Description
This study seeks to examine the motivational and socio-demographic characteristics of
meetings, incentives, conventions and exhibitions (MICE) visitors to Taiwan in order to identify salient
market subgroups or segments. The aim is to establish results with relevance to Asian destinations and
with some more general applicability.
International Journal of Culture, Tourism and Hospitality Research
Taiwan's MICE visitors: business, leisure and education dimensions
Che-Chao Chiang Brian King Thu-Huong Nguyen
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To cite this document:
Che-Chao Chiang Brian King Thu-Huong Nguyen, (2012),"Taiwan's MICE visitors: business, leisure and education dimensions",
International J ournal of Culture, Tourism and Hospitality Research, Vol. 6 Iss 1 pp. 21 - 33
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Taiwan’s MICE visitors: business, leisure
and education dimensions
Che-Chao Chiang, Brian King and Thu-Huong Nguyen
Abstract
Purpose – This study seeks to examine the motivational and socio-demographic characteristics of
meetings, incentives, conventions and exhibitions (MICE) visitors to Taiwan in order to identify salient
market subgroups or segments. The aim is to establish results with relevance to Asian destinations and
with some more general applicability.
Design/methodology/approach – Based on the literature and expert input a questionnaire was
designed and pretested. Using convenience sampling, data were collected from MICE visitors to Taipei
(Taiwan). Principal components, hierarchical cluster, K-means, chi-square and ANOVA analyses of the
data provide information about MICE visitors and their segments.
Findings – Three motivation-based MICE segments are identi?ed. Variable values suggest the
following segment names – value seekers, no-value seekers, and education seekers. Signi?cant
socio-demographic differences are found between the segments. Results provide insight into MICE
visitor decision making showing the role of business, education and leisure-related motives. Discussion
focuses attention on implications of the results for the development of tourism strategies.
Originality/value – The ?ndings enhance understanding of the motivations of MICE visitors. The
information adds to the knowledge that destination marketers can consider in developing a competitive
edge. Since Taipei’s MICE visitors should be similar to those of Asian competitors, results contribute to a
better understanding of MICE business, education and leisure-related motives and activities in Asia.
Keywords Meetings, incentives, conventions and exhibitions sector, Segmentation,
Motivation (psychology), Socio-demographics, Taiwan, Tourism development, Market segmentation
Paper type Research paper
Introduction
The meetings, incentives, conventions and exhibitions (MICE) sector emerges as one of the
fastest growing components of tourismworldwide, with visitor expenditures of approximately
US$743 billion in 2005 (World Tourism Organization, 2006). Dwyer and Mistilis (1999) and
Go and Govers (1999) note that strong competition characterizes the MICE sector in the
Asia-Paci?c region. Understanding consumer motivations and behaviors within this highly
competitive Asia-Paci?c environment is important. Market segmentation can enhance such
understanding by explaining the needs and wants of prospective customers (Kotler and
Armstrong, 2008; Dolnicar and Gru¨ n, 2008). Therefore, identi?cation of segments and their
differences can provide valuable insights for marketing strategy development.
Literature and context for the research
City destinations located within the dynamic Asia-Paci?c region are increasingly popular for
international meetings and conferences (Dwyer and Mistilis, 1999; Go and Govers, 1999).
Internationally MICE visitation accounts for as much as 70 percent of the total sales volume in
major hotels and 15-20 percent in the case of smaller hotels (Astroff and Abbey, 1998). The
MICE sector is rapidly expanding across the Asia-Paci?c region, including in Taiwan.
According to the International Congress and Convention Association (2007), Taiwan is
DOI 10.1108/17506181211206225 VOL. 6 NO. 1 2012, pp. 21-33, Q Emerald Group Publishing Limited, ISSN 1750-6182
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PAGE 21
Che-Chao Chiang is a PhD
candidate, Brian King is a
Professor, and Thu-Huong
Nguyen is a Lecturer, all at
the Centre for Tourism and
Services Research, Victoria
University, Melbourne,
Australia.
Received September 2009
Revised April 2010
Accepted July 2010
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ranked as the 36th MICE destination worldwide, based on the number of international
conventions held, while Taipei is ranked 6th in Asia and 18th worldwide. Re?ective of the
growing impact of the MICE sector, Taiwan’s tourism authorities will be investing US$642.42
million in new exhibition and related facilities (Taiwan Headlines, 2007). As indicated in
Table I, Taiwan has considerable potential for the MICE market based on the economic
performance of Taipei as a leading destination.
Market segmentation and MICE
Tourism segmentation studies are extensive (e.g. see Kozak et al., 2009; Dolnicar, 2007).
Tourism-related segmentation studies (e.g. Kidd et al., 2004; Nguyen et al., 1998) conclude
that marketing strategies should be targeted at relevant market subdivisions. The use of
segmentation to identify and understand consumer needs and wants offers the prospect of
enhancing service (Sarigollu and Huang, 2005; Shoemaker and Lewis, 1999; Woodside and
Jacobs, 1985). Bieger and Laesser (2002) note that understanding motivations is an
essential prerequisite for predicting consumer behavior. Gaining insights into the
socio-demographic characteristics of each segment is important because of the distinct
motivations prevalent amongst travelers originating from different backgrounds and source
markets.
Despite the growth of MICE-related travel, little segmentation research has been undertaken
on the MICE market within the Asia-Paci?c region. However, a number of noteworthy
sub-themes relevant to Asia are evident within the MICE-related research literature (Yoo and
Weber, 2005; Baloglu and Assante, 1999; Crouch and Ritchie, 1998). Yoo and Weber (2005)
review 14 leading tourism and hospitality journals over the period 1983 until 2003, and
identify a relative lack of research publications focusing on MICE tourism.
MICE visitor motivations
Motivations in?uence the needs and wants of tourists and what they expect from tourism
service providers (Dann, 1981; Crompton, 1977). According to Lazarus (1991), motivations
may be viewed as bene?ts which are sought by individuals with a view to undertaking
activities in particular settings. Commenting on a prominent theme within the MICE tourism
related literature, Lazarus suggests that motivations may determine travel decision-making
(Severt et al., 2007; Ngamsom et al., 2001; Callan and Hoyes, 2000; Oppermann, 1998;
Woodside and Jeffrey, 1998). Crompton (1977) identi?es a number of motivation-related
psychological variables, which in?uence decision-making. In order to explain tourist
behavior, Dann (1977) applies a push-pull framework by examining the motives behind
traveler feelings and desires. Viewed within this context, researchers commonly
characterize tourism motivations as pull factors which in?uence destinations and push
factors as in?uencing traveler needs and wants. In supporting this view, Jago and Deery
(2005) and Oppermann and Chon (1997) propose a model to explain convention and
Table I City events ranking (number of meetings organized in 2007)
Rank City Frequency of meetings
1 Vienna 154
2 Berlin 123
3 Singapore 120
4 Paris 115
5 Barcelona 106
6 Budapest 90
7 Lisbon 90
8 Beijing 87
9 Amsterdam 82
10 Madrid 77
17 London 69
18 Taipei 67
Source: The International Congress and Convention Association (2007)
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meeting delegate decision-making. Hiller (1995) notes that most convention and meeting
delegates participate in business related events, as well as attending conference sessions.
Yoon and Uysal (2005) and Uysal and Hagan (1993) note that push factors are commonly
viewed as internal or intrinsic individual desires. Similarly, Gee et al. (1997) maintain that
motivational factors in?uence tourist decisions and behaviors. If the various research
?ndings noted previously were to be implemented, tourism managers would need an
advanced understanding of factors in?uencing travel decision-making. The foregoing
discussion demonstrates the in?uence of motivations on MICE related travel behavior. An
improved understanding of tourism motivations and visitor needs should assist destination
managers to improve their marketing effectiveness.
