CONTENT ANALYSIS OF PROJECTED DESTINATION IMAGE
ONLINE
Drd. Oana Catalina VICOL
Drd. Sinziana SPIRIDON
Doctoral School of Economics
Iasi
vicol_oana@yahoo.com
sinziana.spiridon@gmail.com
Abstract
The purpose of this study is to analyze the way a destination image is projected in online environment
(touristic websites). We use a case study on Romania image as a tourist destination. The chosen
methodology is content analysis which includes text analysis in order to identify specific elements for
Romania as projected destination image.
Keywords
online destination image, content analysis, WordStat,
DESTINATION IMAGE AND ITS APPROACHES
Destination image is considered to be a key factor in the decision of choosing a destination
(Echtner & Ritchie, 1991; Baloglu & McCleary, 1999; Beerli & Martin 2004; Tasci et al.
2007) and therefore the research in the field of destination image is a continuous challenge.
The multidimensional nature of destination image, the formation process and the influence it
has upon the travel decision are important elements to be considered in the tourist
destination promotion strategies.
According to research literature, destination image is a concept that has been approached in
time in three different ways. First approach has benn proposed by Echtner and Ritchie
(1991) and suggested that the destination image construct consists of three dimensions:
attribute-holistic, functional-psychological, and common-unique. They proposed a threecontinua system for the destination image. In the three-continua there can be identified six
components of destination image combined in three pairs of bipolar axes: functionalpsychological, common-unique, attribute-holistic. The functional characteristics refer to the
more observable, measurable attributes like price levels, types of accommodation while the
psychological characteristics represent the intangible attributes like safety and friendliness.
The common-unique continuum is based on the idea that perceptions about the various
characteristics of a destination can range from being considered common or differentiated
and can vary from a person to another. Finally, the attribute-holistic continuum suggests that
a destination’s image involves both specific attributes as well as a bigger picture, the overall
image of the destination.
In the second approach destination image is considered a composite construct, composed of
interrelated cognitive and affective evaluations that influence the overall impressions
(Baloglu & McCleary, 1999).
The third approach was proposed by Gartner (1993) and it classifies the sources of received
information as base of the process of destination image formation as based on organic
and/or induced sources. The organic sources include news, movies books, school, movies,
etc.– materials that don’t have the objective of advertising a specific destination; the induced
sources include videos, brochures, advertisements, posters, the Internet and other marketing
and communication instruments used to promote a destination.
The Internet is an important vehicle and source of information for the potential tourist. On
the government and travel agencies websites they can travel almost near to reality in places
they want to visit. The images, videos, 360˚ panorama and the text can influence the image
that the website visitor has about a destination. This is why the content analysis and the
identification of most frequent words, expressions or images are used to describe a
destination is important. The message transmitted with those words/images can influence
the overall destination.
Perdue (2001) considers that the quality of a website is a result of three technological
elements: ease navigation, accessibility and technological creativity. But when it comes to
destination image, the most important element of websites is the content. Online and offline
information, acts as a stimulus and image-forming agent that shapes destination perceptions
and choices (Baloglu & McCleary, 1999; Beerli & Martin, 2004). And researches articles
have shown that content of a website have a major influence on the perceived image of the
destination (Gretzel, Yuan & Fesenmaier, 2000).
STUDIES OF CONTENT ANALYSES OF DESTINATION IMAGE
Content analysis is part of qualitative methodology used in this research field to analyze
destination image. Qualitative content analysis includes manifest content and latent content
(Albers & James, 1988). The manifest content can be identified through the process of
naming and translation, while analyzing images at a latent level involves the subjective
interpretation of “the structure and set of rules – the codes – that transform a picture’s
compositional appearance into a shared and meaningful message” (Albers & James, 1988).
With the increasingly rich and readily available text data on the web, qualitative assessments
such as content analysis of image formation and other tourism phenomena is gaining in
popularity. MacKay & Fesenmaier (2000) proposed that while visitor-determined images
reflect individual differences in information processing and interpretation, destinationdetermined images mirror the actuality of the destination. Therefore, it is important to test
whether the images presented in promotional materials correspond to those held by visitors.
Although image is formed through the subjective evaluation of external stimuli (Gartner,
1993), it may be as important to discern what stimuli are delivered by destination marketers.
