Description
The main purpose of this study is to draw implications about simultaneous consideration of
demographics, preferences and attitudes in understanding travel behavior decision criteria
International Journal of Culture, Tourism and Hospitality Research
Cross-cultural differences in purchase decision-making criteria
Nuray Selma Ozdipciner Xiangping Li Muzaffer Uysal
Article information:
To cite this document:
Nuray Selma Ozdipciner Xiangping Li Muzaffer Uysal, (2012),"Cross-cultural differences in purchase decision-making criteria", International
J ournal of Culture, Tourism and Hospitality Research, Vol. 6 Iss 1 pp. 34 - 43
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Cheryl Leo, Rebekah Bennett, Charmine E.J . Härtel, (2005),"Cross-cultural differences in consumer decision-making styles", Cross Cultural
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Ignacio Rodríguez del Bosque, Héctor San Martín, J esús Collado, María del Mar García de los Salmones, (2009),"A framework
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Katherine B. Hartman, Tracy Meyer, Lisa L. Scribner, (2009),"Culture cushion: inherently positive inter-cultural tourist experiences",
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Cross-cultural differences in purchase
decision-making criteria
Nuray Selma Ozdipciner, Xiangping Li and Muzaffer Uysal
Abstract
Purpose – The main purpose of this study is to draw implications about simultaneous consideration of
demographics, preferences and attitudes in understanding travel behavior decision criteria.
Design/methodology/approach – Convenience sampling was used to collect data. In 2006 university
students in Turkey collected data from visitors at hotels. From distribution of 1,067 questionnaires, 906
usable questionnaires were collected. Data used are for Turkish (local), European and Asian tourists.
Findings – One expects Turkish tourists, European tourists, and Asian tourists in Turkey will differ in
demographics, preferences and attitudes. Oblique factors are determined to reduce the dimensions for
attitudes. ANOVA and chi-square show variable speci?c differences between groups. However,
considering multiple variables results in showing multiple variables are important in understanding
behavior.
Research limitations/implications – The research demonstrates the importance of joint consideration
of demographics, preference and attitudes. This research does not apply to a particular population
since convenience sampling was used.
Practical implications – Understanding the complexity of the relationship between decision making
and its possible determinants shows the value of having demographic, preference and attitude
information.
Originality/value – The research involves analysis across origin countries or regions, and focuses on
one type of variable (e.g. attitude). This research uses univariate and multivariate results to show the
importance of having and using multivariate data.
Keywords Behaviour, Multiple variables, Reducing dimensions, Regional origin in?uence,
Tourism development, Attitudes, National cultures, Turkey
Paper type Research paper
Introduction
Effective tourism marketing requires that managers understand not only what people do on
vacation but also factors affecting leisure travel decisions. Businesses taking into account
customer preferences and attitudes when making decisions regarding product and service
attributes is imperative. Despite the fact that the travel industry is rather slow in recognizing
and responding to the existence and diversities of tourist demand, academic research
recognizes that tourists are engaged in a variety of behaviors and that tourist roles and
needs, wants and expectations vary considerably.
Being a social activity that involves family, friends, relatives and others, tourism regularly
occurs in social situations (Sirakaya and Woodside, 2005). Tourists usually make purchases
by simultaneously evaluating several decision-making criteria. When individuals take the
decision to travel for leisure, they do so for different reasons or motives. A typical buying
decision might take into account for instance, service quality, delivery, speed, price and any
special buying incentives (Verma et al., 2002). Thus, a destination chance of being chosen
depends on the destination meeting criteria set by those traveling together. Therefore,
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VOL. 6 NO. 1 2012, pp. 34-43, Q Emerald Group Publishing Limited, ISSN 1750-6182 DOI 10.1108/17506181211206234
Nuray Selma Ozdipciner is
a Head of Department at
the University of
Pamukkale, Denizli, Turkey.
Xiangping Li is a Research
Associate and Muzaffer
Uysal is a Professor, both at
the Department of
Hospitality and Tourism
Management, Pamplin
College of Business,
Virginia Polytechnic
Institute and State
University, Blacksburg,
Virginia, USA.
Received September 2009
Revised April 2010
Accepted July 2010
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researchers are interested in the destination choice criteria. Understanding criteria is an
important ?rst step in research that, for example, facilitates market segmentation and thus
research in support of destination promotion.
As international travel markets increase in importance to destination countries,
understanding international travelers’ behaviors has become a prerequisite for successful
destination marketing programs and strategies. Understanding the extent tourists from
different places (e.g. countries or regions) are similar and dissimilar in demographics,
preferences and attitudes bene?ts destination countries. However, often tourism studies
utilize aggregated data as the basis for analysis and treat the respondents from a number of
different origin countries as a homogenous group. This aggregation or homogeneity
prevents the differences that may occur, for example related to culture, from being detected
and analyzed. The presence of value or cultural differences and their impact on many
aspects of human behavior is well documented (Hofstede, 2001). However, in the tourism
literature, cross-cultural tourism research has been dealt with by only a handful of
researchers (Campo and Garau, 2008; Jo¨ nsson and Devonish, 2008; Kozak et al., 2003;
Reisinger and Turner, 2003; You et al., 2000).
Since the mid-1990s Turkey has emerged as a very competitive tourist destination. Turkey
attracts tourists from around the world. Having international tourists and the limited research
on these makes research possible and useful for Turkey. For this research to be of bene?t
beyond Turkey, the study investigates the effects of being a local (Turkish) tourist, of being
European or being Asian. In this regard, the research pursues how regional origin (also
referred to as regional culture) research relates to demographics, preferences and attitudes.
The focus is on extracting information from survey data that allows understanding
differences between tourists from different regions. In this research Asian tourists mainly
consisted of tourists from Japan and Korea. Although nationality is not synonymous with
culture, following ideas in other literature reference is made to culture or regional culture. All
that is implied by referring to culture is that European countries share some common cultural
characteristics, as do Asian countries such as Japan and Korea.
Literature review
Decision-making is a complex and multi-stage process. Following the need recognition
stage of the decision process, consumers can negotiate with travel partners; seek
information to guide their purchase, etc. In some instances, consumers routinize their
purchase based on their experience with a particular product or service (e.g. brand loyalty
or habitual buying) in making purchase decisions. In other cases, consumers follow a
piecemeal process, involving the construction of evaluative criteria to be used in decision
making. Although the relative importance of decision criteria may vary considerably across
consumers, given certain consumer and purchase characteristics recognizing factors
affecting a choice is possible.
The role of national and cultural or nationality characteristics in shaping a tourist’s decisions
and behavior has been discussed. An increasing number of scholars are incorporating
nationality as a key variable to explain patterns in tourists’ preferences and behaviors.
Previous studies have highlighted the variations in the travel characteristics and behavior of
tourists from different countries. Recent empirical studies investigate the similarities and
differences among cultural groups in terms of their motivation, satisfaction, information
search, vacation preferences, travel characteristics, etc. (Pizam and Sussmann, 1995;
Kozak, 2002; Litvin et al., 2004; Yuksel et al., 2004; Reisinger and Mavondo, 2006).
The literature cited shows that tourists’ behavior can be related to country of origin and
cultural background. Among those studies that have investigated the impact, Andriotis et al.
