Research on Strategic Management of User-Generated Content from the Perspectives of Businesses and Platforms

Abstract

With the rapid advancement of digital technologies, user-generated content (UGC) has become a cornerstone of the online ecosystem, with users actively creating content across various platforms. UGC plays a pivotal role in shaping businesses’ financial performance and platforms’ profitability, presenting both opportunities and challenges. On e-commerce platforms, online reviews boost businesses’ revenues by enhancing product credibility but also expose businesses to risks such as review blackmail. On short video platforms, user-generated videos drive personalized recommender systems, increasing engagement while reinforcing filter bubbles that limit content diversity. On social media platforms, user comments and posts enhance businesses’ visibility but also pose risks such as reputation crises fueled by viral negative content. Recognizing the dual impact of UGC, this thesis adopts a new perspective to investigate its influence on businesses and platforms across three representative digital environments: e-commerce, short video, and social media. It provides insights into strategic approaches that help businesses and platforms leverage UGC’s benefits while mitigating its potential risks.

Study 1 examines e-commerce platforms, where UGC exists in the form of consumer reviews, benefiting e-commerce sellers but also exposing them to review blackmail. Some reviewers post malicious negative reviews and demand a ransom for removal, damaging sellers’ reputations and financial performance. While previous studies have focused on legitimate negative reviews, strategies for addressing blackmail-driven reviews remain unexplored. Thus, we propose a conceptual framework to understand whether, how, and under what conditions sellers’ exposure of such blackmail in their responses affects consumers’ perceptions and behaviors. Results from two online experiments and one archival study show that exposing blackmail improves consumers’ perceived response helpfulness and attitudes toward sellers by reducing perceived seller responsibility and increasing perceived sympathy for sellers, leading to greater support for review removal by e-commerce platforms. These effects are context-dependent—third-party involvement (e.g., a logistics provider) diminishes the impact of blackmail exposure, while detailed seller explanations further weaken the diminishing effects of third-party involvement. Our findings offer implications for sellers in managing consumer reviews and for platforms in handling seller–reviewer disputes more effectively.

Study 2 examines short video platforms, where UGC primarily consists of user-generated short videos, driving personalized recommender systems while also reinforcing filter bubbles. To address this, unexpectedness in recommendations is increasingly recognized as a critical factor, extending beyond the traditional focus on personalization. However, limited research has explored how unexpectedness influences the longitudinal performance of these systems. Thus, we use agent-based simulations to systematically examine the optimal timing for introducing unexpectedness to maximize recommender system performance. We find that as users consume more recommendations within a session, the positive impact of personalization diminishes, while the positive effect of unexpectedness increases. As a result, recommender systems should initially prioritize personalization and gradually introduce unexpected recommendations during longer sessions, preserving the early benefits of personalization while leveraging the advantages of unexpectedness later. Moreover, user attributes such as boredom proneness, curiosity, memory capacity, and preference diversity significantly shape the optimal timing for emphasizing unexpectedness. These insights offer valuable guidance for designing nuanced, user-tailored recommender systems on short video platforms.

Study 3 investigates social media platforms, where for businesses, UGC primarily takes the form of user comments on their e-marketing microblogs, influencing engagement and shaping microblog popularity. As businesses increasingly rely on e-marketing microblogs for brand promotion, explaining and predicting microblog popularity has become essential for optimizing their social media marketing strategies. However, previous research on this topic has primarily focused on microblog content and creators, overlooking the potential impact of user comments as a key information source. Applying information integration theory, we propose that comments, alongside content and creator features, shape user behaviors (e.g., liking, commenting, and reposting), ultimately influencing microblog popularity. Using news value theory, key comment features are identified and analyzed through regression analysis, increasing the Cox–Snell Pseudo R2 from 0.34 to 0.49. Prediction analysis shows that integrating early comment features improves F1-scores from 0.56 to 0.71 for popular microblogs and from 0.54 to 0.71 for extremely popular microblogs. Adding time-series comment data further enhances prediction accuracy. These findings highlight the critical role of user comments in explaining and predicting microblog popularity and provide valuable insights for businesses in crafting engaging microblogs and optimizing e-marketing strategies.

Overall, this thesis provides a deeper understanding of how UGC influences businesses and digital platforms across different contexts. It highlights both the opportunities and challenges UGC presents, offering strategic insights to help businesses and platforms maximize UGC’s benefits while mitigating potential risks.

Date of Award	29 Jul 2025
Original language	English
Awarding Institution	City University of Hong Kong
Supervisor	Jingjun David XU (Supervisor) & Xueqing Wang (External Supervisor)