TRAVEL BLOGS AND THE IMPLICATIONS FOR DESTINATION MARKETING
Bing Pan, Ph.D.
Department of Hospitality and Tourism Management
School of Business and Economics
College of Charleston
66 George Street
Charleston, SC 29424
Tanya MacLaurin, Ph.D.
Department of Hospitality and Tourism Management
School of Business and Economics
College of Charleston
66 George Street
Charleston, SC 29424
John Crotts, Ph.D.
Department of Hospitality and Tourism Management
School of Business and Economics
College of Charleston
66 George Street
Charleston, SC 29424
TRAVEL BLOGS AND THE IMPLICATIONS FOR DESTINATION MARKETING
This study first explores the nature of travel blogs as a manifestation of travel experience.
Through a marketing perspective, visitor opinions posted on leading travel blog sites were
analyzed to gain an understanding of the destination image being communicated including its
strengths and weaknesses. Travel blogs on Charleston, South Carolina were collected through
three most popular travel blog sites and three blog search engines. The blogs were analyzed
using semantic network analysis and content analysis methods to gain insight into what bloggers’
were communicating about their travel experience. The qualitative results demonstrated that
travel blogs are an inexpensive means to gather rich, authentic, and unsolicited customer
feedback. With the advances of information technologies and increasingly large numbers of
travel blogs, automated travel blog monitoring is a feasible and cost-effective way to help
destination marketers assess their service quality and improve travelers’ overall experience.
Keywords: travel blogs; destination marketing; semantic network analysis; content analysis
TRAVEL BLOGS AND THE IMPLICATIONS FOR DESTINATION MARKETING
Research has shown that interpersonal influence arising from opinion exchange between
consumers is an important factor influencing consumers’ purchase decisions. Word-of-mouth or
advice from friends and relatives often ranks as the most influential source of pre-purchase
information (Crotts 1999). Though there is a body of literature suggesting Word-of-mouth can be
manipulated by marketers (Smith and Vogt 1995), the bulk of the literature suggests that meeting
and exceeding visitor expectations is the only viable means of inducing positive word-of-mouth.
The Internet has become a major source of information for travelers and a platform for
tourism business transactions. Specifically, the tourism industry is today’s leading application of
the Internet in a business to consumer (B2C) context (Werthner and Ricci 2004). According to
the Travel Industry Association of America, 67 percent of the online travelers in the United
States search for information on destinations or check prices or schedules via the Internet; 41
percent of them book airline tickets, hotel rooms, or rental cars via the medium (TIA 2005). With
the increasing amounts of travel related online information, tourists have enormous number of
choices as to where they travel and what they do, and the Internet will continue to influence and
shape the tourism industry more so than any other sector of the economy (TIA 2005).
To date marketing researchers on the topic of the Internet have primarily focused on
online consumer behavior and Internet advertising strategy (Hoffman and Novak 1996; Werthner
and Ricci 2004; Yadav and Varadarajan 2005). Moreover the marketing of destinations has
primarily focused on the potential of the Internet as a B2C medium where firms and organization
promote and sell their products and build customer relationships (Gretzel, Yuan et al. 2000;
Wang and Fesenmaier 2006). Though the Internet is an important medium for travel information,
the confidence the consumers place in Internet advertisement is low (Kwak, Fox et al. 2002; Cyr,
Bonanni et al. 2005). Often overlooked by many, the Internet provides new ways in which
individuals learn about tourist destinations and their products and services directly from other
consumers. The Internet has provided a new platform of communication which is similar to
word-or-mouth which could empower consumers. Travelers can email each other, post
comments and feedbacks, publish online blogs, and form communities on the Internet. Blogs, as
“push-button publishing for people” have gained more and more popularity (Cayzer 2004).
Currently there are 31.6 million blogs on the Internet (Perseus 2005) with 40,000 new blogs
coming online each day (Baker and Green 2005). As essentially a consumer to consumer (C2C)
medium, Internet blogs has important implications for destination marketers that have been
overlooked by researchers.
