Gene Lee

Prospective Graduate Students / Postdocs

This faculty member is currently not actively recruiting graduate students or Postdoctoral Fellows, but might consider co-supervision together with another faculty member.

Associate Professor

Research Interests

Applied Machine Learning
Business Analytics
Computer Science and Statistics
Information Systems
Mobile Ecosystem
Social Media Analysis
Text Mining

Relevant Thesis-Based Degree Programs

Research Options

I am available and interested in collaborations (e.g. clusters, grants).
I am interested in and conduct interdisciplinary research.

Research Methodology

machine learning
Network Analysis
Natural Language Processing
Big Data

Graduate Student Supervision

Doctoral Student Supervision

Dissertations completed in 2010 or later are listed below. Please note that there is a 6-12 month delay to add the latest dissertations.

Three essays on AI strategies and innovation (2024)

Artificial Intelligence (AI) technologies are transforming many industries and our society. While both academia and industry consider AI closely intertwined with innovation, we have limited knowledge of AI’s opportunities and challenges in the business innovation context. This dissertation seeks to address this gap (i) by proposing a novel construct to identify strategically innovative firms; (ii) by examining how deep learning (DL)-based AI capabilities affect knowledge innovation; and (iii) by investigating the economic impacts of service robotics in a customer-facing service industry. In the first essay, we propose a novel firm-level construct, Strategic Competitive Positioning (SCP), to identify distinctive strategic positioning (i.e., first-movers, followers) and competition relationships. Drawing on network theory, we develop a structural hole-based, dynamic, and firm-specific SCP construct. Using a large dataset of 10-K annual reports from US public firms, we demonstrate the value of the proposed measure by examining the impact of SCP on subsequent IPO performance. In the second essay, we study the impact of DL capabilities on exploration to determine how AI’s value creation can facilitate knowledge innovation. Drawing on the theories of organizational learning and path dependence, we theorize how DL capabilities can help firms overcome path dependence and pursue exploration. The findings show that a firm’s DL capabilities have a positive impact on exploration, and conventional innovation-seeking approaches negatively moderate the positive impact of DL capabilities on exploration. In the third essay, we examine the economic impacts of service robots, embodied AI technologies with physical presence, in customer-facing restaurants. The empirical findings suggest that service robot adoption increases restaurant performance, specifically through improved dining experience of existing customers. By distinguishing two forms of managers’ intent in adopting service robots, we further find that the adoption with collaboration intent positively affects perceived service, management, and atmosphere quality and that the adoption with replacement intent positively impacts perceived atmosphere quality only. In sum, this dissertation makes a significant contribution to the literature on AI and innovation by enhancing our understanding of the opportunities and challenges regarding AI in the business innovation context.

View record

Master's Student Supervision

Theses completed in 2010 or later are listed below. Please note that there is a 6-12 month delay to add the latest theses.

How does AI-generated voice affect online video creation? : evidence from TikTok (2023)

The rising demand for online video content has fostered one of the fastest-growing markets as evidenced by the popularity of platforms like TikTok. Because video content is often difficult to create, platforms have attempted to leverage recent advancements in artificial intelligence (AI) to help creators with their video creation process. However, surprisingly little is known about the effects of AI on content creators’ productivity and creative patterns in this emerging market. Our paper investigates the adoption impact of AI-generated voice – a generative AI technology creating acoustic artifacts – on video creators by empirically analyzing a unique dataset of 4,021 creators and their 428,918 videos on TikTok. Utilizing multiple audio and video analytics algorithms, we detect the adoption of AI voice from the massive video data and generate rich measurements for each video to quantify its characteristics. We then estimate the effects of AI voice using a difference-in-differences model coupled with look-ahead propensity score matching. Our results suggest that the adoption of AI voice increases creators’ video production and that it induces creators to produce shorter videos with more negative words. Interestingly, creators produce more novel videos with less self-disclosure when using AI voice. We also find that AI-voice videos received less viewer engagement unintendedly. Our paper provides the first empirical evidence of how generative AI reshapes video content creation on online platforms, which provides important implications for creators, platforms, and policymakers in the digital economy.

View record



If this is your researcher profile you can log in to the Faculty & Staff portal to update your details and provide recruitment preferences.


Sign up for an information session to connect with students, advisors and faculty from across UBC and gain application advice and insight.