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Market Segmentation in Tourism:
An Operational Assessment Framework
18
Vicky Katsoni, Maria Giaoutzi, and Peter Nijkamp
18.1 Introduction
Distribution channels are the paths by which tourism organ izations carry out the
communication and sales of their products and services. To varying degrees, all
tourism product suppliers depend on these channels for the distribution of their
products (Bitner and Booms 1982; Middleton 1994). While the importance of
understanding and managing the structure and behaviour of such channels has
been clearly identified in many mainstream academic and trade publications
(Holloway and Plant 1988; Duke and Persia 1993; Ryan 1991), relatively little
tourism research has focused on this subject (Uysal and Fesenmaier 1993; Buhalis
2000). Many destinations have als o invested in Information and Communication
Technologies (ICTs), in their quest for more efficient and effective ways of
managing tourism demand and facing domestic and global competition (Sigala
et al. 2004). Consum er behaviour, on the other hand, has attempted to expl ain the
decision-making processes of consumers facing several alternatives or choices. Van
Raaij (1986) posited that “consumer research on tourism should be a cornerstone of
marketing strategy”.
V. Katsoni (*)
Department of Hospitality and Tourism Management, School of Business and Economics, TEI of
Athens, Ag.Spyridonos and Milou 1, Aigaleo Campus, Athens 122 10, Greece
e-mail: katsoniv@teiath.gr
M. Giaoutzi
Department of Geography and Regional Planning, School of Rural and Surveying Engineering,
National Technical University of Athens, Heroon Polytechniou 9 – Zographou Campus, Athens
157 80, Greece
e-mail: giaoutsi@central.ntua.gr
P. Nijkamp
Department of Spatial Economics, VU University, De Boelelaan 1105, Amsterdam 1081 HV, The
Netherlands
e-mail: p.nijkamp@vu.nl
A
´
. Matias et al. (eds.), Quantitative Methods in Tourism Economics,
DOI 10.1007/978-3-7908-2879-5_18,
#
Springer-Verlag Berlin Heidelberg 2013
329
While the tourism literature evidences that several factors influence travellers’
behaviour in consuming tourism products (Lepp and Gibson 2008; Hsu et al. 2009),
to date, investigation into the determinants of tourism consumption remains inade-
quate in the literature; for example, the relat ive importance of the various informa-
tion sources (ICT sources included) used by travellers has not yet been
systematically analysed. Given the increasing importance of this particular market
segment for destinations, additional research is needed to understand the behaviour
of tourists in an attempt to bring further theoretical and practical contributions to
this field of study (Ramkissoon et al. 2011). The present paper provides a compre-
hensive overview of the behaviour patterns of travellers to Arcadia (Greece) and
contributes to the study of information sourcing behaviour in their travel decision
process. It also provides a basis for channel members, especially suppliers, to assess
their distribution strategies
The research took place in Arcadia, a historic land of intense and continuous
presence from antiquity to the Byzantine and modern periods of history. In the
European countries after the Renaissance, the “Arcadian ideal” refers to the dream
of escaping from the disturbed world of violence and exploitation and returning to a
world of eternal innocence and tranquillity that would be based on the good
operation and fair competition of its members. Our research provides a better
understanding of how channels are used by different types of travellers in different
types of travel situations. The study adopts a dynamic situational perspective
(Bieger and Laesser 2002), combining characteristics of travellers with
characteristics of trips, and formulating the relevant hypotheses which are analysed
below.
18.2 Background Literature
18.2.1 Tourist Segmentation
Market segmentation is a technique used to subdivide a heterogeneous market into
homogeneous subgroups that can be distinguished by different variables, such as
consumer needs, characteristics, or behaviour (Kotler 1998; Middleton 1994).
Because people have individualized needs, tastes, and attitudes as well as different
life stages and lifestyles, no single variable can be used to segment travel markets
(Andereck and Caldwell 1994). The primary bases for segmentation include
demography, geography, behaviour, lifestyle, personality, motivations (Cha et al.
1995; Madrigal and Kahle 1994 ), benefits sought (Gitelson and Kerstetter 1990 ),
while some basic characteristics (e.g. demographic and behavioural) are sometimes
criticized for their failure to adequately predict actual consumer behaviour
(Andereck and Caldwell 1994; Cha et al. 1995; Morrison 1996; Prentice et al.
1998). Employing multiple variables should yield greater explanatory power than
using a single variable. In several major hospitality and tourism texts, the use of
“multi-stage segmentation” (Middleton 1994; Havitz and Dimanche 1990;
Morrison 1996) or a “combination” of multiple variables rather than just one has
330 V. Katsoni et al.
been recommended. A review of the literature indicates that there is no single
correct way to segment a market.
Market segmentation is a valuable instrument in planning appropriate marketing
strategies, and can assist in framing management thinking. Segmentation is justified
on the grounds of achieving greater efficiency in the supply of products in order to
meet identified demand and increased cost effectiveness in the marketing process
and maximize financial resources (Perdue 1985). Numerous methods of tourist
segmentation exist, including a posteriori or factor-cluster segmentation, a priori
or criterion segmentation, and neural network models (Mazanec 1992). A priori
market segmentation can be less time consuming and more effective for separating
markets at less cost. In tourism, the importance of segmentation is widely acknowl-
edged (Bieger and Laesser 2002; Cha et al. 1995; Kastenholz et al. 1999; Mo et al.
1994). To date research has assisted us to understand which bases can be used by
tourism destinations to effectively segment tourism markets, and these efforts have
largely centred upon building tourist profiles for a destination, using visitor data
(Frochot 2005).
The purpose of the trip is recognized as one of the non-traditional segmentation
bases closely associated with travel motivation, and has been approached from
different perspectives in formulating marketing segmentation approaches.
Examples of such studies include the interaction of trip purposes with activities
(Hsieh et al. 1992; Morrison et al. 1994; Moscardo et al. 1996), interest (Sorensen
1993; Wight 1996), motivation (Cha et al. 1995; Wight 1996), and opinion and
value (Madrigal and Kahle 1994). In using trip type as a key variable to segment the
travel market, the inclusion of more trip-related characteristics in the analysis is
highly recommended for a comprehensive understanding of the target segment
from a consumer behaviour perspective (Sung et al. 2001). Such characterist ics
include length of stay and size of the travel party (Hsieh and O’Leary 1993).
