SPORT EVENT ATTENDEES’ PRO-ENVIRONMENTAL BEHAVIOR
IN DAILY LIFE VERSUS IN A TOURISM CONTEXT
By
Ju Hyoung Han

A DISSERTATION
Submitted to
Michigan State University
in partial fulfillment of requirements
for the degree of
Sustainable Tourism and Protected Area Management—Doctor of Philosophy
2014

ABSTRACT
SPORT EVENT ATTENDEES’ PRO-ENVIRONMENTAL BEHAVIOR
IN DAILY LIFE VERSUS IN A TOURISM CONTEXT
By
Ju Hyoung Han
Collegiate football games in the U.S. are a growing sector of sport event tourism. While
these sport events clearly generate positive social and economic benefits, the Environmental
Protection Agency estimated that an average college football game produces 50 - 100 tons of
waste and releases 188 - 376 metric tons of CO2. To minimize such negative environmental
impacts, universities have implemented campaigns to motivate football fans to engage in proenvironmental behavior. However, it is challenging to mobilize individuals’ environmental
concerns and transform them into action while they are watching games and participating in
tailgating. The discrepancy of pro-environmental behavior in daily life versus in a sport tourism
context is poorly understood, as most researchers have focused their studies in one or the other
context. By employing goal-framing theory (Lindenberg & Steg, 2007) and the Social Norm
Approach (Perkins & Berkowitz, 1986), this study examined whether and how event attendees’
pro-environmental behavior in daily life differed from their behavior in the sport event setting.
This study employed a quantitative research method. Tailgaters were intercepted for the
first three Michigan State University home football game days in the fall of 2013 using
systematic sampling procedure. A cross-sectional survey design was used to examine which
psychological constructs predict pro-environmental behavior (i.e., recycling) at home versus at
tailgating settings. From the on-site intercepted tailgaters (n=1,468), 405 surveys were used for
statistical analyses after conducting online and mail follow-up surveys. Descriptive statistics,
exploratory factor analysis, multiple regression analysis, paired sample t-test, and mixed

between-within subjects analyses of variance were conducted. Results showed that underlying
mechanisms of pro-environmental behavior differed in daily life versus in sport event tourism.
Personal moral norms, hedonic goals, and perceived behavioral difficulty had effects on proenvironmental behavior in daily life, whereas descriptive social norms had effects on the same
behavior in the event setting, after controlling for the effects of age, gender, and habitual
environmental behavior. Additionally, event attendees were less likely to engage in proenvironmental behavior at events compared to their behavior in daily life. The degree of decrease
in such behavior was different depending on event attendees’ perception of destination’s
environmental responsibility and event attendee types (i.e., tourists vs. non-tourists).
This study discussed different mechanisms across settings. This study also advanced
understanding of spillover of the same pro-environmental behavior from daily life to a sport
event tourism context. In terms of environmental policy and campaign interventions, practical
recommendations included: visualizing descriptive social norms among event attendees; using
peripheral cues in environmental campaigns in the sport event tourism context; informing
attendees of the destination’s greening efforts; and targeting different types of attendees with
tailored campaigns. Although this study presented promising theoretical and practical
contributions, nonprobability sampling approach limits generalizability of the study findings.
Future studies are recommended to replicate the study in additional sport event destinations.
Another limitation is that this study did not control the actual behavioral difficulty. It is
recommended for future studies to utilize GPS to include actual behavioral difficulty (e.g.,
distance to recycling containers) in sport event settings to better understand the of spillover
effects of pro-environmental behavior.

