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This is to certify that the
dissertation entitled

THE IMPACT OF SPORT TOURISM EVENT IMAGE ON
DESTINATION IMAGE AND INTENTIONS TO TRAVEL: A
STRUCTURAL EQUATION MODELING ANALYSIS

presented by

KYRIAKI KAPLANIDOU

has been accepted towards fulfillment
of the requirements for the

Ph.D degree in Park, Recreation and Tourism
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THE IMPACT OF SPORT TOURISM EVENT IMAGE ON DESTINATION IMAGE
AND INTENTIONS TO TRAVEL: A STRUCTURAL EQUATION MODELING
ANALYSIS

By

Kyriaki Kaplanidou

A DISSERTATION

Submitted to
Michigan State University
in partial fulfillment of the requirements
for the degree of

DOCTOR OF PHILOSOPHY

Department of Park, Recreation and Tourism Resources

2006

ABSTRACT

THE IMPACT OF SPORT TOURISM EVENT IMAGE ON DESTINATION IMAGE
AND INTENTIONS TO TRAVEL: A STRUCTURAL EQUATION MODELING
ANALYSIS
By
Kyriaki Kaplanidou

Hosting sport events has been a focus of destination marketers as a strategy to
enhance its destination image and differentiate its tourism products. Communities are
attracted to hosting sport events to draw marketing benefits that will contribute to the
success of the destination in the long run by creating awareness, improving their image
with visitors and attracting tourism business to generate future inbound travel. As such,
destination images can be influenced by the hosting of a sport event and the attributes
associated with this event. In other words, the event has a brand image for its participants
and spectators. Research is scarce on event image perceptions held by sport event
spectators and participants. Participants are the guaranteed “customers” of sport events
and destinations that host them. Consequently, a theoretical framework that focuses on
participants’ psychological processes is needed to examine the factors that influence
participants’ intentions to revisit a destination to participate in leisure activities. For this
purpose, this study utilized the Theory of Planned Behavior (TPB) to examine the impact
of event and destination images as attitudinal constructs, and the role of subjective norms
and perceived behavioral control about event participation on intentions to revisit the
destination to participate in leisure activities. The focus of this research was to examine

the interrelationships between the concepts of sport tourism event image and destination

image and their impacts on intentions to revisit a destination for leisure activities to test
the TPB. Furthermore, this study developed a scale to measure a sport event’s image to
address this gap in the literature using Keller’s (1993) theoretical framework of brand
image associations. A model was proposed that tested eleven hypotheses. Independent
variables included: event image, destination image, subjective norms, perceived
behavioral control, past behavior with the event, and past behavior with the destination.
The main dependent variable was intention to revisit the destination for leisure activities.
The data were collected through focus groups and survey administration to a sample of
720 sport tourism event participants. Focus groups yielded six themes for the
development of the sport tourism event image scale. Based on those themes a definition
and a scale were developed. The model was tested utilizing structural equation modeling
techniques. The results revealed destination image mediates the impact of sport tourism
event image and past visits to the destination on intention to revisit the destination for
leisure activities. Event image had a significant large impact on the image of the
destination. Implications for event and destination marketers include the practice of co-
branding approaches with regard to brand images of event and destinations. Furthermore,
the development of the sport tourism event image is a first step toward the development
of an evaluation tool of sport tourism events’ images from active sport tourists’
(participants’) perspectives. Future research should test the model and the scale proposed
in this study with other populations of active sport tourists (e. g., runners, triathlon
participants, basketball players) to fimher validate the findings. Finally, TPB should test
whether intentions predict actual behavior in the field of tourism to validate the predictive

power of the complete model.

DEDICATION

I would like to dedicate this dissertation to my father Vassilis Kaplanidis who passed
away five years ago (2001). His continuous trust and faith in my abilities made me the
person I am today and urged me to achieve this level of knowledge. As he used to always

say to me: “I have complete faith in the things you undertake to do. Keep going.”

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mg SMXIOTO (popo mung omv uvfiun oou. Xe ayartd) iroln') Kat éépm on 61:01) Kat va stout
pug npootatefietg and 6m to mica. Emma) va eioat nepfitpavog yta uéva Icon rnv
oucoyévewt uou Kat yta to eyyévr 001) 1:01) éxet to évouo oou.

Eioat min/ta omv orcéwn pug...

iv

 

ACKNOWLEDGMENTS

The dissertation project is usually the result of a team effort. My team consisted
of four people led by my fearless, decisive, knowledgeable and well-respected advisor,
Dr. Christine Vogt. Without her guidance I wouldn’t have become a good researcher. But
most of all, without her direction I wouldn’t have acquired qualities such as reflective
critique on my work, creative thinking and problem solving. I was really lucky to have
her as my advisor, to see her in action and to receive constructive feedback on my work. I
was really lucky to have her as my advisor because above all she was a compassionate
and supportive human being. I hope I can become as great as her one day.

However the team members do not stop there. I would like to thank the other team
“players” in my dissertation completion “game”. Dr. Holecek, one of the most
experienced and knowledgeable professors and researchers in the Michigan Tourism
industry and MSU provided useful feedback during my PhD studies. Dr. Nora Rifon
inspired me to look into consumer behavior theories and Dr. Thomas Page provided the
solid knowledge background for the consumer behavior domain. Without them, my
dissertation would not have materialized.

I would like to thank the people at the Michigan Trails and Greenways Alliance
for letting me survey their event participants.

Finally, to all the friends I acquired during my PhD studies, thank you. Thank you
for sharing with me all these feelings I thought I only had as a PhD student. Thank you
for being there for me when I wanted to vent my frustration about the long working hours

and the theoretical “roadblocks.”

I would like to thank my husband who supported me through this long journey. Matt, you
can have your wife back now... although I do not know how long it will last if I get a job
as a faculty member soon... Special thanks to my brother and mother in Greece who
although far away they supported me psychologically through this process and urged me
to keep going and for this I thank them for the bottom of my heart.

Last but not least at all, I would like to thank my baby boy Vassilis who joined
our lives during the last phase of my dissertation writing five months ago and who
suffered lack of attention and endless hours on the floor playing with his toys... My baby

you can have your mom back now. We can take long walks in the park!

vi

 

TABLE OF CONTENTS

LIST OF TABLES ......................................................................... vii

LIST OF FIGURES ........................................................................ ix

LIST OF ABBREVIATIONS ............................................................. x

CHAPTER 1

INTRODUCTION ............................................................................ 1
Statement of the problem .......................................................... 3
Purpose for the study ............................................................... 3
Justification of the study .......................................................... 4
Study hypotheses .................................................................. 6
Delimitations ....................................................................... 7
Limitations ......................................................................... 7
Definitions ......................................................................... 8
Organization of the dissertation ................................................ 10

CHAPTER 2

LITERATURE REVIEW ................................................................. 11
Need for a theory that explains and predicts intentions ................. 14

Theoretical framework —The Theory of Planned Behavior (TPB) ....... 16
Types of sport tourists: participants, spectators and nostalgia

sport tourists ....................................................................... 20
Sport tourism and the theory of planned behavior ............................ 22
Why the TPB? ..................................................................... 23
Subjective norms toward sport tourism events ................................ 25
Perceived behavioral control toward sport tourism events
and destinations .................................................................. 26
The concepts of destination image and event image
as attitudinal constructs ......................................................... 28
Destination image (DI) .......................................................... 29
Sport tourism event image ...................................................... 35
Measurement of a sport tourism event image-creation of scale ............ 43
The role of past behavior/past experience ..................................... 44
Intentions and destination image ................................................ 47
Model presentation ................................................................ 48
Model challenges .................................................................. 50
CHAPTER 3
METHOD ................................................................................... 52
Instrumentation ................................................................... 53
Other scales and items used in this study ............................. 58
Sample selection and survey administration .................................. 6]

vii

Sources of survey error ........................................................... 63

Measurement error ....................................................... 66
Model evaluation .................................................................. 66
Variable recoding and computing ............................................... 70
CHAPTER 4
RESULTS: SCALE DEVELOPMENT AND MODEL FIT .......................... 72
Description of the sample ........................................................ 72
Focus group data analysis ........................................................ 78
Refinement and evaluation of the sport tourism
event image scale ................................................................... 79
Construct validity of the sport tourism event image scale .......... 84
Missing data treatment ............................................................ 86
Analysis of age influence on key dependent variables ....................... 89
Testing of the Structural Equation Model ...................................... 90
Reliability checks-reflective factors justification ..................... 91
Direction and magnitude of impact of path estimates ............... 94
Hypotheses testing results ............................................... 95
Model respecifications ................................................... 97
Summary ............................................................................ 99
CHAPTER 5
DISCUSSION, IMPLICATIONS AND CONCLUSION ............................. 101
Discussion of findings ............................................................. 101
Corroborating the results to earlier studies ............................. 101
The link between event and destination image ........................ 104
Previous behavior, image and intentions ............................... 108
Theoretical implications ........................................................... l 10
Managerial implications .......................................................... 1 13
Overall model managerial implications ............................... 113
Link event and destination images ..................................... 114
Future research ..................................................................... 124
Limitations of the findings ........................................................ 125
Final comments ..................................................................... 126
APPENDIX A ............................................................................... 128
APPENDIX B ............................................................................... 133
APPENDD( C ............................................................................... 140
APPENDIX D ............................................................................... 143
APPENDIX E ............................................................................... 152
REFERENCES ............................................................................... 153

viii

LIST OF TABLES
Table 1. Economic impact of sport events ............................................. 11
Table 2. Studies measuring the sport event image from a spectators’ perspective. 37

Table 3. Factor analysis of the sport tourism event image scale pre-test results... 57

Table 4. Variables utilized in the final mail survey and their source ............... 61
Table 5. Response rate from first and second mailing of the surveys ............... 63
Table 6. Nonresponse check results ...................................................... 65

Table 7. Demographic information of sample participants, US bicyclists
and all sport participants in the USA ............................................ 76

Table 8. Descriptive statistics of variables included in the
SEM model evaluation ........................................................... 77

Table 9. Initial sport tourism event image item evaluation: means,
missing data and actions taken .................................................. 81

Table 10. Rotated component matrix of the 23 remaining items after
initial evaluation sport tourism event image items .......................... 82

Table 11. The sport tourism event image scale ......................................... 83
Table 12. Measurement model factor covariance, item loadings and errors
based on 319 complete cases using EQS robust analysis .................. 87

Table 13. Missing data treatment results: comparison of missing cases
with non-missing cases on key variables ..................................... 89

Table 14. MANOVA results from comparing the variable with mean
replacement and the variable without mean replacement ................. 89

Table 15. MANOVA results for the impact of age on the three items
composing the main dependent variable ................................... 90

Table 16. Reliability coefficients for the event image items
comparing all event image items and parcels ............................ 91

ix

 

 

Table 17. Reliability coefficients for the items composing the factors
involved in the structural regression model .................................. 92

Table 18. Summary of goodness of fit indices for measurement
and structural models tested in this study .................................... 94

Table 19. Correlations, means and standards deviations
among the model variables ..................................................... 98

LIST OF FIGURES

Figure 1. The Theory of Buyer Behavior ............................................ 15
Figure 2. The Theory of Planned Behavior (TPB) .................................. 19

Figure 3. A measurement model of destination image:
Destination image as a reflective factor ................................... 34

Figure 4. A conceptual framework of the sport tourism event image

from the participant’s perspective .......................................... 41
Figure 5. Proposed conceptual theoretical model to be tested in this study ...... 50
Figure 6. Path estimates for the SEM model .......................................... 93
Figure 7. Boston Marathon website ..................................................... 115
Figure 8. Primesport.com a sport travel website ....................................... 117
Figure 9. Part of the ESPN sport travel website homepage ........................... 122

xi

LIST OF ABBREVIATIONS
EI: Event image
DI: Destination image
SN: Subjective norms
PBC: Perceived behavioral control
STEI: Sport tourism event image
PVD: Past visits destination
PPE: Past participation event
I: Intentions
ROI: Return on investment
CFA: Confirrnatory factor analysis

SEM: Structural Equation Modeling

xii

CHAPTER 1
INTRODUCTION

Destination marketers have focused on hosting sport events as a strategy to
enhance destination image and differentiate its tourism products (Chalip & Green, 2001;
Chalip, Green & Hill, 2003; Chalip & McGuirty, 2004; Dimanche, 2003; Jago, Chalip,
Brown, Mules & Shameem, 2003). Sport events can range from mega sporting events
such as the Olympic Games to medium size sport events such as national championships
to smaller size sport events such as local cycling, walking, and running events which can
also be characterized as leisure sport events. Participation in leisure sports has increased
in recent years because people desire more active holidays resulting in a boom in sports
like cycling, golf, jogging and hiking (de Villiers, 2001). For example, sport travel
generated $2.4 billion of spending in Canada while the total person-trips related to sport
travel (over 80km) was 11,982,000 in 2004 (Canadian Sport Tourism Alliance, 2006b).

Communities are attracted to hosting sport events to draw marketing benefits that
will contribute to the success of the destination in the long run by creating awareness,
improve their image with visitors and attract tourism business to generate future inbound
travel (Dimanche, 2003). As such, destination images can be influenced by the hosting
of a sporting event and the attributes associated with this event. Sport events can add to
the attractiveness of a destination for new markets, first time and repeat visitors
(Dimanche, 2003).

The tourism literature has acknowledged the importance of destination image and
its role on destination awareness and decision making process (Baloglu, 1997, 1999;

Baloglu & Brinberg, 1997; Baloglu & Mangaloglu, 2001; Baloglu & McCleary, 1999;

Bigne, Sanchez & Sanjez, 2001; Dann, 1996). The tourism literature has also focused on
the measurement of destination image due to its complex and multidimensional nature
(Echtner & Ritchie, 1991, 1993). Recent research efforts (Blain, Levy & Ritchie, 2005)
using empirical research and insights from experts identified that destination image is an
integral part of a destination’s brand. Destinations can enhance their image by hosting
sport events that attract first time and repeat tourists and sport tourists to the destination
using co-branding, brand leverage and product bundling techniques (Chalip & McGuirty,
2004)

Despite the importance of sporting events as a marketing vehicle to promote a
destination, research on a sporting event’s image is scarce. Studies have used brand
personality scales or adjectives to measure the image of a sporting event and its
similarities to sponsor brands (e. g. F errand & Pages, 1996; Gwinner & Eaton, 1999;
Musante, Milne & McDonald, 1999) which limits the concept of image to that of brand
personality. In addition, these studies focused on spectators’ perceptions leaving out
another major category of sport tourist: the participant. More research is needed to
understand the constituents of a sport tourism event image.

The need for a theoretical framework that explains the psychological processes
involved in understanding the decision making processes of sport tourists has been
identified in the sport tourism field (Weed, 2005). The theory of planned behavior (TPB)
(Ajzen, 1985) is a theoretical framework that aims to understand the variables that
influence the behavioral intentions toward a behavior and offers a parsimonious model to

predict intentions. To the author’s knowledge, TPB has not been previously used in the

context of tourism. This brief introduction leads to the statement of problem and the

purpose of the study.

Statement of the problem

The problem addressed in this research is that the interrelationships between the
concepts of sport tourism event image (STEI) and destination image (DI) and their
combined impact on intentions to revisit a destination to participate in leisure activities is
not understood. Thus, they can not be employed in developing effective marketing

strategies.

Purpose of the study

This study attempted to estimate and understand the relationship between the
image of a smaller scale sport tourism event and its impact on destination image to
provide destination marketers and sport event marketers with insight to “how various
entities should be best combined” (Keller, 2003). The results of this study contribute
toward the practice of co-branding techniques and brand leveraging approaches and
toward creating the optimal positioning of the destination in the minds of consumers.
This study also aimed to improve prediction and understanding of active sport tourists’
intentions to travel to a destination as a result of participating in a sport tourism event, in
an attempt to extend the application of the TPB in the context of active sport tourism.
Finally, the development of a sport tourism event image scale will provide event
marketers with an evaluation tool that estimates image profiles of branded sport tourism

events.

Justification for the study

Sport tourism has increased in importance during the last five years. According to
Travel Wire News (2004), sport tourism is one of the fastest growing areas of the $4.5
trillion global travel and tourism industry. More and more destinations utilize sports to
attract more visitors to their area. For example, biking vacations attracted more than 27
million in the past five years, and they rank as the third most popular outdoor vacation
activity in America (Travel Industry Association of America, n.d.). However, recent
reviews of sport tourism identified the lack of theoretical frameworks, which would
further develop the field of sport tourism (Weed, 2005; Weed & Bull, 2004). More
specifically, Weed (2005, p. 229) utilized the expression of “chaos in the brickyar ”to
describe the status of sport tourism research. He identifies the lack of systematic
production of studies, which do not use a solid theoretical background and “were thrown
at the pile of research without any consideration as to how bodies of knowledge
(“edifices”) could be constructed.”

This study utilizes the established theoretical framework of the TPB (Ajzen, 1985,
1991) created and applied within the social psychology domain. In this study, the TPB
was utilized to understand the psychological processes that underline the decision making
processes of actively participating sport tourists. These processes are examined in relation
to the activity, the participants and the place, which are evident in the conceptualization
of the sport tourism phenomenon by Weed and Bull (2004): “Sports tourism is a social,
economic and cultural phenomenon arising from the unique interaction of activity, people

and place” (p. 37).

The variables of the TPB include attitudes, subjective norms (SN), perceived
behavioral control (PBC) toward the behavior, intentions toward the behavior and actual
behavior (Ajzen, 1985). Also, various meta-analysis of the TPB have indicated that the
inclusion of past behavior increases the predictive validity of the model (Hagger,
Chatzisarantis & Biddle, 2002; Ouellette & Wood, 1998). In this study, event and
destination images are conceptualized as attitudinal constructs. Therefore, the study
examines the impact of the link between the sport tourism event image and destination
image, subjective norms toward the sport tourism event participation, perceived
behavioral control about event participation and past behavior (past experience with the
event and the destination) on intention to revisit the destination to participate in leisure
activities. As such, the main dependent variable of the study is the intent to revisit a
destination for leisure activities. Furthermore, Ajzen and Driver (1992) indicated in the
TPB model presentation that there should be positive correlations between the concepts
of attitudes, subjective norms and perceived behavioral control.

Understanding the interrelationship between the activity, people and place
utilizing a solid theoretical background, will shed light on the psychological processes of
the active sport tourist and will provide more information about the parameters that
influence active sport tourists’ intentions to revisit a destination. This study also
investigates the impact of sport event image on destination image in an attempt to define
the nature of that relationship. Past research studies (Chalip & Green, 2001; Chalip et al.,
2003; Chalip & McGuirty, 2004; J ago et al., 2003) have identified the significance of this
relationship. However, there is lack of empirical research focused on the direct link and

influence of sport tourism event image on destination image. This study will provide

background knowledge for future research on the interrelationship of the latter two
constructs (i.e., sport event image and destination image) and will also offer a platform
for better understanding active sport tourists’ disposition toward that link.

The direct beneficiaries of this study are both sport and tourism marketers and
managers, who will be able to utilize concepts such as event and destination image, past
behavior, SN and PBC to predict intentions and create pertinent communication messages

and programs to attract more visitors to their areas and to their events.

Study hypotheses

The study’s hypotheses are the following:

H1: Event image will positively influence the image of the destination;

H2: Destination image will positively influence intentions to travel to the destination;
H3: Subjective norms will positively influence intentions to travel to the destination;
H4: Perceived behavioral control will positively influence intentions to travel to the
destination;

H5: Past visits to the destination will positively influence intentions to travel to the
destination;

H6: Past visits to the destination will positively influence the image of the destination;
H7: Past participation in the event will positively influence the image of the event;
H8: There will be a positive correlation between past participation in the event and past
visits to the destination; and

H9: There will be a positive correlation between subjective norms and perceived

behavioral control.

Delimitations

This study focuses on active sport tourists (participants) and not passive sport
tourists (spectators). The main purpose of the trip for these participants is the sport
tourism event and not just to vacation. The study also focused on people who already had
participated in a bicycling event in the state of Michigan (post-trip phase) and not on
people who were in the pre-trip or during the event phase. To explore the perceptions of
the event’s image and its relationship with the destination image and intentions to return
to the destination, the goal was to target people that had directly experienced the event
and the destination in order to have a homogeneous sample of respondents for the
evaluation of the event and destination stimuli. Travelers in the pre-trip phase may hold
images influenced by media communications and not realistic perceptions of the actual
experience with the destination and the sport event. Also, sport event participation
ensured that respondents would definitely be predisposed toward the event positively or
negatively and that they would have a complete picture of the entities involved after their
trip. Finally, intention to revisit the destination was delimited to include only leisure
activities and not intentions to revisit for other purposes such as business trips or to visit

friends and relatives.

Limitations

The main limitation of the study was that only one event and one destination was
used to study participants’ perceptions and intentions in the context of TPB. The use of
one event may confound differences in the event and destination image relationship that

could otherwise emerge if multiple sport events were studied. However, in order to

address this limitation, questions were asked to determine whether these respondents
participated in other sport tourism events such as running, walking or jogging. These
questions were included in the model testing to control for that type of past experience

with other sport tourism events.

Definitions (alphabetically)
Active sport tourist (participant)

A person who travels away from home to physically participate in a sport tourism
event.
4%

“A psychological tendency that is expressed by evaluating a particular entity with
some degree of favor or disfavor.” (Eagly & Chaiken, 1993, p. 1)
Destination image

The sum of beliefs, ideas and impressions a person has of a destination
(Crompton, 197 9).
Event image

It is expected that event image will be a function of the event’s attributes, benefits
and attitudes (Keller, 1993) as well as emotional and unique characteristics. The
grounded definition is provided in the results section of this dissertation.
Intentions

“Intentions are assumed to capture the motivational factors that influence a

behavior; they are indicators of how hard people are willing to try, of how much of an

effort they are planning to exert in order to perform the behavior” (Ajzen & Driver, 1992,
p.208)
Leisure activities

In this study leisure activities refers to sport related activities pe0ple engage
during their free time (Kraus, 1998). The general definition of leisure in terms of activity
is that leisure is an activity in which people engage during their free time and this
definition is closely linked to the idea of recreation (Kraus, 1998).
first behavior (previous experience)

The frequency with which a person engaged in an activity in the past.
Perceived behavioral control

“The perceived ease or difficulty of performing the behavior. It is assumed to
reflect past experience as well as anticipated impediments and obstacles” (Ajzen &
Driver, 1992, p. 208).
Sport tourism

“All forms of active and passive involvement in sporting activity, participated in
casually or in an organized way for noncommercial or business/commercial reasons, that
necessitate travel away from home and work locality” (Standeven & DeKnop, 1999, p.
12).
Sport tourism event

An organized form of sport activity, which requires active or passive involvement
on behalf of its participants (e.g., running a race or supporting your team); where there
are other people who participate (actively or passively); and for which participants

traveled away from home.

Mral Equation modeling (SEM)

A family of statistically related procedures that utilizes analysis of covariance
structures, to test the fit of a priori model to a set of data (Kline, 2005).
Subjective norm_s

“The person’s perceptions of perceived social pressure to engage or not in the

behavior” (Ajzen & Driver, 1992, p. 208).

Organization of the dissertation

This dissertation includes four more chapters beyond this introductory chapter.
Chapter two includes the literature review where the theoretical framework of the study
and the relationships between key theoretical variables are discussed. The proposed
model is also presented. Chapter three presents the methodological approach utilized in
this study as well as the steps taken toward the development of a scale that measures the
image of sport tourism events. Chapter four presents the analysis of the data and results
for the scale development and the testing of the model’s proposed hypotheses. Chapter
five discusses the results derived from the data analyses and compares and contrasts these
results with previous research on this topic. Chapter five also presents implications and

recommendations for future research in this field.

10

CHAPTER 2
LITERATURE REVIEW

The sport tourism domain is a continuum comprised of sport and tourism without
the one field being subject to the other (Weed, 2005). The two areas work collaboratively
to produce the experiences/activities at the destination and to develop and apply policies
that advance sport tourism products. Destinations turn to sport tourism “products” such as
events to attract more people, change the image of the destination (Dimanche, 2003) and
generate economic impact in their communities. For example, the Canadian Sport
Tourism Alliance (CSTA) (2006a) conducted economic impact studies for various sport
events in Canada revealing the significance of their impact. The results of these reports
are presented in Table 1. It can be observed that in those events where there is a large
number of participants there is also a large number of spectators, and the economic
impact is significant (e. g. FINA, IAAF world youth athletic championships). Participants
are the guaranteed customers of events and destination services and form an important
segment for target market strategies.

Table 1. Economic impact of goort events (source CSTA, 2006).

 

 

 

Economic
Visitors impact _
(includes participants, estlmated
spectators, media and 30090910 Tax
other sport related actmty revenue
Event Year Place Sport people) (million) (million)
Total: 1 11,268
(106,093 spectators,
3,224 participants,
XI FINA 669 FINA family
World 1,200 media,
Championships 2005 Montreal Aquatics 82 observers) $181.2 $29
Hockey Total:17,l38
Bell Capital (under 12 (4,176 participants,
Cg) 2004 Ottawa years) 12,962 spectators) $11.1 $2.2

 

11

Table 1. Continued

 

 

 

 

 

 

 

 

 

V' . Economic
rsrtors .
(includes Impact —
participants, estimated
spectators, media econorruc Tax
and other sport actlvrty revenue
Event Year Place Sport related people) (million) (million)
Total: 12,640
(12,568
spectators,
72 participants:
athletes and
Nokia Brier 2004 Saskatoon Curang coaches) $23.2 $3.1
Sport Total: 9,435
North American and (2,678 spectators
Indigenous cultural 6,757
Games 2002 Winnipeg activities participants) $25.9 $3.7
MasterCard Total: 3,601
Skate (3,433 spectators,
Canada Figure- 74 participants 94
International 2002 Quebec City skating media personnel) $6.27 $1.36
Bathurst and Multi- Total: 8,312
Cambellton sport (5,112 spectators
Canada Winter (New (13-23 3,200
Games 2003 Brunswick) ages) participants) $70.4 $10.4
Multi- Total: 1,992
sport (1,549 spectators,
Viessmann FIS (winter 384 athletes
World Cup 2005 Vernon, BC sports) 59 media) $6.5 $1.0
Total: 3,229
3"I IAAF World (1,628 spectators
Youth Athletic Sherbrooke, Track 1,601
Championships 2003 Quebec and field participants) $39.6 $6.3
Total: 26, 219
(23, 768
UCI Road Hamilton, spectators
World Cycling Ontario 1,512 participants
Championships 2003 Cycling 939 media reps) $48.3 $8.4
Total: 100,816
(100,000
IIHF World Vancouver, spectators
Junior Kamloops, 316 participants
Championship 2006 Kelowna Hockey 500 media $41.0 $4.6

 

Furthermore, smaller scale sport tourism events bring large economic impact benefits to

smaller communities that otherwise they would not receive (e. g. Canada Winter Games).