18.2.2 Information Search and Distribution Channels’ Usage
Buhalis (2000, p. 113) saw the functions of distribution in these terms: “The
primary distribution functions for tourism are information, combination and travel
arrangement service s. Most distribution channels therefore provide information for
prospective tourists; bundle tourism products together; and also establish
mechanisms that enable consumers to make, confirm and pay for reservations”.
These purposes and functions have received unequal attention from researchers
examining the visitors’ perspective, and relevant studies are often not set squarely
in the literature on distribution channels. This is especially the case with questions
of information search, in which a large discrete body of work has developed as one
take of interest in consumer behaviour. A distinction of tourism distribution
channels can be made between those which are direct and those which are indirect
in character. Direct channels norm ally link suppliers and consumers without the aid
of channel intermediaries. Such channels normally involve suppliers developing
and maintaining direct information and sales cont acts with consumers in specific
18 Market Segmentation in Tourism: An Operational Assessment Framework 331
target market areas. Indirect distribution channels (e.g. travel agents, tour operators
and wholesalers) involve a wide range of organizations communicating and selling
products to consumer markets on behalf of tourism suppliers and destinations (Gee
et al. 1989; Michie and Sullivan 1990). All of these channel operators have the
potential to significantly influence the travel patterns and behaviour of specific
travel markets.
There is no clear answer to the question which type of channel should best be
used, and it is important for tourism suppliers and destination marketing
organizations to understand the product preferences, the prior experiences, per-
ceived risks, travel package price thresholds, use of unique or novel destinations,
and market support needs of channel partners and their customers prior to forming
their marketing strategy (Hsieh and O’Leary 1993; Haukeland 1 995; Snepenger
et al. 1990; Calantone and Mazenec 1991). Generally, the closer the destination is
to the consumer in physical, product awareness, and experiential terms, the more
direct the channel of distribution becomes. Frequently, however, strategic informa-
tion concerning the product preferences of potential channel partners and their
customers is not available (Murray 1991). Understanding how customers acquire
information is important for marketing management decisions. This is especially
true for travel and tourism products, which are delivered away from home, often in
unknown places, inducing functional, financial, physical, psychological, and social
risks (Lovelock and Wright 1999; Teare 1992; Srinivasan 1990; Wilkie and
Dickson 1985). Travel products are mostly intangible personal service products,
involving personal interactions between customers and service providers (Lovelock
and Wright 1999 ; Normann 1996; Teare 1992) and the consumption and production
of tourism produc ts always coincide, creating high personal involvement (Bieger
and Laesser 2002). According to the economics of information, these
characteristics often lead to high pers onal investments of time, effort, and financial
resources for customer decision making (Lambert 1998).
The use of information sources has also been applied empirically as a segmen-
tation variable. When employed as a descriptor to profile the behaviour of tourists
who have been segmented on some other basis, information search has provided
valuable insights for planning marketing strategies and targeting marketing
communications (Moutinho 1987 ). With increasing frequency, tourists have been
directly segmented based on their search behaviour (Bieger and Laesser 2004;
Fodness and Murray 1997; Um and Crompton 1990; Baloglu 1999; Crotts 1998;
Snepenger and Snepenger 1993; Etzel and Wahlers 1985; Perdue 1985; Schul and
Crompton 1983; Woodside and Ronkainen 1980). With regard to information
search behaviour research, three major theoretical streams can be identified
(Schmidt and Spreng 1996; Srinivasan 1990; Bieger and Laesser 2004): namely,
(a) the indivi dual motivation approach; (b) the economic cost-benefit approach; and
(c) the process approach.
(a) The Psychological/Motivational/Individual Characteristics Approach
Traditional perspectives of information search focus on functional needs,
defined as motivated efforts directed at or contributing to, a purpose (Vogt and
Fesenmaier 1998). According to this approach, the search for information enables
332 V. Katsoni et al.
travellers to reduce the level of uncertainty and hence enhance the quality of a trip
(Fodness and Murray 1997 ; Teare 1992 ; Schiffmann 1972). The psychological/
motivational approach can be linked to travel motivation theory, where a differen-
tiation between a push and pull dem and stimulation is stipulated (Cha et al. 1995).
The idea behind this dimensional approach lies in the proposition that people are
pushed by their own internal forces and pulled by the external forces of the
destination attributes (Gitelson and Kerstetter 1990; Yuan and McDonald 1990;
Shoemaker 1989, 1994). Consequently, the individual’s characteristics influence
the utilization of available internal and external information sources (Bonn et al.
2001; Schonland and Williams 1996; Crompton 1992; Snepenger et al. 1990;
Leiper 1990; Hugstad and Taylor 1987).
After identification of needs, customers may first start internal search, using
existing knowledge that is also dependent on consumers’ ability to access stored
knowledge and information contained in memory related to past experiences with
the provider and other related learning about the environment/situation, such as
vicarious learning when actual experience is not available (Peter and Olson 1996).
Examples of vicarious learning include gathering information via word of mouth
about the experiences of others with service providers. (Bettman 1979; Soloman
et al. 1985; Alba and Hutchinson 1987; Brucks 1985; Gursoy and McLeary 2003;
Kim et al. 2007; Vogt and Fesenmaier 1998). If internal search is not successful and
consumers face uncertainty, then they continue with external search, that is infor-
mation seeking from the environment (Murray 1991). Various typologies exist for
classifying external sources of information, including: service-provider dominated
(advocate) versus independent/objective sources (Murray 1991); personal versus
impersonal sources (Hawkins et al. 1998); and, from the tourism literature, profes-
sional versus non-professional sources (Opperman 1999). Typically, the consumer
will prefer one source over another based on the perceived effectiveness of a
particular information source. Implicit in the concept of source effectiveness is
the notion that some types of sources are more influential than others in providing
useful information with which to form pre-service encounter expectations
(Hawkins et al. 1998).
Although information seeking is often coupled with a cultural (and therefore
regionally different) background resulting in different patterns of behaviour (Dawar
1993), a number of common travel-specific denominators regarding information
collection have also been identified, such as length of trip, previous experience and/
or visits to the destination, and travel party characteristics (e.g. composition of the
vacation group, the presence of family and friends at the destination). All these
determine information search behaviour, defined not only in terms of the use of
particular sources but also in terms of information search effort, the number
of sources used, situational influences, produc t characteristics (e.g. the degree of
novelty associated with the destination), and search outcomes (Fodness and Murray
1997; Woodside and MacDonald 1994; Schul and Crompton 1983; Bieger and
Laesser 2002; Snepenger et al. 1990).