(2007) ?nds that nationality, among other factors, is a major in?uence in visiting Crete. In
examining leisure travelers to Hong Kong from seven geographic origins based on their
evaluation of four product-related variables, McCleary et al. (2006) discover many origin
related signi?cant differences relating to satisfaction with the destination, perceived value,
service quality and intent to return. Similarly, Kim and Prideaux (2005) identify differences in
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motivations to travel to Korea for ?ve nationalities (American, Australian, Japanese, Chinese
(Mainland), Chinese (Hong Kong SAR).
Some research refers speci?cally to culture. Yoo et al. (2004) examine the relationship of
culture to international visitors’ trip characteristics using Mainland Chinese and Americans.
A comparison of demographic and trip characteristics of these two cultural groups reveals
that statistically signi?cant differences exist. Speci?cally, these two groups differ in terms of
their trip frequency to Hong Kong, visit purpose, travel mode, party size and length of stay.
Sakakida et al. (2004) con?rm that Japanese and American college students have different
cultural tendencies and travel preferences. For example, American students are more likely
to travel to adventurous destinations than their Japanese counterparts. Kozak (2001, 2002)
and his colleagues (Kozak et al., 2003) have conducted cross-cultural studies in exploring
tourist’s behavior, including destination image, motivation, and satisfaction, while visiting
Turkey and Spain. The most recent study investigates differences of destination image
depending on their country of residence (Spain, the UK, France, Germany, and the rest of
the world). The study suggests that the perception of tourists regarding a speci?c tourist
destination is not homogeneous. His studies on motivation and satisfaction between British
and German tourists visiting Mallorca and Turkey draws the conclusion that country of origin
relates to tourists’ motivation and satisfaction level.
Hypotheses and research strategy
Given cultural differences exist between Turkey, Europe and Asia (hereafter the regional
groups), visitors from the different regions can be expected to differ. H1 expresses this
expectation:
H1. Turkish locals, Europeans and Asians will differ on some demographics, preference
and attitude variables.
Given that data were collected by a convenience sample, statistics used to test H1 (e.g. age
group by regional group), do not necessarily give percents in categories that are valid for
planning or marketing. Accepting the hypothesis merely con?rms expectations based on
the literature. However, does the literature suggest that performing tests demonstrates that
looking at breakdowns of/by regional groups according to demographic, preference and
attitude variables gives good information for planning? Discussion above cites literature that
is not disaggregated by origin (country, region, etc.). Acceptance of H1 would mean
disaggregation was necessary in some cases.
H2 expresses the idea that disaggregation by origin gives good information for planning,
management and decision making:
H2. Disaggregation by regional group of data from tourists on demographics,
preferences and attitudes will result in adequate homogeneity for use of the data for
theory development, planning, marketing and management.
H2 is an assertion that looking at distributions of variables controlling on regional group lets
one understand behavior and decision making, and draw inferences that are not ?awed.
H3 raises the need to reduce data down to be usable. With 18 attitude variables with
responses on a ?ve-point rating scale, one has more cells for responses to be in than one
has respondents (i.e. 5
18
cells). However, if responses lie along a few dimensions factor
analysis may allow determination of the dimensions. H3 speci?es that responses can be
explained by far fewer than 18 dimensions. Dimensions determined and how they are
determined can have rami?cations for other research:
H3. The kind of attitude data collected in this research can be reduced to a few
meaningful dimensions using factor analysis.
Research proceeds by analysis addressing H1. For demographic and preference data,
chi-square can be used to test for differences based on regional group. However, for
attitudes, dealing with 18 variables is not reasonable so creation of scale variables occurs
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using factor analysis. The analysis moves from varimax rotation to a four-factor
non-orthogonal solution to provide a good explanation of variance and reasonable factors
(i.e. H3 is addressed). With factors determined, ANOVA can be used to determine if
attitudes vary by regional group. The ?nal step in analysis is testing H2, which involves
examining cross tabulations of variables with regional group to see if interaction between
demographic, preference and/or attitude variables should be considered.
Method
For this research the questionnaire has three parts. The ?rst two parts have 17 questions to
capture demographic information and travel preferences of tourists. In Table I one sees
demographic variables and their responses. Responses for preferences are ‘‘yes’’ for items
preferred. The third part of the questionnaire includes 18 attitude statements covering
purchase decision-making criteria of tourists. Responses for attitude are on a ?ve-point
rating ranging from 1 ¼ strongly disagree to 5 ¼ strongly agee.
Convenience sampling was used to collect data in 2006. Data were collected by students
with foreign language capabilities from the Department of Tourism at Denizli Vocational
Higher Education School of Pamukkale University. The students distributed the
questionnaire to tourists who stopped off at hotels. The questionnaires were randomly
handed out to the potential respondents at the reception desk area of selected hotels and
were immediately collected upon their completion. If declined, the next person in the area
was contacted to complete the questionnaire. The questionnaire was also made available in
different languages (English, Russian and Germany). However, most of the non-Turkish
visitors preferred the English version of the questionnaire. Students with language
capabilities were also present to assist potential respondents with their questions.
During data collection a total of 1,067 questionnaires were distributed with 906 usable
questionnaires collected fromvisitors from Japan, Korea, the UK, Sweden, Belgium, Poland,
Russia, France, Germany and Turkey. Although a convenience sampling method was
followed by choosing visitors that were easiest to reach, some degree of randomness was
still maintained as a function of randomly generated contacts for the study sample. The
required initial sample size (n ¼ 600) was calculated to represent 55 percent of the visitation
market in the Aegean and Mediterranean regions of Turkey, yet the study generated over 900
usable questionnaires to represent the visitation market. The unit of sampling is not important
to this research because no claim is being made to give estimates for a particular
population. Rather, the important matter is that regional groups were treated equally in
selection and thus differences between groups do not re?ect sampling bias.
Results and hypothesis testing
Respondents’ demographic information and statistical tests for differences between
regional groups appear in Table I. In terms of regional groups, almost 52 percent of the
respondents are local tourists (Turkish) while 48 percent are in the European or Asian group.
Only seven respondents indicated that they are from the US so an American group is not in
the analysis. One sees that for variable after variable signi?cant differences occur across
regional groups. The exception is for education.
Part of Table I provides descriptive statistics and statistical tests for what is here called
preferences but which includes what one may call behavior characteristics. Whether a
response on a variable such as lodging package re?ects preference certainly depends on
whether the respondent would choose the given type of accommodation. In fact, in many
cases a respondent might prefer a different accommodation. However, what they are in may
best re?ect what they or others like them can be expected to use. In other words,
preferences is a word used to describe a collection of variables. Preferences should not be
taken as meaning more than that. For preferences, as for demographics, one sees
signi?cant differences between regional groups for the preference variables.
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Based on differences for demographic and preference variables, one could accept H1.
However, what about attitudes? With 18 variables that are expected to be interrelated,
discussing each variable is not reasonable. Reducing the number of variables by ?nding
common factors is reasonable. An exploratory factor analysis with Varimax rotation was
performed to identify the underlying dimensions. The assumption of factorability is
supported by the Barlett’s test of sphericity showing that the overall correlation matrix is
signi?cant (x
2
¼ 2825:5; p ¼ 0:000). The Kaiser-Meyer-Olkin measure, an overall MSA
statistic, is 0.699. This measure is above the acceptable value of 0.50. The tests indicate that
factor analysis is appropriate.
Varimax factor analysis was executed. Only the factors having latent roots (eigenvalues)
greater than 1 are considered. Results are given in Table II. In considering the factors
Table I Variation of demographic variables and travel preference by cultural groups
Variables No. Turkey (%) Europe (%) Asia (%) Total (%) x
2
Sig.
