-
Singapore as an Exhibition Hub
I
Table of Contents
1.0 Introduction
............................................................................................................................................................................
1
2.0 Literature Review
.................................................................................................................................................................
4
2.1 Destination Competitiveness
......................................................................................................................................
4
2.2 Research Analysis
...........................................................................................................................................................
8
3.0 Methodology
.......................................................................................................................................................................
10
3.1 Questionnaire Development
....................................................................................................................................
10
3.2 Sampling
..........................................................................................................................................................................
10
3.3 Data Analysis
.................................................................................................................................................................
12
4.0 Discussion of Data and Results
...................................................................................................................................
16
4.1 Demographic Profile
...................................................................................................................................................
16
4.2 Factor Analysis: Factor Extraction and Factor Loadings
............................................................................
16
4.3 Interpretation of Factor Structure
..........................................................................................................................
18
4.4 Discussion of Regression Results
.........................................................................................................................
19
4.5 Comparison of Regression Results within Two Subgroups
.......................................................................
21
(i) Attendees with more than 10 years experience of visiting
exhibitions versus Attendees with
10 or less years experience of visiting exhibitions
...........................................................................................
21
(ii) Attendees who visit more than 5 exhibitions per year versus
Attendees who visit 5 or less
exhibitions per year
.......................................................................................................................................................
22
(iii) Singaporean attendees versus Non-Singaporean attendees
................................................................
23
(iv) Attendees from age group 18 to 25 years old versus
Attendees from age group above 25
years old
.............................................................................................................................................................................
24
4.6 Overall Implications of Results
..............................................................................................................................
25
5.0 Conclusion
...........................................................................................................................................................................
26
6.0 Limitations of the Analysis
...........................................................................................................................................
28
7.0 References
............................................................................................................................................................................
29
Appendix
......................................................................................................................................................................................
32
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1.0 Introduction
The meetings, incentives, conferences, and exhibitions (MICE)
industry is rising rapidly and
create remarkable amount of profits worldwide in tourism sector.
The MICE industry plays
an important role in Singapores tourism industry and contributes
significantly to the
economy. The sector has been reported to continue flourishing
with 2.5 million business
visitors who contributed 25% of the countrys total tourism
receipts in the first three quarter
of 2012.
Being recognized with many awards and accolades over the years
for its first-class MICE
infrastructure and environment, Singapore is one of the most
renowned business events
destinations in Asia. In accordance with global rankings by the
Congress and Convention
Association (ICCA), the country upheld its position as Asias Top
Convention City for 11
consecutive years. Besides that, Singapore maintained the spot
as the only Asian city in the
Top Five Convention Cities in the world, along with Vienna,
Barcelona, Paris and Berlin,
since 2006. Furthermore, Singapore was granted the Second Best
City for Business Events at
the CEI Asia Industry Awards 2012.
Strategically located at one of the crossroads of the world,
Singapore provides convenience
for movement of merchandise as well as people. With the country
being ranked as the No. 1
Logistics Hub amongst 155 countries globally in the 2012
Logistics Performance Index, this
indicates that there is efficient distribution of exhibits and
materials within and outside
Singapore. Furthermore, it is also a regional business hub with
over 160 banks and numerous
operational and regional headquarters and purchasing offices
being located. Along with a
large hinterland market, these conveniences present exhibitors
with a huge potential pool of
buyers and efficient transaction processing. In addition,
Singapore is often used as a
springboard to get through to the other Southeast Asian
markets.
1
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Singapore as an Exhibition Hub
2
Another plus point that attracts many exhibitors is stability of
Singapore. In accordance with
IMD World Competitiveness Yearbook 2010 rankings, Singapore is
the most politically
stable country in Asia and has the lowest crime rates worldwide.
Moreover, the country is
placed second in terms of economic freedom for 2011 according to
the Economic Freedom of
the World Report 2013. It secured second for freedom to trade
internationally and top in
freedom of exchange in credit. Eradication of monetary
restrictions will certainly facilitate all
sales and purchases transactions arise from exhibitions.
Although Singapore possesses various qualities that make it an
exhibition destination, rising
competition from its neighbouring countries should not be taken
lightly. Indonesia has seen
an increase of 5.79% growth in incoming tourists as a result of
increasing MICE activities in
the country and it has intended to develop this industry
rigorously to tap on this lucrative
business (Osman, 2013). The Malaysia Convention and Exhibition
Bureau (MyCEB), on the
other hand, has introduced branding campaign to showcase
Malaysia as Asias Business
Events Hub. Through this event, Malaysia aims to attract around
100,000 attendees to its
future international business events by 2015, a drastic increase
from 59,000 attendees in 2009
(Jarakiraman, 2012). Thus, by understanding attendees perception
on what attributes make
Singapore an ideal exhibition hub will help Singapore to better
devise marketing strategies to
continue sustain its image as the top exhibition destination in
Asia.
This study is, therefore, motivated by the increasing need to
sustain Singapores top position
in the exhibition industry. With Singapore expanding its number
of events and participants in
the upcoming years, this study will identify the factors that
determine attendees impressions
of an ideal exhibition destination and the results may be
helpful in branding Singapore and
enhancing service level in the industry. Together with
Singapores proven track record of
successful exhibitions, wide choice of convention and exhibition
venues, advanced
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Singapore as an Exhibition Hub
3
telecommunication network and aggressive support from the public
sector, Singapore looks
set to fulfil its role as an international exhibition city.
This paper focuses on attendees who attended trade shows in
Singapore and evaluates
attributes that affect their choice for attending exhibitions in
Singapore over other countries.
It is organized as follows: in the next section, we provide a
review of studies looking at the
destination factors that influence the competitiveness of an
exhibition destination. We then
discuss methodology and analyses of results. Lastly, we conclude
with implications and
limitations of the study.
