Pricing and Returns in the Era of Big Tech: Implications of Information Asymmetry Reversa
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| Publicat a: | ProQuest Dissertations and Theses (2025) |
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| 100 | 1 | |a Hassani, Kiarash Mohammad | |
| 245 | 1 | |a Pricing and Returns in the Era of Big Tech: Implications of Information Asymmetry Reversa | |
| 260 | |b ProQuest Dissertations & Theses |c 2025 | ||
| 513 | |a Dissertation/Thesis | ||
| 520 | 3 | |a Optimizing product returns is a key concern for manufacturers and retailers. While returns can be costly, they also offer consumers a valuable option: purchase under uncertainty and return if their realized valuation is lower than the price. Traditional economic models assume classical information asymmetry—consumers know their preferences, while firms do not. However, in today’s data-rich environments, firms increasingly gain deeper insights into consumer behavior—sometimes even surpassing the consumers’ own understanding of their preferences—leading to a shift in the balance of informational power.This dissertation explores the implications of reversed information asymmetry and its impact on pricing and return policy. Chapter 1 provides a broad discussion of reversed information asymmetry, emphasizing how advances in data collection and analytics can shift informational power. Increasingly, the previously less-informed party may possess more accurate or comprehensive information than the other party. We examine the consequences of this shift not only in business, but also in education, government, and public policy, where data-driven decisions have the potential to reshape traditional roles and dynamics. The chapter also introduces our central research questions.Chapter 2 reviews the literature on information asymmetry, pricing, and return policies. It positions our contribution within existing research and motivates the need to study different information regimes in pricing-return models. Chapter 3 develops a formal model to optimize the pricing and return policy of a monopolist under three regimes: classical asymmetry, symmetric information, and reversed asymmetry—where the firm fully observes each consumer’s valuation. In this chapter returns are assumed to be costless to consumers. We derive optimal strategies for the firm in each regime and compare profits, consumer surplus.Chapter 4 extends the model by introducing return hassle costs, which consumers incur when returning products but do not benefit the firm. We examine how these costs alter firm strategy under each information regime. We also introduce restocking fees as a decision variable. Chapter 5 provides a brief summary of our findings and offers several directions for future research in this domain. | |
| 653 | |a Behavior | ||
| 653 | |a Predictive analytics | ||
| 653 | |a Profits | ||
| 653 | |a Salvage value | ||
| 653 | |a User generated content | ||
| 653 | |a Reputation management | ||
| 653 | |a Ethics | ||
| 653 | |a Online tutorials | ||
| 653 | |a Distance learning | ||
| 653 | |a Chatbots | ||
| 653 | |a Asymmetry | ||
| 653 | |a Accountability | ||
| 653 | |a Insurance industry | ||
| 653 | |a Consumers | ||
| 653 | |a Valuation | ||
| 653 | |a Power | ||
| 653 | |a Decision making | ||
| 653 | |a Empowerment | ||
| 653 | |a Transparency | ||
| 653 | |a Access to information | ||
| 653 | |a Surveillance | ||
| 653 | |a Customization | ||
| 653 | |a Artificial intelligence | ||
| 653 | |a Educational technology | ||
| 653 | |a Web studies | ||
| 653 | |a Management | ||
| 773 | 0 | |t ProQuest Dissertations and Theses |g (2025) | |
| 786 | 0 | |d ProQuest |t ProQuest Dissertations & Theses Global | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3283373877/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3283373877/fulltextPDF/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |