The Influence of Image on Conference Attendees

Transcription

The Influence of Image on Conference Attendees Future
Behavioural Intentions
by
Linlin Cui
A Thesis
presented to
The University of Guelph
In partial fulfillment of requirements
for the degree of
Master of Science
in
Tourism and Hospitality Management
Guelph, Ontario, Canada
© Linlin Cui, 05, 2016
ABSTRACT
THE INFLUENCE OF IMAGE ON CONFERENCE ATTENDEES FUTURE BEHAVIOURAL
INTENTIONS
Linlin Cui
Advisor:
University of Guelph, 2016
Dr. Statia Elliot
This study investigates the relationship among event image, destination image, and business event
attendees’ future behavioural intention toward a host destination and a conference, building on
cognitive-affective-conative theory and image-satisfaction-loyalty modeling. To gather the data about
event image, destination image, and behavioural intentions, a survey of conference attendees was
conducted at the 2015 Ontario Tourism Summit. Principal component analysis was applied to confirm
variables for each construct, and multiple regression analysis was used to test these relationships. The
results show that, firstly, each image component of the study played a unique role: destination affective
image and destination cognitive image influenced intentions to recommend Toronto, while destination
cognitive image and event image influenced intentions to attend a future conference. Secondly, overall
satisfaction directly influenced intentions to both recommend Toronto, and revisit Toronto, and indirectly
influenced intentions to re-attend the conference. Theoretical and practical implications are interpreted
and future study suggestions are offered.
ACKNOWLEDGEMENTS
This thesis would not have been possible without the support and guidance of many people.
I am grateful to my advisor, Dr. Statia Elliot, who is my guide and constant source of support
through this process. She always gives me advisable suggestions when I was at a loss. Her
decisive leadership style helped me to do so much more than I would have though possible.
I would like to thank my committee members, Dr. Stephen Smith and Dr. Michael Von
Massow. I deeply appreciate the help from Dr. Stephen Smith with my data analysis. I will
always remember the Dr. Stephen Smith’s patience when replying my email, and explaining
methods to me. I would also like to appreciate Dr. Michael Von Massow’s valuable comments,
from which I always generate new ideas.
My thanks also go to the staffs in the School of Hospitality, Food, and Tourism
Management, for providing such a good academic environment.
Last but not least, I would like to thank all my friends and family for their support
throughout my entire graduate experience.
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Table of Content
Chapter1:Introduction........................................................................................................................................1
Chapter2:LiteratureReview..........................................................................................................................3
2.1Conventiontourismfromtheattendees’perspective..................................................................................3
2.2ResearchConcepts........................................................................................................................................................9
2.2.1Imageoftheeventanddestination....................................................................................................................9
2.2.2Satisfaction................................................................................................................................................................11
2.2.3Loyalty(behaviouralintention).......................................................................................................................12
2.3InfluencingFactorsofAttendees’BehaviouralIntentions.......................................................................13
2.3.1Imageandbehaviouralintentions..................................................................................................................13
2.3.2Overallsatisfactionandbehaviouralintentions.......................................................................................15
Chapter3:TheoreticalFrameworkandModelDevelopment............................................................17
Chapter4:Methodology..................................................................................................................................21
4.1Researchdesignandsampling.............................................................................................................................21
4.2Surveyinstrument.....................................................................................................................................................21
4.3Dataanalysis................................................................................................................................................................26
Chapter5:Findings...........................................................................................................................................30
5.1Datacharacteristic.....................................................................................................................................................30
5.2Demographicprofile.................................................................................................................................................33
5.3PrincipalComponentAnalysis.............................................................................................................................36
5.4MultipleRegressionModeling..............................................................................................................................40
5.4.1Test1—fromimagesandoverallsatisfactiontobehaviouralintentiontorecommend
Torontotoothers...............................................................................................................................................................41
5.4.2Test2—fromimagestobehaviouralintentiontorecommendTorontotoothers....................42
5.4.3Test3—fromimagesandoverallsatisfactiontobehaviouralintentiontorevisitTorontoas
aleisuretourist...................................................................................................................................................................43
5.4.4Test4—fromimagestobehaviouralintentiontorevisitTorontoasaleisuretourist............44
5.4.5Test5-fromimagesandoverallsatisfactiontobehaviouralintentiontoattendthe2016
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conference.............................................................................................................................................................................45
5.4.6Test6—fromimagestobehaviouralintentiontoattendthe2016conference..........................46
5.5HypothesisTest..........................................................................................................................................................47
5.5.1Measuringtheeffectofimageonattendees’behaviouralintentions..............................................47
5.5.2MeasuringtheIndirectEffectofOverallSatisfaction.............................................................................49
Chapter6:DiscussionandConclusion.......................................................................................................50
Chapter7:Implications,LimitationsandFutureResearch................................................................53
7.1Implications..................................................................................................................................................................53
7.1.1Implicationsfortheory.........................................................................................................................................53
7.1.2Implicationsforpractice.....................................................................................................................................54
7.2Limitationsandfutureresearch..........................................................................................................................55
Bibliography........................................................................................................................................................57
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List of Tables
Table 2-1 Overview of relevant studies of event image, destination image, and behavioural
intention ...................................................................................................................................6
Table 4-1
The measurements of constructs ................................................................................25
Table 4-2 Multiple regression tests .............................................................................................27
Table 5-1 Data characteristics of 29 items ..................................................................................31
Table 5-2 Times been to Toronto (n=128)..................................................................................33
Table 5-3
Demographic information of respondents (N=128) ...................................................35
Table 5-4
Principal component analysis results with Varimax rotation of 25 image items .......37
Table 5-5
Principal component analysis results with Varimax rotation of 19 image items .....39
Table 5-6 Image and overall satisfaction affecting behavioural intention toward destination
(recommendation) ..................................................................................................................42
Table 5-7 Image affecting behavioural intention toward destination (recommendation)...........43
(revisit) ...................................................................................................................................44
Table 5-9 Image affecting behavioural intention toward destination (revisit) ...........................45
Table 5-10
Image and overall satisfaction affecting behavioural intention toward attending
2016 conference ...................................................................................................................46
Table 5-11
Image affecting behavioural intention toward attending 2016 conference ..............47
Table 5-12
The results of multiple regression tests ....................................................................48
List of Figures
Figure 3-1
Conative-cognitive-affective model (Gartner, 1994) ................................................17
Figure 3-2
Image-satisfaction-loyalty model (Chi & Qu, 2008) ................................................19
Figure 3-3
Conceptual model......................................................................................................20
Figure 4-1
Indirect Effect Equation Judd and Kenny (1981) .....................................................29
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Chapter 1:Introduction
It has been recognized that business events can create powerful momentum for tourism
development (Li & Tan, 2015; Gursoy, Kim & Uysal 2004, Getz 2008,). Business events are not
only a type of destination attractor, but also a marketing attribute to promote a place, to increase
a destination’s competiveness, to attract more visitors, and to increase their spending (Getz &
Page, 2015). Based on a report released by Meeting Professionals International (MPI) Canada
Foundation in April 2014, over 585,000 business events, which include meetings, incentives,
conventions, and exhibition (MICE), were held in 2012, providing more than 160,000 jobs in
Canada (Event Spectrum, 2014). In recent years, there has been a remarkable boom of
convention and exhibition centers in cities and in resorts, aiming to attract higher spending
visitors (Lu & Cai, 2011), to pursue economic benefits, and to expand the tourist season of
destinations (Deng, Li & Shen, 2015).
Rogers (1998) predicated that convention tourism would be a fast growing tourism
segments during the early years of the 21st century. Var, Cesario, and Mauser (1985)
pointed out that the destination managers and the convention organizers should share a
common goal of pursuing a maximum number of attendees. Zhang, Leung, and Qu (2007)
also addressed that the more attendees, the more the host destination and the convention
organizer will gain. Therefore, understanding the factors influencing attendees’ behavioural
intention is important not only to the host destination but also to the convention organizers.
1
In recognizing the significance of the convention, many have researched the topics of
convention planners’ image formation process and decision-making process (Lee, Parrish & Kim,
2015; Oppermann, 1996; Papadopoulos, Elliot & Szamosi, 2014); attendees’ motivations (Price,
1993; Malek, Mohamed & Ekiz, 2015); attendees’ satisfaction (Siu, Wan & Dong, 2012; Wang,
Yang, Zhu & Yu, 2014); attendees’ loyalty to the convention (Li & Tan, 2015; Tanford,
Montgomery & Nelson, 2012). However, the attendees’ future behavioural intention to the host
destination and to the convention are largely ignored in the literature, despite the fact that
investigating this topic has potential to directly benefit the host destination and conference.
The relationship among event image, venue image, destination image, behavioural intention
to the destination and behavioural intention to the convention needs further investigation, not
only to address a research void, but also because investigating this relationship can help
destinations and convention reposition or improve their image to attract more tourists and
attendees, and to gain more economic benefits. Therefore, the purpose of this study is to address
the relationship among event image, venue image, destination image, and conference attendees’
future behavioural intention to the host destination and the conference. Among different types of
business events, this study focuses on conference attendees. Specifically, there are three research
objectives: (1) to identify the influence of image on conference attendees’ behavioural intentions;
(2) to confirm the relationship among cognitive image, affective image, and behavioural
intentions based on Gartner’s (1994) model; and, (3) to examine the effect of overall satisfaction
on conference attendees’ behavioural intentions.