Gursoy and MacLeary (2003) proposed a model of tourist information search
behaviour that integrated internal and external search, cost of search, concepts of
18 Market Segmentation in Tourism: An Operational Assessment Framework 333
familiarity, expertise, and previous visits with the involvement and learning of the
individual. In addition, Zins and Teichmann (2006) conducted a longitudinal study
where they found that the credibility of information channels change from the pre-
trip to the post-trip phase. Bieger and Laesser (2004) also investigated the
differences in information channels before and after a trip decision is made.
Consistent with the Zins and Teichmann (2006) study, the Bieger and Laesser
(2004) study shows that the selection of the information channel differs signifi-
cantly depending on type of trip, degree of packaging, and choice of destination.
They also found that friends or, in the web context, other users are very important
channels, as are guide books, regional and destination information brochures, and
tourist boards (Bieger and Laesser 2004).
(b) Economic Cost/Benefit Approach
According to the cost/benefit approach, tourists’ search for information and the
use of information sources depends on the expected costs and benefits of the
information sourcing alternative. In that regard, most traditional perspectives of
information search are embedded in processing theory and consumer behaviour
models (Assael 1984; Bettman 1979), addressing issues such as the role of product
knowledge (Hirschman and Wallendorf 1982); uncertainty (Murray 1991 ) either
with regard to knowledge uncertainty or choice uncertainty; utility (Bettman and
Sujan 1987); and efficiency (Bet tman 1979). Costs within this framework are either
generated on behalf of risk-limiting search costs or the assumption/acceptance
of risk.
The assessment of risk is perceptual; and the information search strategy with
the greatest possible efficiency reduces risk and uncertainty (Murray 1991; Bettman
1973; Schiffmann 1972). According to Mitra et al. (1999), perceived risk derives
from a cognitive conflict between customer expectations and the anticipated out-
come of the purchase decision, with information sourcing as a reaction to this
conflict in order to re-establish cognitive balance. Murray (1991) and Lutz and
Reilly (1973) further suggested that perceived risk and information search are
positively correlated. Risk encountered in service purchase can be reduc ed by
seeking additional information about the serv ice (Lutz and Reilly 1973; Hugstad
and Taylor 1987). This implies that the higher the perceived risk (associated with
the purchase of services), the more likely it is that there will be a heightened
information search effort on the part of the tourist. Howeve r, consumers’ informa-
tion behaviour is also likely to be influenced by the perceived costs of information
search. When the perceived costs of acquiring additional information are high,
information search declines (Lee and Cunningham 2001; Porter 1985). The eco-
nomics of information perspective implies a consumer trade-off between the
perceived benefits and costs of acquiring additional information.
(c) Process Approach
Recent studies have recognized that travel decision making is complex, involv-
ing multiple decisions including length of trip, primary destinations, companions,
activities, attractions, accommodations, trip routes, food stops, and shopping places
(Fesenmaier and Jeng 2000; Moutinho 1987; Woodside and MacDonald 1994). For
multiple product decisions, travellers search for information and move back and
334 V. Katsoni et al.
forth between, the search and the decision-making stages (Woodside and
MacDonald 1994). In addition, actual travel behaviour does not always follow
plans (March and Woodside 2005). Accordingly, in studying travel behaviour,
researchers should consider interactions or intersections of multiple goals and
decisions, informat ion search as an ongoing process, and differences in planned
and actual behaviors. The process approach focuses on the process of information
search rather than on the action itself.
A number of authors have reported that the choice process adopted by consumers
with regard to non-routinized, high-involvement purchases is phased (Correia 2002;
Vogt and Fesenmaier 1998;HsiehandO’Leary1993; Crompton 1992;Umand
Crompton 1990; Woodside and Lysonski 1989; Bettman and Sujan 1987). A number
of concepts are proposed to describe the process of decision making. Basically, they
include a number of input variables and a phased process that includes an information
acquisition phase, a procession phase, a purchase phase, and, last but not least, a
consumption phase (Vogt and Fesenmaier 1998;Correia2002). Crompton (1992)
identified three stages of this process, including an initial consideration set, a late
consideration set, and an action and interaction set. Leiper (1990) puts forward a
model in which a generating information marker (i.e. information received before
setting out) creates a reaction on the needs/wants of a potential traveller, leading to
positive expectations/motivations and to a travel decision. Vogt and Fesenmeier
(1998) propose a five-stage model, focusing on the heuristics of information finding
and decision making. In this model, purchase and consumption coincide. Correia
(2002) examined and expanded the travellers’ decision-making process and classified
the act of purchasing a trip into three distinctive stages: the pre-decision stage;
the decision stage; and the post-decision stage.
A few researchers have suggested that travel-planning theories are more suitable to
explain or predict complex travel behaviours compared to the single goal-oriented
decision-making theories, because a planning process includes multiple decisions and
interactions among decisions (Pan and Fesenmaier 2003). A plan is a traveller’s
reasoned attempt to recognize and define goals, consider alternative actions that
might achieve the goals, judge which actions are most likely to succeed, and act on
the basis of those decisions (Hoc 1988). This definition of planning includes all
information search behaviours, information uses or applications, purchase behaviours,
actual trip behaviours, and the learning from all these experiences.
The Internet has intensified the complexity of the travel decision-making pro-
cess, as it has become an important channel for travellers’ information search
(Gretzel et al. 2006; Gursoy and McLeary 2003; Pan and Fesenmaier 2006;
Xiang et al. 2008; Jun et al. 2007), creating an environment whereby online
information providers such as tourist boards, hotel and resort websites, travel
agents, bloggers and magazines actively compete for attention to attract searchers
and ultimately, bookers. Many travel decision-maki ng models present information
search and assessment as having been processed before decision making (Um and
Crompton 1990; Woodside and Lysonski 1989); however, the Internet has made it
easier for travellers to collect information, purchase travel products, and change
their decisions at any stage of the decision-making process.