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Singapore as an Exhibition Hub
4
2.0 Literature Review
Singapores successes in tourism are based on a combination of
geographical factors,
first class amenities and comprehensive services. With rising
competitions from
neighbouring countries like Indonesia (Osman, 2013) and Malaysia
(Jarakiraman, 2012),
it become crucial for Singapore to understand the influencing
factors that entice
business travellers to the country for exhibitions so as to
remain its competitiveness in
the industry. Despite many researchers have studied the
selection criteria of exhibition
sites from the organisers point of view (Chacko & Fenich,
2000; Go & Govers, 1999;
Kang, Suh, & Jo, 2005; Qu et al., 2000; Weber & Ladkin,
2003), a lack of opinions
from the attendees point of views has resulted in an incomplete
information of
destination competitiveness. Therefore, this research aims to
identify the key attributes,
in view of the attendees, that shape an exhibition hub. The
findings will potentially be
beneficial to Singapores initiatives to expand business tourism
further.
2.1 Destination Competitiveness
Destination Competitiveness (DC) is the countrys ability to
bring about superior
services to the lives of its resident (Dwyer, Forsyth & Rao,
2000; Enright $ Newton,
2004). To measure DC of a country, Chon and Mayer (1995) had
modified Porters
generic industrial competitiveness and developed a tourism
competitiveness model
based on five dimensions: appeal, management, organization,
information, and
efficiency. However, the model did not provide key variables
associated with
sustainable tourism.
As the competition among exhibition destinations escalates
(Weber & Ladkin, 2004)
understanding key success factors to customers satisfactions (Go
& Govers, 1999) has
become crucial to a destinations competitiveness and
sustainability.
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Singapore as an Exhibition Hub
5
Prior research had shown that accessibility is often a key
attributes for renowned
exhibitions and conventions destinations (Nelson and Rys, 2000;
Russell, 2011). In the
study by Lee, Choi and Breiter (2013), accessibility of a
country as an exhibition
destination depends on whether the country hosts more
international conventions or
more regional tradeshows. The former requires good flight
services which conveniently
connect the world to the exhibition sites; the latter depends
largely on established
highway systems. Singapore is position as an international hub
for the Meetings,
Incentives, Conventions and Exhibitions (MICE) industry. It is
therefore essential to
find out whether the availability of airlines services and ease
of traveling to Singapore
contribute significantly to attendees perception on Singapore as
an exhibition.
On the other hand, accessibility within a country also denotes
its competitiveness as an
exhibition destination. In this study, accessibility within a
country refers to how easy
and convenient it is to access to facilities and infrastructures
in the country. All
information can be obtained in English in Singapore and almost
all public amenities
have instructions in English, Chinese, Malay and Tamil language.
In addition, most
Singaporeans are proficient in English language, this ease of
communication makes
basic amenities extremely easy and convenient to access.
The second factor is the facilities catered for exhibition
events and the basic facilities
for residence stay. These include accommodations as well as food
providers such as
business hotel and restaurants respectively. Well-established
accommodation along with
quality room services is found to associate with increasing
destination competitiveness
(Chacko and Fenich, 2000), and thus it is worth discussing in
this research. It was also
shown in Kangs study (2005) that the top three destinations for
MICE purposes - Hong
Kong, Singapore and Tokyo performed very well for their
facilities attribute in the
Importance-Performance Analysis. Other facilities
telecommunication as well as
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Singapore as an Exhibition Hub
6
wireless signals are essential factors in this study analysis as
well. Every year,
numerous attendees come over to Singapore to attend the various
international
exhibitions hosted; emails and online communication tools, thus,
become an important
mode of communications for them. It is, therefore, essential to
offer the business
travelers stable service which could provide them secure
communication with their
overseas clients and colleagues. With Singapore increasing its
free public wireless
services to four times of current by 2016, this project will
enhance Singapores
competitiveness as an exhibition hub and offer cost-effective
communications services
and convenience to the international attendees (Infocomm
Development Authority of
Singapore, 2014).
Another factor that entices business travelers to a country for
exhibition purposes is its
service level and the brand image of the country. This refers to
the reputation of the
event organiser and the exhibitors, the service level which the
attendees experienced
during the event and within the event destination, as well as
the information available
on the events website, email invitations or the events
brochures. While the reputation
of the event organiser and exhibitors are the basic determinants
that entice attendees to
visit the exhibition, the quality of the overall services
received by attendees during and
outside the event plays a large role in their return to the
country for future events (Kim,
2010). In addition, an informative website that delivers
accurate and complete details of
the exhibition to potential attendees will increase the
destinations credibility and
increase the possibility of attendees to visit Singapore for the
event. As such, these
features will be taken into account in this study for
analysis.
Besides, entertainment features such as shopping, nightlife,
entertainment, and multi-
racial culture in Singapore offer an extraordinary experience to
the attendees. Such
features may encourage the return of attendees for future
business travel in the country.
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Singapore as an Exhibition Hub
7
Also, Singapore is generally free from natural disasters and has
very low crime rate,
these allow business travelers to travel with a peace of mind.
From the study conducted
by Lee, Choi and Breiter (2013), attendees placed importance on
the availability of
safety, security, facilities and environment. As observed by the
Lee and his team
(2013), safety and security were placed with such especially
high importance after
September 11 and SARS events. During the SARS period in 2003,
Singapore suffered a
decline in GDP (0.47 %) and its tourism was negatively impacted
(Lee and McKibbin,
2004). Given that Singapore is vulnerable to such global crises,
it is necessary to
understand how attendees perspective of Singapore in this
aspect.
The last factor is affordability which evaluates the cost of
amenities and services such
as accommodations and service personnel with respect to their
efficiency and quality.
Lee, Choi and Breiter (2013) revealed high cost in top-tier
destinations such as Orlando
will render such as locations to lose its competitive edge in
the long run as the
infrastructures in second and third tier destinations developed
Fenich (2008) also
revealed that most attendees are price sensitive and that even
second-tier destinations
are subjected to criticism of high prices. As Singapore becomes
the most expensive city
in the world (Chen, 2014), it is noteworthy to investigate if
affordability is an important
variable to exhibition tourism sustainability in Singapore.
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Singapore as an Exhibition Hub
8
2.2 Research Analysis
One of the most common models used to analyse destination
competitiveness is the
competitiveness-sustainability (C/S) model developed by Crouch
and Ritchie (1999).