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Chapter 2: Literature Review
2.1 Convention tourism from the attendees’ perspective
Getz and Page (2015) reviewed 755 articles about event tourism from 1909 to 2014, and
discovered that the major themes in the literature on “convention and tourism” include meeting
planners, technology, economic impact assessments, venue selection, evaluation of satisfaction,
the impact of destination image on attendance, and the decision-making processes of attendees.
Yet, gaps still remain in the area of convention tourism from the attendees’ perspective. Event
image and behavioural intention toward the destination and the conference are still an
understudied area of convention tourism.
Researchers have investigated the convention planner’s decision-making process more often
than attendee’s decision-making process, going back to the 1970s (Baloglu & Love, 2005;
Chacko & Fenich, 2000; Oppermann, 1996; Taylor, 1976). Price (1993) was one of the first
authors to systematically conduct research about the attendees’ decision-making process.
However, his study was mainly focused on attendees’ decision to participate in conventions.
After 2005, the research focus shifted to the analysis of the attendees’ satisfaction. Bauer, Law,
Tse and Weber (2008), Breiter and Milman (2006), Robinson and Callan (2005), and Siu, et al.
(2012) are all interested in investigating the attendees’ satisfaction. Based on their studies,
specific factors of attendees’ satisfaction emerged — location and image, price/value,
competence, parking fee, foodservice, directional road signage, facility features, business events’
networking/business opportunities, and event content. Around 2010, many researchers (Kim, Lee
3
& Love, 2009; Severt, Wang, Chen & Breiter, 2007; Tanford, et al., 2012) were intrigued by the
research topic that combined satisfaction with behavioural intention toward the convention. They
believe that attendees highly satisfied with a convention will be more likely to return to the
conference and spread positive word-of-mouth. However, in these studies, the behavioural
intention investigation was mainly focused on the attendees’ behavioural intention toward a
convention, not a destination. The question of whether convention attendees would visit the host
destination as leisure tourists after the business event was almost ignored by researchers until
2011, when Lu and Cai (2011) published their articles about the relation among event image,
venue image, destination image, and convention attendees’ loyalty to the destinations. From their
research, venue and destination image were found to influence attendees’ loyalty to a destination.
Table 2-1 lists the relevant studies of the research topic – the relationship among event
image, venue image, destination image and attendees’ behavioural intention to the host
destination and to the convention.
Discovering the factors that stimulate business attendees to
revisit the host destination as leisure tourists is potentially important because it has been
suggested that around 40% of convention attendees would revisit the host destination as leisure
tourists with their family members (Business Tourism Partnership, 2004). It is also valuable to
continue investigating attendees’ behavioural intentions to attend the convention because it can
help convention organizers to assess the performance of the convention as well as to attract more
attendees for future conventions (Severt, et al., 2007). In an effort to enrich the extant literature,
this study intends to analyze the relations among event image, venue image, destination image
4
and attendees’ behavioural intentions toward the host destination and the convention.
5
Table 2-1 Overview of relevant studies of event image, destination image, and behavioural intention
Study
Mitchell,
Schlegelmilch,
and Mone,
(2015)
Constructs
Attendees’
value, business
event
Methodology
Sampling
Semi-structured
interviews with
18 customers and
respondents from six
17 providers
countries
Analytic
Findings
techniques
Social value, learning value, emotional
value, and hedonic value are four impetus for
attendees to attend the business event
Qualitative
method
In-depth interview,
Deng, Li and
Shen, (2015)
Event image
expert opinion, survey at
725/800
Five factors of Event image are discovered,
2010 Shanghai World
Survey
which are benefit, facility, service, theme,
Expo, 7-point
(Shanghai)
EFA, CFA
and event content
Likert-type scale
Chi and Qu,
(2008)
1.Tourist attribute satisfaction was directly
Destination
On site questionnaire
image,
collected from Eureka
385/427
destination
Spring Welcome
Survey
loyalty,
Center,
(China)
satisfaction
7-point Likert scale
influenced by destination image.
2.Satisfaction
will
influence
tourists’
loyalty to destination.
EFA, SEM,
CFI
3.Destination image has a positive influence
on destination loyalty.
6
1.Destination image can affect not only
Behavioural
Liu, Li, and
Yang, (2015)
intentions,
On sight questionnaire
835
destination
collected on Macau,
respondent
image,
7-point Likert scale
(Macau)
satisfaction
visitors’ satisfaction but also visitors’
EFA,
behavioural intentions.
CFA,
2.Satisfaction is a moderating factor
SEM
between destination image and behavioural
intention.
1.Arrangements carried out at request and
cleanliness of facilities are most important
Robinson and
Callan, (2005)
Satisfaction,
conference,
attributes
Focus groups, in-depth
interviews, Survey
547
attributes of attendees’ satisfaction;
Survey
2. The quality of food, the value for money
(UK)
and the quality of facilities and service are
Focus
groups
also important items of attendees’
satisfaction.
Breiter and
Milman (2006)
Attendees’
need, service
priorities
(2014)
566
self-administered
Survey
associated with parking, foodservice inside
Focus
questionnaire collected
(Orland, FL,
and outside of the building, and quality of the
groups
from attendees
USA)
Content analysis, fuzzy
Wang., Yang.,
Zhu and Yu
Focus group,
Satisfaction
evaluation,
importance-performance
analysis (IPA)
Attendees
concerns
are
about
price
facilities in the meeting room.
Data collected
Quality of event, environment, and services
from 4 tourists,
are
blogs
important
satisfaction
factors
in
attendees,
Content
analysis
7
Image,
Loyalty,
Lu and Cai
(2011)
satisfaction,
convention and
exhibition
tourism
Event image,
Deng & Li
(2013)
destination
image,
behavioural
intentions
1.Event image and destination image
On sight questionnaire
significantly influence attendees loyalty to
collection from business
attendees at convention
242/262
and exhibition,
Survey
interview conducted at
(China)
the events, 7 and 5-point
leisure tourists who
have attended the 2010
Shanghai World Expo
that will influence attendees’ conference
EFA, CFA
loyalty;
loyalty
Self-administered
Chinese domestic
2.Event image is the most influential one
3.Satisfaction has no impact on attendees’
Likert scale
survey collection from
host destination;
1.Event image has a strong positive effect
725/800
Questionnaires
(China)
on the destination image;
2.both event image and destination image
have a positive influence on attendees’
EFA, SEM,
CFA, MLE
intention to destination
8
2.2 Research Concepts
2.2.1 Image of the event and destination
The term “image” is said to be a reflection or representation of sensory or conceptual
information (Dann, 1996). People have their own image of everything or everyplace they have
encountered or been. To understand the relationships among event image, venue image, and
destination image, we firstly need to know these concepts separately.
The concept of event image has not been studied extensively. Several relevant definitions
can be found. Gwinner (1997) defined event image as event attendees’ overall interception of
meanings or associations of the event. In Deng, et al.’s (2015) study, the definition of event
image borrows the definition of brand image presented by Keller (1993). The definition of event
image is “perceptions of an event as reflected by the associations in consumer’s memory” (Deng,
et al, 2015, p.294). The brand image proposed by Keller (1993) is based on the associative
network memory model (ANMM), which emphasizes that human memory can be understood as
a network of nodes and connecting links (node means stored information or concepts; link means
the strength of association between this information and concepts). The reason why event image
can be interpreted as brand image is that Deng, et al. (2015) thinks event image can also be seen
as a part of human memory or knowledge. The current study will follow Deng, et al.’s (2015)
definition of event image given its comprehensive theoretical basis.
However, it becomes evident that the majority of these researchers are conceptualizing
event image in terms of lists of attributes, and not in terms of holistic impressions. Activities,
9
opportunities, and educational benefits have been utilized as the attributes of cognitive business
event image (Lu & Cai, 2011; Severt, et al., 2007). Deng, et al. (2015) also includes facilities,
services, theme, and event content as factors of event image. Li, Song and Collins (2014) and Lu
and Cai (2011) also emphasized and investigated the affective image of business event, as:
sleepy / arousing, unpleasant / pleasant, gloomy / exciting, stimulating / unstimulating, poor /
excellent, healthy / unhealthy, ugly / beautiful, unsupportive / supportive, and distressing /
relaxing.
The research conducted by Lu and Cai (2011) and Deng, et al. (2015) introduced the venue
image. Unlike Deng, et al (2015), who combined event image (benefit, event content, and theme)
together with venue image (facilities and service), Lu and Cai (2011) believe venue image
should be investigated separately from event image and destination image. Similar to event
image, venue image is typically accessed by listing attributes. Three factors were included in the
Lu and Cai’s (2011) cognitive venue image construct — service encounters, facilities and
equipment, and overall environment. Other studies related to business events focus on the
conference center factors (Breiter & Milman, 2006; Siu, et al., 2012; Wu & Weber, 2005).