Copyright by
JU HYOUNG HAN
2014

DEDICATION

To my beloved husband Eunsub Lee,
To my family,
To all of you who believed in me,

To my future.

v

ACKNOWLEDGEMENTS

I am deeply grateful to my advisor, Dr. Charles Nelson, whose teaching and guidance
have been greatly appreciated. He has been a terrific mentor, with whom I have shared so much
excitement on this journey. I am grateful to him for his guidance, suggestions, and insights for
improving my dissertation and broadening my academic thinking. To Drs. Christine Vogt, Dan
McCole, and Eunsil Lee, my committee members, I would like to express my heartfelt thanks.
They provided their time, knowledge, experience, wisdom and emotional support, and kept me
focused on the end goal. I have learned a lot from them and have been very fortunate to have had
such a good supervisory team in my doctoral program. It has been an amazing journey, and one I
could not have made without their help.
I also want to thank the faculty, staff, and members of the graduate office in the
Department of Community Sustainability, and colleagues and friends in the Sustainable Tourism
and Protected Area Management Program. They showed their interest in what I was doing, and
offered friendly words of encouragement. They stayed by my side through all the stress of this
difficult but awesome time. They have been a source of everyday merriment. Thank you all. It
has given me great peace of mind to know that I have been cared for and loved by you.
I am extremely thankful to my family for believing in me. They are witnesses to my
challenges and the cornerstone of my success. Their incredible tolerance and encouragement are
greatly appreciated and will always be remembered. Finally, I greatly appreciate the continuous
support and understanding of my husband Eunsub Lee. He has made my academic and personal
journeys burst with love. I could not have done it without you.

vi

TABLE OF CONTENTS

LIST OF TABLES ··························································································· x
LIST OF FIGURES ························································································xii
CHAPTER 1 ·································································································· 1
INTRODUCTION ··························································································· 1
Background ·································································································· 1
Need for the Study·························································································· 3
Sport Event Tourism in College Towns ······························································· 3
Promoting Pro-environmental Behavior in Daily life vs. in a Tourism Context ··············· 4
Introduction of Study Constructs ········································································· 6
Problem Statement ························································································· 8
Purpose of Study ···························································································· 9
Conceptual Model ·························································································· 9
Research Questions and Hypotheses···································································· 11
Definitions of Terms ······················································································ 12
Delimitations ······························································································· 14
CHAPTER 2 ································································································· 16
LITERATURE REVIEW ················································································· 16
Sport Event Tourism ······················································································ 16
Tourism Definitions ···················································································· 16
Sport Definitions ························································································ 17
Event Tourism ··························································································· 18
Sport Tourism ··························································································· 20
Sport Event Tourism ···················································································· 20
Pro-environmental Behavior and Underlying Psychological Constructs ·························· 21
Pro-environmental Behavior ·········································································· 21
Personal Moral Norms: Norm Activation Model and Value-Belief-Norm Theory ············ 22
Descriptive and Injunctive Social Norms: Social Norm Approach ······························ 24
Hedonic and Gain Goals: Goal-framing Theory ··················································· 26
Perceived Behavioral Difficulty: Contextual Influence ··········································· 27
Difference of Environmental Behavior in Daily Life and Tourism Contexts····················· 28
Sport Event Attendees ·················································································· 29
Destination Environmental Responsibility ·························································· 30
Recreation Involvement ················································································ 31
Experience-Use History················································································ 32

