Perspectives on Michelin Green Star Restaurants: Inspector and Customer Review Analysis
DOI:
https://doi.org/10.52731/liir.v006.357Keywords:
Michelin Green Star Restaurant, Online Reviews, Text mining, BERTopicAbstract
Rapid technological advancements have transformed people’s lifestyles and eating habits. While technological innovations have brought convenience, they have also contributed to the growing challenges of global warming and environmental pollution. This study aims to explore the perspectives of customers and Michelin inspectors on the service quality and sustainability practices of Michelin Green Star restaurants. Data were collected from the global Michelin Guide website and TripAdvisor, and text mining techniques were applied to analyze online reviews from both inspectors and customers. Using the BERTopic model, the study identified seven key aspects of restaurant service quality considered in Michelin restaurant evaluations, nine aspects specific to Green Star restaurant assessments, and eight aspects highlighted in customer reviews. These findings reveal the similarities and differences in the evaluation criteria used by inspectors and customers, providing valuable insights for restaurant operators.
References
Ogrean, C., & Herciu, M. Corporate Sustainability–From a Fuzzy Concept to a Coherent Reality. Studies in Business and Economics, 13(1), 112-127. (2018).
Lorenzini, B. The green restaurant, part II: Systems and service. Restaurant & Institutions, 104(11), 119-136. (1994).
Schubert, F., Kandampully, J., Solnet, D., & Kralj, A. Exploring consumer perceptions of green restaurants in the US. Tourism and Hospitality Research, 10(4), 286-300. (2010).
Hu, H. H., Parsa, H. G., & Self, J. The dynamics of green restaurant patronage. Cornell Hospitality Quarterly, 51(3), 344-362. (2010).
Chiang, C. F., & Guo, H. W. Consumer perceptions of the Michelin Guide and attitudes toward Michelin-starred restaurants. International Journal of Hospitality Management, 93, 102793. (2021).
Ma, G., Ma, J., Li, H., Wang, Y., Wang, Z., & Zhang, B. Customer behavior in purchasing energy-saving products: Big data analytics from online reviews of e-commerce. Energy Policy, 165, 112960. (2022).
Kim, M. J., Hall, C. M., & Kim, D. K. Why do investors participate in tourism incentive crowdfunding? The effects of attribution and trust on willingness to fund. Journal of Travel & Tourism Marketing, 37(2), 141-154. (2020).
Kwok, L., Huang, Y. K., & Hu, L. Green attributes of restaurants: What really matters to consumers?. International Journal of Hospitality Management, 55, 107-117. (2016).
Moon, S. J. Investigating beliefs, attitudes, and intentions regarding green restaurant patronage: An application of the extended theory of planned behavior with moderating effects of gender and age. International Journal of Hospitality Management, 92, 102727. (2021).
Han, H., Moon, H., & Hyun, S. S. Uncovering the determinants of pro-environmental consumption for green hotels and green restaurants: A mixed-method approach. International Journal of Contemporary Hospitality Management, 32(4), 1581-1603. (2020).
Chaturvedi, P., Kulshreshtha, K., Tripathi, V., & Agnihotri, D. Investigating the impact of restaurants' sustainable practices on consumers' satisfaction and revisit intentions: a study on leading green restaurants. Asia-Pacific Journal of Business Administration, 16(1), 41-62. (2024).
Zanetta, L. D. A., Xavier, M. C., Hakim, M. P., Stedefeldt, E., Zanin, L. M., Medeiros, C. O., & da Cunha, D. T. How does the consumer choose a restaurant? An overview of the determinants of consumer satisfaction. Food Research International, 114369. (2024).
Leong, A. M. W., Chen, K. Y., Chen, H. B., Chiang, T. E., & Huan, T. C. Exploring the effects of customers' assessments of a restaurant's atmospherics and emotional assessments on a fine-dining experience and intentions to return. Tourism Management Perspectives, 47, 101121. (2023).
Liu, Y., Song, Y., Sun, J., Sun, C., Liu, C., & Chen, X. Understanding the relationship between food experiential quality and customer dining satisfaction: A perspective on negative bias. International Journal of Hospitality Management, 87, 102381.
Yi, J., & Oh, Y. K. (2022). The informational value of multi-attribute online consumer reviews: A text mining approach. Journal of Retailing and Consumer Services, 65, 102519. (2020).
Wu, J., Yang, T., Zhou, Z., & Zhao, N. Consumers' affective needs matter: Open innovation through mining luxury hotels' online reviews. International Journal of Hospitality Management, 114, 103556. (2023).
Garner, B., Thornton, C., Pawluk, A. L., Cortez, R. M., Johnston, W., & Ayala, C. Utilizing text-mining to explore consumer happiness within tourism destinations. Journal of Business Research, 139, 1366-1377. (2022).
Kurowski, Ł., & Smaga, P. Analysing financial stability reports as crisis predictors with the use of text-mining. The Journal of Economic Asymmetries, 28, e00322. (2023).
Shamshiri, A., Ryu, K. R., & Park, J. Y. Text mining and natural language processing in construction. Automation in Construction, 158, 105200. (2024).
Kumar, A., Chakraborty, S., & Bala, P. K. Text mining approach to explore determinants of grocery mobile app satisfaction using online customer reviews. Journal of Retailing and Consumer Services, 73, 103363. (2023).
Grootendorst, M. BERTopic: Neural topic modeling with a class-based TF-IDF procedure. arXiv preprint arXiv:2203.05794. (2022).
Ng, Q. X., Yau, C. E., Lim, Y. L., Wong, L. K. T., & Liew, T. M. Public sentiment on the global outbreak of monkeypox: An unsupervised machine learning analysis of 352,182 twitter posts. Public Health, 213, 1-4. (2022).
Jeon, E., Yoon, N., & Sohn, S. Y. Exploring new digital therapeutics technologies for psychiatric disorders using BERTopic and PatentSBERTa. Technological Forecasting and Social Change, 186, 122130. (2023).
Niroomand, K., Saady, N. M. C., Bazan, C., Zendehboudi, S., Soares, A., & Albayati, T. M. Smart investigation of artificial intelligence in renewable energy system technologies by natural language processing: Insightful pattern for decision-makers. Engineering Applications of Artificial Intelligence, 126, 106848. (2023).