Overall, these five articles, including Deng, et al.’s (2015) and Lu and Cai’s (2011) articles, all
emphasis the importance of the “cleanliness of the convention center”, “convention service
facilities and equipment”, and “the service from staff”.
The current study will follow Lu and Cai’s (2011) study separating venue image from event
image. The definition of venue image will also use Deng, et al.’s (2015) definition of event
10
image, which is “perceptions of an event as reflected by the associations in consumer’s memory”
(Deng, et al., 2015, p.294), because, in Deng, et al.’s (2015) study, venue image is included in
event image.
Destination image has been extensively investigated (Baloglu & McCleary, 1999; Beerli &
Martin, 2004; Lu & Cai, 2011). The definition of destination image applied by most emphasizes
the cognitive (beliefs and knowledge about a place or destination) and affective (the feeling
about a place or destination) components of image. Baloglu and McCleary (1999) define image
as “a set of beliefs, ideas, and impressions that people have of a place or destination” (p. 871).
Tasci, Gartner, and Cavusgil (2007) believe image should be studied in an integrated manner,
and define image as “ an interactive system of thoughts, opinions, feelings, visualizations and
intentions toward a place or destination” (p. 200). The current study will follow the definition of
destination image by Baloglu and McCleary (1999), because their definition includes both
cognitive image and affective image, both considered as important components of a destination.
2.2.2 Satisfaction
According to Olive (1993), satisfaction is a sense of fulfillment: the purchase behavior can fulfill
customers’ needs, desires, goals, and so on, and can make customers feel pleasure. Satisfaction
in the context of tourism has been viewed as a mediator variable between destination image and
loyalty (Chen & Tsai, 2007; Chi & Qu, 2008; Žabkar, Brenčič & Dmitrović, 2010). Satisfied
tourists will be more likely to spread positive word-of-mouth, and be repeat visitors (Chi & Qu,
2008, Žabkar, et al., 2010; Yoon & Uysal, 2005).
11
There are two common ways to investigate tourists’ satisfaction — overall satisfaction and
attribute satisfaction. Kaplanidou and Vogt (2007), Lu and Cai (2011), and Li, et al. (2014)
utilized overall satisfaction as a mediator variable between destination image and loyalty from
event attendees’ perspective. However, according to Olive (1993), attribute satisfaction and
overall satisfaction are two distinctive but related concepts. It is believed that attribute
satisfaction has important, positive, and direct effects on overall satisfaction; and it captures a
significant amount of variation in overall satisfaction (Olive, 1993; Chi & Qu, 2008). Kim, et al.
(2009) used service delivery, food content, recognition of personal preference, and menu
selection as four attributes of attendees’ satisfaction. The research of Tanford, et al., (2012)
included five factors of attendees’ satisfaction — program, networking, external activities,
location, and cost.
2.2.3 Loyalty (behavioural intention)
Loyalty is one of the most popular and important areas in destination research. The theory of
tourists’ loyalty was built from the theory of consumers’ loyalty to products. According to
Oppermann (2000), customers’ loyalty includes two parts: behavior and attitude. Under behavior,
three items are identified: sequence of purchase, proportion of purchase, and probability of
purchase. The reason why attitude needs to be investigated when measuring customers’ loyalty is
that sometimes customers buy products repeatedly just because of the time convenience,
monetary rewards, lack of substitutes or lack of information on substitutes, rather than the
commitment to the brand (Day, 1969). Revisit behaviour and word-of-mouth are most often
12
considered by researchers, when they consider the theory of tourists’ loyalty to destination and to
conference (Chen & Tsai, 2007; Lu & Cai, 2011; Žabkar, et al., 2010).
However, there are many authors using the term “Behavioural Intention” instead of
“Loyalty” (Chen & Chen, 2010; Chen & Tsai, 2007; Jang & Feng, 2007; Lee, Graefe & Burns,
2004;), but the measurement of these two constructs is quite similar. When measuring the
tourists’ behavioural intention or loyalty, both tend to use revisit behavior and recommendation
(Chen & Chen, 2010; Chen & Tsai, 2007; Kim, Lee & Kim, 2012; Lee, et al., 2004; McDowall,
2010;). The reasonable explanation of this situation is that: behavioural intention is one stage of
loyalty. Oliver (1999) discovered that loyalty could be divided into four stages — cognitive
loyalty (loyalty based on brand belief only), affective loyalty (liking the brand), conative loyalty
(behavioural intention), and action loyalty (motivated intention transformed into readiness to act).
Therefore, it may be that researches investigating tourists’ loyalty to a destination are
investigating tourists’ conative loyalty, which can also be termed as behavioural intention (Chen
& Chen, 2010; Kim, et al., 2012; Oliver, 1999).
This study will use the term “Behavior intention” to investigate business attendees’ loyalty
to the host destination and to the conference.
2.3 Influencing Factors of Attendees’ Behavioural Intentions
2.3.1 Image and behavioural intentions
The relationship between image and behavioural intentions has been primarily studied from
tourists’ perspectives, more so than from conference attendees’ perspectives. Studies about
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convention tourism show that attendees’ behavioural intentions will be influenced by event
image, venue image, and destination image (Lu & Cai, 2011, Deng & Li, 2013)
As for the relation between convention’s event image and behavioural intention, Lu and Cai
(2011) made great contribution, by analyzing the relation among convention’s event image,
destination image and attendees’ behavioural intention to host destination and convention, by
distinguishing venue image from event image. From their research, event image has no influence
on attendees’ loyalty to host destinations, but venue image will influence attendees’ loyalty to
host destinations. In addition, their results also show that among event image, venue image, and
destination image, venue image is the most influential to influence attendees’ loyalty to
convention. However, Deng and Li’s (2013) study, including venue image in event image, shows
that event image does not directly influence attendees’ behavioural intentions. Their results show
that event image indirectly influences attendees’ behavioural intentions through destination
image.
Baloglu, (2000), Chen and Tsai (2007), and Zhang, Fu, Cai, and Lu (2014) find a positive
impact of destination image on tourists’ intentions, while fewer have conducted research from
the attendees’ perspective. Chen and Tsai (2007) discovered that destination image would have a
direct influence on tourist’s behavioural intentions (intention to revisit and willingness to
recommend). Their results show that destination image not only influences the decision-making
process but also conditions after-decision-making behaviors of tourists. Lu and Cai (2011), and
Deng and Li’s (2013) study also show that destination cognitive image and affective image will
14
influence event attendees’ behavioural intention toward the host destination and conference.
Therefore, it is necessary to investigate the relation between Image and behavioural
intention from conference attendees perspective, because conference attendees likely have a
different image formation process than leisure tourists, and because finding out this relationship
can help the destination attract more tourists and can help the conference planners select an ideal
destination.
2.3.2 Overall satisfaction and behavioural intentions
The relationship between attendees’ satisfaction and behavioural intention toward the conference
has been widely studied (Kim, et al., 2009; Severt, et al., 2007; Tanford, et al., 2012), more so
than the relationship between attendees’ satisfaction and behavioural intention toward the
destination.
Tanford, et al. (2012) discovered that if attendees feel satisfied with a convention, they
would be more loyal and less likely to switch conventions. The research conducted by Severt, et
al. (2007) deeply investigated behavioural intentions toward the conference. Their results show
that satisfaction will have a more significant impact on return intention than WOM, and the more
conferences an attendee attends, the more positive recommendation an attendee will promote.
However, research conducted by Hahm, Breiter, Severt, Wang, and Fjelstul (2016) to understand
the influence of the sense of community on attendees’ future intention did not show a direct line
between satisfaction and future intention to attend a future conference. The result indicates that
the sense of community (social bonding, sense of belonging, emotional connection, and
15
relationship) is a better predictor of future intention than overall satisfaction.
By contrast, there are not a lot of studies about the relationship between attendees’
satisfaction and behavioural intention to revisit a destination. In addition, the relationship
between satisfaction and behavioural intention to a destination from tourists’ perspective is
different from conference attendees’ perspective.
Lu and Cai (2011) and Kaplanidou and Vogt
(2007) discovered that attendees’ satisfaction has very little impact on attendees’ loyalty to
destination, which is opposite to the conclusion found by most (Chen & Tsai, 2007; Chi & Qu,
2008; Kim, et al., 2009).
In sum, because of contradictory conclusions, the relationship between satisfaction and
attendees’ future intention toward the destination and the future conference needs more
investigation.
16
Chapter 3: Theoretical Framework and Model Development
The focus of this study is on examining the relationships among event image, venue image,
destination image, and attendees’ behavioural intentions toward the host destination and toward
the conference. To achieve this aim, a cognitive-affective-conative model (Gartner, 1994) and
image-satisfaction-loyalty model (Chi & Qu, 2008) will be the foundation for this study.
In order to understand the behavioural intentions of convention attendees, this study will
build upon Gartner’s model (1994), which shows that tourists’ conative image (also known as
behavior intention) is influenced by cognitive image and affective image (Figure 3-1). Cognitive
image refers to beliefs and knowledge about the destination, affective image means the feelings
about the destination, and conative image means tourist’s behavioural intentions based on this
information (Agapito, Oom do Valle & da Costa Mendes, 2013; Baloglu & McCleary, 1999;
Dann, 1996; Pike & Ryan, 2004).
