COMMITTEE CHAIR: Dr. Oluwagbemiga Ojumu

TITLE: ALGORITHMIC INFLUENCE AND CONSUMER PROTECTION: HOW DIGITAL PLATFORMS SHAPE CONSUMER DECISIONS, POLITICAL BEHAVIOR, AND FIRM ADVANTAGE

ABSTRACT: Algorithmic systems have become fundamental to digital platforms, shaping how consumers access information, evaluate alternatives, form preferences, and make decisions. Through data collection, tracking, personalization, recommendation systems, and behavioral prediction, algorithms influence what individuals see, believe, purchase, and support politically. While these technologies are often promoted as tools that enhance convenience and user experience, they also create opportunities for firms and platforms to influence consumer behavior in ways that may be difficult for consumers to recognize or resist. This dissertation examines algorithmic influence as a consumer protection issue, focusing on how digital platforms shape consumer decisions, political behavior, and firm advantage. The dissertation consists of three interconnected studies. The first study develops a theoretical framework explaining how algorithms influence consumer behavior through a recursive process involving data collection, personalization, repeated exposure, identity reinforcement, and behavioral feedback loops. Drawing on Reinforcing Spirals Theory and Social Identity Theory, the study argues that algorithmic influence is an ongoing process in which platforms continuously learn from user behavior and adjust future exposure to reinforce existing attitudes, identities, and behavioral tendencies. The second study investigates partisan loyalty and vote switching in the 2020–2024 U.S. presidential elections. Although it does not directly measure algorithmic exposure, it examines political behavior as an identity-based outcome and explores how partisan identity shapes voter loyalty and electoral decision-making across demographic groups. The findings contribute to understanding how identity-driven behaviors may be reinforced within increasingly personalized digital information environments. The third study examines how firms and digital platforms transform consumer data, attention, and behavior into strategic advantage. Drawing on platform economics and strategic management perspectives, it explores how organizations leverage targeting, personalization, and predictive analytics to generate competitive advantage and create economic value from consumer engagement. Collectively, the three studies demonstrate that algorithmic systems are not merely technological tools but influential structures that shape consumer behavior while generating substantial benefits for firms and platforms. The dissertation contributes to research on consumer behavior, political behavior, platform strategy, and consumer protection, while highlighting the need for stronger transparency requirements, regulatory oversight, and consumer safeguards in increasingly algorithm-driven digital environments.

Keywords: Price Convergence, Market Integration, Spatial Economics, Regional Pricing Strategy, Geographic Arbitrage

Zoom Link:

https://pvpanther.zoom.us/j/99418712996?pwd=66bbJFCip3caOzTdB6CqyYQmVpywLp.1

Room Location: Northwest Center, Room 205