Exhibition Reference

Singapore as an Exhibition Hub

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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

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.

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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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


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


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


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.


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.


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.


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


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.


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.


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


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%


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)


35

Figure 4.2: Rotated Factor Loadings (Eigenvalue>=1)

Figure 4.3: PCA Extraction (6 Components)


36

Figure 4.4: Rotated Factor Loadings (6 Components)

Figure 4.5: PCA Extraction (5 Components)


37

Figure 4.6: Rotated Factor Loadings (5 Components)


38

Figure 4.7: Scree Plot


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


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


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


42

Figure 4.11: Residuals vs fitted values plot


43

Figure 4.12: Normal Probability plot for Residuals

Figure 4.13: Test Results for Overall Competitiveness


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


45

Figure 4.16: Test results for Competitiveness perceived by attendees with 10 or less years of

experience of attending exhibitions


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


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


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


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

Documents

Exhibition Reference