Although, the model has been consistently revised by the
authors, its niche focus on the
relationships between tourism and societal prosperity as well as
its complicated features
make empirically analysis difficult to do (Lee, Choi and
Breiter, 2013).
The ability to quantify the various factors is, therefore, most
preferable for analysis of
destination competitiveness. However, Kim and Dwyer (2003)
pointed out that it is very
hard to define or uniformly calculate factors which are
associated with destination
competitiveness. Gooroochurn and Sugiyarto (2005) also noted
that given its qualitative
nature, it is often difficult to measure individual factors
accurately.
However, in order to test destination competitiveness
empirically, Gooroochurn and
Sugiyarto (2005) yielded eight main indicators, namely price,
openness, technology,
infrastructure, human tourism, social environment, natural
environment, and human
resources of over 200 countries using factor analysis. The
weights for each indicator
were used to compute the Composite Tourism Competitiveness
Index. The research
team also used cluster analysis to categorise the countries
based on their performance as
tourist destinations. However it is to note that as Gooroochurn
and Sugiyarto (2005)
only used data from published secondary sources to derive the
indicators, the results do
not provide an overall view of destination competitiveness as
not all relevant factors
were taken into analysis.
Given the unquantifiable nature of the issue, the team has
designed an open-ended
questionnaire to captures all the relevant and common aspects
that set Singapore as an
exhibition hub. Surveys were done when the six exhibitions as
listed in Table 6 were
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Singapore as an Exhibition Hub
9
held and factor analysis was then used to reduce the survey
results into the respective
factors for analysis. ANOVA was then conducted to identify any
statistical significant
differences between the factors obtained from factor analysis
and the surveyees
demographic data.
Unlike customers in normal selling situations, exhibitions
attendees are exposed to an
enormous amount of information within a short time (Levinson,
Smith and Wilson,
1997). Understanding the factors that influence the quality of
the destinations, therefore,
becomes crucial to promote sustainability in the exhibition
industry in Singapore. As
this study has taken reference from various journal sources to
identify the aspects that
would influence the destination competitiveness of Singapore,
the indicators measured
will be more related to the exhibition industry in Singapore and
may be helpful in
maximizing the overall performance of Singapore as an exhibition
hub in the future.
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Singapore as an Exhibition Hub
10
3.0 Methodology
3.1 Questionnaire Development
A questionnaire with three sections was designed. Based on the
7As of Convention Destination
Competitiveness from Lee, Choi & Breiter (2013) as shown in
Figure 3.1, 20 relevant
attributes were chosen from a thorough review of the attributes
affecting exhibition
attendees perception of exhibition destination competitiveness
found from previous studies
shown in Figure 3.2. Section I comprises respondents ratings of
these 20 attributes relating
to the performance of Singapore as an exhibition destination.
The 7-point Likert scale ranges
from Very Poor to Excellent. Section II consists of their rating
of the overall perceived relative
competitiveness of Singapore as a regional exhibition
destination. The 7-point Likert scale
ranges from Not Competitive At All to Very Competitive. Section
III captures demographics
information of respondents, including information on their
history of attending exhibition.
3.2 Sampling
As this study aims to examine Singapores competitiveness as
trade exhibition destination
from exhibition attendees perceptive, the target population of
this study was the individuals,
both local and foreign, who attended trade exhibitions held in
Singapore.
After the teams considerable efforts to find exhibitions from
which to conduct survey, five
exhibitions, that serve the different industries and hosted at
different exhibition centers in
Singapore, were selected for on-site data collection. The
exhibitions include MEDLAB Asia
Pacific, BeautyAsia/SpaAsia/HealthAsia/NaturalAsia, World Low
Cost Airlines World
Asia Pacific, MAISON&OBJET ASIA, International Furniture
Fair Singapore/ASEAN
Furniture Show (IFFS/AFS). As clearly suggested by the names of
the exhibitions, all of
them are regional trade shows targeting Asia or Asia Pacific
markets. The exhibitions were
held at three different venues in Singapore, namely The Sands
Expo and Convention Center,
Suntec Singapore Convention & Exhibition Center, Singapore
EXPO, reducing the
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Singapore as an Exhibition Hub
11
possibility of venue-specific biases in the data. To enhance the
diversity of the samples for
the analysis reliability and validity, the team also conducted
the questionnaire in the Central
Business District of Singapore.
The technique employed for data collection was systematic random
sampling. A verbal
assessment was carried out to confirm that the respondent had
attended trade exhibitions in
Singapore before the questionnaire form was provided to him/her.
Team members could
speak both English and Chinese and so language difficulties were
reduced to a minimum. The
questionnaires were conducted in the morning, afternoon, and
evening for each source to
minimize selection biases. A field editing was conducted at the
data collection venue to check
for the completeness of the questionnaires, unusable
questionnaires were discarded. A total of
259 complete questionnaires were obtained from the six different
sources, which respective
percentages are shown in Table 3.1.
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Singapore as an Exhibition Hub
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3.3 Data Analysis
The demographic profile of the respondents was first analyzed to
obtain some characteristics
of the sample, including Gender, Age, Region of Residence,
Highest Educational
Qualification, Industry of Current Employment, Years of
Attending Exhibitions and Number
of Exhibitions Attended Per Year.
A 20-item instrument was used to evaluate the respondents
perception of Singapores
competiveness as an exhibition destination. Factor analysis was
used to reduce these 20
attributes to a smaller set by assembling common variables into
descriptive categories
(Rummel, 1970). Principal Components analysis is used to extract
maximum variance from
the data set with each component (Tabachnick & Fidell,
2007). Varimax rotation was
performed to attain an optimal simple structure for unamibiguous
interpretation, which
attempts to have each variable load on as few factors as
possible, but maximizes the number
of variables with high loadings on each factor (Rummel,
1970).
Factors can be initially identified by the largest loadings, but
it is important to examine the
smaller yet significant loadings to confirm the identification
of the factors (Gorsuch, 1983).
