PhD Dissertation Defense by HOANG Ai Phuong Emmy

About The Talk

IT innovations disrupt traditional business models and challenge conventional thinking. Thus, industry incumbents face fierce competition from start-ups with new business models and new ways of engaging customers. Digital entertainment goods and personalized services have become a lucrative market, which has undergone a transformation enabled by seamless Internet connections. Meanwhile, social networks and other online platforms have brought people and business even closer. As consumer relationships with firms span geographic boundaries, so does their spending. It is no longer effective nor appropriate to segment consumers based on socioeconomic factors; instead, consumers can be characterized by their direct relationship with the goods and services, or their relationship with technology. Thus, firms need to develop innovative products and services, and adjust their marketing and delivery systems to address this new level of sophistication in consumer informedness. There is one fundamental, yet intriguing question: How can firms recover consumer preferences in the digital space, to keep themselves and their consumers informed about the offerings that are suitable for the consumers?

This dissertation examines how IT creates new capabilities for extracting business and consumer insights to inform traditional marketing activities, for physical products in the retail industry as well as on-demand services in the entertainment industry. It consists of two essays that employ Computational Social Science approaches involving explanatory empiricism, scientific theory, and machine learning methods to assess and evaluate different strategic marketing strategies. The first one discusses household informedness and its impact on the marketing of digital information goods, via free content samples for on-demand TV series. The second one proposes a methodological advance related to censored observation recovery using temporal sequences and iterative data simulation that improves statistical power for causal inference in data-driven exploratory research. The dissertation contributes to the growing body of research on consumer and business analytics by looking at the impact of consumer informedness on the sales of products and services in digital space. It also constitutes a methods innovation to improve the sequential completeness of the dataset for causal inference in explanatory research, to produce business policy-relevant findings for industry practitioners.

Speaker Biography

Ai-Phuong HOANG is a PhD candidate under the Interdisciplinary Doctoral Programme in Information Systems and Marketing at Singapore Management University. She works under the guidance of Professor Robert J. Kauffman. Her research interests involve consumer behavior, social network, marketing strategy for digital information goods, and methodology innovations for causal inference with consumers’ digital trace data. Her research articles appear in Journal of Management Information Systems (JMIS), Electronic Commerce Research and Applications (ECRA), and leading conferences and workshops in IS. From 2015 to 2016, she was a visiting Ph.D. student at Carnegie Mellon University. In 2017, she received a Young Scholar Award at the Pacific Telecommunications Council Conference in Honolulu, Hawaii. She obtained her Master of Applied Information Systems from SMU in 2013, and Bachelor of Business from RMIT in 2011.