When word-or-mouth goes digital, it poses new possibilities and challenges for tourism
marketers (Dellarocas 2003). The purpose of this study was to understand the nature of travel
blogs as a manifestation of travel experience, and through a marketing perspective, assess visitor
opinions posted on leading Internet travel blogs and understand the image being communicated
about the destination including its strengths and weaknesses. Furthermore, its purpose is to
highlight and refine a qualitative methodology that destinations could use to assess visitor
perceptions as well as their positions in the marketplace. Traditional visitor intercept surveys
asking subjects to rate a fixed set of destination attributes along Likert scales might be an overly
blunt and clumsy tool in understanding visitors likes and dislikes and the information they will
likely share with others. Instead, travel blogs as uncensored and rich expression of travel
experience are a cost-effective way to collect travelers’ feedbacks and potentially provide a
quality control mechanism.
This section reviews relevant literature in marketing, consumer behavior, and information
technology areas on the nature of word-of-mouth and online blogs in order to provide a
conceptual basis for the discussion on travel blogs and the relevant research methodology.
Opinion Leadership and a Model of Word-of-Mouth
Consumers learn about the attributes of a product in various ways: through
advertisements, word-of-mouth, price, and sale quantity (Vettas 1997). However, a tourism
product is in essence an ‘experience good’ meaning that the product is based upon a bundle of
services and experiences by their very nature that are hard to assess prior to purchase (McIntosh
1972). The advice from other consumers who have prior experience with the tourist destination
and who are interpersonally available will no doubt rank as not only the preferred source of pre-
purchase information but the most influential in travel decision making (Crotts 1999). Word-of-
mouth has been defined as informal communication between consumers regarding the
characteristics, ownership, and usage of a service or product (Westbrooke 1987). Different from
marketing information from mass media, personal communication or word-of-mouth is viewed
as a more credible source of consumer information.
Lazarsfeld, Berelson, and Gaudet (1944) have discussed a two-step flow of
communication in which political views were influenced largely by the communication between
voters themselves instead of the influence of mass media. Thus, political information was firstly
transmitted from the mass media sources to the audience, and then secondly within the audience
themselves through word-of-mouth. The opinion leader is the agent who is an active user and
who interprets the meaning of media messages or content for other users. Usually the opinion
leader is held in high esteem by those that accept their opinions and their advice is sought by
other consumers (opinion seekers). As the originators of word-of-mouth, opinion leaders are
especially interested in a product field, are exposed to mass media about the product category,
and are trusted by opinion seekers to provide knowledgeable advice regarding the product (Piirto
1992; Weimann 1994; Walker 1995).
Past research has investigated the sources, mediating variables, motivations, and
outcomes of word-of-mouth. Consumers often learn about a product through real consumption
experience or mass media (Dichter 1966). Driven by the motivations of altruism, self-interest, or
the expectation of reciprocation, some consumers may spread product knowledge through word-
of-mouth (Grewal, Cline et al. 2003). However, this process from product knowledge to
voluntarily spreading word-of-mouth is mediated by several variables. Research has shown that a
good customer-employee relationship could foster the produce of positive word-of-mouth
(Gremler, Gwinner et al. 2001); higher level of consumer involvements will lead to higher level
of word-of-mouth activity; the consumers’ affective levels will also influence its amount
(Westbrook 1987); the intensity of surprises in the consumption process is positively correlated
with the volume of word-of-mouth (Derbaix and Vanhamme 2003). On the recipient’s end, both
positive and negative of word-of-mouth could influence a consumer’s loyalty, product
evaluation, and purchase decision (Westbrook 1987). However, the changes of attitude of
behavior of the recipients are mediated by their evaluation of information sources. Research
shows that they do not readily change their attitude based on negative word-of-mouth since their
sources may not be trustworthy (Laczniak, DeCarlo et al. 2001).
Blogs as Online Word-of-Mouth
With the advancement of the Internet, consumers now are able to access not only
opinions from close friends, family members, and coworkers, but also strangers from all around
the world who may have used the product, visited a certain destination, or patronized a property.
