IDENTIFYING AND DETERMINING CROWDSOURCING SERVICE STRATEGIES: AN EMPIRICAL STUDY ON A CROWDSOURCING PLATFORM IN CHINA

The crowdsourcing platforms, as mediators and service providers, play a critical role in crowdsourcing initiatives. The service quality of a plat-form has a direct impact on solver satisfaction, and ultimately affects the plat-form's continuous operation. Service quality can be measured by service qual-ity attributes (SQAs). Thus, identifying and quantifying SQAs are crucial to enhance solver satisfaction. Besides, choosing pertinent strategies and deter-mining priorities for the SQAs are another core issue. To address these issues, this study proposes a novel decision framework that combines the Fuzzy An-alytical 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 strate-gies 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.