framework that combines the Fuzzy Analytical Kano (FAK) and the Importance-performance analysis (IPA) models. Firstly, 24 related SQAs are identified from five dimensions of service quality. Secondly, we quantify these SQAs into a polar form representation scheme in accordance with the FAK model. In addition, the pertinent service strategies and priorities of the SQAs are confirmed by using the IPA model and Kano categories. Finally, decision priority rules for corresponding strategies and priorities of SQAs are constructed. An empirical study is presented to demonstrate our proposed decision framework on ZBJ platform, which is one of the most widely used online crowdsourcing platform in China.</p>