Recently, “The Interplay of Earnings, Ratings, and Penalties on Sharing Platforms: An Empirical Investigation” co-authored by professor Dai Hongyan of the Business School, professor Xu Yuqian from the University of North Carolina at Chapel Hill, postdoctor Lu Baile from National University of Defense Technology, professor Anindya Ghose from New York University and professor Zhou Weihua from Zhejiang University was published in the top international academic journal “Management Science”, which marked another important achievement of our first-class discipline construction program titled “New Business Construction under the Background of Platform Economy”. Founded in 1954 and sponsored by the Institute for Operations Research and Management Sciences (INFORMS), the journal “Management Science” is the oldest and most highly acclaimed top journal in the field of management science and operations research, and also a UTD24 journal and a FT50 journal.

    This paper is based on the background of crowdsourcing economy and takes the crowdsourced distribution platform as the object to carry out relevant empirical research. Under the crowdsourced delivery model, the crowdsourced workforce has the freedom and flexibility to make work decisions, which poses a great challenge for the platform to manage the crowdsourced workforce. Based on this, this paper is devoted to studying the behaviors and motivation of the crowdsourced workforce, and adopts the Heckman two-stage model and the instrumental variable method to explore the interaction between the platform’s performance feedback mechanism and salary mechanism manifested in the form of scoring and fines and the work decision-making of the crowdsourced workforce. According to the research findings, higher scores will motivate crowdsourced workforce to work more actively; nevertheless, when scoring and pay work together, the two positive effects can replace each other. Secondly, higher fines will hinder the crowdsourced workforce from working more actively, and the workers who receive higher fines are often more sensitive to salary increases. Through follow-up surveys, this paper explores the underlying mechanism of the moderating effect from the perspectives of psychology and economics. The results of this paper can be applied to the design of incentive mechanisms in the crowdsourcing mode, because they're of important reference significance for helping platform managers understand how salary, scoring and fines jointly affect the work decision-making of the crowdsourced workforce as well as how to manage high-quality and low-quality employees.

    Professor Dai Hongyan has conducted long-term and in-depth research in such fields as data-driven optimization decision-making, data-driven operation optimization, new retail supply chain management and the Internet of Things, obtained rich research results, published more than 40 papers, including those published in MS, EJOR, Journal of Management Science and Engineering, Journal of Management Engineering and other domestic and foreign flagship journals, and presided over multiple national, provincial and ministerial-level programs, such as the Training Program of the Major Research Plan of the National Natural Science Foundation of China and the General Program of the National Natural Science Foundation of China.