More consumers are relying on online opinions for their purchase decisions, for example, from
which movies to watch, to which stocks to buy (Guernsey 2000). Searching and reading other’s
opinions about a product can help a consumer save decision making time and also make better
decisions (Hennig-Thurau and Walsh 2003). More importantly, according to Bickart and
Schindler: “the Internet extended and changed the nature of word-of-mouth communication and
its impact on consumer behavior as well as the methodological approaches used to examine it”
(Bickart and Schindler 2002). Thus, a new methodology is needed in understanding blogs as
As an important format of digitized word-of-mouth, blogs are gaining more and more
popularity. Blog is a shortened word originating from ‘web log” (Chow 2005). The form is
familiar, frequently updated, reverse-chronological entries on a single Web page (Blood 2004).
Recently, audio and video blogging from mobile devices is also available (Baker and Green
2005). Currently there are 31.6 million blogs on the Internet (Perseus 2005) with 40,000 new
blogs each day (Baker and Green 2005). Perseus (2005) randomly surveyed 10,000 blogs on
twenty leading blog-hosting services; sample demographics reported 68.1 percent were females,
ages ranged from 13 to above 50 with 94 percent under the age of 30. Pew Internet Research
shows that around 7% of the 120 million U.S. adults have created at least one blog and 27% of
Internet users have read blogs (Rainie 2005). The number is expected to continue rising (Figure
1). Both blog creators and readers are more likely to be male, young, broadband users, Internet
veterans, relatively well-off financially and well educated (Rainie 2005). The distribution of
influences as measured by the number of in-links to the blogs follows the power-law distribution,
which makes only a small percentage of blogs the most popular and well-read ones. Most blogs
are read and linked infrequently especially for those recently built blogs (Marlow 2003).
Furthermore, according to Nardi et al. (Nardi, Schiano et al. 2004), Internet users blog for
various reasons: to document one’s life; as a commentary; as catharsis and outlet for their
feelings; and as a thinking tool. These are all intrinsic motivations indicating the genuineness of
travel blogs as visitors’ experiences which are similar to travel journals. The various bloggers
have made blogs a different media other than the mere communication of product information.
___________________ Insert Figure 1 Here _______________________________
In a aggregated level, when word-of-mouth goes digital, this form of communication is
different from traditional word-of-mouth in three important ways (Dellarocas 2003): 1. with the
low cost of access and information exchange, the new types of word-of-mouth will appear in a
unprecedented large scale and will create new dynamics in the market; 2. the format and
communication type between those communications could be controlled and monitored
precisely; 3. new problems may arise since users could be anonymous or intentionally
misleading, and 4. online blogs may be captured out of contexts and may induce multiple
interpretations. Blogs and the information clusters formed around them through links could
provide connections between otherwise disconnected smaller customer groups. Thus, they
possess the potential to transform the blogspace into large virtual communities. However, the
anonymity and almost free accessing and posting of online space make collusion a potential
problem for both customer and marketers (Dellarocas 2003).
Recent study confirmed that blogspace is a complex and rich environment. Based on
more than one million blogs in livejournal.com, Kumar et. al. (2004) demonstrated that the
blogspace exists at least three layers: the individual bloggers who are defined by their
demographic characteristics; a middle layer of pairs of bloggers is constructed based on
friendship; a higher layer of interest groups and virtual communities explained by geographic or
demographic correlations (Kumar, Novak et al. 2004). In a global scale of online space, Google
bombing is a phenomenon in which savvy users of Google take advantage of Google’s algorithm
and blogs, and further manipulate the search results when the users type in a query in Google
(Tatum 2005). Google’s PageRank algorithm utilizes the link structure of the web space to locate
the most authoritative web pages (Brin and Page 1998). Blogs are actually the tool that Google
bombers used in manipulating the hyperlink structure of the web space and subsequently
influenced the returned results in Google. This demonstrated that different from physical word-
of-mouth, blogspace can create virtual relationships and communities and its influence move far
beyond the readers of blogs; it actually creates a new type of reality through search engines in
the online space.
Travel Blogs and Online Word-of-Mouth
Similarly, the emergence of travel blogs will inevitably influence the link structure and
the content of the information space for visitors, and will induce different information content
when a visitor searches for destination-specific information on the Internet. A tourism product is
in essence an ‘experience good’ (McIntosh 1972). At the moment of decision making, the
consumer must act on impressions of the product’s attributes gathered from often imperfect
sources of information. Thus word-of-mouth and digital word-of-mouth will inevitably become
the preferred travel information source (Crotts 1999). In this sense, tourism marketers need to
understand this new technological phenomenon and its implications for marketing and promotion
of a destination, instead of being driven by it blindly. With the rapid development of Internet
technology, it is vital for the tourism marketers and the tourism industry in general to understand
the nature of travel blogs and their implications for destination marketing.
