Key destination attributes of behavioural intention: An application of neural networks

Authors

  • Maria M. Santos Silva Assistant professor, Faculty of Economics of the University of Coimbra. Av. Dias da Silva, Coimbra, Portugal; email address: nelinha@fe.uc.pt.
  • Tahir Albayrak Associate professor, Akdeniz University, Tourism Faculty, Campus. Antalya, Turkey; email address: tahiralbayrak@akdeniz.edu.tr
  • Meltem Caber Associate professor, Akdeniz University, Tourism Faculty, Campus. Antalya, Turkey; email address: meltemcaber@akdeniz.edu.tr
  • Luiz Moutinho Professor, DCU Business School, Dublin City University, Collins Avenue Dublin 9, Ireland; email address: luiz.moutinho@dcu.ie

Keywords:

destination, artificial neural networks, service quality, behavioural intention, value perception, customer satisfaction

Abstract

In the tourism literature artificial neural networks (ANNs) are mainly employed in forecasting and market segmentation. Hitherto, no existing research applies ANNs in assessing the effect of destination attributes on customer behavioural intention. Thus, the aim of this study is to identify the main destination attributes, and to evaluate the effects of these attributes, customer satisfaction and perceived value on behavioural intention by using ANNs. Data obtained from 332 foreign tourists visiting Antalya-Turkey were analyzed for exploring the relationships among the research variables. ANN results show that five explanatory variables (shopping, transportation, accommodation, customer satisfaction, perceived value), which are the input nodes, correspond to output node of behavioural intention. Findings indicate the role of value perception and the importance of the basic functional attributes in the formation of behavioural intention, and the strength of ANNs in terms of exploration and prediction of tourist behaviour against regression analysis.

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Published

2016-10-01

How to Cite

Silva, M., Albayrak, T., Caber, M., & Moutinho, L. (2016). Key destination attributes of behavioural intention: An application of neural networks. European Journal of Tourism Research, 14, 16–28. Retrieved from https://ejtr.vumk.eu/index.php/about/article/view/240