The Evolving Retail Ecosystem: Spatiotemporal Data, Partnerships, and Crowd Wisdom for Strategic Advantage

Abstract

The retail landscape is undergoing a profound transformation driven by technological advancements, changing consumer behaviors, and intensifying competition between online and offline channels. This dissertation explores three critical aspects of this evolving ecosystem: leveraging spatiotemporal data for strategic decision-making, forging partnerships between physical retailers and e-tailers for consumer returns, and harnessing crowd wisdom in the crowdfunding context. Chapter 3 employs tensor completion methods to estimate the treatment effect of deploying smart vending machines across various urban settings, demonstrating the superiority of this approach over traditional causal inference techniques. Chapter 4 investigates the impact of a returns partnership between a physical retailer and an e-tailer on their demand-side competition, revealing conditions under which both firms can benefit, but consumer surplus may decrease. Chapter 5 examines how atypical idea combinations on crowdfunding platforms influence project success, identifying an optimal balance between familiarity and novelty. Collectively, these chapters contribute to the literature on retail strategy, data-driven decision-making, and platform economies, offering valuable insights for firms navigating the complex and dynamic retail ecosystem.