DFR – A New Model to Identifying Loyal Tourists on the Destination
Abstract
Regarding the increasing tendency to travel in recent years, many cities offer more amusement plans to have the tourists visit them again. The importance of these plans is due to maintaining the current tourists, but not attracting the new ones. Hence, recognizing loyal tourists can reduce the expenses and flourish the tourism. Accordingly, in this research, we have attempted to develop a new model based on data mining techniques to reconnoiter loyal tourists more accurately. The presented model is named DFR and is based on the development of the well-known model RFM. In addition to DFR model, the Imperialist Competitive Algorithm (ICA) has been used to cluster the tourists. The DFR model was implemented on 1100 tourists visiting Shiraz between 2013 and 2015, show that this model has a higher accuracy than other existing models and has identified loyal tourists more precisely.
References
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[34] Møller Jensen, J., and Hansen, T. 2006. An empirical examination of brand loyalty. Journal of Product and Brand Management, 15(7): 442-449.
[35] Nam, J., Ekinci, Y., and Whyatt, G. 2011. Brand equity, brand loyalty and consumer satisfaction. Annals of Tourism Research, 38(3): 1009-1030.
[36] Ngai, E. W., Xiu, L., and Chau, D. C. 2009. Application of data mining techniques in customer relationship management: A literature review and classification. Expert Systems with Applications, 36(2): 2592-2602.
[37] Nicolau, J. L., Zach, F. J., and Tussyadiah, I. P. 2016. Effects of distance and first-time visitation on tourists’ length of stay. Journal of Hospitality and Tourism Research, 1096348016654972.
[38] Niknam, T., and Amiri, B. (2010. An efficient hybrid approach based on PSO, ACO and k-means for cluster analysis. Applied Soft Computing, 10(1) 183-197.
[39] Niknam, T., Fard, E. T., Pourjafarian, N., and Rousta, A. 2011. An efficient hybrid algorithm based on modified imperialist competitive algorithm and K-means for data clustering. Engineering Applications of Artificial Intelligence, 24(2): 306-317.
[40] Pappu, R., and Quester, P. 2006. Does customer satisfaction lead to improved brand equity? An empirical examination of two categories of retail brands. Journal of Product and Brand Management, 15(1): 4-14.
[41] Prayag, G., and Ryan, C. 2012. Antecedents of tourists’ loyalty to Mauritius: The role and influence of destination image, place attachment, personal involvement, and satisfaction. Journal of Travel Research, 51(3): 342-356.
[42] Prentice, C., and Loureiro, S. M. C. 2017. An asymmetrical approach to understanding configurations of customer loyalty in the airline industry. Journal of Retailing and Consumer Services, 38: 96-107.
[43] Ruzzier, M. K., and Chernatony, L. 2010. A SYSTEMATIC APPROACH TO BRANDING SLOVENIA. Paper presented at the The Steering Committee. The 6th Thought Leaders International Conference on Brand Management.
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[45] Srivastava, M., and Kaul, D. 2014. Social interaction, convenience and customer satisfaction: The mediating effect of customer experience. Journal of Retailing and Consumer Services(0). doi: http://dx.doi.org/10.1016/j.jretconser.2014.04.007
[46] Sun, X., Chi, C. G.-Q., and Xu, H. 2013. Developing destination loyalty: The case of Hainan Island. Annals of Tourism Research, 43: 547-577.
[47] Velmurugan, T. 2014. Performance based analysis between k-Means and Fuzzy C-Means clustering algorithms for connection-oriented telecommunication data. Applied Soft Computing, 19: 134-146.
[48] Viglia, G., Mauri, A., and Carricano, M. 2016. The exploration of hotel reference prices under dynamic pricing scenarios and different forms of competition. International Journal of Hospitality Management, 52: 46-55.
[49] Yeh, I.-C., Yang, K.-J., and Ting, T.-M. 2009. Knowledge discovery on RFM model using Bernoulli sequence. Expert Systems with Applications, 36(3): 5866-5871.
