讲座题目： Task Management in Decentralized Autonomous Organization
讲座人： 赖福军教授
主持人： 李凯教授
讲座时间：2023年6月23日（星期五）10：30
讲座地点：商学院A501-3
讲座人简介：
Fujun Lai is McCarty Distinguished Professor of MIS and Decision Sciences at the University of Southern Mississippi. He received a Ph.D. from the City University of Hong Kong. His research interests include supply chain and logistics management, quality management, and enterprise information systems. His work on these topics has been published in Journal of Operations Management, Production and Operations Management, Journal of Management Information Systems, Decision Sciences,Communications of the ACM, Journal of Supply Chain Management, and among others.
讲座摘要：
In the emerging platform economy, blockchain technologies are reshaping the digital economy. Moreover, disintermediation and decentralization have broken new ground for platform organizations and management mechanisms and instigated the concept of a DAO (Decentralized Autonomous Organization). Recent literature on operations management has called for further research on governance issues related to DAOs. In response to this call, we explore the relationship between DAO management efforts and platform performance in this study. Specifically, we propose and theoretically articulate decentralized voting tasks in DAOs as a new form of organizing. Harnessing both online and on-chain data from seven sources, we empirically examine how voting task division, task allocation, reward distribution, and information provision affect platform performance in the context of MakerDAO (an Ethereum-based stablecoin issuance platform). Our findings reveal that strategic decisions arrived at through voting have a positive impact on platform operational performance under certain conditions, whereas operational decisions resulting from voting have a negative impact. Moreover, we elucidate the moderating effects of voting task execution characteristics on the relationship between completed decision tasks and operational performance. These findings have important implications from both theoretical and practical perspectives. We also share all the raw data we use to promote the development of blockchain-related empirical research.
