Estudo Exploratório do Setor Gastronômico Brasileiro: Uma Análise de Dados de Plataformas Online
Resumo
Este estudo exploratório analisa a influência de atributos chave nas avaliações online do Google Reviews de restaurantes brasileiros. Focando em aspectos como classificações, preços e tipos de cozinha, foi possível identificar padrões significativos nas preferências dos clientes, como a predominância da conveniência nas avaliações positivas. Esses resultados oferecem orientações para melhorar estratégias no setor gastronômico e turístico, contribuindo para uma compreensão mais profunda das dinâmicas que afetam as decisões dos consumidores, ressaltando a necessidade de adaptação aos contextos locais.Referências
de Oliveira Garcia, R. K., Zottis, A. M., and Bonho, D. V. (2015). A gastronomia e seu uso turístico: a contribuição do projeto de extensão “turismo e gastronomia: unindo sabores e saberes” para a valorização do patrimônio cultural imaterial. Gestão e Desenvolvimento, 12(2):67–82.
Dong, B., Li, M., and Sivakumar, K. (2019). Online review characteristics and trust: A cross-country examination. Decision Sciences, 50(3):537–566.
Gavilan, D., Avello, M., and Martinez-Navarro, G. (2018). The influence of online ratings and reviews on hotel booking consideration. Tourism Management, 66:53–61.
Huang, Y., Li, C., Wu, J., and Lin, Z. (2018). Online customer reviews and consumer evaluation: The role of review font. Information & Management, 55(4):430–440.
Li, H. and Hecht, B. (2021). 3 stars on yelp, 4 stars on google maps: a cross-platform examination of restaurant ratings. Proceedings of the ACM on Human-Computer Interaction, 4(CSCW3):1–25.
Mathayomchan, B. and Taecharungroj, V. (2020). “how was your meal?” examining customer experience using google maps reviews. International Journal of Hospitality Management, 90:102641.
Navitha Sulthana, A. and Vasantha, S. (2023). Effect of online review rating on purchase intention. In Artificial Intelligence for Societal Issues, pages 127–140. Springer.
Park, C. W., Sutherland, I., and Lee, S. K. (2021). Effects of online reviews, trust, and picture-superiority on intention to purchase restaurant services. Journal of Hospitality and Tourism Management, 47:228–236.
Sadiq, S., Umer, M., Ullah, S., Mirjalili, S., Rupapara, V., and Nappi, M. (2021). Discrepancy detection between actual user reviews and numeric ratings of google app store using deep learning. Expert Systems with Applications, 181:115111.
Shahhosseini, M. and Khalili Nasr, A. (2024). What attributes affect customer satisfaction in green restaurants? an aspect-based sentiment analysis approach. Journal of Travel & Tourism Marketing, 41(4):472–490.
Siering, M. (2021). Leveraging online review platforms to support public policy: Predicting restaurant health violations based on online reviews. Decision Support Systems, 143:113474.
Wang, C., Zhang, X., and Hann, I.-H. (2018). Socially nudged: A quasi-experimental study of friends’ social influence in online product ratings. Information Systems Research, 29(3):641–655.
Xiang, Z., Du, Q., Ma, Y., and Fan, W. (2017). A comparative analysis of major online review platforms: Implications for social media analytics in hospitality and tourism. Tourism Management, 58:51–65.
Yoo, K.-H., Sigala, M., and Gretzel, U. (2016). Exploring tripadvisor. Open tourism: open innovation, crowdsourcing and co-creation challenging the tourism industry, pages 239–255.
Dong, B., Li, M., and Sivakumar, K. (2019). Online review characteristics and trust: A cross-country examination. Decision Sciences, 50(3):537–566.
Gavilan, D., Avello, M., and Martinez-Navarro, G. (2018). The influence of online ratings and reviews on hotel booking consideration. Tourism Management, 66:53–61.
Huang, Y., Li, C., Wu, J., and Lin, Z. (2018). Online customer reviews and consumer evaluation: The role of review font. Information & Management, 55(4):430–440.
Li, H. and Hecht, B. (2021). 3 stars on yelp, 4 stars on google maps: a cross-platform examination of restaurant ratings. Proceedings of the ACM on Human-Computer Interaction, 4(CSCW3):1–25.
Mathayomchan, B. and Taecharungroj, V. (2020). “how was your meal?” examining customer experience using google maps reviews. International Journal of Hospitality Management, 90:102641.
Navitha Sulthana, A. and Vasantha, S. (2023). Effect of online review rating on purchase intention. In Artificial Intelligence for Societal Issues, pages 127–140. Springer.
Park, C. W., Sutherland, I., and Lee, S. K. (2021). Effects of online reviews, trust, and picture-superiority on intention to purchase restaurant services. Journal of Hospitality and Tourism Management, 47:228–236.
Sadiq, S., Umer, M., Ullah, S., Mirjalili, S., Rupapara, V., and Nappi, M. (2021). Discrepancy detection between actual user reviews and numeric ratings of google app store using deep learning. Expert Systems with Applications, 181:115111.
Shahhosseini, M. and Khalili Nasr, A. (2024). What attributes affect customer satisfaction in green restaurants? an aspect-based sentiment analysis approach. Journal of Travel & Tourism Marketing, 41(4):472–490.
Siering, M. (2021). Leveraging online review platforms to support public policy: Predicting restaurant health violations based on online reviews. Decision Support Systems, 143:113474.
Wang, C., Zhang, X., and Hann, I.-H. (2018). Socially nudged: A quasi-experimental study of friends’ social influence in online product ratings. Information Systems Research, 29(3):641–655.
Xiang, Z., Du, Q., Ma, Y., and Fan, W. (2017). A comparative analysis of major online review platforms: Implications for social media analytics in hospitality and tourism. Tourism Management, 58:51–65.
Yoo, K.-H., Sigala, M., and Gretzel, U. (2016). Exploring tripadvisor. Open tourism: open innovation, crowdsourcing and co-creation challenging the tourism industry, pages 239–255.
Publicado
21/07/2024
Como Citar
MELO, Tiago de.
Estudo Exploratório do Setor Gastronômico Brasileiro: Uma Análise de Dados de Plataformas Online. In: BRAZILIAN WORKSHOP ON SOCIAL NETWORK ANALYSIS AND MINING (BRASNAM), 13. , 2024, Brasília/DF.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2024
.
p. 200-206.
ISSN 2595-6094.
DOI: https://doi.org/10.5753/brasnam.2024.2002.