[50] Zalaghi, Z., and Varzi, Y. 2014. Measuring customer loyalty using an extended RFM and clustering technique. Management Science Letters, 4(5): 905-912.
[2] Ahn, H., Ahn, J. J., Oh, K. J., and Kim, D. H. 2011. Facilitating cross-selling in a mobile telecom market to develop customer classification model based on hybrid data mining techniques. Expert Systems with Applications, 38(5): 5005-5012. DOI: http://dx.doi.org/10.1016/j.eswa.2010.09.150
[3] Albaity, M., and Melhem, S. B. 2017. Novelty seeking, image, and loyalty—The mediating role of satisfaction and moderating role of length of stay: International tourists' perspective. Tourism Management Perspectives, 23: 30-37. DOI: http://dx.doi.org/10.1016/j.tmp.2017.04.001
[4] Alderighi, M., Cento, A., and Piga, C. A. 2011. A case study of pricing strategies in European airline markets: The London–Amsterdam route. Journal of Air Transport Management, 17(6): 369-373.
[5] Armstrong, G., Cunningham, M. H., and Kotler, P. (2012). Principles of marketing: Pearson Australia.
[6] Atashpaz-Gargari, E., and Lucas, C. 2007. Imperialist competitive algorithm: an algorithm for optimization inspired by imperialistic competition. Paper presented at the Evolutionary computation, 2007. CEC 2007. IEEE Congress on.
[7] Baloglu, S. 2002. Dimensions of customer loyalty: Separating friends from well wishers. The Cornell Hotel and Restaurant Administration Quarterly, 43(1): 47-59.
[8] Barros, C. P., and Machado, L. P. 2010. The length of stay in tourism. Annals of Tourism Research, 37(3): 692-706.
[9] Bosnjak, M., Sirgy, M. J., Hellriegel, S., and Maurer, O. 2011. Postvisit destination loyalty judgments: Developing and testing a comprehensive congruity model. Journal of Travel Research, 50(5): 496-508.
[10] Buil, I., Martínez, E., and de Chernatony, L. 2013. The influence of brand equity on consumer responses. Journal of consumer marketing, 30(1): 62-74.
[11] Buttle, F. 2009. Customer relationship management: concepts and technologies: Routledge.
[12] Chan, C. C. H. 2008. Intelligent value-based customer segmentation method for campaign management: A case study of automobile retailer. Expert Systems with Applications, 34(4): 2754-2762.
[13] Cheng, L.-C., and Sun, L.-M. 2012. Exploring consumer adoption of new services by analyzing the behavior of 3G subscribers: An empirical case study. Electronic Commerce Research and Applications, 11(2): 89-100.
[14] Chi, C. G.-q. 2011. Destination loyalty formation and travelers’ demographic characteristics: A multiple group analysis approach. Journal of Hospitality and Tourism Research, 35(2): 191-212.
[15] Chi, C. G.-q. 2012. An examination of destination loyalty: Differences between first-time and repeat visitors. Journal of Hospitality and Tourism Research, 36(1): 3-24.
[16] Cossío-Silva, F.-J., Revilla-Camacho, M.-Á., Vega-Vázquez, M., and Palacios-Florencio, B. 2016. Value co-creation and customer loyalty. Journal of business research, 69(5): 1621-1625.
[17] DehghaniZadeh, M., Fathian, M., and Gholamian, M. 2016. LDcFR: A new model to determine value of airline passengers. Tourism and Hospitality Research, 1467358416663821.
[18] DehghaniZadeh, M., Fathian, M., and Gholamian, M. R. 2014. A new method for clustering based on development of Imperialist Competitive Algorithm. China Communications, 11(12): 54-61.
[19] Dursun, A., and Caber, M. 2016. Using data mining techniques for profiling profitable hotel customers: An application of RFM analysis. Tourism Management Perspectives, 18: 153-160.