Nationality 899 51.9 40.5 7.6
Age (n ¼ 906) 96.609 0.000
18-24 131 11.1 17.0 25.0 14.6
25-34 119 15.4 12.4 2.9 13.2
35-44 242 32.8 19.8 25.0 26.9
45-64 279 34.9 28.8 13.2 30.8
65 þ 135 5.8 22.0 33.8 14.5
Gender (n ¼ 906) 38.122 0.000
Male 472 61.7 40.1 51.5 52.5
Female 434 38.3 59.9 48.5 47.8
Marital status (n ¼ 905) 9.009 0.011
Married 592 68.3 64.5 50.0 65.4
Single 313 31.7 35.5 50.0 34.6
Education (n ¼ 905) 8.331 0.215
Primary school 17 2.8 1.1 0.0 1.9
Middle school 62 6.9 5.8 10.3 6.7
High school 254 29.1 25.9 29.4 27.8
University 572 61.2 67.2 60.3 63.6
Occupation (n ¼ 889) 192.211 0.000
Civil servant 112 14.4 10.5 10.9 12.6
Worker 78 5.8 11.3 14.1 8.6
Self-employed 276 47.7 15.0 0.0 31.2
Student 118 9.5 15.9 28.1 13.4
Retired 212 10.8 38.5 37.5 23.8
Other 93 11.8 8.8 9.4 10.4
Tourist expenses (n ¼ 666) 131.259 0.000
Below e1,500 197 40.1 5.1 20.0 29.6
e1,500-3,000 245 38.4 31.8 60.0 36.8
Above e3,000 224 21.5 63.1 20.0 33.6
Lodging package type (n ¼ 901) 80.161 0.000
Room and breakfast 88 4.9 13.3 23.5 9.7
Half board 189 22.3 17.8 30.9 21.1
Full board 286 41.4 21.4 20.6 31.8
All inclusive 338 31.3 47.5 25.0 37.4
Reason for lodging package preference (n ¼ 588) 282.502 0.000
Comfort and safety 136 20.3 31.6 28.0 23.2
Quality 242 54.8 10.3 6.0 41.7
Cost 66 15.9 0.0 0.0 11.4
Amusement and rest 96 1.4 51.3 50.0 15.7
Other 48 7.5 6.8 16.0 8.1
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obtained items with factor loadings greater than 0.45 were retained. Items that double-load
were excluded. Given the conditions, the factor analysis yields a six-factor solution that
explains 56.8 percent of the total variance. However, a close examination of the factors
reveals that some items had factor loadings of less than 0.45.
With factor analysis, an option is to seek an alternative (e.g. even oblique) solution. A four-factor
solution was obtained when seven items that had factor loadings of 0.45 and less were
eliminated from the analysis. The ?nal four-factor structure (Table II) includes 11 items and
explains 67.9 percent of the total variance in the 11 items. The items of the scale that make the
?nal cut all have a factor loading of more than 0.76 with the exception of one item, which has a
loading of 0.473. However, this particular itemstill exceeds the cut-off value of 0.45. Cronbach’s
alphas for each factor are given and exceed minimal criteria for reliability (Hair et al., 2005).
The factor explaining the largest variance can be described as ‘‘domestic familial decisions.’’
This factor explains 28.0 percent of the variance and has an alpha of 0.88. Its four items (see
Table II) are: ‘‘we decide as a family how long we should stay on vacation,’’ ‘‘we make the
decision of buying a vacation as a family,’’ ‘‘we decide how much to pay as a family,’’ and ‘‘we
decide where to go on vacation on familial grounds.’’ ‘‘Trademark decisions’’ as a variable
explain over 16 percent of the variance and has an alpha of 0.591. Its three items are: ‘‘the
name of the accommodation is important for me in the decision-making processes, ‘‘the name
of the tourist site or country name is important in my decision making’’, and ‘‘the fact that I
purchased from an accommodation previously makes it easy for me to re-prefer the same
accommodation’’. Collectively these items re?ect the trademark factor. ‘‘Price decisions’’
explains almost 13 percent of the variance with reliability of 0.54. This scale has two items: ‘‘the
price of the service to be extended affects the decision of vacation,’’ and ‘‘the discount of the
price on a speci?c tourist service leads me to that product’’. The last factor, ‘‘decisions of circle
of acquaintances’’, explains almost 11 percent of the variance and has a relatively low
reliability of 0.484. This factor also includes two items: ‘‘I purchase the tourist service
considering the individuals’ aspects around me’’, and ‘‘the attitudes and ideas of the people
around me have an impact on my purchase decision-making’’.
Factors are quantitative variables so ANOVA can be used to see if differences in factor
scores show differences between regional groups on the four factors of purchase
decision-making criteria.
Table II Factor analysis of purchase decision attributes
Factors
Factor
loading
Variance
explained Reliability Mean
Domestic familial decisions 28.0 0.884 3.81
We decide as a family how long we should stay on vacation 0.900
We make the decision of buying a vacation as a family 0.886
We decide how much to pay as a family 0.845
We decide where to go on vacation on familial grounds 0.806
Trademark decisions 16.1 0.591 3.45
The name of the accommodation is important for me in the decision-making process 0.824
The name of the tourist region or country is instrumental to my decision-making 0.797
The fact that I purchased from an accommodation previously makes it easy for me to
re-prefer the same accommodation 0.573
Price decisions 12.9 0.535 3.82
The price of the service to be extended affects the decision of vacation 0.804
The discount of the price on a speci?c touristic service leads me to that product 0.773
Decisions of circle of acquaintances 10.9 0.484 3.08
I purchase the touristic service considering the individuals’ aspects around me 0.838
The attitudes and ideas of the people around me have an impact on my purchase
decision-making 0.762
Total 67.9
Notes: Response coding: 1 – strongly disagree to 5 – strongly agree
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The results in Table III indicate that signi?cant differences exist across different groups
in terms of the four purchase decision factors. Speci?cally, Turkish tourists make
decisions more on family basis than their European and Asian counterparts do
(domestic familial decisions), as most of them are married (68.3 percent). In addition,
they are more price sensitive than European and Asian tourists (price decisions), as
they spend much less than their European and Asian counterparts do. Furthermore, they
are less dependent on word of mouth (WOM) from their friends or acquaintances
(decisions of circle of acquaintances); possibly, because they are living in Turkey and
have other more reliable sources of information such as direct experience and acquired
knowledge with the place.
With ANOVA results, one has the information to support H1 for the three types of data.
Therefore, H1 is accepted. Actually, developing the factor structure for attitudes has
provided justi?cation for accepting H3.
What remains is addressing H2. The important matter to note is that, for example,
demographic information can yield information about, for example, family decision making.
In general, people who are not traveling with family should not be specifying family as
in?uencing what they choose to do. People (e.g. Asians just speaking Korean or Japanese)
can be expected to spend more on transportation and have language issues that Turkish
and many Europeans do not face.
Logically H3 is established by one example that shows a difference based a cross tabulation
is deceptive. However, practical rami?cations exist when evidence suggests many
signi?cant differences may merely show a failure to control based on values of variables.
