 Understanding value perceptions and propositions: A machine learning approachYuliya Kolomoyets and Astrid Dickinger Journal of Business Research, 2023, vol. 154, issue C Abstract: It is well established in marketing literature that aligning value creation with customers' aspirations promotes satisfaction, repurchasing, and competitiveness. This study employs structural topic modeling and sentiment analyses on online documents to provide an empirical account of value alignment from customers' and service providers' perspectives. This generates insights into i) the attributes valued by customers and service providers, respectively, ii) the valence of those attributes, iii) the sources of value formation, iv) the value alignment between customers and service providers, and v) the relative importance of value attributes for budget and upscale hotels. The results indicate that guests focus on interaction, cleanliness, and comfort, while service providers most frequently discuss service-related aspects; however, the first two attributes also affect hotel ratings. Furthermore, the sources of value differ in terms of valence. These insights show that structural topic modeling is a scalable approach to understanding value from both perspectives. Keywords: Structural topic modeling; Value proposition; Value-in-use; Machine learning; Hotel attributes (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:jbrese:v:154:y:2023:i:c:s0148296322008207 DOI: 10.1016/j.jbusres.2022.113355 Access Statistics for this article Journal of Business Research is currently edited by A. G. Woodside More articles in Journal of Business Research from Elsevier |
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