Wen Wen

Dr. Wen Wen is a tenured Associate Professor at the McCombs School of Business, University of Texas at Austin. Dr. Wen received her Ph.D. in Information Technology Management and M.S. in Statistics from Georgia Institute of Technology.

Dr. Wen is an award-winning researcher whose work spans the future of work, digital innovation and platforms, artificial intelligence (AI), and healthcare. One stream of her recent research investigates the development and implications of digital technologies for the future of work. She investigates how generative AI, digital collaboration technologies, and enterprise platforms transform knowledge work, software development, workforce productivity, and reskilling. Her research in this area collectively advances understanding of how digital technologies are reshaping the nature of work, collaboration, skill development, and productivity.

Another stream of her recent work explores AI and digital technologies in healthcare. She explores how information technologies, artificial intelligence, and advanced analytics can improve healthcare delivery, patient outcomes, and healthcare value. Collectively, her research advances a vision for the future of healthcare in which digital innovation, data-driven decision-making, and human-centered design work together to enhance healthcare value and promote equitable access to high-quality care.

Dr. Wen’s research also examines digital platforms, intellectual property, and open innovation. She studies how complementors and platform owners respond to competitive threats and ecosystem governance. Her work further investigates how intellectual property (IP) strategies, standards-setting organizations, and IP enforcement mechanisms influence entrepreneurial activity, innovation incentives, and technology diffusion. Together, these studies provide new insights into the balance between protecting innovation and fostering openness and competition in technology markets.

Being trained as a multi-method researcher, Dr. Wen's methodological expertise spans econometric analyses of observational data, natural experiments, field experiments, and controlled laboratory experiments. She frequently combines multiple methodological approaches within a single research project—such as integrating empirical analyses based on both observational and experimental data, combining design science research with controlled experiments, and applying machine learning techniques in conjunction with econometric methods. This methodological versatility enables her to address complex research questions using complementary approaches and generate insights that are both rigorous and practically relevant.