Consider, for example, the rami?cation of 50 percent of Asians not being married whereas for
Turkish and Europeans the percents are respectively, 32 and 36 percent. Furthermore,
consider that only 6 percent of Turkish visitors are over 65 while for Europeans and Asians
the percents are 22 and 34 percent. Then, a young Asian segment (i.e. 25 percent of Asians
under 24) exists that greatly exceeds this age segment for Turkish (11 percent) and for
Europeans (17 percent). What is the point? Well, one expects differences by national group
on family travel because family is playing the least role for Asians and the greatest for
Turkish. Furthermore, treating Asians as old because they have a large 65þ segment would
ignore that they have the largest less than 25 segment. Does one expect young Asians to
have attributes like the older ones?
The kind of commentary introduced in the last paragraph could continue. This
commentary would just reinforce that accepting H1 is of little value because having
differences does not show that breaking out information by regional group is yielding
good information for theory development, planning, managing or marketing. Cases may
exist when a bivariate breakdown yields proportions that should be used in theory
development, planning, managing or marketing. However, given youth, families, etc. may
be similar across regional groups, or differ; analysis should go beyond consideration of
regional groups if one wants good information for theory development and practical
application. What one sees is that interrelations between variables mean that differences
between regional groups could disappear if one controlled on certain variables. In other
words, H2 should be rejected.
Table III Variation of delineated purchase decision-making factors by cultural groups
Decision making Turkey Europe Asia F-test p-value
Domestic familial decisions 3.9* 3.7** 3.5** 8.229 0.000
Trademark decisions 3.5** 3.3* 3.8** 6.819 0.001
Price decisions 4.0* 3.7** 3.5** 12.271 0.000
Decisions of Circle of acquaintances 2.9* 3.2** 3.4** 11.729 0.000
Notes: Using post hoc tests *p , 0.05; **p , 0.001; For example, in terms of factor of domestic familial decisions, Turkish tourists were
signi?cantly different from European and Asian tourists; while European tourists did not differ from Asian tourists
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Discussion and research implications
As indicated in formulating hypotheses, the expectation was that H1 would be accepted.
One expects that locals, European visitors to Turkey and Asian visitors will differ on a number
of attributes. However, knowing differences, such as given in Table I, arise does not, in
general, give useful information for theory development, planning, managing, marketing,
etc. Differences not being useful have been discussed in rejecting H2. The general strategy
employed in discussing differences being explainable can be described as common sense
or a priori segmentation (Dolnicar, 2008; Bigne´ et al., 2008). Bivariate tables can show
signi?cant relations because one does not control on a variable, for example, showing a
person is not traveling with family. In this special issue, Liu et al. (n.d.) raise various matters
relating to being able to use information. Asking too many questions and having various
segments to which questions apply necessitates a priori segmentation. However, within
limits, having too much information is better than not having information that logically you
know is important. If you do not collect a variable, for example, language capability, you will
not know if a person must have support to function in Turkey.
Rejecting H2 has theory development and applied research implications that merit
discussion. Consider that Taiwan or other countries collect data similar to data collected in
this research (e.g. see Liu et al., n.d.). As suggested in reviewing literature, civil servants
may see preparing a booklet with information such as one ?nds in Table I. A page or two
could be devoted to each country of origin. However, if young Koreans (or Europeans) differ
substantially fromolder ones on distributions of attributes, an aggregate table for an origin is
not of much use. In fact, as occurs in Taiwan’s inbound data (Liu et al., n.d.) questions about
being on a tour or having some self organization, occur in surveys because survey planners
are aware that many visitors are on tours. However, knowing that a person is on a tour has
implications. People on tours stay in certain places and carry out certain activities. Having a
table in which activity, accommodation preference, etc. information is mixed with data from
self organized travelers does not clarify why a tour person chose a tour and can present a
confusing picture about a self organized tourist making choices.
Thinking about what is to be achieved by research involving a survey is critical to research
success. If the goal is to meet development, planning and marketing needs, one needs to do
speci?c thinking about how information will be used. A statement like differences between
origin groups will be used to plan for them and market to them is not explicit enough to guide
data collection or analysis. On the other hand, having a long list (e.g. of locations or
activities) can present a problem. Say, the idea is that one will segment respondents based
on 18 activities (Liu et al., n.d.). Dolnicar (2008) indicates that one should have at least 2
18
(¼262,144) cases. One is not likely to have enough cases to support analysis for any origin
group. Now, consider one has a fewvariables (e.g. 5) and responses are ratings (e.g. 1 to 5),
Dolnicar and Gru¨ n (2007) specify that one must take care to see that cultural differences in
rating are not confused with meaningful differences. Logic is a powerful tool. If Turkish
suppliers know that older Asians usually need to work through a translator and are on certain
types of tours, this knowledge should be taken into account because recognizing this
knowledge is necessary for supplying needs. If one knows younger Asians and Europeans
want certain things, data collection and analysis need to build on that knowledge. In
summary, research should not focus on getting/?nding signi?cant differences.
Even given convenience sampling, some results merit mention since they relate to general
attributes of the population from which the convenience sample was drawn. Speci?cally,
demographics may be off for the population and may affect how preferences and attitudes
are distributed. However, since factor analysis is justi?ed (i.e. by statistical tests), results on
factor analysis are quite speci?c. Being speci?c is appropriate because factor structure
should not be heavily in?uenced by deviation of the sample from a random sample. In that
regard, the research implies that whether Asian (at least, largely Japanese and Korean)
tourists are visiting Turkey or some other destination outside their region (e.g. Australia or the
US), they attach more importance to brand names of accommodation than Europeans. In
order to attract those tourists, well-known international hotel chains should be emphasized.
Local Turkish tourists are more price-sensitive; therefore, special discounted products could
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be used as incentives to entice themto travel more. In addition, local tourists make decisions
more on a family basis. As a result, products and services catering to family should be
stressed when targeting the domestic market.
Finally, using convenience sampling has not compromised the most important ?nding of this
research. Yes, the proportions tested for differences for H1 do not apply to a particular
population. In theory, differences could even result from ?awed sampling. However, reasons
why large differences observed would have anything to do with convenience sampling are
hard to imagine. In fact, since observations of data collection did not result in seeing bias in
selection of respondents, another sample carried out in similar circumstances is likely to
yield similar differences. Furthermore, a second sample is likely to yield a similar factor
structure. Therefore, the factor results are seen as providing guidance for new research.
Most importantly, examination of the results shows that simply looking at univariate data for
variables does not give good information for theory building or practical application. For
example, treating responses about family in?uencing the same when some people are
traveling in a family and others are traveling alone or with friends is problematic. A priori
segmentation to put people into categories so that questions apply to them or so that
differences, for example, of age, language or trip purpose are controlled, just makes sense.
Stated differently, the data serve to reject H2 even though convenience sampling was used.
Conclusion
This study examines the relationship between origin and demographics, preference and
attitude variables. Three regional (origin) groups of Turkish, European and Asian differ on
most variables and factors. As noted earlier this ?nding is expected as the ?nding is
supported by previous research. However, the important matter is that the signi?cant
differences do not mean much because interactions of variables need to be considered to
develop theory and to have good information for planning, marketing and managing. Getting
signi?cant relations can play too big a role in research. Whether one is building theory or
planning for and managing tourism, overly simplistic tables such as tables giving attributes
of visitors by origin (e.g. region of origin) are easy for governments to produce and easy to
discuss. However, when a combination of variables de?nes segments that relate to behavior
to be planned for, one should have the right information.
