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Exploring the underlying factors of customer value in restaurants: A machine learning approach |
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| Institution: | 1. Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, Tennessee;2. Department of Neurology, Vanderbilt University Medical Center, Nashville, Tennessee;3. Vanderbilt Kennedy Center, Vanderbilt University Medical Center, Nashville, Tennessee;4. Warren Center for Neuroscience Drug Discovery, Vanderbilt University, Nashville, Tennessee;5. Department of Pharmacology, Vanderbilt University, Nashville, Tennessee;6. Quantitative and Chemical Biology Program, Vanderbilt University, Nashville, Tennessee;7. Vanderbilt Brain Institute, Vanderbilt University, Nashville, Tennessee;8. Vanderbilt Center for Addiction Research, Vanderbilt University, Nashville, Tennessee;1. Northern Arizona University English Department, Room 140 BuIlding 18 705 S. Beaver Street Flagstaff, AZ, 86011-6032, USA;2. University Writing Program English Department, Room 140 Building 18 705 S. Beaver Street Flagstaff, AZ, 86011-6032, USA |
| Abstract: | This study explores the underlying factors of customer value in a restaurant setting by applying machine learning-based natural language processing techniques to the analysis of a vast amount of online customer reviews. The study identifies 14 factors that reflect a holistic view of previous research on customer value. The findings suggest that the comprehensive approach incorporating cognitive and affective aspects into the experiential perspective of value gain deeper insights into the nature of customer value. Moreover, the study uniquely finds new factors that prior research has rarely investigated. This study proposes a methodological framework that enables researchers to test the quantitative measure of customer perceptions derived from unstructured online reviews. Practitioners will be able to use the findings to understand customers better and enhance service operations. |
| Keywords: | Customer value Machine learning Natural language processing Word embedding Topic modeling Online review |
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