Tourism researchers have explained a variety of consumer motivations within the MICE
sector. Oppermann and Chon’s (1997) integrated model consists of four factors or
determinants of convention delegate decision-making. These determinants are respectively:
1. personal and business factors;
2. association and conference factors;
3. locational factors; and
4. intervening opportunities.
According to a report by the MPI Foundation (2000), most convention and meeting
delegates regard participation in meeting related events as a way of accessing new
knowledge that will assist their future professional development. They anticipate
encountering novel skills or techniques, which are being used by others in their
day-to-day activities. In another frequently cited investigation of MICE delegates, Price
(1993) suggests that the major motivating factors are education, networking, career path
and leadership enhancement. A variety of motivations are also identi?ed for delegates in
association-related MICE events (Ngamsom and Beck, 2000). Var et al. (1985) examine the
motivations of convention and meeting delegates. They propose a theoretical model to
explore the in?uence of meeting and convention related attractions on destination selection.
Var et al. (1985) concludes that the perceived appeal and accessibility of attractions
associated with meetings and conventions is a predictor of delegate behavior.
Rittichainuwat Ngamson et al. (2001) examine the in?uence of motivations, inhibitors and
facilitators on delegates participating in international conferences. Severt et al. (2007)
identify ?ve motivational factors: activities and opportunities; networking; convenience of
conference; education bene?ts; and products and deals. They identify a relationship
between motivations and overall satisfaction, which then connects with tourism
performance. The most sought-after activities and opportunities are not the formal
convention sessions. Oppermann and Chon (1997) and Price (1993) suggest that
participation in MICE events provides an opportunity for professionals to enhance their
careers by gathering information and forming networks. Previous tourism studies suggest
that the opportunity to travel overseas and participate in outdoor recreation activities is a
major motivation for conference, convention and exhibition delegates (Ngamsom and Beck,
2000).
Socio-demographics and segmentation
Customer socio-demographic pro?ling can highlight the connection between market
segmentation and competitive advantage (Kattiyapornpong and Miller, 2008; Tsiotsou and
Vasioti, 2006; Dolnicar and Leisch, 2004; Grant and Weaver, 1996). Gladwell (1990)
demonstrates that socio-demographics help to explain differences between tourists
traveling to state parks in the state of Indiana, USA. Nichols and Snepenger (1988) stress the
important in?uence of socio-demographics on decision-making and their implications for
marketing performance. Kattiyapornpong and Miller (2008) stress that traveler
socio-demographics (such as age, income and life-stage) help to explain overseas
holiday preferences.
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Hypotheses and research strategy
Drawing upon previous research, the present study proposes that travel motivations and
socio-demographics have reasonable and useful relations to MICE segments for Taiwan
(Figure 1). This proposition is manifested in the following hypotheses:
H1. A limited number of dimensions explain a high percentage of variation in
motivations for MICE travel to Taiwan.
H2. Segmentation of MICE visitors to Taiwan results in a limited number of segments
with attributes consistent with those speci?ed in the literature.
H3. MICE segments of Taiwan have distinctive socio-demographic characteristics.
The structure of MICE segments for Taiwan is studied using data collected from MICE
visitors to Taipei. The provision of information about questionnaire development and survey
data collection precedes description of data reduction using principal components analysis
(PCA). Drawing upon data from reduced dimensions of PCA, sampling is used for
hierarchical clustering input to gain information supporting the use of K-means to segment
all usable data respondents. Differences are ascertained between segments based on
socio-demographic and motivational characteristics. A discussion of the results of the
segmentation process can facilitate an understanding of MICE tourism. Discussion ?nally
turns to providing practical and theoretical insights into the MICE phenomenon.
Methodology
To test the research hypotheses, a questionnaire was developed for data collection
purposes. The questionnaire includes 20 variables drawn from previous tourism motivation
studies (see: Bauer et al., 2008; Rittichainuwat Ngamson et al., 2001; Ngamsom and Beck,
2000; Rutherford and Kreck, 1994; Crompton, 1977). Respondents were asked to assess
the importance of various motivations are from 1 (not important) to 5 (extremely important).
The respondents were asked to indicate the importance in decision-making with particular
reference to their current trip (i.e. a MICE visit to Taipei).
Neuman (2006) stresses the importance of evaluating the reliability of any proposed instrument.
Pre-testing of the questionnaire was conducted using convenience sampling (n ¼ 50). The
internal consistency of key constructs was assessed using Cronbach’s alpha. Cronbach alpha
values were greater than 0.60 and hence acceptably reliable (Hair et al., 2006).
The survey was administered in person. The target population consisted of MICE visitors
participating in business related events being staged at Taipei’s International Convention Centre
and at the Taipei World Trade Centre. The surveys were distributed by means of convenience
sampling. The administrator encountered people in the lobby areas of the two centers. If a
person encountered was determined to be a MICE visitor, she/he was asked to complete the
questionnaire. About 80 percent of potential respondents agreed to complete the questionnaire.
Of 700 questionnaires returned, 518 were usable (i.e. the responses were complete).
Figure 1 Segmentation research model
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Convenience sampling was used for two reasons. First, no obvious way to obtain a random
sample of MICE visitors to Taiwan exists. Secondly, some dimensions (e.g. for motivation)
apply across segments. In this context convenience samples that possess a diversity of
information in order to de?ne the dimensions can be used. This sampling method is an
appropriate strategy to collect a large sample in a relative short time (Hair et al., 2006). Using
non-probability sampling is justi?ed because people were involved in different meetings.
Even if lists were obtained, contacting people selected as a sample of unique MICE visitors
presented logistical problems. One issue associated with sampling by encounter was
over-representation of those most likely to be encountered. Without correction
overrepresentation can cause biased or invalid results. Given the population sampled can
be described as possible person-encounters and people in particular segments may spend
more time in areas where encounters occur, weights like length of stay may be needed for
correction purposes (Lucas, 1963) or other methods may be used (e.g. Tyrrell and Johnston,
2002) to get to unique MICE visitors. No viable way of determining the likelihood of an
encounter was determined (e.g. by determining times a person is likely present during data
collection). Thought then turned to some survey results being invariant between a sample for
unique MICE visitors and a convenience sample by encounter. Logical consideration of
research objectives suggested no need to produce statements about speci?c attributes
(e.g. x percent of Taiwan’s MICE are male and y percent give a rating of 5 for some variable),
to obtain information representative of the unique MICE visitor. Research objectives can be
achieved by determining motivational dimensions that can be taken as invariant between the
unique MICE visitor and possible person-encounters populations. Assuming invariance is
like deriving other scales for use across diverse sub populations (i.e. assuming that
dimensions that apply to people in different segments).
Analysis was undertaken using applications within the Statistical Package for the Social
Sciences (SPSS 15.0).
Results and discussion
Analysis began by undertaking tabulations to determine the socio-demographic
characteristics of respondents. Though no predictable distribution for this group exists
(e.g. MICE ?gures produced by Taiwan Tourism Bureau, 2007), some results (sample
column of Table II) show broad coverage by the survey. Nearly 80 percent of respondents
are male. The respondents range across ages with about a third between 40 to 49 years.
Almost half of the respondents possess a postgraduate degree. Approximately two-thirds
identify themselves as a director or manager. About 20 percent of respondents report annual
income of over US$99,000. Japan accounted for 32.4 percent of respondents with 21
percent from Europe and 5.4 percent from China (including Hong Kong).
The principal components analysis (PCA)
To reduce the number of dimensions for motivation variables, PCA was employed. PCA was
used with varimax rotation so the reduction of twenty motivations was in groups of related
variables. The appropriateness of the PCA is supported by the KMO score of sampling
adequacy being 0.89 and the Bartlett Test of sphericity being 7000.54 (df of 171) which has
near zero probability of occurring.
Four components were extracted with Eigen values of greater than 1.0. These components
explain about 69 percent of total variance. Cronbach’s alpha for these components ranges
from 0.84 to 0.89. Values above 0.6 exceed the minimum level recommended by Hair et al.