Hopefully, this research encourages a priori thinking in formulating research strategy, data
collection and analysis.
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Corresponding author
Muzaffer Uysal can be contacted at: [email protected]
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doc_194534916.pdf
The main purpose of this study is to draw implications about simultaneous consideration of
demographics, preferences and attitudes in understanding travel behavior decision criteria
International Journal of Culture, Tourism and Hospitality Research
Cross-cultural differences in purchase decision-making criteria
Nuray Selma Ozdipciner Xiangping Li Muzaffer Uysal
Article information:
To cite this document:
Nuray Selma Ozdipciner Xiangping Li Muzaffer Uysal, (2012),"Cross-cultural differences in purchase decision-making criteria", International
J ournal of Culture, Tourism and Hospitality Research, Vol. 6 Iss 1 pp. 34 - 43
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Cross-cultural differences in purchase
decision-making criteria
Nuray Selma Ozdipciner, Xiangping Li and Muzaffer Uysal
Abstract
Purpose – The main purpose of this study is to draw implications about simultaneous consideration of
demographics, preferences and attitudes in understanding travel behavior decision criteria.
Design/methodology/approach – Convenience sampling was used to collect data. In 2006 university
students in Turkey collected data from visitors at hotels. From distribution of 1,067 questionnaires, 906
usable questionnaires were collected. Data used are for Turkish (local), European and Asian tourists.
Findings – One expects Turkish tourists, European tourists, and Asian tourists in Turkey will differ in
demographics, preferences and attitudes. Oblique factors are determined to reduce the dimensions for
attitudes. ANOVA and chi-square show variable speci?c differences between groups. However,
considering multiple variables results in showing multiple variables are important in understanding
behavior.
Research limitations/implications – The research demonstrates the importance of joint consideration
of demographics, preference and attitudes. This research does not apply to a particular population
since convenience sampling was used.
Practical implications – Understanding the complexity of the relationship between decision making
and its possible determinants shows the value of having demographic, preference and attitude
information.
Originality/value – The research involves analysis across origin countries or regions, and focuses on
one type of variable (e.g. attitude). This research uses univariate and multivariate results to show the
importance of having and using multivariate data.
Keywords Behaviour, Multiple variables, Reducing dimensions, Regional origin in?uence,
Tourism development, Attitudes, National cultures, Turkey
Paper type Research paper
Introduction
Effective tourism marketing requires that managers understand not only what people do on
vacation but also factors affecting leisure travel decisions. Businesses taking into account
customer preferences and attitudes when making decisions regarding product and service
attributes is imperative. Despite the fact that the travel industry is rather slow in recognizing
and responding to the existence and diversities of tourist demand, academic research
recognizes that tourists are engaged in a variety of behaviors and that tourist roles and
needs, wants and expectations vary considerably.
Being a social activity that involves family, friends, relatives and others, tourism regularly
occurs in social situations (Sirakaya and Woodside, 2005). Tourists usually make purchases
by simultaneously evaluating several decision-making criteria. When individuals take the
decision to travel for leisure, they do so for different reasons or motives. A typical buying
decision might take into account for instance, service quality, delivery, speed, price and any
special buying incentives (Verma et al., 2002). Thus, a destination chance of being chosen
depends on the destination meeting criteria set by those traveling together. Therefore,
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VOL. 6 NO. 1 2012, pp. 34-43, Q Emerald Group Publishing Limited, ISSN 1750-6182 DOI 10.1108/17506181211206234
Nuray Selma Ozdipciner is
a Head of Department at
the University of
Pamukkale, Denizli, Turkey.
Xiangping Li is a Research
Associate and Muzaffer
Uysal is a Professor, both at
the Department of
Hospitality and Tourism
Management, Pamplin
College of Business,
Virginia Polytechnic
Institute and State
University, Blacksburg,
Virginia, USA.
Received September 2009
Revised April 2010
Accepted July 2010
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researchers are interested in the destination choice criteria. Understanding criteria is an
important ?rst step in research that, for example, facilitates market segmentation and thus
research in support of destination promotion.
As international travel markets increase in importance to destination countries,
understanding international travelers’ behaviors has become a prerequisite for successful
destination marketing programs and strategies. Understanding the extent tourists from
different places (e.g. countries or regions) are similar and dissimilar in demographics,
preferences and attitudes bene?ts destination countries. However, often tourism studies
utilize aggregated data as the basis for analysis and treat the respondents from a number of
different origin countries as a homogenous group. This aggregation or homogeneity
prevents the differences that may occur, for example related to culture, from being detected
and analyzed. The presence of value or cultural differences and their impact on many
aspects of human behavior is well documented (Hofstede, 2001). However, in the tourism
literature, cross-cultural tourism research has been dealt with by only a handful of
researchers (Campo and Garau, 2008; Jo¨ nsson and Devonish, 2008; Kozak et al., 2003;
Reisinger and Turner, 2003; You et al., 2000).
Since the mid-1990s Turkey has emerged as a very competitive tourist destination. Turkey
attracts tourists from around the world. Having international tourists and the limited research
on these makes research possible and useful for Turkey. For this research to be of bene?t
beyond Turkey, the study investigates the effects of being a local (Turkish) tourist, of being
European or being Asian. In this regard, the research pursues how regional origin (also
referred to as regional culture) research relates to demographics, preferences and attitudes.
The focus is on extracting information from survey data that allows understanding
differences between tourists from different regions. In this research Asian tourists mainly
consisted of tourists from Japan and Korea. Although nationality is not synonymous with
culture, following ideas in other literature reference is made to culture or regional culture. All
that is implied by referring to culture is that European countries share some common cultural
characteristics, as do Asian countries such as Japan and Korea.
Literature review
Decision-making is a complex and multi-stage process. Following the need recognition
stage of the decision process, consumers can negotiate with travel partners; seek
information to guide their purchase, etc. In some instances, consumers routinize their
purchase based on their experience with a particular product or service (e.g. brand loyalty
or habitual buying) in making purchase decisions. In other cases, consumers follow a
piecemeal process, involving the construction of evaluative criteria to be used in decision
making. Although the relative importance of decision criteria may vary considerably across
consumers, given certain consumer and purchase characteristics recognizing factors
affecting a choice is possible.
The role of national and cultural or nationality characteristics in shaping a tourist’s decisions
and behavior has been discussed. An increasing number of scholars are incorporating
nationality as a key variable to explain patterns in tourists’ preferences and behaviors.
Previous studies have highlighted the variations in the travel characteristics and behavior of
tourists from different countries. Recent empirical studies investigate the similarities and
differences among cultural groups in terms of their motivation, satisfaction, information
search, vacation preferences, travel characteristics, etc. (Pizam and Sussmann, 1995;
Kozak, 2002; Litvin et al., 2004; Yuksel et al., 2004; Reisinger and Mavondo, 2006).
The literature cited shows that tourists’ behavior can be related to country of origin and
cultural background. Among those studies that have investigated the impact, Andriotis et al.
(2007) ?nds that nationality, among other factors, is a major in?uence in visiting Crete. In
examining leisure travelers to Hong Kong from seven geographic origins based on their
evaluation of four product-related variables, McCleary et al. (2006) discover many origin
related signi?cant differences relating to satisfaction with the destination, perceived value,
service quality and intent to return. Similarly, Kim and Prideaux (2005) identify differences in
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motivations to travel to Korea for ?ve nationalities (American, Australian, Japanese, Chinese
(Mainland), Chinese (Hong Kong SAR).