12

Beyond economic impact, a question that rises is whether participants and other visitors
to the area develop favorable images about the destination they were exposed to because
they went to the event. Also, if sport tourists develop favorable images, are those
favorable images adequate to generate repeat visitation in the future?

The focus of this study was on evaluating the relationship between the image of a
sport tourism event and the image of a destination and its impact on intentions to revisit a
destination for leisure activities. The following section of the literature review will
discuss theories of consumer behavior. Consumer behavior theories have focused on
intentions (among other variables) as the best predictor of behavior (F ishbein & Ajzen,
1975; Howard & Sheth, 1969). The TPB (Ajzen, 1985) is utilized to explain intentions to
return to the destination where the sport tourism event took place. The appropriateness of
TPB for this study will be discussed in more detail. Finally, key behavioral variables,
such as past behavior (experience) identified in the tourism literature as predictors of
intentions to revisit a tourism destination will be discussed as well.

The literature review is organized in six thematic areas: 1) need for a theory in
sport tourism behavior; 2) sport tourist classification; 3) the theory of planned behavior
and its utility in explaining sport tourists intentions to visit a destination; 4) the constructs
of destination image and sport tourism event image and their influences on intention to
visit or revisit a destination; 5) the lack of a scale that measures the image of a sport
tourism event from the participant’s point of view; and 6) other variables, such as past
behavior, that influence the key constructs of the TPB. After the discussion of these

thematic areas, the proposed model and the hypotheses tested in this study are presented.

13

Need for a theory that explains and predicts intentions

Consumer behavior has been at the focus of academic marketing research for the
past 40 years attempting to explain theoretically the factors that influence consumers’
decision making. Howard and Sheth (1969) proposed a theory of buyer behavior to
explain and capture the decision making processes of the consumer (see Figure 1). In this
theory, a consumer is exposed to a stimulus (e. g. product or service) which contains
characteristics related to significance, symbolism, and social environments. These
stimulus characteristics influence buyers’ perceptions and learning about the product or
service and eventually lead to behavioral outputs. These outputs include attention, brand
comprehension, attitude, intention and purchase (behavior).

The theory of buyer behavior by Howard and Sheth (1969) was a grand theory
that could not be easily tested (Bagozzi, 1992; Sirnonson, Carrnon, Dhar, Drolet &
Nowlis, 2001). Furthermore, “consumer behavior is too complex to be meaningfully
captured in a single model” (Simonson et al., 2001, p. 251). Consumer research has also
utilized other theories related to persuasion and attitude change. These belong to the
family of consistency theories and “assume that individuals strive toward consistency
among their beliefs, attitudes, and behaviors” (Ajzen & Fishbein, 1980, p. 22). These
theories include balance theory (Heider, 1958; Newcomb, 1956), cognitive dissonance
theory (F estinger, 1957), cognitive consistency, attitude change and wishful thinking
(McGuire, 1960), congruity theory (Tannenbaum, 1968), self-persuasion (Bern, 1965)

and social judgment theory (Hovland, Harvey & Sherif, 1957).

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The afore mentioned theories neither specified which psychological variables
influence behavior nor explained inconsistencies between attitude and behavior observed
in studies during the 19705 and 19805 (Ajzen, 1985; Ajzen & Fishbein, 1980).

Bagozzi (1992) indicated the need for middle range theories that incorporate
micro and macro perspectives. As a result of developing and testing middle range
theories, the multi-attribute attitude model and the theory of reasoned action (TRA)
(Fishbein & Ajzen, 1975) emerged to explain whether intentions mediate the effects of
attitudes and subjective norms (SN) on behavior. The TRA specified a limited number of
psychological variables that can influence behavior (Albarracin, Johnson, Fishbein &
Muellerleile, 2001) offering a more parsimonious model for explaining behavioral
intentions.

Research on the TRA peaked during the 19705 and 19805 in the consumer
research field (Simonson et al., 2001). The TRA aimed to explain behavioral intentions
that were under the volitional control of the individual (Ajzen & F ishbein, 1980). All
behavioral goals, however, are not under the volitional control of the individual. In order
for the behavioral goals to be attained they depended on the person’s skills, ability,
willpower and opportunity, which varies from behavior to behavior (Ajzen, 1985). To
account for behaviors that are under the limited behavioral control of the individual the
theory of planned behavior (TPB) (Ajzen, 1985) was proposed as an extension to the
TRA adding the variable of perceived behavioral control (PBC). The TRA and the TPB
explicitly study behavioral intentions and behavior as their dependent variables. “The two
theories are identical when the subjective probability of success and the degree of control

over internal and external factors reach their maximal values”(Ajzen, 1985, p.36).

16

However, Eagly and Chaiken (1993) questioned whether having control over a
behavior should predict behavior and they indicated that control should be relevant when
people intend to perform the behavior in question. As such, PBC was viewed by Eagly

and Chaiken as a better predictor of intentions than behavior.

Theoretical framework —The Theory of Planned Behavior (TPB)

The TPB as discussed previously evolved from the theory of reasoned action
(TRA) (Ajzen & Fishbein, 1980). The theory of planned behavior aims to capture the
factors that influence a person’s intentions to perform a behavior accounting also for
behaviors that are not under the volitional control of the individual (Ajzen, 1985). This
evolution in the theory included the concept of perceived behavioral control in addition to
attitudes and subjective norms toward the behavior. The PBC variable was added to the
theory of reasoned action to account for behaviors that were not under the volitional
~ control of the individual. A detailed discussion of each TPB construct follows.

According to the theory, attitudes are a function of beliefs regarding the outcome
of the behavior (Ajzen & F ishbein, 1980). These beliefs are called behavioral beliefs.
Subjective norms are also a function of beliefs that focus on what other people important
to the individual think about performing that behavior. These beliefs are called normative
beliefs. Referents with whom a person is motivated to comply think that he should
perform a behavior or not, create social pressure for that person to engage or not engage
in that behavior.

Perceived behavioral control accounts for behaviors that are not under the
volitional control of the individual. According to Ajzen (1991) “the importance of actual

behavioral control is self evident: the resources and opportunities available to a person

17

must to some extent dictate the likelihood of behavioral achievement. Of greater
psychological interest than actual control, however, is the perception of behavioral
control and its impact on intentions and actions” (p.183). The concept of perceived
behavior control is compatible with the concept of self-efficacy (Bandura, 1982). Self
efficacy focuses on “judgments of how well one can execute courses of action required to
deal with prospective situations” (Bandura, 1982, p. 122). Conceptually, perceived
behavioral control is expected to moderate the effect of intention on behavior, such that a
favorable intention produces the behavior only when perceived behavioral control is
strong. In practice, intentions and perceptions of behavioral control are often found to
have main effects on behavior, but no significant interaction (Ajzen, 2006).

A central variable in the theory of planned behavior is the individual’s intention to
perform a behavior (Ajzen, 1985, 1991). “Intentions are assumed to capture the
motivational factors that influence a behavior; they are indicators of how hard people are
willing to try, of how much of an effort they are planning to exert in order to perform the
behavior” (Ajzen & Driver, 1992, p. 208). Beyond the direct impact of the three key
independent variables (attitudes, SN, PBC), Ajzen (2006) in his TPB model
representation correlates the concepts of attitudes, SN and PBC. Figure 2 presents the

theory of planned behavior.

18

Figge 2. The Theory of Planned Behavior (TPB) (Ajzen, 2006)

Attitudes
toward the
behavior I,

  
   
 
     
   

Intentions to
perform the
behavior

Subjective
norms

 
   
   
 

Perceived
behavioral
control

 
   
 

Intention to engage in a behavior has been a key variable of study among tourism
and social psychology scholars (e.g. Ajzen, 1985; Albarracin et al., 2001; Armitage &
Conner, in press; Bigne et al., 2001; Cunningham & Kwon, 2003; Pctrick & Backman,
2001; Pctrick, Morais & Norman, 2001). Intentions in the tourism domain have been
researched in relation to other variables such as destination image, satisfaction and
service quality (Bigne et al., 2001; Pctrick & Backman, 2001; Pctrick et al., 2001).
However, a solid theoretical framework that accounts for travelers’ psychological
processes toward behaviors of tourism product providers and how these psychological
variables interrelate to predict intentions to travel to destinations has not been proposed in
the literature. To promote destinations and understand tourists’ intentions to travel to
these destinations, theoretical models such as the TPB which incorporate the
multidimensional (e. g. social, cognitive) determinants of behavioral intentions to travel to
a destination should be utilized.

The TPB has been successfully used in other domains such as exercise and leisure

behavior to predict behavioral intentions (e. g. Ajzen & Driver, 1992; Downs &

19

Hausenblas, 2005; Hagger et al., 2002). In the context of sport tourism, various scholars
have identified the need to proceed from descriptive research to more theoretically based
research studies that utilize theories from other domains such as cognitive psychology
(Gibson, 2004; Weed, 2005). This dissertation aimed to account for the lack of theoretical
frameworks that explain phenomena related to the consumer behavior of active sport

tourists.

Types of sport tourists: participants, spectators and nostalgia sport tourists

It is important to understand how sport tourists are classified. According to the
focus of the sport tourism activity (active or passive), sport tourists may have different
behavioral characteristics (Shamir & Ruskin, 1984). Looking at the definition of sport
tourism by Standeven and DeKnop (1999) sport tourism is “all forms of active and
passive involvement in sporting activity, participated in casually or in an organized way
for noncommercial or business/commercial reasons, that necessitate travel away from
home and work locality” (Standeven & DeKnop, 1999, p. 12). Following this definition,
it can be observed that two main behavioral characteristics of sport tourists are related to
active and passive components.

Sport tourists have been classified as participants/active tourists, spectators/
passive tourists and nostalgia tourists, which includes sport tourists who travel to visit
major sport related attractions and areas (e. g., the locations where major sport events took
place in the past) (Gibson, 1998). Each category (spectators, participants, and sport
museums visitors) is approached separately in the literature, where their different

characteristics, needs, motives, attitudes and behaviors are discussed (Gibson, 1998,

20

2004; Weed & Bull, 2004). For example, for active participants there may be more
physical health, environmental and exercise related factors influencing their decision to
attend a sport tourism event compared to spectators who may seek entertainment and
socialization. The nostalgia sport tourists may seek to reminisce and escape to a famous
sport place to re-live the experience.

There has been research examining spectator behaviors regarding consumption
behavior, team identification, emotional responses and motivation (Trail, Anderson &
Fink, 2000, 2002; Trail, Fink & Anderson, 2003; Trail & J arnes, 2001; Wann, 1996;
Wann & Branscombe, 1990, 1992, 1993; Warm & Dolan, 1994; Wann, Dolan,
McGeorgem & Allison, 1994; Wann & Schrader, 1997). Research on participants
(mainly recreationists-people who participate in activities near home) has looked at
motivations and place attachment variables but not behaviors. For example, Kyle,
Bricker, Graefe and Wickharn (2004) examined recreationists attachment with place and
activities utilizing the theoretical framework of place attachment. Personal investment
theory (Maehr & Braskamp, 1986) was used by various studies (Kaplanidou, 2004;
Raedeke & Burton, 1997; Tappe, Duda & Menges-Ehmwald, 1990) to understand
participants’ motivation. However, for most of the previously mentioned studies, the
main focus was on exercise participants and not on sport tourists who participated in a
sport tourism event. Due to the lack of research with active sport tourists (participants),
this study focused on that segment to understand the factors that predict their behaviors.
For this study, active sport tourists were defined as those people who participate

physically in an organized sport tourism event.

21

Sport tourism and the theory of planned behavior

Weed (2005) suggested sport tourism can be conceptualized and studied within a
framework of interrelationships among people, place and activities. The need for theory
is becoming stronger since the field is moving from the “what” and the “who” to
understanding “why” and “how” (Gibson, 2004; Weed, 2005). A recent study aimed to
capture intentions to attend a sport event as a spectator using the theory of planned
behavior as its main theoretical framework to fill theoretical shortcomings in explaining
sport consumers’ game attendance behaviors (Cunningham & Kwon, 2003). Cunnigham
and Kwon (2003) found spectators’ previous behavior, attitudes, subjective norms and
perceived behavioral control were positively associated and explained 67% of the
variance in intentions. The large amount of variance explained indicates the efficacy of
the theory in prediction intentions to attend a game. However, spectators tend to have
different leisure behavior patterns than active sport participants in terms of socialization
and motivation (Shamir & Ruskin, 1984). It is expected that active sport tourists have
different dispositions toward activities, events, places and people within the sport tourism
domain than participants.

The TPB has been used to predict behaviors such as playing video games,
performing cognitive tasks, election participation, voting choice, shoplifting and giving a
gift (Ajzen, 1991). Two studies supported the applicability of the TPB (Ajzen & Driver,
1992; Hrubes, Ajzen & Daigle, 2001) in recreation and leisure domains, studying
activities such as hunting, biking, jogging and boating. The first study (Ajzen & Driver,
1992) studied five recreational activities/behaviors among college students and found the

TPB was more appropriate to use than the TRA. The authors also found that attitudes but

22

not subjective norms were important predictors for spending time at the beach, jogging
and biking, while subjective norms made significant contributions to jogging, mountain
climbing and boating activities/behaviors. Furthermore, they found the addition of
perceived behavioral control improved prediction of intentions to perform each of these
recreational activities. Ajzen and Driver’s study distinguished between instrumental and
affective attitudes toward specific leisure activities concluding that people evaluate a
leisure behavior in terms of its instrumental costs and benefits generating positive or
negative feelings, which can offset the lack of benefits or the presence of costs.

The second study (Hrubes et al., 2001) focused on the prediction of hunting
intentions. The results of that study were that attitudes, subjective norms and perceived
behavioral control made significant contributions to the prediction of hunting intentions
when controlling for other variables, such as wildlife enjoyment, wildlife rights, self-
transcendence and openness. The respondents, however, were not described in the study
to be people who traveled to participate in an organized sport event such as a hunting
competition or a bicycle tour. In addition, these respondents were asked about their
intentions to participate in the physical activity and were not questioned about their

intentions to revisit areas associated with the activity itself in the future.

Why the TPB?

The TPB was deemed appropriate for this study because the main dependent
variable of the study was behavioral intentions to visit a destination. As mentioned
previously, the focus of the TPB is on behavioral intentions. Intentions to visit a

destination can be influenced by destination image (Baloglu, 1999; Bi gne et al., 2001;

23

Chalip et al., 2003), which in turn can be influenced by destination tourism product
characteristics such as the image of a sport tourism event (Chalip & McGuirty, 2004;
Pennington-Gray & Holdnak, 2002). Images entail attitudinal components (e. g.
evaluation of attributes) (Echtner & Ritchie, 1991; Jenkins, 1999). In this study, images
were conceptualized as attitudinal constructs. They replaced attitudes to explore the
interrelationship between the attitudinal constructs of sport tourism event image and
destination image and their impact on intentions.

As far as other components of the TPB are concerned, various meta-analyses
(Downs & Hausenblas, 2005; Hagger et al., 2002) showed the adequacy of TPB in
predicting exercise intentions and behavior. Because active sport tourists engage in
physical activities as participants in sport tourism events, the TPB was deemed
appropriate for this study. Also, Hagger et al.’s study has shown the importance of
subjective norms and perceived behavioral control in exercise behavior and beliefs. Since
this study is focusing on sport tourism participants, exercise is part of the event and as
such the social norms and perceived behavioral control related to the event should be
included in prediction of intentions. A more recent meta analysis of TPB studies
(Armitage & Conner, in press) has shown the adequacy of the theory in explaining
intentions. The latter meta-analytic study also revealed that the SN construct is generally
found to be a weak predictor of intentions mainly due to a combination of poor
measurement and the need for expansion of the normative component. In the same study,
PBC accounted for a significant amount of variance on intentions controlling for SN and

attitudes.

24

If the focus of this study was the evaluation of the link between sport tourism
event image- destination image with a focus on changing attitudes toward the destination,
other theories such as assimilation and contrast effects (Sherif & Hovland, 1961), the
match-up hypothesis (Kamins & Gupta, 1994) and schema congruity theory (Lynch &
Shuler, 1994; Meyers-Levy & Tybout, 1989) would have been adopted. However, this
study focused on intentions and consequently the TPB was deemed the most appropriate

for this study.

Subjective norms toward sport tourism events

Subjective norms (SN) can play an important role in activities that have a social
nature because SNs deal with the influence of the social environment on intentions and
behaviors (Ajzen & F ishbein, 1980). An example of an activity that has a social nature is
participation in sport tourism events such as biking, running, and walking. Subjective
norms refer to the person’s perception that most people, who are important to him/her
think he/she should or should not perform the behavior in question (Ajzen & Fishbein,
1980). Their role in the theory of planned behavior has been found to be contradictory.
In a meta-analysis of TPB studies in the context of physical activity Hagger et al. (2002)
found attitudes were strong predictors of intentions to participate in a physical activity
than subjective norms. Armitage and Conner (in press) also found SN to be a weak
predictor of intentions.

Subjective norms are becoming more important within the context of attending a
sport event as a spectator (Wakefield, 1995; Wann, 1995) due to the need for

socialization or social identification with the rest of the group or teams. In a meta-

25

analysis of elicitation studies for the theory of planned behavior on exercise beliefs
(Downs & Hausenblas, 2005), the most salient normative referents were found to be
family members and friends. The same may apply for sport tourism group activity events
like bicycling, walking or running. Family members and friends may be those salient
referents active sport tourist think they should trust. These referents may be supportive of
events where people with similar interests participate in a common task/activity. In a
study by Vogt, Nelson, Stynes and Fridgen, (2000) that asked bicyclists’ motives to
attend the event, the participants indicated that socializing was an important reason. This
motivation may influence sport participants’ intentions to participate in a sport event in
the future and also revisit places where the event took place. However, qualitative data
are needed to reveal the type of referent a sport tourist participant finds influential in his
decision making process.

Although results fiom previous research have been contradictory regarding the
role of subjective norms due to the nature of the activity studied, the role of subjective
norms was deemed important to be included in the conceptual model used in this study to

firrther test and validate its contribution to the predictive power of TPB.

Perceived behavioral control toward sport tourism events and destinations

The TPB is an extension to the theory of reasoned action with the addition of the
perceived behavioral control construct, which is hypothesized to influence intentions and
behavior directly. Perceived behavioral control refers to “the perceived ease or difficulty
of performing the behavior and it is assumed to reflect past experience as well as

anticipated impediments and obstacles” (Ajzen & Driver, 1992, p. 208). In the context of

26

this study, perceived behavioral control variables could include the lack of physical,
financial or time resources that can alter the intentions of a person. Downs and
Hausenblas (2005) found that the most frequently reported beliefs obstructing exercise
were health issues, inconvenience, lacking motivation and energy, time, and lacking
social support. Although, their study was about exercise beliefs and not participation in
sport tourism events, these control beliefs are examples of influential factors in decision
making processes regarding exercise participation. Different populations may have
different perceptions of perceived behavioral control variables. Consequently, qualitative
approaches are required to elicit these beliefs and validate their impact through
quantitative approaches (Downs & Hausenblas, 2005).

Ajzen and Driver (1992) provided two rationales for the inclusion of perceived
behavioral control in the theory. The first rationale involved the perceptions of skill and
effort a person believes to have regarding the behavior. The higher these perceptions are
the more inclined this person would be to engage in the behavior. The second rationale
involves the use of perceived behavioral control as a proxy measure for actual control.
However, the measure of actual control depends on how accurate the perceptions of
engaging in the behavior are (Ajzen & Driver, 1992). Hagger, Chatzisarantis and
Biddle’s (2002) meta analysis of studies utilizing the TPB in the physical activity context
showcased the importance of the perceived behavioral control in predicting intentions to
participate. Therefore, perceived behavioral control is necessary to be included in the
theoretical framework of this study to account and explain more variance in sport

tourists’ intentions to revisit a destination for leisure activities.

27

The concepts of destination image and event image as attitudinal constructs

In his theoretical framework on brand equity, Keller (1993) suggested that brand
image consists of attributes, benefits and attitudes toward an entity. Based on that
approach, sport tourism event image and destination image were conceptualized as
attitudinal constructs. Although not used in this study, a brief discussion of the concept of
attitude will provide a better understanding of the similarities between the concept of
image and attitudes. Attitude has been defined as “a psychological tendency that is
expressed by evaluating a particular entity with some degree of favor or disfavor” (Eagly
& Chaiken, 1993, p. 1). As Eagly and Chaiken (1993) explained, “psychological
tendencies” refer to the state that is internal to the person while “evaluating” refers to all
classes of evaluating responses whether cognitive, affective or behavioral that are elicited
from a stimuli. “Evaluating responses are those that express approval or disapproval,
favor or disfavor, like or dislike, approach or avoidance, attraction or aversion or similar
reactions” (Eagly & Chaiken, 1993, p. 3). Most individuals through some type of
communication or past experience will have positive or negative dispositions toward an
event and destination which means that they will respond to an event in a favorable or
unfavorable manner (Gwinner, 1997). The same applies to the overall evaluation of
destination image where people can perceive it as very positive or very negative (Baloglu
& McCleary, 1999) favorable or unfavorable. Images, therefore, can be attitudinal
constructs.

Aj zen and Driver’s (1992) studied attitudes toward leisure activities. As
previously discussed, images can be conceptualized as attitudinal constructs as they entail

beliefs and emotional components (Baloglu, 1999). Aj zen and Driver’s study of students’

28

recreational activities (e.g. biking, jogging) tested the theory of planned behavior and
found that attitudes toward leisure activities have affective and instrumental components.
The affective component is closely linked to the moods and emotions associated with the
performance of the leisure activity in question. Aj zen and Driver’s study segmented
attitudes into instrumental and affective because in the context of leisure, recreation and
tourism, not only can activities have instrumental benefits (e. g. health and fitness, making
friends) but they can also evoke certain emotions like exhilaration, fear, relaxation and
excitement. As such, the image of a sport tourism event can potentially be defined as a
cognitive and affective construct but further research is needed to understand the
dimensions of these constructs. A more detailed discussion of the concepts of destination

image and sport event image follows.

Destination image

Destination image is a construct that has drawn a lot of attention among tourist
scholars. The construct has been studied from various perspectives. Many studies have
investigated the destination image formation process (Beerli & Martin, 2004; Gallarza,
Saura & Garcia, 2002; Gartner, 1993; Govers & Go, 2003; MacKay & Fesenmaier,
1997). Other scholars aimed to understand the influences of destination image on
intentions to travel to a destination (e. g. Baloglu, 1999; Gartner, 1993; Lee, Lee & Lee,
2005; Tapachai & Waryszak, 2000; Woodside & Lysonski, 1989). Another stream of
research (e. g. Bigne et al., 2001; Court & Lupton, 1997) examined the relationship of
destination image with variables such as destination tourist service quality and tourists’

satisfaction and their impact on intentions to return to a destination.

29

However, the concept of destination image is not without its own conceptual
problems. The tourism literature does not seem to agree upon a universal definition of
destination image. The most cited definition (Jenkins, 1999) is by Crompton (1979, p.18)
“the sum of beliefs, ideas and impressions a person has of a destination.” Jenkins (1999)
indicated that Crompton’s definition relates to the individual, while there are other
definitions which acknowledge that images are shared by groups. Jenkins (1999) cited
another definition of image by Lawson and Baud Bovy (1977) that includes both personal
images and those images shared by groups. Lawson and Baud Bovy (1977) defined
destination image as the expression of all objective knowledge, impressions, prejudice,
imaginations, and emotional thoughts an individual or group might have of a particular
place. Various other definitions of destination image have been cited in reviews of the
destination image literature with the most recent review by Gallarza et a1. (2002) who
conceptualized the nature of destination image as complex, multiple, relativistic and
dynamic.

Gunn (1972) was among the first scholars to propose a theory of destination
image formation. He posited that the destination image formation process includes
organic, induced and modified induced images. Organic images represented the
accumulation of mental images of a place through life. Induced images represented the
modified images derived from researching prior to the decision to travel, while modified
induced images represent the images acquired at the destination through the interaction
with the tourism products and services (Gunn, 1972, p. 120). Destination images are also
known to be complex (F akeye & Crompton, 1991) consisting of cognitive, affective,

conative (Gartner, 1996) and global (overall) evaluations (Baloglu & McCleary, 1999).

30

Cognitive evaluations refer to the beliefs about a destination’s attributes whereas the
affective evaluations refer to the feelings toward the destination. Conative evaluations are
analogous to behavior because they evaluate the action component of the image (Gartner,
1993). These conative evaluations depend on the images developed during the cognitive
stage of image formation and evaluated during the affective stage (Gartner, 1993).
Global evaluations refer to the overall image perceptions of visitors; at least, that was
how they were measured (Baloglu & McCleary, 1999). Echtner and Ritchie (1991; 1993)
identified the multidimensionality of the image construct and indicated the existence of
three dimensions that support the image of any tourism destination. These dimensions
included the functional/psychological, the common/unique and the holistic /attribute.
Echtner and Ritchie (1993) explained “functional and psychological characteristics may
be perceived as individual attributes or as more holistic impressions. On the attribute side
there are numerous perceptions of the individual characteristics of the destination,
ranging from functional to psychological. On the holistic side, the fiurctional impression
consists of the mental picture (or imagery) of the physical characteristics of the
destination, while the psychological impression could be described as the atmosphere or
mood of the place” (p. 3).

Destination image has also been viewed as part of a destination’s brand (Blain et
al., 2005). This is stated explicitly in a revised definition of destination branding provided
by Blain, Levy and Ritchie (2005, p. 337): “Destination branding is the set of marketing
activities that (1) support the creation of a name, symbol, logo, word mark or other
graphic that readily identifies and diflerentiates a destination; that (2) consistently convey

the expectation of a memorable travel experience that is uniquely associated with the

31

destination; that (3) serve to consolidate and reinforce the emotional connection between
the visitor and the destination; and that (4) reduce consumer search costs and perceived
risk. Collectively these activities serve to create a destination image that positively
influences consumer destination choice” (p. 337).