Cognitive image
Conative image
Affective image
Figure 3-1 Conative-cognitive-affective model (Gartner, 1994)
This model has been empirically supported by many authors (Pike & Ryan, 2004; del
Bosque & San Martin, 2008; Agapito, et al. 2013). For example, a study by del Bosque and San
Martin (2008), conducted in a destination in Spain, further reinforces this finding. The study
found that tourists who have a more positive cognitive and affective destination image would be
17
more likely to revisit the destination and have positive word-of–mouth (WOM). Agapito, et al.
(2013) conducted research in Lagos using interviews and surveys, and the results show that
cognitive and affective image of destination can be the predictor variables of tourists’
behavioural intentions to the destination.
Besides the direct influence of destination image on behavioural intentions, satisfaction is
widely accepted as a mediator variable between destination image and behavioural intentions
(Chen & Tsai, 2007; Chi & Qu, 2008; Žabkar, et al., 2010). The results of Chi and Qu’s (2008)
study show that tourist overall satisfaction was determined by destination image, and tourists
behavioural intention was influenced by overall satisfaction. Figure 3-2 shows the general
relations among destination image, overall satisfaction, and behavioural intentions. These
relations are also tested in the context of convention tourism (Lu & Chi, 2011, Tanford, et al.,
2012; Kim, et al., 2009; Severt, et al., 2007; Li, et al., 2014). Most studies (Tanford, et al., 2012;
Kim, et al., 2009; Severt, et al., 2007) support the indirect influence of overall satisfaction on
attendees’ behavioural intentions, while some studies (Hahm, et al., 2016; Lu & Cai, 2011) did
not support it.
18
Image
Behavioural intentions
Overall satisfaction
Figure 3-2 Image-satisfaction-loyalty model (Chi & Qu, 2008)
In sum, a cognitive-affective-conative model (Gartner, 1994) and a mediator role played by
overall satisfaction needs more investigation in the area of convention tourism. Figure 3-3 is the
conceptual model of this study, which combines cognitive-affective-conative model (Gartner,
1994) and image-satisfaction-loyalty model (Chi & Qu, 2008). The overriding hypothesis of this
research is:
H1: Conference attendees’ images influence their behavioural intention toward both the
destination and future conferences.
To test this hypothesis, this study will explore the influence of various components of event
and destination image, and the role of satisfaction in indirectly influencing behavioural intentions.
In order to discover the most influential factors of attendees’ behavioural intentions, three
behavioural intentions will be tested as dependent variables separately, with four image factors
as independent variables. At the same time, the indirect effect of overall satisfaction on
behavioural intentions will be tested using Difference of Coefficients Approach (Judd & Kenny,
1981), in two steps—conducting a regression analysis with image and overall satisfaction
predicting each behavioural intention, and conducting a simple regression analysis with image
predicting each behavioural intention.
19
Event image
Recommendation
Venue image
Cognitive image
Destination
cognitive
image
Image
Revisit
Behavioural
intentions
Destination
affective
image
Affective image
Re-attend
conference
Figure 3-3 Conceptual model
20
Chapter 4: Methodology
4.1 Research design and sampling
A questionnaire was used in this study to collect data in a systematic way (Tanford, er al., 2012).
The questionnaire was developed through a two-stage process. Firstly, in order to generate the
measurement items of the questionnaire, an in-depth literature review was conducted based on
the concepts of event image, destination image, and behavioural intention. Secondly, in order to
fit the selected conference theme, the questionnaire was modified after discussion with the
conference manager.
In this study, seven well-trained researchers were responsible for data collection, which was
conducted during the 2015 Ontario Tourism Summit at the Westin Prince hotel in Toronto,
Ontario, Canada. The respondents were randomly approached at the Westin Prince Conference
Center during a 30-minute networking break time. Among 467 attendees who attended this
summit, 136 attendees filled in the questionnaire, and 31 attendees rejected the request. Of the
136 collected questionnaires, 128 were completed and usable for data analysis, resulting in 94.1%
effective completion rate, and an 81.4% response rate.
4.2 Survey instrument
The final questionnaire has three parts and 10 questions to measure a total of 35 items. Part one
has one question, to know how many times participant have been to Toronto. Part two has five
questions, which are about four constructs—event image, venue image, destination image,
21
satisfaction—and behavioural intentions. Part three has four background questions. The
measurements of each construct are generated from the business event tourism, destination
image, and destination loyalty literatures. This study will use overall satisfaction as the
measurement of satisfaction, and the items of the other four constructs are proposed as follow
(Table 4-1).
(1) Event image. Based on existing research, three factors of event image are used most often
(Deng & Li, 2013; Deng, et al., 2015; Lu & Cai 2011; Severt, et al., 2007). These are
quality of attendees, activities and opportunities, and venue image. This study will also
mainly focus on these three factors (Table 4-1).
A question of the quality of attendees is the first item. As for the activities and opportunities,
four items will be included — 1. Association-related activities: activities that attendees join
during the business event; 2. Business opportunities: attendees who attend a business event
typically represent their company, so they may have opportunities to broaden their
company’s business by establishing relationships with other attendees; 3. Learning
opportunities: the knowledge gained from the business event; 4. Social opportunities: meet
different people and build relationships socially. The selected five event image items were
rated on a 5-point Likert-type scale where 1=poor, 3= average, and 5=excellent.
(2) Venue image. There are three important venue image factor proposed by different authors
(Breiter & Milman, 2006; Deng & Li, 2013; Deng, et al., 2015; Lu & Cai, 2011; Siu, et al.,
2012; Wu & Weber 2005), which are venue environment, venue service, and venue
22
facilities/equipment. For venue environment, it includes hotel conference environment,
hotel accommodation environment, and food and beverage facilities. For venue service, it
includes hotel conference service, hotel accommodation service, and food and beverage
service. For venue facilities/equipment, it includes hotel conference amenities, hotel
accommodation amenities, and food and beverage quality. The respondents were asked to
rate items on a 5-point Likert-type scale where 1=poor, 3= average, and 5=excellent.
(3) Destination image. Most studies of destination image are conducted from a leisure tourist’s
perspective. However, leisure tourists and business tourists have both similarities and
differences in terms of the formation of destination image. Through reviewing the literature
about destination image, there are eleven cognitive factors mentioned by many authors —
attractions, weather, culture, friendly people, accommodation, service, value, transportation,
activities, shopping facilities, and safety (Chi & Qu, 2008; Kaplanidou & Vogt, 2007; Kim
& Yoon, 2003; Lin, Morais, Kerstetter, & Hou, 2007; Mendes, Do Valle, & Guerreiro, 2011;
Sönmez & Sirakaya, 2002; San Martín & Del Bosque, 2008). The formation process of
destination image of these two types tourists is greatly influenced by the attractions and
high quality of accommodation (Chi & Qu, 2008; Echtner & Ritchie, 1991; Kaplanidou &
Vogt, 2007; Kim & Yoon, 2003; Lin, et al., 2007; Mendes, et al., 2011; Sönmez & Sirakaya,
2002; San Martín & Del Bosque, 2008). However, leisure tourists focus more on variety of
activities (Lin, et al., 2007; Sönmez & Sirakaya, 2002). Business attendees visit a
destination mainly for a conference or exhibition; that means he/she cannot decide where to
23
go and has less time to explore a destination. Therefore, they may be less concerned about
the activities, but more influenced by the accommodation, safety and security (Lu & Cai,
2011; Mendes, et al., 2011). Based on the discussion above, and because the measurement
of service and accommodation is included in the event image, the cognitive destination
image will include climate, attractions, price, shopping facilities, safety and security, and
accessibility. The affective image will include sleepy/arousing, unpleasant/pleasant,
gloomy/exciting, distressing/relaxing, and unfriendly/friendly. Items of the destination
cognitive image were also rated on a 5-point Likert-type scale where 1=poor, 3= average,
and 5=excellent. The items of the destination affective image were rated by Semantic
differential scale where 1= Sleepy, Unpleasant, Gloomy, Distressing, and Unfriendly, and
7= Arousing, Pleasant, Exciting, Relaxing, and Friendly.
(4) Overall satisfaction. Along a 5-point Likert-type scale, attendees were asked to evaluate
their satisfaction with overall experience (1= Very dissatisfied, 3= Neither satisfied nor
dissatisfied, and 5=Very satisfied).
(5) Behavioural intention. Behavioural intention has been widely studied in the context of
tourist destination. Because revisit intention and positive world-of-mouth are commonly
used as the measurements of behavioural intention (Chen & Chen, 2010; Chen & Tsai, 2007;
Kim, et al., 2012; Lee, et al., 2004; McDowall, 2010), this study will also use these two
items to measure business attendees’ behavioural intention to visit the destination, and use
24
future intention to attend the conference to measure business attendees’ behavioural
intention toward the conference. The respondents were asked to rate items on a 5-point
Likert-type scale where 1= not at all, 3= neither unlikely nor likely, and 5=extremely likely.
Table 4-1 The measurements of constructs
Constructs
Event
image
Factors
Activities and
opportunities
Items
Reference
Quality of attendees
Deng and Li
Association-related activities
(2013); Deng, et al.
