Measuring the resemblance between pictorial and verbal city image spaces

Purpose – This study focuses on the emotional aspects of destinations and employs two different scales for capturing the affective component of city destination image. The aim of this paper is not only to measure the emotions assigned to different European cities, but also to compare these two instruments/scales by means of Procrustes analysis. Design/methodology/approach – The authors collected measurements on two different scales (verbal and pictorial) for capturing the emotional (affective) component of destination image – both based on Russell's circumplex model of affect – in two independent surveys. Findings – Significant differences were found between the multidimensional scaling (MDS) results of both scales. Because the two samples match in terms of demographics and psychographics, the differences of the perceptual spaces are likely due to the form of stimuli (pictures compared to verbal items) presented. The results indicate that pictures are easy to use, but they also are subject to broader interpretations. In contrast, verbal items are more concise, but perhaps respondents may find it harder to assign them to cities (especially negative emotions). Research limitations/implications – The paper contributes to the literature by suggesting a methodology for detailed analysis of the differences in measurements, and introducing implications that apply primarily to researchers once new measurement methods are introduced. The limitations of this research relate to the sample (convenience sample): the respondents were solely Austrians, and thus only one culture was represented. Originality/value – Although several other researchers suggest measuring both the cognitive and the affective image, very few studies incorporate both aspects in evaluating destination image. In contrast, this study applies different scales to incorporate emotions in destination image measurement and demonstrates the applicability of the scales in the tourism context.