Number of sources and coded references

First, it is necessary to clarify the terminology used by the software NVivo to name different content characteristics. The following explains the meaning of source, reference, and node. On the one hand, every page of the website, captured in a separate pdf, is considered to be one source. In the present study, 779 different pages were counted. An important factor to successfully work with NVivo in this project was to properly organize the sources so that they could later be easily identified as a part of a single website, allowing the analysis of separate cases.

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In contrast, every piece of content that is selected and coded at one node is considered a reference. Thus, main content boxes, navigational banners, and other differentiated units of content within the same page constitute different references. Finally, the term node is used to designate the different categories proposed by the author that will be used to identify, distinguish, and classify the different content units.

Returning to the specific research results, a total of 779 sources from all websites were separately analyzed and coded, shown in Table 6.1. Even though each website was downloaded based on the same criteria (homepage plus two levels deep in the hierarchy), the number of sources for each website differs. As seen in Table 6.1, each website comprises a different number of pages with the same length. Furthermore, the process of coding all the sources became more complex than expected and concluded with a higher number of identified content units.

Table 6.1. Number of coded sources, references, and words

Pages References Inf. Nav. Trans. Words

Websites are complex communication sources that can display a variety of different content simultaneously and even interlink pages to each other. Interactive media is known to contain a wide variety of content unit types (Neuendorf, 2017; Strijbos et al., 2006). Thus, not all the content within a website page is linked to a single node; on the contrary, in this study all coded sources presented more than one reference about different topics. This became a major difficulty during the coding process.

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While one could expect that each page of a website would focus on a single topic, this is not the case. For example, the page http://en.parisinfo.com/shopping should only contain information about shopping in Paris. However, within the same page, content units were identified about different ways of shopping, tourism for disabled people, buying theater tickets, where to eat, and climate, among others.

Therefore, the coders explored, analyzed, and coded each source’s specificities separately. This process resulted in a total of 10,172 identified references, representing almost 13 different content units per source on average. However, significant differences between cases were identified in terms of number of references, as seen in Table 6.1. For this reason, all average results and comparisons were calculated based on relative percentages and not absolute numbers. The percentages of references were calculated considering the total number of content units identified in each case.

In this regard, two additional complications encountered during the coding process must be considered: the variety of website designs and how information was restructured in the downloaded pdfs.

 On the one hand, the coders discussed the variety of architectonic models, design solutions, and different layout hierarchies found on different websites. The criteria to delimitate a different unit of content and thus a different reference were precisely described and agreed after a pilot analysis (see Chapter 5).

 On the other hand, downloading pages in pdf format was the most suitable way to capture the website content in a readable format for NVivo. In addition, doing so respected most of the layout design needed to identify different blocks of content. However, not all pdf downloads were optimal, as some divided long website pages into several consecutive DIN A 4 (or similar format). Consequently, in some cases unexpected splits of content units occurred, causing later difficulties in coding them as single references. This became an unsolvable issue, so coders were asked to code the split parts separately even though they were part of the same content unit. The number of such cases was low and common to all websites, so the effect of a possible bias is minimal.

Furthermore, to conduct a more precise analysis, every piece of content within each website page was coded based on three different criteria: product-related category, content type, and non-related attributes (tourist profile). Even though the main interest was

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related associations, it was interesting to keep track of each type of content unit:

informational, navigational, or transactional, as seen in Table 6.1. These three types of content units usually present different characteristics (see Chapter 5); hence, it can be inferred from the data in Table 6.1 that the 1,843 informational references represent a higher volume of content than the 8.030 navigational ones.

All in all, the coding process identified 10,172 different references across the content; all of them were linked to one product-related category and one content type node, both of which are mandatory. From these, 79% of the references were distinguished as navigational content, 3% as transactional content, and 18% as informational content. Furthermore, some of these references were also linked to tourist profile nodes, which were complementary and optional categories. Not all content units referred to a specific target: only a total of 994 references were also coded at the non-product-related node.