Research Seminar by LIAW Shao Yi and ZHAO Anqi

Talk #1: How Does Combining Old and New Ideas Drive Product Innovation Success? Evidence from the Movie Industry 
by LIAW Shao Yi, PhD Candidate

Innovation can stem from groundbreaking new ideas, novel recombination of existing ideas, or a combination of both. Using both regression models and causal forests, an advanced machine learning technique as a robust framework to uncover causal relationships, this paper investigates how the introduction of new ideas and the recombination of existing ideas jointly drive innovation success within the dynamic landscape of the movie industry.  Leveraging human annotation on the platform of TvTropes.org, we decompose each movie into a set of tropes used, including new tropes debut by the movie and existing ones by previous movies. With such decomposition and movie data from IMDB and Box Office Mojo, we analyze how introducing new tropes combined with existing ones affects the artistic and economic success of a movie. We find that both new idea and recombination novelty positively affect movie success while recombination diversity negatively affects its success. We also find that recombination novelty and diversity negatively moderates the positive effect of new idea on success. Insights from this analysis can provide recommendations to filmmakers and industry stakeholders to strategically leverage new and existing idea combinations for product innovation to foster creativity, enhance audience engagement, and improve the success of their products.

Talk #2: How Reviews for Online Healthcare Services Affect Physician Performance?
by ZHAO Anqi, PhD Candidate

With the increasing adoption of online healthcare services among patients, online healthcare platforms (OHPs) are introducing the review function specifically for online services, alongside the existing review function for offline services, to allow patients to provide feedback on their online experiences. Besides informing potential patients, such feedback may in turn affect physician performance in online healthcare services. Using detailed service and review data from an OHP, we conduct a staggered difference-in-differences analysis to examine the effect of online service reviews on physician performance. The results show that online service reviews incentivize physicians to improve their response speed and informational support, but not emotional support. We further find that the more positive the online service reviews are, the greater the improvement in the physician’s performance. Additionally, online service reviews substitute online gifts while complementing offline service reviews in motivating physicians to improve their online performance. Furthermore, providing online service reviews leads to decreased physician performance in a patient’s subsequent online consultations. Our findings contribute to research on performance feedback and offer practical implications for improving feedback interventions in digital healthcare.

LIAW Shao Yi is a Ph.D. candidate in Information Systems, supervised by Prof. TANG Qian. Her general research interests are in Product and Human Innovation.

ZHAO Anqi is a Ph.D. candidate in Information Systems, supervised by Prof. TANG Qian. Her general research interests are in IT-enabled services on online healthcare platforms. Her recent research focuses on online healthcare reviews.