Reducing the Bias in Online Reviews Using Propensity Score Adjustment

被引:5
作者
Han, Saram [1 ,2 ]
Mikhailova, Daria [1 ]
机构
[1] Seoul Natl Univ Sci & Technol, Seoul, South Korea
[2] Seoul Natl Univ Sci & Technol, Coll Business & Technol, 232 Gongneung Ro, Seoul 01811, South Korea
关键词
selection bias; online reviews; text analyses; propensity score adjustment; WORD-OF-MOUTH; SOCIAL MEDIA; PARTICIPATION; HOSPITALITY;
D O I
10.1177/19389655231223364
中图分类号
F [经济];
学科分类号
02 ;
摘要
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.
引用
收藏
页码:429 / 441
页数:13
相关论文
共 54 条
[1]   Sentiment Analysis in Tourism: Capitalizing on Big Data [J].
Alaei, Ali Reza ;
Becken, Susanne ;
Stantic, Bela .
JOURNAL OF TRAVEL RESEARCH, 2019, 58 (02) :175-191
[2]  
ANDERSON EW, 1998, J SERV RES-US, V1, P5, DOI [DOI 10.1177/109467059800100, DOI 10.1177/109467059800100102]
[3]   Big Data, Big Insights? Advancing Service Innovation and Design With Machine Learning [J].
Antons, David ;
Breidbach, Christoph F. .
JOURNAL OF SERVICE RESEARCH, 2018, 21 (01) :17-39
[4]   Understanding and overcoming biases in online review systems [J].
Askalidis, Georgios ;
Kim, Su Jung ;
Malthouse, Edward C. .
DECISION SUPPORT SYSTEMS, 2017, 97 :23-30
[5]   An Introduction to Propensity Score Methods for Reducing the Effects of Confounding in Observational Studies [J].
Austin, Peter C. .
MULTIVARIATE BEHAVIORAL RESEARCH, 2011, 46 (03) :399-424
[6]   Uniting the Tribes: Using Text for Marketing Insight [J].
Berger, Jonah ;
Humphreys, Ashlee ;
Ludwig, Stephan ;
Moe, Wendy W. ;
Netzer, Oded ;
Schweidel, David A. .
JOURNAL OF MARKETING, 2020, 84 (01) :1-25
[7]  
Bethlehem J., 2011, HDB NONRESPONSE HOUS
[8]   The Differential Effects of the Quality and Quantity of Online Reviews on Hotel Room Sales [J].
Blal, Ines ;
Sturman, Michael C. .
CORNELL HOSPITALITY QUARTERLY, 2014, 55 (04) :365-375
[9]   Latent Dirichlet allocation [J].
Blei, DM ;
Ng, AY ;
Jordan, MI .
JOURNAL OF MACHINE LEARNING RESEARCH, 2003, 3 (4-5) :993-1022
[10]   Word of mouth communication within online communities: Conceptualing the online social network [J].
Brown, Jo ;
Broderick, Amanda J. ;
Lee, Nick .
JOURNAL OF INTERACTIVE MARKETING, 2007, 21 (03) :2-20