Business opportunities
(2015); Lu and Cai
Learning opportunities
(2011); Severt,
Wang, Chen and
Social opportunities
Hotel conference environment
Breiter and Milman
Hotel accommodation
(2006);
environment
Deng and Li
Food & beverage facilities
(2013); Deng, et al.
Hotel conference service
(2015); Lu and Cai
Hotel accommodation service
(2011); Siu, et al.
Food & beverage service
(2012);
Hotel conference facilities
Wu and Weber
Hotel accommodation facilities
(2005)
Venue environment
Venue
image
Venue service
Venue facilities
Breiter (2007)
Food & beverage quality
Destination
image
Toronto Climate
Chi and Qu (2008);
Toronto Attractions
Echtner and
Toronto price
Ritchie, (1991);
Toronto shopping facilities
Kaplanidou and
Toronto Accessibility
Vogt (2007);
Toronto Safety and security
Kim and Yoon
Cognitive image
25
Sleepy/arousing
Unpleasant/pleasant
(2003);
Lin, et al.
(2007);
Gloomy/exciting
Mendes, et al.
Distressing/relaxing
(2011);
San Martín and Del
Affective image
Bosque (2008);
Unfriendly/friendly
Sönmez and
Sirakaya (2002).
Lu and Cai (2011);
Satisfaction
Kaplanidou and
Vogt (2007); Li, et
al (2014).
Behavioral intention
Recommendation
toward host
destination
Revisit
(2010);
Chen and Tsai
Behavioural
intentions
Chen and Chen
(2007);
Behavioral intention
Attend 2016 Ontario Tourism
toward conference
Summit
Kim, et al., (2012);
Lee, Graefe and
McDowall (2010);
4.3 Data analysis
The data were analyzed according to the following processes. Firstly, frequency analysis was
performed to understand the data distribution. Secondly, principal component analysis was
carried out to identify the components, or, dimensions of the data that best explain its variance
(event image, venue image, destination cognitive image, destination affective image). Thirdly,
multiple regression analysis was used to test the relationship between the independent variables
26
(event image, venue image, and destination image and overall satisfaction) and each dependent
variable (recommend the destination, revisit the destination, and attend a future conference). All
data analysis was performed using SPSS 23.0.
Six multiple regressions are run in this study, as listed in Table 4-2. Based on the research
objectives and Gartner’s (1994) cognitive-affective-conative model, three behavioural intentions
represent the dependent variables; and based on the research model (Figure 3-3) and the
hypothesis, event image, venue image, destination cogitative image, and destination affective
image represent the independent variables for each test.
Table 4-2 Multiple regression tests
Test
Independent variables
Dependent variables
Event image
Venue image
1
Destination cognitive image
Destination affective image
Behavioural intention toward destination
(Recommend Toronto to others)
Event image
2
Venue image
(Recommend Toronto to others)
Event image
Venue image
3
(Revisit Toronto as a leisure tourist)
4
Event image
27
Venue image
(Revisit Toronto as a leisure tourist)
Event image
Venue image
5
Behavioural intention toward conference
(Attend the 2016 conference)
Event image
6
Venue image
Behavioural intention toward conference
(Attend the 2016 conference)
The indirect effect of overall satisfaction is tested in two steps, as proposed by Judd and
Kenny (1981). They suggested that the indirect effect can be estimated by computing the
difference between two regression coefficients: Bindirect = B − B1 (Figure 4-1), where B1 is from
Model 1 (with Overall Satisfaction) and B is from model 2 (without Overall Satisfaction). Using
the difference of coefficients approach in this study, X represents four image factors, M
represents overall satisfaction, and Y represents three behavioural intentions. Therefore, the
indirect effect of overall satisfaction is tested in two steps: first, conducting a regression analysis
with all image and overall satisfaction constructs predicting each behavioural intention (Test 1,
Test 3, Test 5); second, conducting regression analyses with only image constructs predicting
each behavioural intention (Test 2, Test 4, Test 6).
28
c’
X
Model1:
M
c
b
X
Y
Y
Model2:
Figure 4-1 Indirect Effect Equation Judd and Kenny (1981)
29
Chapter 5: Findings
5.1 Data characteristic
In total, 35 variables are included in the data set. Those are “number of times to Toronto”,
fourteen items for “Event image”, six items for “Destination cognitive image”, five items for
“Destination affective image”, “Satisfaction”, three items for “Behavioural intention”, “rotation”.
Additionally, four items were included to capture background information.
Table 5-1 shows the mean values and standard deviations of each factor and each item.
Event image, venue image, destination cognitive image, overall satisfaction, and behavioural
intentions were measured by 5-point Likert-type scale, and destination affective image was
measured by 7-point semantic differential scale. Comparing the mean values of image factors
using 5-point Likert-type scale, venue image has the highest mean value (4.11) followed by
event image (4.08), and destination cognitive image has the lowest mean value (3.88), but is well
above the average point. The Standard Deviations (SD) of all factors are below point 1, where
the highest SD is for destination affective image (0.872), and the lowest SD is for event image
(0.505).
The mean values of event image items range from 3.94 to 4.19 with standard deviation (SD)
from 0.684 to 0.785, which indicates participants, on average, believe that the image of the 2015
Ontario Tourism Summit was in the above average to below excellent range. Like the items of
event image, the items of venue image were also rated as above average level from the
participants’ perspective (mean values from 4.06 to 4.14, with SD from 0. 698 to 0. 899). As for
30
destination cognitive image, the mean values of three items score are above 4, which are Toronto
climate (mean=4.04, SD=0.680), Toronto attractions (mean=4.16, SD=0.827), and Toronto
shopping facilities (mean=4.10, SD=0.856). However, the mean values of the other three items
are below 4: Toronto price (mean=3.30, SD=0.829), Toronto accessibility (mean=3.68,
SD=0.900), and Safety in Toronto (mean=0.397, SD=0.786). This indicates that participants, on
average, believe Toronto price, Toronto accessibility, and safety to be somewhat average level,
but rate them below climate, attractions, and shopping facilities. Participants, on average, have a
position evaluation on destination affective image. Expecting an item: distressing/relaxing
(mean=4.55), the rest four items’ mean values are above 5 to below 6. The SD values of
destination affective image are above 1, which shows that responses are relatively variable. The
mean of overall satisfaction is 4.4 (SD=0.614). Participants, on average, had a positive response
about their intention to recommend Toronto to others (mean=4.713, SD=0.638), as well as their
intention to attend the 2016 conference (mean=4.43, SD=0837). Compared with these two
intentions, the intention to revisit Toronto as a leisure tourist received a relatively low evaluation
(mean=3.86), and the responses were more polarized (SD=1.070).
Table 5-1 Data characteristics of 29 items
Factors
Event image
Items
Standard
Factor Mean
Deviation (SD)
(SD)
Item Mean
Quality of attendees
4.24
0.687
Association-related activities
3.94
0.684
4.08
Business opportunities
3.98
0.684
(0.505)
Learning opportunities
4.07
0.785
31
Social opportunities
4.19
0.614
Hotel conference environment
4.12
0.767
4.10
0.767
Food & beverage facilities
4.13
0.772
Venue
Hotel conference service
4.06
0.773
4.11
image
Hotel accommodation service
4.14
0.899
(0.606)
Food & beverage service
4.07
0.790
Hotel conference amenities
4.12
0.711
Hotel accommodation amenities
4.14
0.851
Food & beverage quality
4.09
0.698
Toronto Climate
4.04
0.680
Toronto Attractions
4.16
0.827
Toronto price
3.30
0.829
3.88
Toronto shopping facilities
4.10
0.856
(0.567)
Toronto Accessibility
3.68
0.900
Toronto Safety and security
3.97
0.786
Sleepy/arousing
4.01
0.682
Destination
Unpleasant/pleasant
4.01
0.677
affective
Gloomy/exciting
3.95
0.756
image
Distressing/relaxing
3.37
0.817
Unfriendly/friendly
3.79
0.809
4.04
0.614
Recommendation
4.37
0.638
Revisit
3.86
1.070
Hotel accommodation
environment
Destination
cognitive
image
Satisfaction
Behavioural
intentions
3.83
(0.581)
4.04 (0.614)
4.20
(0.632)
Attend 2016 Ontario Tourism
4.34
0.837
Summit
Note: Event image, venue image, destination cognitive image were evaluated by 5-point
Likert-type scale, where 1=poor, 3= average, and 5=excellent.
32
Destination affective image was evaluated by 7-point semantic differential scale, where 1=
Sleepy, Unpleasant, Gloomy, Distressing, and Unfriendly, and 7= Arousing, Pleasant,
Exciting, Relaxing, and Friendly.
Overall satisfaction was evaluated by 5-point Likert-type scale, where 1= Very dissatisfied,
3= neither satisfied nor dissatisfied, and 5=Very satisfied.
Behavioural intentions were evaluated by 5-point Likert-type scale, where 1= not at all, 3=
neither unlikely nor likely, and 5=extremely likely.
5.2 Demographic profile
In the sample of 128 attendees (Table 5-2), most are familiar with the city, in that 64 (50.0%)
have come to Toronto more than 5 times, followed by 43 (33.6%) attendees who live in Toronto,
11 (8.6%) who commute to Toronto for work, and only 4 (3.1%) had never been to Toronto
before.