(2006) for scales to be internally consistent. Though results are for MICE visitors at two
locations in Taipei, the assumption that the dimensions apply to MICE travelers visiting
Taipei’s Asian competitors seems reasonable. Subject to results being generalizable, H1 is
accepted (i.e. a limited number of dimensions explain a high percentage of variance in
motivation responses).
To demonstrate that the four component solution makes sense and conveys useful
information, the solution loadings are presented in Table III. Since the components are
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orthogonal, being a strong seeker of educational values has no implication for how a person
responds for other components. In other words, being strongly educationally motivated
does not convey information about motivation for exploration of the novel. The components
do not de?ne groups exhibiting related values. This occurs in cluster analysis.
The following paragraphs describe the components.
Component 1: educational values, consists of four variables associated with participation in
education related activities (see Table III). This component accounts for almost half (45
percent) of the total variance and is indicative of education related motivations amongst
MICE visitors. MICE events may offer different opportunities for activities such as presenting
a conference paper or serving as chair of a conference session. Because activities relate to
what occurs at MICE events, this component may show as much about the structure of
meetings as about respondent motivations. Future research should clarify how responses
relate to opportunities.
Component 2: exploration of the novel, consists of six variables associated with novel
experiences and with the search for comfort. The two experience-related variables ‘‘life
Table II Frequencies and percents for samples and clusters chi-square analysis of MICE segments
Motivation-based clusters
Sample Cluster 1 Cluster 2 Cluster 3
(n ¼ 518) (n ¼ 262) (n ¼ 152) (n ¼ 104)
Variables n % n % n % n % x
2
df p
Gender 3.1 2 0.2
Male 405 78 204 78 125 82 76 73
Female 113 22 58 22 27 18 28 27
Age 36 8 , 0.001
20-29 66 13 36 14 3 2 27 26
30-39 114 22 64 24 32 21 18 17
40-49 189 37 92 35 67 44 30 29
50-59 89 17 44 17 29 19 16 15
60 or over 60 12 26 10 21 14 13 13
Education 31 6 , 0.001
Secondary school 62 12 43 16 19 13 0 0
Vocational ed. 15 3 9 3 3 2 3 3
Bachelor degree 200 39 94 36 48 32 58 56
Masters/PHD 241 47 116 44 82 54 43 41
Some occupations 98 14 , 0.001
Director/manager 335 65 153 58 129 85 53 51
Professional 43 8 19 7 3 2 21 20
Technical 48 9 20 8 11 7 17 16
Annual income ($US) 86 14 , 0.001
8,000 or less 28 5 24 9 0 0 4 4
8,001-16,500 60 12 42 16 6 4 12 12
16,501-33,000 35 7 25 10 7 5 3 3
33,001-49,500 46 9 23 9 14 9 9 9
49,501-66,000 82 16 27 10 37 24 18 17
66,001-82,500 75 15 29 11 34 22 12 12
82,501-99,000 89 17 61 23 12 8 16 15
More than 99,000 103 20 31 12 42 28 30 29
Some countries of residence 146 22 , 0.001
China/Hong Kong 28 5 22 8 3 2 3 3
Germany 33 6 11 4 19 13 3 3
Japan 168 32 101 39 39 26 28 27
USA 26 5 11 4 9 6 6 6
Other Asia 40 8 40 15 0 0 0 0
Other Europe 62 12 12 5 28 18 22 21
Notes: Frequencies and percents are omitted for some occupations and countries; Chi-square uses all cells in the data; Also, Cluster 1 –
value seekers; Cluster 2 – no-value seekers; Cluster 3 – education seekers
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experience gained when travelling’’ and ‘‘comfortable place to stay’’ have the heaviest
loadings for the component. Given that the variable explains 13 percent of the variance,
marketing and meeting planners should be able to position themselves by considering this
variable. In determining components means have been extracted. In this component,
means of importance on the variables not loadings or variance, must be examined in
assessing the value of pursuing visitors based on the quest for novel life experiences.
Component 3: career enhancement, is comprised of six variables. These are centered upon
work related requirements and on the potential to develop networks by forming friendships
and contacts within the relevant ?eld. This variable only explains 5.8 percent of the variance.
However, as comments for component 2 indicate, being in a component shows a pattern in
variation. A respondent that rates one variable of the component higher than the mean tends
to rate others high. Understanding the role of career enhancement in going to meetings
involves examining importance and not scores that have the means of importance extracted.
The variables that loaded highest are: required by employers; employer funded and social
networking. These are most consistently marked higher or lower in terms of importance but
are not necessarily the variables of highest mean importance.
The fourth and ?nal component contains variables associated with a personal desire to visit
new destinations, and to be entertained. This component explained 5.2 percent of total
variance and is labeled travel opportunities. Because some variance exists, emphasis on
opportunities to travel overseas in?uences motivation.
Cluster analysis
An important topic of research has been the merit of further research on how to determine
clusters with the kind of data collected in this topic. In this research, the scores for
individuals within the four components identi?ed are used as the basis for the identi?cation
of segments based on MICE visitor motivations. An alternative would be deriving clusters
based on the importance responses received.
Table III Principal components analysis of MICE visitor motivations
Tourism-related motivational dimensions Loadings for components
Component 1: educational value
Presenting a paper 0.87
Serve as chair or moderator 0.87
Education-related purposes 0.79
Self-esteem enhancement 0.59
Component 2: exploration of the novel
Life experience gained when traveling 0.81
Comfortable place to stay 0.81
See new things 0.75
Escape from routine 0.61
Experience a different culture 0.58
Opportunity to relax on vacation 0.55
Component 3: career enhancement
Work requirement 0.77
Employer funded 0.77
Networking opportunities 0.72
Interesting conference program 0.62
A good conference package 0.56
A reasonably priced conference 0.49
Component 4: travel opportunities
Sightseeing 0.86
An opportunity to visit a new town or city 0.85
Combining leisure and business trips 0.56
An opportunity for entertainment 0.55
Percentage of variance explained 45.5 12.8 5.8 5.2
Cronbach’s alpha 0.89 0.89 0.86 0.84
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A problemof cluster analysis is that unless theory suggests/implies the number of clusters to
seek, the number of segments is unknown. Hierarchical cluster analysis is appropriate to
gather information about the number of clusters to seek (Hair et al., 2006; Yu¨ ksel and Yu¨ ksel,
2002; Ketchen and Shook, 1996). Based on practicality, Malhotra et al. (2002) suggest that
the number of cases within each cluster should be enough to draw inferences. Even with ten
cases in a cluster, one can be hard pressed to draw inferences (e.g. about the group as a
target market). Consistent with the suggestion made by Hair et al. (2006), hierarchical
clustering with a subsample (n ¼ 50) was used for the purposes of the present research to
determine the number of clusters. The result shows that two, three, four or ?ve clusters might
provide a good approximation reality.
To obtain clusters for the entire data set, K-means cluster analysis was used to obtain four
different cluster solutions (n ¼ 2, 3, 4 and 5). The results obtained from these solutions were
then compared and the three-cluster solution was selected for further analysis. Selection is
based on heterogeneity across clusters on the four motivational components. The
three-cluster solution also yields the most readily interpretable, and meaningful results.
Cluster differences
To examine the ?nal cluster solution, an analysis of variance (ANOVA) was applied based on
post-hoc tests. The goal of the ANOVA analysis is determining if statistically signi?cant
differences in appropriately weighted importance scores (i.e. component importance
scores) across clusters.
The ANOVA results are noted in Table IV. The F-test values in Table IV indicate differences in
component importance scores so large that the probability of chance occurrence is near
zero. Table IV displays the three clusters with mean importance scores highlighted for the
four motivational components. The cluster scores are included in Table IV only if one-way
analysis of variance was signi?cant at ,0.001. Each cluster is labeled based on the
contribution of motivational components.
