OPTIMAL DISTINCTIVENESS AND AUDIENCE EVALUATION IN DIGITAL PLATFORM MARKETS

Abstract

This dissertation examines how audiences evaluate distinctiveness in digital platform markets, with a focus on online knowledge markets. Drawing on optimal distinctiveness theory and audience evaluation research, I explain why being different can help some producers and offerings while hurting others. Across three essays using data from Udemy, I examine distinctiveness at both the product and producer levels. The first essay studies course distinctiveness and shows that the value of distinctiveness depends on learner expertise and the distance between the course and the instructor’s area of expertise. The second essay examines within-producer distinctiveness and shows that, within the same instructor’s portfolio, individual courses that deviate more from the instructor’s established portfolio prototype have lower market appeal than the instructor’s more typical courses. However, this penalty is weaker for courses targeting experienced learners. The third essay examines instructor focus breadth and finds an inverted U-shaped relationship between portfolio breadth and market appeal . This finding suggests that instructors with moderately broad portfolios do better on average than those with either very narrow or excessively broad portfolios. This dissertation contributes to research on optimal distinctiveness and platform-based entrepreneurship by showing that distinctiveness is not evaluated uniformly. It shows that the performance consequences of distinctiveness depend on who evaluates the offering, how well it fits the producer, and how broadly the producer positions their portfolio.