Percepción de la calidad en restaurantes: un análisis mixto con redes neuronales

Asdrúbal López-Chau; J. Patricia Muñoz-Chávez; David Valle-Cruz

doi:10.18046/j.estger.2022.165.5235

Autores

Asdrúbal López-Chau Profesor de Tiempo Completo, CU UAEM Zumpango, Laboratorio de Tecnologías Computacionales Aplicadas, Universidad Autónoma del Estado de México, Zumpango, México.
J. Patricia Muñoz-Chávez Profesora de Tiempo Completo, Área Académica de Desarrollo de Negocios, Universidad Tecnológica de la Zona Metropolitana del Valle de México, Tizayuca Hidalgo, México.
David Valle-Cruz Profesor Investigador, Unidad Académica Profesional Tianguistenco, Universidad Autónoma del Estado de México, Santiago Tianguistenco, México.

DOI:

https://doi.org/10.18046/j.estger.2022.165.5235

Palavras-chave:

qualidade, restaurantes, turismo, redes neurais artificiais, inteligência artificial

Resumo

Este estudo se concentra em identificar os fatores que influenciam a percepção do consumidor de qualidade em restaurantes de serviço de mesa na cidade mágica de Real del Monte, Hidalgo, México. A metodologia baseia-se em duas perspectivas: em primeiro lugar, na análise dos fatores mais importantes dos resultados de uma pesquisa de 22 itens por meio de redes neurais artificiais aplicada a 320 comensais e, em segundo lugar, na aplicação de entrevistas semiestruturadas a oito comensais. Os achados mostram que os aspectos fundamentais que influenciam a percepção dos consumidores são a capacidade de resposta dos funcionários, a música ambiente, bem como a qualidade e sabor dos alimentos.

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Referências

Akhil, A. y Suresh, M. (2021). Assessment of service quality in restaurant using multi-grade fuzzy and importance performance analysis. Materials Today: Proceedings [preprint]. https://doi.org/10.1016/j.matpr.2021.01.767

Albrecht, J. Danielmeier, T. y Boudreau, P. (2019). The importance of architecture in food and drink experiences within a tourism context. Journal of Gastronomy and Tourism, 4(1), 41-50. https://doi.org/10.3727/216929719X15657857907789

Antón, C., Camarero, C. y Laguna-García, M. (2017). Towards a new approach of destination royalty drivers: Satisfaction, visit intensity and tourist motivation. Current Issues in Tourism, 20(3), 238-260. https://doi.org/10.1080/13683500.2014.936834

Antun, J.M., Frash, R.E., Costen, W. y Runyan, R. (2010). Accurately assessing expectations most important to restaurant patrons: The creation of the DinEX scale. Journal of Foodservice Business Research, 13(4), 360-379. https://dx.doi.org/10.1080/15378020.2010.524539

Aragón, C. L., González, A. y Lagarda-Leyva, E. A. (2022). Cultura organizacional y competitividad de las empresas restauranteras y hoteleras de Sonora, México. Ciencias Administrativas, 10(19), 1-13. https://doi.org/10.24215/23143738e095

Assadi, D. y Flandrin, A. (2009). L´impact de la musique sur le comportament d´achat. Cahiers du Ceren, (26). 2-16.

Barber, N. y Scarcelli, J. (2009). Clean restrooms: how important are they to restaurant consumers? Journal of Foodservice, 20(6), 309-320. https://doi.org/10.1111/j.1748-0159.2009.00155.x

Barbosa, D. M. E. y Ayala, A. H. (2014). Los determinantes de la orientación exportadora y los resultados en las pymes exportadoras en Colombia. Estudios Gerenciales, 30(133), 430-440. https://dx.doi.org/10.1016/j.estger.2014.05.002

Callejo, J. (2002). Observación, entrevista y grupo de discusión: el silencio de tres prácticas de investigación. Revista Española de Salud Pública, 76,(5), 409-422.

Campos-Vázquez, R. M., y Esquivel, G. (2020). Niveles y Patrones de Consumo en la Era del COVID-19. Revista Nexos en Línea, 6. https://www.nexos.com.mx/?p=48034

Chen, C-F. y Chen, F-S. (2010). Experience quality, perceive value, satisfaction, and behavioral intentions for heritage tourists. Tourism Management, 31(1), 29-35. https://doi.org/10.1016/j.tourman.2009.02.008

Clemente-Ricolfe, J. S. (2016). Atributos relevantes de la calidad en el servicio y su influencia en el comportamiento poscompra. El caso de las hamburgueserías en España. Innovar, 26(62), 69-78. https://doi.org/10.15446/innovar.v26n62.59389

Contreras, T. J. C., Flores, S. G. Z., Hernández, I. Z. y Zamorano, Z. M. V. (2020). Desafíos del turismo en el noroeste de México entre Ciudad Juárez, Chihuahua y San Luis Río Colorado ante la incertidumbre del COVID 19. En Conferencia Internacional sobre Turismo, Tecnología y Sistemas (pp. 509-520). Springer.