The examination of the effects of projected images through various information channels
could become an integral part of image formation research. Some previous studies have
investigated the pictorial or verbal contents of promotional materials from the perspectives
of the research subjects after exposure to the messages (MacKay & Fesenmaier, 2000).
O’Leary & Deegan (2009) argued that content analysis of written information, such as
guidebooks and travel brochures, could provide a great deal of information about the images
projected by a tourism destination. They conducted an exploratory study on the contents of
Ireland’s promotional materials distributed in the French market and developed a master list
of attributes measuring French residents’ perceptions of Ireland as a destination.
However, this study only examined the printed promotional materials provided by the Irish
national tourism organization. Online information and other commercial information
sources were not analyzed. Further, the destination attributes identified were based on the
researchers’ subjective judgments. Stepchenkova & Morrison (2006), however, examined
the online content of tour operator websites about Russia. They found that within the 212
websites analyzed the US and Russian sites were portraying different images of Russia as a
tourism destination.
STUDY OBJECTIVES
O1. Identify destinations within Romania mentioned most frequently in the websites.
O2. Identify the most frequent used words in websites that refer to Romania as a tourist
destination. The most frequent meaningful words were interpreted as the induced image
variables and were used to compare them with the most frequent used images resulted
from a previous research on the same subject.
METHODOLOGY
Websites selection
Using Google AdWords Keywords tool we generated 3187 keywords starting with a first
seeding keyword “Romania tourism” and using at each step new seeding words from the list
generated by the previous keyword until the new generated list had very few or no new
relevant keywords.
This method of identifying websites from which to select text content to analyze has been
chosen due to the fact that the present paper has as purpose to evaluate the image
transmitted by Internet not just created and posted on the Internet. We therefore chose to
use a method of selecting the websites with the most exposure to Internet users.
In a second stage, from the 3187 keywords obtained we have selected 115 keywords on the
following criteria: to contain the word “Romania’ or “Romanian” and one or more of the
following terms: “travel”, “tour”, “tourism”, “tourists”, touristic attraction”, “travel agency”,
“travel guide”, “touristic guide” – indicating an interest in the touristic aspects of our
country.
Using these 115 keywords in Google search engine we generated a number of 2186 websites
from which we have selected for analysis a number of 9 websites each of them representing
more than one percent of the links offered by Google in the firts two pages of results. We
had eliminated 2 from these websites because of the lack of an English version of the site,
analyzing therefore a final set of 6 websites.
Tabel 1 – List of websites from which the text has been colected
Website
Freq.
98
48
%
4,5
2,2
www.deltatravel.ro
42
1,9
www.rotravel.com
39
1,8
www.infohub.com
38
1,7
www.tripadvisor.com
23
1,1
www.romaniatourism.com
www.wordtravels.com
Data collection
Our pilot study uses a database containing 548 text files representing the content of 548 web
pages extracted from a sample of 6 websites promoting Romania as a touristic destination
towards foreigners and therefore in English or containing English pages. We selected the
pages upon the following criteria: pages accessed with at most 3 clicks from the index page,
containing text in English, referring to Romania and containing at least a phrase of at least 3
rows (in order to exclude pages containing only links or enumerations).
We used the Spider Simulator software from http://www.seochat.com in order to extract de
text from each page and also the links to follow on the next level (index page has been
considered level 0, the pages emerging from the index page level 1, pages emerging from
pages at level 1 have been considered level 2 and pages emerging from pages at level 2 have
been considered level 3). For the pages appearing in the data base at more than one level we
considered the page only once at the first level where it has been encountered.
We analyzed the data using WordStat software which is a text analysis module specifically
designed to study textual information such as responses to open-ended questions,
interviews, titles, journal articles, public speeches, electronic communications, etc.
Data preparation
WordStat is case-insensitive and therefore accepts files in either upper or lowercase.
Because of the tool used to collect the data it was not necessary to manually verify
uppercase and lowercase letters in order to ensure that the same word beginning with
uppercase and respectively with lowercase is considered as a single word and not two
different words. Also, as we have extracted the text using Spider Simulator it has not been
necessary to check for hyphenation, square brackets and braces which can determine errors
in word coding leading to imprecise or invalid conclusions, since Spider Simulator does not
collect punctuation signs of any kind.