Some research refers speci?cally to culture. Yoo et al. (2004) examine the relationship of
culture to international visitors’ trip characteristics using Mainland Chinese and Americans.
A comparison of demographic and trip characteristics of these two cultural groups reveals
that statistically signi?cant differences exist. Speci?cally, these two groups differ in terms of
their trip frequency to Hong Kong, visit purpose, travel mode, party size and length of stay.
Sakakida et al. (2004) con?rm that Japanese and American college students have different
cultural tendencies and travel preferences. For example, American students are more likely
to travel to adventurous destinations than their Japanese counterparts. Kozak (2001, 2002)
and his colleagues (Kozak et al., 2003) have conducted cross-cultural studies in exploring
tourist’s behavior, including destination image, motivation, and satisfaction, while visiting
Turkey and Spain. The most recent study investigates differences of destination image
depending on their country of residence (Spain, the UK, France, Germany, and the rest of
the world). The study suggests that the perception of tourists regarding a speci?c tourist
destination is not homogeneous. His studies on motivation and satisfaction between British
and German tourists visiting Mallorca and Turkey draws the conclusion that country of origin
relates to tourists’ motivation and satisfaction level.
Hypotheses and research strategy
Given cultural differences exist between Turkey, Europe and Asia (hereafter the regional
groups), visitors from the different regions can be expected to differ. H1 expresses this
expectation:
H1. Turkish locals, Europeans and Asians will differ on some demographics, preference
and attitude variables.
Given that data were collected by a convenience sample, statistics used to test H1 (e.g. age
group by regional group), do not necessarily give percents in categories that are valid for
planning or marketing. Accepting the hypothesis merely con?rms expectations based on
the literature. However, does the literature suggest that performing tests demonstrates that
looking at breakdowns of/by regional groups according to demographic, preference and
attitude variables gives good information for planning? Discussion above cites literature that
is not disaggregated by origin (country, region, etc.). Acceptance of H1 would mean
disaggregation was necessary in some cases.
H2 expresses the idea that disaggregation by origin gives good information for planning,
management and decision making:
H2. Disaggregation by regional group of data from tourists on demographics,
preferences and attitudes will result in adequate homogeneity for use of the data for
theory development, planning, marketing and management.
H2 is an assertion that looking at distributions of variables controlling on regional group lets
one understand behavior and decision making, and draw inferences that are not ?awed.
H3 raises the need to reduce data down to be usable. With 18 attitude variables with
responses on a ?ve-point rating scale, one has more cells for responses to be in than one
has respondents (i.e. 5
18
cells). However, if responses lie along a few dimensions factor
analysis may allow determination of the dimensions. H3 speci?es that responses can be
explained by far fewer than 18 dimensions. Dimensions determined and how they are
determined can have rami?cations for other research:
H3. The kind of attitude data collected in this research can be reduced to a few
meaningful dimensions using factor analysis.
Research proceeds by analysis addressing H1. For demographic and preference data,
chi-square can be used to test for differences based on regional group. However, for
attitudes, dealing with 18 variables is not reasonable so creation of scale variables occurs
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using factor analysis. The analysis moves from varimax rotation to a four-factor
non-orthogonal solution to provide a good explanation of variance and reasonable factors
(i.e. H3 is addressed). With factors determined, ANOVA can be used to determine if
attitudes vary by regional group. The ?nal step in analysis is testing H2, which involves
examining cross tabulations of variables with regional group to see if interaction between
demographic, preference and/or attitude variables should be considered.
Method
For this research the questionnaire has three parts. The ?rst two parts have 17 questions to
capture demographic information and travel preferences of tourists. In Table I one sees
demographic variables and their responses. Responses for preferences are ‘‘yes’’ for items
preferred. The third part of the questionnaire includes 18 attitude statements covering
purchase decision-making criteria of tourists. Responses for attitude are on a ?ve-point
rating ranging from 1 ¼ strongly disagree to 5 ¼ strongly agee.
Convenience sampling was used to collect data in 2006. Data were collected by students
with foreign language capabilities from the Department of Tourism at Denizli Vocational
Higher Education School of Pamukkale University. The students distributed the
questionnaire to tourists who stopped off at hotels. The questionnaires were randomly
handed out to the potential respondents at the reception desk area of selected hotels and
were immediately collected upon their completion. If declined, the next person in the area
was contacted to complete the questionnaire. The questionnaire was also made available in
different languages (English, Russian and Germany). However, most of the non-Turkish
visitors preferred the English version of the questionnaire. Students with language
capabilities were also present to assist potential respondents with their questions.
During data collection a total of 1,067 questionnaires were distributed with 906 usable
questionnaires collected fromvisitors from Japan, Korea, the UK, Sweden, Belgium, Poland,
Russia, France, Germany and Turkey. Although a convenience sampling method was
followed by choosing visitors that were easiest to reach, some degree of randomness was
still maintained as a function of randomly generated contacts for the study sample. The
required initial sample size (n ¼ 600) was calculated to represent 55 percent of the visitation
market in the Aegean and Mediterranean regions of Turkey, yet the study generated over 900
usable questionnaires to represent the visitation market. The unit of sampling is not important
to this research because no claim is being made to give estimates for a particular
population. Rather, the important matter is that regional groups were treated equally in
selection and thus differences between groups do not re?ect sampling bias.
Results and hypothesis testing
Respondents’ demographic information and statistical tests for differences between
regional groups appear in Table I. In terms of regional groups, almost 52 percent of the
respondents are local tourists (Turkish) while 48 percent are in the European or Asian group.
Only seven respondents indicated that they are from the US so an American group is not in
the analysis. One sees that for variable after variable signi?cant differences occur across
regional groups. The exception is for education.
Part of Table I provides descriptive statistics and statistical tests for what is here called
preferences but which includes what one may call behavior characteristics. Whether a
response on a variable such as lodging package re?ects preference certainly depends on
whether the respondent would choose the given type of accommodation. In fact, in many
cases a respondent might prefer a different accommodation. However, what they are in may
best re?ect what they or others like them can be expected to use. In other words,
preferences is a word used to describe a collection of variables. Preferences should not be
taken as meaning more than that. For preferences, as for demographics, one sees
signi?cant differences between regional groups for the preference variables.
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Based on differences for demographic and preference variables, one could accept H1.
However, what about attitudes? With 18 variables that are expected to be interrelated,
discussing each variable is not reasonable. Reducing the number of variables by ?nding
common factors is reasonable. An exploratory factor analysis with Varimax rotation was
performed to identify the underlying dimensions. The assumption of factorability is
supported by the Barlett’s test of sphericity showing that the overall correlation matrix is
signi?cant (x
2
¼ 2825:5; p ¼ 0:000). The Kaiser-Meyer-Olkin measure, an overall MSA
statistic, is 0.699. This measure is above the acceptable value of 0.50. The tests indicate that
factor analysis is appropriate.
Varimax factor analysis was executed. Only the factors having latent roots (eigenvalues)
greater than 1 are considered. Results are given in Table II. In considering the factors
Table I Variation of demographic variables and travel preference by cultural groups
Variables No. Turkey (%) Europe (%) Asia (%) Total (%) x
2
Sig.
