{"title":"Reducing the Bias in Online Reviews Using Propensity Score Adjustment","authors":"Saram Han, Daria Mikhailova","doi":"10.1177/19389655231223364","DOIUrl":null,"url":null,"abstract":"Online hotel reviews on platforms like TripAdvisor are crucial in shaping customer choices and steering business strategies in the hospitality sector. However, the effectiveness of these platforms is partially hindered by the self-selection bias found in voluntary reviews. This bias can create false expectations and unsatisfactory experiences, mainly as the feedback generally comes from a non-representative group of self-motivated reviewers (SMRs). A common strategy to mitigate this bias is increasing the number of reviews through customer surveys, generating retailer-prompted reviews (RPRs). However, these RPRs, despite reducing selection bias, tend to lack the depth and insight of SMRs, resulting in a credibility gap and reduced representativeness. To address this, our study presents a novel approach using the propensity score adjustment (PSA) technique. This method leverages the distribution of RPRs to refine the accuracy of text data from SMRs, aiming to enhance the reliability and representativeness of online reviews. By combining the strengths of both RPRs and SMRs, we aim to create an online review environment that is both accurate and reliable. In conclusion, this research marks an important step toward improving online review platforms, aiming for a more transparent and trustworthy environment for reviews.","PeriodicalId":47888,"journal":{"name":"Cornell Hospitality Quarterly","volume":null,"pages":null},"PeriodicalIF":3.4000,"publicationDate":"2024-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Cornell Hospitality Quarterly","FirstCategoryId":"91","ListUrlMain":"https://doi.org/10.1177/19389655231223364","RegionNum":4,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"HOSPITALITY, LEISURE, SPORT & TOURISM","Score":null,"Total":0}
引用次数: 0
Abstract
Online hotel reviews on platforms like TripAdvisor are crucial in shaping customer choices and steering business strategies in the hospitality sector. However, the effectiveness of these platforms is partially hindered by the self-selection bias found in voluntary reviews. This bias can create false expectations and unsatisfactory experiences, mainly as the feedback generally comes from a non-representative group of self-motivated reviewers (SMRs). A common strategy to mitigate this bias is increasing the number of reviews through customer surveys, generating retailer-prompted reviews (RPRs). However, these RPRs, despite reducing selection bias, tend to lack the depth and insight of SMRs, resulting in a credibility gap and reduced representativeness. To address this, our study presents a novel approach using the propensity score adjustment (PSA) technique. This method leverages the distribution of RPRs to refine the accuracy of text data from SMRs, aiming to enhance the reliability and representativeness of online reviews. By combining the strengths of both RPRs and SMRs, we aim to create an online review environment that is both accurate and reliable. In conclusion, this research marks an important step toward improving online review platforms, aiming for a more transparent and trustworthy environment for reviews.
期刊介绍:
Cornell Hospitality Quarterly (CQ) publishes research in all business disciplines that contribute to management practice in the hospitality and tourism industries. Like the hospitality industry itself, the editorial content of CQ is broad, including topics in strategic management, consumer behavior, marketing, financial management, real-estate, accounting, operations management, planning and design, human resources management, applied economics, information technology, international development, communications, travel and tourism, and more general management. The audience is academics, hospitality managers, developers, consultants, investors, and students.