{"title":"Patient text reviews and preference estimation","authors":"Nah Lee, Richard Staelin","doi":"10.1007/s11002-024-09738-2","DOIUrl":null,"url":null,"abstract":"<p>The goal of this paper is to illustrate how customer text reviews can be used to identify (a) the factors underlying consumers’ preference for a product offering and (b) the magnitude of each of these factors on the consumers’ overall assessment of the product offering experience. The authors do this using approximately 317k Google patient reviews for U.S. acute care hospitals. They first analyze the texts using Natural Language Processing and find eleven valenced topics well-describe the types of healthcare experiences. Then, after describing the structure of these reviews, they use regression analysis to estimate the magnitude of each type of experience on the patient’s overall evaluation of the experience after adjusting for any halo effect associated with the dominantly discussed topic, which has the potential of influencing the impact of the other discussed experiences. The authors conclude by providing numerous managerially significant insights coming from these analyses.</p>","PeriodicalId":48068,"journal":{"name":"Marketing Letters","volume":"17 1","pages":""},"PeriodicalIF":2.5000,"publicationDate":"2024-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Marketing Letters","FirstCategoryId":"91","ListUrlMain":"https://doi.org/10.1007/s11002-024-09738-2","RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"BUSINESS","Score":null,"Total":0}
引用次数: 0
Abstract
The goal of this paper is to illustrate how customer text reviews can be used to identify (a) the factors underlying consumers’ preference for a product offering and (b) the magnitude of each of these factors on the consumers’ overall assessment of the product offering experience. The authors do this using approximately 317k Google patient reviews for U.S. acute care hospitals. They first analyze the texts using Natural Language Processing and find eleven valenced topics well-describe the types of healthcare experiences. Then, after describing the structure of these reviews, they use regression analysis to estimate the magnitude of each type of experience on the patient’s overall evaluation of the experience after adjusting for any halo effect associated with the dominantly discussed topic, which has the potential of influencing the impact of the other discussed experiences. The authors conclude by providing numerous managerially significant insights coming from these analyses.
期刊介绍：
Marketing Letters: A Journal of Research in Marketing publishes high-quality, shorter paper (under 5,000 words including abstract, main text and references, which is equivalent to 20 total pages, double-spaced with 12 point Times New Roman font) on marketing, the emphasis being on immediacy and current interest. The journal offers a medium for the truly rapid publication of research results.
The focus of Marketing Letters is on empirical findings, methodological papers, and theoretical and conceptual insights across areas of research in marketing.
Marketing Letters is required reading for anyone working in marketing science, consumer research, methodology, and marketing strategy and management.
The key subject areas and topics covered in Marketing Letters are: choice models, consumer behavior, consumer research, management science, market research, sales and advertising, marketing management, marketing research, marketing science, psychology, and statistics.
Officially cited as: Mark Lett
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