Insights into Suspicious Online Ratings
Direct Evidence from TripAdvisor
Bibliographic Data
| ID | 12463003 |
|---|---|
| Authors | Markus Schuckert (0000-0003-1912-8672, Hong Kong Polytechnic University, corresponding author), Xianwei Liu (0000-0002-0206-9410, Harbin Institute of Technology), Robin Law (0000-0001-7199-3757, Hong Kong Polytechnic University) |
| Year | 2015 |
| Volume | 21 |
| Issue | 3 |
| Pages | 259-272 |
| Publication date | 2015-04-21 |
| Peer Reviewed | Yes |
| Open Access | No |
| Type | ARTICLE |
| Venue | Asia Pacific Journal of Tourism Research (JOURNAL) |
| Journal identifiers | ISSN: 1094-1665 • E-ISSN: 1741-6507 |
| Publisher | Routledge (PUBLISHER • GB) |
| DOI | 10.1080/10941665.2015.1029954 |
| OpenAlex | W2080013587 |
| Language | EN |
| Citations received | 20 |
| References cited | 29 |
Online ratings and online reputation management are becoming increasingly popular and important. With this increasing importance, attempts to manipulate online reviews through fake reviews have become more prevalent. Suspicious online reviews (ratings) exist on many e-commerce platforms, but these reviews have rarely been observed and reported as manipulation in academic studies using different test methods. In our research, we examine empirical evidence of suspicious online ratings based on 41,572 ratings on TripAdvisor. Applying quantitative analytics, we find three important results: (1) the gap between overall rating and individual ratings does exist and is significant, especially among the lower class hotels; (2) the proportion of suspicious ratings is about 20% at a standard of 0.5; and (3) reviewers who tend to post excellent ratings are less likely to generate big gaps when posting ratings. We offer specific managerial implications for hotel managers on online reputation management and selected suggestions for future research based on the empirical findings
Analytics · Business · Data science · Empirical evidence · Empirical research · Political science · Reputation · Reputation management · Test (biology · Computer Science · Digital Marketing and Social Media · Psychology · Sentiment Analysis and Opinion Mining · Spam and Phishing Detection · Marketing
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| Unique citing works | 20 |
|---|---|
| Citations per year | 2,22 |
| Citation span | 2017 - 2026 (10) |
| Citation velocity | current |
| Highly cited | No |
| Citation types | Neutral: 20 |