嘉　宾：周俊杰  PhD. Candidate National University of Singapore

主持人：刘佳璐  助理教授  金沙威尼斯欢乐娱人城

时　间：2024年11月22日（周五）9:30-11:00

地　点：金沙威尼斯欢乐娱人城徐汇校区安泰楼A511

内容简介:

Firms have widely adopted crowdsourcing because of its advantages in conducting parallel search for innovative solutions to challenging problems. Prior research suggests that no individual solver from the crowd may have the full range of requisite knowledge to develop an effective solution, necessitating interactions among individual solvers to collectively solve the crowdsourced problem. Although interactions among solvers have been shown to be associated with solution level performance, they inevitably alter the distribution of solutions via reducing the number of independently developed solutions. There is scant research examining whether and to what extent interactions could compensate for the loss in the parallel search and eventually increase the likelihood of firms obtaining high quality solutions, namely crowdsourcing effectiveness. In this paper, we focus on a certain type of interaction among solvers -- i.e., teaming, teams formed on the fly within crowdsourcing contests. We posit that solvers are likely to make use of a variety of publicly available information that crowdsourcing platforms provide as quality signals in determining potential teammates for teaming. In turn, this reliance on observable quality signals will shape the self-selected teaming process and subsequent crowdsourcing effectiveness. Using simulation experiments, we find that the impact of teaming on crowdsourcing originates from the immediate returns from identifying other solvers (or their solutions) to integrate with and potential returns from teamwork-based collaboration, albeit conditionally depending on problem complexity and the timing of teaming. Moreover, under certain conditions, teaming may not compensate for the loss in parallel search and will hamper global crowdsourcing effectiveness. The findings shed new light on crowdsourcing effectiveness and design and point to exciting new lines of inquiry.

演讲人简介：

Junjie Zhou is a Ph.D. candidate in Information Systems and Analytics at the National University of Singapore. His research investigates the impact of information technology on organizational knowledge work, with an emphasis on innovation production activities. He uses agent-based modeling and econometric analyses in his studies.

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