Table 5-2 Times been to Toronto (n=128)
How many times have you been to Toronto
Frequency
Percentage
First time
4
3.1
1-5 Times
1
0.8
More than 5 times
64
50.0
I live here
43
33.6
I commute here for work
11
8.6
Other
5
3.9
Total
128
The demographic profile of the respondents is presented in Table 5-3. Of the 128
33
participants, 77 (60.2%) were female and 51 (39.8%) were male. The age distribution
demonstrates that the largest group was participants between 25 and 54 years old (75%). The
Global Business Travel Association Canada (GBTA)’s report (2015) also indicates that the age
of most Canadian business travelers are likely to be from 35 to 55 years old. A convention study
conducted by Breiter and Milman (2006) also shows that participants from 31 to 60 of age
represent the largest share (74.7%). Most participants hold a bachelor’s degree or higher (69.5%),
which is similar to the Breiter and Milman’s (2006) education profile. The education background
of respondents in Breiter and Milman’s (2006) study also showed that most respondents have a
college or higher degree (64.9%). In the current study, the number of participants who identified
as having a “High school degree”, “Some college”, or “Some graduate school” is almost the
same (5 to 4). The household annual income level of 69.6% of the participants was higher than
$70,000 (CAD), which is above the Ontario median income (CAD$76,510) in 2013 (Statistic
Canada, 2015). The annual income of 15.6% of participants was lower than $49,000 (CAD),
which is probably because a small group of students attending the Summit belong to this group.
Only 7.8% of participants indicated that their annual income was between $50,000 (CAD) and
$69,999 (CAD).
34
Table 5-3 Demographic information of respondents (N=128)
Gender
Frequency
Percentage
Male
51
39.8
Female
77
60.2
Total
128
Age (Years)
Frequency
Percentage
18-24
14
10.9
25-34
27
21.1
35-44
32
25.0
45-54
37
28.9
55-64
16
12.5
>64
2
1.6
Total
128
Education
Frequency
Percentage
High school degree
5
3.9
Some college
6
4.7
Certificate or diploma
27
21.1
Bachelor’s degree
59
46.1
Some graduate school
5
3.9
Graduate degree
25
19.5
Other
1
0.8
Total
128
Income level ($CAD)
Frequency
Percentage
35
<=49,999
20
15.6
50,000-69,999
10
7.8
70,000-99,999
24
18.8
100,000-129,999
29
22.7
>=130,000
36
28.1
Total
119
5.3 Principal Component Analysis
Principal Components Analysis (PCA) with varimax rotation was performed to identify
dimensions underlying these 25 image items. An eigenvalue of 1.0 was utilized for factor
extraction as well as communalities of 0.6 and loadings of 0.6 were used for item inclusion
(Carmines and Zeller 1979; Stevens 2012). Reliability for each factor is obtained by using the
Cronbach α coefficient. According to Nunnally (1978), the Cronbach α should be greater than
0.7. The construct validity is tested by PCA with varimax Rotation.
Through PCA of the 25 image items (Table 5-4), six items are removed because of low
commonalities (conference learning opportunities, conference business opportunities, the
Toronto climate, the Toronto price, safety in Toronto, and distressing/relaxing), and five factors
are generated. Factor 5 in Table 5-5, which has only one item, is excluded in six multiple
regression tests, as it is hard to give a rational interpretation of why sleepy/arousing would
represent a distinct factor. Smith (2002) pointed out that the component with low level
significance or eigenvalues can be ignored. In this case, factor 5, a single variable factor, has an
36
eigenvalue of 1.187 is the least meaningful among the five factors. Therefore, four factors will be
used in study, which are named as Event Image, Venue Image, Destination Cognitive Image, and
Destination Affective Image (Table 5-5).
The Cronbach α coefficients for the four factors are 0.942, 0.818, 0.727, and 0.744
respectively. Thus the reliability for the four factors is accepted. The Kaiser-Meyer-Olkin (KMO)
measure of 19 image items is 0.821 (Table 5-5), indicating that the items will yield an
appropriate factor result (Kaiser 1974). Bartlett’s Test of Sphericity indexes were statistically
significant at a 0.000 level and adequacy for further analysis (Field, 2005). The eigenvalue of
factor 1(venue image) is 6.559 with 34.52% % of variance explained; factor 2 (destination
affective image) is 3.213 with 16.91 % of variance explained; factor 3 (event image) is 1.677
with 8.83 % of variance explained; and factor 4 (destination cognitive image) is 1.329 with 6.99 %
of variance explained.
Table 5-4 Principal component analysis results with Varimax rotation of 25 image items
Items
Qualityofattendees
Conferencerelated
activities
Conferencebusiness
opportunities
Conferencelearning
opportunities
Conferencesocial
opportunities
Communalities
Factor
Factor Factor Factor Factor
1
2
3
4
5
0.684
0.802
0.700
0.816
0.302
0.533
0.573
0.662
0.612
0.659
37
Hotelconference
0.647
0.742
0.539
0.673
0.661
0.757
Torontoaccessibility
0.642
0.621
SafetyinToronto
0.565
0.635
Sleepy/Arousing
0.659
0.700
Unpleasant/pleasant
0.741
0.802
Gloomy/Exciting
0.704
0.796
Distressing/Relaxing
0.578
0.662
Unfriendly/Friendly
0.656
0.777
0.687
0.671
0.662
0.764
0.794
0.872
0.730
0.836
0.746
0.831
0.816
0.875
0.749
0.852
0.736
0.833
0.736
0.781
TheTorontoclimate
0.508
TheTorontoattractions
0.714
TheTorontoPrice
environment
Hotelaccommodation
environment
Hotelfood&beverage
facilities
Hotelconference
service
Hotelaccommodation
service
Hotelfood&beverage
service
Hotelconference
amenities
Hotelaccommodation
amenities
Hotelfood&beverage
quality
Torontoshopping
facilities
38
EigenValueafter
VarimaxRotation
ExplainedVariance
OverallAccumulated
Variance
KMOandBarlett’s
sphericitytest
7.170
3.853
2.282
1.912
1.286
28.68% 15.41% 9.13%
7.65% 5.15%
66.02%
KMO=0.815;CHI-SQUARE=1362.986;
P-VALUE=0.000
Table 5-5
Principal component analysis results with Varimax rotation of 19 image items
Factor
Eigen
Explained
loading
Value
Variance
6.559
34.52%
0.942
Hotelconferenceenvironment
0.666
Hotelaccommodationenvironment
0.762
Hotelfood&beveragefacilities
0.873
Hotelconferenceservice
0.836
Hotelaccommodationservice
0.832
Hotelfood&beverageservice
0.885
Hotelconferenceamenities
0.860
Hotelaccommodationamenities
0.841
Hotelfood&beveragequality
0.794
3.213
16.91%
0.818
Factorsitems
Factor1--VenueImage
Factor2--Destinationaffective
image Unpleasant/pleasant
0.856
Gloomy/Exciting
0.542
Unfriendly/Friendly
0.779
Cronbach α
39
Factor3--EventImage
1.677
8.83%
0.727
Qualityofattendees
0.854
Conferencerelatedactivities
0.825
Conferencesocialopportunities
0.654
1.329
6.99%
0.744
TheTorontoattractions
0.694
Torontoshoppingfacilities
0.852
Torontoaccessibility
0.706
Factor5(excluded)
1.187
6.25%
0.789
Factor4--Destinationcognitive
image Sleepy/arousing
OverallAccumulatedVariance
KMOandBarlett’ssphericitytest
73.50%%
KMO=0.821;CHI-SQUARE=1093.599;
P-VALUE=0.000
5.4 Multiple Regression Modeling
This study explores the relationship among event image, venue image, destination image, and
attendees’ behavioural intention toward destination and conference. Multiple regression analysis
was applied to investigate these relationships. Multiple regression analysis is often used to
explore correlations among a small number of independent variables and a dependent variable,
especially when the sample size is small. All independent variables are entered into the equation
at the same time. This is also an appropriate method when the number of independent variable is
small and when the researcher does not know which independent variables will create the best
40
predictive equation.
In this study, the independent variables are the four factor scores generated from PCA, and
the dependent variables are overall satisfaction, behavioural intention toward the destination
(recommend Toronto to others), behavioural intention toward the destination (revisit Toronto as
leisure tourists), and behavioural intention toward the conference (attend the 2016 conference).
5.4.1 Test 1—from images and overall satisfaction to behavioural intention to recommend
Toronto to others
In the Table 5-6 below, the behavioural intention toward the destination (recommend Toronto to
others) is used as a dependent variable. Four factor scores (event image, venue image,
destination cognitive image, and destination affective image) and overall satisfaction are used as
independent variables.
As can be seen from the table below, 30.5% of variance in the rating of attendees’
recommendation is attributable to two dimensions: factor 2 – destination affective image
( β = 0.269 , P < 0.05 ), and overall satisfaction ( β = 0.367 , P < 0.05 ). The variables have no
multicollinearity problem (VIF<10.000). The F-statistic for the regression model is 9.063 with
p = 0.000 .