In this research a case study method was adopted to explore the nature of travel blogs and
their implications for destination marketing. Various research methods were used to gain insight
on the nature of travel blogs about a specific tourist destination. Bloggers’ demographic
information was researched as were the characteristics of the travel blogs. The text contained in
blogs was collected and analyzed by word frequency, semantic network analysis (Doerfel 1998)
and content analysis. Various qualitative data analysis techniques were adopted in hopes that
other researchers would replicate this research to gather information on visitors’ experiences at
their tourist destinations.
Charleston, South Carolina, United States
Charleston, South Carolina was selected as the tourist destination to collect qualitative
blog data. Charleston is located in the Low Country of South Carolina and faces the Atlantic
Ocean. The area is composed of three major cities or towns: Charleston, North Charleston, and
Mount Pleasant with a combined population around 600, 000 (U.S. Census Bureau 2005).
Charleston boasts a rich history with the firing of the first shot of the Civil War of the United
States. It is also one of the best preserved cities in America’s Old South with many Pre-
Revolutionary War buildings (Porter 2005). In 2004, Charleston received 4.7 visitors. The
tourism industry created about 105,000 jobs and proves to be a leading economic
driver(Charleston CVB 2005). Charleston is ranked as the 6th “Top City in the United States &
Canada” according to Travel and Leisure magazine’s tenth annual World’s Best Poll. It has
consistently placed in the top 10 domestic travel destination for the pat 12 years by Conde Nast
Traveler magazine’s prestigious Readers Choice Awards (Charleston CVB 2005).
The three most popular travel blog sites, travelblog.org, travelpod.com, and
travelpost.com were identified through searches on Google on September 7, 2005. Searches on
the three sites for blogs on Charleston, South Carolina yielded 30 blogs. Additional search
engines, technorati.com, Google blog search, and IceRocket, were also used to identify an
additional 24 blogs on travel experience to Charleston. In total, 54 blogs were collected on
September 12, 2005. Of these, fourteen did not contain content relevant to a travel experience in
Charleston, South Carolina. Instead, the bloggers might have been Charleston locals, or stopped
in Charleston en route to another destination. The remaining 40 blogs contained content relating
to visitors’ experiences in Charleston. These 40 blogs were placed in a master file for qualitative
data analysis. Each blog retained its title and identification information.
After detailing the general characteristics of bloggers (demographical information) and
their blogs (time of blogging and the lengths of blogs), the full text of the 40 blogs were
aggregated and analyzed using semantic network analysis and content analysis. Semantic
network analysis has been found to be a useful framework for the construction and analysis of
communication content (Doerfel and Barnett 1999). Many researchers have argued that the
meaning of a concept can only be determined by the relationship with other concepts, and a
model of networked concepts can accurately determine their meanings (Woelfel and Fink 1980;
Barnett, Palmer et al. 1984; Palmer and Barnett 1984). Programs such as CATPAC II (Doerfel
1998) and TextAnalyst (Megaputer 2005) can be used to generate semantic network from
communication content. In this study, a semantic network of the bloggers’ experiences was
generated from the analysis of the full text of all the travel blogs to the Charleston area using
TextAnalyst. Content analysis was also performed on the travel blogs (Weber 1990). An iterative
process of content analysis was followed. Two researchers independently coded the blogs; upon
further discussion, a master coding scheme was constructed. The blogs were finally were coded
using two dimensions, which are the aspects of tourism amalgam model (Cooper 2005) and the
directions of the comments (positive or negative).