18 Market Segmentation in Tourism: An Operational Assessment Framework 335
The Internet provides an opportunity for travel and tourism service providers to
intermix traditional marketing channels (i.e. distribution, transaction, and commu-
nication), which were previously considered independent processes (Zins 2009). A
single interaction on the Internet can provide product information, a means for
payment and product exchange, and distribution, whereas more traditional interac-
tion frequently separates these functions (Jun et al. 2007). Particularly interesting
studies have considered the use of online information sources relative to more
conventional ones. The application and extension of Information Technology (IT)
in the tourism sector (Buhalis and Law 2008; Buhalis and Zoge 2007) has greatly
favoured the dissemination of information about tourism destinations and their
promotion, mainly through the World Wide Web, which some consider to be the
ideal source for the distribution of such information. Nonetheles s, a considerable
part of the studies produced on the new IT deal with the possibilities that this IT can
offer to market tourism destinations.
As a consequence o f all this, we conclude that a gap in the tourism literature
concerns the need for more research about information at destinations. The aim of
this present research is to examine the tourists’ requireme nts for information, the
effects of socio-demographic characteristics on information search, and the
tourists’ information search behaviour, for instance, the influence of the informa-
tion on trip characteristics such as the composition of the traveller party.
18.3 Research Method
18.3.1 Data Collection
The present investigation was designed to further understand the tour ism market in
the province of Arcadia, Greece, over a period of 12 months, between July 2007 and
July 2008 in order to eliminate seasonality. The survey, included Greek and foreign
tourists in the region. In most cases, the hotel owner or manager agreed to collect
the data for the study, as the survey questionnaires were distributed to the survey
sites, and respondents freely participated in answering the survey questionnaire
after they had stayed in the hote l for at least one night. Then, the researchers visited
each hotel and collected the completed survey questionnaires. Data were collected
by using a four-page self-administered questionnaire primarily designed to gather
information on the subjects’ general motivations for travel. A total of 3,500
questionnaires were given to tour ists. Ultimately, 820 usable questionnaires were
collected, which leads to the response rate of 23.43 %.
18.3.2 Analysis
The survey data were coded and analysed using R, an open-source statistical
package. A descriptive-statistical analysis was applied to the collected data to
explore the overall sample profile. Chi-square tests were conducted to verify
336 V. Katsoni et al.
whether differences between the above mentioned two tourist sub-groups, as
regards the particular characteristics of the population of tourists, are due to chance
variation or reveal some statistically significant trend. Chi-squared tests were
chosen for use in this exploratory investigation to aid in making an inference
about the uniform distribution (or not) of the two sub-groups in relation to demo-
graphic variables, trip characteristics, selection of information sources for their
journey, and their degree of satisfaction from the use of these information sources.
18.3.3 Research Objectives and Hypotheses
All the previously mentioned approaches demonstrate the complexity of the infor-
mation search proce ss, illustrate a range of approaches (psychological-motivational
and cost-benefit being the most prominent), and emphasize a concern with
determinants, information sources, decision making, and segmentation. The overall
goals of the present research was to combine research about information both at the
tourism destinations and before the trip, question whether segmentation based on
the information search behaviour is an appropriate way to develop marketing
strategies and target marketing communications; and analyse the importance of
information at destinations from the tourists’ perspective. The specific objectives of
the study were to compare the importance that international and domestic tourists
attribute to various forms of information, both at tourism destinations and in the
pre-trip context, and make an analysis of their information sourcing behaviour,
based on internal and external information sources, including the use of the Internet.
This would provide a better understanding of how channels are used by differ ent
types of travellers in different types of travel situations, thus taking a dynamic
situational perspective (Bieger and Laesser 2002), combining characteristics of
travellers with characteristics of trips. Bearing in mind the objectives of this
study, the hypotheses formulated state the following:
H1. The composition of the travel party has an effect on the way tourists seek
information about their journey (trip-related, situational descriptor).
H2. The socio-demographic characteristics of the traveller (gender, age, education
level, occupation, nationality) have an effect on the way tourists seek informa-
tion about their trip.
H3. The purpose of the trip has an effect on the way tourists seek information.
These hypotheses are now tested in our subsequent analysis.
18.4 Results
18.4.1 The Travel Party (H1)
Table 18.1 displays the results from the comparison of the distribution of the
population according to how the travel party is composed (out of the total popula-
tion: 49 % travel with friends, 41.7 % with family, and 6.2 % on their own), with the
18 Market Segmentation in Tourism: An Operational Assessment Framework 337
Table 18.1 Comparison between the sources of information and the composition of the travel company/team
On you own
(%)
With friends
(%)
With family
(%) Total Chi-squared test
Total population 6.2 49.0 41.7 820
Information brochures (source 1) 4.6 44.3 48.9 131 X-squared ¼ 2.3649, df ¼ 2,
p-value ¼ 0.3065
Hotel listings (source 2) 3.9 51.0 43.1 51 X-squared ¼ 0.4671, df ¼ 2,
p-value ¼ 0.7917
Oral information provided by retailer/agency (source 3) 6.8 29.6 59.1 44 X-squared ¼ 6.3642, df ¼ 2,
p-value ¼ 0.0415
Oral information provided by tourist information at the destination or
from local tourist offices (source 4)
0 50.0 43.8 16 X-squared ¼ 1.03, df ¼ 2,
p-value ¼ 0.5975
Advertisements and articles in newspapers/magazines(source 5) 5.4 38.8 51.2 129 X-squared ¼ 4.9285, df ¼ 2,
p-value ¼ 0.08507
Travel guidebooks and travel magazines (source 6) 4.0 51.1 42.8 278 X-squared ¼ 2.0926, df ¼ 2,
p-value ¼ 0.3512
Radio and TV broadcasts (source 7) 7.1 44.7 45.9 170 X-squared ¼ 1.2653, df ¼ 2,
p-value ¼ 0.5312
Video,CDROM,DVD,video-text (source 8) 6.8 31.8 52.3 44 X-squared ¼ 3.7485, df ¼ 2,
p-value ¼ 0.1535
INTERNET (source 9) 5.1 54.9 37.0 430 X-squared ¼ 4.0599, df ¼ 2,
p-value ¼ 0.1313
Recommendation from friends and relatives (source 10) 5.2 49.8 42.5 442 X-squared ¼ 0.5747, df ¼ 2,
p-value ¼ 0.7503
Personal experience/knowledge (source 11) 10.4 45.1 41.8 131 X-squared ¼ 4.2729, df ¼ 2,
p-value ¼ 0.1181
Note: Significant differences (p ¼ <0.05) in mean scores printed in bold
338 V. Katsoni et al.
distribution of sub-groups of the population according to the same criterion, i.e. the
composition of the travel team/party. The sub-groups are determined by the use of
the different sources of information displayed on Table 18.1. The results of the Chi-
squared test reveal that statistically significant differences are observed only with
regard to the ‘Oral Information provided by retailer/agency’ (source 3). Significant
percentage of tourists who made use of this particular source travel with their
family compared with their share in the total population. Other than that, we do
not observe any other significant differences in the distribution of the total popula-
tion and the individual sub-groups according to the com position of the trip party.
