Virtual Tourism Destination Image, Innovating measurements methodologies

Abstract

This paper reports on an innovative research study that utilised the enabling technologies of the internet and computerised content analysis to approach the measurement of destination image from a phenomenographic post-positivist perspective. In an online survey, respondents were asked to describe their image of one of seven case-study destinations that they had never visited before, in story format. The large amount of qualitative data of 1,100 respondents was content analysed using artificial neural network software. The results produce a vivid three dimensional picture of the differences and commonalities between seven sample destinations. For each destination unique characteristics are identified, but common attributes across destinations are observed as well. The measured image components match results reported in literature, both from a theoretical perspective as well as through applied studies that report on the case-study destinations that were included here. The limitation of our approach is that common attributes move towards the centre of our three-dimensional clustering space, as these attributes are shared by several of the destinations that were included in the study. This makes it virtually impossible to assess the relative position of different destinations on these common attributes. Hence, this is where traditional attribute-based measurement scales will come in useful in future research.