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

Maria M. Santos Silva¹ , Tahir Albayrak^2*, Meltem Caber³ and Luiz Moutinho⁴

Received: 01/04/2016 Accepted: 21/05/2016

¹Assistant professor, Faculty of Economics of the University of Coimbra. Av. Dias da Silva, Coimbra, Portugal;
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²Associate professor, Akdeniz University, Tourism Faculty, Campus. Antalya, Turkey; email address:
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³Associate professor, Akdeniz University, Tourism Faculty, Campus. Antalya, Turkey; email address:
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⁴Professor, DCU Business School, Dublin City University, Collins Avenue Dublin 9, Ireland; email address:
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* Corresponding author

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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Keywords: destination, artificial neural networks, service quality, behavioural intention, value perception, customer satisfaction
Citation: Santos Silva M., T. Albayrak, M. Caber and L. Moutinho (2016) Key destination attributes of behavioural intention: An application of neural networks. European Journal of Tourism Research 14, pp. 16-28