The existence of signi?cant clusters supports an acceptance of Asian MICE visitors as
classi?ed into a limited number of segments with attributes expected from the literature. On
this basis, H2 is accepted.
The clusters found can be described and labeled as follows.
Cluster 1: value seekers
This cluster includes over half of the sample. Compared with the other two clusters (see
Table IV), cluster 1 ranks high on motivational components associated with professional and
recreation. The strong components identi?ed are congruent with the ?ndings of previous
studies (Rutherford and Kreck, 1994). The results con?rmthe work of Severt et al. (2007) and
Ngamsom and Beck (2000), who suggest that business and recreation related motivations
are both important determinants for understanding the behavioral intentions of MICE
delegates. In order to address the needs and wants of the delegates included in this cluster,
marketers should consider providing travelers with rewards to encourage engagement with
MICE related events.
Table IV ANOVA for tourism motivation components with mean scores for clusters
Cluster mean scores
Tourism motivation components
Value seekers
(n ¼ 262)
No-value seekers
(n ¼ 152)
Education seekers
(n ¼ 104) df F Value p
Educational value 3.64 1.95 3.69 2 17.16 ,0.001
Exploration of the novel 3.36 1.36 1.69 2 25.50 ,0.001
Career enhancement 3.76 2.04 2.31 2 16.39 ,0.001
Travel opportunities 3.63 1.73 3.56 2 19.47 ,0.001
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Cluster 2: no-value seekers
This cluster includes about 30 percent of the sample population. The component means
differ substantially from those of cluster 1. Relative to the other groups, means are lower on
all components with mean component importance ratings ranging from 1.36 to 2.04. The
means suggest that respondents within this group are less interested in professional and/or
recreational related activities than their counterparts in the other two groups. Cluster 2
represents a group of visitors who are relatively indifferent to the concept of value, and who
do not travel explicitly for reasons of education, novelty or career enhancement. In targeting
this group, destination marketers and service providers should focus more on work-related
values and on the provision of enjoyable and interactive social events.
Cluster 3: education seekers
This cluster includes approximately 21 percent of the total sample. The segment has a high
mean score on determining factors associated with education based values (mean ¼ 3.69)
and travel opportunities (mean ¼ 3.56). One can infer that members of cluster 3 tend to plan
their travel around both educational and leisure related opportunities. Their expectations of
the current trip emphasize sightseeing opportunities, visiting a new place, presenting a
paper and serving as a session chair or moderator. These ?ndings provide insights into the
roles of educational value and ability to travel as signi?cant decision-making factors. An
inference is that respondents in this cluster are seeking MICE-related events which provide a
high quality travel environment, as well as good value in terms of education and learning. To
attract visitors within this cluster, results suggest that marketers should design interesting
destination-based events that provide participants with educational, professional and travel
related opportunities.
Segment socio-demographic characteristics
In order to explore differences between the demographic characteristics of respondents
across the three motivation-based clusters (e.g. see Table III), gap Chi-square tests were
run. Results are noted in Table II. Notable statistically signi?cant differences ( p , 0.001) are
evident for age, education, occupation, household income and country of residence. Asian
MICE segments have distinctive socio-demographic characteristics. On this basis, H3 can
be accepted.
A description is possible based on the univariate statistics. Consistent with the age
composition of clusters, one can propose that respondents aged 30-39 tend to be seeking
career enhancement, while downplaying education. Nearly 85 percent of no-value seekers
are directors or managers. No-value seekers are less interested in professional and
recreational related activities than the other two groups. Directors and managers are
distinct. More than two-thirds of value seekers reported having an annual income in excess
of US$82,500 compared with 44 percent of education seekers. In terms of country of
residence, nearly 40 percent of value seekers were Japanese compared with 26 percent of
no-value seekers and 27 percent of education seekers. Does this result just re?ect that
Japan is close to Taiwan (i.e. as offering good travel value)? The results also indicate that
European visitors (from Germany, the UK and other) placed a greater emphasis on work
related values. These respondents appeared to be less interested in engaging in
recreational activities. As may be expected on the basis of stereotypes, Japanese
respondents were strongly motivated to participate in business related events (for
work-related reasons) and to engage in a range of leisure activities.
Discussion: implications and opportunities for further research
As previously noted, various studies have explored the connections between customer needs
and expectations and the services and/or products which contribute to the overall tourism
experience (Galloway, 2002; Pearce and Caltabiano, 1983; Dann, 1981, 1977). From a
theoretical perspective, the ?ndings of this study have provided an enhanced understanding
of MICE visitor motivations. This enhanced understanding has been achieved by identifying
dimensions and clusters of MICE visitors with distinctive socio-demographic characteristics.
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The validity of motivational components of educational value, exploration of the novel, career
enhancement and travel opportunity was consistent with previous research undertaken by
Rittichainuwat Ngamson et al. (2001). The present study also supports the view that
profession-related motivations are important determinants for explaining MICE delegates’
decision making. This research also offers veri?cation of the interrelationship between leisure
and business motivations for MICE travel and thus for MICE decision-making (Oppermann,
1998; Oppermann and Chon, 1997; Rutherford and Kreck, 1994).
The discussion of PCA results for motivation variables may appear obtuse. Avoiding wording
which suggests that importance ratings rather than their variation (standardized or not)
around means is important. Recognizing groupings of variables does not suggest groupings
of people but, given orthogonality of components, that on average persons who rate high on
one component have responses distributed randomly over the response distribution of other
variables. In this research PCA establishes a 4-dimensional space in which one looks for
concentrations of points using cluster analysis. Even if points in the space correspond to a
random distribution, cluster analysis can ?nd groupings. However, the groupings will be
unstable (starting clustering from different initial values will result in different clusters that are
just as tight). Some measures are taken in this research to see that cluster results are stable.
Further research can elucidate how stable they are for the analysis of data collected.
Generalizability involves getting similar PCA and cluster results with data for Taipei’s
competitors.
A technical matter is whether segmentation should be done on scores fromPCA with zero as
a mean, component score values or on raw importance values. As noted above, scores
produced to reproduce a correlation or covariance matrix do not re?ect mean importance
ratings. Component score values created to produce the ANOVA results are scores that
approximate raw scores with their means. Future research must address appropriate
versions of PCA and ways to develop scores.
The hypothesis proposes that useful segmentation of motivation has been achieved partly
relates to the idea that segments can be considered in planning, managing or marketing.
The study has highlighted signi?cant socio-demographic differences between three MICE
visitor segments. However, univariate pictures of MICE visitors do not underlie the segments.
Future research must consider the need for multivariate socio-demographic pictures. Given
the income increases with age and with certain occupational categories, control on variables
will show that some signi?cant univariate differences (Table II) re?ect the structure of the
multivariate categories that segment members tend to occupy. Future research should be
conducted in other Asian countries by the same survey questionnaire to validate the results
of this study.
Though this study has contributed to the tourism literature at both the managerial and
theoretical levels, more research is needed on MICE visitors. Future research should go
beyond the suggestions made here. Examining linkages between past behavior, intentions
and future behaviors to provide better predictive capacity is not necessary. Research should
contribute to understanding underlying values which are sought by MICE visitors and the
role of these values in drawing visitors to a destination.
Conclusion
This work is exploratory yet has rami?cations for methodology, application and theory. Reason
suggests that MICE visits to Taipei’s competitors will differ in origin and other
socio-demographics (e.g. because of proximity of countries). Research and data from
MICE visitors to Taiwan yields scales, dimensions and clusters similar to those of competitors.
Only further research will show the general applicability results for Taiwan. A line of research
has started. The results should facilitate further study involving data from multiple locations.
Research issues raised also point to methodological issues. Understanding behavior can
rarely be based on univariate differences. How best to determine clusters is a methodological
matter. Multivariate consideration of cluster differences to produce information can serve
planning, managing and marketing as well as the development of theory.
