Neural network analysis for hotel service design in Madrid: the 3ps methodology and the frontline staff

Sandra Flores-Ureba, Javier De Esteban-Curiel, Mª Luisa Delgado-Jalón, José Ángel Rivero-Menéndez

Abstract


This paper takes into account a fresh approach to hotel service experience based on the concept of “3Ps Methodology” and “Neural Network Analysis” (NNA). Traditional tools of experience management have been implemented by hotel management in the last decades in order to fulfil the requirements of customer experiences.

A more discriminating distinction is proposed in this research, based on the neural network algorithm (the Multilayer Perceptron) in order to achieve more efficiency. In this sense, Madrid hotels are analyzed from the visitor’s perspective for showing the relationships established between front-line employees and customers. A face-to-face survey were conducted in several tourism hotspots in Madrid in order to investigate the hotel frontline service and the emotional experience of customers.

The findings have been treated with statistical procedures and, after carrying out the neural network analysis, we have concluded that the Servicescape model could be classified under the category of “People-processing service”.


Keywords


-line employees, Neural Network Analysis, Hotels, Visitor emotional experience

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References


Ak?n, M. (2015). A novel approach to model selection in tourism demand modeling. Tourism Management, 48, 64-72.

Alpayd?n, E. (2004). Introduction to machine learning. Cambridge: The MIT Press.

Berenguer, T.M.; Berenguer, J.A.M.; García, M.E.B. (2015) Models of artificial neural networks applied to demand forecasting in nonconsolidated tourist destinations. Methodology. European Journal of Research Methods for the Behavioral and Social Sciences, Vol 11(2), 35-44.

Binkhorst, E. (2006). The co-creation tourism experience. ESADE. Information available in: http://www.esade.edu/cedit2006/pdfs2006/papers/esther_binkhorst_paper_esade_may_06.pdf (visited 20/09/2015).

Bitner, M.J. (1992) Servicescapes: The Impact of Physical surroundings on customers and employees. Journal of Marketing Vol. 56.

Bitner, M. J. (2007). Service Blueprinting: A Practical Technique for Service Innovation. USA: Arizona State University.

Burkhardt, L. (1995). Design ist unsichtbar. Berlin: Hatje Cantz Verlag.

Cang, S.(2014) A comparative analysis of three types of tourism demand forecasting models: Individual, linear combination and non-linear combination. International Journal of Tourism Research, vol.16, Issue 6, 596-607.

Chen, K.-Y. (2011). Combining linear and nonlinear model in forecasting tourism demand. Expert Systems with Applications, 38(8), 10368-10376.

Cherkassky, V., Friedman, J. H., and Wechsler, H. (1994). From statistical to neural networks. Springer – Verlag. Berlin.

Claveria, M.; Monte, E.; Torra, S.(2015) Tourism demand forecasting with Neural Network Models: Different ways of treating information. International Journal of Tourism Research, vol.17, Issue 5, 492-500.

Costa, Á., and Markellos, R. N. (1997). Evaluating public transport efficiency with neural network models. Transportation Research Part C: Emerging Technologies, 5(5), 301-312.

Creswell, J. (2012). Planning, conducting, and evaluating quantitative and qualitative research, 4th ed. Boston: Pearson Education.

García, P.(2002). Aplicaciones de las redes neuronales en las Finanzas. Documento de Trabajo. Universidad Complutense de Madrid, Facultad de CC. Económicas y Empresariales.

Gray, D. E. (2011). Doing research in the real world, 2nd ed. Hampshire: Ashford Color Press.

Hair, J.; Bush, R. and Ortinau, D. (2006). Marketing Research. Within a changing environment,3rd ed. New York: McGraw-Hill.

Instituto de Turismo de España. Turespaña (2015 a). Boletín trimestral de coyuntura turística, II trimestre 2015 (Coyuntur, 2015). Information available in www.iet.tourspain.es (visited 20/09/2015)

Instituto de Turismo de España.Turespaña (2015 b). Movimientos turísticos en fronteras. Julio 2015 (Frontur, 2015). Information available in www.iet.tourspain.es (visited 20/09/2015)

Instituto Nacional de Estadística (2015). Encuesta de ocupación e infraestructura hotelera. Information available in www.ine.es (visited 13/10/2015).

Karlaftis, M. G., and Vlahogianni, E. I. (2011). Statistical methods versus neural networks in transportation research: Differences, similarities and some insights. Transportation Research Part C: Emerging Technologies, 19(3), 387-399.

Kjaer Mansfeldt, O.; Vestager, E. M.and Baek Iversen, M. (2008) Experience Design in city tourism. Copenhagen: Nordic Innovation Center.

Lane, M. (2007). The Visitor Journey: the new road to success. International Journal of Contemporary Hospitality Management, 19 (3), 245-254.

Lovelock C., Wirtz J. and Chew P. (2009). Essentials of Service Marketing. Singapore: Pearson Education.

Martín del Brío, B. and Sanz, A. (2001). Redes neuronales y sistemas borrosos: introducción, teoría y práctica. Ra-ma. Madrid.

Pérez Ortiz, J. A. (2002). Modelos predictivos basados en redes neuronales recurrentes de tiempo discreto. Tesis doctoral. Universidad de Alicante.

Pine, B. J. and Gilmore, J. H. (1999). The Experience Economy. Boston: Harvard Business School Press.

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

Prahalad, C. K. and V. Ramaswamy (2004). The Future of Competition: Co-creating Unique Value with customers. Boston: Harvard Business School Press.

Pullman, M. E., and Gross, M. A. (2003). Welcome to your experience: Where you can check out anytime you'd like but you can never leave. Journal of Business and Management, 9(3), 215-231.

Ripley, B.D. (1996). Pattern Recognition and Neural Networks. Cambridge University Press, Cambridge, G.B.

Ritchie, J. R. B. and Crouch, G. I. (2000). “The competitive destination: A sustainable perspective”. Tourism Management, 21, 1-7.

Sánchez de Lara, M. (2013). Estudio predictivo de costes y financiación del servicio de transporte urbano colectivo en las empresas españolas mediante la aplicación de redes neuronales artificiales. Tesis doctoral. Universidad Rey Juan Carlos.

Shachmurove, Y. and Witkowska, D. (2000). Utilizing artificial neural network model to predict stock markets. University of Pennsylvania, Center for Analytic Research in Economics and the Social Sciences.

Stickdorn, M. (2009). Service Design in Tourism, in Miettinen S, and Koivisto, M. (eds.), Designing Services with Innovative Methods. Helsinki: Taik Publications.

Trigo, L., and Costanzo, S. (2007). Redes neuronales en la predicción de las fluctuaciones de la economía a partir del movimiento de los mercados de capitales. El Trimestre Económico, 415-440.

Vellas, F. and Bécherel, L. (1999). The International Marketing of Travel and Tourism (A Strategic Approach). Publishing House: Palgrave Macmillan. New York.

Ziethaml, V. and Bitner, M. J. (1996). Services Marketing. New York: McGraw Hill.






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