In a review of the concept of brand image by Dobni and Zinkhan (1990), it was
suggested that brand image is a perceptual phenomenon formed through consumers’
logical and emotional interpretations which is affected by stimulus elements and
perceiver’s characteristics. Keller (1993) proposed that brand image consists of attributes,
benefits and attitudes which can be viewed as favorable (or unfavorable), strong (or
weak) and unique (or common). Attributes can be product or non-product related (e. g.
user imagery or usage imagery which is usually tied to brand personality
attributes/descriptors such as youthful, energetic, fun, boring).

As far as the measurement of the destination image concept is concemed,
cognitive evaluations are measured through a list of attributes that are related to the
individual’s cognitive perceptions of the destination. Affective evaluations have recently
been measured using four semantic differentials identified in environmental psychology
(Russell, Ward & Pratt, 1981) as the appropriate dimensions to describe affect toward a
place. These four semantic differential scales are: “pleasant/unpleasant”,
“relaxing/distressing”, “arousing/sleepy” and “exciting/gloomy”. These
adjectives/dimensions of affect form an affective response grid where each dimension is
not independent of each other but instead they represent a circumflex model of affect
(Russell & Pratt, 1980; Russell & Snodgrass, 1987). In the destination image literature,

Baloglu and Brinberg (1997) showcased how the affective response grid can be applied

32

to destination image measurement. Other studies that used this affective measurement in
destination images are by Baloglu and McCleary (1999), Baloglu and Mangaloglu
(2001) and more recently by Pike and Ryan (2004). Global evaluations are a function of
cognitive and affective image evaluations (Baloglu & McCleary, 1999). These global
evaluations of destination image have been measured with one item. However, global
evaluations are useful in studies that aim to examine the destination image formation
process. Tourism studies where key dependent variables are intentions to visit or revisit a
destination, positioning or differences of image among visitors utilize a set of cognitive
and affective items to measure destination image (e. g. Bonn, Sacha & Dai, 2005; Chen,
2001; Lee et al., 2005; Pike & Ryan, 2004). As a result when the goal of a study (such as
this one) is not the evaluation of the formation of destination image, global evaluations
are not deemed necessary to be used in the measurement process. The most frequent
measurement of destination image involves using cognitive, affective and conative
evaluations with a greater focus on cognitive evaluations (Pike, 2002). A visual
representation of the measurement components of a destination image is depicted in
Figure 3. In this figure, destination image is conceptualized as a reflective factor and not
a formative one. Reflective factors are viewed as affected by the same underlying
concept (i.e., the latent variable) while formative factors are viewed as the result of a
combination of relatively uncorrelated variables (Chin, 1998). The image of the
destination is also highly related to touristic benefits sought. The benefits determine the

image of the destination before and after the visit (Baloglu & McCleary, 1999).

33

Figge 3. A measurement model of destination image: Destination image as a reflective
factor

 

Furthermore, Gallarza et a1. (2002) indicated the concept of tourism destination
image has a relativistic nature. Relativistic is subject to subjective tourists’ perceptions
of different tourism services offered by a destination: accommodation, food,
transportation, as well as, other activities. A sport tourism event offered at a destination
will be part and parcel of the benefits sought, especially for those individuals who travel
to the destination for that event. Under this perspective, the image of the destination
could be directly influenced by the image of the sport tourism event. The ways
destination image works synergistically with other tourism “products” present at a
destination, such as sport tourism events is unexplored. Sport tourism events have
distinct image for consumers but their image have only been examined in a few studies
from the spectator’s perspective (Chalip et al., 2003; Ferrand & Pages, 1996). More

research is needed to understand the image perceptions of other sport tourist types such as

34

active participants. A more analytical discussion about the concept of sport tourism event

image follows.

Sport tourism event image

Destination marketers seek smaller recuning events that take place on a regular
basis to promote their destination (Dimanche, 2003; Getz, 1998). In addition, event
marketers need to know how to achieve a better brand image for their events. It is
therefore important to understand and define sport tourism event image and its
components. Local sport tourism events such as bicycling, running or walking are taking
place in smaller communities and have not been the focus of sport tourism studies
(Chalip & Green, 2001). As far as the measurement of a sport tourism event’s image is
concerned, the literature does not currently provide a scale or a definition for a sport
tourism event’s image. The closest attempt to identify the image of an event was offered
by Gwinner (1997) who proposed that an event’s image is a function of the type of event
(e. g. sports, festival, arts), event characteristics (e. g. size, professional status, history,
venue, promotional appearance) and individual factors (e. g. meanings associated with the
event, strength of meanings and past history with the event). However, Gwinner’s
approach is generic and not customized for sport tourism events that feature elements
such as competition, socialization, skill requirement and knowledge. Other studies (e. g.
Ferrand & Pages, 1996; Gwinner & Eaton, 1999; Musante et al., 1999) have measured
the image of the sport event in the context of matching it with a sponsor using adjectives
as the event’s image descriptors. Musante et al. (1999) used the brand personality scale

(Aaker, 1997) to measure the personality match of sponsor brands and sports. Overall,

35

these studies have aimed to measure the fit between sport event image and sponsor image
and have found that the image of the sport event was perceived as sophisticated, exciting
(Musante et al., 1999), strong, methodical, young, masculine (Martin, 1994), popular,
entertaining, dynamic and successful (Ferrand & Pages, 1996) by spectators. These

results are presented in more detail in Table 2.

36

Table 2. Studies m_e_asurin2 thwrt event im_age from a spectators’ persmctive

 

 

 

 

Author Journal Title Variables Measurement Results
Musante International Sport Evaluate the Rate of subjects Study 1:
and Journal of sponsorship personality of favorability of the five subjects
Milne Sport evaluating five brands sports (nine point scale, students
1999 Marketing the sport and five sports 9=favorable, l=not Personality fit
and and brand on the adapted favorable) —used as between sport
Sponsorship image Aaker brand covariates to control for and brand is a
match personality individual preferences better predictor
scale than
(4 items: Five brands and five demographics.
exciting, sports were evaluated on Sport brand
wholesome, the four brand factors
rugged, personality scale factors sophisticated
sophisticated) (1=least represents the and exciting
brand, 5=best represents accounted for
Favorability the brand) most of the
with sport and variance on the
brand Sponsorship fit on a nine overall
point scale when brands sponsorship fit.
Demographics and sports were matched
(1=poor fit, 9=good fit) Study 2: 153
Perceived fit NBA spectators
NBA Use a brand sport all star
attendance, personality fit indices: weekend-data
NBA interest, Brand-sport personality collected at a
NBA fit= festival
viewership , organized by the
(high-low) l r city of
income, _ ‘ Cleveland as
gender . 2(bpfi Spfi) part of the event
1 =1 (evaluated 1
sport: basketball
Where bpfl=the in, brand andfl: 0 brands
personality factor score on e four
, personality
(average) and Spfi 15 the factors).

 

 

 

 

ith sport personality
factor score
Technique used:
Regression model

 

Results: highest
fit: functional
(athletic
footwear and
isotonic
beverage).

 

37

 

flble 2. Coming

 

 

 

Author Journal Title Variables Measurement Results
Martin, Sport Using a Rated similarity Subjects: 141 college S_ix_ dimensions
1994 Marketing perceptual among 10 sports students based on their
Quarterly map of the using a paired Instrument: similarity
consumer’s comparison questionnaire
sport procedure to Strength vs.
schema to understand why Rate the similarity of methodical
help make some sports are pairs of sports on a
sponsorship perceived scale from 1=not at all Athletes vs.
decisions similar and similar, 7=extremely recreationists
other different similar

 

 

 

 

Pretest with another
student sample to select
which sports to include
in the questionnaire
based on their
familiarity with the
sports and also how to
pair the sports based on
how highly
similar/moderately
similar/highly
dissimilar they were
perceived

153 pairs of sports

Multidimensional
scalirg

 

Group practice vs.
individual practice

Object skill vs.
body skill

Young participant
vs.

various ages from
young to old

More masculine
vs. less masculine

 

38

 

Table 2. Continued.

 

 

 

Author Journal Title Variables Measurement Results
Ferrand Journal of Image Event: Sample: Common

and Sport sponsoring: A Annual representative of image

Pages Management methodology Lyon the event’s dimensions

1996 to match event TENNIS spectators based were found for

and sponsor Grand prix on variables both event and
event such as sex, age, sponsor: these
Sponsor: occupation and were “being
Perrier event interest popular” and
“entertaining”
Symbolic Pilot test of the and
meaning of 300 adjectives being
Perrier and with a “dynamic” and
the Tennis convenience “successful”
through a sample of 80
300 subjects familiar
adjective with both
list entities—select
most

Intensity of representative
association adjectives for

 

 

 

 

the event and
sponsor

Resulted in 23
adjectives for
the Tennis and
16 for Perrier

Association
intensity of
brand and
adjectives “do
you think the
brand is. . .?”
Agree/disagree
5-point scale

Analysis:
Canonical
correlation

 

 

39

 

Keller’s (1993) seminal work on brand equity presents a thorough theoretical
framework on brand knowledge and its constituents: brand awareness and brand image.
Brand awareness “is related to the strength of the brand node or trace in memory, as
reflected by consumers ability to identify the brand under different conditions. . .in
particular brand name awareness relates to the likelihood that a brand name will come to
mind and the ease with which it does so” (Keller, 1993, p. 3). Brand image was defined
by Keller as “perceptions about a brand as reflected by the brand associations held in
consumer memory” (1993, p. 3). Keller (1993) postulated that brand image is defined by
the types of brand associations which in turn are defined by attributes, benefits and
attitudes toward the brand. Attributes are product related (ingredients necessary for
performing the product or service) and non-product related (external aspects of the
product or service that relate to its purchase or consumption) (Keller, 1993). Benefits are
related to the personal value consumers assign to the product or service and consist of
functional, experiential, and symbolic dimensions (Keller, 1993). Functional benefits are
related to intrinsic advantages of the product or service; experiential benefits relate to
what it feels like to use the product or service; and symbolic benefits are the extrinsic
advantages of the product or service consumption (Keller, 1993). Finally, brand attitudes
are consruners’ overall evaluations of a brand (Keller, 1993).

Sport tourism events can have a brand image which is formed through media
exposure, word-of-mouth, advertisement and personal experiences. Sport tourism events
consist of certain attributes, have benefits or costs for the sport tourist (participant and
spectator) and can be the object of attitudes especially for those having participated in

such an event. Keller’s (1993) framework is suitable to explore the dimensionality of a

40

sport tourism event image because a sport tourism event can have all of these
components. This framework is suitable for both participants and spectators since both
groups will have formed perceptions about these components. However, since the nature
of the experience is different for active participants than spectators, this study focuses on
exploring the participants’ perspective, as there are not many studies that have
investigated the images this segment of sport tourists hold. Figure 4 presents a visual
representation of what may constitute the image of a sport tourism event from the
participant’s point of view.

Figre 4. A conceptual framework of the sport tourism image event from the participant’s
perspective (adapted from Keller, 1993).

 

Although Keller’s framework is indicative of what may constitute the image of a
sport tourism event, qualitative research is required to understand and define the brand
image associations that a consumer has about a sport tourism event (Dobni & Zinkhan,
1990; Keller, 1993). This type of research is necessary because sport tourism event

organizers need to understand the event participants’ salient image components to better

41

promote the event in their prospective target markets. Also, event organizers can benefit
from this information by working cooperatively with destination marketers to promote
the event and promote the area where it is taking place.

A number of studies have examined the image of a sport tourism event from the
spectator’s perspective in an attempt to match the image of the event with that of the
sponsor or athlete (F errand & Pages, 1996; Gwinner & Eaton, 1999; Martin, 1996;
Martin, 1994; Musante et al., 1999). Other studies have examined the impact of media
coverage of a sport event on destination image (Chalip et al., 2003; Green, Costa &
Fitzgerald, 2003) while other scholars have researched the impact of mega events on
hosting destinations/cities (Bieger, Laesser, Scherer, Johnsen & Bischof, 2003; Brown,
Chalip, J ago & Mules, 2002; Chalip & McGuirty, 2004; Kang & Perdue, 1994; Kim &
Morrison, 2005; McCartney, 2005; Mossberg, 1996; Ritchie & Smith, 1991; Ritchie &
Yangzhou, 1987; Ritchie, 1984; Roche, 1994; Rooney, 1988; Smith, 2005). None of
these studies, however, defined or measured the image of a sport event as a stand alone
concept.

Mega sport events have been shown to have a positive impact on destination
image (Chalip & Green, 2001; Chalip et al., 2003; Kim & Morrison, 2005; Ritchie &
Smith, 1991; Ritchie & Yangzhou, 1987; Smith, 2005). However, for recuning sport
tourism events of a smaller scale, like local running, bicycling and walking events there
are no studies to showcase the effects of the event’s perceived image on destination
image and the users’ intentions to revisit the destination. Based on the literature that
examines the impact of mega sport events, a positive relationship is expected between a

sport event’s image and destination image. In other words, the image of a sport event is

42

expected to positively influence the image of the destination but this has not been
confirmed. In the case of an unsuccessful sporting event (e. g. bad organization), the
image of the sport event may be influenced in an unfavorable way. This influence may

cause the relationship of event and destination image to be negative.

Measurement of a sport tourism event image-creation of scale

There is no available research that examines the image perceptions of a sport
tourism event from the participant’s point of view. Brand personality scales used in the
past for spectators may not be adequate to capture the concept of image. Brand image as
presented in Keller’s (1993) framework has components related to attributes, benefits and
attitudes. Brand personality scales seem to only capture the human characteristics
associated with a brand (Aaker, 1997). In the case of the image of a sport tourism event,
brand personality scales are not adequate because they capture only the symbolic or self-
expressive function of a brand (Aaker, 1997). Sport tourism events have other aspects
attached to them such as participation experience related attributes, benefits associated
with the event, social and spatial aspects (Bouchet, Lebrun & Auvergne, 2004; Kurtzman
& Zauhar, 2003). Qualitative approaches are needed to understand and identify the
important attributes/components of the images a person has of a sport tourism event
(Dobni & Zinkhan, 1990). Dobni and Zinkman (1990) in their analysis of brand image,
discussed the various elements present in definitions of brand image. These elements put
emphasis on symbolism, on meanings and messages, on personification and on cognitive
or psychological elements. Qualitative approaches will reveal those attributes that are

related to the image of a sporting event from the participant perspective. Churchill (1979)

43

indicated that, in order to generate the sample of items to construct a scale, various
approaches could be used. Literature search, experience survey, insight-stimulating
experiences, critical incidents or focus groups are some of the methods that can produce
these items. Specifically, focus groups allow for efficient gathering of qualitative data
compared to interviews. Focus groups also allow for group interaction which can provide
interesting insights to the participants interests and experiences (Morgan, 1997).
Furthermore, focus groups “provide preliminary research on specific issues in a larger
project” (Morgan, 1997, p. 17) when combined with other research methods and they
contribute in the creation of survey items (Morgan, 1997). Morgan (1997) stated that the
three basic ways focus groups contribute to the creation of survey items are that: a) they
capture all domains that need to be measured in the survey, b) they determine the
dimensions that make up each of these domains and 0) they provide item wordings that
effectively convey the researchers’ intent to the survey respondent (Morgan, 1997, p. 25).
This study will develop the items for a sport tourism event image scale utilizing focus
groups and validating these items through quantitative methods utilizing Keller’s (1993)

theoretical propositions of brand image.

The role of past behavior/past experience

Another variable for consideration in the TPB is past behavior or past experience.
“The addition of past behavior to the model is eminently sensible from the behaviorist
perspective, which postulates that behavior is influenced by habit or more generally, by
various types of conditioned releases or learned predispositions to respond that are not

readily encompassed by the concepts of attitude and intention” (Eagly & Chaiken, 1993,

44

p. 179). Hagger, Chatzisarantis and Biddle (2002) emphasized the need for inclusion of
past behavior in the theory as an important variable for the predictive validity of the TPB
model. More specifically, the latter study found that past behavior is a strong predictor of
intentions to participate in a physical activity. Cunningham and Kwon (2003) also found
that the addition of the variable of past behavior significantly contributed to an
understanding of intentions to attend a game, explaining 2% unique variance beyond the
effects of other independent variables. It is also expected that the image of the sport event
is influenced by previous participation in the event since the participants will know what
to expect and they will hold certain event images. However, there is no evident research
to support this assumption, but it will be tested with this study.

Research in the tourism field indicates that previous experience with a destination
increases intentions to return to a destination (Hu & Ritchie, 1993; Kozak, 2001;
Mazursky, 1989; Milrnan & Pizam 1995; Perdue, 1985; Pctrick et al., 2001; Sonmez &
Graefe, 1998). One explanation for the influence of past experience on intentions to
return to the destination is that destination choice is perceived as less risky and safer.
Gitelson and Crompton (1984) indicated that the common factor predicting the reasons
travelers repeat a vacation experience is because past experience reduces “the risk that an
unsatisfactory experience is forthcoming” (p.199). Furthermore, past experience with the
destination positively influences destination image. For example, Milrnan and Pizam
(1995) and Fakeye and Crompton (1991) found previous experience with a destination
positively influenced the image of the destination. Other studies showed previous
experience influenced more positively specific attributes of the destination image such as

outdoor recreation than nightlife (Ahmed, 1996). Additionally they found differences

45

between non visitors and visitors in dimensions of safety, scenic beauty, shopping and
general attitudes toward the destination (Chon, 1991).

Baloglu (2001) found that previous experience with the destination influence
positively the destination image perceptions. Visitation experience with the destination
creates a more realistic image compared to images held before visiting (Gartner, 1989;
Gunn, 1972). In addition, previous visits resulted in more affective destination image
perceptions (Baloglu & Brinberg, 1997; Dann, 1996). However, there are other studies
which found no significant influence of prior visitation on a destination’s image (Chen &
Kerstetter, 1999; Hunt, 1975). Most of the studies nevertheless show support for the
positive effect of prior visitation on destination image.

Past behavior (past experience) with a sport tourism event is also expected to
positively correlate with past behavior (past experience) with the destination. In other
words, the experience sport tourists gain with the event participation would correlate with
the experience they had when they previously visited the destination that is currently
hosting the event. They would see the destination as part of the event and as such any
previous experience with the one would impact the other.

Past behavior with the event and the destination was included in the model
proposed in this study. Its impact will be associated with the sport tourism event image,
the destination image and also with intentions to travel to the destination. In summary,
past experience (past participation) in the event is expected to positively influence event
images perceptions while past experience (visits) with the destination is expected to
influence destination image and intention to revisit the destination. Also, past

participation in the event will be positively correlated with past visits to the destination.

46

Intentions and destination image

In the context of tourism, intentions to travel to destinations have been studied in
theoretical contexts other than the TPB. Those theoretical frameworks include the role of
satisfaction and quality on intentions to return to a destination (e. g. Bigne et al., 2001;
Pctrick & Backman, 2001; Pctrick et al., 2001) and relationships between psychological
variables and destination selection (6. g. Baloglu, 1999; Weaver, McCleary, Lepisto &
Damonte, 1994). For example, a study by Bigne et al. (2001) studied the impact of
destination image, quality and satisfaction on intentions to return to a destination
concluding that the more favorable the image of the destination the higher the probability
the tourist will return in the future. Court and Lupton (1997) found the image of the
destination to be positively affected intentions to revisit. Blain et a1. (2005) in their
definition of destination branding explicitly stated that destination image influence
consumers’ destination choices. These three studies showcased the direct positive
influence of destination image on intentions. However, destination image can also
mediate the relationship between visitation intentions and stimuli such as information
sources and social-psychological travel motivations (benefits sought) (Baloglu, 1999).

Other studies that examined the positive impact of destination image on intentions
include Tapachai and Waryszak (2000) and Woodside and Lysonski (1989). Tapachai
and Waryszak (2000) assessed the role of beneficial image on decision to visit a vacation
destination concluding that beneficial images contain essential information that influence
decisions to revisit a destination. Beneficial image was defined as an amalgam of five
consumption values: functional (utilitarian), social, emotional, epistemic (provide novel

experiences) and conditional (value of alternatives) (Tapachai & Waryszak, 2000).

47

Woodside and Lysonski (1989) investigated the impact of travelers’ destination
awareness on preferences and on intentions to visit specific places. Their results revealed
that there is a positive relationship between images of countries found in their

consideration sets, preferences and intentions to travel.

Model presentation

The sport tourism market is growing in importance. Many destinations seek to
differentiate their tourism product. More and more cities are trying to be the host of a
sport tourism event. It is important, therefore, to understand the perceptions sport tourists
have of a sport tourism event and how these perceptions relate to the image of the
destination. It is important to understand the factors that impact sport tourists’ behaviors
and create demand for this activity. This study proposes a model that aims to test the
impact of the link between the image of a sport tourism event and the image of the
hosting destination on intentions to revisit the destination. This study also aims to
develop a sport tourism event image scale to measure the mental factors that constitute
sport tourism event image.

Figure 5 depicts the model that was tested in this study based on the theory of
planned behavior incorporating relationships between event and destination image and
past behavior on intentions to revisit the destination.

The hypotheses established for this model are the following:
H1: Event image will positively influence the image of the destination;
H2: Destination image will positively influence intentions to travel to the destination;

H3: Subjective norms will positively influence intentions to travel to the destination;

48

H4: Perceived behavioral control will positively influence intentions to travel to the
destination;

H5: Past visits to the destination will positively influence intentions to travel to the
destination;

H6: Past visits to the destination will positively influence the image of the destination;
H7: Past participation in event will positively influence the image of the event;

H8: There will be a positive correlation between past participation in the event and past
visits to the destination; and

H9: There will be a positive correlation between subjective norms and perceived

behavioral control.

49

Figge 5. Proposed conceptual theoretical model to be tested in this study
Note: He observed variables measuring each of the latent factors are not depicted in this figure
for clarity purposes.

      
        

         
   
  
  

 

Past Past
behavior- behavior-
sport tourism destination

   

event

      

Destination
Image (DI)

Sport tourism
event image
(ST El)

 
 

    
  
 

 
   

 

Subjective norms
about the event
participation

{...

Perceived
behavioral control
about the event
participation (PBC

 
 
   

 
   

Intentions to revisit the
destination to
participate in leisure
activities

    
     
 
     
     
   

Model challenges

Ajzen (1991) suggested that the predictive validity of the model of the TPB is
enhanced when the behavior of interest is defined in terms of its Target, Action, Context
and Time (TACT). Defining the TACT elements is somewhat arbitrary (Aj zen, 2006).
But, no matter how the TACT elements are defined, there should be compatibility

between the constructs of the theory of planned behavior (e. g. SN, PBC), intentions and

50

behavior. For example, if the behavior is to “visit the destination to participate in leisure
activities in the following two years”, the other measures of attitudes, SN and PBC
should be phrased in terms of the behavior’s target (leisure activities), action (visit),
context (destination) and time (following two years) for the model to increase its
predictive validity. However, in the model tested in this study, there are two attitudinal
constructs: event and destination image. One of the study objectives was to test the link
between these two concepts utilizing TPB. Since the two attitudinal constructs differ in
TACT, there had to be some discrepancy in the measures of image, SN, PBC and
intentions. To manage this challenge the intentions construct was designed to consist of
three items that all together aimed to create better compatibility between SN, PBC, event
image and destination image. The following chapter discusses the method followed to

test this model and the analysis of the data.

51

CHAPTER 3
METHOD

The problem of the study was to investigate the impact of sport tourism event
image on destination image and intentions to revisit a destination utilizing the TPB. A
scale that measures the image of a sport tourism event was necessary but not available in
the literature. The literature review revealed the directionality of the relationships among
the model variables proposed in this study. It is hypothesized that destination image
mediates the relationship between sport tourism event image and intentions to revisit the
destination where the event took place to participate in leisure activities. It is also
hypothesized that SN and PBC about event participation will positively influence
intentions to revisit the destination. Finally, it is also hypothesized that participants’ past
participation in the event and past visits to the destination will positively influence their
event image, their destination image perceptions and their intentions to revisit the

\
destination. The correlations between the factor variables of event image, subjective
norms, perceived behavioral control, past participation in the event and past visits to the
destination are expected to be positive.

The method used in this study included the following steps: a) instrumentation b)
sample selection and survey administration; 0) evaluation of sources of survey error; (1)
model evaluation; and e) variable recoding and variable computing descriptions.

Instrumentation included the development of a scale to measure the image of a
sport tourism event from the participant’s perspective. This was achieved through focus

groups in order to elicit the scale items for the event tourism image construct. The same

focus groups were also used to investigate beliefs behind other key variables in the study:

52

destination image, SN and PBC. Furthermore, a questionnaire was designed for the
purpose of this study, and the scale was tested for reliability and validity. The sample
selection and survey administration step included the identification and delimitation of
the population, the determination of an appropriate sample size, the selection of the
participants and the administration of the survey. The evaluation of the sources of survey
error phase included checks for measurement error and non response error (Dillrnan,
2000). The model evaluation phase of this chapter discusses the strategies involved to

test the hypotheses and the fit of the model to the data.

Instrumentation

As the literature review revealed, a scale for the measurement of a sport tourism
event image did not exist. Consequently, one of the objectives of this study was to
develop a reliable and valid scale that can be used by event organizers and tourism
marketers. To develop the scale, the steps proposed by DeVellis (2003) were followed.
The first step was to determine clearly what was going to be measured using theory to
delimit the meaning of the construct. This was accomplished by a thorough literature
review of the marketing, consumer behavior and tourism fields related to the concept of
brand image. Keller’s (1993) conceptualization of brand image was adapted for the sport
tourism event image. This conceptualization was the main source for the formulation of
questions for the focus groups. The script utilized in the focus group discussions can be
found in Appendix A.

The second step included the generation of the large pool of items “that are

candidates for eventual inclusion in the scale” (DeVellis, 2003, p. 63) and reflect the

53

scale’s purpose. This stage of the scale development process requires redundancy of
items in order for the relevant items to come together and “the irrelevant idiosyncrasies to
cancel out”(DeVellis, 2003, p. 65). The pool of items was derived from two focus groups.
The one focus group consisted of eight bicyclists who belonged to a university bicycling
club. The other focus group consisted of four participants from the target sample. Focus
groups took place in conference rooms at Michigan State University under the guidance
of a moderator and the presence of one assistant moderator. The duration of each focus
group was one hour and the participants were each given $15 dollars for their
participation. The discussion was tape recorded. Other information recording items
included paper and pencil exercises and flip charts. The discussion was semi-structured,
and the moderator made sure all participants contributed to the discussion by asking each
member of the group to communicate their thoughts. One of the discussion segments was
a free elicitation of image descriptors for both the event and the destination and a
commentary from each of the respondents on the choice of their answers. The focus
groups data analysis is reported more analytically in the results section. The qualitative
analysis yielded 41 items as the initial pool for the scale development process.

