Influencers and digital platforms are reshaping consumer decision-making and firms’ strategies. My dissertation studies the strategic interactions between firms, influencers, consumers, and digital platforms. The first three essays take the perspective of the firm. In Essay 1, I develop a conceptual framework to integrate influencer marketing into marketing strategy. Drawing from interviews with 15 marketing managers, I identify the challenges managers face in the design and execution of influencer marketing strategy and develop a framework to help managers select the right portfolio of influencers for each stage of the customer decision-making journey. In Essay 2, I study how duopoly firms optimally choose influencer effort types and product pricing strategies. The model extends horizontal and vertical differentiation thinking to the influencer context. I examine when firms should engage influencers in effort overflow—educating consumers about the product category—and when they should engage influencers in effort variety—diversifying content formats to combat creativity fatigue. My findings indicate that effort overflow increases firm profits by alleviating influencer competition in effort levels, while effort variety intensifies such competition. In Essay 3, I employ a Bayesian persuasion perspective to study how influencers strategically design their reviews in a market where early adopters’ reviews contribute to information dissemination. I find that the optimal influencer strategy depends on the valence and informativeness of early adopters’ reviews, and I characterize the conditions under which the seller should use non-performance-based contracts or sales-based commissions. These results highlight the influencer’s role as a strategic information intermediary who corrects information distortions to facilitate product-consumer matching. In Essay 4, I shift the focus from firms to platforms to open the black box of personalized recommendation algorithms. Motivated by Etsy’s front page recommendation algorithm, I characterize an optimal recommendation algorithm that enables the platform to incentivize a monopoly seller to charge low prices. The algorithm induces the seller to self-select into different pricing tiers, a phenomenon I term Laissez-faire–Pooling–Regulated–Excluded (LaiPREx) pricing. Together, these essays provide a deeper understanding of how information, influencers, and algorithms interact in shaping consumer behavior and firm decision-making in the digital marketing environment.
Jiaming Wei (Fri,) studied this question.