There should be few item crossloadings (i.e., when an item loads
significantly on two or
more factors) so that each factor defines a distinct cluster of
interrelated variables (Costello &
Osborne, 2005). For a sample size of at least 300, Comrey and
Lee (1992) suggest that
loadings in excess of 0.71 (50% overlapping variance) are
considered excellent, 0.63 (40%
overlapping variance) very good, 0.55 (30% overlapping variance)
good, 0.45 (20%
overlapping variance) fair, and 0.32 (10% overlapping variance)
poor which is also least
rotated factor loading to be considered statistically meaningful
(% overlapping variance =
(Factor loading)2). For the smaller sample size of 259, a larger
loading of 0.55 is believed to
be a suitable cut-off for statistically meaningful
interpretation of the rotated factor loadings.
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Singapore as an Exhibition Hub
13
The Kaisers eigenvalues criterion, scree test (i.e., scree plot)
and conceptual analysis of the
meanings of the factors extracted are considered when
determining how many factors to
retain. The Kaisers criterion suggests retaining all factors
that are above the eigenvalue of 1
(Kaiser, 1960). The scree test suggests the number of factors to
be retained is the data points
that are above the break (i.e., point of inflexion) in the scree
plot (Cattell, 1978). With
conceptual analysis, the number of expected factors should be
based upon a sound theoretical
framework of the structural model under investigation. To
determine the number of factors to
retain, researchers evaluate each method and choose the solution
that provides the most
desirable rotated factor structure (Yong & Pearce,
2013).
After appropriate interpretation of factors based on rotated
factor loadings and retainment of
desirable number of rotated factors, the mutually uncorrelated
factor scores were produced
using the regression method for further analysis. All
computations of factor analysis were
done using the SPSS package.
The backward stepwise multiple regression analysis was then used
to study which extracted
factors had more significant impact on the respondents overall
perception of Singapores
competitiveness as an exhibition destination. P-value of 0.05
was used to determine the
significance of a particular factor. To test for the validity of
the regression model,
analysis was performed on residual plots against the fitted
values to determine the
fitness of model as well as the Normal Probability plot to test
normality of data.
Three kinds of tests were also carried out to test for
multicollinearity, autocorrelation
and heteroskedascity. These tests are important because any
violation to these tests will
result in an inappropriate model. The test for multicollinearity
is the use of Variance
Inflation Factor. According to the rule of thumb for Variance
Inflation Factor (VIF), if
the VIF for the independent variable is below 10, there is no
significant
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Singapore as an Exhibition Hub
14
multicollinearity (Hair, Anderson, Tatham, & Black, 1995).
The test for Autocorrelation
is BreuschGodfrey test that checks whether there is serial
correlation between the
residuals of the independent variables which determines whether
the dependent variable
is independent (Powell, n.d.). It checks whether there is
presence of higher-order serial
correlation as compared to Durbin Watson test which only tests
for first-order serial
correlation. If there is significant autocorrelation, the
estimated standard error will be
smaller than the actual standard error, resulting in an
ineffective model. In the Breusch-
Godfrey test, if the p-value of the chi-squared is more than
0.05, it indicates that the
null hypothesis should not be rejected, justifying absence of
serial correlation. To check
for heteroskedascity, the test for it is Breusch-Pagan Test. It
checks whether variance of
the residuals of the independent variables are constant. In the
case of heteroskedascity,
the standard errors, test statistics and confidence intervals
will be biased. In the
Breusch-Pagan Test, if p-value of the chi-squared is more than
0.05, it indicates that the
null hypothesis should not be rejected, justifying constant
residuals variances
(Heteroscedascity, n.d.).
Univariate ANOVA was conducted to identify the presence of
statistically significant
differences in the four factors identified Accessibility,
Appropriate Services &
Appealing Images, Agreeable Environment & Attraction and
Availbility of Exhibition
Facilities between the demographic responses of the surveyees.
These demographic
responses include gender, age and region of residence. The
regrouped subsample size
was at least 30 or more to meet the statistical assumption of
normality on the samples
distribution (Bhattacharyya and Johnson, 1997).
Finally, the multiple regression model was applied to two
different subgroups across selected
demographic dimensions to gain further insights, i.e. whether
the relative importance of the
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Singapore as an Exhibition Hub
15
factors that affect the perception of Singapores competitiveness
as an exhibition hub
will differ from the overall results within each subgroup.
Firstly, it would be based on
the number of years they have been attending trade exhibitions,
i.e. one group with less
than or equal to 10 years of experience and one with more than
10 years of experience.
Secondly, it would be based on the number of times per year the
respondents attend
trade exhibitions, i.e. one group with more 5 times of visiting
and the other group who
visits 5 or less exhibitions per year. Thirdly, we would also
like to find out whether
region of residence would make a difference in terms of
expectations for an exhibition
hub. Hence we would do that by analyzing the responses made by
Singaporean
surveyees separate from Non-Singaporean attendees. Lastly, we
would investigate on
how age groups affect ones view of importance of factors that
affect Singapores
competitiveness as an Exhibition Hub by conducting an analysis
based on the age group
18 to 25 years old with the age group of 25 years and above. All
computations on
regression were done using Stata.
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Singapore as an Exhibition Hub
16
4.0 Discussion of Data and Results
4.1 Demographic Profile
As shown in Table 4.1, the respondent profile is relatively
balanced in gender and reasonably
spread across different age groups. Most of the respondents
received higher education, with
50.58% attained Bachelors Degree, while 35.52% attained
Postgraduate Degree. Also
majority of the respondents, 85.78%, are from either Singapore
or other Asian countries,
while only 14.22% are from non-Asia region. Due to the
limitations of sampling sources,
there are four dominating industries accounting for 76.83% of
the data, namely Banking &
Financial Services (17.37%), F&B, Retail and Hospitality
(23.55%), Healthcare &
Pharmaceuticals (21.62%), Raw Materials and Manufacturing
(14.29%). Most of the
respondents have 5 years or less of experience of attending
exhibitions, with 2 to 5
exhibitions attended per year.
4.2 Factor Analysis: Factor Extraction and Factor Loadings
In order to achieve the optimal results for factor analysis, the
appropriate rotated factor
structure has to be determined, through a iterative process of
factor extraction and
interpretation of factor loadings.