Folgieri, R., Baldigara, T. y Mamula, M. (2017). Artificial neural networks-based econometric models for tourism demand forecasting. ToSEE-Tourism in Southern and Eastern Europe, 4, 169-182. https://dx.doi.org/10.20867/tosee.04.10

Garson, G. D. (1991). Interpreting neural network connection weights. AI Expert. Scientific Research an Academic Publisher, (6). 47-51.

Graupe, D. (2013). Principles of artificial neural networks (vol. 7). World Scientific.

Ha, J. y Jang, S. (2010). Effects of service quality and food quality: The moderating role of atmospherics in an ethnic restaurant segment. International Journal of Hospitality Management, 29(3), 520-529. https://doi.org/10.1016/j.ijhm.2009.12.005

Hernández-Rojas, R. D., Folgado-Fernandez, J. A. y Palos-Sánchez, P. R. (2021). Influence of the restaurant brand and gastronomy on tourist loyalty. A study in Córdoba (Spain). International Journal of Gastronomy and Food Science, 23. https://doi.org/10.1016/j.ijgfs.2021.100305

Hirudayaraj, M. y Clay, C. (2019). Experiences of women veterans within the private sector: Examining the intersection of gender and veteran status. Human Resource Development Quarterly, 30(4), 473-494. https://doi.org/10.1002/hrdq.21367

Horng, J. S., Chou, S. F., Liu, C. H. y Tsai, C. Y. (2013). Creativity, aesthetics and eco-friendliness: A physical dining environment design synthetic assessment model of innovative restaurants. Tourism Management, 36, 15-25. https://dx.doi.org/10.1016/j.tourman.2012.11.002

Hu, Y. C., Jiang, P. y Lee, P. C. (2019). Forecasting tourism demand by incorporating neural networks into Grey-Markov models. Journal of the Operational Research Society, 70(1), 12-20. https://doi.org/10.1080/01605682.2017.1418150

Hwang, J. y Zhao, J. (2010). Factors influencing customer satisfaction or dissatisfaction in the restaurant business using answertree methodology. Journal of Quality Assurance in Hospitality & Tourism, 11(2), 93-110. https://doi.org/10.1080/15280081003800355

Jia, S. S. (2020). Motivation and satisfaction of Chinese and US tourists in restaurants: A cross-cultural text mining of online reviews. Tourism Management, 78. https://doi.org/10.1016/j.tourman.2019.104071

Kim, J., Wei, S. y Ruys, H. (2003). Segmenting the market of West Australian senior tourists using an artificial neural network. Tourism Management, 24(1), 25-34. https://doi.org/10.1016/S0261-5177(02)00050-X

McCulloch, W. S. y Pitts, W. (1943). A logical calculus of the ideas immanent in nervous activity. The Bulletin of Mathematical Biophysics, 5(4), 115-133.

Mira, J., Pérez-Jover, V., Lorenzo, J. y Vitaller, J. (2002). La investigación cualitativa: una alternativa también válida. Atención primaria, 34(4), 161-165.

Namkung, Y. y Jang, S. (2008). Are highly satisfied restaurant customers really different? A quality perception perspective. International Journal of Contemporary Hospitality Management, 20(2), 142-155. https://doi.org/10.1108/09596110810852131

Negnevitsky, M. (2005). Artificial intelligence: A guide to intelligent systems. Pearson Education.

Olalla, G. (2020). COVID-19: Un único aspecto positivo para el turismo. https://www.hosteltur.com/comunidad/003987_covid-19-un-unico-aspectopositivo-para-el-turismo.html

Organización Mundial del Turismo. (2020). Barómetro OMT del turismo mundial. UNWTO, 18(2). https://www.unwto.org/es/taxonomy/term/347

Orgaz, F. y Moral, S. (2016). El turismo como motor potencial para el desarrollo económico de zonas fronterizas en vías de desarrollo. Un estudio de caso. El Periplo Sustentable, 31, 1-17.

Palmer, A., Montano, J. J. y Sesé, A. (2006). Designing an artificial neural network for forecasting tourism time series. Tourism Management, 27(5), 781-790.

Pitarque, A., Ruiz, J. C. y Roy, J. F. (2000). Las redes neuronales como herramientas estadísticas no paramétricas de clasificación. Psicothema, 12(Su2), 459-463.

Pérez-Gálvez, J., Medina-Viruel, M., Jara-Alba, C. y López-Guzmán, T. (2020). Segmentation of food market visitors in World Heritage Sites. Case study of the city of Córdoba (Spain). Current Issues in Tourism, 4(8), 1139-1153. https://doi.org/10.1080/13683500.2020.1769570

Pérez-Ramírez, C. y Antolín-Espinoza, D. (2016). Programa pueblos mágicos y desarrollo local: Actores, dimensiones y perspectivas en El Oro, México. Estudios sociales, 25(47), 219-242.

Quecedo, R. y Castaño, C. (2002). Introducción a la metodología de la investigación cualitativa. Revista de Psicodidáctica, (14), 5-40.

Rodríguez, R. M. A. S., Pulido-Fernández, J. I. y Herrera, I. M. R. (2017). El producto turístico en los Pueblos Mágicos de México. Un análisis crítico de sus componentes. Revista de Estudios Regionales, (108), 125-163.