The characteristics of bloggers and blogs revealed the nature of travel blogging. The
results of semantic network analysis and content analysis for travel blogs to Charleston were
detailed to reveal the strengths, weaknesses, and the competitive environment of Charleston as a
The demographic information of the bloggers was gleaned from the hosting sites through
different methods. All the sites allow users to post their personal portraits in their profiles; their
demographic and personal information can also be inferred from their blogs and usernames; on
travelpost.com, the users can choose to disclose their age, gender, location, and occupation in
their public accessible profiles. Through these methods, certain demographic information was
obtained on 34 out of the 40 bloggers. For all bloggers, most were from the United States except
one from Thailand and one from the United Kingdom. For the 13 users who choose to disclose
their ages, their ages ranged from 21 to 64 with an average age of 38. Two cohorts of users
seemed to be present: one cohort in their 20s to 30s and the other in their 50s and 60s. For those
users who disclosed their gender, there were eleven females (55%) and nine males (45%).
For most blogs, each piece contains the description of one trip from one traveler. There
are exceptions: for example, in one case two bloggers produced several posting for one trip in
which they described one day’s experience in each. All the blog sites show the time the blog was
added; some sites such as travelpod.com and travelpost.com ask the bloggers to enter the date
they traveled. Additional information about the trip could also be inferred from the blogs
themselves. There were 18 blogs which the researchers can infer both the trip dates and blog
posting dates. One blog was posted the same day the trip was taken; four blogs were composed a
few days after the trip; four blogs were posted 2-10 months after the trips; the rest of the blogs
were posted more than one year after the trips were taken. The longest delay was a post in 2005
about a trip taken in 1968. Interestingly, one account is not always associated with one blogger:
there is one family and two couples who co-own one blog account besides other single
individuals. However, it was not clear who in the group composed the blogs. In addition, some
blogs contain both pictures and text about their trip to Charleston. Among 40 blogs, 11 contain
pictures. The number of pictures range from 1 to 53. The number of words range from 4 to 1,972
with a mean number of 444 words.
Frequency Analysis and Semantic network Analysis
The travel blog text was analyzed by word occurrence and frequency using TextAnalyst
(Megaputer 2005). Words commonly used in constructing sentences (stop words) were
eliminated using a stop word list such as “the”, “a”, “of”, and “is”. The analysis resulted in the
construction of a table of the ‘Most Frequently Used Keywords’ that appeared in the blogs
(Table 1). The most frequently used keywords demonstrated that travelers express every aspect
of travel experience in their travel blogs, including attractions (e.g. “plantation”, “city”, and
“Fort Sumter”), accommodations (e.g. “hotel” and “inn”), dining (e.g. “restaurant”, “dinner”,
“menu”, and “lobster”), and transportation (e.g. “car”, “drive”, and “road”). The travelers were
mostly impressed by Charleston’s “plantations”, and they tend to talk about their en-route (“car”
and “drive”) and accommodation (“hotel”) experience. Dining also is one type of most
remembered experience (“restaurant”, “dinner”, and “menu”).
_____________________ Insert Table 1 Here _____________________________________
Semantic Network Analysis
Semantic network analysis provided a useful framework for the construction and analysis
of meanings and impressions of Charleston as a tourist destination. The most frequently used
keywords reported above were used to construct a semantic network diagram from TextAnalyst
(Megaputer 2005). Word frequency was illustrated in the diagram by size and color of circle
surrounding the word, i.e. large frequency is illustrated by a larger and darker colored circle, a
less frequent occurrence is illustrated by a smaller and lighter colored circle. Lines drawn
between the word circles illustrate the proximity of occurrence of the words. The semantic
diagram provides a graphic representation of Charleston as a destination (Figure 2). From the
graph, several clusters of keywords could be identified, indicating the types of travel experience
as represented in travel blogs on Charleston. Charleston with its major tourist attractions and
related accommodations and dining is the most prominent cluster; the second major cluster is the
driving experience related to the trip; the third one is specifically associated with plantations, an
unique type of attraction in Charleston; smaller clusters of keywords are connected with dining
experience, camping and museum attractions. The results demonstrated the kaleidoscopic nature
of travel blogs in representing travel experience. Every aspect of the travel experience, from
visiting attractions, dining at restaurants, to relatively ancillary activities such as driving and
camping all become the major content of blogging and constitute a part of travel experience.
