2024年3月25日（周一）

10：00-11：30

信管学院308会议室

Xuanqi Liu earned her Ph.D. degree in Information Systems from School of Computing at National University of Singapore (NUS). Her research focuses on machine learning, social network analysis, business intelligence, and Fintech. She employs different research methods in her studies, including various machine learning techniques and traditional econometric methods. Her papers have been published at leading IS conferences and are under review at top IS journals.

Text-Based Technological Risk and Firm Innovation: An Empirical Analysis

While innovation is a critical driver of firm success, firms face a variety of risks during the innovation. Technological risk, in particular, poses a significant challenge to innovation, as the rapid pace of technological change can make it challenging for firms to keep up with evolving consumer demands and preferences. Despite its importance, technological risk is rarely discussed due to the lack of a direct measure of firm-level ex-ante technological risk.

To fill this gap, we first propose a novel framework to measure firm-level technological risk measures based on risk disclosures in 10-K reports. Specifically, the proposed framework incorporates distant learning, sentiment analysis, and NLP techniques to identify and quantify technological risk. Subsequently, we examine the effect of technological risk on future innovation performance, as measured by patent counts and average patent citations.

Our empirical results show that firms exposed to technological risk are more likely to improve innovation performance, i.e., increase their patenting activities and develop more impactful patents. In addition, firms operating in highly competitive environments adopt more proactive innovation strategies to mitigate technological risks, while firms with a narrow technological knowledge base tend to focus on developing high-quality patents.

Our study contributes to the growing IS literature on employing novel machine-learning techniques for large-scale business analytics. Our findings also have important implications for managers, investors, and financial regulators.