vii

CHAPTER 3 ································································································· 34
METHODS ··································································································· 34
Study Site and Study Participants ······································································· 35
Sport Event Tourism Destination: Michigan State University ··································· 35
Study Participants: Football Tailgaters ····························································· 36
Research Design ··························································································· 36
Sampling Days ·························································································· 36
Systematic Sampling ···················································································· 37
Spatial Zoning Plan ···················································································· 39
Sample Size Determination ············································································ 42
Sample Frame ··························································································· 44
First Phase of Survey on Days 1 and 2 ······························································ 45
Second Phase of Survey on Day 3 ···································································· 48
Survey Instrument Development ········································································ 50
The Questionnaires ····················································································· 50
Pro-environmental Behavior ·········································································· 51
Perceived Behavioral Difficulty ······································································ 52
Psychological Constructs of Environmental Behavior ············································ 52
Destination Environmental Responsibility ·························································· 54
Recreation Involvement ················································································ 55
Experience-use History ················································································ 55
Socio-demographics and Tailgating Characteristics ·············································· 56
Preparation for Data Analysis ··········································································· 57
Data Cleaning ··························································································· 57
Reliability and Validity Tests·········································································· 59
Data Analysis Plan ····················································································· 61
CHAPTER 4 ································································································· 64
RESULTS····································································································· 64
Description of the Study Participants ··································································· 64
Socio-demographic Profile ············································································ 64
Tailgating Experience Profile········································································· 67
Pro-environmental Behavior and Related Psychological Constructs Profile·················· 69
Research Question 1 ······················································································ 74
Exploratory Factor Analysis ·········································································· 75
EFA for Psychological Constructs································································· 75
EFA for Perceived Behavioral Difficulty························································· 78
EFA for Pro-environmental Behavior ····························································· 80
Multiple Regression Analysis ········································································· 81
Variable Description ················································································· 82
Assumptions Test ···················································································· 85
Hypothesis Test ······················································································ 86
Research Question 2 ······················································································ 90
Paired-sample t-test ···················································································· 90
Mixed between-within subjects ANOVA ····························································· 92
Variable Description ················································································· 92
viii

Assumptions Test ···················································································· 96
Preliminary Analysis ················································································ 96
Hypothesis Test ······················································································ 99
CHAPTER 5 ······························································································· 105
DISCUSSION AND CONCLUSION ································································· 105
Summary of the Study ·················································································· 105
Discussion of Key Findings and Theoretical Contributions ······································· 107
Psychological Constructs of Environmental Behavior ·········································· 107
Different Mechanisms of Environmental Behavior in Daily Life versus Event Setting ····· 110
Moderating Effects of Event Attendees’ Characteristics on Environmental Behavior
Discrepancy between Daily Life and Event Setting ·············································· 113
Managerial Implications ················································································ 118
Limitations and Future Studies ········································································ 124
Conclusion ································································································ 127
APPENDICES ····························································································· 131
Appendix A IRB Approval Letter ····································································· 132
Appendix B Sampling Log Sheet ····································································· 133
Appendix C Survey Administrator Pre-meeting Agenda ·········································· 134
Appendix D On-site Questionnaire Used at the First Phase Survey ····························· 136
Appendix E Consent Form for Follow-up Mail Survey Used at the First Phase Survey······ 139
Appendix F Survey Items for Follow-up Mail Survey Used at the First Phase Survey ······· 141
Appendix G Reminder Messages ····································································· 146
Appendix H Email Message for Follow-up Online Survey Used at the First Phase Survey · 147
Appendix I Non-response Check for the First Phase Survey ····································· 148
Appendix J On-site Survey Used at the Second Phase Survey ··································· 153
Appendix K Skewness and Kurtosis of Measurement Items ······································ 157
REFERENCES ···························································································· 159

ix

LIST OF TABLES

Table 1 Sampling schedule ........................................................................................................... 37
Table 2 On-site sampling frame at first phase .............................................................................. 45
Table 3 Follow-up sample frame at first phase ............................................................................. 47
Table 4 On-site sampling frame at second phase.......................................................................... 49
Table 5 Comparison between intercepted tailgaters and study participants ................................. 58
Table 6 Affiliation and residential area of study participants ....................................................... 65
Table 7 Socio-demographic characteristics of study participants................................................. 66
Table 8 Recreation involvement profile ....................................................................................... 67
Table 9 Experience-use history profile ......................................................................................... 68
Table 10 Pro-environmental behavior profile ............................................................................... 70
Table 11 Perceived behavioral difficulty profile .......................................................................... 71
Table 12 Psychological constructs of recycling behavioral profile .............................................. 72
Table 13 Event attendee perceived destination environmental responsibility .............................. 73
Table 14 Exploratory factor analysis: Psychological constructs .................................................. 77
Table 15 Exploratory factor analysis: Perceived behavioral difficulty ........................................ 80
Table 16 Exploratory factor analysis: Recycling behavior ........................................................... 81
Table 17 Variables used in regression models .............................................................................. 83
Table 18 Descriptive statistics of variables in regression analysis ............................................... 84
Table 19 Results of regression full models for recycling behavior at home and at tailgating ...... 87
Table 20 Results of reduced regression model for recycling behavior at home ........................... 89
Table 21 Results of reduced regression model for recycling behavior at tailgating ..................... 89
x