The next step in the scale development process was to determine the format for
measurement. The semantic differential was chosen because the goal of the scale was to
measure image. Semantic differentials are ideal for image measurement (Alreck & Settle,
1995). “The major advantage of the semantic differential is its ability to portray images
clearly and effectively. Since several pairs of bipolar adjectives are used, the results
provide a profile of the image of the topic that is rated” (Alreck & Settle, 1995, p. 129).

The format of the scale included a set of opposite adjectives that described the

54

phenomenon based on the focus groups data. The focus group data analysis yielded the
initial adjective list (41 items) and the author generated their opposite. The scale
adjectives were rated on a seven-point response format. Specifically, the seven points
were labeled as extremely, quite, slightly, neither, slightly, quite, extremely, and the
respondents had to check a line that represented their perception of the sport tourism
event image amongst the set of opposite adjectives. The seven-point scale format was
used both at the pre-test and during the final mailing of the survey.

The fourth step in scale development was to have the initial pool of items
reviewed by experts. Due to time pressure this step was combined with the pilot testing
phase. Part of the pilot testing sample were four PhD students from the tourism and
recreation field. These students were asked to confirm or invalidate the definition of the
phenomenon and evaluate the items’ clarity and conciseness (DeVellis, 2003, p. 86) by
providing comments. The aim of the pilot-testing was to reveal discrepancies and further
purify the measure.

The next step in scale development involved the inclusion of validation items.
This was achieved by including five items that would support the construct validity of the
measurement. These items were borrowed from the brand personality scale developed by
Aaker (1997) which has been used by researchers (e.g., Musante et al., 1999) to measure
the brand personality of sport events from the spectators’ perspective. This scale used
adjectives which respondents rated based on how descriptive they were of the event.

The sixth step in the scale development process was to administer the items to a
development sample. This step in the scale development process served as the pilot

testing phase and was combined with step four. For this purpose, a web survey was

55

developed and used, which included the forty-one items and a few questions about the
respondents. The sample of respondents was based on a non-probability sampling
technique called snowball sampling. “This procedure is appropriate when the members of
a special population are difficult to locate” (Babie, 2001, p. 180). In this case, sport
tourist participants were the target sample. The requirement for the participation in the
pilot test phase of the scale development was to have participated actively in an organized
sport event which required travel away from home. As such, people who participated in a
sport event told a friend who participated in an event to complete the scale evaluation and
so on and so forth. Forty-four sport tourists of various events (e. g. marathons, triathlons,
golf tournaments, bike events) participated in the pilot testing of the scale. These
participants rated the scale items and provided qualitative comments for the items and the
content validity of the scale.

Beyond the qualitative comments analysis, the items were factor analyzed using
varimax rotation with an eigenvalue criterion value of one. The results fi'om the factor
analysis and reliability analysis with the pilot testing sample are presented in Table 3. All
factors and their items were retained for use in the final mail survey except for those
which generated negative comments by the pilot-testing sample. Due to the small sample
(n=44) of the pre-test, the results had to be treated with caution. If the items that loaded
on the factors were not inclusive enough based on the focus groups data, then the
remaining items were reviewed to make sure that unique items were not excluded.
Reliability analysis was undertaken for the extracted factors which captured the concept

of sport tourism event image.

56

Table 3. Factor analysis of the sport tourism event image scale pre-test results

 

 

 

Factor Factor Factor
Items 3 Item behavior Action taken
Expensive-cheapl -0. 18 V 0.66 I 0.25 Reworded"
Bad-good2 0.69 0. 19 -0.08 Reworded”
Unsuccessful- Reworded"
Accomplishing’ 0.62 0.37 -024
Unpolluted-polluted‘ -0.62 0.01 037 Negative loading Reworded"
Passive-competitive 0.28 047 -046 Did not load on any factor Included“
Distressing—relaxing 0,18 0.00 044 Did not load on any factor Included”
Inefficient-efficient 0.31 0.20 0. 16 Included“
Individualistic-collective -0. 16 0.31 026 Did not load on any factor Included"
Disorganized—organized 0.60 0.25 0.09 Included
Artificial-natural 0.52 -0.35 0.49 Included
Unappealing-appealing 0.50 -0.36 0. l4 Included
Easy-challenging 0.65 -0.3 5 -0. 1 6 Included
Inactive-active 0. 5 8 0.45 0.05 Included
Worthless-valuable O. 70 0.3 7 -0.04 Included
Unfriendly-friendly 0.75 -0.02 0.24 Included
Unsupportivc-supportive 0.65 0.30 -0. 1 0 Included
Unsafe-safe 0.45 0.04 0.40 Included
Ugly-beautiful 0.53 -0.43 0.41 Included
Stressful-carefree 0.09 -0.38 0.55 Included
Gloomy-cheerfirl 0.54 024 0.34 Included
Boring-exciting 0.68 -0.1 1 -0.24 Included
Enervating-invigorating 0.70 0.02 -0. 1 5 Included
Sad-joyful 0.63 0.24 0.07 Included
Uninspiring-inspiring 0.73 -0.04 -0.05 Included
Unadventurous-adventurous 0.58 0.04 -0.52 Included
Unstimulating-stimulating 0.72 0.0 1 -0. 20 Included
Mental-physical 0.35 0.57 0.03 InCluded
Unhealthy-healthy 0.48 0.5 l 0. 20 Included
Impersonal-personal 0.64 -0.24 -0. 13 Dropped”
Unhappy-happy 0.72 -0.09 0. 1 7 Dropped"
Uneventful-cventful 0.35 -0.24 —0.60 Dropped“
Antagonistic-synergistic 0.59 0.30 -0.29 Dropped"
Diversified-monotonous —0.49 0.5 6 -0. l 3 Dropped"
Embarrassing-Proud 0.53 0.03 -0.30 Dr0pped"
Dirty-clean 0.38 -034 016 Did not load on any factor Dropped
Simple-luxurious -0.03 -0.53 -0.29 Negative loading Dropped
Inconvenient-convenient 0.08 0.24 0.38 Did not load on any factor Dr0pped
Unhelpful-helpfirl 0,35 0,05 .028 Did not load on any factor Dropped
Aroused-unaroused —0,22 0.29 0, 33 Did not load on any factor Dropped
Uncomfortable-comfortable 0.20 -0.37 0.21 Did not load on any factor Dropped
Mature-youthful 0.13 0.44 0.17 Did not load on any factor Dropped
Cronbach‘s a 0.92 0.58
Variance explained 26.35% 10.40% 8.40%

 

57

Note: a) The negative items were on the left side of the scale and rated with l and the positive items were
on the right side of the scale and rated with 7. b) Only the included items were subject to reliability
analysis.

"‘ Based on focus groups qualitative data; *"‘ Based on pilot-testing comments

1 . . . .
. Reworded to inexpensrve-expensrve-rncluded

2. Re-worded to poor-excellentO-included

3 . Reworded to unfirlfilling-fulfilling-included

4. Reworded to polluted-clean—included.

Cronbach’s alpha for the first factor was 0.92 and for the second factor was 0.58.
Although the alpha coefficient for the second factor was not satisfactory, the items were
retained in the pool to further test with the larger sample of respondents in the final mail
survey.

Further validation and measure purification was to take place with the
administration of the scale to the target sample. From the factor analysis results, the
comments provided by the participants and the focus group data, a list of 28 items (some
of which were reworded based on the pilot-testing recommendations) was included in the
final mail survey. These items included descriptors of attitudes, benefits and attributes,
which based on Keller’s framework (1993), comprise the concept of brand image.
However, the list of final scale items was yet to be determined with the final mail survey.
The seventh step in the scale development process was to evaluate the items and the
eighth step was to optimize scale length. These two steps will be reported at the results

section of this dissertation.
Other scales and items used in this study

The scales to measure the construct of destination image were derived from the

focus group data, previous studies and a review of the destination’s (South Haven)

58

promotional material such as its destination travel guide and its website. These
destination image scales included a set of cognitive and a set of affective items. The set
of cognitive items was generated based on previous destination image measurement
studies (for a review see Gallarza et a1. (2002)) which studied destinations with similar
geographical, environmental and cultural characteristics. As a result, a set of 17 cognitive
items and a set of five affective items were generated and used in this study to measure
the image of the destination under study (see Table 4). The cognitive items involved
destination attributes, and the respondents had to evaluate how much of each of those
attributes the destination offered. The affective items were based on a semantic
differential derived from studies focusing on the affective meaning of the environment by
Russell and his colleagues (Russell & Pratt, 1980; Russell et al., 1981). This set of four
affective items was used in various destination image formation and measurement
studies (e. g. Baloglu & Brinberg, 1997; Baloglu & McCleary, 1999). This study added a
fifth item in the measurement of the affective dimension (fiiendly-unfiiendly) because
focus group data revealed “friendliness” as a characteristic of the South Haven image.
Two items were used for the SN construct and two for the PBC construct. The
measurement of these items was based on a seven-point likert scale. These four items
were derived from studies that utilized the theory of planned behavior in the leisure and
recreation field (Ajzen & Driver, 1992; Cunningham & Kwon, 2003; Hrubes et al.,
2001). Past participation in the event was measured with three open-ended items, which
asked the respondents about the frequency of participation in the Michi gander event, the
frequency of participation in similar sport events and the frequency of bike vacation trips

in the past five years. Past visits to the destination was measured with two open ended

59

items, which asked the respondents about visiting frequency to the destination during the
past five years for either vacation or to participate in a sport tourist event. Intentions were
measured with three items which asked participants about the likelihood of going back to
the destination in the following two years to participate in a sport or recreation activity,
take a vacation there or ride the trails. Finally, one more scale was included in the
measurement instrument to test the construct validity of the developed event image scale.
This scale consisted of five items (five adjectives-sincere, spirited, reliable, sophisticated,
rugged- which respondents had to evaluate on how descriptive were of the event if that
event had a personality).

A set of items also measured the respondents’ demographics. More specifically,
employment and income information were collected from the mail survey while gender,
age and residence (in-state, out-of—state) were matched through questionnaire
identification number to the list of respondents. Table 4 depicts the number of items used
for each construct and their source. The full survey can be found in Appendix B and the

questionnaire items involved in the model estimation in Appendix E.

60

Table 4. Variables utilized in the final mail survey and their source

 

 

Variables Items-scale Source
-Studies:
(Baloglu & McCleary, 1999)
(Herbowicz, 2004)
Destination (Echtner & Ritchie, 1993)
image: cognitive 22 (17 cognitive and 5 (Russell, Ward, & Pratt, 1981)
and affective affective) seven-point -Destination brochures and website
components likert scale type items -Focus groups: for unique descriptors

28 seven-point
semantic differential scale From focus groups data, pilot-testing and

Event image type items theoretical framework of brand image
Subjective 2 seven-point (Aj zen & Driver, 1992; Cunningham & Kwon,
norms likert scale type items 2003; Hrubes et al., 2001).
Perceived
behavioral 2 seven-point (Ajzen & Driver, 1992; Cunningham & Kwon,
control likert scale type items 2003; Hrubes et al., 2001).
Past behavior- (Aj zen & Driver, 1992; Cunningham & Kwon,
Event 3 open ended items 2003; Hrubes et al., 2001).
Past behavior (Ajzen & Driver, 1992; Cunningham & Kwon,
destination 2 open ended items 2003; Hrubes et al., 2001).

3 seven-point likert scale (Aj zen & Driver, 1992; Cunningham & Kwon,
Intentions type items 2003; Hrubes et al., 2001).
Construct
validity items for 5 seven-point likert type
event image scale items (Aaker, 1997)

 

Sample selection and survey administration

This study aimed to test perceptions of sport tourists at the post-trip phase. The
population of this study was active sport tourists. Due to this delimitation, respondents
had to be people who physically participated in a sport tourism event and held image
perceptions of the event and destination through their participation. The population
consisted of individuals who participated in an annual bicycling tour, which takes place
in the state of Michigan and is called the “Michigander.” The total number of the
population was 981. A random sample was chosen to include one person per household

to ensure independency of responses.

61

The measurement instrument for this study was a self-administered four-page
questionnaire (see Appendix B for the questionnaire). Due to financial reasons, there was
no option to have a lengthier questionnaire which could have included more items per
factor. Each survey was ntunbered on the back page using a unique identification number
that matched the subject’s information to that of the survey and was maintained in a
master list. This approach allowed for non-response tracking and follow-up mailing
procedures.

A modified Dillrnan (2000) mailing procedure was used. Two waves of survey
mailings were used. A reminder postcard was sent between the two mailings to the non-
respondents. The modifications involved the absence of a pre-notice letter and the final
third contact. The contact information for the sample was obtained from the event
organizers at the beginning of October 2005. The population size was 981 individuals.
However, the statistical analysis of this study required independent observations which
led to the refinement of the population to study households not individuals (one person
per household). The final number of the sample utilized in this study was 720.
Consequently, 720 surveys were mailed (on November 1”, 2005) along with a detailed
personalized cover letter printed on a Michigan State University letterhead and signed by
the research project’s investigators. The letter explained the purpose of the study and the
reasons a response is important (cover letters available in Appendix C). The packet
included a cover letter, a copy of the questionnaire and a prepaid return envelope. One
week later, a reminder postcard was mailed to the entire sample to thank those who
already responded and to remind to the non-respondents that their responses were

important to the research project. Two weeks after the postcard a second mailing to the

62

non-respondents took place which included a revised personalized cover letter (see
Appendix C), a copy of the survey and a pre-paid return envelope. In an effort to achieve
a high response rate an incentive was offered to the recipients of the surveys. The
incentive was the chance to win two $50 discounts from the following year’s

“Michi gander” event participation fees. The winners were notified through the event
organizers in February 2006, and their names were announced in a newsletter event
organizers distribute to event participants. From the 720 mailed surveys, 4 came back
undeliverable (0.005%) while 12 replied but didn’t fill out the questionnaire. These 12
people reported that they registered but they didn’t participate in the event due to
personal reasons. Consequently, the total effective sample was 704. The response rates
achieved are shown in Table 5. The modified Dilhnan survey administration method
yielded a satisfactory response rate (70.3%) minimizing the potential impact of
nonresponse error.

Table 5. Responpe rate from first and second mailing of the surveys

 

 

Percent
Frequency %

Overall sample 720

Effective sample 704

Returned from first mailing 409 58.1
Returned from second mailing 86 12.2
Total returned 495

Response rate 70.3%

 

Sources of survey error

The self-administered survey process utilized in this study could introduce two
sources of survey error into the data (Groves et al., 2004). These are measurement error
and nonresponse error. Measurement error can be introduced due to poor item wording or

questionnaire design. In this study, a few items created problems for a small number of

63

participants. These items were related to the use of the semantic differential scale. Also,
due to the nature of the study many event image descriptors or related concepts were used
(c. g. construct validity items) which led some participants not to answer some of the
items due to fatigue reasons (they wrote this information on the survey). Overall, the
wording and the design of the questionnaire seemed not to be a problem. Sampling error
was not a concern in this study because the full list of participants was used and it was
filtered to include one person per household.

The response rate of this study was satisfactory and was not a reason to be
concerned with nonresponse error. However, individual t-tests were performed between
the first and second wave participants to test for differences in the main dependent
variable (factor: 3 items). The main dependent variable was chosen because of its
importance in the statistical analysis. Also, Host and chi-square were estimated to test
differences of respondents with the non-respondents on demographic variables such as
age and gender. This non-response check was feasible because the age and gender data of
the non-respondents were available on the registration form of the event. The results
revealed there were no differences among the first and second wave respondents with
regards to the dependent variables. No differences were found with regard to gender
when the respondents were compared with the non-respondents. The t-test for age
differences between non-respondents and respondents, however, showed significant
differences. The respondents were significantly older (mean=47 years old) than the non
respondents (mean=43 years old). Table 6 shows the results from the non-response tests.

Overall, this investigation regarding non-response error did not reveal significant

differences in key dependent variables and demographics except for age. More analysis

64

will follow in the results section to show that age may not influence the behavior of the

dependent variable.

kble 6. Non-response check results

 

 

Mean
Mean Mean Mean (or %)
Non-response 1" 2"d (or %) non
check Variable wave wave respondents respondents t 12 Sig:—
Dependent
variable 1:
Ride the
Between lst Kal Haven
and 2‘“1 wave Trail 446* 431* .64 p=.52
Dependent
variable 2:
Visit South
Haven (SH)
Between lst for a
and 2Ml wave vacation 4.09* 4.15* -.27 p= .78
Dependent
variable 3:
Visit SH
area to
participate
in a sport or
outdoor
Between lst recreation
and 2"d wave activity 400* 395* .24 p= .80
Between
respondents
and non
respondents Age 47.8 43.1 4.35 p<.001
Between
respondents
and non 45.9% (F) 41.7 % (F)
respondents Gender 54.1% (M) 58.3% (M) 1.10 p=.32

 

"‘ Note: Scale from 1-7, where 1=low end of the scale and 7=high.

65

Measurement error

Another potential source of error is measurement error. To reduce measurement
error, pilot testing of the questionnaire was completed by two graduate students in the
field of tourism who checked the wording of the items. Additionally, previously applied
measures from the destination image and consumer behavior literature were used.
Improvements were made in the wording as a result of the pilot testing phase. Another
form of measurement error can occur when the questions are answered incorrectly.
Careful and detailed instructions were used in the questionnaire and cover letter to avoid
this type of error. There was no evidence to believe the answers were answered
incorrectly. A few respondents (n=10) were confused with how to complete the event
image semantic differential scale. The data from these respondents were not entered in

the data file and were treated as missing data.

Model evaluation

The purpose of the model evaluation was to test the fit of the model to the data
and the significance of the proposed paths between sport tourism event image, destination
image, SN and PBC of event participation, past behavior with both the event and the
destination and intentions to return to the destination.

The model fit was achieved by using structural equation modeling (SEM)
analysis. “The term structural equation modeling (SEM) does not designate a single
statistical technique but instead refers to a family of related procedures. . ..SEM is a priori
and requires researchers to think in terms of models. But being a priori does not mean

that is exclusively confirmatory. Many applications of SEM are a blend of exploratory

66

and confirmatory factor analysis. The explicit representation of the distinction between
observed and latent variables is characteristic of many structural equation models. This
distinction makes it possible for researchers to test a wide variety of hypotheses. The
basic statistic in SEM is the covariance. It is possible, however, to analyze other types of
data, such as means... SEM is still a large sample technique” (Kline, 2005, p. 9-10).

In a more detailed discussion about SEM, Vinokur (2005) indicated five major
steps precede SEM analysis. The first step involves model specification where the
researcher builds a measurement model by specifying which observed variables are the
indicators of which reflective factors and which are causal indicators of formative factors.

The second step involves model identification where the researcher determines
whether the specified model including its structural part is formally identified. That is,
the analysis determines whether the estimation of the model can provide a unique
solution to the set of equations derived fiom the variances and covariances of the
observed variables.

The third step is the estimation of the model by applying a statistical procedure to
estimate the model’s parameters’ in such a way the discrepancy between the parameters
of the specified model and the model based on the empirical data set is minimized.

The fourth step is the model evaluation which reveals how well the results fit the
data. A note about SEM is that it tests relationships between exogenous (independent)
and endogenous (dependent) factors. Exogenous variables can correlate among
themselves but endogenous variables cannot correlate with the exogenous. Finally, errors

(the remaining unexplained variance of exogenous factor items) and disturbances (the

67

remaining unexplained variance of endogenous factors) should not correlate (Kline,
2005).

Various goodness of fit measures guide the researcher to accept or reject the
model. The evaluation process also examines more specific questions of which particular
paths demonstrate statistically significant influence on latent factors and how much of the
factor variance is accounted for by the interdependent factors or variables. The evaluation
of the model may also include inspection of results that suggest modification or re-
specification to achieve a better model fit.

The fifth step is the re-specification of a new model based on information
supplied as part of the estimation and evaluation process regarding possible improvement
in the model’s fit. A word of caution with the causal status of paths and estimates is that
good fitting models and their parameters provide support for the hypothesized model and
its implied hypotheses rather than providing proof or confirmation of the model validity.

Consequently, to test the hypotheses several statistical methods and procedures
were used. The statistical analysis followed a three-step process. The first step involved
data management and reduction techniques. Initially, the distributions of the variables
were checked for non-normality patterns. Next, missing data were treated by replacing
the missing cases with the series mean. Subsequently, exploratory factor analysis of the
28oitem event image scale was conducted to identify underlying dimensions and to
further purify the measure from the pilot testing phase. The event image scale was
reduced to include 13 items which loaded on the first factor and explained most of the
variance in the latent construct. The scale was tested for convergent and discriminant

validity with EQS 6.1. Composite scores for the destination image variables (i.e. create

68

one item for the destination image cognitive factor, and one item for the affective factor)
were created to specify a model with two and three indicators per factor. In addition, the
items composing the event image scale were parceled to form three indicators. “A parcel
is a total score (linear composite) across a set of homogeneous items” (Kline, 2005, p.
197). Parcels increase stability of parameter estimates, improve the variable-to-sample-
size ratio, and remedy small sample sizes and reduce measurement error (Holt, 2004).
Furthermore, correlation tests were applied among the items which measured each factor
to check whether structure of the factors was indeed reflective and not formative.

The second step involved the two-step modeling approach (Anderson & Gerbing,
1988). More specifically, “the structural regression model is first re-specified as a
confirmatory factor analysis (CF A) measurement model. The CFA model is then
analyzed to determine whether it fits the data. . .The first part of the two-step modeling
involved finding an acceptable CFA model” (Kline, 2005, p. 216). As a result, testing
and evaluation of the measurement model through a confirmatory factor analysis was
conducted. The loadings of the items were evaluated and the factor structure (i.e., the
items composing each factor) were retained or dropped accordingly to prepare for the
testing of the structural model.

The second part of the two-step modeling approach of the structural regression
model evaluation analyzed data based on the multivariate distribution assumption and
tested all the hypothesized relationships simultaneously (Kline, 2005). It also provided
means for assessing and modifying the theoretical model (Anderson & Gerbing, 1988).

Data were analyzed using SPSS 12.1 and EQS 6.1. All analyses were performed

using listwise covariance matrices utilizing the maximum likelihood estimation method

69

because listwise provides more powerful results compared to pairwise covariance
matrices analysis (Kline, 2005). The recommendations of Raykov, Tomer and
Nessleroade (1991) were followed to report the following goodness-of-fit measures:
normed fit index (NF 1), nonnonned fit index (NNFI), and comparative fit index (CFI).
Also, one widely used misfit index is reported: the root mean square error of
approximation (RMSEA). Fit indices that exceeded .90 and RMSEA misfit indices at or
below .06 are considered to indicate acceptable fit (Hu & Bentler, 1999). Because large
sample sizes tend to produce a statistically significant 35:, measures such as NFI, NNFl
and CFI were used as indicators of goodness of fit (Klem, 2000). Also, the significance
and the magnitude of the path estimates were examined. The path estimates are presented
in standardized form (regression coefficients, i.e. betas) to be able to make meaningful
comparison between the path and the each variable’s contribution to the variance

explained in the dependent variable.

Variable recoding and computing

Some of the questionnaire items were worded negatively to avoid respondents’
acquiescence (Krosnick, 1998) in answering. Those items were recoded to reflect the
structure of the rest of the questionnaires item scales (1=low, 7=high). Next, the mean of
the 17 cognitive destination image items and the mean of the 5 affective image items
were estimated to represent one item each. This was done in order to avoid “noise” in the
SEM model by the large numbers of items consisting each factor which reduces the
model’s degrees of freedom and jeopardizes the identification of the model (Kline, 2005).

In total, destination image was represented by two items (1 cognitive, 1 affective). It is

70

preferable to have two (Bollen, 1989) or three indicators per factor (Kline, 2005). The
following chapter presents the results of the data analysis and the scale development

process. A definition for the concept of event image is provided.

71

CHAPTER 4
ANALYSIS AND RESULTS: SCALE DEVELOPMENT AND MODEL FIT
In this chapter a series of statistical analyses are reported in six sections. The first

section presents the estimation of descriptive statistics of key variables. The second
section presents the focus group data analysis and reports on the purification, reliability
and construct validity of the event image scale. This section also presents the
measurement model of this study as part of the construct validity testing of the scale. The
third section reports reliability checks performed for the items comprising each factor in
the structural equation model. The fourth section presents missing data treatment and the
fifth section the impact of age on respondents and non-respondents. The sixth section
reports on the structural regression model testing. For the first five sections, SPPS 12.1

and EQS 6.1 were used. For the last section, EQS 6.1 was used to test the SEM model.

Description of the sample

Demographic information of the participants in this study are presented in Table
7. Also, information from a_ll types of sport participants in the USA found in the SGMA
report (2004) and 2003 adult bicycling demographics found in an online report by Bikes
Belong Coalition (n.d.) are presented for comparison purposes. It can be observed that
the sample characteristics are somewhat different from those of the 2003 adult bicycling
demographics and the sport participants in general. The adult bicycling demographics
show a younger population compared to the sample of respondents used in this study.
Age wise, both samples (adult USA bicyclists and sample respondents) are similar. With

regards to income distributions, 76% of USA adult bicyclists have income under $79,999

72

compared to 62% of the sample respondents. Sample respondents seem to have higher
incomes compared to USA adult bicyclists. Finally, comparing the sample with all types
of sport participants, there are differences in the age distribution, gender and income. All
sport participants are mainly younger (48.6% are under 34 years old) while the sample
participants consist mainly of baby boomers (57.3% were between 45-64 years old: note
that the younger sample participants under 12 years old were filtered during the sample
selection process). Comparing gender between all sport participants and the sample
respondents it can be observed that there are differences between the two populations,
with all sport participants being mainly female. The income distribution for all sport
participants seem to be slightly different with the majority (64.7%) of all sport
participants showing income under $75,000 compared to 62% of the sample respondents.
Overall, the sample population seems to be older and wealthier compared to both USA
bicyclists and all sport participants.

The following sections will present sample statistics on past experience with the
event and destination, satisfaction with the event, event image, subjective norms (SN),
perceived behavioral control (PBC), destination image, intentions, spending and
demographics (see Appendix D for detailed tables).