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Corresponding author
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doc_962259650.pdf
This study seeks to examine the motivational and socio-demographic characteristics of
meetings, incentives, conventions and exhibitions (MICE) visitors to Taiwan in order to identify salient
market subgroups or segments. The aim is to establish results with relevance to Asian destinations and
with some more general applicability.
International Journal of Culture, Tourism and Hospitality Research
Taiwan's MICE visitors: business, leisure and education dimensions
Che-Chao Chiang Brian King Thu-Huong Nguyen
Article information:
To cite this document:
Che-Chao Chiang Brian King Thu-Huong Nguyen, (2012),"Taiwan's MICE visitors: business, leisure and education dimensions",
International J ournal of Culture, Tourism and Hospitality Research, Vol. 6 Iss 1 pp. 21 - 33
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Taiwan’s MICE visitors: business, leisure
and education dimensions
Che-Chao Chiang, Brian King and Thu-Huong Nguyen
Abstract
Purpose – This study seeks to examine the motivational and socio-demographic characteristics of
meetings, incentives, conventions and exhibitions (MICE) visitors to Taiwan in order to identify salient
market subgroups or segments. The aim is to establish results with relevance to Asian destinations and
with some more general applicability.
Design/methodology/approach – Based on the literature and expert input a questionnaire was
designed and pretested. Using convenience sampling, data were collected from MICE visitors to Taipei
(Taiwan). Principal components, hierarchical cluster, K-means, chi-square and ANOVA analyses of the
data provide information about MICE visitors and their segments.
Findings – Three motivation-based MICE segments are identi?ed. Variable values suggest the
following segment names – value seekers, no-value seekers, and education seekers. Signi?cant
socio-demographic differences are found between the segments. Results provide insight into MICE
visitor decision making showing the role of business, education and leisure-related motives. Discussion
focuses attention on implications of the results for the development of tourism strategies.
Originality/value – The ?ndings enhance understanding of the motivations of MICE visitors. The
information adds to the knowledge that destination marketers can consider in developing a competitive
edge. Since Taipei’s MICE visitors should be similar to those of Asian competitors, results contribute to a
better understanding of MICE business, education and leisure-related motives and activities in Asia.
Keywords Meetings, incentives, conventions and exhibitions sector, Segmentation,
Motivation (psychology), Socio-demographics, Taiwan, Tourism development, Market segmentation
Paper type Research paper
Introduction
The meetings, incentives, conventions and exhibitions (MICE) sector emerges as one of the
fastest growing components of tourismworldwide, with visitor expenditures of approximately
US$743 billion in 2005 (World Tourism Organization, 2006). Dwyer and Mistilis (1999) and
Go and Govers (1999) note that strong competition characterizes the MICE sector in the
Asia-Paci?c region. Understanding consumer motivations and behaviors within this highly
competitive Asia-Paci?c environment is important. Market segmentation can enhance such
understanding by explaining the needs and wants of prospective customers (Kotler and
Armstrong, 2008; Dolnicar and Gru¨ n, 2008). Therefore, identi?cation of segments and their
differences can provide valuable insights for marketing strategy development.
Literature and context for the research
City destinations located within the dynamic Asia-Paci?c region are increasingly popular for
international meetings and conferences (Dwyer and Mistilis, 1999; Go and Govers, 1999).
Internationally MICE visitation accounts for as much as 70 percent of the total sales volume in
major hotels and 15-20 percent in the case of smaller hotels (Astroff and Abbey, 1998). The
MICE sector is rapidly expanding across the Asia-Paci?c region, including in Taiwan.
According to the International Congress and Convention Association (2007), Taiwan is
DOI 10.1108/17506181211206225 VOL. 6 NO. 1 2012, pp. 21-33, Q Emerald Group Publishing Limited, ISSN 1750-6182
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PAGE 21
Che-Chao Chiang is a PhD
candidate, Brian King is a
Professor, and Thu-Huong
Nguyen is a Lecturer, all at
the Centre for Tourism and
Services Research, Victoria
University, Melbourne,
Australia.
Received September 2009
Revised April 2010
Accepted July 2010
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ranked as the 36th MICE destination worldwide, based on the number of international
conventions held, while Taipei is ranked 6th in Asia and 18th worldwide. Re?ective of the
growing impact of the MICE sector, Taiwan’s tourism authorities will be investing US$642.42
million in new exhibition and related facilities (Taiwan Headlines, 2007). As indicated in
Table I, Taiwan has considerable potential for the MICE market based on the economic
performance of Taipei as a leading destination.
Market segmentation and MICE
Tourism segmentation studies are extensive (e.g. see Kozak et al., 2009; Dolnicar, 2007).
Tourism-related segmentation studies (e.g. Kidd et al., 2004; Nguyen et al., 1998) conclude
that marketing strategies should be targeted at relevant market subdivisions. The use of
segmentation to identify and understand consumer needs and wants offers the prospect of
enhancing service (Sarigollu and Huang, 2005; Shoemaker and Lewis, 1999; Woodside and
Jacobs, 1985). Bieger and Laesser (2002) note that understanding motivations is an
essential prerequisite for predicting consumer behavior. Gaining insights into the
socio-demographic characteristics of each segment is important because of the distinct
motivations prevalent amongst travelers originating from different backgrounds and source
markets.
Despite the growth of MICE-related travel, little segmentation research has been undertaken
on the MICE market within the Asia-Paci?c region. However, a number of noteworthy
sub-themes relevant to Asia are evident within the MICE-related research literature (Yoo and
Weber, 2005; Baloglu and Assante, 1999; Crouch and Ritchie, 1998). Yoo and Weber (2005)
review 14 leading tourism and hospitality journals over the period 1983 until 2003, and
identify a relative lack of research publications focusing on MICE tourism.
MICE visitor motivations
Motivations in?uence the needs and wants of tourists and what they expect from tourism
service providers (Dann, 1981; Crompton, 1977). According to Lazarus (1991), motivations
may be viewed as bene?ts which are sought by individuals with a view to undertaking
activities in particular settings. Commenting on a prominent theme within the MICE tourism
related literature, Lazarus suggests that motivations may determine travel decision-making
(Severt et al., 2007; Ngamsom et al., 2001; Callan and Hoyes, 2000; Oppermann, 1998;
Woodside and Jeffrey, 1998). Crompton (1977) identi?es a number of motivation-related
psychological variables, which in?uence decision-making. In order to explain tourist
behavior, Dann (1977) applies a push-pull framework by examining the motives behind
traveler feelings and desires. Viewed within this context, researchers commonly
characterize tourism motivations as pull factors which in?uence destinations and push
factors as in?uencing traveler needs and wants. In supporting this view, Jago and Deery
(2005) and Oppermann and Chon (1997) propose a model to explain convention and
Table I City events ranking (number of meetings organized in 2007)
Rank City Frequency of meetings
1 Vienna 154
2 Berlin 123
3 Singapore 120
4 Paris 115
5 Barcelona 106
6 Budapest 90
7 Lisbon 90
8 Beijing 87
9 Amsterdam 82
10 Madrid 77
17 London 69
18 Taipei 67
Source: The International Congress and Convention Association (2007)
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meeting delegate decision-making. Hiller (1995) notes that most convention and meeting
delegates participate in business related events, as well as attending conference sessions.
Yoon and Uysal (2005) and Uysal and Hagan (1993) note that push factors are commonly
viewed as internal or intrinsic individual desires. Similarly, Gee et al. (1997) maintain that
motivational factors in?uence tourist decisions and behaviors. If the various research
?ndings noted previously were to be implemented, tourism managers would need an
advanced understanding of factors in?uencing travel decision-making. The foregoing
discussion demonstrates the in?uence of motivations on MICE related travel behavior. An
improved understanding of tourism motivations and visitor needs should assist destination
managers to improve their marketing effectiveness.
