04/23/2025
By Becky Lawrence

The Operations and Information Systems Department in the Manning School of Business invites you to a doctoral dissertation proposal defense by Jie Li, Ph.D. Candidate in Decision Sciences, on "Algorithms, Attention, and Advantage: An AI-Driven Analytical Study of Business Competitiveness and Customer Experience in the Platform Economy."

Date: May 6, 2025
Time: 10:30 a.m. – 12 p.m.
Location: Via Zoom

Thesis/Dissertation Title: Algorithms, Attention, and Advantage: An AI-Driven Analytical Study of Business Competitiveness and Customer Experience in the Platform Economy

Dissertation Committee Members

  • Asil Oztekin, Ph.D., Dissertation Chair, Professor of Analytics, Department of Operations and Information Systems, Manning School of Business, UMass Lowell
  • Hongwei (Harry) Zhu, Ph.D., Professor of Information Systems, Department of Operations and Information Systems, Manning School of Business, UMass Lowell
  • Hadi Amiri, Ph.D., Assistant Professor of Computer Science, Miner School of Computer & Information Sciences, UMass Lowell

Dissertation Proposal Abstract:
In the contemporary platform-driven economy, online platforms such as Yelp and TripAdvisor play a pivotal role in shaping consumer decision-making. These platforms significantly reduce search costs, facilitate pre-purchase evaluation, and enable the dissemination of post-purchase experiences, thereby influencing business reputation. Small businesses, especially those with a predominant offline presence, often encounter challenges in accessing competitive intelligence and deciphering dynamic market conditions. This dissertation investigates the role of digital platforms and consumer-generated reviews in enabling firms to assess competitive positioning and interpret customer feedback, thereby facilitating more informed and strategic decision-making.

Chapter 1 develops a data-driven analytical framework to measure business competitiveness on digital platforms, leveraging spatial, categorical, and sentiment-based indicators. This study examines the relationship between these indicators and business survival, with a particular focus on the differential impact of various consumer cohorts—specifically new versus returning customers—on business outcomes. Utilizing a longitudinal dataset comprising over 9,000 restaurants across a sixteen-year span (2005–2021), we employ a survival analysis framework augmented with machine learning techniques to integrate temporally-lagged consumer sentiments and competitive attributes. Explainable AI (XAI) methods are utilized to elucidate the relative contribution of each competitiveness feature to business outcomes. Findings suggest that businesses exhibiting high similarity to proximate competitors and minimal differentiation face an elevated risk of closure. Furthermore, reviews from new customers exert a disproportionately strong influence on survival outcomes, underscoring the reputational importance of first-time customer experiences. These insights offer strategic guidance for entrepreneurs and managers, emphasizing the need for deliberate differentiation, geospatial awareness, and effective management of initial consumer interactions.

Chapter 2 shifts focus to the boutique hotel sector, examining customer satisfaction through an NLP-based analysis of 12,949 TripAdvisor reviews. This study identifies core themes within customer feedback, revealing how variables such as booking convenience, service quality, amenities, and overall stay experience influence satisfaction ratings. A cross-national comparative analysis highlights significant geographic variation: U.S. consumers tend to emphasize the holistic stay experience, whereas U.K. consumers assign greater weight to the availability and quality of amenities. By integrating structural topic modeling (STM) and ordinal logistic regression, the chapter elucidates the relationship between review sentiment, narrative richness, and satisfaction outcomes. These findings equip boutique hotel operators with nuanced insights into customer expectations and preferences, offering actionable implications for the design and delivery of guest experiences.

By synthesizing these two empirical investigations, this dissertation proposal offers a holistic perspective on how digital platforms and user-generated content (UGC) shape business competitiveness and consumer satisfaction. The research provides actionable insights for businesses seeking to enhance their digital footprint, adapt to evolving competitive landscapes, and make evidence-based decisions to optimize customer experience and long-term viability in an increasingly digital marketplace.