_____________________ Insert Figure 2 Here _____________________________________
The researchers used NVivo (2004) software to perform content analysis on the blog
data. Each of two researchers independently coded the content by constructing topical categories
dependent on their own analysis of the blog content. No prior discussion of categories took
place between the researchers. NVivo tools enable the researcher to create category trees to
illustrate relationships between different categories. Strengths and weaknesses were also coded
for all content that possessed positive or negative comments related to the blogger’s travel
experience while in Charleston. Upon completion of the researchers’ independent coding of the
blog data, coding trees were compared. This comparison demonstrated many similarities and
differences in coding. All categories identified by the researchers were used to build a composite
enabling the formation of a master list of categories. The tourism amalgam model developed by
Cooper (2005) was adapted by adding subcategories identified in the independent coding
process. Cooper considers destinations as amalgams creating an inseparable tourist product. His
original destination amalgam model includes attractions (artificial features, natural features, and
events which provides the initial motivation to visit), amenities (accommodations, food,
beverage, retailing and other services), ancillary services (related marketing efforts of tourism
organizations and others), and access (transportation, car rental, and local transport) (Cooper
2005). The model could also be used to categorize travel experience since they encounter every
aspect of the destination as described in the model and their experience is also integrated, holistic
and inseparable from one aspect to another. However, from initial coding, the researchers
discovered that the aspect of “ancillary services” is almost irrelevant; a new category of “overall
impression” is necessary since sometimes the travel bloggers talk about their overall experience
with evaluation on the whole trip without referring to any single aspect of the trips. The final
standardized coding categories with the numbers of codes were listed in Table 2.
__________________________ Insert Table 2 Here _______________________________
Researchers repeated the independent coding of the blog data using the standardized
category codes shown in Table 2. Upon completion, coding comparison indicated that
researchers were more consistent in their coding but significant differences remained in the
number of items coded. Upon closer examination it was revealed that most differences could be
attributed to varying coding practice. One researcher coded multiple lines as one item, while the
second researcher coded four keywords in the same text lines as four items. A detailed set of
coding procedures were developed to assist in the third coding. In the final data coding, the
researchers followed the rule that one sentence should be taken as one coding unit. Thus, the
final coding scheme follows two dimensions: one dimension follows the different aspect of
tourism amalgam model; the other dimension is on the personal evaluation of various aspects of
the amalgam, either positive or negative, in order to understand the strengths or weaknesses of
Charleston as a destination (Table 2). Pure neutral descriptions of the trip are very few and
ignored since the goal of the content analysis on strengths and weaknesses of Charleston. Figure
3 depicts the complete procedure for the content analysis.
___________________ Insert Figure 3 Here _______________________________
Strengths and Weaknesses as Reflected from Travel Blogs to Charleston
Strength and weaknesses identified in the coding process were calculated. A total of 177
positive and negative comments were articulated about Charleston in the travel blog text
including 134 positive and 43 negative comments. Thus, three out of four comments made about
Charleston were positive (75.5%). Looking at the major categories, the results show that
attractions are the major strength of Charleston (17% negative comments), especially on the
history, hospitality environment, and water attractions. The major complains about the city come
from access issues, especially on automobile travel (6 and 75% negative comments). Amenities
also have a relatively high percentage of negative comments (32%). Looking more in-depth in
the coding on the secondary level, the results show that the four major complaints were weather
(too hot in the summer), food (trashy food in fast-food restaurants and hotels), parking and road
signs (hard to find a parking space and unclear road signs). Three of the four complains on
overall impression were about the high price of the destination in general (Figure 4).
_______________ Insert Figure 4 here ________________________________________
The visitors also compared Charleston with the following cities: Chapel Hill in North
Carolina, Santa Barbara in California, New Orleans in Louisiana and Savannah in Georgia.
Those cities are more likely to be the competitors of Charleston in drawing visitors. This
provides destination marketers with benchmarking cities. In the future marketing campaign of
Charleston, marketers need to pay special attention to these cities in terms of monitoring their
marketing efforts and tourist volumes.