Tourism researchers have explained a variety of consumer motivations within the MICE
sector. Oppermann and Chon’s (1997) integrated model consists of four factors or
determinants of convention delegate decision-making. These determinants are respectively:
1. personal and business factors;
2. association and conference factors;
3. locational factors; and
4. intervening opportunities.
According to a report by the MPI Foundation (2000), most convention and meeting
delegates regard participation in meeting related events as a way of accessing new
knowledge that will assist their future professional development. They anticipate
encountering novel skills or techniques, which are being used by others in their
day-to-day activities. In another frequently cited investigation of MICE delegates, Price
(1993) suggests that the major motivating factors are education, networking, career path
and leadership enhancement. A variety of motivations are also identi?ed for delegates in
association-related MICE events (Ngamsom and Beck, 2000). Var et al. (1985) examine the
motivations of convention and meeting delegates. They propose a theoretical model to
explore the in?uence of meeting and convention related attractions on destination selection.
Var et al. (1985) concludes that the perceived appeal and accessibility of attractions
associated with meetings and conventions is a predictor of delegate behavior.
Rittichainuwat Ngamson et al. (2001) examine the in?uence of motivations, inhibitors and
facilitators on delegates participating in international conferences. Severt et al. (2007)
identify ?ve motivational factors: activities and opportunities; networking; convenience of
conference; education bene?ts; and products and deals. They identify a relationship
between motivations and overall satisfaction, which then connects with tourism
performance. The most sought-after activities and opportunities are not the formal
convention sessions. Oppermann and Chon (1997) and Price (1993) suggest that
participation in MICE events provides an opportunity for professionals to enhance their
careers by gathering information and forming networks. Previous tourism studies suggest
that the opportunity to travel overseas and participate in outdoor recreation activities is a
major motivation for conference, convention and exhibition delegates (Ngamsom and Beck,
2000).
Socio-demographics and segmentation
Customer socio-demographic pro?ling can highlight the connection between market
segmentation and competitive advantage (Kattiyapornpong and Miller, 2008; Tsiotsou and
Vasioti, 2006; Dolnicar and Leisch, 2004; Grant and Weaver, 1996). Gladwell (1990)
demonstrates that socio-demographics help to explain differences between tourists
traveling to state parks in the state of Indiana, USA. Nichols and Snepenger (1988) stress the
important in?uence of socio-demographics on decision-making and their implications for
marketing performance. Kattiyapornpong and Miller (2008) stress that traveler
socio-demographics (such as age, income and life-stage) help to explain overseas
holiday preferences.
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Hypotheses and research strategy
Drawing upon previous research, the present study proposes that travel motivations and
socio-demographics have reasonable and useful relations to MICE segments for Taiwan
(Figure 1). This proposition is manifested in the following hypotheses:
H1. A limited number of dimensions explain a high percentage of variation in
motivations for MICE travel to Taiwan.
H2. Segmentation of MICE visitors to Taiwan results in a limited number of segments
with attributes consistent with those speci?ed in the literature.
H3. MICE segments of Taiwan have distinctive socio-demographic characteristics.
The structure of MICE segments for Taiwan is studied using data collected from MICE
visitors to Taipei. The provision of information about questionnaire development and survey
data collection precedes description of data reduction using principal components analysis
(PCA). Drawing upon data from reduced dimensions of PCA, sampling is used for
hierarchical clustering input to gain information supporting the use of K-means to segment
all usable data respondents. Differences are ascertained between segments based on
socio-demographic and motivational characteristics. A discussion of the results of the
segmentation process can facilitate an understanding of MICE tourism. Discussion ?nally
turns to providing practical and theoretical insights into the MICE phenomenon.
Methodology
To test the research hypotheses, a questionnaire was developed for data collection
purposes. The questionnaire includes 20 variables drawn from previous tourism motivation
studies (see: Bauer et al., 2008; Rittichainuwat Ngamson et al., 2001; Ngamsom and Beck,
2000; Rutherford and Kreck, 1994; Crompton, 1977). Respondents were asked to assess
the importance of various motivations are from 1 (not important) to 5 (extremely important).
The respondents were asked to indicate the importance in decision-making with particular
reference to their current trip (i.e. a MICE visit to Taipei).
Neuman (2006) stresses the importance of evaluating the reliability of any proposed instrument.
Pre-testing of the questionnaire was conducted using convenience sampling (n ¼ 50). The
internal consistency of key constructs was assessed using Cronbach’s alpha. Cronbach alpha
values were greater than 0.60 and hence acceptably reliable (Hair et al., 2006).
The survey was administered in person. The target population consisted of MICE visitors
participating in business related events being staged at Taipei’s International Convention Centre
and at the Taipei World Trade Centre. The surveys were distributed by means of convenience
sampling. The administrator encountered people in the lobby areas of the two centers. If a
person encountered was determined to be a MICE visitor, she/he was asked to complete the
questionnaire. About 80 percent of potential respondents agreed to complete the questionnaire.
Of 700 questionnaires returned, 518 were usable (i.e. the responses were complete).
Figure 1 Segmentation research model
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Convenience sampling was used for two reasons. First, no obvious way to obtain a random
sample of MICE visitors to Taiwan exists. Secondly, some dimensions (e.g. for motivation)
apply across segments. In this context convenience samples that possess a diversity of
information in order to de?ne the dimensions can be used. This sampling method is an
appropriate strategy to collect a large sample in a relative short time (Hair et al., 2006). Using
non-probability sampling is justi?ed because people were involved in different meetings.
Even if lists were obtained, contacting people selected as a sample of unique MICE visitors
presented logistical problems. One issue associated with sampling by encounter was
over-representation of those most likely to be encountered. Without correction
overrepresentation can cause biased or invalid results. Given the population sampled can
be described as possible person-encounters and people in particular segments may spend
more time in areas where encounters occur, weights like length of stay may be needed for
correction purposes (Lucas, 1963) or other methods may be used (e.g. Tyrrell and Johnston,
2002) to get to unique MICE visitors. No viable way of determining the likelihood of an
encounter was determined (e.g. by determining times a person is likely present during data
collection). Thought then turned to some survey results being invariant between a sample for
unique MICE visitors and a convenience sample by encounter. Logical consideration of
research objectives suggested no need to produce statements about speci?c attributes
(e.g. x percent of Taiwan’s MICE are male and y percent give a rating of 5 for some variable),
to obtain information representative of the unique MICE visitor. Research objectives can be
achieved by determining motivational dimensions that can be taken as invariant between the
unique MICE visitor and possible person-encounters populations. Assuming invariance is
like deriving other scales for use across diverse sub populations (i.e. assuming that
dimensions that apply to people in different segments).
Analysis was undertaken using applications within the Statistical Package for the Social
Sciences (SPSS 15.0).
Results and discussion
Analysis began by undertaking tabulations to determine the socio-demographic
characteristics of respondents. Though no predictable distribution for this group exists
(e.g. MICE ?gures produced by Taiwan Tourism Bureau, 2007), some results (sample
column of Table II) show broad coverage by the survey. Nearly 80 percent of respondents
are male. The respondents range across ages with about a third between 40 to 49 years.
Almost half of the respondents possess a postgraduate degree. Approximately two-thirds
identify themselves as a director or manager. About 20 percent of respondents report annual
income of over US$99,000. Japan accounted for 32.4 percent of respondents with 21
percent from Europe and 5.4 percent from China (including Hong Kong).
The principal components analysis (PCA)
To reduce the number of dimensions for motivation variables, PCA was employed. PCA was
used with varimax rotation so the reduction of twenty motivations was in groups of related
variables. The appropriateness of the PCA is supported by the KMO score of sampling
adequacy being 0.89 and the Bartlett Test of sphericity being 7000.54 (df of 171) which has
near zero probability of occurring.
Four components were extracted with Eigen values of greater than 1.0. These components
explain about 69 percent of total variance. Cronbach’s alpha for these components ranges
from 0.84 to 0.89. Values above 0.6 exceed the minimum level recommended by Hair et al.