Table 22 Variables in mixed between-within subjects ANOVAs ................................................ 95
Table 23 Demographic, psychographic, and behavioral profiles between groups ....................... 97
Table I - 1 Non-response check for Day 1 sample..................................................................... 149
Table I - 2 Non-response check for Day 2 sample..................................................................... 151
Table K - 1 Skewness and kurtosis of measurement items ........................................................ 157

xi

LIST OF FIGURES

Figure 1 Conceptual model ........................................................................................................... 10
Figure 2 Event tourism at the nexus of tourism and event studies ............................................... 19
Figure 3 Schematic representation of variables in the VBN theory ............................................. 23
Figure 4 Sampling zone ................................................................................................................ 40
Figure 5 Four groups in experience-use history ........................................................................... 95
Figure 6 Recycling scores at home and at a tailgating setting between DER groups ................. 100
Figure 7 Recycling scores at home and at a tailgating setting between Tourist and Non-tourist
groups .......................................................................................................................................... 101
Figure 8 Recycling scores at home and at a tailgating setting between RI groups ..................... 102
Figure 9 Recycling scores at home and at a tailgating setting between EUH groups ................ 103
Figure 10 Summary of managerial implications ......................................................................... 123
Figure 11 Three domains for greening sport event tourism ........................................................ 128

xii

CHAPTER 1
INTRODUCTION

This chapter includes the following sections: (1) Background; (2) Need for the Study; (3)
Introduction of Study Constructs: (4) Problem Statement: (5) Purpose of Study; (6) Conceptual
Model; (7) Research Questions and Hypotheses; (8) Definitions of Terms; and (9) Delimitations.

Background
Event tourism is generally recognized as tourism that is inclusive of all planned events,
ranging from mega events to local sport events (Getz, 2008). As a research topic, sport event
tourism, as a sub-set of event tourism, became firmly established in the 1990s, and has been
expanding substantially since 2000 (Getz, 2008). Sport event tourism is unique in that sporting
events are the dominant attractions for travelers (e.g., sports spectators, sport event participants)
(Ritchie & Adair, 2002). Yet, sport event tourism shares many aspects with tourism in general in
that it involves travelers who are away from their everyday life (Standevan & Deknop, 1999),
seeking leisure and recreational opportunities in their travel experiences (Gamon & Robinson,
1997).
Several studies have noted that sport event tourism, like tourism in general, brings
positive and negative impacts to the host destinations and communities. The positive side
includes increased revenue and improved infrastructure such as new facilities for the host
communities, building a positive image of the destination, and creating a sense of community
pride and involvement (Chalip, Green, & Hill, 2003; Lee & Taylor, 2005; Smith, 2005). At the
same time, however, sport event tourism brings negative impacts such as crowding, crime, traffic
1

