题　目：When Local Organizing Undermines Algorithmic Learning

嘉　宾：Runjia Zhang, PhD Candidate, Peking University

主　持：王薇, 助理教授, 上海交通大学

时　间：2026年10月21日（周三）13:30-15:00

地　点：上海交通大学安泰经济与管理学院 包兆龙图书馆 A305室

内容简介:

Machine learning (ML) algorithms are increasingly deployed to manage organizational work because of their potential to continuously improve by learning from accumulated data. However, drawing on a qualitative study of a retail chain that implemented algorithmic governance, we find that ML-enabled algorithms instead degrade over time. Our feedback loop model shows how three forms of local organizing: performative accommodation, operational compensation, and discrepancy concealment, though enabling store operation, distort the learning process of the algorithmic system. Organizational knowledge, therefore, deteriorates at both the individual and collective levels. We contribute to emerging research on algorithms as organizational learning stock by illustrating how machine learning challenges the previous way of organizational knowledge accumulation.

演讲人简介：

Runjia Zhang is a PhD candidate at the Guanghua School of Management at Peking University. Her research explores how emerging technologies reshape the ways we organize, collaborate, and govern. She has published in journals such as Academy of Management Review and Organization Studies.

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