(2006) for scales to be internally consistent. Though results are for MICE visitors at two
locations in Taipei, the assumption that the dimensions apply to MICE travelers visiting
Taipei’s Asian competitors seems reasonable. Subject to results being generalizable, H1 is
accepted (i.e. a limited number of dimensions explain a high percentage of variance in
motivation responses).
To demonstrate that the four component solution makes sense and conveys useful
information, the solution loadings are presented in Table III. Since the components are
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orthogonal, being a strong seeker of educational values has no implication for how a person
responds for other components. In other words, being strongly educationally motivated
does not convey information about motivation for exploration of the novel. The components
do not de?ne groups exhibiting related values. This occurs in cluster analysis.
The following paragraphs describe the components.
Component 1: educational values, consists of four variables associated with participation in
education related activities (see Table III). This component accounts for almost half (45
percent) of the total variance and is indicative of education related motivations amongst
MICE visitors. MICE events may offer different opportunities for activities such as presenting
a conference paper or serving as chair of a conference session. Because activities relate to
what occurs at MICE events, this component may show as much about the structure of
meetings as about respondent motivations. Future research should clarify how responses
relate to opportunities.
Component 2: exploration of the novel, consists of six variables associated with novel
experiences and with the search for comfort. The two experience-related variables ‘‘life
Table II Frequencies and percents for samples and clusters chi-square analysis of MICE segments
Motivation-based clusters
Sample Cluster 1 Cluster 2 Cluster 3
(n ¼ 518) (n ¼ 262) (n ¼ 152) (n ¼ 104)
Variables n % n % n % n % x
2
df p
Gender 3.1 2 0.2
Male 405 78 204 78 125 82 76 73
Female 113 22 58 22 27 18 28 27
Age 36 8 , 0.001
20-29 66 13 36 14 3 2 27 26
30-39 114 22 64 24 32 21 18 17
40-49 189 37 92 35 67 44 30 29
50-59 89 17 44 17 29 19 16 15
60 or over 60 12 26 10 21 14 13 13
Education 31 6 , 0.001
Secondary school 62 12 43 16 19 13 0 0
Vocational ed. 15 3 9 3 3 2 3 3
Bachelor degree 200 39 94 36 48 32 58 56
Masters/PHD 241 47 116 44 82 54 43 41
Some occupations 98 14 , 0.001
Director/manager 335 65 153 58 129 85 53 51
Professional 43 8 19 7 3 2 21 20
Technical 48 9 20 8 11 7 17 16
Annual income ($US) 86 14 , 0.001
8,000 or less 28 5 24 9 0 0 4 4
8,001-16,500 60 12 42 16 6 4 12 12
16,501-33,000 35 7 25 10 7 5 3 3
33,001-49,500 46 9 23 9 14 9 9 9
49,501-66,000 82 16 27 10 37 24 18 17
66,001-82,500 75 15 29 11 34 22 12 12
82,501-99,000 89 17 61 23 12 8 16 15
More than 99,000 103 20 31 12 42 28 30 29
Some countries of residence 146 22 , 0.001
China/Hong Kong 28 5 22 8 3 2 3 3
Germany 33 6 11 4 19 13 3 3
Japan 168 32 101 39 39 26 28 27
USA 26 5 11 4 9 6 6 6
Other Asia 40 8 40 15 0 0 0 0
Other Europe 62 12 12 5 28 18 22 21
Notes: Frequencies and percents are omitted for some occupations and countries; Chi-square uses all cells in the data; Also, Cluster 1 –
value seekers; Cluster 2 – no-value seekers; Cluster 3 – education seekers
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experience gained when travelling’’ and ‘‘comfortable place to stay’’ have the heaviest
loadings for the component. Given that the variable explains 13 percent of the variance,
marketing and meeting planners should be able to position themselves by considering this
variable. In determining components means have been extracted. In this component,
means of importance on the variables not loadings or variance, must be examined in
assessing the value of pursuing visitors based on the quest for novel life experiences.
Component 3: career enhancement, is comprised of six variables. These are centered upon
work related requirements and on the potential to develop networks by forming friendships
and contacts within the relevant ?eld. This variable only explains 5.8 percent of the variance.
However, as comments for component 2 indicate, being in a component shows a pattern in
variation. A respondent that rates one variable of the component higher than the mean tends
to rate others high. Understanding the role of career enhancement in going to meetings
involves examining importance and not scores that have the means of importance extracted.
The variables that loaded highest are: required by employers; employer funded and social
networking. These are most consistently marked higher or lower in terms of importance but
are not necessarily the variables of highest mean importance.
The fourth and ?nal component contains variables associated with a personal desire to visit
new destinations, and to be entertained. This component explained 5.2 percent of total
variance and is labeled travel opportunities. Because some variance exists, emphasis on
opportunities to travel overseas in?uences motivation.
Cluster analysis
An important topic of research has been the merit of further research on how to determine
clusters with the kind of data collected in this topic. In this research, the scores for
individuals within the four components identi?ed are used as the basis for the identi?cation
of segments based on MICE visitor motivations. An alternative would be deriving clusters
based on the importance responses received.
Table III Principal components analysis of MICE visitor motivations
Tourism-related motivational dimensions Loadings for components
Component 1: educational value
Presenting a paper 0.87
Serve as chair or moderator 0.87
Education-related purposes 0.79
Self-esteem enhancement 0.59
Component 2: exploration of the novel
Life experience gained when traveling 0.81
Comfortable place to stay 0.81
See new things 0.75
Escape from routine 0.61
Experience a different culture 0.58
Opportunity to relax on vacation 0.55
Component 3: career enhancement
Work requirement 0.77
Employer funded 0.77
Networking opportunities 0.72
Interesting conference program 0.62
A good conference package 0.56
A reasonably priced conference 0.49
Component 4: travel opportunities
Sightseeing 0.86
An opportunity to visit a new town or city 0.85
Combining leisure and business trips 0.56
An opportunity for entertainment 0.55
Percentage of variance explained 45.5 12.8 5.8 5.2
Cronbach’s alpha 0.89 0.89 0.86 0.84
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A problemof cluster analysis is that unless theory suggests/implies the number of clusters to
seek, the number of segments is unknown. Hierarchical cluster analysis is appropriate to
gather information about the number of clusters to seek (Hair et al., 2006; Yu¨ ksel and Yu¨ ksel,
2002; Ketchen and Shook, 1996). Based on practicality, Malhotra et al. (2002) suggest that
the number of cases within each cluster should be enough to draw inferences. Even with ten
cases in a cluster, one can be hard pressed to draw inferences (e.g. about the group as a
target market). Consistent with the suggestion made by Hair et al. (2006), hierarchical
clustering with a subsample (n ¼ 50) was used for the purposes of the present research to
determine the number of clusters. The result shows that two, three, four or ?ve clusters might
provide a good approximation reality.
To obtain clusters for the entire data set, K-means cluster analysis was used to obtain four
different cluster solutions (n ¼ 2, 3, 4 and 5). The results obtained from these solutions were
then compared and the three-cluster solution was selected for further analysis. Selection is
based on heterogeneity across clusters on the four motivational components. The
three-cluster solution also yields the most readily interpretable, and meaningful results.
Cluster differences
To examine the ?nal cluster solution, an analysis of variance (ANOVA) was applied based on
post-hoc tests. The goal of the ANOVA analysis is determining if statistically signi?cant
differences in appropriately weighted importance scores (i.e. component importance
scores) across clusters.
The ANOVA results are noted in Table IV. The F-test values in Table IV indicate differences in
component importance scores so large that the probability of chance occurrence is near
zero. Table IV displays the three clusters with mean importance scores highlighted for the
four motivational components. The cluster scores are included in Table IV only if one-way
analysis of variance was signi?cant at ,0.001. Each cluster is labeled based on the
contribution of motivational components.