[20] Fedorco, Ľ., and Hospodka, J. 2013. Airline Pricing Strategies In European Airline Market. Faculty of Transportation Sciences, 2(8), 33-41.
[21] Forgas-Coll, S., Palau-Saumell, R., Sánchez-García, J., and Callarisa-Fiol, L. J. (2012). Urban destination loyalty drivers and cross-national moderator effects: The case of Barcelona. Tourism Management, 33(6): 1309-1320. DOI: http://dx.doi.org/10.1016/j.tourman.2011.12.013
[22] Ghaffari, M., Rezayi, H., and Dehghani, F. 2014. Analysis of factors affecting tourists’destination loyalty. Organizational Culture Management Journal, 12(3): 551-568.
[23] Ghanavati, M., Gholamian, M. R., Minaei, B., and Davoudi, M. 2011. An efficient cost function for imperialist competitive algorithm to find best clusters. Journal of Theoretical and Applied Information Technology, 29(1).
[24] Guizzardi, A., Pons, F. M. E., and Ranieri, E. 2017. Advance booking and hotel price variability online: Any opportunity for business customers? International Journal of Hospitality Management, 64: 85-93.
[25] Hallowell, R. 1996. The relationships of customer satisfaction, customer loyalty, and profitability: an empirical study. International journal of service industry management, 7(4): 27-42.
[26] Ho, G. T., Ip, W., Lee, C., and Mou, W. 2012. Customer grouping for better resources allocation using GA based clustering technique. Expert Systems with Applications, 39(2): 1979-1987.
[27] Jones, J. L., Easley, R. F., and Koehler, G. J. (2006). Market segmentation within consolidated E-markets: A generalized combinatorial auction approach. Journal of Management Information Systems, 23(1): 161–182.
[28] Jraisat, L. E., Akroush, M. N., Alfaouri, R. J., Qatu, L. T., and Kurdieh, D. J. (2015). Perceived brand salience and destination brand loyalty from international tourists’ perspectives: the case of Dead Sea destination, Jordan. International Journal of Culture, Tourism and Hospitality Research, 9(3): 292-315.
[29] Kim, S.-Y., Jung, T.-S., Suh, E.-H., and Hwang, H.-S. (2006). Customer segmentation and strategy development based on customer lifetime value: A case study. Expert Systems with Applications, 31(1): 101-107. doi: http://dx.doi.org/10.1016/j.eswa.2005.09.004
[30] Konecnik, M., and Gartner, W. C. (2007). Customer-based brand equity for a destination. Annals of Tourism Research, 34(2): 400-421.
[31] Kumar, V., Shah, D., and Venkatesan, R. (2006). Managing retailer profitability—one customer at a time! Journal of Retailing, 82(4): 277-294. doi: http://dx.doi.org/10.1016/j.jretai.2006.08.002
[32] Kursunluoglu, E. (2011). Customer service effects on customer satisfaction and customer loyalty: A field research in shopping centers in Izmir City-Turkey.iternational Journal of business and social science, 2(17).
[33] Lai, W.-T., and Chen, C.-F. (2011). Behavioral intentions of public transit passengers—The roles of service quality, perceived value, satisfaction and involvement. Transport Policy, 18(2): 318-325. DOI: http://dx.doi.org/10.1016/j.tranpol.2010.09.003
[34] Møller Jensen, J., and Hansen, T. 2006. An empirical examination of brand loyalty. Journal of Product and Brand Management, 15(7): 442-449.
[35] Nam, J., Ekinci, Y., and Whyatt, G. 2011. Brand equity, brand loyalty and consumer satisfaction. Annals of Tourism Research, 38(3): 1009-1030.
[36] Ngai, E. W., Xiu, L., and Chau, D. C. 2009. Application of data mining techniques in customer relationship management: A literature review and classification. Expert Systems with Applications, 36(2): 2592-2602.