Initially, with Kaisers criterion for factor extraction, only
factors with eigenvalue more than
1 were retained and altogether four factors were extracted,
accounting for 65.40% of the
variation in the data as shown in Figure 4.1. Figure 4.2 shows
the rotated factor loadings of
the four components with the highest loadings of each variable
highlighted and colour-coded
according to the 7As of Lee, Choi & Breiter (2013), namely
Accesibility(Red),
Appropriate Service(Orange), Appealing Image(Yellow),
Agreeable
Environment(Green), Attractions(Turquoise), Affordability(Blue),
Availability of
Exhibition Facilities(Purple). As seen, based on four factors,
the attributes are not
cleassified meaningfully: attributes for Appropriate Service,
Appealing Image and
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Singapore as an Exhibition Hub
17
Agreeable Environment are split across two different factors.
With a cut-off of 0.55,
despite the lack of crossloadings, all the attributes for
Appropriate Service are deemed
as insignificant. Thus, the rotated factor structure derived
from Kaisers extraction
criterion of eigenvalue more than 1 does not seem to
desirable.
For alternative factor extraction, the scree test is performed.
As shown in Figure 4.7, the scree
plot is relatively hard to interpret, with only one major point
of inflexion at Factor 2. After
careful scrutiny, a minor break can be seen at Factor 7, where
the gradient changes slightly
and the eigenvalues start to level off. With that, analysis was
rerun with manual extraction of
six factors. As seen in Figure 4.3, Factor 5 and 6 has
eigenvalue of 0.853 and 0.765
respectively, and altogether the six factors account for 73.49%
of the variation in the data.
Similarly, with the highest loadings highlighted and
colour-coded, Figure 4.4 shows a much
better clasification of the attributes while all the highlighed
loadings above the cut-off of 0.55
and no crossloadings. However, now that attributes from
Appropriate Service & Appealing
Image, Agreeable Environment and Attractions are clustered
together into Factor 1 and 2
respectively, attributes for Accesibility are still split across
Factor 4 and 6.
After careful consideration of the homogeneity of the attributes
within each factor and the
consitency with prior factor categories to ensure sensible
interpretation, the final analysis is
run with five factors manually extracted, accounting for near
70% (69.67%) of the variation
in the data as shown in Figure 4.5. The rotated component
loadings, with highlights and
colour-codings, are shown in Figure 4.6, and none of the 7As are
split across different factors.
With the same cut-off of 0.55, there is all of the highlighted
loadings are significant and there
is no crossloadings. Thus, the rotated factor structure derived
with five factors is deem as
desirable and used to compute factor scores for regression.
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Singapore as an Exhibition Hub
18
4.3 Interpretation of Factor Structure
The results of factor analysis performed on the 20 attributes
are given in Table 4.2. The five
factors obtained can be considered as the factors that influence
the exhibition attendees
perception of Singapores competiveness as an exhibition
destination. Altogether, the five
factors can account for about 70% (i.e. 69.67%) of the variation
in the data.
Factor 1, labeled as Accessibility, consists of variables that
reflect the desirability of
international location of Singapore and exhibition venue,
convenience of air transport and
local transport, and ease of communication. Factor 2,
Appropriate Service & Appealing
Image, consists of five variables that reflects the perceived
quality of the exhibition in terms
of the relevance of exhibitor, service quality provided at
exhibition venue, quality of other
related services, and perceived image of exhibition in terms of
quality of online information
and reputation. Agreeable Environment & Attractions form the
third factor and it relate to
the general safety, cleanliness, social and political stability
of Singapore, as well as the
variety of leisure activities, entertainment and tourist
attractions of Singapore. The
reasonableness of prices of hotel accommodation, transportation,
food and commodities,
forms the next factor Affordability, whereas the last factor
Availability of Exhibition
Facilities consists of variables related to availability of
restaurants and shops at exhibition
venue; the convenience of exhibition/conference facilities; and
the quality of
telecommunication/wireless services at the exhibition venue.
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Singapore as an Exhibition Hub
19
4.4 Discussion of Regression Results
Based on the stepwise regression results as shown in Figure 4.8,
Accessibility,
Appropriate Service & Appealing Image, Availability of
Exhibition Facilities as well as
Agreeable Environment & Attractions are the significant
factors that determine
Singapores competitiveness as an Exhibition Hub and all portray
a positive relationship
in affecting Singapores competitiveness as an Exhibition Hub.
However, the R-square
of this model is at 0.3, which shows that only 30% of
variability in the overall
competitiveness of Singapore as an Exhibition Hub can be
explained by the differences
of the significant independent variables that are included in
this model. A normality plot
was also plotted as shown in Figure 4.9. Though the normality
pattern is not obvious,
we can safely assume normality given that n is large (Normal
Distribution, n.d.).
According to the Augmented Dickey Fuller test for Unit Root
which tests whether the
data is stationary, a p-value smaller than 0.05 indicates the
null hypothesis of a presence
of unit root can be rejected (Dfuller - Augmented Dickey-Fuller
unit-root test, n.d).
Based on the observations in Figure 4.10, the dependent variable
exhibits stationary due
to absence of a unit root. Hence a R-squared of 0.3 is
considered respectable given that
the dependent variable is a properly stationarised series (Whats
a good value for a R-
squared?, n.d.). Besides the R-square, standard error is at
0.80248 which is 11.4% of
our 7-point scale. This shows that the overall forecast of the
competitive index is likely
to deviate the actual perception of competitiveness by 11.4%. In
addition, with the
large F-statistic of 28.36> 2.40718240 with numerator degrees
of freedom at 4 and
denominator degrees of freedom at 258, it clearly shows our
model fits the data well.
This can be substantiated based on the residuals versus the
fitted values which show no
linear pattern as shown in Figure 4.11 and normal distribution
of residuals as shown in
Figure 4.12. To measure the validity of our test results, the
three tests mentioned
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Singapore as an Exhibition Hub
20
previously was also conducted. Based on our findings in Figure
4.13, there is no
presence of multicollinearity, autocorrelation of residuals as
well as heteroskedascity.