18.4.2 Socio-demographic Characteristics (H2)
In the following paragraphs we analyse the use of the different sources of informa-
tion with regard to the socio-demographic characteristics of the participants for this
survey, i.e. gender, age, education level, occupation and nationality. In the analysis,
the results of which are presented in the following Tables 18.2, 18.3, 18.4 and 18.5,
we have made comparisons between the distribution of the total population and that
of sub-groups of the population. These sub-groups are created according to the use
of the different sources of information. Statistically significant results (i.e. p-value
< 0.05) reveal that the characteristic under analysis is not independent of the use of
the information sources.
18.4.2.1 Gender
The total population comprises 55.4 % women and 42.2 % men. This distributio n
pertains for sub-groups of the population (see Table 18.2), with the exception of the
users of Source 5 (Advertisements and articles in newspapers/magazines), Source 6
(Travel guidebooks and travel magazines) and Source 7 (Radio and TV broadcasts).
In these three sources we observe greater participation of women (above 65 %)
compared to the total population.
18.4.2.2 Age
In the total population, the age group between 25 and 34 years accounts for
approximately one third (30.4 %) of the total population, while only a small
proportion of the population are above 65 (3.9 %). This distribution pertains in
most sub-groups (see Table 18.2), with the exception of Source 3 (‘Oral Informa-
tion provided by retailer/agency’), Source 4 (‘Oral information provided by tourist
information at destination or from local tourist offices’) and Source 9 (‘Internet’).
In particular, for users of Source 3 we observe higher frequencies (27.3 %) in the
ages above 55, compared with the total population (12.6 %) and accordingly
frequencies in the younger ages are smaller. The situation is similar with users of
Source 4, while the majority (63.3 %) of users of Source 9 are between 25 and
44 years’ old, significantly above the corresponding frequencies for the total
population.
18 Market Segmentation in Tourism: An Operational Assessment Framework 339
Table 18.2 Chi-square analysis of socio-demographic characteristics for users of the different sources of information – gender, age
Gender Age
Men
(%)
Women
(%) Chi-squared test
15–24
(%)
25–34
(%)
35–44
(%)
45–54
(%)
55–64
(%)
