Category definitions

Classifying the associations into broader categories was essential to compare the different destinations in the sample. Thus, the correct definition and delimitation of categories was a key phase of this research. Krippendorff (2013) highlights that categories must group together content units with the same meaning, and at the same time be clearly different from other categories.

In this regard, despite the wide range of literature analyzing tourism offer, there is no universal set of attributes relevant to describing destinations' functional image (Enright & Newton, 2005). Different literature reviews on the subject have identified a considerable diversity of items and labels to describe destinations’ offers (Enright & Newton, 2005; Hong-bumm, 1998).

Therefore, academic articles that propose a list of destinations' attributes were thoroughly examined to define a broad categorization useful for the purpose of this study.

In this process, the present author identified several studies proposing a listing of the main functional attributes associated with destinations. This was achieved in three different steps.

Firstly, some articles were identified while conducting the literature reviews in Chapters 2 and 3. Secondly, an additional search was conducted using the academic database SCOPUS using the keywords “destination branding” and “attributes”. Finally, the author also considered references within the identified papers pointing to studies matching the criteria: a snowball compilation. After a first inspection, only those articles explicitly providing a list of attributes were selected. A total of nine studies, shown in Table 5.4, were found useful in forming the basis of the attributes’ categorization.

As shown in Table 5.4, these studies differ in different aspects: different case studies, different methodologies, and different numbers of identified attribute categories. Therefore, all the attributes and categories of attributes were analyzed and regrouped into broader categories common to all studies. This process of synthesizing and regrouping considered the following characteristics of the previous lists:

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Table 5.4. Previous investigations offering a set of functional attributes associated with destinations¹⁹

Source: Author

 Label diversity. Different investigations use different names to identify the same attribute.

For example, landscape, sightseeing, visual appeal, scenery, and bird’s eye are different labels used to designate the landscape and natural environment category of attributes. All of them are considered to be part of a single category: in this case, “landscape & natural resources”.

 Different approaches to the same attributes. Some studies highlight the same attribute more than once with a different qualitative approach. For example, Mazanec and Wöber (2010) identify booking accommodation as a relevant attribute up to four times: in terms of overall quality of accommodation, comfort and cleanliness of accommodation, low-priced accommodation, and low-low-priced package with transport and accommodation. In the present study, all were reduced to a single category, called “accommodation”, corresponding to the functional attribute and omitting the qualitative approach. At this stage, the interest was in the subjects of destinations’ discourse, the core attributes, and not in how they described those attributes.

 Different study, same list. Three of the sample studies are related. Firstly, Echtner and Ritchie’s studies from 1993 and 2003 are part of the same ongoing research. The 2003 publication is a reprint of an article published in 1991, where the authors propose an initial

19 Echtner and Ritchie's (1993) study highlights 35 different items counting both symbolic and functional attributes. Only 16 of them are closer to the functional dimension.

Ref Reference Scope Methodology Nº attributes

[1] Echtner & Ritchie 1993 Journal of Travel Research Country Theoretical Case study: Jamaica [3] Enright & Newton 2005 Journal of Travel Research Urban

dest.

Morrison 2007 Tourism Management Urban

dest. Case study: Macau Content analysis &

categorization 11

[6] Tang, Choi,

Morrison & Lehto 2009 Journal of Vacation Marketing Urban

dest. Case study: Macau Content analysis &

categorization 9

[7] Mazanec & Wöber 2010 Book: SpringerWienNewYork Urban

dest. Case study: Vienna Survey 23

[8] Wong & Teoh 2015 Journal of Destination Marketing

& Management

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list of attributes. In the 1993 publication, the authors propose a final list after testing it in the analysis of the Jamaican case. Secondly, Wong and Teoh (2015) use the same list proposed by Echtner and Ritchie (2003), and only slightly adjust it to their study. Given these facts, Echtner and Ritchie's (2003) list of attributes is not considered in the analysis to avoid biasing the categories’ unification, since the same scale is used in Wong and Teoh (2015).

 Cross-case attributes. Most of the analyzed articles focus on case studies. Therefore, to obtain an overall categories list that would be useful for more than a single case, only the attributes highlighted by more than two studies were considered significant and valid across cases.

 New categories. Since the sample of the present study differs from those of previous investigations, there was the possibility of identifying new attributes. Hence, the present author did not want to narrow the study to only already identified categories. For this reason, during the analysis, a category called “others” was included for those content units that did not match previous categories. This was subsequently explored and some interesting data was found.

Taking into account the above considerations, the analysis of the literature resulted in 17 different categories common to different studies: landscape and natural resources; tourism attractions and activities; architecture and heritage; local culture and history; events, fairs, and festivals; food and drink; shopping; nightlife; sports; social life and locals; infrastructure and transportation; accommodation; tourist products and packages; climate; service; political and economic factors; and safety. This list served as the starting point of the analysis of product-related attributes associated with urban destinations.

Before concluding, it is necessary to note that two additional categories were included in the project after the pilot study, also represented in Figure 5.1 in grey. After the pilot, the category

"others" was explored and, together, coders identified interesting data worth including in the final analysis. On the one hand, some references to spas, relaxation, or massages emerged from the content. These topics were grouped in a “wellness” category to complete the categories' representativeness of the offer. Therefore, the final analysis assessed the relevance of this category in destinations' communication.