The automatic content analysis feature of WordStat involves numerous operations of word
recognition and generally requires each word to be spelled correctly. Any misspelled word
may be left uncoded and lead to imprecise or invalid conclusions. Hence part of the data
preparation has been to check misspellings. The text files have been imported in WordStat,
each webpage representing a case.
Database cleaning
WordStat includes a default list of common English words which can be excluded from
analysis if chosen too. Cleaning the database included completing the exclusion lists with
words identified in our text files and considered to be irrelevant to the analysis (e.g. traveler,
acting, day, time etc.). We have defined 13 relevant categories including elements of 1, 2, 3
and 4 words identified in the text.
Analysis and results
Fig. 1 Text elements of 1 word length
The most frequently used word (Fig. 1) from the analyzed set is the name of the capital
“Bucharest” with a frequency of 6923 in 548 text files, followed by names of cities
“Brasov” (2605), “Sibiu” (2455), “Constanta” (2118), “Timisoara” (1555), a region
“Transylvania” (1803) and a generic administrative term “City” (1551).
The presence analysis (Fig. 2) revealed that the name of the capital has been used in 73,3%
of the analyzed file texts, the word “Europe” in 68,8%, “City” in 65%, “Transylvania” in
60,9% and “International” in 58,9%.
Analysing text elements including two words (Fig. 3) shows that the most frequently
mentioned element is the Danube with 2 forms: “Danube Delta” (frequency of 609),
“Danube River” (frequency of 534), followed by “Sea coast” (frequency of 355),
“Carphatian Mountains” (frequency of 311), “Medieval Towns” (frequency of 297) and
“Traditional Villages” (frequency of 287).
Fig. 2 – Text elements of 1 word length. Presence analysis
Fig. 3 – Text elements of 2 words length. Frequency analysis
The presence analysis of text elements with 2 words (Fig. 4) indicates that “Danube Delta”
is present in 38,9% of the analyzed text files, “Lowest prices” in 36,9%, “Sea Coast” in
32,1% and “Carpathian Mountains” in 25%.
Fig. 4 – Text elements of 2 words length. Presence analysis
Fig. 5 – Text elements of 3 words length. Frequency analysis.
The analyses of text elements with 3 words shows that the most used expressions of this
form are “castle and fortresses” (frequency of 487), “Food and Wine” (frequency of 295),
“Banat and Crisana” (frequency of 289), “Arts and Crafts” (frequency of 269) and
“Festivals and Events” (frequency of 269).
Presence analysis indicates that “Banat and Crisana” appears in 29,6% of pages, “Food and
wine” in 24,6%, “Arts and crafts” in 23,7%, “Excellent customer care” in 23,7% and
“Danube river cruises” in 21,5% of pages.
Fig. 6 – Text elements of 3 words. Presence analysis.
Fig. 7 – Text elements of 4 words length. Presence analysis.
Analyzing phrases with 5 words revealed a new series of frequently used elements. Besides
regions “Transilvania Banat and Crisana” (present in 29,4% of web pages analysed) we
meet references to cultural elements as museums: “National Military Museum Bicharest”
(present in 7,3% of cases) and “National Museum of Art” (present in 5,7% of cases). Also,
we see elements as hotels “Golden Tulip Ana Tower” (present in 4,9% of cases) and
referrences to the “History of Jewish” (present in 4,6% o cases).
Fig. 8 – Text elements of 5 words length. Frequency analysis.
Conclusions and further research
The most frequently used elements in describing Romania on the analyzed websites refer to
cities (Bucharest, Sibiu, Brasov, Constanta, Timisoara), country regions (Transilvania,
Banat and Crisana). Including larger phrases in the analyses revealed more interesting
themes contained in the analyzed web pages as names of museums, hotels, natural beauties
and historical elements.
Compared with the results obtained in the previous research Vicol, Spiridon (2010), results
obtained in the present study also underline the commercial aspects of Romania as touristic
destination through references made by hotel names. The significant difference observed is
the presence of cities which is missing from the previous study due to the fact that in most
of the pictures we could not identify the location.
The main conclusion and also further research direction emerging from the present study is
the necessity of extending the analysis on a larger group of websites, refining the analysis
methodology at the first level, when working with 1 word phrases.
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