Toward a more populous online platform: The economic impacts of compensated reviews

• Published in: Transportation research. Part E, Logistics and transportation review Vol. 202; p. 104294
• Main Authors: Li, Peng, Park, Arim, Cho, Soohyun, Zhao, Yao
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.10.2025
• Subjects: Classification; Compensated reviews; Online reviews; Text analysis; Topic modeling; Transportation; Topic modeling; Compensated reviews; Text analysis; Transportation; Classification; Online reviews
• ISSN: 1366-5545
• DOI: 10.1016/j.tre.2025.104294

Many companies nowadays offer compensation to online reviews (called compensated reviews), expecting to increase the volume of their non-compensated reviews and overall rating. Does this strategy work? On what subjects or topics does this strategy work the best? These questions have still not been answered in the literature but draw substantial interest from the industry. In this paper, we study the effect of compensated reviews on non-compensated reviews by utilizing online reviews on 1240 auto shipping companies over a ten-year period from a transportation website. Because some online reviews have missing information on their compensation status, we first develop a classification algorithm to differentiate compensated reviews from non-compensated reviews by leveraging a machine learning-based identification process, drawing upon the unique features of the compensated reviews. From the classification results, we empirically investigate the effects of compensated reviews on non-compensated. Our results indicate that the number of compensated reviews does indeed increase the number of non-compensated reviews. In addition, the ratings of compensated reviews positively affect the ratings of non-compensated reviews. Moreover, if the compensated reviews feature the topic/subject of a car shipping function, the positive effect of compensated reviews on non-compensated ones is the strongest. Besides methodological contributions in text classification and empirical modeling, our study provides empirical evidence on how to prove the effectiveness of compensated online reviews in terms of improving the platform’s overall online reviews and ratings. Also, it suggests a guideline for utilizing compensated reviews to their full strength, that is, with regard to featuring certain topics or subjects in these reviews to achieve the best outcome. •Identify compensated reviews from transportation review platforms using a machine learning approach.•Examine the impact of compensated reviews on non-compensated reviews in the transportation industry.•Provide insights into how compensated reviews influence online ratings.•Show compensated reviews increase both the volume and ratings of non-compensated reviews.•Offer practical strategies for transportation platforms to enhance online reputations using compensated reviews.