41
(recommendation)
Sample
DV
Adjust
R2
Collinearity
Items
Recommendation
(N=128)
All participants
Factor 2
β
T
Sig T
statistic
TOL
VIF
0.170
0.269
2.881
0.005
0.830
1.205
0.376
0.367
3.843
0.000
0.870
1.149
Factor 1
-0.008
-0.013
-0.147
0.883
0.998
1.002
Factor 3
-0.066
-0.104
-1.201
0.233
0.999
1.001
Factor 4
0.112
0.177
1.981
0.051
0.830
1.205
Overall
0.305
B
satisfaction
NOTE: Total explained variance (R2) = 0.342, Adjusted R2 = 0.305, Dependent variable:
recommend Toronto to others; Constant=2.772; t=7.001 (Sig=0.000); F=9.063; P =0.000. TOL:
Tolerance.
5.4.2 Test 2—from images to behavioural intention to recommend Toronto to others
In the Table 5-7 below, the behavioural intention toward the destination (recommend Toronto to
others) is used as a dependent variable. Four factor scores (event image, venue image,
destination cognitive image, and destination affective image) are used as independent variables.
As can be seen from the table below, 19.6% of variance in the rating of attendees’
recommendation is attributable to two dimensions: factor 2 – destination affective image
( β = 0.251, P < 0.05
, and factor 4 – destination cognitive image( β = 0.160, P < 0.05 ) . The
variables have no multicollinearity problem (VIF=1.000). The F-statistic for the regression
model is 6.603 with p< 0.000.
42
Table 5-7 Image affecting behavioural intention toward destination (recommendation)
DV
Recommendation
(N=128)
All participants
Sample
Adjust
R2
0.196
Collinearity
Items
B
β
T
Sig T
statistic
TOL
VIF
Factor 2
0.251
0.298
4.252
0.000
1.000
1.000
Factor 4
0.160
0.254
2.712
0.008
1.000
1.000
Factor 1
0.001
0.001
0.015
0.988
1.000
1.000
Factor 3
-0.058 -0.092 -0.987 -0.326 1.000
1.000
NOTE: Total explained variance (R2) = 0.231, Adjusted R2 = 0.196, Dependent variable:
recommend Toronto to others; Constant=4.280; t=72.839 (Sig=0.000); F=6.603; P =0.000.
TOL: Tolerance.
5.4.3 Test 3 —from images and overall satisfaction to behavioural intention to revisit
Toronto as a leisure tourist
In the Table 5-8 below, the behavioural intention toward the destination (revisit Toronto as
leisure tourists) is used as a dependent variable. Four factor scores (event image, venue image,
As can be seen from the table below, 14.7% of variance in the rating of attendees’ revisit
behavioural is attributable to one item: overall satisfaction ( β = 0.839, P < 0.05 ). There is no
multicollinearity problem (VIF<10.000). The F-statistic for the regression model is 3.406 with p
value equals 0.009.
43
(revisit)
DV
R2
Collinearity
Items
Revisit
(N=85)
β
T
Sig T
statistic
VIF
0.000
0.900
1.111
Factor 1
-0.108 -0.086 -0.752 0.455
0.933
1.071
Factor 2
0.126
0.112
0.987
0.327
0.945
1.059
Factor 3
0.070
0.060
0.538
0.592
0.974
1.027
Factor 4
0.030
0.027
0.240
0.811
0.936
1.069
satisfaction
0.147
B
TOL
Overall
Toronto residents
Participants excluding
Sample
Adjust
0.839
0.432
3.708
NOTE: Total explained variance (R2) = 0.208, Adjusted R2 = 0.147, Dependent variable: revisit
Toronto as leisure tourists; Constant=0.365; t=0.407 (Sig=0.686); F=3.406; P =0.009. TOL:
Tolerance.
5.4.4 Test 4—from images to behavioural intention to revisit Toronto as a leisure tourist
In the Table 5-9 below, the behavioural intention toward the destination (revisit Toronto as
leisure tourists) is used as a dependent variable. Four factor scores (event image, venue image,
independent variables. However, there is no significant item generated, because the p values of
the four factors are greater than 0.05.
44
Table 5-9 Image affecting behavioural intention toward destination (revisit)
DV
Revisit
(N=85)
Toronto residents
Participants excluding
Sample
Adjust
R2
Collinearity
Items
B
β
T
Sig T
statistic
TOL
VIF
Factor 1
-0.020 -0.016 -0.128 0.899
0.960
1.042
Factor 2
0.219
0.195
1.606
0.133
0.983
1.017
Factor 3
0.006
0.005
0.042
0.967
0.991
1.009
Factor 4 0.078
0.134
0.578
0.565
0.946
1.057
0.147
NOTE: Total explained variance (R2) = 0.208, Adjusted R2 = 0.147, Dependent variable: revisit
Toronto as leisure tourists; Constant=0.365; t=0.407 (Sig=0.686); F=3.406; P =0.009. TOL:
Tolerance.
5.4.5 Test 5-from images and overall satisfaction to behavioural intention to attend the
2016 conference
In the Table 5-10 below, the behavioural intention toward the conference (attend the 2016
conference) is used as a dependent variable. Four factor scores (event image, venue image,
As can be seen from the table 15, 12.0% of variance in the rating of attending the 2016
conference is attributable to two dimensions: factor 3 – event image ( β = 0.234, P < 0.05 ), and
factor 4 – destination cognitive image ( β = 0.271, P < 0.05 ). The variables have no
multicollinearity problem (VIF<10.000). The F-statistic for the regression model is 3.505 with p
45
value equals 0.006.
Table 5-10 Image and overall satisfaction affecting behavioural intention toward attending
2016 conference
Sample
DV
Adjust
R2
Collinearity
Items
conference
Attend the 2016
(N=128)
All participants
Overall
β
T
Sig T
statistic
TOL
VIF
0.839
0.432
3.708
0.000
0.900
1.111
Factor 1
-0.108
-0.086 -0.752
0.455
0.933
1.071
Factor 2
0.126
0.112
0.987
0.327
0.945
1.059
Factor 3
0.070
0.060
0.538
0.592
0.974
1.027
Factor 4
0.030
0.027
0.240
0.811
0.936
1.069
satisfaction
0.147
B
NOTE: Total explained variance (R2) = 0.168, Adjusted R2 = 0.120, Dependent variable: attend
2016 conference; Constant=4.074; t=7.572 (Sig=0.000); F=3.505; P =0.006. TOL: Tolerance.
5.4.6 Test 6—from images to behavioural intention to attend the 2016 conference
In the Table 5-11 below, the behavioural intention toward the conference (attend the 2016
conference) is used as a dependent variable. Four factor scores (event image, venue image,
destination cognitive image, and destination affective image) are used as independent variables.
As can be seen from the table below, 12.7% of variance in the rating of attending 2016
conference is attributable to two dimensions: factor 3 – event image ( β = 0.236, P < 0.05 ), and
factor 4 – destination cognitive image ( β = 0.281, P < 0.05 ). The variables have no
multicollinearity problem (VIF=1.000). The F-statistic for the regression model is 4.341 with p
value equals 0.003.
46
Table 5-11 Image affecting behavioural intention toward attending 2016 conference
conference
DV
Attend 2016
(N=128)
All participants
Sample
Adjust
R2
0.127
Collinearity
Items
B
β
T
Sig T
statistic
TOL
VIF
Factor 3
0.180
0.236
2.421
0.018
1.000
1.000
Factor 4
0.216
0.283
2.904
0.005
1.000
1.000
Factor 1
0.102 -0.134 1.377
0.172
1.000
1.000
Factor 2
-0.080 -0.105 -1.082 0.282
1.000
1.000
NOTE: Total explained variance (R2) = 0.165, Adjusted R2 = 0.127 Dependent variable: attend
2016 conference; Constant=4.366; t=59.043 (Sig=0.000); F=4.341; P =0.003. TOL: Tolerance.
5.5 Hypothesis Test
5.5.1 Measuring the effect of image on attendees’ behavioural intentions
ThroughMultipleregressionanalysis,theresultsofhypothesistestarelistedinTable5-12.
First,fromtheresultsofTest1andTest2,conferenceattendees’intentiontorecommenda
destinationtoothersisinfluencedbydestinationcognitiveimage,anddestinationaffective
image.Second,fromresultsofTest3andTest4,conferenceattendees’intentiontorevisit
thedestinationisnotinfluencedbycognitiveimageoraffectiveimage.Third,fromTest5
andTest6,conferenceattendees’intentiontoattendafutureconferenceisinfluencedby
cognitiveimage—eventimageanddestinationcognitiveimage,ratherthanaffectiveimage. 47
Table 5-12 The results of multiple regression tests
Test
1
Including
overall
satisfaction
Significant independent
Dependent variables
variables
Behavioural intentions
Adjust R2
Destination affective
image
( β = 0.269 )
Recommend Toronto to others
0.305
N=128
Constant=2.772
( β = 0.367 )
Destination cognitive
2
image
Excluding
( β = 0.160 )
Recommend Toronto to others
0.196
overall
Destination affective
N=128
Constant=4.280
satisfaction
image
( β = 0.251 )
3
Including
overall
satisfaction
Revisit Toronto as leisure
tourists
( β = 0.839 )
N=85
4
Excluding
overall
Including
overall
satisfaction
6
Constant=0.365
Revisit Toronto as leisure
None
tourists
None
N=85
satisfaction
5
0.147
Event image
( β = 0.234 )
Attend 2016 conference
image
N=128
0.120
Constant=4.074
( β = 0.271 )
Event image
Attend 2016 conference
0.127
48
Excluding
( β = 0.236 )
overall
satisfaction
image
N=128
Constant=4.366
( β = 0.281 )
5.5.2 Measuring the Indirect Effect of Overall Satisfaction
Based
on
the
coefficients
and
P-value
for
Overall
Satisfaction
from
Test
1
( β = 0.367, P-value = 0.000 ) and Test 3 ( β = 0.839, P-value = 0.000 ). Overall Satisfaction has a
statistically significant influence on intention to recommend Toronto to others, and to revisit
Toronto as a leisure tourist. However, Overall Satisfaction did not have a direct influence on
attendee’s intention to attend future conferences, thus its indirect influence is considered.