Ryu, K. y Han, H. (2010). Influence of the quality of food, service, and physical environment on customer satisfaction and behavioral intention in quick-casual restaurants: Moderating role of perceived price. Journal of Hospitality & Tourism Research, 34(3), 310-329.

Ryu, K. y Han, H. (2011). New or repeat customers: how does physical environment influence their restaurant experience? International Journal of Hospitality Management, 30(3), 599-611.

Sabir, R., Ghafoor, O., Hafeez, I., Akhtar, N. y Rehman, A. (2014). Factors affecting customers satisfaction in restaurants industry in Pakistan. International Review of Management and Business Research, 3(2), 869-876.

Seo, S. y Yun, N. (2015). Multi-dimensional scale to measure destination food image: Case of Korean food. British Food Journal, 117(12), 2914-2929. https://doi.org/10.1108/BFJ-03-2015-0114

Slack, N., Singh, G., Ali, J., Lata, R., Mudaliar, K. y Swamy, Y. (2020). Influence of fast-food restaurant service quality and its dimensions on customer perceived value, satisfaction and behavioural intentions. British Food Journal, 123(4), 1324-1344. https://doi.org/10.1108/BFJ-09-2020-0771

Sultan, I. (2019). Religious tourism and its impact on local economy and environment: A case study on Dakshineswar and Adyapeath, West Bengal, India. International Journal of Humanities and Social Science, 6(6), 85-91. https://doi.org/10.14445/23942703/IJHSS-V6I6P112

Torres, M. y Mora, C. (2017). Experiencia de consumo y los niveles de satisfacción de los usuarios de establecimientos de comida rápida en el municipio Libertador del estado Mérida, Venezuela. Visión Gerencial, 3(1), 43-58.

Tuzunkan, D. y Albayrak, A. (2016). The importance of restaurant physical environment for Turkish customers. J Tourism Res Hospitality 5(1). https://doi.org/10.4172/2324-8807.1000154

Uysal, M. y El Roubi, M. S. (1999). Artificial neural networks versus multiple regression in tourism demand analysis. Journal of Travel Research, 38(2), 111-118.

Van der Walt, R., Greyling, M. y Kotzé, T. (2014). Customers' perceptions of restaurant experience in Gauteng. Contemporary Management in Theory and Practice, 149-157.

Valle-Cruz, D., Gil-Garcia, J. R. y Fernandez-Cortez, V. (2020). Towards smarter public budgeting? Understanding the potential of artificial intelligence techniques to support decision making in government. En dg.o 2020: 21th Annual International Conference on Digital Government Research (dg.o 2020), junio 15-19, 2020, Seoul, Korea. ACM, New York, EE. UU. https://doi.org/10.1145/3396956.3396995

Vareiro, L. Ribeiro, J. y Remoaldo, P. (2019). What influences a tourist to return to a cultural destination? International Journal of Tourism Research, 21(2), 280-290. https://doi.org/10.1002/jtr.2260

Vega-Falcón, V., Castro-Sánchez, F. y Romero-Fernández, A. J. (2020). Impacto de la Covid-19 en el turismo mundial. Revista Universidad y Sociedad, 12(S1), 207-216.

Vera, J. y Trujillo, A. (2017). Escala mexicana de calidad en el servicio en restaurantes (EMCASER). Innovar, 27(63), 43-60. https://doi.org/10.15446/innovar.v26n63.60665.

Weckman, G. R., Dravenstott, R. W., Young II, W. A., Ardjmand, E., Millie, D. F. y Snow, A. P. (2020). A prescriptive stock market investment strategy for the restaurant industry using an artificial neural network methodology. En Deep Learning and Neural Networks: Concepts, Methodologies, Tools, and Applications (pp. 217-237). IGI Global. https://doi.org/10.4018/IJBAN.2016010101

Xue, L. y Kerstetter, D. (2018). Rural tourism and livelihood change: and emic perspective. Journal of Hospitality & Tourism Research, 20(10), 1-22. https://doi.org/10.1177/1096348018807289

Youn, H. y Kim, J-H. (2017). Effects of ingredients, names and stories about food origins on perceived authenticity and purchase intentions. International Journal of Hospitality Management, (63), 11-21. https://doi.org/10.1016/j.ijhm.2017.01.002

Zeithaml, V., Berry, L. y Parasuraman, A. (1988). Communication and control processes in the delivery of service quality. Journal of Marketing, 52, 35-48.

Zhang, T., Chen, J. y Hu, B. (2019). Authenticity, quality, and loyalty: Local food and sustainable tourism experience. Sustainability, 11(12), 34-37. https://doi.org/10.3390/su11123437

Zhang, H., Fu, X., Cai, L. y Lu, L. (2014). Destination image and tourist loyalty: A meta-analysis. Tourism Management, (40), 213-223. https://doi.org/10.1016/j.tourman.2013.06.006

Zheng, B., Thompson, K., Lam, S. S., Yoon, S. W. y Gnanasambandam, N. (2013). Customers' behavior prediction using artificial neural network. En IIE Annual Conference. Proceedings (p. 700). Institute of Industrial and Systems Engineers (IISE).