Pάno apo
´
65 (%) Chi-squared test
Total
population
42.20 55.40 14.4 30.4 23.2 17.4 8.7 3.9
Source 1 35.11 61.83 X-squared ¼ 1.9405, df ¼ 1,
p-value ¼ 0.1636
16.79 22.14 25.19 17.56 13.74 3.05 X-squared ¼ 6.5593, df ¼ 5,
p-value ¼ 0.2555
Source 2 37.25 62.75 X-squared ¼ 0.48, df ¼ 1,
p-value ¼ 0.4884
23.53 19.61 27.45 17.65 7.84 1.96 X-squared ¼ 5.4417, df ¼ 5,
p-value ¼ 0.3644
Source 3 47.73 52.27 X-squared ¼ 0.1824, df ¼ 1,
p-value ¼ 0.6693
18.18 11.36 15.91 25 13.64 13.64 X-squared ¼ 18.036, df ¼ 5,
p-value ¼ 0.002902
Source 4 31.25 62.50 X-squared ¼ 0.2553, df ¼ 1,
p-value ¼ 0.6133
6.25 12.5 31.25 12.5 18.75 18.75 X-squared ¼ 12.8597, df
¼ 5,
p-value ¼ 0.02473
Source 5 30.23 68.22 X-squared ¼ 6.592, df ¼ 1,
p-value ¼ 0.01024
11.63 27.91 27.91 20.93 6.2 3.88 X-squared ¼ 3.4317, df ¼ 5,
p-value ¼ 0.6337
Source 6 32.73 65.47 X-squared ¼ 7.886, df ¼ 1,
p-value ¼ 0.004982
12.59 28.78 25.54 16.55 8.99 4.68 X-squared ¼ 1.5665, df ¼ 5,
p-value ¼ 0.9053
Source 7 33.53 64.12 X-squared ¼ 4.1323, df ¼ 1,
p-value ¼ 0.04207
15.29 22.94 27.65 18.82 9.41 3.53 X-squared ¼ 4.2274, df ¼ 5,
p-value ¼ 0.5172
Source 8 47.73 47.73 X-squared ¼ 0.4904, df ¼ 1,
p-value ¼ 0.4838
25 13.64 18.18 27.27 11.36 2.27 X-squared ¼ 10.4696, df ¼ 5,
p-value ¼ 0.06297
Source 9 44.19 53.26 X-squared ¼ 0.409, df ¼ 1,
p-value ¼ 0.5225
14.19 36.98 26.28 15.81 3.49 1.4 X-squared ¼ 22.1264, df ¼
5,
p-value ¼ 0.0004954
Source 10 41.63 56.79 X-squared ¼ 0.0689, df ¼ 1,
p-value ¼ 0.793
12.22 31.9 24.66 16.06 8.82 5.43 X-squared ¼ 3.2711, df ¼ 5,
p-value ¼ 0.6583
Source 11 45.60 51.10 X-squared ¼ 0.7432, df ¼ 1,
p-value ¼ 0.3886
14.29 26.92 24.73 19.78 8.79 3.85 X-squared ¼ 1.2034, df ¼ 5,
p-value ¼ 0.9446
Note: Significant differences (p ¼ <0.05) in mean scores printed in bold
340 V. Katsoni et al.
Table 18.3 Chi-square analysis of socio-demographic characteristics for users of the different sources of information – education level, nationality
Education level Nationality
Primary
(%)
Secondary/
high school
(%)
Tertiary
(%)
Postgraduate
studies (%)
Other
(%) Chi-squared test
Greeks
(%)
Foreigners
(%) Chi-squared test
Total
population
3.7 24.3 40.5 21.5 6.8 85.4 14.6
Source 1 1.53 30.53 35.11 19.85 9.92 X-squared ¼ 5.7294, df ¼ 4,
p-value ¼ 0.2203
83.2 16.8 X-squared ¼ 0.2622,
df ¼ 1, p-value ¼ 0.6086
Source 2 1.96 39.22 33.33 17.65 5.88 X-squared ¼ 5.5849, df ¼ 4,
p-value ¼ 0.2324
80.4 19.6 X-squared ¼ 0.5847,
df ¼ 1, p-value ¼ 0.4445
Source 3 4.55 20.45 40.91 25 9.09 X-squared ¼ 0.82, df ¼ 4,
p-value ¼ 0.9357
79.6 20.5 X-squared ¼ 0.7027,
df ¼ 1, p-value ¼ 0.4019
Source 4 18.75 12.5 31.25 25 6.25 X-squared ¼ 10.6906,
df ¼ 4, p-value ¼ 0.03027
87.5 12.5 X-squared ¼ 0.0139,
df ¼ 1, p-value ¼ 0.906
Source 5 3.1 24.81 44.19 21.71 4.65 X- squared ¼ 1.2433, df ¼ 4,
p-value ¼ 0.871
83.0 17.1 X-squared ¼ 0.3405,
df ¼ 1, p-value ¼ 0.5595
Source 6 2.52 24.82 48.2 17.63 3.96 X-squared ¼ 8.0167, df ¼ 4,
p-value ¼ 0.09097
81.7 18.4 X-squared ¼ 1.9017,
df ¼ 1, p-value ¼ 0.1679
Source 7 4.12 18.82 45.29 15.88 11.76 X-squared ¼ 9.3049, df ¼ 4,
p-value ¼ 0.05391
78.8 21.2 X-squared ¼ 4.0607,
df ¼ 1, p-value ¼ 0.04389
Source 8 4.55 43.18 11.36 18.18 18.18 X-squared ¼ 22.8263,
df ¼ 4, p-value ¼ 0.0001372
54.6 45.5 X-squared ¼ 26.9882,
df ¼ 1,
p-value ¼ 2.047e-07
Source 9 2.56 19.77 45.81 25.35 4.42 X-squared ¼ 9.9672, df ¼ 4,
p-value ¼ 0.04098
82.1 17.9 X-squared ¼ 2.036, df ¼ 1,
p-value ¼ 0.1536
Source 10 2.94 28.05 44.57 18.1 4.52 X-squared ¼ 7.2209, df ¼ 4,
p-value ¼ 0.1247
85.3 14.7 X-squared ¼ 0.0024,
df ¼ 1, p-value ¼ 0.9609
Source 11 5.49 21.43 38.46 21.98 8.79 X-squared ¼ 2.7372, df ¼ 4,
p-value ¼ 0.6027
89.0 11.0 X-squared ¼ 1.3571,
df ¼ 1, p-value ¼ 0.2440
Note: Significant differences (p ¼ <0.05) in mean scores printed in bold
18 Market Segmentation in Tourism: An Operational Assessment Framework 341
Table 18.4 Chi-square analysis of socio-demographic characteristics for users of the different sources of information – occupation
Occupation
Scientific, free
professional,
technical, etc.
(%)
Administrative/
managerial (%)
Clerical
(%)
Trade
and
sales
(%)
Farmer,
fisherman,
etc. (%)
Craftmanworker,
operator (%)
Pensioner
(%)
Housework
(%)
Unemployed,
looking for
job (%)
Student
(%) Chi-squared test
Total
population
27.7 14.6 18 7.1 2 4.9 5.6 4.6 4.4 10
Source 1 32.82 8.4 16.79 7.63 0.76 3.82 6.87 3.82 6.87 10.69 X-squared ¼ 7.5712,
df ¼ 9,
p-value ¼ 0.5779
Source 2 25.49 11.76 19.61 13.73 0 3.92 1.96 9.80 0 11.76 X-squared ¼ 10.6041,
df ¼ 9,
p-value ¼ 0.3038
Source 3 18.18 29.55 6.82 9.09 2.27 0 18.18 2.27 6.82 6.82 X-squared ¼ 24.6377,
df ¼ 9,
p-value ¼ 0.003399
Source 4 12.5 0 18.75 25 0 6.25 12.5 6.25 0 12.5 X-squared ¼ 13.6691,
df ¼ 9,
p-value ¼ 0.1346
Source 5 30.23 8.53 20.16 4.65 2.33 0.78 4.65 10.85 6.2 11.63 X-squared ¼ 17.9038,
df ¼ 9,
p-value ¼ 0.03631
Source 6 29.5 14.75 16.55 5.76 1.08 2.52 6.83 7.55 5.04 10.07 X -squared ¼ 8.6267,