Nationality 899 51.9 40.5 7.6
Age (n ¼ 906) 96.609 0.000
18-24 131 11.1 17.0 25.0 14.6
25-34 119 15.4 12.4 2.9 13.2
35-44 242 32.8 19.8 25.0 26.9
45-64 279 34.9 28.8 13.2 30.8
65 þ 135 5.8 22.0 33.8 14.5
Gender (n ¼ 906) 38.122 0.000
Male 472 61.7 40.1 51.5 52.5
Female 434 38.3 59.9 48.5 47.8
Marital status (n ¼ 905) 9.009 0.011
Married 592 68.3 64.5 50.0 65.4
Single 313 31.7 35.5 50.0 34.6
Education (n ¼ 905) 8.331 0.215
Primary school 17 2.8 1.1 0.0 1.9
Middle school 62 6.9 5.8 10.3 6.7
High school 254 29.1 25.9 29.4 27.8
University 572 61.2 67.2 60.3 63.6
Occupation (n ¼ 889) 192.211 0.000
Civil servant 112 14.4 10.5 10.9 12.6
Worker 78 5.8 11.3 14.1 8.6
Self-employed 276 47.7 15.0 0.0 31.2
Student 118 9.5 15.9 28.1 13.4
Retired 212 10.8 38.5 37.5 23.8
Other 93 11.8 8.8 9.4 10.4
Tourist expenses (n ¼ 666) 131.259 0.000
Below e1,500 197 40.1 5.1 20.0 29.6
e1,500-3,000 245 38.4 31.8 60.0 36.8
Above e3,000 224 21.5 63.1 20.0 33.6
Lodging package type (n ¼ 901) 80.161 0.000
Room and breakfast 88 4.9 13.3 23.5 9.7
Half board 189 22.3 17.8 30.9 21.1
Full board 286 41.4 21.4 20.6 31.8
All inclusive 338 31.3 47.5 25.0 37.4
Reason for lodging package preference (n ¼ 588) 282.502 0.000
Comfort and safety 136 20.3 31.6 28.0 23.2
Quality 242 54.8 10.3 6.0 41.7
Cost 66 15.9 0.0 0.0 11.4
Amusement and rest 96 1.4 51.3 50.0 15.7
Other 48 7.5 6.8 16.0 8.1
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obtained items with factor loadings greater than 0.45 were retained. Items that double-load
were excluded. Given the conditions, the factor analysis yields a six-factor solution that
explains 56.8 percent of the total variance. However, a close examination of the factors
reveals that some items had factor loadings of less than 0.45.
With factor analysis, an option is to seek an alternative (e.g. even oblique) solution. A four-factor
solution was obtained when seven items that had factor loadings of 0.45 and less were
eliminated from the analysis. The ?nal four-factor structure (Table II) includes 11 items and
explains 67.9 percent of the total variance in the 11 items. The items of the scale that make the
?nal cut all have a factor loading of more than 0.76 with the exception of one item, which has a
loading of 0.473. However, this particular itemstill exceeds the cut-off value of 0.45. Cronbach’s
alphas for each factor are given and exceed minimal criteria for reliability (Hair et al., 2005).
The factor explaining the largest variance can be described as ‘‘domestic familial decisions.’’
This factor explains 28.0 percent of the variance and has an alpha of 0.88. Its four items (see
Table II) are: ‘‘we decide as a family how long we should stay on vacation,’’ ‘‘we make the
decision of buying a vacation as a family,’’ ‘‘we decide how much to pay as a family,’’ and ‘‘we
decide where to go on vacation on familial grounds.’’ ‘‘Trademark decisions’’ as a variable
explain over 16 percent of the variance and has an alpha of 0.591. Its three items are: ‘‘the
name of the accommodation is important for me in the decision-making processes, ‘‘the name
of the tourist site or country name is important in my decision making’’, and ‘‘the fact that I
purchased from an accommodation previously makes it easy for me to re-prefer the same
accommodation’’. Collectively these items re?ect the trademark factor. ‘‘Price decisions’’
explains almost 13 percent of the variance with reliability of 0.54. This scale has two items: ‘‘the
price of the service to be extended affects the decision of vacation,’’ and ‘‘the discount of the
price on a speci?c tourist service leads me to that product’’. The last factor, ‘‘decisions of circle
of acquaintances’’, explains almost 11 percent of the variance and has a relatively low
reliability of 0.484. This factor also includes two items: ‘‘I purchase the tourist service
considering the individuals’ aspects around me’’, and ‘‘the attitudes and ideas of the people
around me have an impact on my purchase decision-making’’.
Factors are quantitative variables so ANOVA can be used to see if differences in factor
scores show differences between regional groups on the four factors of purchase
decision-making criteria.
Table II Factor analysis of purchase decision attributes
Factors
Factor
loading
Variance
explained Reliability Mean
Domestic familial decisions 28.0 0.884 3.81
We decide as a family how long we should stay on vacation 0.900
We make the decision of buying a vacation as a family 0.886
We decide how much to pay as a family 0.845
We decide where to go on vacation on familial grounds 0.806
Trademark decisions 16.1 0.591 3.45
The name of the accommodation is important for me in the decision-making process 0.824
The name of the tourist region or country is instrumental to my decision-making 0.797
The fact that I purchased from an accommodation previously makes it easy for me to
re-prefer the same accommodation 0.573
Price decisions 12.9 0.535 3.82
The price of the service to be extended affects the decision of vacation 0.804
The discount of the price on a speci?c touristic service leads me to that product 0.773
Decisions of circle of acquaintances 10.9 0.484 3.08
I purchase the touristic service considering the individuals’ aspects around me 0.838
The attitudes and ideas of the people around me have an impact on my purchase
decision-making 0.762
Total 67.9
Notes: Response coding: 1 – strongly disagree to 5 – strongly agree
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The results in Table III indicate that signi?cant differences exist across different groups
in terms of the four purchase decision factors. Speci?cally, Turkish tourists make
decisions more on family basis than their European and Asian counterparts do
(domestic familial decisions), as most of them are married (68.3 percent). In addition,
they are more price sensitive than European and Asian tourists (price decisions), as
they spend much less than their European and Asian counterparts do. Furthermore, they
are less dependent on word of mouth (WOM) from their friends or acquaintances
(decisions of circle of acquaintances); possibly, because they are living in Turkey and
have other more reliable sources of information such as direct experience and acquired
knowledge with the place.
With ANOVA results, one has the information to support H1 for the three types of data.
Therefore, H1 is accepted. Actually, developing the factor structure for attitudes has
provided justi?cation for accepting H3.
What remains is addressing H2. The important matter to note is that, for example,
demographic information can yield information about, for example, family decision making.
In general, people who are not traveling with family should not be specifying family as
in?uencing what they choose to do. People (e.g. Asians just speaking Korean or Japanese)
can be expected to spend more on transportation and have language issues that Turkish
and many Europeans do not face.
Logically H3 is established by one example that shows a difference based a cross tabulation
is deceptive. However, practical rami?cations exist when evidence suggests many
signi?cant differences may merely show a failure to control based on values of variables.
Consider, for example, the rami?cation of 50 percent of Asians not being married whereas for
Turkish and Europeans the percents are respectively, 32 and 36 percent. Furthermore,
consider that only 6 percent of Turkish visitors are over 65 while for Europeans and Asians
the percents are 22 and 34 percent. Then, a young Asian segment (i.e. 25 percent of Asians
under 24) exists that greatly exceeds this age segment for Turkish (11 percent) and for
Europeans (17 percent). What is the point? Well, one expects differences by national group
on family travel because family is playing the least role for Asians and the greatest for
Turkish. Furthermore, treating Asians as old because they have a large 65þ segment would
ignore that they have the largest less than 25 segment. Does one expect young Asians to
have attributes like the older ones?
