Prof. YANG2

Digital Platform and Pricing

Why is this topic important? 
The proliferation of digital platforms has revolutionized how tourism products are distributed, priced, and evaluated. Beyond pricing economics, the post-purchase behavior of tourists—specifically their online reviews—has become a critical determinant of business performance. Understanding what makes a review “helpful” to others, how the timing of a review affects its rating (temporal contiguity), and how different user traits lead to rating variations (scaling heterogeneity) is essential for interpreting consumer feedback. Furthermore, hotel response strategies have emerged as a vital tool for reputation management. How a manager responds—whether through personalized topics, linguistic matching, or emotional positivity—can significantly alter future ratings and consumer trust.
 
What has our team done so far? 
In pricing, our team analyzed “opaque” channels and Airbnb’s impact on hotels, finding that higher-class hotels use opaque discounts to protect brand equity. We have significantly expanded this area by examining tourist post-review behavior. We identified “scaling heterogeneity” using a Hierarchical Ordered Probit (HOPIT) model, revealing that different demographic groups (e.g., business travelers vs. couples) use different rating thresholds to express the same level of satisfaction. Our team also explored “temporal contiguity,” finding that reviews posted closer to the travel date tend to be more extreme, particularly for negative experiences. Regarding hotel response strategies, we found that “topic-matched” responses (addressing specific issues raised by guests) effectively increase future ratings, especially for economy hotels. Our team also found that matching the guest’s linguistic style enhances perceived helpfulness. However, on peer-to-peer platforms like Airbnb, we identified a “positivity paradox,” where overly positive responses backfire by reducing perceived authenticity. Additionally, we examined the impact of “review novelty” and “inconsistency,” finding that novel information increases usefulness while inconsistency decreases it. Finally, we analyzed the influence of “expert identity,” showing that while expert reviews boost a hotel’s ratings, the experts themselves tend to become more critical after achieving that status.

Recommendation

Related Publications (click for all related publication)

  • Article
  • US
  • Econometrics, Big Data

Balancing the clock: How predictive scheduling laws influence employee satisfaction in the U.S. tourism and hospitality industry

Yang, Yang; Tan, Karen Pei-Sze; Liu, Yi Vanessa

2026

TOURISM MANAGEMENT

10.1016/j.tourman.2026.105419

  • Article
  • US
  • Econometrics, Big Data

How does the number of attribute ratings affect a product’s overall rating? Evidence from TripAdvisor

Zhang, Ziqiong; Yang, Yang; Wang, Xueyan; Wang, Chuxin; Zhang, Zili

2026

INFORMATION & MANAGEMENT

10.1016/j.im.2025.104275

  • Article
  • US
  • Econometrics, Big Data

How market structure and market power shape hotel price discrimination – Evidence from big data

Tian, Fengjun; Wang, Zhonglie; Yang, Yang; Mao, Zhenxing

2025

TOURISM ECONOMICS

10.1177/13548166241281757

Related Presentations

Related Resources

  • Tool

Rating Adjustment Tool

Tool link: https://uflyy.github.io/rating-adjustment/

 

The Rating Adjustment Tool is an advanced analytical web application designed to standardize hotel online reviews by correcting for “scaling heterogeneity”—the phenomenon where different types of reviewers interpret and use rating scales differently. Powered by a Hierarchical Ordered Probit (HOPIT) model grounded in peer-reviewed academic research , the tool mathematically controls for systematic response biases tied to traveler demographics (such as age and gender) and trip characteristics (such as travel type and reviewer expertise). By offering individual review adjustments, hotel-level aggregate score calculations, and batch CSV processing, the tool effectively translates subjective, raw user ratings into objective, comparable latent scores and standardized 1–5 metrics, ensuring fairer and more accurate hotel evaluations.

This rating adjustment tool is built on the theoretical framework and empirical results from the following paper:

Leung, X. Y., & Yang, Y. (2020). Are all five points equal? Scaling heterogeneity in hotel online ratings. International Journal of Hospitality Management, 88, 102539.

Resource link

  • Tool

PS-SAT: Predictive Scheduling & Satisfaction Analytics Tool

This web-based dashboard is designed to visualize tourism and hospitality employee satisfaction data across U.S. geographic units at both the City and Metropolitan Statistical Area (MSA) levels. The application provides interactive mapping, subgroup breakdowns, and (where applicable) legislative pre– and post–comparisons.

Key Features:

  • Dual-Level Geographic View: Users can switch between City-Level (point markers) and MSA-Level (polygon map) visualization.
  • Interactive Search & Selection: Users can search locations dynamically and select them either via the map or the dropdown search interface.
  • Subgroup Breakdown Analysis: Satisfaction scores are displayed by:
    • Job tenure
    • Business model
    • Skill level
    • Front-of-house vs. back-of-house
  • Legislative Effect Comparison: For locations with valid policy-period data, the dashboard displays Pre-Law vs. Post-Law satisfaction comparisons.
  • Visual Encoding of Sample Size: City marker size scales with sample size (N), allowing immediate identification of data-rich locations.

Link: https://uflyy.github.io/ps-tool/

Resource link

Prof. Yang Yang (YANG2)

School of Sport, Tourism and Hospitality Management, Temple University

U.S.-Asia Center for Tourism and Hospitality Research

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