Brand, Identity and Reputation: Exploring, Creating New Realities ...

Profiling the Image of Urban Business Destinations: Innovative Segmentation Criteria
Francesca d‘Angella, IULM University
Manuela De Carlo, IULM University
Topic and purpose
In today‘s highly competitive tourism marketplace, image is a fundamental asset for the competitiveness of urban
destinations (d‘Angella, & De Carlo, 2009). According to the literature on place-competition (Dwyer, & Chulton, 2003;
Gordon, & Buck, 2005; Markusen, & Shrock, 2006), cities and territories have to distinguish themselves from other
destinations and create a positive, distinct and sustainable image in order to attract different targets, governmental
funding and corporate inward investment. Even if this topic has already been explored by a wide stream of literature
since the Nineties (Padgett, & Allen, 1997; Echtner, & Ritchie, 2003; Gallarza et al 2002; Beerli, & Martìn, 2004;
Tapachai & Wariszak, 2000), the centrality of this theme in management practices has led to a revitalization of the
current academic concern. This issue seems less developed concerning urban destinations, particularly those with a
business vocation. The paper is set in the context of tourism marketing and, more specifically, in the process of
destination image formation (Hunt, 1975; Gartner, & Hunt, 1987; Gartner, 1993; Go, & Govers, 2009, 245-249). It
contributes to this ongoing debate by profiling the perceived image of a business oriented urban destination, according
to nontraditional segmentation criteria. Moreover, the measurement of the perceived destination‘s image is based on the
evaluation of specific services and tourism features of the destination instead of intangible and subjective elements. In
particular, the paper aims at testing the following macro hypotheses:
HP1. The image of an urban destination differs by the scope of the trip
Hp2 a. the image is different according to the trip itinerary
Hp2 b. the image is different according to the trip organization
HP2. The image of an urban destination differs by the knowledge depth of tourists
Hp3 a. the image is different according to the number of times the tourist has already been to the destination
Hp3 b. the image is different according to the number of nights spent in the destination
HP3. The image of an urban destination differs by the perception shift of tourists
Methodology
In order to answer our research questions we surveyed tourists in Milan. We selected Milan as our single case study
because it‘s a business destination, without any deliberated communication strategy. In this sense, it is an interesting
clinical case study to assess differences in the image perception according to tourists‘ characteristics rather than
destination‘s policies. Questions are based on the attributes that determine the perceived destination image as proposed
in tourism literature (Beerli, & Martin, 2004a, 2004b; Etchner, & Ritchie, 1991; Embacher, & Buttle, 1989; Walmsley,
& Jekins, 1993; Walmsley, & Young, 1998) and on specific destination‘s features. In fact, the choice to study an urban
destination obliged the authors to formulate the questions giving less relevance to weather and natural environment and
more space to the other attributes. In total, 17 variables are used to assess destination image. We asks tourists their
personal opinions about Milan regarding: urban facilities, transports, hospitality, accommodation, events, culture and
entertainment using a ten-point Likert-type scale, from ‗extremely unsatisfied ( = 1)‘ to ‗definitely satisfied ( = 10)‘.
The sample includes 1,032 tourists coming to Milan for different purposes 4 . All questionnaires were carried out by face
to face interviews conducted in summer and autumn 2009 in leisure and cultural places; airports and train stations;
business hotels and fairs & exhibitions.
The data analysis was conducted in two stages. First, we used a k-means analysis to group respondents into clusters
diverse in terms of global satisfaction of their tourist experience in Milan (perceived image). In this step of analysis, the
challenging part is the estimation of the optimal number of clusters. Since stopping rules are heuristic (Milligan et al.,
1985), to assess the proper number of clusters we adopt the criterion suggested by Ball and Hall (1965), which is based
on the average distance of the items to their respective cluster centroids. The largest difference between levels has been
used to indicate the optimal solution. In the second part of the analysis, we conduct ANOVA tests to understand the
influence of traditional segmentation variables (Hp0), scope (Hp1), knowledge depth (Hp2) and perception shift (Hp3)
on destination‘s image according to the clusters identified.
In the two steps we opted for cluster and ANOVA methods because, as Jenkins (1999) notes, in studies about
destination image, structured methods such as factor analysis, cluster analysis and other multivariate analyses are more
common than unstructured ones. In fact, the methodologies used in this research have been often used in tourism studies
to analyze image formation, differences according to different tourism segments (Hankinson, 2005; Kozak, 2002) and
destination residents (Long, Perdue, & Allen, 1990). Furthermore, we run two OLS logistic regressions to test the
4
To guarantee the representativeness of the sample we conducted all the analyses considering both the sample as it is
and weighted the data according the purpose of the trip (business tourists are much higher than leisure tourists) and the
nationality (foreigners are more than domestic visitors). In both the cases we obtained similar results, but with a higher
number of observation using non-weighted data. For this reason we opted for this solution. Please consider the analyses
with weighted data available for further close examinations.
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