The kind of commentary introduced in the last paragraph could continue. This
commentary would just reinforce that accepting H1 is of little value because having
differences does not show that breaking out information by regional group is yielding
good information for theory development, planning, managing or marketing. Cases may
exist when a bivariate breakdown yields proportions that should be used in theory
development, planning, managing or marketing. However, given youth, families, etc. may
be similar across regional groups, or differ; analysis should go beyond consideration of
regional groups if one wants good information for theory development and practical
application. What one sees is that interrelations between variables mean that differences
between regional groups could disappear if one controlled on certain variables. In other
words, H2 should be rejected.
Table III Variation of delineated purchase decision-making factors by cultural groups
Decision making Turkey Europe Asia F-test p-value
Domestic familial decisions 3.9* 3.7** 3.5** 8.229 0.000
Trademark decisions 3.5** 3.3* 3.8** 6.819 0.001
Price decisions 4.0* 3.7** 3.5** 12.271 0.000
Decisions of Circle of acquaintances 2.9* 3.2** 3.4** 11.729 0.000
Notes: Using post hoc tests *p , 0.05; **p , 0.001; For example, in terms of factor of domestic familial decisions, Turkish tourists were
signi?cantly different from European and Asian tourists; while European tourists did not differ from Asian tourists
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Discussion and research implications
As indicated in formulating hypotheses, the expectation was that H1 would be accepted.
One expects that locals, European visitors to Turkey and Asian visitors will differ on a number
of attributes. However, knowing differences, such as given in Table I, arise does not, in
general, give useful information for theory development, planning, managing, marketing,
etc. Differences not being useful have been discussed in rejecting H2. The general strategy
employed in discussing differences being explainable can be described as common sense
or a priori segmentation (Dolnicar, 2008; Bigne´ et al., 2008). Bivariate tables can show
signi?cant relations because one does not control on a variable, for example, showing a
person is not traveling with family. In this special issue, Liu et al. (n.d.) raise various matters
relating to being able to use information. Asking too many questions and having various
segments to which questions apply necessitates a priori segmentation. However, within
limits, having too much information is better than not having information that logically you
know is important. If you do not collect a variable, for example, language capability, you will
not know if a person must have support to function in Turkey.
Rejecting H2 has theory development and applied research implications that merit
discussion. Consider that Taiwan or other countries collect data similar to data collected in
this research (e.g. see Liu et al., n.d.). As suggested in reviewing literature, civil servants
may see preparing a booklet with information such as one ?nds in Table I. A page or two
could be devoted to each country of origin. However, if young Koreans (or Europeans) differ
substantially fromolder ones on distributions of attributes, an aggregate table for an origin is
not of much use. In fact, as occurs in Taiwan’s inbound data (Liu et al., n.d.) questions about
being on a tour or having some self organization, occur in surveys because survey planners
are aware that many visitors are on tours. However, knowing that a person is on a tour has
implications. People on tours stay in certain places and carry out certain activities. Having a
table in which activity, accommodation preference, etc. information is mixed with data from
self organized travelers does not clarify why a tour person chose a tour and can present a
confusing picture about a self organized tourist making choices.
Thinking about what is to be achieved by research involving a survey is critical to research
success. If the goal is to meet development, planning and marketing needs, one needs to do
speci?c thinking about how information will be used. A statement like differences between
origin groups will be used to plan for them and market to them is not explicit enough to guide
data collection or analysis. On the other hand, having a long list (e.g. of locations or
activities) can present a problem. Say, the idea is that one will segment respondents based
on 18 activities (Liu et al., n.d.). Dolnicar (2008) indicates that one should have at least 2
18
(¼262,144) cases. One is not likely to have enough cases to support analysis for any origin
group. Now, consider one has a fewvariables (e.g. 5) and responses are ratings (e.g. 1 to 5),
Dolnicar and Gru¨ n (2007) specify that one must take care to see that cultural differences in
rating are not confused with meaningful differences. Logic is a powerful tool. If Turkish
suppliers know that older Asians usually need to work through a translator and are on certain
types of tours, this knowledge should be taken into account because recognizing this
knowledge is necessary for supplying needs. If one knows younger Asians and Europeans
want certain things, data collection and analysis need to build on that knowledge. In
summary, research should not focus on getting/?nding signi?cant differences.
Even given convenience sampling, some results merit mention since they relate to general
attributes of the population from which the convenience sample was drawn. Speci?cally,
demographics may be off for the population and may affect how preferences and attitudes
are distributed. However, since factor analysis is justi?ed (i.e. by statistical tests), results on
factor analysis are quite speci?c. Being speci?c is appropriate because factor structure
should not be heavily in?uenced by deviation of the sample from a random sample. In that
regard, the research implies that whether Asian (at least, largely Japanese and Korean)
tourists are visiting Turkey or some other destination outside their region (e.g. Australia or the
US), they attach more importance to brand names of accommodation than Europeans. In
order to attract those tourists, well-known international hotel chains should be emphasized.
Local Turkish tourists are more price-sensitive; therefore, special discounted products could
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be used as incentives to entice themto travel more. In addition, local tourists make decisions
more on a family basis. As a result, products and services catering to family should be
stressed when targeting the domestic market.
Finally, using convenience sampling has not compromised the most important ?nding of this
research. Yes, the proportions tested for differences for H1 do not apply to a particular
population. In theory, differences could even result from ?awed sampling. However, reasons
why large differences observed would have anything to do with convenience sampling are
hard to imagine. In fact, since observations of data collection did not result in seeing bias in
selection of respondents, another sample carried out in similar circumstances is likely to
yield similar differences. Furthermore, a second sample is likely to yield a similar factor
structure. Therefore, the factor results are seen as providing guidance for new research.
Most importantly, examination of the results shows that simply looking at univariate data for
variables does not give good information for theory building or practical application. For
example, treating responses about family in?uencing the same when some people are
traveling in a family and others are traveling alone or with friends is problematic. A priori
segmentation to put people into categories so that questions apply to them or so that
differences, for example, of age, language or trip purpose are controlled, just makes sense.
Stated differently, the data serve to reject H2 even though convenience sampling was used.
Conclusion
This study examines the relationship between origin and demographics, preference and
attitude variables. Three regional (origin) groups of Turkish, European and Asian differ on
most variables and factors. As noted earlier this ?nding is expected as the ?nding is
supported by previous research. However, the important matter is that the signi?cant
differences do not mean much because interactions of variables need to be considered to
develop theory and to have good information for planning, marketing and managing. Getting
signi?cant relations can play too big a role in research. Whether one is building theory or
planning for and managing tourism, overly simplistic tables such as tables giving attributes
of visitors by origin (e.g. region of origin) are easy for governments to produce and easy to
discuss. However, when a combination of variables de?nes segments that relate to behavior
to be planned for, one should have the right information.
Hopefully, this research encourages a priori thinking in formulating research strategy, data
collection and analysis.
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Corresponding author
Muzaffer Uysal can be contacted at: [email protected]
VOL. 6 NO. 1 2012
j
INTERNATIONAL JOURNAL OF CULTURE, TOURISM AND HOSPITALITY RESEARCH
j
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