An Auto-Coding Process for Testing the Cognitive-Affective and Conative Model of Destination Image

Ainhoa Serna (), Elena Marchiori (), Jon Kepa Gerrikagoitia (), Aurkene Alzua-Sorzabal () and Lorenzo Cantoni ()
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Ainhoa Serna: Mondragon Unibertsitatea
Elena Marchiori: UniversitÁ della Svizzera italiana (USI - University of Lugano)
Jon Kepa Gerrikagoitia: Competence Research Center in Tourism, CICtourGUNE
Aurkene Alzua-Sorzabal: Competence Research Center in Tourism, CICtourGUNE
Lorenzo Cantoni: UniversitÁ della Svizzera italiana (USI - University of Lugano)

A chapter in Information and Communication Technologies in Tourism 2015, 2015, pp 111-123 from Springer

Abstract: Abstract Current research on online contents analysis relies mainly on human coding procedures, and it is still under research the creation of automatic tools for content analysis in the eTourism domain. Thus, considering the current research gap in the field of automatic coding procedure for content analysis, this study aims at contributing to the auto-coding analysis of the three image components: the cognitive, the affective (feelings expressed), and conative ones (behavioral intentions towards a destination) which might be reported in the tourism-related online conversations. Hence, an ad-hoc software has been developed and tested for the auto-coding analysis of online conversations, together with a human-coding procedure used for coding unclassified entities. The image of the Basque Country has been used as case study and data have been collected from Minube, a popular travel experience community. Results of this study show that the proposed approach can be apt for the analysis of cognitive-affective and conative components of destination image, and in turn help destination managers in their web marketing strategies.

Keywords: Destination image; Image model; Content analysis; Natural language processing; Text mining; Online experiences; Social media analysis (search for similar items in EconPapers)
Date: 2015
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DOI: 10.1007/978-3-319-14343-9_9

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