题　目：When Experimentation Becomes Inexpensive: How Surprises (More than Failures) Drive Learning in Product Innovation 

嘉　宾：沈茜蓉 助理教授 香港科技大学

主　持：欧阳璨 副教授 上海交通大学

时　间：2026年09月17日（周四）13:30-15:00

地　点：上海交通大学 徐汇校区安泰楼A407室

内容简介:

Experimentation and learning play a pivotal role in guiding organizations to find new product innovation. It is long believed that learning is largely driven by failures, which incur significant loss and consequently motivate reflections and change. In this paper, we explore whether and how organizational decision-makers learn when failures no longer incur losses—a question increasingly relevant with the wide adoption of digital experimentation, which enables organizations to systematically experiment with new product strategies at minimal costs. We investigate this question through an inductive, comparative case study, drawing on participant observations at two internet companies and supplementary semi-structured interviews at 9 additional organizations. We found that in a variety of contexts where experiments are perceived as costless, performance outcomes are no longer categorized into successes versus failures; Instead, they are categorized into outcomes that confirms prior beliefs or belief-violating “surprises”. Surprises, rather than failures, captures attention of decision-makers, triggers curiosity, and drive causal investigation to fill knowledge gap. Drawing on these findings, we theorize surprise-driven learning as a new form of learning that guides product innovation in the age of inexpensive digital experimentation and advanced data analytics, in sharp contrast to the conventional failure-driven learning. This paper contributes to the literature on organizational learning, product innovation, and digital transformation.

演讲人简介：

Prof. Subrina Shen is an Assistant Professor in the Department of Management at The Hong Kong University of Science and Technology. She received her PhD in Management and Organization from Cornell University in 2021. Prior to joining HKUST in 2025, she served as an Assistant Professor in the Department of Management at the McCombs School of Business, University of Texas at Austin. Her research explores firm behavioral strategy, organizational learning, the strategic management of multi-purpose technologies (AI), digital experimentation, and team status dynamics. In her research, she combines a wide range of methods including large-sample statistical analysis, lab experiments, computational methods and qualitative methods. Her work has been published in leading business journals such as Organization Science and Strategic Management Journal. She serves on the editorial board of Strategic Management Journal. 

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