Tuesday, April 25th, from 10:00 a.m. to – 11:30 a.m. (ET) in Room 311A, Scheller College of Business.

You can also attend virtually via the following Zoom link: https://gatech.zoom.us/j/94314841014

The dissertation abstract is included below. Copies of the dissertation are available upon request.

Best,

Hao Hu | Ph.D. Candidate
Information Technology Management, Scheller College of Business
Georgia Institute of Technology

Area: Information Technology Management

Committee Members: Dr. Marius Florin Niculescu (Co-chair), Dr. D. J. Wu (Co-chair), Dr. Eric Overby, Dr. Mingfeng Lin, Dr. Yifan Dou (Fudan University)

Title: Essays on Digital Goods and Online Markets

Essay 1: Score High with a Free Kick: Seeding vs. Time-Limited Freemium as Catalysts for the Adoption of Software Applications (joint work with Dr. Marius Florin Niculescu, Dr. D. J. Wu, Dr. Yifan Dou)

In software markets, the sheer number of available applications make it rather challenging for any given new one to stand out and be noticed by consumers. While word-of-mouth (WOM) effects may help developers gradually gain visibility for their products, efficiently jumpstarting and propagating adoption prior to product obsolescence are by no means trivial. Often, relying solely on paid adoption may result in sub-optimal outcomes. We explore two popular strategies through which developers can catalyze adoption by helping consumers directly or indirectly learn the value of their products - seeding (free full-feature product giveaways to a subset of the consumer base) and time-limited freemium (TLF). Seeding, as a business strategy, existed for a long time. On the other hand, the feasibility to offer market-wide TLF became mainstream more recently, with the advent of digital goods and services. Thus, a natural question emerges - if TLF represents nowadays a feasible and easily implementable strategy for software applications, has seeding approach been rendered irrelevant in these markets? In this study, we provide managerial recommendations on when each of these strategies with a free full-feature-consumption component is optimal, based on social and self-learning dynamics, consumer priors, adoption costs, and individual product value depreciation. While we see TLF showing up as optimal in some parameter range for each scenario explored, the same cannot be said about seeding. We identify two particular conditions under which the latter can still emerge as a dominant strategy - the presence of (i) user adoption costs and/or (ii) individual depreciation of value by usage. While WOM effects alone are not enough for seeding to dominate other strategies, we do see that in the presence of any of the aforementioned additional market conditions, the parameter range where seeding is dominant expands as social learning is more efficient. We further show that our results are robust under diverse assumptions regarding seeding and the distribution of consumer priors.

Essay 2: An Empirical Investigation on Amazon's Self-preferencing Strategy and its Launch of Private-label Products

In today's e-commerce landscape, many platforms are serving dual roles as gatekeepers that connect third-party sellers with consumers and as sellers offering their own products. This has led to increasing concerns that platforms may adopt self-preferencing strategies that favor their own brands in search, further intensifying the competition. In this paper, we document evidence of Amazon's engagement in self-preferencing and examine the consequences of Amazon launching its private-label products while employing such a strategy. We first present two pieces of evidence for self-preferencing. In the direct evidence, we find that Amazon private-label products are ranked higher than third-party products even when accounting for other observables. To control for unobserved product qualities, we further leverage a scenario where Amazon becomes the seller of an existing third-party product, i.e., the product itself remains unchanged. We find that product sales immediately increase after Amazon becomes the seller, indirectly showing that Amazon may actively promote its own products more than third-party counterparts. We then examine the effects of Amazon launching private-label products on third parties in the same category. We find that although Amazon favors its own products in search, the average sales of third-party products in affected categories increase more than those in the unaffected categories. We further explore the mechanisms that may explain the changes. We find that private-label products displace lower-rated sellers, stimulate innovation and variety in product designs, and serve as valuable guidance for third-party sellers to enhance their searchability by improving product descriptions. These factors potentially lead to higher sales and ultimately an increase in consumer welfare, with prices being largely unchanged.