嘉　宾：陆天 Assistant Professor  Arizona State University

主持人：房思含  助理教授  上海交通大学安泰经济与管理学院

时　间：2024年12月25日（周三）10:30-12:00

地　点：上海交通大学徐汇校区安泰楼A305

内容简介:

Artificial intelligence (AI) algorithm capabilities are increasingly augmented by integrating expertise from domain specialists and top performers to better align with human objectives. While prior research suggests experienced individuals often resist algorithmic guidance, we challenge this in the context of algorithm capability augmentation. Drawing on self-regulation theory, we argue that high-experienced individuals, recognizing enhanced algorithm capabilities, are more likely to follow and adapt to algorithmic recommendations. Using a natural experiment in the on-demand food delivery industry, we uncover several key findings. Specifically, high-experienced riders demonstrate greater compliance with enhanced algorithms, leading to improved productivity and eventual gains in on-time delivery rates through adaptation over time. Structural modeling and other empirical endeavors, supported by a granular proprietary dataset, reveal the mechanisms underlying their long-term learning and adaptation processes. Additionally, low-experienced riders also benefit from the algorithm‘s enhancements through consistent adherence to its guidance. Our study highlights the pivotal interplay between self-regulation (i.e., observation, evaluation, and adaptation) and algorithm capability augmentation in enhancing human–algorithm collaborations, thereby advancing both market efficiency and worker welfare.

演讲人简介：

Tian Lu is an Assistant Professor in the Department of Information Systems at the W. P. Carey School of Business, Arizona State University. Previously, he was a post-doctoral research fellow at Carnegie Mellon University, and he completed his Ph.D. at Fudan University, China. His research interests center around dynamically learning the interaction between humans, algorithms, and IT applications, leading to adaptive decision-making in high-stakes contexts. Particularly, his research is driven by mindful AI, aiming to develop principled innovations that enhance both economic and social welfare in emerging business models. Currently, he has a particular interest in human–AI collaboration issues in diverse contexts. His research has been published in journals such as Information Systems Research, MIS Quarterly, Management Science, Production and Operations Management, INFORMS Journal on Computing, Journal of the Association for Information Systems, among others. He has received multiple Best Paper Awards at premier information systems conferences including ICIS 2019, CSWIM 2021 and 2019, and PACIS 2017. He has also received several research grants, including the Amazon AWS AI Research Grant, NET Institute Summer Grant, Facebook Research Grant, and Tencent Research Grant.

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