From our results, it can be interpreted based on the
coefficients of the independent
variables that Accessibility is the most important factor in
determining the
competitiveness of Singapore as an exhibition hub. Appropriate
Service & Appealing
Image, Availability of Exhibition Facilities and lastly,
Agreeable Environment &
Attractions follow the importance accordingly. In order to draw
the link between our
independent variables and the attendees perception of Singapores
competitiveness as
an Exhibition Hub, we will analyse the results based on their
perception of performance
rated for each category with the final overall competitiveness
perception scoring. In this
case, Affordability is not significant here as compared to past
literature study by Lee,
Choi & Breiter (2013) which has the most impact of visitors
perception of a countrys
exhibition destination competitiveness. Being a developed
country, Singapore is a top
tier destination for holding of exhibitions. Thus our result can
be explained by Nelson
and Ryss (2000) findings whereby costs may be an advantage for
second-tier
exhibition locations as compared to top-tier destinations. It
means that it is relevant to
say that affordability may not be important in determining
Singapores competitiveness
given that we are a top-tier exhibition destination. In terms of
the ranking of importance
of the other factors, the order of importance also differs from
the literature studys
results by Lee, Choi & Breiter (2013). This can be explained
by the different types of
people being surveyed. Since the study by Lee, Choi &
Breiter (2013) is conducted in
the United States of America, the targeted surveyees may come
from a different cultural
background as compared to the targeted surveyees for our survey
conducted in
Singapore whereby 42% of our respondents are Singaporeans.
According to Acar, Taura,
Yamamoto & Yusof (2011), culture does play a part in
perception of objects due to the
-
Singapore as an Exhibition Hub
21
different visual inference habits people have, living in
different environments since they
were born.
Our ANOVA results show that that there are no significant
differences between the
demographic responses gender, age and region of residence, in
all the factors except
Agreeable Environment & Attraction. Similar to the ANOVA
results revealed by Hui
and Wan (2003), there are no significant statistical differences
in the perception of
different gender group in all four factors. The only demographic
variables that present
significant differences among different subgroups in the
Agreeable Environment &
Attraction factor are age (Figure 4.23) and surveyees region of
residence (Figure 4.24).
4.5 Comparison of Regression Results within Two Subgroups
(i) Attendees with more than 10 years experience of visiting
exhibitions versus Attendees
with 10 or less years experience of visiting exhibitions
Based on our observations of both results, perception of
importance does change with
increasing number of years of experience of visiting
exhibitions. For attendees who
have been attending exhibitions for 10 or less years, they view
Accessibility as the most
important factor, followed by Appropriate Service &
Appealing Image, Availbility of
Exhibition facilities and Agreeable Environment &
Attarction, shown in Figure 4.14.
This result is similar to that of our overall result. This can
be explained from the fact
that 81% of respondents fall into this group and hence their
perception will tend to
coincide with the overall results. However, for attendees who
have more than 10 years
of visiting exhibitions, this perception changes as Appropriate
Service & Appealing
Image becomes the most important factor followed by Availbility
of Exhibition
Facilities, Accessibility and finally Agreeable Environment
& Attractions, shown in
Figure 4.15. This shows that when people have more experience of
visiting exhibitions,
they focus more on the experience itself since they have already
attended various
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Singapore as an Exhibition Hub
22
exhibitions previously. Thus for this group of people, what they
get out of visiting these
exhibitions for them are more important. With respect to image,
because having more
experience than the rest, they may be more selective in terms of
visiting exhibitions and
hence to them, the reputation of the country as well as the
organisers will play an
important role in determining their choice of attendance. In
addition, particularly, in
this model, the adjusted R-square at 0.5, the smaller standard
error at 0.668 despite the
smaller number of observations and the F-statistic at 12.79>
2.58366743 with numerator
degrees of freedom and denominator degrees of freedom at 4 and
44 respectively, shows
a better fitted model for this group of attendees. For both
models, it is tested there is
absence of multicollinearity as well as autocorrelation of
residuals, shown in Figure
4.16 and Figure 4.17.
(ii) Attendees who visit more than 5 exhibitions per year versus
Attendees who visit 5 or
less exhibitions per year
Within these two sub groups, clear differences can be
highlighted. For attendees who
visit 5 or less exhibitions per year, the results are similar to
the overall results, which
can be explained by 91% of respondents being in this group as
shown in Figure 4.18. In
contrast, for attendees who visit more than 5 exhibitions per
year, affordability seems to
be only factor that affects their perception of a countrys
competitiveness as an
Exhibition Hub and exhibits a negative relationship with
dependent variable, shown in
Figure 4.19. This is reasonable since as people purchase more,
they will tend to be more
concerned with the price assuming that the other aspects are
kept constant. This concept
is similar to the concept of frequency bias in inflation
perceptions whereby people are
more focused on price increases on items which they purchase
more frequently when
forming inflation perceptions of the economy (Georganas, Healy
& Li, 2014). However,
it is important to note that for this model, due to the small
number of observations, the
-
Singapore as an Exhibition Hub
23
results may not be representative of the entire population
though there is absence of
autocorrelation among residuals and constant variance of
residuals as shown in Figure
4.20.
(iii) Singaporean attendees versus Non-Singaporean attendees
Between Singaporean and Non-Singaporean attendees, there is also
a slight difference
of perception in the importance of factors that affect their
final perception of
Singapores competitiveness as an Exhibition Hub. For
Non-Singaporeans,Appropriate
Service & Appealing Image, Accessibility and Exhibition
Facilities are the important
factors in determining their perception in order of importance
while for Singaporeans,
Accessibility, Appropriate Service & Appealing Image,
Availbility of Exhibition
Facilities and Agreeable Environment & Attractions are
significant factors in order of
importance. For Non-Singaporeans, the greater emphasis
inAppropriate Service &
Appealing Image could be justified due to the fact of having
more experience visiting
exhibitions held overseas and hence would have greater
tendencies to compare their
experience with that in Singapores. Both results can be seen in
Figure 4.21 and Figure
4.22 respectively. The Non-Singaporean model shows very small
R-squared and
relatively high standard error despite the large F-statistics at
9.85>2.66700561 when
numerator degrees of freedom and denominator degrees of freedom
is 3 and 145
respectively. The significance of the results in which
Non-Singaporeans do not place as
much emphasis on Agreeable Environment as compared to
Singaporeans is further
supported by the ANOVA analysis. The P-value of 0.00001 and
F-statistic of 20.45
indicate significant differences in the perceptions Singaporeans
and other Nationalities
towards the factor on Agreeable Environment. Like mentioned
before, it can be
explained due to the cultural differences that could affect
their perception in evaluating
objects.