congestion, and community displacement (Barker, Page, & Meyer, 2002; Higham, 1999). In the
sport event tourism domain, there has been a great deal of emphasis on the social and economic
benefits of event tourism development. Yet until recently, there has been relatively little concern
about sustainability (Presbury & Edwards, 2005), and little attention placed on the negative
environmental consequences (Getz, 2008).
Sustainability can be widely defined as encompassing economic, socio-cultural, and
environmental responsibilities, referred to as the triple-bottom-line of sustainability (Font &
Harris, 2004; Getz, 2009; Hede, 2008). Sustainability comes in many forms, and tourism
planners and event organizers continuously seek ways to pursue environmental and social
sustainability alongside financial sustainability in their long-term plans. Sustainable practices in
the tourism domain include greening events. A greening event can be defined as an event that
incorporates sustainable practices into its management and operations (Laing & Frost, 2010).
Sustainable practices in greening events include, but are not limited to, access to public
transportation, recycling, source reduction, availability of renewable energy (solar, wind, hydro,
biomass, etc.), promoting green messages, and educating tourists to engage in pro-environmental
behavior. Goldblatt (2011) pointed out three core strategies for event organizers to move toward
greening their events: (1) Innovation: creatively harnessing emerging strategies and green
technology for increased energy efficiency and environmentalism; (2) Conservation: responsible
use of natural resources and waste minimization; and (3) Education: promoting ethical behavior
toward energy and the environment while creating memorable event experiences.
Michigan State University (MSU) has gone greener by implementing various greening
practices on campus through “Reduce, Reuse, and Recycle” (MSU Surplus Store and Recycling
Center, 2013) and keeping materials out of landfills. At MSU, going green refers to personal
2

responsibility that is shared by students, faculty, and staff. Through both small steps and large
collective actions, they show how such effort can make a big impact and a better world (MSU
Office of Campus Sustainability, 2013).
The MSU campus, as a tourism destination on game days during the football season,
makes special efforts toward green initiatives. This began in 2007 when the MSU Athletics
Department launched a multiyear "GO GREEN" initiative and is continuously expanding its
beverage container-recycling program (MSU Athletics News, 2007). The Surplus Store and
Recycling Center runs “The Game Day Challenge” to promote waste reduction and recycling at
football games (MSU Recycling, 2013). All event attendees are encouraged to deposit their trash
in the appropriate receptacles and to participate in waste reduction and recycling program in
keeping the campus “green and clean” (MSU Game Day, 2013).
However, it is challenging to mobilize individuals’ environmental concerns and
transform them into action while they are traveling or participating in sport events as their focus
is on the event rather than their environmental behaviors. It is critical to motivate event attendees
to take an active role in responding to event organizers’ green efforts. Motivating event attendees
to engage voluntarily in pro-environmental behavior at tourism destinations is expected to
contribute to environmental sustainability by reducing greenhouse gas emissions, reducing waste
products, increasing recycling, etc.

Need for the Study
Sport Event Tourism in College Towns
Sport event tourism ranges from mega events such as the Olympics to local sporting
events. Collegiate football games in the U.S. are a growing sector of sport event tourism.
3

According to the National Collegiate Athletic Association (NCAA) (2014), a total of 50,291,275
fans attended college football games at the 657 NCAA football-sponsoring schools in 2013. The
total attendance at these sports events has been increasing continuously. The average number of
attendees per game at national level was 12,684 in 2001 and increased to 13,589 in 2013.
College campuses, as sport event tourism destinations, are visited by a huge number of fans on
football game days. For example, in 2013, the University of Michigan had 111,592 fans per
game, Ohio State Univ. had 104,933 fans, and Michigan State University had 72,328 fans per
game (NCAA, 2014).
While these sport events clearly generate positive social and economic benefits, the
Environmental Protection Agency (EPA) estimated that an average college football game
produces 50 to 100 tons of waste, and releases 188 to 376 metric tons of carbon dioxide into the
environment (EPA, 2012). In response to this negative environmental consequence, the EPA
implemented the “Game Day Challenge” initiative in 2009, to promote a “Zero Waste” program
on game days. In an effort to reduce the amount of waste accumulated during college football
games, 75 participating colleges and universities, including MSU, reduced the amount of waste
from football games by nearly 500,000 pounds, which prevented more than 810 metric tons of
carbon dioxide from being released in 2011 (EPA, 2012). This environmental campaign included
promotional efforts motivating football fans to engage in pro-environmental behavior while
attending sporting events (i.e., watching games and participating in tailgating).