For 75% of the participants the Michigander trip was not their first participation
in the event. Respondents had on average a long relationship with bike riding as a leisure
activity. The average number of years of bicycling for recreation on trails was 15.6.
Furthermore, for 48% of the participants, bicycling was a weekly activity as long as the
weather permitted. The average number of participations in similar sporting events in the

past five years was 4.7 times. The average number of bike vacations during the past five

73

years was 2.5 trips. The average number of family members who rode in the event was
2.7 persons.

Most of the participants (73.6%) fell under the categories of “very” and
“extremely satisfied” with the overall event experience. Their perceptions regarding the
image of the event were on the positive side of the scale for the majority of the items (see
appendix D for means and percentages). Participants’ responses on the two items
regarding the support of their friends and family for participation in the event (SN) were
also on the positive side of the scale. More specifically, 90% of the respondents indicated
their close friends support their participation in the event. The majority of the respondents
(91.4%) indicated their family approves of their participation in the event. As far as their
perceived behavior control measurement is concerned, most of the respondents (90.5%)
indicated that they have the financial resources to participate in the event in the following
year. Most of the respondents (96.6%) indicated that they have the physical ability to
participate in the Michigander next year.

The cognitive and affective destination images of South Haven were positively
perceived by most of the respondents. Most items were evaluated positively (mean range
was 4.3 to 6.2- see Appendix D for means and percentages). Most of the respondents
(58.1%) indicated likelihood to ride the Kal Haven trail (where parts of the event took
place) in the next two years. Slightly less than half of the participants (46%) indicated
likelihood to visit South Haven for a vacation in the next two years. Less than half
(43.9%) indicated likelihood to visit South Haven to participate in a sport or outdoor

recreation activity in the next two years.

74

Participants spent $213 on average for their participation in the Michigander sport
tourism event excluding their registration fee for the event. On demographics (beyond
age, gender and income depicted in Table 7), the majority (67.8%) of the participants
were employed full-time, 15% were retired, 7% were employed part-time, 4% were self
employed and 3% were students. The rest (2.2%) were either homemakers, unemployed
or indicated “other”. Finally, the 2004 total annual household income from all sources
and all taxes for most of the participants (68.3%) was over $60,000. Less than one-fifth
(19.2%) of the participants had income between $40,000-$59,000, while the remaining
12.5% had income less than $40,000. The income categories in Table 7 have been
collapsed to match the adult bicycling demographics for comparison purposes. Details for

the respondents’ demographics are in Appendix D.

75

Table 7. Demoggaphic informgtion for sample participants, US bicyclists and all sport
Dartimants in the USA

 

2003 USA adult bicycling

demographics 2004
Sample (source Bikes Belong All USA sport participants
(N=490) Coalition) (source SGMA)
% % %
Age
0-17 1.6 23.9
(16-24)
18-24 3.7 28 11.5
25-34 8.4 23 13.2
35-44 20.8 22 12.9
45-54 35.3 (45-59) 11.2
55-64 22.0 20 12.6
7
65+ 8.2 (60+) 14.7
Gender
N=492
Male 53 56 47.1
Female 47 44 52.9
Income
=448
O-$49,999
0- $39,999 12.5 35 35.3
$50,000-$75,000
$40,000
-$79,999 39.5 41 29.4
$75,000+

$80,000 + 48.0 24 35.3

 

Key descriptive statistics from the variables used in the model evaluation are
presented in Table 8. The data show that most of the seven-point scale variables are
negatively skewed. EQS analysis relies on the normal multivariate distribution
assumptions. EQS is fairly robust against departures from normality especially when the

sample is large. However, EQS offers an alternative analysis called “robust” which

76

accounts for arbitrary distributions. The robust method accepts the normal theory but
scales the T statistic to yield a robust test statistic using a theory developed by Satorra
and Bentler (Bentler, 2006; Satorra & Bentler, 1986, 1994). It also improves standard
error estimates using a so-called robust covariance matrix (Bentler, 2006). The Sattora-
Bentler methodology is currently the most accurate method for dealing with non-normal

data (Bentler, 2006). This analysis was used in this study.

flble 8. Descriptive sfitatistics of vagipbles included in the SEM model evaluation

 

 

 

N Min Max Mean Std.
Deviation

*Vl--Past behavior 471 .00 14.00 4.12 3.88
event_l
V2-—Past behavior 41 1 .00 100.00 4.72 7.69
event_2
V3--Past behavior 479 .00 22.00 2.82 3.95
event___3
V4--Intentions 1 489 1.00 7.00 4.43 1.66
V5--Intentions 2 489 1.00 7.00 4.11 1.64
V6--Intentions 3 490 1.00 7.00 3.99 1.61
V7--Past behavior 488 .00 15.00 .92 1.69
destination_l
V8--Past behavior 486 .00 12.00 .62 1.34
destination_2
V9--EI_1 464 3.00 7.00 6.26 .61
V10--EI_2 464 1.50 7.00 6.08 .71
V1 l--EI_3 463 1.60 7.00 6.00 .65
V12--SN_1 491 1.00 7.00 6.31 1.18
Vl3--SN_2 489 1.00 7.00 6.49 1.36
V14--PBC_1 493 1.00 7.00 6.22 1.28
V15--PBC_2 491 1.00 7.00 6.54 .91
Vl6--DI cognitive 495 2.71 7.00 5.35 .36
V17--DI affective 453 2.80 7.00 5.45 .77
Valid N (listwise) 319

 

‘Vl-V17 is the coding of the variables for the SEM model testing. The detailed description of
each variable item is available in Appendix E.

77

Focus group data analysis

Keller (1993) suggested to apply qualitative techniques to understand brand image
associations. To examine the brand image associations for sport tourism events, two
focus groups were used. The one group consisted of eight Michigan State University
(MSU) bicyclists who belonged to the bicycling club of the university and the other
groups consisted of four participants of the target sample that were later excluded from
the mail survey list. The assistant moderator took notes during both focus groups. After
the completion of the focus groups, note-based analysis of the data took place (Krueger &
Casey, 2000). The moderator prepared a brief written summary of the key points after the
end of the focus groups and discussed the key points with the assistant moderator in order
to reach agreement. Themes were identified and agreed upon between the moderator and
assistant moderator based on the fiequency (how many times something was said), the
specificity (detail), the emotion (how emotionally they were expressed) and the
extensiveness (how many people said something). Data were derived from questions (see
appendix A) based on Keller’s (1993) framework that proposed that brand images consist
of attitudes, benefits and attributes toward an entity. The themes derived fi'om the
analysis were the following:

[:1 Organizational theme (safer routes, avoid roads/use trails, organized
transportation, convenient, event atmosphere, expensive/inexpensive entry fees,
registration deadlines, vacation destinations for overnight stays, event activities at
destinations, good services —e. g. showers);

Cl Environment theme (beautiful scenery, country side, new places, the best of an

area);

78

I3 Physical activity theme (healthy, endurance, perseverance, good physical
condition, training);

El Social activity theme (socialization, meeting other people, vacation with family
and friends, new things to do);

[:1 Fulfillment theme (self fulfillment, accomplishment, challenge); and

El Emotional theme (relaxing, exciting, enjoyment, pride, happy, fiiendly, range of
emotions before, during and after).

These themes yielded 41 potential event image items. The two focus groups seem
to differ in perceptions of physical activity and training. The MSU group was geared
more toward the competitive nature of the events, while the Michigander participants
were more about the healthy and more vacation oriented nature of the activity. The
words in parentheses are actual words used by both focus groups participants. Those 41
items were used in the pilot testing phase of the scale. After the pilot-testing phase
mentioned in chapter three, 28 items were left for inclusion in the survey. These 28 image
items derived from the focus group data were associated with Keller’s (1993) conceptual
framework: attributes (e. g. organization theme), benefits (e. g. physical activity,
fulfilhnent and social themes) and attitudes (emotional theme). These 28 items were
included in the final survey to be further tested with an adequate developmental sample

size.

Refinement and evaluation of the sport tourism event image scale

The initial evaluation of the items (28) from the survey sample involved the

estimation of frequencies and descriptive statistics to check for missing data patterns and

'79

the means associated with each of the items. These results are shown in Table 9. Those
items which featured means close to the middle of the scale (4.0) which stood for
“neither” an_d those items that had missing data more than 5% were dropped from the
analysis (five items). These five items coincided with those items some respondents
(n=15) indicated in writing that they did not understand. The range of responses for the
event image items were 450 people for the lowest response and 487 people for the
highest. The actions taken for the scale items based in missing data and means are
depicted in Table 9. After that first evaluation, there were 23 remaining items for the
factor analysis. Principal components analysis was utilized with varimax rotation
(uncorrelated or orthogonal factor assumption) and eigenvalues set to 1. Varimax rotation
was used to explore the independent dimensions the scale featured. The results are
depicted in table 10. Thirteen items loaded on the first factor which explained most of the
variance (28.5%) of the latent construct. To further test the reliability of the components,
reliability tests were estimated with SPSS, using Cronbach’s a as the reliability
coefficient (DeVellis, 2003). The results are depicted in table 10. The first extracted
factor consisting of thirteen items is high in internal consistency (or=.92). The other three
factors, despite their contribution to the variance explained, featured low reliability
coefficients. Although, there was no available literature to indicate the dimensions that
the sport tourism event image scale should have, these exploratory factor analysis results
point to a uni-dimensional scale which captures the latent construct of sport tourism event
image and shows good reliability. The items that loaded on the first factor were retained
for the event image scale and for further analysis with the structural regression modeling.

Multidimensional scales consist of more than one dimension. Future testing of the scale

80

should use all 28 items to test the stability of the uni-dimensionality with other

populations and to further validate the uni-dimensionality of the scale.

Table 9. Initial sport toufigm event image item evaluation: means, missing data and actions taken

 

 

Missing

Items data Mean Action
safe unsafe (R)* 1 .61% 5 .95 Retained
friendly unfriendly(R) 3.03% 6.07 Retained
unhealthy healthy 3.03% 6.42 Retained
unsupportive supportive 3.23% 6.15 Retained
disorganized organized 3.23% 6.11 Retained
clean polluted(R) 3.23% 5.69 Retained
sad joyful 3.23% 6.09 Retained
unstimulating stimulating 3.23% 6.03 Retained
unfulfilling fulfilling 3.23% 6.33 Retained
ugly beautiful 3.43% 6.19 Retained
easy challenging 3.63% 4.78 Retained
stressful carefree 3.63% 5.50 Retained
gloomy cheerful 3.63% 6.21 Retained
poor excellent 3.63% 6.13 Retained
inspiring uninspiring(R) 3.83% 5.85 Retained
unadventurous adventurous 3.83% 5.88 Retained
expensive inexpensive(R) 3.83% 4.41 Retained
boring exciting 4.04% 5.95 Retained
distressing relaxing 4.24% 5.81 Retained
active inactiveat) 4.64% 5.80 Retained
inefficient efficient 4.64% 5.58 Retained
worthless valuable 4.64% 6.22 Retained
passive competitive 4.84% 3.82 Retained
enervating invigorating 5.65% 5.90 Dropped
artificial natural 6.06% 5.80 Dropped
unappealing appealing 7.47% 6.13 Dropped
individualistic collective 7.47% 4.91 Dropped
mental physical 9.09% 5.66 Dropped

 

*R: recoded items (1=low end of the scale, 7= high end of the scale)

81

Table 10. Rotated commnent matrix of the 23 ' ' items after initial evaluation sport
tourism event image items

 

 

 

Component

1 2 3 4
Fulfilling .80 .10 .12 .13
Stimulating .77 .09 .22 .12
Excellent .77 .25 -.06 .17
Joyful .76 .25 -.03 .03
Healthy .72 .02 -.07 .14
Exciting .71 .1 l .21 .14
Cheerful .69 .22 -.l 1 .10
Valuable .65 .35 .07 .06
Beautiful .64 .33 -.02 .17
Relaxing .63 . 14 -.45 .02
Adventurous .62 .09 .45 .13
Inspiring .54 .06 . 13 .37
Supportive .51 .47 .03 .12
Organized .29 .63 -. 16 .09
Efficient .26 .64 .03 .14
Inexpensive .07 .58 .03 -.02
Carefree .24 .15 -.63 .08
Competitive .24 .02 .57 -. l 3
Challenging .16 .09 .72 .01
Active .16 -.15 .11 .71
Friendly . 16 .04 -.14 .68
Safe .17 .20 -.24 .50
Clean .05 .34 -.00 .55
Variance
explained 34.51% 9.06% 6.04% 4.87%
Cronbach’s a 0.92 0.49 0.48 0.57
Action Retain Drop Drop Drop
Reason for High Low- Low- Low-
action reliability reliability reliability reliability

 

The results from the factor analysis concur with the qualitative data analysis in
terms of the underlying themes in the sport tourism event image concept. The focus
group data analysis yielded six thematic areas related to: organization, environment,
physical activity, socialization, fulfillment and emotional involvement. The factor

analysis results relate to these themes. For example, valuable and excellent refer to the

82

mental representation about the organization of the event, beautiful refers to the
environment of the event; healthy refers to the physical activity; supportive refers to the
socialization aspect in terms of camaraderie and group homogeneity; fulfilling,
stimulating, adventurous and inspiring refers to the fulfillment the event provides the
participants; and exciting, cheerful, relaxing and joyful refer to the emotional imagery.
Consequently, based on the results from the factor analysis and the qualitative data a
definition of the sport tourism event image is proposed:

The image of a sport tourism event is the mental representations active sport
tourism participants have about the organization, environment, physical activity,
socialization, fulfillment and emotional involvement with the event.

Table 11 presents the final version of the sport tourism event image scale.

Table 11. The sport tourism event ime scalp

 

Extremely Quite Slightly Neither Slighfly Quite Extremely

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

1 2 3 4 5 6 7
Unfulfilling Fulfilling
Stimulating
(R)* Unstimulating
Poor Excellent
Sad Joyful
Healthy (R) Unhealthy
Boring Exciting
Gloomy Cheerful
Valuable (R) Worthless
Ugly Beautiful
Distressing Relaxing
Unadventurous Adventurous
Inspiring (R) Uninspiring
Unsupportive Supportive

 

 

 

 

*(R): Items that need to be recoded in statistical analysis to follow the rest of the item structure:
l=low end , 7=high end of the scale.

83

Construct validity of the sport tourism event image scale

To test the construct validity of the scale, a scale related to the concept of brand
image (brand personality) was used (Aaker, 1997). “To establish the construct validity of
a measure, the analyst must determine: 1) the extent to which the measure correlates with
other measures designed to measure the same thing, and 2) whether the measure behaves
as expected” (Churchill, 1979, p. 70). Construct validity consists of convergent and
discriminant validity (Churchill, 1979). Convergent validity is whether the scale highly
correlates with scales that measure similar concepts (Shadish, Cook & Campbell, 2002).
Discriminant validity refers to the extent to which the measure is novel and not a
reflection of some other variable that is conceptualized to be different fi'om it (Churchill,
1979; Shadish et al., 2002). Convergent validity was tested with CFA (using EQS 6.1
robust statistical analysis) and was compared to the brand personality scale (Aaker,
1997). Brand personality is related to brand image (Dobni & Zinkhan, 1990) and as such
was deemed as the appropriate concept for the testing of construct validity. Discriminant
validity was tested during the measurement model evaluation. The results supported the
convergent and discriminant validity of the scale.

As far as the convergent validity of the scale is concerned, the covariance between
the event image factor (13 items) and the brand personality factor (five items) was
significant and relatively high (r= .59, p<.05) which supports the convergent validity of
the scale. The goodness of fit measures for this model were not acceptable ()8 =354.05,
df=l34, NFI=.74, NNFI=.79, CFI=.82, RMSEA=.06 with 90% confidence interval
between .05 and.07). Examining the factor loadings for each factor, it was revealed that

the brand personality scale had two items (sophisticated and rugged) that had low

84

loadings (.44 and .41 respectively). Despite the unacceptable fit of the model to the data,
convergent validity was shown with the high correlation coefficient between the two
factors. Potentially, if the two brand personality items were dropped, the overall fit of the
model might have been better.

The measurement model provided support for the discriminant validity of the
scale. Table 14 depicts the standardized estimates of the measurement model’s factor
covariances, factor item loadings and the error variances. The covariance between the
sport tourist event image scale and past behavior with the event was not significant and
very low (r = -.03). The same applied for the covariance between the scale and past
behavior with the destination (r=-.01). The covariance between the event image scale and
intentions to return to the destination was not significant and low (r=.12). These results
discriminate the sport tourism event image scale from the other three variables with
which the scale was not supposed to correlate.

The robust goodness of fit indices for the measurement model were the following:
x2=184.11, df=99, p<.001, NNFI=.83, NFI=.88, CF1=.91, RMSEA=.05 (n=319). These

indices show mixed results. Two of the fit indices show acceptable fit of the model to the
data while the other two do not. These mixed results come potentially from some of the
low factor loadings. For example, the SN items both had low loadings. However, because
the factor was needed for the model estimation a decision was made to keep both items.
Ajzen and Driver (1992) found similar results on SN in their study but decided to keep
the items in the analysis. A few other items had low loadings as well. However, these
items were part of the two or three item factor structure, and their removal would

transform some of the factors to one item observed variables. One item variables,

85

however, carry some amount of measurement error which can result in biased estimates
of direct effects and can affect other path coefficients of exogenous (independent)
variables (Kline, 2005). In general, item loadings should be at least .60 and above to
indicate that each measure is accounting for 50% or more of the variance in the latent
variable (Chin, 1998). All loadings, however, were significant and a decision was made
to include all factor items in the model based also on the mixed results. Furthermore,
McDonald and Ho (2002) indicated each latent variable should have at least two
indicators if the factors are correlated and three if the they are not. Also, McDonald and
Ho (2002) indicated the most popular unbiased relative fit indices are the CF I and
RMSEA. Based on these two indicators the results of the analysis provide an acceptable

fit of the model to the data.

Missing data treatment

One of the questions in the survey asked respondents to evaluate the destination
of South Haven as part of their bike tour experience. Many people did not provide
answers to all seventeen items composing the cognitive destination image factor. The
transformation of this variable into one indicator of cognitive image (the mean of the
seventeen items was estimated) intensified that problem because data were not missing
systematically from one item. Rather data were missing from all items which reduced
drastically the number of cases (N=118) with which the data would be analyzed using
listwise deletion. Listwise deletion includes in the analysis only cases with complete data
and provides more powerful results. One way to treat the missing data is to replace the

missing data with the series mean as long as the missing data happen at random (Kline,

86

2005). To check randomness of missing data, various statistical tests were run to compare

the missing data cases with those cases fully answered.

Table 12. Measurement model factor covariance, item loadings and errors based on 319 complete
cases using EQS robust anflsis

 

Factor covariance

 

 

F 1 F2 F3 F4 F5 F6 F7
F l- PPE .14 .06 -.03 -.32* -.25 .002
F2- I .44* .12 .14 .10 .49*
F3 - PVD -.01 -.10 -.01 .33“
F4 - STEI .40* .16* .61*
F5 - SN .55* .66*
F6 -PBC .20*

F7 -DI

Item loadings

Fl—Past participation event (PPE)
Loadings Errors

Item 1 .21* .97

Item 2 .99* .04

Item 3 .55* .83

F 2-Intentions to revisit the destination for leisure activities (I)
Item 4 .75 * .65

Item 5 .79* .61

Item 6 .88* .47

F3- Past visits destination (PVD)
Item 7 .84* .53

Item 8 .50* .86
F4-Sport tourism event image (STEI)
Parcel 9 .91* .40
Parcel 10 .88* .46
Parcel ll .90* .43
F5-Subjective norms (SN)

Item 12 .53"‘ .84
Item 13 .24* .97
F6-Perceived behavioral control (PBC)
Item 14 .57"‘ .81

Item 15 .83* .55
F7-Destination image (DI)

Item 16 .54* .83

Item 17 .70* .71

 

*significant at p< .05

87

A new variable was computed that estimated the mean of all the destination image
cognitive items was taken. Next, 05 were placed to the missing data cells. Then, the new
variable was recoded into two groups: 0=missing, >l=not missing (values from 1-7).
Independent sample t-tests were estimated to compare the missing data with non-missing
data for key variables. The results showed no differences between cases with missing
data and fully-answered cases for all the variables involved in the model testing, except
for the two out of the three items comprising intentions to return to the destination to
participate in a sport activity. Kline (2005) indicated that “if the missing observations on
some variable X differ from the observed scores on that variable only by chance, the data
loss pattern is said to be MAR [missing at random]. . .there is no magical statistical “fix”
that will remedy systematic data loss. About the best that can be done is to attempt to
understand the nature of the underlying data loss mechanisms and take preventive
measures” (p. 53). Since most of the variables did not exhibit statistical differences
between missing and non-missing data cases, the missing data were replaced with the
mean of the series to proceed with the analysis in order to avoid the small sample size
from the listwise deletion and the potential unstable results (Kline, 2005).However, tests
followed to check the impact of the new variable compared to the old variable on the
dependent variable. To test the differences between the two variables (the variable with
the mean replacement and the one with the missing data) MANOVA was used to test the
impact of each variable on the multivariate distribution of the three items comprising the
dependent variable factor (intentions). The results were the same for each variable. Both

had significant impacts on all three dependent variables. This testing further supports the

88

mean replacement action. Table 15 and 16 summarize the results from the missing data

analysis and treatment.

Bible 13. Missing data treatment results: comparison of missing cases with non-missing cases on
key variables

 

 

Variable t-test Sig.
Sport tourism event image (parcel 1) 1.22 .22
Sport tourism event image (parcel 2) .68 .49
Sport tourism event image (parcel 3) -.44 .65
Affective image (one item- computed mean from the five items) .54 .58
Subjective norms (item 1) 1.57 .11
Subjective norms (item 2) .35 .72
PBC (item 1) 1.50 .13
PBC (item 2) 1.12 .26
Intentions 1: Ride the Kal-Haven trail -1 .70 .09
Intentions 2: Visit South Haven for vacation -2.37 .01*
Intentions 3: Visit South Haven area to participate in a sport/recreation
activity -2.81 .005 *

 

*significant results, p<.05

Table 14. MANOVA results from comparing the vargble with mean rmlacement and the variable
without mean replacement

 

 

 

Type III
Dependent Sum of Mean Wilk’s
Source Variable Squares df Square F Sig. A

Destination image
cognitive item
(with mean .96
replacement,
N=485) Intentions 1 47.14 1 47.14 17.49 .000 p<.001

Intentions 2 28.44 1 28.44 10.69 .001

Intentions 3 31.45 1 31.45 12.25 .001
Destination image Intentions 1 47.49 1 47 .49 19.08 .000
cognitive item Intentions 2 28.81 1 28.81 13.16 .000
(without mean .83
replacement
N=118) Intentions 3 31.90 1 31.90 14.59 .000 p<.001

 

Analysis of age influence on key dependent variables
The non-response check showed significant differences in the age variable

between respondents and non-respondents. The non-respondents were younger compared

89

to the respondents. Consequently, further analysis had to be performed to show that age
was not significantly impacting the dependent variable. In order to check whether age
influences the main dependent variable (intentions) of the model, MAN OVA was
performed to test the impact of age on the three items that composed the intentions
dependent variable. The results from this analysis are presented in Table 15. The results
revealed age was not a significant predictor for any of the three items that composed the
main dependent variable in this study.

Table 15. MANOVA results for the imact of age on the three items composing the ma_ir_r
demndent varim

 

 

Type HI
Dependent Sum of Mean Wilk’s
Source Variable Squares df Square F Sig. A
Age 99
(N=480) Intentions 1 6.95 1 6.95 2.51 .11 '
Intentions 2 2.47 1 2.47 .90 .34 p=.47
Intentions 3 3.14 l 3.14 1.20 .27

 

Reliability checks-reflective factors justification

“An underlying assumption for SEM analysis is the items or indicators used to
measure a LV [latent variable] are reflective in nature. Such items are viewed as affected
by the same underlying concept (i.e., the LV). . ..All items must be reflective to be
consistent with the statistical algorithm that assumes that the correlations among
indicators for a particular LV are caused by that LV” (Chin, 1998, p. ix). The other
measurement indicator is called formative. Formative indicators “are measures that form
or cause the creation or change in a LV” (Chin, 1998, p. ix). To check on the structure of
the factors, reliability analyses were estimated for those items composing each factor
(Chin, 1998). The results revealed strong reliability for some of the scales and acceptable

or low for others. For early stages of basic research, Nunnally (1967) suggested that

90

reliabilities of .50 to .60 suffice. The reliability results are depicted in Table 17. Overall,
the results supported the notion that all factors are reflective (not formative). The
subjective norms factor had a low reliability coefficient (or=.29). Ajzen and Driver (1992)
found low reliability coefficients in their SN and PBC measures as well, and they related
this low reliability to the number of items measuring each factor. Also, due to the
theoretical conceptualization of the items, the low coefficient may have been a result of
other effects (i.e., item sequence, negative wording) and not an indicator of a formative
factor. Consequently the decision was made to include the SN factor and treat it as
reflective in the structural equation modeling analysis. Future research could improve
upon the wording of these two items and add more items to the measurement of

subjective norms.

Table 16. Reliability coefficients for the items composing the factors involved in the structural
regression model

 

 

Reliability
Factors Number of items (Cronbach’s (1)
Event image 3 parcels .92
Destination cognitive image 17 .93
Destination affective image 5 .84
Past behavior event 3 .60
Past behavior destination 2 .65
Perceived behavioral control 2 .65
Sub'Lective norms 2 .29

 

Testing of the Structural Equation Model

To proceed with the measurement and SEM analysis, the 13 sport tourism event
image items were transformed to parcels. Parcels are total scores —or linear composites-
across a set of homogeneous factors for those items composing each of the latent factors

after exploratory factor analysis indicated their uni-dimensionality. Uni-dimensionality is

91

a prerequisite for parceling (Kline, 2005). Parcels create indicators with better
distribution properties and better approximation to the normal distribution (Bandalos &
F inney, 2001). Parceling was done by placing every 4th item into groups which resulted
in three parcels. The thirteenth item was included in the last parcel as the remaining item
from the selection process. Parcels are supposed to have the same reliability as the
individual items. Reliability analysis was estimated and parcels featured the same
reliability as the individual items. Table 16 depicts this information.

