Customer engagement and bias in online reviews: comparative analysis between Airbnb and traditional hotels

He, Mengwei (2025) Customer engagement and bias in online reviews: comparative analysis between Airbnb and traditional hotels. PhD thesis, University of Glasgow.

Abstract

This study advances the understanding of customer engagement and online review dynamics in the hospitality industry by comparing traditional hotels and Airbnb within the sharing economy framework. Although online reviews have become central to shaping consumer decision-making, prior research has underexplored the distinct engagement mechanisms and biases between standardised hotel services and peer-to-peer accommodation platforms. In particular, limited attention has been given to how cognitive, emotional, and behavioural engagement dimensions influence review content, or how service-dominant logic (SDL; Vargo and Lusch, 2008) and customer-dominant logic (CDL; Heinonen et al., 2010) shape these patterns. This study addresses these gaps by examining the role of reciprocity, emotional connection, and platform design in driving review biases.
A robust multi-qualitative design was employed, integrating large-scale text-mining of TripAdvisor and Airbnb reviews using Leximancer with purposive manual coding, 26 semi-structured interviews, and a follow-up quantitative survey (Ryan, 2019; Creswell, 2014). This methodological integration ensures scalability in detecting thematic patterns while retaining contextual validity, aligning with critical realism’s emphasis on combining objective structures with subjective interpretations and with recent recommendations for ensuring paradigm–method fit in qualitative international business research (Aguzzoli et al., 2024).
Findings reveal that SDL-driven hotel engagement more emphasises structured, standardised interactions centred on service consistency and reliability, with relational elements such as guest recognition contributing to loyalty. In contrast, CDL-driven Airbnb engagement prioritises personalised, emotionally resonant experiences, often resulting in positively biased reviews reinforced by reciprocal host–guest relationships and bilateral review systems. Platform-specific features were found to amplify positive sentiment while potentially obscuring dissatisfaction.
Academically, the research bridges SDL and CDL within a comparative platform context, offering a novel conceptual model that links engagement dimensions with review biases. Methodologically, it demonstrates the value of combining automated semantic analysis with qualitative triangulation and quantitative validation. Managerially, the findings provide actionable insights for hotels to incorporate personalised engagement strategies and for sharing economy platforms to strengthen review authenticity. By addressing a critical gap in understanding engagement and bias in online reviews, the study contributes to both theory development and practice in digital hospitality management.

• Item Type: Thesis (PhD)
• Qualification Level: Doctoral
• Subjects: H Social Sciences > HF Commerce
• Colleges/Schools: College of Social Sciences > Adam Smith Business School
• Supervisor's Name: Anker, Professor Thomas and Chen, Professor Bowei
• Date of Award: 2025
• Depositing User: Theses Team
• Unique ID: glathesis:2025-85658
• Copyright: Copyright of this thesis is held by the author.
• Date Deposited: 06 Jan 2026 14:33
• Last Modified: 06 Jan 2026 14:42
• Thesis DOI: 10.5525/gla.thesis.85658
• URI: https://theses.gla.ac.uk/id/eprint/85658