CONCLUSIONS AND DISCUSSIONS
This study revealed that travel blogs are authentic and untainted manifestation of
travelers’ experiences. Analysis on the blogs revealed strengths and weaknesses of the tourist
destination. Travel blogs can also be a useful tool in monitoring the competitive environment of
a destination and provide valuable customer feedback that is superior to Likert response survey
The Nature of Travel blogs
Travel blogs qualitatively cover every aspect of a visitor’s trip. From the overall
experience of traveling, anticipation, planning, packing, departure, driving, flying and delays en
route were all reflected in their travel blogs. Their experience involved the kaleidoscopic
perception and senses of a destination: from attractions, accommodation, dining, to access and
overall impressions. Most of the descriptions were experiential and subjective in nature. For
example, driving was always one major part of their traveling experience. On the other hand, a
part of that experience was positive for some visitors while negative for others. For example,
food was found to be both a positive experience for many visitors but a negative one for others.
In a more detailed analysis, it was revealed that the major complaints about food were mainly
coming from quick service or free breakfast in hotels; the satisfactory dining experience was
from fine dining restaurants in the Charleston area.
Strengths and Weaknesses of the Destination and Its Implications for Marketing
The results revealed that the major strengths of the destination lie in its attractions: the
historic charm, the southern hospitality, the beaches and water activities; the major weaknesses
were weather, infrastructure (roads and traffic), and fast food restaurants. “Pricy”, as one of the
overall complaints, also stood out. Again, different types of dining experience were reported as
both strength and weaknesses.
Compared with New York and Boston, lodging, attractions, and food in Charleston are
not significantly higher, if not cheaper. Why did travelers complained about the price? Despite
the majority of comments are positive, the travelers also have expressed many complains about
the infrastructure, especially on limited parking space, road conditions and traffic signs. In order
to understand these complaints, the researchers looked into the city as a whole. Charleston has
gained tremendously growth in the last decade. The Charleston-North Charleston Metropolitan
Statistical Area is the 96th largest metropolitan area in the United States; its population has
grown from 430,346 in 1980 to 583,434 in 2004 with a 35.6% growth (U.S. Census Bureau
2005); the visitor volume has steadily increased from 3.2 million in 1997 to 4.7 million in 2004
(Charleston CVB 2005). The data show that the greater Charleston area is moving toward a
major metropolitan region in South Carolina as well as the coastal south. However, the images
developing from the travel blogs reflected a discrepancy between the image of Charleston as a
town, versus the reality of a major metropolitan city. Thus in order to build a healthy industry in
Charleston, more investment needs to put into the infrastructure. In addition, marketers need to
promote a transformation of the image of Charleston, from a “town” with a single dimension of
historic heritage, to a metropolitan area with multi-dimensional attractions, such as beach,
seashore, golf, historic heritages and natural beauty. These results demonstrated that analysis on
travel blogs can reveal detailed and in-depth information about the characteristics of a
destination, which can not be gathered from the Likert scale measurement on visitor surveys.
It should be noted that blogspace, like the Internet in general, is changing in real-time
with new blogs being added and deleted everyday. In general, an automated monitoring system
for online blogs could be proposed. The system starts with the definition of research questions.
From the help of web crawlers or RSS (Real Syndication Feeds, which allow Internet users to
subscribe the websites or blogs) (Wikipedia 2005), the destination marketers could monitor the
dynamics of customer feedbacks. Figure 5 proposes automated mechanism and procedures for
monitoring travel blogs and thus providing a real-time customer feedback and quality control
tool for destination marketing. First, the researchers need to identify and define the question and
the goal of the study, whether it is the analysis for strengths and weaknesses of a destination or a
hotel, or the effects of a marketing campaign; then those keywords related to the questions could
be generated, for example, a city’s name or most commonly used complain words; the
researchers could then manually search the blogspace using identified keywords, or using RSS to
track blogs in real time; those blogs could be manually or automatically downloaded, aggregated
and analyzed using semantic network analysis or content analysis. Similar to the results of this
study, this procedure will likely produce insightful and cost-effective marketing implications for
tourism organizations and businesses.
______________________ Insert Figure 5 here __________________________________
This study represents the first of its kind in applying semantic network analysis and
content analysis on blog data in order to understand the competitiveness and customer feedbacks
of tourist destinations. Several issues need to be taken into consideration when applying this
methodology for other destinations. These include data collection issues and the issues of unit of
Data collection methods proposed here are the initial steps in understanding the nature of
blogs and the implications for destination marketing. The researchers need to be aware of the
following issues in adopting the methodology: the travel blogs are not collected from a random
sample of all the blogs about Charleston; rather, they are collected from searching through major
travel blog sites and three major travel blog search engines. The manual data collection method
is a form of non-random sampling of the blogspace, which can and should be replaced by
automated and complete random sampling in the future, with technologies such as RSS (Really
Simple Syndication) as described above.