'_l'_able 16. Reliability coefficients for the event image items comparing all event imge items and
parcels

 

 

 

Reliability
Factors Number of items (Cronbach’s (1)
Event image all items Q3) .92
Event image 3 parcels .92

 

The structural model testing results are depicted in Figure 6. In this model there
were four exogenous (independent) factors and three endogenous (dependent) factors.
The EQS maximum likelihood solution with robust statistical analysis revealed the model
had a good fit to the data. The model converged in 13 iterations. “The goal of iterative
estimation is to derive better estimates at each stage, ones that improve the overall fit of
the model to the data. When improvements from step to step become very small, iterative
estimation stops because the solution is stable” (Kline, 2005, p.57). The latter comment
by Kline implies the model may not converge in a certain number of iterations which in

the case of EQS is set to 30.

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If the model does not converge the solutions are not trustworthy. For this model, the chi-
) square value (x2=191.72, df=108) was significant at p<.001. All fit indices, except for the
NFL were above .90, providing evidence of good fit of the model to the data (NF I=.84
NNFI=.92 CFI=.93). RMSEA was .04 with the 90% confidence interval between .02 and
.05. Table 18 provides the summary of goodness of fit measures for both the

measurement and structural regression models.

Table 18. Summary of goodness of fit indices for measurementiand structural models tested in
this study

 

 

N x2 df p NFI NNFI CFI RMSEA
Measurement 3 19 184.1 1 99 p<.001 .83 .88 .91 .05
model
Structural
model 319 171.85 110 p<.001 .84 .92 .93 .04

 

Direction and magnitude of impact of path estimates

The path diagram for the structural equation model is depicted in Figure 6 and
presents the standardized path estimates and correlations in the model. The model also
shows the negative or positive direction and impact of the standardized path coefficients.

The overall model explained 34% of the variance in intentions to travel to the destination

to partlmpate 1n sport/recreation (F2) (R =.34) and 44% of the vanance on destinatlon

. 2 . . .
image (F7) (R =.44). Fl (PBE) d1d not explain any of the variance on F4 (STEI) (R2=

.00) because the only predictor of event image (past behavior with sport tourism events)
had a very small insignificant beta coefficient. The model also depicts the measurement
loadings for the items comprising each factor. The factor item loadings are similar with

the measurement model (see Table 12). This is a positive indication regarding the

94

measurement part of the model because it is not indicative of interpretational
confounding (which means that the empirical definitions of the constructs change based
on the structural model) (Kline, 2005). The subjective norms items had lower loadings
in both the measurement and structural model. In order to test whether the one SN item
that had the lower loading affected the overall model fit, that item was dropped and the
overall model was re-tested. The results were the same as with the two SN items. As
such, this lower loading did not impact the overall model fit and the decision to keep it in

the analysis was further supported with this testing.

Hypotheses testing results

The hypotheses tested with this study were the following:
H1: Event image will positively influence the image of the destination;
H2: Destination image will positively influence intentions to travel to the destination;
H3: Subjective norms will positively influence intentions to travel to the destination;
H4: Perceived behavioral control will positively influence intentions to travel to the
destination;
H5: Past visits to the destination will positively influence intentions to travel to the
destination;
H6: Past visits to the destination will positively influence the image of the destination;
H7: Past participation in the event will positively influence the image of the event;
H8: There will be a positive correlation between past participation in the event and past

visits to the destination; and

95

H9: There will be a positive correlation between subjective norms and perceived
behavioral control.

The results of the analysis support most of the hypotheses. More specifically, the
first hypothesis was supported indicating the image of a sport tourism event positively
influences the image of the destination that is hosting that event (13:58). The path
estimate from event image to destination image is fairly large, which indicates the
importance of the influence of event image on destination image. The second hypothesis
stated destination image impacts intentions to revisit the destination and it was also
supported (13:30). The third amd fourth hypotheses were not supported. The impact of
subjective norms on intentions (H3) and the impact of PBC (H4) on intentions were
insignificant and low. The fifth hypothesis was supported because of its positive
significant impact on intentions (13:.40). Past behavior with the destination (previous
visits) influenced positively (B=.32) the image of the destination which supports H6. Past
behavior with the event (previous experience with similar events and vacations) did not
influence the image of the event and as a result H7 was not supported. Past behavior with
the event and past behavior with the destination were positively correlated, but the
correlation was not statistically significant which implies H8 was not supported.
However, it has to be mentioned that the correlation coefficient was not negligible
(r=.20). Subjective norms and PBC were highly correlated with each other (r=.58, p<.05)

(H9 was supported).

96

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98

Summary

This chapter presented the results on the sport tourism event image scale
development process and the model testing including nine hypotheses tests. The event
image development process resulted in a sport tourism event image scale comprised of
thirteen items and a definition of the sport tourism event image. The construct validity of
the scale was shown to be adequate although future research should test the uni-
dimensionality of the scale with other sport tourist samples. With regard to the model
testing, most of the hypotheses were supported, and the model fit to the data was
acceptable. The impact of the sport tourism event image on destination image was large
and significant. Destination image was a strong predictor of intentions to revisit the
destination in the future to participate in a leisure activity. Sport tourism event image had
indirect positive effects on intent to revisit the destination for leisure activities. Past
behavior (previous experience) with the destination was a significant predictor of
destination image. The image of the sport tourism event under study was positively and
significantly correlated with subjective norms and perceived behavioral control about the
event. Previous experience with sport tourism events was not significantly correlated with
previous experience with the destination. Previous experience with sport tourism events
did not influence the image of the event, and previous experience with the destination
was not a significant predictor of intentions to revisit a destination to participate in a
sport/recreation activity. Although some of the paths were not statistically significant,
path coefficients were large and that is an important topic for discussion.

The next chapter discusses the results and implications of the study, as well as,

opportunities for future research. The results are discussed in perspective with current

99

Model respecifications

EQS 6.1 provides the user with the Lagrance modification index of adding
parameters to improve the fit of the model to the data. However, according to Kline
(2005), model modifications should take place according to theoretical criteria and not
empirical criteria such as statistical significance. If the model is respecified by empirical
criteria, the researcher should worry about capitalization on chance. This means that a
path may be statically significant due to chance variation. The proposed modifications by
the EQS program had no theoretical rationale and as a result no action was taken to re-
specify and retest the model.

SEM analysis requires providing the reader with a table where correlations among
the model variables, their means and SD are presented. Table 19 depicts the correlations

between the model variables, their means and standard deviations.

97

literature perspectives. The theoretical and methodological contributions of the study are

presented. Theoretical and managerial implications are discussed.

100

CHAPTER 5
DISCUSSION, IMPLICATIONS AND CONCLUSIONS

The research objective of this study was to examine the impact of a sport tourism
event image on destination image and intentions to revisit a destination for leisure
activities. Furthermore, the study aimed to define and develop a scale for measuring sport
tourism event image. A model was tested utilizing robust statistics to decide on statistical
significance. The results fi'om the model testing showed an acceptable fit of the model to
the data and the overall support of the model’s relationship for the specified sample of
respondents. Moreover, the scale development process yielded a sport tourism event
image scale consisting of thirteen items. In addition, a sport tourism event image
definition from the active sport tourist perspective was developed. The following sections
will focus on the discussion of findings and how they compare and contrast with the
existing literature. Theoretical and managerial implications will be provided and future
research approaches will be suggested. The chapter concludes with the discussion of

limitations and final comments.

Discussion of findings
Corroborating the results to earlier studies

This section will compare and contrast the results of this study to previous studies.
The model tested in this study was not previously tested in the literature, which implies
direct comparisons to the model evaluation cannot be performed. However, the TPB

application in the context of sport tourism will be discussed. In addition the link between

101

event and destination image will be discussed in relationship to previous research. Then
the role of past behavior will be discussed.

Overall, the TPB was adequate in its explanatory power for the model tested in
this study. The overall model explained 34% of the variance in the dependent variable of
intentions to return to the destination to participate in leisure activities. Armitage and
Conner (in press) in a meta-analysis of TPB studies found TPB to explain 39% of the
variance on intentions. The model had correspondence challenges that had to be
overcome, as discussed in the literature review. The correspondence challenges between
two different attitudinal measures, subjective norms (SN), perceived behavioral control
(PBC) and intentions, could potentially have been the reason for the insignificant path
estimates from SN and PBC to intentions.

Another interesting finding was the insignificant impact of SN factor to
intentions; this result however can be explained. Subjective norm items asked participants
about the support their family and fi'iends provided for their event participation.
However, the endogenous factor (dependent) of intentions consisted of items that asked
participants about their general disposition to return to the destination where the event
took place to participate in other leisure activities. The results suggested perceived
support of family and friends regarding the event participation is not impacting intentions
to revisit the destination for activities other than the event participation. The impact of
family and fi'iends’ approval and support on intentions to revisit could be significant in
the case that participants intended to go back to the destination to participate in the same

event. As such, the model could potentially utilize another variable as a mediator of SN

102

to intentions. This variable could be intentions to participate in the same event in the
future.

Another plausible explanation is related to perceptions of event participation as an
exercise activity. SN’s relationship to intentions to revisit a destination to participate in
leisure activities may not be high. Ajzen and Driver (1992) in their TPB application study
of students’ leisure choice with regards to leisure activities found no significant influence
of SN on intentions to engage in biking (beta=.13, p>.05). When studying hunters’
intentions, however, Hrubes et al. (2001) found both SN and PBC to be significant and
SN to have a larger beta coefficient compared to PBC (SN B=.37, p<.01, PBC B=.O7,
p<.05). Hagger et a1. (2002) found the meta analytic path estimate of physical
activity/exercise SN to intentions (based on 72 studies), although significant, was very
small (beta=.09, p<.01) which was the reason for Hagger et al. to indicate the weak role
of subjective norms.

Hagger et al. also found PBC’s influence was not as pervasive, but it was still an
important predictor of physical activity behavior. However, the results of this study
showed that PBC was not a significant predictor of intentions. PBC positively influenced
intentions to return to the destination for other activity purposes, which supports the
hypothesis pertaining to PBC as proposed in this study, but it did not reach significance.
One explanation for this non-significance is that participants’ financial and physical
resources could influence intentions to participate again in the event but not to revisit the
destination for leisure activities. Since the event was the main reason participants visited
the destination, future intentions to revisit the destination were not predicted by PBC.

Potentially, if the event was held in the same place every year, PBC could influence

103

intentions to revisit the destination. But the event takes place in different cities each year.
Cunnigham and Kwon (2003) found PBC was less important and partly insignificant (one
of the two betas for the variables used to measure PBC did not reach significance levels)
in explaining fan’s intentions to attend a hockey game. They concluded that attitudes and
SN were more salient predictors of intentions to attend a hockey game than PBC.

In summary, the application of the TPB in active sport tourism showed that the
attitudinal concepts of event and destination image were strong predictors of intentions to
revisit the destination to participate in leisure activities. Subjective norms and PBC did

not significantly predict intentions.

The link between event and destination image

Research in spectators’ perceptions has shown that sport events positively impact
the image of destinations (Bieger et al., 2003; McCartney, 2005; Ritchie & Smith, 1991;
Ritchie & Yangzhou, 1987). This study focused on bicycling event participants and
reinforced the positive impact of a sport tourism event image on the image of the
destination from the participants’ perspective. The impact of the event’s image on
destination image was quantified. This quantification reveals the magnitude of the
indirect impact (through destination image) of a smaller scale sport tourism event on
participants’ intentions to revisit the destination for leisure activities. In regression terms,
one unit change in the event image perceptions would bring .58 positive change in
destination image perceptions which in turn would increase intentions to revisit the
destination by .30. The total impact of the event and destination image link is .88. So in

other words, the 495 people who responded in this study are .88% more prone to revisit

104

the destination in the following two years. For the city of South Haven this increase in
visitation has come about without major investing in the event hosting. The city offered
the tourism infrastructure available which implies that the repeat visitation was generated
without major costs.

The previously mentioned studies (Bieger et al., 2003; McCartney, 2005; Ritchie
& Smith, 1991; Ritchie & Yangzhou, 1987) discussed the impact of sport events on
destination images, none of these studies utilized a measurement of a sport tourism event
image. This study developed a sport tourism event image scale to account for this gap in
the literature and to provide event marketers with an evaluation tool. This scale was
designed to tap into the common image elements participants find in a sport tourism
event and measure that commonality. Also, in the previously mentioned studies (e. g.,
Bieger et al., 2003; McCartney, 2005; Ritchie & Smith, 1991; Ritchie & Yangzhou,
1987), the image of the event was evaluated on the same characteristics as those of the
destination. This type of evaluation is useful for image comparison purposes. However,
the literature has not provided an estimate of the size of the impact of the event image
variable on destination image. This study accounted for this gap in the literature (i.e. lack
of quantification of the impact). Furthermore, the research showcased the positive impact
the event had on the destination’s image.

Sport events, however, can sometimes negatively impact destination images.
There have been studies which demonstrated the negative influence of sport events on
destination image perceptions. Chalip, Green and Hill (2003) found that a sport event (car
race) negatively impacted some aspects of destination image perceptions (natural

environment), but these results pertain to viewers of the event and not attendees or

105

participants. Furthermore, Smith (2005) in his study of three “sport cities” in England
indicated “it is problematic to regard sport as a congruent theme per se, as it depends on
which sports are emphasized in strategies and whether they reflect established urban
images”(p. 230) and “sport initiatives can provide imageable urban phenomena, but the
combination of events, event bids and indoor arenas developed in the three cities
[Birmingham, Manchester and Sheffield] have not been particularly effective for this
purpose.” (Smith, 2005, p.231). However, Smith (2005) concluded “although it may not
be popular among the majority of potential tourists, sport’s positive meaning in
contemporary culture and its exposure in contemporary society assist its potency as an
imaging theme” (p. 231). Therefore, it is not straight forward that sport tourism events
will benefit a community. Rather, systematic evaluation of these events should take place
on behalf of destination and event marketers based on the fit of the event with a
destination (J ago et al., 2003) and on the support of the community’s resources on the
hosting of that event.

The impact of smaller-scale sport tourism events on destination image and future
intentions to travel for leisure activities is illustrated by this study. Participants in this
annual smaller scale bicycling event positively rated the relationship between event and
destination image. These types of sport tourism events expose participants to destinations
they haven’t seen before. Based on the results of this study, sport tourism events
positively influenced participants’ perceptions of destination image. Potentially, larger
scale events (e. g. Olympic Games) may have the same impact on participants’
perspectives of a hosting destination. However, future research should examine

perceptions of the event and destination images that elite sport tourists hold and the ways

106

these images impact their athletic performance and their intentions to revisit the
destination (Copeland & Hirtler, 2002).

Events have also been viewed as part of the destination’s brand (J ago et al.,
2003). J ago et a1. (2003) acknowledged that community support, a good strategic, and
cultural fit with the destination are necessary requirements for building events into
destination branding approaches. The results from the scale development process can be
linked to earlier studies on the brand image of a sport tourism event in relation to sponsor
images. More specifically, one of Musante and Milne’s findings (1999) was that the
brand image of the sport event was considered exciting for event spectators. The same
result was found in this study from the participants’ perspective. Also, Martin (1994)
found that skill and strength are two qualities people use to attribute similarities among
events and his result can be related to the physical activity theme found in this study.
F errand and Pages (1996) found the sport event image to be entertaining, pleasant,
popular, active and full of life when evaluated in correlation with the sponsor. These
results are similar to the findings of this study for a smaller scale event (e. g., emotional
and physical activity components can be found in Ferrand and Pages’ results).

In summary, the impact of sport tourism events on destinations as shown in this
study and in other studies has been found to be mostly positive (Bieger et al., 2003;
Brown et al., 2002; Chalip & Green, 2001; Chalip et al., 2003; Copeland & Hirtler,
2002). The image of the bicycling tourism event studied in this research was related to
organization, physical activity, socialization, environment, fulfillment and emotional

themes.

107

Previous behavior, image and intentions

Past research has shown that past behavior can be influenced by habit, and it can
influence people’s attitude accessibility with regards to a specific behavior (Eagly &
Chaiken, 1993). Past behavior can bias the interpretation of attitudinal concepts (Roskos-
Ewoldsen, Arpan-Ralstin & St Pierre, 2002), but in this case previous behavior with sport
tourism events (i.e. previous participation in the event or similar events and sport
vacations) had insignificant effect on the image perceptions of the event. Also, there was
a negative sign in front of the path coefficient. This negative relationship can be
explained. The more experience participants get from attending sport tourism events the
less the image they have for the specific event studied in this study. Each event offers
different experiences to participants. Each sport tourism event takes place in a unique
setting with different co—participants. Each event creates a different past experience.
Unless a recurring event takes place in the same place, previous experience will not
potentially influence the mental representation participants have of each event. In
addition, this finding can be explained in terms of the event duration. Vogt and Stewart
(1998) found that after the first day in a vacation trip, there were no difference between
first and repeat visitors in terms of the vacation attributes and experiences. Consequently,
past experience with the event does not influence image perceptions of the event.

Past experience (past visits) with the destination positively influenced the
destination image perceptions supporting findings of previous studies (Ahmed, 1991;
Chon, 1991; Milrnan & Pizam 1995; Pearce, 1982; Shelby & Morgan, 1996).
Furthermore, past experience with the destination influenced significantly intentions to

return to the destination. The path estimate was positive and large (B=.40). This path

108

estimate indicates the positive influence of previous experience on intention prediction.
For example, Pctrick et a1. (2001) found the beta coefficient from past visit to the
destination to intentions was .22 compared to .40 found in this study. This result, agrees
with previous findings where it was supported that past experience with a destination
influenced intention to return to the destination in the future (Gitelson & Crompton,
1984; Hu & Ritchie, 1993; Kozak, 2001; Mazursky, 1989; Milrnan & Pizam 1995;
Perdue, 1985; Pctrick et al., 2001; Sonmez & Graefe, 1998).

Past research has also showed that destination image influences tourists’ choices
of destinations (Ahmed, 1996; Court & Lupton, 1997; Dann, 1996; Fakeye & Crompton,
1991; Fridgen, 1987; Gartner, 1989; Gartner, 1993; Hunt, 1975; Ross, 1993; Tapachai &
Waryszak, 2000). The results of this study support findings of the previously mentioned
studies. It was shown that destination image positively impacted intentions to revisit the
destination to participate in a leisure activity. This study empirically reinforces the impact
of the post trip destination image experience on intention to revisit. More research is
needed to understand which component of the destination image (i.e. cognitive, affective)
is more dominant in the tourists’ decision making process with regards to sport tourism
phenomena.

In summary, this research has shown that past experience with the destination
directly and indirectly influences intentions to revisit through the mediating impact of
destination image perceptions, while past experience with the event does not have a

significant impact on the event’s image.

109

Theoretical implications

The TPB was utilized in this study in a post-trip context. In other words,
participants were asked to make a decision about revisiting a destination they had been
exposed to from event participation. In this context, this study utilized the TPB as a
theoretical background to predict people’s intentions to return to the destination
combining the theory’s concepts of SN and PBC toward the event participation and a link
between two attitudinal constructs: event and destination image. Past behavior variables
were also added to the model to examine their role in the overall predictive validity of the
theory in the context of sport tourism and their impact on the attitudinal concepts of event
and destination image and intentions. Although SN and PBC path estimates were not
significant (potentially due to the indirect correspondence of measures to intentions), the
overall model explained 34% of the variance in intentions to return to the destination to
participate in leisure activities. This result shows the parsimonious model of the TPB is
adequate to explain the behavioral intentions of sport tourism participants to revisit the
destination. However, the results raise some questions with regards to the predictive
power of SN and PBC variables on intentions.

It may be possible to improve the theory in the post-trip context by examining the
role of intentions to participate in the event that takes place in the same destination. In
other words, are event participation intentions a predictor of intentions to return to a
destination? In addition, based on statistical results, the SN and PBC were not significant
predictors of intentions. Potentially, the impact of these two factors did not reach
significance due to two reasons. One is the indirect correspondence between the measures

of SN, PBC and intentions. The second one involves the trip phase. People were surveyed

110

about their SN and PBC perceptions in the post trip context. Potentially, in the post-trip
phase the relationships between future intentions, SN and PBC are not significant. This
may be because there is the element of direct experience that potentially influences these
relationships. In other words, no matter how supportive the family and fi'iends are of the
event participation and despite having all resources required to participate in the event in
the future, if the participant had a dreadful experience with the event in a specific
destination, SN and PBC will not be good predictors of intentions. In the post-trip context
people experienced a specific level of services with which they were satisfied or
dissatisfied. Consequently, future research could examine the role of satisfaction with
both the event and destination services because it could potentially play an important role
in intentions to revisit (Fakeye & Crompton, 1991; Pctrick & Backman, 2001; Pctrick et
al., 2001; Ross, 1993). As such, the model could include satisfaction with the event and
with the destination services as predictors of intentions.

The important role of past experience with the destination was also verified in this
study. Past behavior positively influenced destination image perceptions and as such its
inclusion in future models of destination image formation should be considered. Past
research has also shown the value of past behavior on intentions (Ouellette & Wood,
1998). This study also demonstrated the sizeable positive impact of past experience with
the destination on intentions. Its inclusion is advised in models of TPB because its
explanatory power has been shown in recent meta-analytical studies (Ouellette & Wood,
1998) and in this study contrasting Ajzen’s older research (1987), it was concluded that
past behavior serves no useful purpose in models predicting future behavior. As far as the

inclusion of past event behavior is concerned, this study showed no support for its

111

inclusion in future model testing. However, more studies are needed to conclude its
effectiveness or ineffectiveness with regard to the prediction of the image of a sport
tourism event.

The TPB explained intention to revisit a destination to participate in leisure
activities satisfactorily despite the two different attitudinal measures and the indirect
correspondence between measures but including past behavior variables. The results of
this study indicate that the attitudinal measures of event image and destination image
should be targeted when destination marketers aim to increase intentions to revisit the
destination where the event took place. This study emphasizes the synergy that needs to
be created between event and destination marketers. Currently, there are not many cases
where the event and destination marketers have worked together effectively to promote a
destination in correlation with the event (Chalip & McGuirty, 2004; J ago et al., 2003). In
addition, destination marketers should use the theory to target, through marketing
communications, the physical activity benefits of event participation in a destination and
promote the destination’s facilities that can achieve those benefits (e. g. trails).
Promotional communications can target subjective norm salient beliefs to increase the
likelihood to visit a destination that hosted a sport tourism event in the future. For
example, if friends perceive a destination that hosted a sport tourism event as exciting
and with great activities and things to do, they may be more positive toward visiting the
destination.

Another example relates to the role of PBC beliefs. Event organizers can work
with destination marketers to attract more people to the destination by promoting PBC

beliefs such as the ease with which participants can physically achieve the event

112

completion. Also, the promotion of inexpensive event participation prices in combination
with destination accommodation prices may increase intentions to return to the
destination. The role of PBC although not significant in this study, should not be
neglected by event and destination marketers because it did have a small positive

influence on intentions.

Managerial implications

Sport event marketers and destination marketers are interested in understanding
the factors that influence people’s participation in sport events that take place in their
surroundings (Weed & Bull, 2004). This study provides theoretical support which can
translate into practical and managerial applications for each one of these two
management groups. These implications will be presented in terms of the overall model,
the link between sport tourism event image and destination image, past behavior,
destination image and their impact on intentions to revisit a destination for leisure activity

participation.

Overall model managerial implications

In terms of the overall model, destination marketers can test the type and extent of
impact various sport tourism events have on a hosting destination’s image. This implies
the use of this model as an intentions barometer model. The impact of sport tourism
events and other events such as festivals, conferences and cultural happenings that attract
visitors in the area can be evaluated. Bigger impact sport tourism events could be chosen

from the destination tourism authorities as a tourism product diversification strategy.

113

Event marketers can utilize the model as a tool to support and promote the effectiveness
of sport tourism events for marketing destinations. This implication is especially true for
recurring events because the SN and PBC will be directly related to intentions to revisit a
destination that is part of the event itself.

Although the results of this study showed that SN and PBC were not significant
predictors of intentions, their role should not be discounted until further research has
established their role more clearly in the field of sport tourism. The role of the support
network of participants (SN) and whether they have the physical and financial means to
participate (PBC) are two other factors that can be manipulated, in terms of marketing
communication campaigns, by event managers. Also, event managers could use the
model by replacing the destination image factor with the image of the sponsor. They can
evaluate the link between the branded image of a sport tourism event and the sponsor
image separately showcasing which sponsor benefits the most fi'om such linkages and the
type of event that offers the greatest impact. This model is also applicable for those cases
where destinations function as sponsors for events. For example, the Las Vegas
Convention and Visitor Bureau (CVB) has been sponsoring conferences or parts of
conferences in the past four years. Consequently, the Las Vegas CVB could utilize the
model to assess the effectiveness of this sponsorship to the image of Las Vegas and
conference participants’ intentions to visit Las Vegas in the future. Another example is
portrayed in Figure 7. The jetBlue airline teamed with the Boston Marathon event and a
travel company (Marathon Tours and Travel) to facilitate participants to book their trips

at better prices.

114

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The model could also be used in the pre-trip context to assess people’s intentions to visit
a destination they have never visited before. This utilization, however, would require the
removal of the past experience variables since the target market would be first-time
visitors.

Destinations invest firnds in creating or improving event related infrastructure and
promoting events to attract more visitors to their area. This model can provide support for
events that better fit the destination by evaluating the impact of the event’s image on
destination image and assessing the return on investment (ROI) for each event. If, for
example, sport tourism events (e. g. bicycling) result in larger economic impact in the area

than business conferences, and the model shows that the image of the sport event had a

115

substantial positive impact on the destination image and on intentions, then sport tourism
events are a better fit for a destination than business conferences. Recent research on
economic impact of bicycling facilities in North Carolina found visitors who bicycle have
a significant economic impact on the area (Transportation Research Board, 2006). This
finding influenced the policies of the North Carolina Department of Transportation to
study more bicycle facilities in order to allocate public funding more effectively
(Transportation Research Board, 2006).

In addition, the evaluation of SN and PBC would allow event marketers to apply
proper marketing communications strategies that would target beliefs related to similar
group subcultures (Green, 2001) and promote the perceived ease or difficulty of engaging
in the event according to the profile of their target market. For example, if their target
market is young and adventurous, promotional material could utilize images that portray
challenging (or less challenging) elements of the event as well as adventurous features of
the destination (or venue) where the event is held. For outdoors events, natural resources
managers can become directly involved with the promotion of the event because they
provide the natural environment for the event happening.