Based on Judd and Kenney’s (1989) process, the relationships among images (IMA),
overall satisfaction (SAT), and behavioural intention to attend a future conference (BI1) (Table
5-12: Test 5, and Test 6) is tested.
The formulas below show that the overall satisfaction has an indirect effect ( Bindirect = 0.014)
on attendees’ behavioural intentions to attend future conferences. Therefore, if an attendee feels
satisfied, he/she will be more likely to re-attend a conference.
Model 1:BI1 = 4.074 + (0.234 + 0.271)IMA + 0SAT + e
(1)
Model 2 : BI1 = 4.366 + (0.236 + 0.283)IMA + e
(2)
Bindirect = (0.236 + 0.283)-(0.234 + 0.271) = 0.014
(3)
49
Chapter 6: Discussion and Conclusion
This study investigated the relationship among event image, venue image, destination image,
overall satisfaction, and attendees’ future behavioural intentions. To be specific, this study tested
which image will influence behavioural intentions, and tested the effect of overall satisfaction on
behavioural intentions.
There are four conclusions that can be generated from this research. First is about attendees’
behavioural intention to recommend the destination to others. The results of Test 1 and Test 2
show that attendees’ intention to recommend the destination to others is influenced by
destination cognitive image and destination affective image, supporting the finding of Chen and
Tsai (2007), Baloglu (2000), Gartner (1994), Deng and Li (2013). In addition, this study finds
that attendees’ overall satisfaction directly influences attendees’ behavioural intentions toward
recommending Toronto to others.
Second conclusion is about attendees’ behavioural intention to revisit the destination as a
leisure tourist. Overall satisfaction directly influences attendees’ behavioural intention to revisit
Toronto as a leisure tourist, in fact, the result of Test 3 shows that only overall satisfaction
influences attendees’ behavioural intention to revisit Toronto. This finding is contrary to most
studies (Lu & Cai, 2011; Deng & Li, 2013), which indicate that destination image is able to
influence attendees’ behavioural intention to revisit the host destination. One explanation for this
contradiction is that destination image has a more important effect on revisit intention when
50
consumers decide to visit a destination for the first time (Court & Lupton, 1997; Baloglu, 1999).
In Lu and Cai’s (2011) study, about 16% of participants had never been to the destination before,
in Deng and Li’s (2013) study, almost half of the participants were first-time visitors. However,
in the current study, 97% of participants have been to Toronto before.
Third conclusion is about attendees’ behavioural intention to re-attend a future conference.
Consistent with the findings of Chen and Tsai (2007), Chi and Qu (2008), Kim et al. (2009),
Severt et al. (2007), Tanford et al. (2012), Chen and Tsai (2007), Baloglu (2000), Gartner (1994),
and Deng and Li (2013), this study finds that attendees’ overall satisfaction indirectly influences
attendees’ behavioural intentions toward re-attending a future conference through its influence
on image. In addition, The results of Test 5 and Test 6 show that behavioural intention toward
the conference is influenced directly by event image and destination cognitive image, which is
somewhat different from the conclusion of Lu and Cai’s (2011) study. They discovered that
attendees’ behavioural intention toward an event is influenced by event image, venue image, and
destination image. However, the results of multiple regression tests 5 and 6 show that only event
image and destination cognitive image explain attendees’ behavioural intention to attend the
2016 conference. The reason may be because of the difference between study foci. Lu and Cai
(2011) focused on a world expo, a leisure event, but this study focused on a conference. The
conference selected in this study is held in difference places and in difference hotels in Ontario
each year, so the conference is unlikely to be held in the same hotel during a certain period, and
thus attendees will not have the same venue image from year to year. Therefore venue image
51
does not influence the intention to visit next year’s conference. However, the same destination
would be more likely to be selected to hold the conference in the future, so this study supports
the destination cognitive image influence on attendees’ behavioural intention toward the
conference.
Fourthly, differing from the conclusion generated by Lu and Cai (2011), Kaplanidou and
Vogt (2007), Li et al. (2014), and Mendes et al. (2011), one of the conclusions based on the
result of multiple regression test1, 2, 3, and 4 (Table 5-12) shows that event image and venue
image have no influence on attendees’ behavioural intention toward the destination, which
supports the research conducted by Deng & Li (2013). Deng and Li’s (2013) study shows that
the influence of event image and venue image on behavioural intention toward a destination will
be achieved only through influencing destination image in the first place.
52
Chapter 7: Implications, Limitations and Future Research
7.1 Implications
7.1.1 Implications for theory
The findings of this study contribute most to the knowledge of the cognitive-affective-conative
model (Gartner, 1994) by broadening its application to the research of conference attendees.
Attendees’ behavioural intention to recommend a destination to others (conative) is influenced
by destination cognitive image and destination affective image. However, attendees’ behavioural
intention to revisit a destination is not influenced by destination cognitive image or destination
affective image. One reasonable explanation may be that most participants do not have a very
strong intention to revisit Toronto as leisure tourists, presumably because they have been to
Toronto many times. Even though the Toronto residents were excluded when analyzing this
relationship between image and intention to revisit, 89.4% (76/85) of participants had been to
Toronto before, and many commute regularly there for work. The results also show that
attendees’ behavioural intention to attend the 2016 conference will be influenced by event image
and destination cognitive image.
Additionally, the results show that attendees’ behavioural intention to recommend Toronto
to others and to revisit Toronto as leisure tourists is directly influenced by their overall
satisfaction. This study also supports the indirect effect of overall satisfaction on attendees’
behavioural intention to attend a future conference. This inconsistency shows that the
53
image-satisfaction-loyalty link (Chi & Qu, 2008) may vary by circumstance when analyzing
attendees’ intention.
7.1.2 Implications for practice
Destination marketers understand the value of attracting events to destinations. The results of the
current research suggest the benefits to a destination should be extended beyond just generating
visitor revenue to shaping or enhancing the destination’s image in order to increase the
likelihood of future visits. Both the theoretical and empirical evidence show that most of
attendees will have a positive behavioural intention toward a destination. Destination managers
need to find ways to improve their destination cognitive image and destination affective image,
not only through promotion, such as advertising, but also through holding events. In addition,
Satisfaction, which is able to influence attendees’ behavioural intention toward a destination,
should be valued by destination managers. Event attendees’ satisfaction is different from leisure
tourists’ satisfaction. Destination managers should not only focus on leisure tourists’ need, but
also focus on event attendees’ needs and fulfill those needs in order to increase attendees’
satisfaction level and thus attract more tourists. Although this study indicates that destination
image does not influence attendees’ behavioural intention to revisit the destination, destination
managers should not underestimate the impact of destination image. They should make an effort
to attract conference attendees to revisit the host destination. For example, destination marketers
can provide more detailed information about attractions, family and couple activities, and so on.
54
In order to attract attendees and encourage them to re-attend a conference, conference
planners are encouraged to pay more attention to the conference image management and the host
destination selection. The finding that both the event image and destination cognitive image are
able to influence attendees’ behavioural intention to attend future conferences suggests that
conferences could achieve a competitive advantage by combining destination image with event
image. For example, when promoting a conference, conference organizers could add several
photos of the host destination, as well as introduce local attractions. This study indicates that
overall satisfaction directly influences on attendees’ behavioural intention toward the destination,
and indirectly toward the conference, thus event planners should not underestimate the impact of
satisfaction and should fulfill attendees’ needs as much as is realistically possible.
7.2 Limitations and future research
This study has some limitations that require critical examination. Firstly, the sample size of this
study is not large enough to generate generalizable results, and a large sample size may support
multiple research techniques, such as Structural Equation Modeling, to generate multiple results.
Secondly, some additional questions could be added in the survey to gather more information.
For example, because behavioural intention has two items—recommendation and revisit—a
question like “will you recommend the Ontario Tourism Summit to others” would generate
findings about the relationship between images and intention to re-attend the conference.
Future studies could test the findings of this study by conducting research in other
55
geographic regions that are popular conference destinations. Future studies could also gather
broader results by perfecting the questionnaire, collecting more data, and conducting Structural
Equation Modeling.
56
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The Influence of Image on Conference Attendees

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