Unit of Analysis
Another issue in the process is the unit of analysis in the content analysis stage.
Depending on the goals of the research, and the researcher conducting the research, a word, a
sentence, or a paragraph could be the unit of analysis. For example, a travel bloggers who visited
Patriot’s Point, one of the major attractions in Charleston, wrote about her experience in five
paragraphs with very detailed description on each ship and room she visited. If the goal of the
research is a destination, one coding unit would be sufficient for the analysis; if the goal is to
specifically understand the strength and weaknesses of Patriot’s Point, then each sentence, or
even each phrase, should be coded as one unit. It also should be noted that the coding categories
are not defined but emerged through the initial analysis. Depending on different goals of
research, coding categories might be different.
In general, this case study shows that blog analysis can be a useful way to detect the
strengths and weaknesses of a tourist destination. Special attention must be given to the coding
and analysis process. Depending on different goals of research, the unit of analysis and coding
categories will be distinct. Novel software tools could be used to automatically tracking the
dynamics of blogspace and potentially provide a quality control mechanism.
It is our hope that this research will be refined and replicated by others to better
understand a destinations strengths and weaknesses from the impressions of prior visitors who by
their actions are willing opinion leaders. In addition, we hope that this paper stimulates interests
among researchers as to the characteristics or profiles of these online bloggers. For those who
choose to pursue this line of inquiry, we encourage you to capture subjects’ national culture of
birth and residence since the role of opinion leaders has often been shown to vary across national
cultures (De Mooij 2004). In particular, subjects from countries along Hoftstede (2001) power
distance, masculinity, and uncertainty avoidance dimensions will likely vary in terms of role,
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List of Figures
Figure 1. Growth of number of blogs from three hosting sites (Persus 2005)
Boone Hall Plantation
Figure 2. Semantic Network of Travel Experience to Charleston
Two Researchers' Independent Coding on
Travel Experience and Positives and
Comparing Two Topical Coding Categories
Constructing Master Coding Tree Following
Tourism Amalgam Model
Independent Coding by Two Dimensions:
Dimensions of Amalgam and Positive or
Comparing Two Coding, Cleaning Up and
Coding by Sentences
Figure 3. Coding Procedure for Content Analysis on Travel Blogs
Figure 4. Positive and Negative Comments in Travel Blogs to Charleston
Food & Beverage
Other Visitor Services
Overall Impression 0 20 40 60 80 100 120
Identify research questions
Generate keywords relevant to research
Search blog sites or aggregate through RSS
Filter and download relevant travel blogs
Automated semantic network analysis or
manual content analysis
Market analysis/quality control report
Figure 5. Automated Quality Control Mechanism/Market Analysis through Monitoring Travel
List of Tables
Table 1. Most Frequently Used Keywords in Travel Blogs to Charleston
Keywords Frequency Keywords Frequency
Charleston 75 owner 8
plantation 24 lobster 8
city 23 history 7
car 14 cafe 7
hotel 13 Magnolia 6
drive 13 Boone 6
town 12 museum 6
restaurant 11 Yorktown 5
Carolina 11 mansion 5
dinner 11 acre 5
Fort Sumter 10 Sunday 5
inn 9 salad 5
fort 8 Folly Beach 4
road 8 harbor 4
Table 2. Number of Positive and Negative Comments
Attractions 81 16 97 17
History 35 2 37 5
Natural Environment 15 8 23 35
Environment 5 1 6 17
Attractions/Activities 14 2 16 13
Other Attractions 12 3 15 20
Amenities 32 15 47 32
Accommodations 8 7 15 47
Food and Beverages 22 8 30 27
Shopping 2 0 2 0
Other Services 0 0 0
Access 3 8 11 73
Air Travel 0 1 1 100
Local Transport 1 1 2 50
Car Travel 2 6 8 75
Train Travel 0 0 0
Overall Impression 18 4 22 18
Total 134 43 177 24