df ¼ 9,
p-value ¼ 0.4724
Source 7 25.29 13.53 14.71 4.71 2.94 10 5.88 4.71 8.24 9.41 X-squared ¼ 13.7054,
df ¼ 9,
p-value ¼ 0.1332
342 V. Katsoni et al.
Source 8 15.91 4.55 6.82 0.00 9.09 15.91 6.82 20.45 2.27 15.91 X-squared ¼ 51.5289,
df ¼ 9,
p-value ¼ 5.549e-08
Source 9 29.77 17.44 19.53 6.51 1.16 3.49 1.63 4.65 3.95 10.7 X-squared ¼ 15.4161,
df ¼ 9,
p-value ¼ 0.08012
Source 10 28.05 11.54 21.04 7.01 2.26 1.36 7.24 6.11 4.3 10.18 X-squared ¼ 15.7455,
df ¼ 9,
p-value ¼ 0.07239
Source 11 30.77 17.03 16.48 3.3 2.75 4.95 7.14 4.4 4.95 7.69 X-squared ¼ 6.5691,
df ¼ 9,
p-value ¼ 0.6819
Note: Significant differences (p ¼ <0.05) in mean scores printed in bold
18 Market Segmentation in Tourism: An Operational Assessment Framework 343
Table 18.5 Mean scores and ranking for combinations of trip purpose and the source of information
Source
1
Source
2
Source
3
Source
4
Source
5
Source
6
Source
7
Source
8
Source
9
Source
10
Source
11 ANOVA
Visiting natural attractions and
enjoying the quiet nature of the region
4.09 (1) 4.04 (3) 4 (2) 4.07 (2) 4.15 (2) 4.15 (2) 3.75 (3) 3.65 (2) 4.13 (2) 4.14 (2) 4.11 (1) F-value ¼ 3.4676,
df ¼ 10,
p-value ¼ 0.0001562
Learning about local culture/history 4.05 (2) 4.14 (2) 3.83 (4) 4 (3) 4.05 (3) 4.08 (3) 3.89 (2) 3.26 (4) 4 (3) 4.04 (3) 4.05 (3) F-value ¼ 4.0015,
df ¼ 10,
p-value <<0.00005
Sunbathing/swimming 4.05 (3) 4.23 (1) 4.2 (1) 4.5 (1) 4.17 (1) 4.18 (1) 4.04 (1) 4.22 (1) 4.17 (1) 4.15 (1) 4.08 (2) F-value ¼ 0.7247, df ¼ 10,
p-value ¼ 0.7018
Night life/entertainment 3.76 (4) 3.54 (4) 3.37 (7) 3.38 (6) 3.52 (4) 3.6 (4) 3.6 (4) 3.05 (7) 3.64 (4) 3.58 (4) 3.64 (4) F-value ¼ 2.0808,
df ¼ 10,
p-value ¼ 0.02302
Shopping 3.56 (5) 3.44 (5) 3.52 (3) 3.38 (5) 3.41 (5) 3.31 (5) 3.34 (5) 2.69(10) 3.31 (6) 3.29 (5) 3.49 (5) F-value ¼ 2.8819,
df ¼ 10,
p-value ¼ 0.001416
Mountaineering and other intense
athletic activities
3.31 (6) 2.96 (9) 2.97(10) 3.54 (4) 3.24 (6) 3.23 (6) 3.25 (8) 3.19 (5) 3.37 (5) 3.27 (6) 3.27 (7) F-value ¼ 0.9548, df ¼ 10,
p-value ¼ 0. 0.4814
Winter holiday in the snow 3.25 (7) 3.08 (8) 3.05 (9) 3 (9) 3.13 (8) 3.12 (8) 3.26 (7) 2.6 (11) 3.29 (7) 3.22 (7) 3.17 (9) F-value ¼ 1.6514, df ¼ 10,
p-value ¼ 0.08683
Visiting friends and relatives 3.2 (8) 3.17 (7) 3.44 (5) 3.14 (7) 3.3 (7) 3.22 (7) 3.24 (9) 3.65 (3) 3.13 (8) 3.17 (8) 3.35 (6) F-value ¼ 1.4577, df ¼ 10,
p-value ¼ 0.1493
Visiting agricultural sites 3.11 (9) 2.62(10) 3.38 (6) 3 (8) 3.04(10) 2.97(10) 3.27 (6) 2.95 (9) 3.01 (9) 3.06 (9) 3.2 (8) F-value ¼
2.1924,
df ¼ 10,
p-value ¼ 0.01594
Visiting religious sites 3.08(10) 3.34 (6) 3.35 (8) 3.07(10) 3.07 (9) 2.99 (9) 2.87(10) 3.12 (6) 2.95(10) 3.04(10) 3.1 (10) F-value ¼ 1.2377, df ¼ 10,
p-value ¼ 0.2615
Watching sporting events 2.75(11) 2.42(11) 2.61(11) 2.92(11) 2.4 (11) 2.46 (11) 2.67(11) 3 (8) 2.54(11) 2.46(11) 2.49(11) F-value ¼ 1.8092, df ¼ 10,
p-value ¼ 0.05426
Mean score 3.47 3.36 3.43 3.45 3.41 3.39 3.38 3.22 3.41 3.40 3.45
344 V. Katsoni et al.
18.4.2.3 Education
40.5 % of all the participants of this survey have tertiary education. Second in
frequency come those with secondary education (24.3 %), and third holders of
postgraduate degrees (21.5 %). Th e results of Table 18.3 reveal that this distribution
is similar for all sub-groups, with the exception of Source 4 (‘Oral information
provided by tourist information at the destination or from local tourist offices’),
Source 8 (‘Video, CD- Rom, DVD, Videotext’) and Source 9 (‘Internet’).
In particular for users of Source 4, we observe that a significantly higher
percentage of tourists (18.8 %) have primary education, while the corresponding
percentage for the total popul ation is considerably lower (3.7 %). For users of
Source 8, we always see higher frequencies, compared with the total population, in
the group who have secondary education (43.2 % compared to 24.3 % in the total
population). Users of Source 8 who have tertiary education are considerably less
(11.4 % compared to 40.5 % in the total population). Finally users of the Internet are
mostly gathered in the categories ‘Tertiary education’ and ‘Postgraduate studies’
(71.2 % compared with 62 % in the total population).
18.4.2.4 Nationality
The majority of the participants in this survey were Greeks (85.4 %) as opposed to
14.6 % foreigners. When comparing the total population with the sub-groups of
users of the different sources of information significant differences were only
observed for users of Source 7 (‘Radio and TV broadcasts’) and Source
8 (‘Video, CD- Rom, DVD, Video-text’). (The results of Table 18.1). In particular,
foreign users of Source 7 are significantly more (21.2 %) than foreigners in the total
population (14.6 %). The same happens with users of Source 8, with even higher
frequency of foreigners (45.5 %) in this group.
