Despite the growing interest in crowdsourcing, this new labor model has recently received severe criticism. The most important point of this criticism is that crowdworkers are often underpaid and overworked. This severely affects job satisfaction and productivity. Although there is a growing body of evidence exploring the work experiences of crowdworkers in various countries, there have been a very limited number of studies to the best of our knowledge exploring the work experiences of Chinese crowdworkers. In this paper we aim to address this gap. Based on a framework of well-established approaches, namely the Job Demands-Resources model, the Work Design Questionnaire, the Oldenburg Burnout Inventory, the Utrecht Work Engagement Scale, and the Organizational Commitment Questionnaire, we systematically study the work experiences of 289 crowdworkers who work for ZBJ.com - the most popular Chinese crowdsourcing platform. Our study examines these crowdworker experiences along four dimensions: (1) crowdsourcing job demands, (2) job resources available to the workers, (3) crowdwork experiences, and (4) platform commitment. Our results indicate significant differences across the four dimensions based on crowdworkers' gender, education, income, job nature, and health condition. Further, they illustrate that different crowdworkers have different needs and threshold of demands and resources and that this plays a significant role in terms of moderating the crowdwork experience and platform commitment. Overall, our study sheds light to the work experiences of the Chinese crowdworkers and at the same time contributes to furthering understandings related to the work experiences of crowdworkers.
Department, Program, or Center
School of Interactive Games and Media (GCCIS)
Yihong Wang, Konstantinos Papangelis, Ioanna Lykourentzou, Hai-Ning Liang, Irwyn Sadien, Evangelia Demerouti, and Vassilis-Javed Khan. 2020. In Their Shoes: A Structured Analysis of Job Demands, Resources, Work Experiences, and Platform Commitment of Crowdworkers in China. Proc. ACM Hum.-Comput. Interact. 4, GROUP, Article 07 (January 2020), 40 pages. DOI:https://doi.org/10.1145/3375187
RIT – Main Campus