[37] Nicolau, J. L., Zach, F. J., and Tussyadiah, I. P. 2016. Effects of distance and first-time visitation on tourists’ length of stay. Journal of Hospitality and Tourism Research, 1096348016654972.
[38] Niknam, T., and Amiri, B. (2010. An efficient hybrid approach based on PSO, ACO and k-means for cluster analysis. Applied Soft Computing, 10(1) 183-197.
[39] Niknam, T., Fard, E. T., Pourjafarian, N., and Rousta, A. 2011. An efficient hybrid algorithm based on modified imperialist competitive algorithm and K-means for data clustering. Engineering Applications of Artificial Intelligence, 24(2): 306-317.
[40] Pappu, R., and Quester, P. 2006. Does customer satisfaction lead to improved brand equity? An empirical examination of two categories of retail brands. Journal of Product and Brand Management, 15(1): 4-14.
[41] Prayag, G., and Ryan, C. 2012. Antecedents of tourists’ loyalty to Mauritius: The role and influence of destination image, place attachment, personal involvement, and satisfaction. Journal of Travel Research, 51(3): 342-356.
[42] Prentice, C., and Loureiro, S. M. C. 2017. An asymmetrical approach to understanding configurations of customer loyalty in the airline industry. Journal of Retailing and Consumer Services, 38: 96-107.
[43] Ruzzier, M. K., and Chernatony, L. 2010. A SYSTEMATIC APPROACH TO BRANDING SLOVENIA. Paper presented at the The Steering Committee. The 6th Thought Leaders International Conference on Brand Management.
[44] Shian-Chang, H., Jo-Ting, W., Shih-Yen, L., and Hsin-Hung, W. (2012, 4-6 June 2012). Using Bayesian Network and LRFM Model in a Pediatric Dental Clinic. Paper presented at the Computer, Consumer and Control (IS3C), 2012 International Symposium on.
[45] Srivastava, M., and Kaul, D. 2014. Social interaction, convenience and customer satisfaction: The mediating effect of customer experience. Journal of Retailing and Consumer Services(0). doi: http://dx.doi.org/10.1016/j.jretconser.2014.04.007
[46] Sun, X., Chi, C. G.-Q., and Xu, H. 2013. Developing destination loyalty: The case of Hainan Island. Annals of Tourism Research, 43: 547-577.
[47] Velmurugan, T. 2014. Performance based analysis between k-Means and Fuzzy C-Means clustering algorithms for connection-oriented telecommunication data. Applied Soft Computing, 19: 134-146.
[48] Viglia, G., Mauri, A., and Carricano, M. 2016. The exploration of hotel reference prices under dynamic pricing scenarios and different forms of competition. International Journal of Hospitality Management, 52: 46-55.
[49] Yeh, I.-C., Yang, K.-J., and Ting, T.-M. 2009. Knowledge discovery on RFM model using Bernoulli sequence. Expert Systems with Applications, 36(3): 5866-5871.
[50] Zalaghi, Z., and Varzi, Y. 2014. Measuring customer loyalty using an extended RFM and clustering technique. Management Science Letters, 4(5): 905-912.
Published
2018-10-29
How to Cite
SEDAGHAT, Shahrzad; DEHGHANI ZADEH, Mohammad Reza; AMIRI, Vahid.
DFR – A New Model to Identifying Loyal Tourists on the Destination.
Journal of Environmental Management and Tourism, [S.l.], v. 9, n. 4, p. 879-890, oct. 2018.
ISSN 2068-7729.
Available at: <https://journals.aserspublishing.eu/jemt/article/view/2394>. Date accessed: 24 apr. 2024.
doi: https://doi.org/10.14505//jemt.v9.4(28).22.
Section
Article
Copyright© 2023 The Author(s). Published by ASERS Publishing 2023. This is an open access article distributed under the terms of CC-BY 4.0 license.