145 Figure 5.1. Resulting product-related categories

Source: Author Database: see Table 5.4

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On the other hand, the coders also agreed on the division of the original “tourist attractions and activities” into two differentiated categories: “cultural and leisure attractions” and

“activities”. Coders stressed the difference between content related to casinos, theme parks, swimming pools in summer, etc., and content related to theaters, museums, cultural events, etc., which were originally part of the same category. Considering that one of the goals of this study was to explore the relationship between personality and product-related associations, coders hypothesized that the difference between these two types of offer might have an effect. Therefore, in the final study, this content was considered as two separate categories even though previous research handled it as one.

The 19 resulting categories are highlighted in Figure 5.1 in bold. Below each category, the figure displays the attributes extracted from the literature that the present author considered to be part of the same attribute category. Each of these attributes is identified with a corresponding reference to the original study, matching the numeration in Table 5.4. For example, the attribute “[3] cuisine” is highlighted in [3] Enright and Newton's (2005) research and included in the “food & drink” category.

Once all the product-related categories were correctly defined, two additional groups of categories were added, as seen in Figure 5.2. Firstly, given the importance of personalization in website communication, one non-product-related attribute was also considered: target. A group of categories related to tourist profiles (user imagery) was included in the study.

This decision is also supported by some of the studies in Table 5.4.

Some attributes analyzed in the literature review included

“family” or “for children”, which are types of annotations that are related to user imagery attributes. In these cases, the messages were directed to a specific tourist profile. For these reasons, the category “target” was also included, referring not only to family but also to any other content that specified characteristics of the tourist and his or her style of travel (i.e. couple, with kids, gay, luxury, young, disabled…).

Secondly, it was also useful to incorporate additional categories describing the typology of each content unit. As noted in the previous section, this study analyzed the prevalence of content units and not overall words counts; however, not all content units have the same Figure 5.2. Complementary categories

Source: Author

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characteristics. Even though previous literature supports this decision to assess the visibility of different product-related categories within a website’s discourse, it does mean that information about content units’ characteristics can be missed. For instance, some units might contain a higher volume of content than others.

For this reason, a categorization was included to distinguish different types of content units:

informational, navigational, and transactional. This taxonomy was adopted from the one proposed by Broder (2002) for web search. While the original research explored search engine queries, it is also applicable to describe the different content unit characteristics within a single website. For the present study, the three categories are described as follows.

 Informational units are the main content units, usually occupying the main body text, and usually located on the top left side of the screen (Miller, 2005). These offer a greater amount of information about a precise topic and are usually confined to a single page, even though they can present some links within the discourse. As Broder (2002) highlights, this is static information (Broder, 2002). In other words, more information about a single topic is included on a single page; the discourse starts and ends on the same page.

 Navigational units work as a bridge between the introduction to a certain topic and the related informational content page. Certainly, these units show a lower volume of content, but they grant more visibility to the topic throughout the website: these content units are usually located on different pages to guide more users towards the topic. Navigational units are characterized by a lower amount of content, and they aim to engage users to keep exploring specific topics on other pages that often contain informational content.

 Transactional units, in contrast, promote further interaction between users and content with additional operations. In this case, these units refer mainly to the commercialization of services and products, such as booking services, tickets reservations and purchases, online shopping, etc.

This final categorization provides some additional information about the characteristics of the coded content units. By looking at this information, the specific purpose of the content and the volume of content expected to be contained in this unit can be inferred in each reference.

Target and content type categories are considered complementary to product-related attributes. In other words, all content units are coded exclusively in one product-related category, but they must also be associated with one content type node and can also be coded

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at one target node, if necessary. Each website's pages were coded by associating all content units to at least two attribute categories, product-related and content type, and, optionally, at an additional target node. Subsequently, counting the number of content units linked to each node allowed for the assessment of their prevalence on different pages of the website. All in all, counting the amount of content units dedicated to each category is a relevant indicator of the intentionality of the DMOs, and a significant sign of the projected image (Stepchenkova &

Zhan, 2013).

Nevertheless, some limitations of this phase of content analysis must be noted:

 Firstly, this study only analyzed manifest content. This raises the ongoing discussion about the importance of analyzing manifest and latent content in textual information analysis (Bos & Tarnai, 1999; Krippendorff, 2013; Malterud, 2001). However, considering that this investigation focused on a more quantitative content analysis perspective, the manifest content analysis approach was judged to be suitable to achieve the research goals. In addition, previous research supports this decision (Berganza Conde & Ruiz San Román, 2005; Li & Stepchenkova, 2011; Stepchenkova, Kirilenko, & Morrison, 2008; Stepchenkova

& Zhan, 2013).

 Secondly, similar to in the WQI analysis phase, the influence of personal judgment on the coding process must be minimized (Krippendorff, 2013). To this end, two measures were adopted: analysis software was used to minimize human errors, and a coding pilot was carried out by two coders to unify criteria. The team of coders was compound by the author of the present thesis and another coder with a superior degree in communication sciences.