Finally, sport travel has become a luring business and websites are created to
facilitate fans’ traveling behaviors (McManus, 2006). Internet sport travel websites have
started to increase in number to facilitate sport fans’ event attendance. This model can be
applied in the online world of information search to investigate the impact of sport
tourism event images on destination images and intentions to purchase packages that
include accommodation, event tickets and other activities that are taking place in the area.

McCartney (2005) found that 60% of spectators engage in other activities (e. g. food

116

festival) during their stay in the destination for a sport event. The city of South Haven
could utilize this application and create a segment for sport tourists customized to offer
sport tourism event opportunities, as well as, accommodation and other activities

packages. Figure 8 shows one of these websites (wwwprimesportcom).

F igge 8. Primesport.com a sport travel website

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Link event and destination images

Sport tourism events have a specific brand image in the minds of their
participants. This brand image was found to be an amalgam of organizational,
environmental, physical activity related, social, fulfillment and emotional themes.

Through marketing communications (brochures, websites, magazines, word-of-mouth)

117

branded products have to communicate the proper brand image for their target markets
and the entities that are part of their image (e. g. environment is related to the hosting
destination’s resources). J ago et a1. (2003) suggested sport event marketers and
destination marketers should work together to capitalize on sport events as poles of
tourism attraction. The results from this study reinforce this view. In their latest tourism
business magazine, the Canadian Tourism Commission (CTC) (2006b) discusses the role
of events in selling destinations. Although the connection between athletic achievement
and tourism is not plainly evident, the achievement on the one field will have an impact
on the other (Canadian Tourism Commission, 2006b). The CTC also presents a website
that is the one stop shopping resource for athletes, coaches and spectators. The example
of Canada as a tourism destination that aims to capitalize on the hosting of sport events to
enhance their destination image was also evident in another article by CTC (Canadian
Tourism Commission, 2006a). In that article, it was presented that the sport and tourism
communities work together to enable the industry to take advantage of a range of
opportunities due to the upcoming Olympic Games in 2010 in combination with
Canada’s strong brand image as a hosting destination (Canadian Tourism Commission,
2006a).

The contribution of this study also extends to the development of a measurement
tool for the image of the event among active participants. The definition of sport tourism
event image produced in this study identifies those image items participants favor about a
sport tourism event. These items can be utilized in the development of a destination
brand. The results from the scale development process revealed that destinations can

utilize the organizational, environmental, physical activity, socialization, fiilfillment and

118

emotional involvement themes to create related branding images for their destination.
The goal would be to attract more people to destinations that identify with those images
and increase the numbers of participants in the event because event image indirectly
influences intentions to return to the destination that hosted that event. For example,
respondents of this study rated the event highly on how fulfilling and healthy it was.
Destination and event marketers could utilize promotional images related to fulfillment
and healthy activities and places in relation to re-hosting the event to achieve brand
leveraging or brand development.

This information can also be used to attract sponsoring companies that carry
similar brand images and aim for congruency enhancements of their company’s image. If
the event scores high in items the sponsor company aim to promote, then potentially
brand image enhancement could occur. Also, if destinations are the sponsor of an event,
then the same implications apply to them.

Event marketers usually focus on spectators. However, participants and their
families coming along to support them, are guaranteed visitors to the destination that
hosts the sport tourism event. Understanding the active sport tourist (participant) can help
event marketers better target participants’ needs. For example, Chalip and McGuirty
(2004) revealed in a study of runners, who could be potential participants to the Old
Coast marathon event in Australia, four clusters of runners: dedicated runners, running
tourists, active runners and runners who shop and concluded that each group prefers
different activities offered at the destination to be bundled with the event (e. g. dedicated
runners preferred marathon official parties as an activity bundled with the event). These

results are examples of event marketing customization for participant clusters. These

119

clusters are identified based on their activity preferences at the destination but also could
be segmented based on their event image perceptions. For example, dedicated runners
may have higher perceptions of the health and organization image item, while running
tourists may have higher perceptions about emotional image items and socialization
image items. Based on these perceptions, target marketing approaches can be customized
to fit the needs and perceptions of each segment.

For the online world of information, travel websites could be developed to
customize itineraries for participants based on their event image perceptions and
destination perceptions. For example, ESPN.com. teamed up with Orbitz (online travel
company) to develop content that is customized for passionate fans. More specifically,
according to a press release by Orbitz (2006) "ESPN Sports Travel online will offer
sports travel planning information and tools for more than 50 major North American
cities (16 at launch with others following in the coming weeks). The site will provide
users with the ability to book hotels, vacation packages, flights and destination services
on Orbitz.com. Planned content for ESPN Sports Travel powered by Orbitz includes:

> Features: Travel articles about major sports towns, events and experiences from
ESPN.com, ESPN The Magazine and contributing writers;

> The Sports Fans' Guide: Editorial on cities, including in-town tips, sports bar and
restaurant reviews, addresses and maps, general recommendations, average daily
weather and more;

‘P The "Power Weekend" Profiles: Ideas for perfect sports getaways;

> The "Hot List": Briefs on the top spots in the best sports towns in America;

120

> Calendars: Complete calendar of the biggest sporting events, and travel guides for
attending them;

> Sports Schedules: Full schedules for all teams in more than 30 professional,
collegiate and amateur sports leagues and conferences;

> E-Cards: The ability to send picture postcards from sporting events around the
globe;

> Polls and Message Boards: The perfect venue for fans to share opinions and travel
tips from featured cities.

> Directory: Addresses, maps and contact information for more than 200 North
American venues (50 venues at launch with others following in coming weeks);

> Other editorial content from ESPN.com content providers, such as Golf Digest
course reviews and golf features, a J ayski.com NASCAR travel guide and more!”

Figure 9 shows one part of the ESPN website.

121

Figge 9. Part of the ESPN sport travel website homepage

 

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The ESPN sport travel website showcases the potential of joint marketing efforts

between destination and events. Similar websites, however, could be created for the
needs of participants or the same websites could be modified to accommodate
participants’ (active sport tourists) needs. Finally, Internet communications can become
the medium for brand image building. The Internet can provide faster and cost-effective
brand image development and improvement through linkages with successful events and
destinations.

Finally, for Internet marketing communications on sport travel behaviors, profiles
of sport tourists who surf the World Wide Web can be created (through login usemarnes
and passwords) and customized packages could be generated based on previous behavior

patterns and the types of activities those sport tourists enjoy engaging. The customization

122

can promote destination products and services that are not easily traceable in destination
websites and can project to the user a different destination image that is customized to
what the online sport tourist seeks to find.

Beyond event marketing strategies, destinations’ marketing strategies should
communicate to the tourist the availability of tourism products that are in conjunction
with visitors’ destination image perceptions. This indicates that each destination should
identify and understand its destination’s imagery. Also, this information can be derived
by residents’ images of their own destination. Brarnwell and Rawding (1996) found that
locals’ perceptions of projected image is important because they define their attitudes and
support for tourism development. In addition, sport event managers should understand
that the image of the destination matters should they want to host the sport tourism event
in the same place. Destinations can benefit from recurring events in term of synergistic
approaches marketing approaches with events (McCartney, 2005). Finally, repeat
visitors are more likely to revisit the destination as shown with this model. Destination
marketers should customize their marketing communication campaigns for the needs of
repeat visitors so as to maintain high probability of return visitation. For example, the
diversification of the tourism product through the use of various events could be a pole of

attraction for the repeat visitor.

Future research
This study examined active sport tourists’ intentions to return to a destination they
experienced through sport tourism event participation. The model presented in this study

satisfactorily fit the data, nevertheless fiiture developments could potentially improve the

123

overall model. Active sport tourists travel to places to participate in sport events. Their
intentions to participate in the event could be a mediator between SN, PBC and intentions
to retum to that destination in the future to participate in leisure activities. Consequently,
future research should test the model inserting a new variable of intentions to participate
in the event at the same destination in the future as a precursor of intentions to revisit the
destination.

Future research should also test the overall model with events other than sport
tourism events. For example, festivals, conferences, concerts could be used as events and
the model can be used to evaluate their impact on destination images.

Sport events are important for residents as well. Future research should examine
how events impact the destination images of local people and potentially investigate the
impact of these events and the changes they bring on residents’ quality of life. This type
of research would require a change in the dependent variable from intentions to quality of
life and intentions to use local event facilities.

Furthermore, future research should test the reliability and validity of the scale
developed in this study with other sport tourist types such as spectators. Differences may
arise in perceptions of how beautifiil the environment is (e. g. artificial vs. natural) as well
as perceptions of the nature of the activity (e. g. healthy). However, spectators may relate
the image of a sport event to mental health since it provides them with an opportunity to
engage in an enjoyable different activity rather than sitting at home (Wann & Schrader,
1997). The scale could be also tested with participants in other sport tourism events such

as running, golfing, soccer tournaments or basketball tournaments

124

Limitations of the findings

This study used one sample of sport tourists (bicyclists) and one event and one
destination to test the model. Consequently, the results cannot be generalized to the
whole population of sport tourists. However, generalization of the results at this stage
was not a necessary requirement because this study also aimed to test a theoretical model
that was not previously tested per se in the tourism literature (Calder, Phillips & Tybout,
1981; Lynch, 1999). The results, however, can be used in relationship to branded events
such as the “Michigander” sport tourist (bicyclist). This study is limited also by the poor
reliability exhibited in some of the measured items. Future research should account for
item improvement and reliability. The space constraints in the questionnaire layout due to
financial reasons (4 pages) limited the inclusion of more items for the measurement of
each factor which could have potentially accounted for some of the poor reliability
measures. Finally, the results are limited to applications with sport tourist participants in
the post-trip phase. Potentially, research of the participants at the pre-trip phase would
yield different results especially with regards to subjective norms and perceived
behavioral control because people would have perceptions and not direct experiences

with the event and destination.

Final comments

This study aimed to test the impact of event and destination images on intentions
to revisit a destination to participate in leisure activities. The role of event and destination
image, past experience, subjective norms and perceived behavioral control was examined

through the application of the TPB. The results of the study should be viewed in

125

relationship with the increasing numbers of bike vacationers (more than 27 million in the
past 5 years (Travel Industry Association of America, n.d.) and the increasing need for
synergy among tourism entities and sport organizations (J ago et al., 2003). The
increasing number of sport travel websites is an indicator of this trend. Marketing
communications should be customized to address the needs of a new target market: the
independent sport tourists who plan their own trips based on their sport participation

interests or their spectatorship interests and passions.

126

 

APPENDICES

127

Appendix A
Focus group consent form and script

 

Focus group consent form and script for MS U bike club and Michigander participants
approved by UCHRIS: IRB # 04106

CONSENT FORM

The Department of Community Agriculture, Recreation and Resource Studies (CARRS) in
cooperation with the Michigan Department of Transportation are conducting a research project to
understand the impact of sport tourist events on local communities.

You are being asked to take part in a research study. To join the study is voluntary. You may
refuse to join, or you may withdraw your consent to be in the study, for any reason, without
penalty.

Details about this study are discussed below. It is important that you understand this information
so that you can make an informed choice about being in this research study. You will be given a
copy of this consent form. You should ask the researchers or staff members who may assist
them, any questions you have about this study at any time.

The duration of the focus group will be approximately 1 hour.

Purpose of the study
We would like to ask you about your experiences with sport tourist events, your impressions,

attitudes and overall perceptions of similar events and their impact on communities’ tourism
approaches within a focus group context.

What will happen if you take part in the study?
The group will be asked to discuss how the participation in a sport tourist event can impact the

communities involved in its organization and its image. No questions will be directed to you
individually, but instead will be posed to the group. You may choose to respond or not respond at
any point during the discussion. The focus group discussion will be audiotaped so we can capture
comments in a transcript for analysis.

What are the possible benefits from being in this study?

Research is designed to benefit society by gaining new knowledge. You may not benefit
personally from being in this research study.

What are the possible risks or discomforts involved from being in this study?

We do not anticipate any risks or discomfort to you from being in this study. Even though we
will emphasize to all participants that comments made during the focus group session should be
kept confidential, it is possible that participants may repeat comments outside of the group at
some time in the future. Therefore, we encourage you to be as honest and open as you can, but
remain aware of our limits in protecting confidentiality.

How will your privacy be protected?

Every effort will be taken to protect your identity as a participant in this study. You will not be
identified in any report or publication of this study or its results. Your name will not appear on
any transcripts; instead, you will be given a code number. The list which matches names and code
numbers will be kept in a locked file cabinet. After the focus group tape has been transcribed, the
tape will be destroyed, and the list of names and numbers will also be destroyed.

Will it cost you anflhing to be in this study?

You may incur a small transportation cost for being in the study.

Will you receive anfihing for participating in this study?

You will receive $15 dollars for your time and participation.

128

What if you have Questions about this study?
You have the right to ask, and have answered, any questions you may have about this research

project. If you have questions, or concerns, you should contact the researchers listed on the
bottom of this form.

What if you have Questions about your rights as a research participant?
If you have any questions about your rights as a human subject of research, please contact the

Michigan State University Committee on Research Involving Human Subjects, Peter Vasilenko,
Ph.D., Chair of the University Committee on Research Involving Human Subjects (U CRIHS) by
phone: (517) 355-2180, fax: (517) 432-4503, e-mail: ucrihs@msu.edu. or regular mail: 202 Olds
Hall, East Lansing, MI 48824.

Participant’s Agreement:
I voluntarily agree to participate in this research study.

 

 

Signature of Research Participant Date

 

Printed Name of Research Participant

 

 

Signature of Person Obtaining Consent Date

 

Printed Name of Person Obtaining Consent

 

Kiki Kaplanidou Dr. Christine Vogt
Research project assistant Associate Professor
Michigan State University Michigan State University
E-mail: Qplanid@msu.edu E-mail: vogtc@msu.edu
Tel: 517 432 0320 Tel: 517 432 0318

129

FOCUS GROUP SCRIPT
Objective: Use the focus group format to identify the impact of sport events on local
communities. Duration: Approximately 1:00 hour.

Group/Location:
Number of Participants:
Date:
Moderator:
Assisted by:

 

 

 

 

 

Supplies — flip charts and markers, 12-15 copies of exercise, 3 copies of script

Introduction (5 minutes)
0 Moderator: self-introduction (hired by a client to moderate, no mention of MSU
or MDOT).
- Describe study:

0 The purpose of today is to gain YOUR perspective on participation of
sport tourism events and the decision making process involved with
attending a sport event as a participant (e. g. bicycling, running)

0 Session will be taped and transcribed. No information and names will ever be
matched. You will be assured complete confidentiality.

Focus Group “Rules” (1 minute):

0 Today’s session will last no longer than 90 minutes.

a The goal of this focus group is to allow everyone to participate by sharing their
ideas and opinions.

0 Many of you may have much to say on this topic and we value that. However, we
need to make sure that everyone is allowed to share his or her views. My role as
the moderator is to help us stay on track and provide an opportunity for everyone
to participate.

Warm-Up (10 minutes)
Let us begin by introducing ourselves to each other, sharing how long you have lived in
Michigan and what you like best about Michigan. Other questions:

1. Have you taken any trips in the last year or two that included participation in a
sport tourist activity? (if needed ask: How about bicycling tours?)

2. What important is it to you to participate in a sport tourism activity like bicycling
and why?

Sport event image questions (25 minutes)
1. When you think of your participation in a sport tourist event (e. g. Michigander),
what words/images come to mind?
a. PAPER AND PENCIL ACTIVITY
b. After that discuss their ideas

130

2. What are the benefits (e. g. functional, experiential, symbolic) that you get from

participating in a sport tourist event? Any costs or negative outcomes?
a. FLIP CHART ACTIVITY

3. Do you find that the participation in a such an event can be characterized overall
as very interesting/uninteresting, pleasant/unpleasant, boring/exciting,
worthless/valuable (from Cunningham and Kwon, 2003) and why?

4. What other emotions do you have of participating in this event?

What are those features of a sport tourist event (e. g. Michigander) that you

consider when deciding on events? (probing if needed: price, amenities, the whole

package, other sport tourists?)

a. PAPER AND PENCIL ACTIVITY

b. After that discuss their ideas

6. How about where this event is held? Does the location or destination influence
your decision to travel to participate to the event? Why?

7. Would you return to the destinations where sport events occurred for vacation
purposes, reuse the facility or to participate to the same event? Why?

8. Do you think that there are certain sport events that fit with certain communities
or trails better than others? (e. g. Michi gander)

Destination image questions (15 minutes)

1. What are those attributes that you find important in a destination you travel to for
vacation?

2. What are the three words, images, characteristics that come to mind when you
think of XXX? (e. g. South West Michigan, South Haven -if participants have
been there)

a. PAPER AND PENCIL ACTIVITY
b. Discuss their ideas afterwards
3. Please list any distinctive or unique elements that you can think of XXX?
a. PAPER AND PENCIL ACTIVITY
b. Discuss their ideas afterwards

4. What are some of the tourist or other activities you engage in during a vacation
trip?

5. Why would you revisit a destination?

Subjective norms (5 minutes)

1. When you make a decision to participate in a sport activity (take a trip for the
activity), who are those people from your environment that influence your
decision? How?

a. FLIP CHART ACTIVITY

2. Do you think that you will change your mind if these people have a different

opinion from yours with regards to participating in this sport tourist activity?
Perceived behavioral control (5 minutes)

1. What are some factors that have prevented you or may prevent you from engaging
in a sport tourist activity? Why?

a. PAPER AND PENCIL ACTIVITY
b. Discuss their ideas afterwards

LII

131

Past behavior (5 minutes)
1. How important is previous experience with an event on your intentions to
participate again in this event? Why?
2. If you experience a destination through some sport activity, how willing would
you be to revisit that area in the future? Why? Do you consider that past
experience is important in your decision? Why?

132

Appendix B
Survey instrument

 

Michigander Bike Event Survey
Sponsored by Michigan State University, Michigan Agricultural Experiment
Station, Michigan Dept. of Transportation

Thank you for agreeing to complete this survey about your Michigander bike
experience. Please read each question carefully before responding. Answer to the best
of your ability and save any additional comments for the end. Your responses will help
the organizers of the event, the destinations involved and the organizations which build
and maintain trails in Michigan.

 

E This first section asks about your experience with the Michigander.

1. Was the 2005 Michigander your first ride in this bike event? (/ one)
”—13 YES, CONTINUE TO QUESTION 2
I] NO A. How MANY PREVIOUS TIMES HAVE YOU RODE IN THE EVENT? TIMES
B. IN WHAT YEAR DID YOU LAST RIDE IN THE EVENT? YEAR

 

L 2. How many times in the last 5 years have you participated in similar sport tourist
events?
(fill in the # of events and describe their type) (# OF EVENTS)

 

Please describe below the types of the events you participated (e.g. running, biking,
swimming)

 

3. Was the 2005 Michigander the primary purpose of your trip? (/ one)
C] YES
[:1 NO

4. How much influence did the location of the trails’ route have on your decision to
ride in the 2005 Michigander? (r/ one)

 

 

NO INFLUENCE NOT MUCH SOME MODERATE A LOT OF
WHATSOEVER INFLUENCE INFLUENCE INFLUENCE INFLUENCE
El El [:1 El

 

5. Who accompanied you on the 2005 Michigander bike event and trip?
(Fill in a # for those that apply)

In your travel party: How many?

FAMILY MEMBERS WHO RODE IN THE EVENT

 

OTHERS BESIDE FAMILY WHO RODE IN THE EVENT

 

FAMILY OR FRIENDS WHO DID NOT RIDE IN THE EVENT

 

133

6. How many days did you ride in the Michigander and how many nights were you
away from home? (fill in a #)

NUMBER OF DAYS RODE IN EVENT

NUMBER OF NIGHTS AWAY FROM HOME

7. How satisfied were you with the 2005 Michigander on the following items?
(circle a response per line)

 

Extremely Very Very Extremely
dissatisfied dissatisfied Dissatisfied Neutral Satisfied satisfied satisfied

 

Condition of
the Kal Haven
trail 1 2 3 4 5 6 7

 

Overall
Michigander
experience 1 2 3 4 5 6 7

 

Condition of

other trails

that were part

of the event 1 2 3 4 5 6 7

 

8. In the next m years, how likely are you to...? (circle a response per line)

 

 

 

 

Extremely Very Somewhat Somewhat Very Extremely
unlikely unlikely unlikely Neutral likely likely likely

Ride the Kal

Haven trail 1 2 3 4 5 6 7
Visit south

haven for a

vacation 1 2 3 4 5 6 7
Visit south

haven area to

participate in a

sport or outdoor

recreation

activity 1 2 3 4 5 6 7

 

 

This second section includes questions about the Michigander event
and the destination of South Haven.

9. In the past 5 years how many times have you visited South Haven for vacation
purposes (excluding this trip)?
(please write the # in the box)

I J

 

134

10. In the past 5 years how many times have you taken a trip to South Haven area
to participate in a sport tourist
event (excluding this trip)? (please write the # in the box)

 

11. How would you describe all aspects of the Michigander bike event based on how
you feel now about the event? ( r/ the line for each set of opposite adjectives that is
closer to how you feel)

FOR ME THE MICHIGANDER BIKE EVENT IS. . ..

 

-O
C
:‘
0

Extremely Quite . Slightly Neither .., “Slightly-“ “Extremely” . .

....--ng mm. -. .

 

 

 

 

 

iNATURAL’

 

. ARTIFICIAL . __ __ _ __. __.. __ _____ .
UNABEEALWG,;L ' . '. .. ] __.. -.ff§f;APl‘EA’LmG“ .
_ *___ EASY _ j ,_ _ _ __ , WCHALLENGING
PASSIVEJ, ...___.- __.... _._..__ __.... __.-"LCOMPETHTNE .-
STRESSFUL . _ CAREFREE
T “ACTIVE. _' .______ _. __._ ._ .______ ..______..‘..; mACTIVE..ii.-_
. INEFFICIENT , . . _. EFFICIENT _
....NVORmLESS:fi.L.I ...__.__. _..... . _ _.___..__ . VALUABLE if
1 FRIENDLY UNFRIENDLY _
p "“UNSUPPORTIVE Lg" , . . __ ' '. . j . ' ‘ ' _ SUPPORTNE
INDIVIDUALISTIC . . . COLLECTIVE
1 QDISQRGANIZED . ._ __. -_. .___‘ __.. __.. ...___ .i. ORGANIZED/.5
.- UGLY __ __.. __ _ ___ ____ _____ BEAUTIFUL
j” CLEAN” __.. ..'_'__. '_ ,_..__ '.;_. __.... ...___."7'7T’iPGLLUTED-m'f 13'.
GLOOMY _. _ __ __ _ __ __ CHEERFUL
"H BORING); , H . _' ' ' , ‘ WyliXClTING, ‘
. ENERVATING . _, ._ . INVIGORATINO
‘ iIf.'DIS'I..RES.-LLZSNGQ.Z:ZZ.l ‘. .. ‘ ‘ ' " " ' _RELAXING
WWWSAD ________ __ ________ __ __ __. ____ JOYFUL
. POOR ::._.______ __.... _. _..... .___, ..____ __,__- EXCELLENT f,
INSPIRING UNINSPIRING
. UNA‘DVENTUROUSj j . . . . _ ' ._ _ . . ' . _.L.LADVEN'IUROUSjj
UNSTIMULATINO STIMULATING
UNFULFILLING 'FULFlLLING
EXPENSIVE INEXPENSIVE

 

135

12. How much do you agree or disagree with the following statements about
participation in the Michigander? (Please circle one answer on each line)

 

Totally Moderately Somewhat Somewhat Moderately Totally
disagree disagree disagree Neutral agree agree agree

 

My fn'ends

who are

important to

me support

my

participation

in the

Michigander 1 2 3 4 5 6 7

 

My family

disapproves

of my

participation

in the

Michigander 1 2 3 4 5 6 7

 

I have the

financial

resources to

participate in

the

Michigander

next year 1 2 3 4 5 6 7

 

I have the

physical

resources to

participate in

the

Michigander

next year 1 2 3 4 5 6 7

 

136

13. The following statements are about what South Haven offered during your stay.
Please indicate your opinion on a seven-point scale from l=offers very little to
=offers very much. (Circle a response on each line or check not applicable N/A)

 

Offers Offers Offers Neither Offers Offers Offers
extremely very somewh little nor somewh very extremely

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

little little at little much at much much much N/A
Good nightlife
and
entertainment 1 2 3 4 5 6 7 E]
Quality of
infrastructure 1 2 3 4 5 6 7 C]
Personal
safety 1 2 3 4 5 6 7 [3
Standard
hygiene and
cleanliness 1 2 3 4 5 6 7 E]
Suitable
accommodatio
ns 1 2 3 4 5 6 7 [:1
Good quality
restaurants 1 2 3 4 5 6 7 Q
Great beaches 1 2 3 4 5 6 7 E]
Friendly
pegfle 1 2 3 4 5 6 7 Q
Great
museums 1 2 3 4 5 6 7 [J
Interesting
historical
attractions 1 2 3 4 5 6 7 E]
Beautiful
scenery/natura
l attractions 1 2 3 4 5 6 7 E]
Good value for
money 1 2 3 4 5 6 7 E]
Unpolluted/
unspoiled
environment 1 2 3 4 5 6 7 1:]
Good climate 1 2 3 4 5 6 7 El
Opportunities
for sport
activities 1 2 3 4 5 6 7 Cl
Shopping
facilities 1 2 3 4 5 6 7 D
Great trails 1 2 3 4 5 6 7 E]

 

137

14. How do you feel now about South Haven as a vacation destination?

( V the line that is closer to the adjective that represents how you feel - provide an

answer for each set of adjectives)

FOR ME, SOUTH HAVEN AS A VACATION DESTINATION IS...