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Singapore as an Exhibition Hub
24
(iv) Attendees from age group 18 to 25 years old versus
Attendees from age group above 25
years old
ANOVA results reveal a significant difference among attendees
from different age
groups in the Agreeable Environment factor. The factor is then
split into its individual
component which includes Safety, Cleanliness, Social and
Political Stability and
Leisure, Entertainment and Tourist Attaration and ANOVA was
conducted on these
components against different age groups. It was found that all
components present
significant differences between attendees aged between 18 to 25
years old and attendees
who are older than 25 years old. Likert scores given by
attendees aged between 18 to
25 years old are more impartial, with average score of 5.97 for
safety, 5.73 for
cleanliness and 5.24 for shopping and nightlife while average
scores given from
attendees aged above 25 are shown in Table 4.3, which
demonstrated higher rating for
the respective components.
These can be explained by the fact that majority of the
attendees aged 25 and below are
students and are inexperienced in attending exhibitions. They
may lack the experience
to compare Singapore and other exhibition destinations and
result in more neutral
responses collected from them. Another possibility for the
significant difference in
responses towards Leisure, Entertainment and Tourist Attaration
in Singapore was
explained by Hui and Wan (2003), who mentioned that different
shopping capacity due
to differing purchasing power between respondents of different
age groups may lead to
different perceptions towards Singapore as a Shopping
destination.
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Singapore as an Exhibition Hub
25
4.6 Overall Implications of Results
Based on our findings, exhibition organisers can understand the
needs of their clients
and look for suitable destinations for holding of exhibitions
either in Singapore or
worldwide according to their targeted group of attendees. Since
Accessibility has been
deemed as an extremely important factor in terms of overall
results and in various
subgroups, it is important that exhibition organisers take note
of this to consider their
future location of exhibition halls. The Singapore Exhibition
& Convention Bureau can
also consider more on the accessibility of location in their
future planning of
constructing new exhibition halls in Singapore. Affordability is
not seen as a significant
factor in the overall results as well as in most of the
subgroups. This shows that despite
Singapore being the most expensive city in the world (Singapore
named the world's
most expensive city, 2014), it may play a small role in
affecting ones perception of
Singapore being competitive as an Exhibition Hub.
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Singapore as an Exhibition Hub
26
5.0 Conclusion
This paper provides an in-depth study of the exhibition industry
in Singapore. We provide a
detailed discussion of factors affecting the competitiveness of
Singapore as exhibition
destination from the viewpoint of attendees and the impact of
these factors on Singapore
competitiveness. It can be concluded that Accessibility,
Appropriate Service & Appealing
Image, Availbility of Exhibition Facilities as well as Agreeable
Environment & Attarctions
are the four main factors, in order of importance, that have
impact on Singapores
competitiveness as an exhibition hub. Furthermore, all the four
factors have a positive
influence on the countrys competitiveness. In contrast to past
literature study by Lee, Choi &
Breiter (2013), our study finds that Affordability is not a
significant factor affecting attendees
perception of a countrys competitiveness as an exhibition
centre.
We have discussed the ratings provided by the attendees based on
four categories number
of years attended exhibitions, frequency of attending
exhibitions annually, nationality and age
group. Firstly, attendees who have been attending exhibitions
for 10 or less years,
Accessibility is the most important factor whereas those who
attended more than 10 years
view Appropriate Service & Appealing Image as the most
important competitive factor. The
latter focuses more on the experience provided by exhibitors
throughout the entire visiting
journey. Next, we also deduce that people who attend exhibitions
more than five years
annually perceive Affordability as the only factor that affect a
countrys competitiveness as
an exhibition hub. Moreover, this factor has an inverse
relationship with the countrys
competitiveness. On the other hand, attendees attending
exhibitions five or less times per
annum deem Accessibility as the most important factor. Then,
locals view Accessibility as
the most significant factor while foreigners consider
Appropriate Service & Appealing Image
the most important factor in influencing the competitiveness of
a country as an exhibition
destination. The different viewpoints could be due to cultural
differences. Lastly, attendees
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Singapore as an Exhibition Hub
27
who are above 25 years old provided higher rating for the
individual components for
Agreeable Environment & Attarctions as compared to those who
are 25 years old and below.
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Singapore as an Exhibition Hub
28
6.0 Limitations of the Analysis
In our study, there is a concentration of surveyees in a few
industries, namely the Food
and Beverages, Retail and Hospitality industry, Raw materials
and Manufacturing
Industry, Banking and Financial Services Industry as well as the
Healthcare and
Pharmaceutical Industry. Thus it may not be a completely good
representation of the
perception of exhibition attendees in general. In addition,
within each subgroup, the
number of surveyees are also not proportionate with more
attendees concentrated at
attendees who visit 5 or less exhibition per year and have less
than 10 years of
experience and thus, the comparison between the two subgroups
may not be justified
enough but it still does give a rough idea on difference in
perceptions since clear
differences could be highlighted.