The existence of signi?cant clusters supports an acceptance of Asian MICE visitors as
classi?ed into a limited number of segments with attributes expected from the literature. On
this basis, H2 is accepted.
The clusters found can be described and labeled as follows.
Cluster 1: value seekers
This cluster includes over half of the sample. Compared with the other two clusters (see
Table IV), cluster 1 ranks high on motivational components associated with professional and
recreation. The strong components identi?ed are congruent with the ?ndings of previous
studies (Rutherford and Kreck, 1994). The results con?rmthe work of Severt et al. (2007) and
Ngamsom and Beck (2000), who suggest that business and recreation related motivations
are both important determinants for understanding the behavioral intentions of MICE
delegates. In order to address the needs and wants of the delegates included in this cluster,
marketers should consider providing travelers with rewards to encourage engagement with
MICE related events.
Table IV ANOVA for tourism motivation components with mean scores for clusters
Cluster mean scores
Tourism motivation components
Value seekers
(n ¼ 262)
No-value seekers
(n ¼ 152)
Education seekers
(n ¼ 104) df F Value p
Educational value 3.64 1.95 3.69 2 17.16 ,0.001
Exploration of the novel 3.36 1.36 1.69 2 25.50 ,0.001
Career enhancement 3.76 2.04 2.31 2 16.39 ,0.001
Travel opportunities 3.63 1.73 3.56 2 19.47 ,0.001
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Cluster 2: no-value seekers
This cluster includes about 30 percent of the sample population. The component means
differ substantially from those of cluster 1. Relative to the other groups, means are lower on
all components with mean component importance ratings ranging from 1.36 to 2.04. The
means suggest that respondents within this group are less interested in professional and/or
recreational related activities than their counterparts in the other two groups. Cluster 2
represents a group of visitors who are relatively indifferent to the concept of value, and who
do not travel explicitly for reasons of education, novelty or career enhancement. In targeting
this group, destination marketers and service providers should focus more on work-related
values and on the provision of enjoyable and interactive social events.
Cluster 3: education seekers
This cluster includes approximately 21 percent of the total sample. The segment has a high
mean score on determining factors associated with education based values (mean ¼ 3.69)
and travel opportunities (mean ¼ 3.56). One can infer that members of cluster 3 tend to plan
their travel around both educational and leisure related opportunities. Their expectations of
the current trip emphasize sightseeing opportunities, visiting a new place, presenting a
paper and serving as a session chair or moderator. These ?ndings provide insights into the
roles of educational value and ability to travel as signi?cant decision-making factors. An
inference is that respondents in this cluster are seeking MICE-related events which provide a
high quality travel environment, as well as good value in terms of education and learning. To
attract visitors within this cluster, results suggest that marketers should design interesting
destination-based events that provide participants with educational, professional and travel
related opportunities.
Segment socio-demographic characteristics
In order to explore differences between the demographic characteristics of respondents
across the three motivation-based clusters (e.g. see Table III), gap Chi-square tests were
run. Results are noted in Table II. Notable statistically signi?cant differences ( p , 0.001) are
evident for age, education, occupation, household income and country of residence. Asian
MICE segments have distinctive socio-demographic characteristics. On this basis, H3 can
be accepted.
A description is possible based on the univariate statistics. Consistent with the age
composition of clusters, one can propose that respondents aged 30-39 tend to be seeking
career enhancement, while downplaying education. Nearly 85 percent of no-value seekers
are directors or managers. No-value seekers are less interested in professional and
recreational related activities than the other two groups. Directors and managers are
distinct. More than two-thirds of value seekers reported having an annual income in excess
of US$82,500 compared with 44 percent of education seekers. In terms of country of
residence, nearly 40 percent of value seekers were Japanese compared with 26 percent of
no-value seekers and 27 percent of education seekers. Does this result just re?ect that
Japan is close to Taiwan (i.e. as offering good travel value)? The results also indicate that
European visitors (from Germany, the UK and other) placed a greater emphasis on work
related values. These respondents appeared to be less interested in engaging in
recreational activities. As may be expected on the basis of stereotypes, Japanese
respondents were strongly motivated to participate in business related events (for
work-related reasons) and to engage in a range of leisure activities.
Discussion: implications and opportunities for further research
As previously noted, various studies have explored the connections between customer needs
and expectations and the services and/or products which contribute to the overall tourism
experience (Galloway, 2002; Pearce and Caltabiano, 1983; Dann, 1981, 1977). From a
theoretical perspective, the ?ndings of this study have provided an enhanced understanding
of MICE visitor motivations. This enhanced understanding has been achieved by identifying
dimensions and clusters of MICE visitors with distinctive socio-demographic characteristics.
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The validity of motivational components of educational value, exploration of the novel, career
enhancement and travel opportunity was consistent with previous research undertaken by
Rittichainuwat Ngamson et al. (2001). The present study also supports the view that
profession-related motivations are important determinants for explaining MICE delegates’
decision making. This research also offers veri?cation of the interrelationship between leisure
and business motivations for MICE travel and thus for MICE decision-making (Oppermann,
1998; Oppermann and Chon, 1997; Rutherford and Kreck, 1994).
The discussion of PCA results for motivation variables may appear obtuse. Avoiding wording
which suggests that importance ratings rather than their variation (standardized or not)
around means is important. Recognizing groupings of variables does not suggest groupings
of people but, given orthogonality of components, that on average persons who rate high on
one component have responses distributed randomly over the response distribution of other
variables. In this research PCA establishes a 4-dimensional space in which one looks for
concentrations of points using cluster analysis. Even if points in the space correspond to a
random distribution, cluster analysis can ?nd groupings. However, the groupings will be
unstable (starting clustering from different initial values will result in different clusters that are
just as tight). Some measures are taken in this research to see that cluster results are stable.
Further research can elucidate how stable they are for the analysis of data collected.
Generalizability involves getting similar PCA and cluster results with data for Taipei’s
competitors.
A technical matter is whether segmentation should be done on scores fromPCA with zero as
a mean, component score values or on raw importance values. As noted above, scores
produced to reproduce a correlation or covariance matrix do not re?ect mean importance
ratings. Component score values created to produce the ANOVA results are scores that
approximate raw scores with their means. Future research must address appropriate
versions of PCA and ways to develop scores.
The hypothesis proposes that useful segmentation of motivation has been achieved partly
relates to the idea that segments can be considered in planning, managing or marketing.
The study has highlighted signi?cant socio-demographic differences between three MICE
visitor segments. However, univariate pictures of MICE visitors do not underlie the segments.
Future research must consider the need for multivariate socio-demographic pictures. Given
the income increases with age and with certain occupational categories, control on variables
will show that some signi?cant univariate differences (Table II) re?ect the structure of the
multivariate categories that segment members tend to occupy. Future research should be
conducted in other Asian countries by the same survey questionnaire to validate the results
of this study.
Though this study has contributed to the tourism literature at both the managerial and
theoretical levels, more research is needed on MICE visitors. Future research should go
beyond the suggestions made here. Examining linkages between past behavior, intentions
and future behaviors to provide better predictive capacity is not necessary. Research should
contribute to understanding underlying values which are sought by MICE visitors and the
role of these values in drawing visitors to a destination.
Conclusion
This work is exploratory yet has rami?cations for methodology, application and theory. Reason
suggests that MICE visits to Taipei’s competitors will differ in origin and other
socio-demographics (e.g. because of proximity of countries). Research and data from
MICE visitors to Taiwan yields scales, dimensions and clusters similar to those of competitors.
Only further research will show the general applicability results for Taiwan. A line of research
has started. The results should facilitate further study involving data from multiple locations.
Research issues raised also point to methodological issues. Understanding behavior can
rarely be based on univariate differences. How best to determine clusters is a methodological
matter. Multivariate consideration of cluster differences to produce information can serve
planning, managing and marketing as well as the development of theory.
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