 

Extremely Quite Slijgitly Neither Slightly Quite Extremely

 

 

 

 

 

 

 

 

 

 

 

 

 

 

exciting gloomy
unpleasant pleasant
arousing sleepy
distressing relaxing
unfriendlL friendly
15. If you thought of the Michigander as a sport tourism event that has a
personality, to what extent would the following personality traits describe the
Michigander? (Circle a response for each adjective)
Neither
descriptive
Extremely Very Somewhat nor Somewhat Very Extremely
undescriptive undescriptive undescriptive undescriptive descriptive descriptive descrjptive
sincere l 2 3 4 5 6 7
spirited 1 2 3 4 5 6 7
reliable I 2 3 4 5 6 7
sophisticated l 2 3 4 5 6 7
rugged 1 2 3 4 5 6 7

 

 

I This third section includes questions about your general biking interest. I

16. How many years have you ridden bikes on roads and trails for recreation?

(fill in a #)
NUMBER OF YEARS RIDING BIKES

17. How often do you bike ride on trails as a recreational activity? (/ one)
[:1 EVERY DAY (AS WEATHER PERMITS) El ONCE A WEEK
[3 ONCE A MONTH [I SEVERAL TIMES A WEEK
[:1 SEVERAL TIMES A MONTH E] FEW TIMES A YEAR

18. How many bikes are in your household? (fill in a #)

NUMBER OF MOUNTAIN BIKES
NUMBER OF ROAD BIKES

19. How many bike vacations other than the 2005 Michigander have you taken in the

past five years?(fill in a #.)
NUMBER OF BIKE VACATIONS IN PAST FIVE YEARS

138

20. What groups are you a member of this year? (/ all that apply)

I] LEAGUE OF AMERICAN BICYCLISTS D LEAGUE OF MI BICYCLISTS

D RAILS TO TRAILS CONSERVANCY D MICHIGAN MOUNTAIN BIKE ASSOCIATION

E] INTERNATIONAL MOUNTAIN BIKE ASSOCIATION D LOCAL BICYCLE ORGANIZATION

E] NATIONAL OFF ROAD BICYCLE ASSOCIATION U MICHIGAN TRAILS AND GREENWAYS
ALLIANCE

21. Had you previously ridden on the KAL HAVEN Trail before the 2005 Michigander
event?

[I NO DYES, HOW MANY TIMES IN THE PAST 12 MONTHS? (# OF TIMES)

 

 

The final section of the survey asks about spending patterns and
demographics. This information will be kept confidential and used for
statistical purposes only.

 

22. Approximately, how much did you spend out of pocket on your 2005 Michigander
trip, including all expenses (e.g. accommodations, food, etc.) but excluding the entry
fee? (fill in a #)

 

23. How many adults and children live in your household? (fill in a #)
NUMBER OF ADULTS INCLUDING YOURSELF
NUMBER OF CHILDREN (UNDER 19)

 

24. What is your present employment status? ( I one)

D EMPLOYED, FULL-TIME U RETIRED El UNEMPLOYED U STUDENT
C] EMPLOYED, PART-TIME C] SELF-EMPLOYED D HOMEMAKER I] OTHER

25. Which statement best describes your total 2004 annual household income (from all
sources and before taxes)? (I one)

C] LESS THAN $20,000 U $40,000 - $59,999 [3 $80,000 OR MORE
[:1 $20,000 - $39,999 El $60,000 - $79,999

Thank you for completing this survey. Please return it in the provided self addressed envelope to C. Vogt,
Michigan State Univ., 13] Natural Resources Bldg., East Lansing, MI. «18824-1222. If there is anything else to
add, please include it on an additional sheet.

139

 

Appendix C
Cover letters for the survey administration approved by UCHRIS : IRB # 04106

 

First wave survey cover letter

November 1, 2005
INSERT NAME
INSERT ADDRESS

Dear NAME,

Michigan State University, Michigan Department of Transportation, Michigan
Agricultural Experiment Station and Michigan Trails and Greenways Alliance, (former Rails to
Trails Conservancy) are cooperating to assess the use and values of rail trails and improve their
management in Michigan. Our studies have focused on many trails in Michigan including the
Pere Marquette Rail-Trail, the Traverse Area Recreation and Transportation Trails, the Paint
Creek Trail, the White Pines Trail and the Lansing River Trail.

One use of the trail is for events, like the recent Michigander. The enclosed
questionnaire asks about your experiences in the event, your experiences bicycling in general, the
communities you visited, and descriptive information about you and your household. You will be
entered into a drawing held on November 23, 2005 for one of two $50 price reductions toward
your 2006 Michigander participation fee.

Please take the 10 or so minutes to complete the questionnaire. Your participation in this
study may contribute to the shaping of Michigan’s trail opportunities and tourism development.
There are no known risks associated with participation in this study.

You indicate your voluntary agreement to participate by completing this questionnaire.
However, if you choose not to complete all or part of the questions, you will not suffer any
penalty.

When you have completed the questionnaire, please mail it back to us in the postage paid
envelope provided. Your responses will be kept confidential, your privacy will be protected to
the maximum extent allowable by law and your name will not be associated with any results.

If you have any questions about this study, please contact Christine Vogt at (517) 432 0318, or
vogtc@msu.edu. If you have questions or concerns regarding your rights as a study participant, or
are dissatisfied at any time with any aspect of this study, you may contact — anonymously, if you
wish —Peter Vasilenko, Ph.D., Chair of the University Committee on Research Involving Human
Subjects (UCRIHS) by phone: (517) 355-2180, fax: (517) 432-4503, e-mail: ucfihsébfliedu. or
regular mail: 202 Olds Hall, East Lansing, MI 48824.

Sincerely,
Christine Vogt Kiki Kaplanidou
Associate Professor PhD Candidate and research project assistant
Michigan State University Michigan State University

Nancy Krupiarz
Executive Director
Michigan Trails and Greenways Alliance

140

Reminder postcard text

11/9/05

Last week you should have received a survey mailed from Michigan State University
titled “Michigander Bike Event Survey.” It is part of an important study concerning the
study of trails and sport tourism events in Michigan. If you have already returned it,
please accept our sincere thanks. If not, we ask that you do so as soon as possible. Your
views and information will be very helpful.

If you did not receive the survey, or it was misplaced, please call me at 517-432-0318 or
e-mail me at vogtc@msu.edu. We would be glad to mail you another survey. Thank you
for your help.

Christine A. Vogt, Associate Professor, MSU

141

Second wave cover letter to the survey

November 29, 2005
INSERT NAME
INSERT ADDRESS

Dear NAME,

Recently you should have received a survey in the mail fiom Michigan State
University. We have not yet received your completed survey and we are very interested
in your opinions. If you mailed the survey already we must not have received it when this
letter was written.

Your participation in this study is very important because your answers will
contribute to the shaping of Michigan’s trail opportunities and tourism development. The
questionnaire asks about your experiences with the 2005 Michigander and due to its
theoretical approach it also features a few questions about your feelings with the event,
the trails and destinations that were part of the event. There are no known risks associated
with participation in this study. Ifyou have not completed the survey, please take 10
minutes to complete the enclosed survey. If you reply by December 10, 2005, you will be
entered into a drawing for one of two $5 0 price reductions toward your 2006
Michigander participation fee. You indicate your voluntary agreement to participate by
completing this questionnaire. However, if you choose not to complete all or part of the
questions, you will not suffer any penalty.

When you have completed the questionnaire, please mail it back to us in the
postage paid envelope provided. Your responses will be kept confidential, your privacy
will be protected to the maximum extent allowable by law and your name will not be
associated with any results.

If you have any questions about this study, please contact Christine Vogt at (517)
432 0318, or vogtc@msu.edu. If you have questions or concerns regarding your rights as
a study participant, or are dissatisfied at any time with any aspect of this study, you may
contact — anonymously, if you wish —Peter Vasilenko, Ph.D., Chair of the University
Committee on Research Involving Human Subjects (UCRIHS) by phone: (517) 355-
2180, fax: (517) 432-4503, e-mail: ucrihs@mgu.edu. or regular mail: 202 Olds Hall, East
Lansing, MI 48824.

Sincerely,

Christine Vogt
Associate Professor
Michigan State University

142

Appendix D
Data tables from the survey administration

 

1. Was the 2005 Michigander your first ride in this bike event?
NO 75% —DHow many previous time have you ridden in the event? 5.53 (mean)
YES 25%

The following table provides information on the number of people that participated for the first time
and the number of days they rode in the event.

 

First ride?

 

 

 

 

 

 

 

No Yes Total
Days rode in the From 1-2 days away Count 147 64 211
event? from home % within Days
participants rode in 69.7% 30.3% 100.0%
the event
3 days through Count 222 58 280
highest % within Days
participants rode in 79.3% 20.7% 100.0%
the event
Total Count 369 l 22 491
% within Days
participants rode in 75.2% 24.8% 100.0%
the event

 

2. How many times in the last 5 years have you participated in similar smrt tourist events?
Mean: 4.7

3. Was the 2005 Michigander the primary purpose of your trip?

 

 

 

NO 0.6%
YES 99.4%
4. How much influence did the location of the trails’ route have on your decision to ride in the 2005
Michigander?
(mean 2.7)
NO INFLUENCE NOT MUCH SOME MODERATE A LOT OF
WHATSOEVER INFLUENCE INFLUENCE INFLUENCE INFLUENCE
1 2 3 4 5
24.8% 22.1% 21.5% 16.2% 15.4%

 

5. Who accompanied you on the 2005 Michigander bike event and trip? (Fill in a # for those that
apply)

 

 

 

In your travel party: How many? (mean)
FAMILY MEMBERS WHO RODE IN THE EVENT 2.7 people
OTHERS BESIDE FAMILY WHO RODE IN THE EVENT 1.9 people
FAMILY OR FRIENDS WHO DID NOT RIDE IN THE EVENT __1.0 person _

143

6. How many days did you ride in the Michigander and how many nights were you away from

 

 

 

 

 

 

home?
Number of days rode in event 4.3 (mean days)
Number of nights away from home 4.6 (mean nights)
Days participants rode in the event
Frequency Valid Percent
Valid From 0-2 days
away from home 211 42'9
3 days through
highest 28 1 5 7. 1
Total 492 100.0
Missing 3
Total 495

 

Number of nights away from home

Frequency Valid Percent

 

 

 

 

 

Valid From 0-2 nights
away from home 168 35 '7
3 nights through
highest 303 64.3
Total 471 100.0
Missing 24

Total 495

 

7. How satisfied were you with the 2005 Michigander on the following items?

 

CONDITION
OF THE KAL
HAVEN
TRAIL

Mean: 6.0
OVERALL
MICHIGAND
ER
EXPERIENC
E

Mean: 5.94
CONDITION
OF OTHER
TRAILS
THAT WERE
PART OF
THE EVENT
Mean: 5.22

Extremely
dissatisfied

1

0.4%

0.8%

1.5%

Very

0.6%

0.8%

3% 6.4%

.6%

1.8%

dissatisfied Dissatisfied Neutral
2 3

4

3.3%

1.0%

11.9%

144

Satisfied

5

17.9%

21.9%

29.9%

Very
satisfied
6

46.2%

42.2%

32.2%

Extremely
satisfied
7

31.0%

31.4%

15.1%

8. In the next _th years, how likely are you to...?

 

 

 

 

Extremely Very Somewhat Somewhat Very Extremely
unlikely unlikely unlikely Neutral likely likely likely
I 2 3 4 5 6 7
RIDE THE
KAL HAVEN
TRAIL 12.5
Mean: 4.43 6.3% 9.4% 12.5% 13.7% 33.7% % 11.9%
VISIT SOUTH
HAVEN FOR
A VACATION 10.2
Mean: 4.11 7.0% 12.7% 15.3% 19.0% 27.6% % 8.2%
VISIT SOUTH
HAVEN AREA
TO
PARTICIPATE
IN A SPORT
OR OUTDOOR
RECREATION
ACTIVITY 10.4
Mean: 3.99 8.0% 13.9% 14.3% 20.0% 28.0% % 5.5%

 

9. In the past 5 years how many times have you visited South Haven for vacation purposes
(excluding this trip)?
(This answer includes people that put 0 as their answer).

 

0.9 (mean)

 

 

 

10. In the past 5 years how many times have you taken a trip to South Haven area to participate in a
sport tourist event (excluding this trip)?
(This answer includes people that put 0 as their answer).

 

0.6 (mean)

 

 

 

145

11. How would you describe all aspects of the Michigander bike event based on how you feel now

about the event?

FOR ME THE MICHIGANDER BIKE EVENT IS. . ..

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Extremely Quite Slightly Neither Slightly Quite Extremely
_ I 2 3 4 5 6 7
UNSAFE .. . .2. .. _. ._ - "SAFE ..
mean” ., ...0.-6% 2-9‘%>...-~;.3...7% ”.2799; ---..339/9-.. .-....5.§-9°./9.--... 395%. . -.. A. ..
ARTIFICIAL NATURAL
5.3 1.9% 1.5% 13.3% 4.1% 54.4% 24.3%
UNATF'FEALING ‘ .--~ . - “ '1 - » = - ‘ AFPEALW
- .. ..6.1. 0.7% ., 1.1% 1.7% 2.3%... 3.9%. 53.3% ....,36.5% . ...
EASY CHALLENGIN
4.7 2.7% 9.0% 7.3% 9.0% 36.9% 23.3% 6.3% G
PASSIVE “ ‘ " ‘ “15.5 " * “i “ '" r ' T " "‘COIv‘IFETrr‘IVE”:
,. 3.3 . 4.9%. ...... %, . . ..8..l% - . 43.9% . 20.3%. 4.9%. .. - 1.9%. . ...
STRESSFUL CAREFREE
5.5 0.4% 2.5% 3.4% 11.3% 11.9% 43.2% 22.2% __
, . .. .- . 5.3 , . . 1.5%. ...4.4% - 1.7% 3.6% ..10.4% , 49.3% 28.6%., .. .
lNEFFlClENT EFFICIENT
5.5 0.4% 1.1% 2.1% 17.3% 10.3% 50.4% 17.4%

' “‘WORTHLESS ‘ ‘ ‘ ‘ * ‘ ‘ 4 . ' ”VALUAELE“"~“
6.2, . 02% - 0.2% 5.3%--.. 59%.... -.48.5%.-...40.;%. 2....
UNFRIENDLY FRIENDLY

6.0 2.7% 5.2% 0.3% 0.2% 3.3% 33.3% 43.5%

UNSUPPORTIVE . ~ ‘ " . ‘ i ~‘ "”"SUW
6.1 . . 0.4% . ...:.0..4% ...0.6% 5.4% . 5.3% 49.9% “37.4% . g .. -..
lNDlVlDUALlSTlC COLLECTIVE

4.9 f 0.9% 5.5% 5.0% 23.2% 19.9% 31.2% 9.4%
f‘fr ‘DEWIANIZED ‘ ‘ ‘ ’ "' " ‘ , ‘- . W
1,6.1 .. ..o.3% ...... .4%._. - 3.1% . .....,2.7%.'..,5.2%., ..50.5.%,., 37.3%... .'. .._
UGLY BEAUTIFUL
6.1r 0.2% 6.1% 7.1% 47.3% 39.3% T
.POLLmED . . . .. CLEAN ..
,. 5.6. . .. 40.3%,... 5.3% 3.1%... 5.0% 7.5%. 53.7%.. 34.0%.. .
GLOOMY CHEERFUL
6.2 0.2% 0.2% 3.4% 6.1% 54.3% 35.3%
BORING “ ‘ -““““ ““““ 3‘ “ “ ‘ ~ W
. .. 5.9 , _ 02% 1.3% . 4.0%. .-13.7%.,, 47.8% 27.3%
ENERVATING INVIGORATIN
i 5.9 1.3% 2.4% 1.5% 2.3% 12.0% 52.0% 23.1% .... G
"'DISTRESSING " ‘ “ ° ‘ ' 3' " '~ ‘* ' ‘ ‘ “ * ‘RELAXING"“
5.3- 40.4%.. , 0.3% .~,2.1%. 5.9% ..116.9%...”..51.9%.,,721.9%. _
SAD JOYFUL
6.0g 0.2% 0.2% 0.2% 4.6% 10.2% 53.4% 31.1%

POOR ,I . I. -.I .. II “15W
-. j 6.1 .. .. 0.2% ' _0.2%. . .. 4.0% ...Io.5% . 51.8% 339% , . . _.
UNINSPIRING INSPIRING

5.3 0.2% 3.3% 1.7% 5.5% 12.2% 47.1% 29.6%
mom " 3 * ** j ' ‘ “' -‘ ' ** * ‘ “ADVW
‘._. . 5.3-.. . .- ~- ..0.2% , 0.2% ...-1.3% 5.7%- .2__1_.2% 45.0%»... ~».26.5% . . 1 s . ; -3

UNSTIMULATING STIMULATING

6.0 0.2% 0.2% 0.6% 4.0% 12.7% 54.1% 23.2%
it? “1m“ ALTHY ~' ”* = " "'-‘- W
. 6.4,, . .. 0.2% .- 0.6%. 1.5% . .4.4%._....-.40.6%. 52.7% .

UNFULFILLING FULFILLING

6.3 0.2% 0.4% 0.2% 1.7% 6.1% 45.5% 45.9%

_... 5.6 .. . , .1.3% 1.8%- .....1.3% .* 10.9% «19.1%. 49.9%,ng ..
EXPENSIVE [NEXPENSIVE
4.4 1.3% 5.9% 24.6% 24.4% 13.2% 23.3% 7.4%

 

146

12. How much do you agree or disagree with the following statements about participation in the

Michigander?

 

Totally
disagree

1

Moderately
Disagree

2

Somewhat
disagree

3

Neutral

4

Somewhat

agree
5

Moderately

Agree
6

Totally
agree
7

 

My friends
who are
important to
me support
my
participation
in the

Michi gander

mean: 6.31 1.4%

0.4%

1%

7.1%

6.7%

19.1%

64.2%

 

My family
approves of
my
participation
in the

Michi gander

6.49 3.3%

1.4%

1.4%

2.5%

1.6%

7.8%

82%

 

I have the
financial
resources to
participate
in the
Michigander
next year

6.22 1.4%

1.4%

2.4%

4.3%

10.3%

18.3%

61.9%

 

I have the
physical
resources to
participate
in the

Michi gander
next year

6.54 1%

0.4%

2%

5.9%

20.2%

70.5%

 

147

13. The following statements are about what South Haven offered during your stay. Please indicate

your opinion on a seven-point scale from l=offers very little to 7=offers very much.

 

Good nightlife
and
entertainment
mean: 5.1

Offers

extremely

little
I

1.5%

Offers

very
little
2

1.9%

Offers
somewhat

little
3

3%

Neither

little nor

much
4

8.9%

Offers

somewhat

much
5

21.8%

Offers
very
much
6

27.3%

Offers

extremely

much
7

6.1%

N/A
%

29.4

 

Quality of
infrastructure
5.3

0.7%

0.7%

0.9%

11.5%

23%

33.4%

5.1%

24.8

 

Personal safety
5.6

0.8%

8.5%

21.6%

42.7%

13.1%

13.3

 

Standard
hygiene and
cleanliness 5.6
Suitable
accommodation
3 5.4

Good quality
restaurants

5.6

Great beaches
6.2

Friendly people
5.8

Great museums
4.3

Interesting
historical

attractions
4.9

0.8%

0.8%

0.2%

0.2%%

1.3%

0.4%

0.4%

0.8%

0.6%

0.2%

0.2%

1.7%

1.5%

1.1%

2.5%

1.1%

0.4%

0.6%

1.1%

3.2%

8.2%

8.1%

8.9%

4.0%

5.9%

15.8%

14.9%

16.9%

18.5%

16.5%

7.8%

16.6%

5.6%

15.3%

48.6%

36.3%

37%

33.3%

43.7%

3.7%

11.4%

12.3%

10.4%

13.5%

36.9%

20.4%

1.9%

5.6%

11.6

22.5

22.2

17.1

12.5

68.8

47.6

 

Beautiful
scenery/natural
attractions

6.2

Good value for

money
5.4

0.6%

0.4%

3.2%

2.6%

13.8%

11.5%

19.5%

37.1%

32.2%

40.9%

11.4%

7.5

19.3

 

Unpolluted/
unspoiled
environment
5.6

0.4%

0.2%

0.9%

8.5%

22.6%

41.0%

15.0%

11.3

 

Good climate
5.8
Opportunities
for sport
activities 5.7
Shopping
facilities 5.4
Great trails 6.0

0.2%

0.2%

0.2%

0.2%

0.4%
0.6%

1.3%

0.6%

1.5%
0.6%

148

7.0%

8.7%

10.8%
4.0%

17.1%

15.8%

16.6%
12.9%

43.3%

33.0%

25.7%
42.8%

20.3%

18.8%

8.9%
30.2%

10.7

22.8

35.9
8.9

14. How do you feel now about South Haven as a vacation destination? (scale is from I to 7 where 1 is the
lowest-negative and 7 is the highest-positive)

FOR ME, SOUTH HAVEN AS A VACATION DESTINATION IS...

 

 

 

 

 

 

Extremely Quite Slightly Neither Slightly Quite Extremely
l 2 3 4 5 6 7

GLOOMY
mean: 5.2 0.2% 2.2% 2.6% 17.2% 29.9% 39.8% 8.2% EXCITING
UNPLEASANT
5.8 0.2% 0.9% 11.3% 12.3% 56.3% 19.0% PLEASANT
SLEEPY
4.65 0.4% 2% 8.3% 40.0% 23.0% 22.8% 3.5% AROUSING
DISTRESSING
5.77 0.2% 0.9% 12.3% 12.6% 56.3% 17.7% RELAXING
UNFRIENDLY
5.78 0.4% 0.4% 12.4% 12.6% 55.3% 18.9% FRIENDLY

 

15. If you thought of the Michigander as a sport tourism event that has a personality, to what extent would
the following personality traits describe the Michigander? These items are measured on a 7 point scale where I
is the lowest and 7 is the highest.

 

 

 

 

 

 

 

Somewhat Neither
Extremely Very undescripti descriptive nor Somewhat Very Extremely
undescriptive undescriptive ve undescriptive Descriptive descriptive descriptive
l 3 4 5 6 7

SINCERE
mean 5.4 1.3% 1.7% 2.1% 15.2% 22.9% 41.8% 15.0%
SPlRlTED
5.7 1.1% .4% .9% 3.8% 20.9% 56.4% 16.6%
RELIABLE
5.6 1.3% .4% 2.3% 8.1% 20.2% 49.5% 18.3%
SOPHISTICA
TED 4.3 4.3% 7.1% 11.8% 30.9% 22.3% 18.0% 5.6%
RUGGED
4.9 1.1% 2.4% 5.1% 16.7% 45.3% 21.6% 7.9%

 

149

 

16.

17.

How many years have you ridden bikes on roads and trails for recreation?
15.6 (mean)

How often do you bike ride on trails as a recreational activity?

 

 

 

 

 

 

EVERY DAY (AS WEATHER PERMITS) 4.7%
SEVERAL TIMES A WEEK 23.5%
ONCE A WEEK 14.3%
SEVERAL TIMES A MONTH 24.3%
ONCE A MONTH 3.7%
FEW TIMES A YEAR 19.0%

 

18.How many bikes are in your household?

19.

NUMBER OF MOUNTAIN BIKES
2.5 (mean)

NUMBER OF ROAD BIKES
1.6 (mean)

How many bike vacations other than the 2005 Michigander have you taken in the past five
years?

NUMBER OF BIKE VACATIONS IN PAST FIVE YEARS
2.8 (mean)

20. What groups are you a member of this year?

21.

>

>

V

V V VV

LEAGUE OF AMERICAN BICYCLISTS (5.1%)

RAILS TO TRAILS CONSERVANCY (32.7%)
INTERNATIONAL MOUNTAIN BIKE ASSOCIATION (2.2%)
NATIONAL OFF ROAD BICYCLE ASSOCIATION (0.4%)
LEAGUE OF MI BICYCLISTS (11.3%)

MICHIGAN MOUNTAIN BIKE ASSOCIATION (2.6%)
LOCAL BICYCLE ORGANIZATION (12.3%)

MICHIGAN TRAILS AND GREENWAYS ALLIANCE ( 16.4%)

Had you previously ridden on the KAL HAVEN Trail before the 2005 Michigander event?
NO 38.6%
YES 61.4%
HOW MANY TIMES IN THE PAST 12 MONTHS?

2.4 (mean # of times-this mean calculation includes Os)

150

22. Approximately, how much did you spend out of pocket on your 2005 Michigander trip,
including all expenses (e.g. accommodations, food, etc.) but excluding the entry fee?
Range: $0-$1,500
$213 (mean)
$150 (median)

23. How many adults and children live in your household?

NUMBER OF ADULTS INCLUDING YOURSELF
2.0 (mean)

NUMBER OF CHILDREN (UNDER 19) FOR ALL HOUSEHOLDS
0.8 (mean)

24. What is your present employment status?

> EMPLOYED, FULL-TIME 67.8%
> EMPLOYED, PART-TIME 7.4%
> RETIRED 15.3%
> SELF-EMPLOYED 4.1%
> UNEMPLOYED 0.6%

> HOMEMAKER 1.4%

> STUDENT 3.1%
> OTHER 0.2%

25. Which statement best describes your total 2004 annual household income (from all
sources and before taxes)?

> LESS THAN $20,000 3.6%
> $20,000 - $39,999 8.9%
> $40,000 - $59,999 19.2%
> $60,000 - $79,999 20.3%
> $80,000 OR MORE 48.0%

Age

Range: 12-78
47.8 (mean)
49.0 (median)

Gender
Male: 53.0%
Female: 47.0%

State

91.1% of the respondents were from MICHIGAN
8.9% of the respondents were from out-Of-state

151

no

 

 

APPENDIX E

Variable coding description included in figure 6

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Code used for SEM Variable description Source
V1 Past Behavior with sport Transformed from
tourism event (PPE)—Times questionnaire item I
participated in Michi gander
V2 PPE-Times participated in questionnaire item 2
Similar events
V3 PPE-Number of bike questionnaire item 19
vacations
V4 Intentions (1): Ride the Kal questionnaire item 8 (1)
Haven Trail
V5 I-Visit South Haven (SH) for questionnaire item 8 (2)
vacation
V6 I-Visit SH area to participate questionnaire item 8 (3)
in a sport/recreation activity
V7 Past behavior destination questionnaire item 9
(PVD)-Number Of times
visited SH for vacation in
past 5 years
V8 PVD-Number of times visited questionnaire item 10
SH area to participate in a
Sport tourist event
V9 Event image parcel 1 transformed from
questionnaire item 11
V10 Event image parcel 2 transformed from
questionnaire item 11
V11 Event image parcel 3 transformed from
questionnaire item 11
V12 SN-Friends support event questionnaire item 12 (1)
participation
V13 SN—Family disapproves Of questionnaire item 12 (2)
event participation
V14 PBC-Financial resources to questionnaire item 12 (3)
participate in the event
V15 PBC-Physical resources tO questionnaire item 12 (4)
participate in the event
V16 Destination image (DI) Transformed from
aggregate score-cognitive questionnaire item(s)13
items-mean replacement
V17 DI-aggregate score of Transformed from

affective items

questionnaire item(s) 14

 

152

 

 

 

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