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Singapore as an Exhibition Hub
29
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Appendix
Figure 3.1: Seven As for Convention Destination
Competitiveness
Source Venue Business Sector N Percentage
MEDLAB Asia Pacific The Sands Expo
and Convention
Center
Laboratory, Medicine
57 22.01%
BeautyAsia / SpaAsia /
HealthAsia /
NaturalAsia
Suntuc Singapore
Convention &
Exhibition Center
Cosmetics, Perfumery, Hairdressing,
Health, Whole Food
11 4.25%
World Low Cost
Airlines World Asia
Pacific
Suntuc Singapore
Convention &
Exhibition Center
Aviation, Hospitality, Tourism, Food
and Beverages
23 8.88%
MAISON&OBJET
ASIA
Marina Bay Sands Glassware, China, Ceramics,
Household, Interior Decoration,
Tableware, Textiles, Fabrics, Home
Textiles 22 8.49%
IFFS/AFS / The Dcor
Show / Hospitality 360
The Sands Expo
and Convention
Center
Furniture, Interior Decoration, Hotel,
Restaurant
63 24.32%
Street Survey Central Business
District
Banking, Finance, Business
83 32.05%
Table 3.1: Distribution of Data from Different Sources
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Singapore as an Exhibition Hub
33
Characteristics N Percentage
Gender
Male 131 50.58%
Female 128 49.42%
Age
18 - 25 33 12.74%
26 35 80 30.89%
36 50 97 37.45%
51 and above 49 18.92%
Region of Residence
Singapore 110 42.47%
Asia (Excluding Singapore) 112 43.31%
Non-Asia 37 14.22%
Highest Educational Qualification
Diploma/Technical 36 13.90%
Bachelors Degree 131 50.58%
Postgraduate (Masters, PhD) 92 35.52%
Industry of Current Employment
Banking & Financial Services industry 45 17.37%
F&B, Retail and Hospitality 61 23.55%
Healthcare & Pharmaceutical industry 56 21.62%
Raw Materials and Manufacturing industry 37 14.29%
Others 60 23.17%
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Singapore as an Exhibition Hub
34
Years of Attending Exhibition
5 years or less 148 57.14%
6 - 10 years 62 23.94%
More than 10 years 49 18.92%
Number of Exhibitions Attended per Year
Once 99 38.22%
2 - 5 times 138 53.28%
More than 5 times 22 8.50%
Table 4.1: Demographic Profile of Respondents
Figure 4.1: PCA Extraction (Eigenvalue>=1)
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Singapore as an Exhibition Hub
35
Figure 4.2: Rotated Factor Loadings (Eigenvalue>=1)
Figure 4.3: PCA Extraction (6 Components)
-
Singapore as an Exhibition Hub
36
Figure 4.4: Rotated Factor Loadings (6 Components)
Figure 4.5: PCA Extraction (5 Components)
-
Singapore as an Exhibition Hub
37
Figure 4.6: Rotated Factor Loadings (5 Components)
-
Singapore as an Exhibition Hub
38
Figure 4.7: Scree Plot
-
Singapore as an Exhibition Hub
39
Measures and Factors Variance
Explained (%)
Factor
Loading
Mean
Factor 1: Accessibility 16.909
Ease of Air Transportation Access 0.648 6.17
Proximity to Regional Markets 0.618 6.09
Location of Exhibition Venue 0.692 5.68
Convenience of Local Transportation 0.746 5.70
Ease of Communication 0.588 6.05
Factor 2: Appropriate Service & Appealing Image 14.634
Relevance of Exhibitors/Participants 0.669 5.37
Service Quality at Exhibition Venue 0.729 5.39
Service Quality of Hotels, Restaurants etc 0.618 5.62
Quality of Information Online 0.630 5.57
Organizer/Exhibition Reputation 0.635 5.47
Factor 3: Agreeable Environment & Attraction 13.645
Safety and Security 0.629 6.32
Cleanliness 0.708 6.08
Social and Political Stability 0.766 6.20
Leisure, Entertainment, Tourist Attractions 0.671 5.73
Factor 4: Affordability 12.408
Hotel Accommodation Rates 0.738 4.49
Transportation Cost 0.768 5.18
General Cost of Food and Commodities 0.780 4.47
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Singapore as an Exhibition Hub
40
Factor 5: Availbility of Exhibition Facilities 12.066
Availability of Restaurants and Shops 0.661 5.60
Convenience of Exhibition/Conference Facilities 0.705 5.63
Quality of Telecommunication/Wireless Services 0.805 5.44
Table 4.2: Factor Analysis Results
-
Singapore as an Exhibition Hub
41
Figure 4.8: Stepwise regression results of survey
Figure 4.9: Competitiveness Indexs Normal Probability Plot
Figure 4.10: Dickey-Fuller test for unit root of Competitiveness
Index
0
2
4
6
8
0 20 40 60 80 100 120
Co
mp
eti
tiv
en
es
s
Sample Percentile
Normal Probability Plot
-
Singapore as an Exhibition Hub
42
Figure 4.11: Residuals vs fitted values plot
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Singapore as an Exhibition Hub
43
Figure 4.12: Normal Probability plot for Residuals
Figure 4.13: Test Results for Overall Competitiveness
-
Singapore as an Exhibition Hub
44
Figure 4.14: Stepwise Regression Results for attendees who have
10 or less years experience
of attending exhibitions
Figure 4.15: Stepwise Regression Results for attendees who have
more than 10 years
experience of attending exhibitions
-
Singapore as an Exhibition Hub
45
Figure 4.16: Test results for Competitiveness perceived by
attendees with 10 or less years of
experience of attending exhibitions
-
Singapore as an Exhibition Hub
46
Figure 4.17: Test results for Competitiveness perceived by
attendees with more than 10 years
of experience of attending exhibitions
Figure 4.18: Stepwise Regression Results for Competitiveness
perceived by attendees who
visit 5 or less exhibitions per year
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Singapore as an Exhibition Hub
47
Figure 4.19: Stepwise Regression Results for Competitiveness
perceived by attendees who
visit more than 5 exhibitions per year
Figure 4.20: Test Results for Competitiveness perceived by
attendees who attend more than 5
exhibitions per year
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Singapore as an Exhibition Hub
48
Figure 4.21: Stepwise Regression Results for Competitiveness
perceived by Singaporean
attendees
Figure 4.22: Stepwise Regression Results for Competitiveness
perceived by Non-
Singaporean attendees
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Singapore as an Exhibition Hub
49
Figure 4.23 ANOVA results for different age group in Agreeable
Environment
Figure 4.24 ANOVA results for different age group in Agreeable
Environment
Age Groups
26 to 35 36 to 50 >
50
Safety 6.35 6.36 6.41
Cleanliness 6.20 6.05 6.18
Political Stability 6.24 6.24 6.31
Shopping and Nightlife 5.99 5.65 5.78
Table 4.3: Likert scores for attendees based on Age Groups