18.4.2.5 Occupation
The most commonly reported occupa tions in order of frequency in the total
population are: Scientific and free professional (27.7 %), Clerical worker (18 %),
Administrative and Managerial worker (14.6 %) and Students (10 %). The results of
the analysis reveal that the distribution of tourists according to their occupation is
not significantly different in the various sub-groups when compared with their
distribution in the total population, with the exception of Source 3 (‘Oral Informa-
tion provided by retailer/agency’), Source 5 (‘Advertisements and articles in
newspapers/magazines’), and Source 8 (‘Video, CD- Rom, DVD, Videot ext’). In
particular:
• The most common jobs among users of Source 3 are Administrativ e and
Managerial workers (29.6 %), Scientific and free professionals (18.2 %),
Pensioners (18.2 %) and Trade and sales workers (9.1 %);
• The most common jobs among users of Source 5 are Scientific and free
professionals (30.2 %), Clerical workers (20.2 %), Students (11.6 %) and
Housework (10.9 %);
18 Market Segmentation in Tourism: An Operational Assessment Framework 345
• The most common jobs among users of Source 8 are Housework (20.5 %),
Scientific and free professional (15.9 %), Craftsm en, workers, operators
(15.9 %) and Students (15.9 %).
18.4.3 Trip Purposes (H3)
Table 18.5 shows the results from the Analysis of Variance (ANOVA) that has
been applied to identify sign ificant differences in the mean scores (ranging betwee n
1 – ‘Very unlikely’ and 5 – ‘Very likely’) that users of the different sources of
information gave to the different trip purposes. The table summarizes for each ‘trip
purpose’ and ‘source of information’ combination the mean score, along with a
ranking that shows the preference that users of each source had for the different trip
purposes. The table has been arranged according to the ranking derived for users of
Source 1 (i.e. Information Brochure). The results on the table reveal that differences
in the mean scores are found to be significant (p-value < 0.05) only with respect to
the following ‘trip purposes’: Visiting natural attractions and enjoying the quiet
nature of the region; Le arning about local culture/history; Night life/entertainment;
Shopping and Visiting agricultural sites. The results shown on this table also
indicate that Sunbathing/Swimming comes first in the preference of users of all
the different sources of information, with the exception of the Information
Brochures, which comes third in the preference of users of this source. Visiting
natural attractions and enjoying the quiet nature of the region and Learning about
local culture/history also come high (in the first three most- popular purposes) in the
preferences of users of all sources. Differentiation is observed only with respect to
the magnitude of preference, expressed by the mean score.
18.5 Conclusions
This paper supports the view that developing alliances with well-positioned,
knowledgeable distribution channels is especially important for the assessment of
tourism policies. The research implies that a segmentation based on the information
search behaviour is an appropriate way to develop marketing strategies and target
marketing communications. The promotion of attractions should ideally be based
on an understanding of travellers’ behaviour in order to achieve the long-term
success of tourism, and providers of tourism products need to acknowledge and
support the efforts of regional and national tourism organizations. The accuracy of
the information is an important quality factor for building and maintaining trust in a
specific source (Bieger and Laesser 2004).
Hypothesis 1 which postulates “The composition of the traveller party has an
effect on the way tourists seek information about their journey” is not verified by
the results of our survey. The only exception is with regard to the ‘Oral Information
provided by retailer/agency’, where a significantly higher percentage of tourists
346 V. Katsoni et al.
who made use of this particular source, travel with their family compared with their
share in the total population. Thus, this trip-related, situational descriptor, i.e. the
composition of the traveller party seems to have no effect on information search
behaviour
The present study agrees with other researchers that travellers usually rely on
multiple information channels depending on, as postulated by Hypothesis 2, their
socio-demographic characteristics (Katsoni 2011). However, this hypothesis is only
partially verified by the results of this analysis. It important to note that women
make greater use than men of information sources such as advertisements and
articles in newspapers/magazines, travel guidebooks and travel magazines and
radio and TV broadcasts. The analysis of education and age characteristics, also
shows the Internet to be a favoured source of information among travellers who
have university/college education and postgraduate studies, irresp ective of gender,
and who are in the age group 25–44 years old. Travellers in Arcadia are mainly
scientific and free professionals (27.7 %), Clerical workers (18 %), Administrative
and Managerial workers (14.6 %) and Students (10 %), and this distribution of
tourists according to their occupation applies to all sources of information, with the
exception of “Oral Information provided by retailer/agency’, ‘Advertisements and
articles in newspapers/magazines’ and ‘Video, CD- Rom, DVD, Videotext’. The
analysis of the similarities and differences between international and domestic
tourists regarding the importance of the information at destinations shows that
correspondences exist betwee n both groups on the order of use of the information
source, with the exception of Radio and TV broadcasts’ and ‘Video, CD- Rom,
DVD, Videotext’ which are slightly more preferred by foreign travellers.
Hypothesis 3: “The purpose of the trip has an effect on the way tourists seek
information” is only partially verified by the results of this analysis. Differences are
found only with respect to the following ‘trip purposes’: Visiting natural attractions
and enjoying the quiet nature of the region; Learning about local culture/history; Night
life/entertainment; Shopping; and Visiting agricultural sites, which come first, second,
third, forth and fifth, respectively, in the preference of users of all the different sources
of information. It is also noteworthy that Watching sporting events comes last in the
preference of users of all the different sources of information, with the exception of
Source 8, Video, CDROM, DVD and Videotext.
The results of this study have important implications from the managerial
perspective at the tourism destinations. The present study can help managers
carry out their task in a more informed and strategic manner by examining the
effects of demographic traits on tourist consumption and considering the effects
that the provision of information has for the tourists at the destinations. This
information can increase the economic impacts from travel and tourism at the
destinations, and lead to the adoption of the necessary measures to reinforce the
forms of information analysed in this study in order to attract the most suitable
target market. The implementation of the forms of communication analysed
requires the collaboration of diverse tourist agencies, and the creation of the
Destination Management Systems (DMSs) or the Destination Management
Organizations (DMO) to integrate all this information in a manner that meets the
needs of the tourists.
18 Market Segmentation in Tourism: An Operational Assessment Framework 347
A main limitation of this study is that the research does not cover all important
aspects associated with the information available at destinations, such as the
modification of the image conveyed by a flood of information at dest inations and
the economic effects of the information on the destinations. “Internet” also is
considered as a homogeneous source of information, as it neglects the different
types and sources of information a tourist can collect in the web, such as social
networks, DMO’s websites, etc. More research on all these topics is necessary to
develop a more complete understanding of the information at tourism destinations.
As tourism industry grows in both capacity and services, so will its need for a
wide variety of distribution channels. This research has identified a range of
strategies for developing and supporting links with them. It seems evident that
Tourist Boards can have a significant impact on these processes, and the present
findings will possibly help them by providing a brief examination of these issues.
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