Promoting Pro-environmental Behavior in Daily life vs. in a Tourism Context
Although individuals may have positive environmental attitudes and engage in proenvironmental behavior in their daily lives, they may be less likely to engage in such behavior
4

while they are traveling or are enjoying recreational activity. This topic is poorly understood, as
most researchers have focused their studies on understanding individuals that do engage in proenvironmental behavior with a single contextual perspective: either in their daily life or in a
tourism context. Also, if the behaviors between settings differ, it is possibly because the effects
of psychological constructs of pro-environmental behavior are differentiated between daily-life
and tourism contexts. For example, individuals place less attention to environmental concerns or
moral obligations in a tourism setting and they place more attention to their needs for
convenience or comfort while they are travelling. If environmental campaigns are designed
based on theories and models developed based on daily life, then applied in promoting tourists’
or recreationists’ pro-environmental behavior in the tourism context, the campaign may not be
effective. Accordingly, a question arises as to whether promotional campaigns for inducing proenvironmental behaviors need to differentiate between daily-life and tourism contexts.
Event attendees enjoying special events tend to behave in a more liberated way, less
constrained by moral obligations than in their routine life. A few researchers have suggested that
pro-environmental behavior is more pervasive in the daily-life context, compared to the tourism
context (e.g., Dolnicar, 2010; Miao & Wei, 2013). Given the contextual nature of the behavior of
tourists who are escaping routines, and are seeking leisure time and recreational opportunities
(Pearce, 1982), the challenges to motivate pro-environmental behavior in a tourism context
appear to differ from those in a daily-life context. To increase the effectiveness of environmental
campaigns in tourism destinations, it is critical to understand whether and how individuals’ proenvironmental behavior, and the underlying psychological constructs of such behavior, differ
between two different contexts.

5

Introduction of Study Constructs
Many theoretical frameworks have been developed and used for explaining individuals’
pro-environmental behaviors, including the Value-Belief-Norm (VBN) theory (further developed
by Stern [2000]), the Social Norm Approach (SNA; developed by Perkins & Berkowitz [1986]),
and the Goal-Framing Theory (developed by Lindenberg & Steg [2007]). Although these
theories are by no means exhaustive in explaining all types and situations of pro-environmental
behaviors, they have been widely supported by identifying the underlying psychological
constructs of individuals’ pro-environmental behaviors. According to the previous research that
employed these theories, personal moral norms (e.g., to obligate one to environmental
conservation), descriptive social norms (e.g., to a follow what others actually do), injunctive
social norms (e.g., to follow what others ought to do), hedonic goals (e.g., to feel better) and gain
goals (e.g., to get personal benefits) facilitate individuals’ environmental behaviors. On the other
hand, researchers have also generally agreed that perceived behavioral difficulty inhibits
individuals’ environmental behavior (Ajzen, 1985; Kollmuss & Agyeman, 2002). These
facilitators and inhibitors have been addressed in separate studies. Empirical research has yet to
be conducted that combines these multiple psychological constructs into one holistic model. In
other words, little research has focused on whether the relationships between psychological
constructs and environmental behavior in an everyday-life context are transferable to a tourism
context, or vice versa.
Event attendees or recreationists visit destinations to get away from home, experience a
change of pace from regular daily life patterns, and to experience recreational activities (Smith,
1978). When people willingly travel to or enter into an event-specific place for a defined period
of time, they engage in activities that are out of the ordinary and experience a temporary
6

difference from everyday ordinary life (Cohen, 1979; Jafari, 1987). Therefore, it is expected that
a model explaining effects of facilitators and inhibitors on individuals’ environmental behavior
in the daily life context is not the same as a model describing such relations in a tourism context.
There is growing recognition of destination social responsibility, especially with the
recent environmental responsibility focus in event literature (e.g., Babiak & Trendafilova, 2011;
Laing & Frost, 2010). Event destinations’ environmental responsibility includes waste
management/recycling system operation and environmental campaign development. With
respect to the ‘green event attendees,’ destinations expect such operations to encourage event
attendees to adopt pro-environmental behavior. Yet, little research has examined whether event
attendee’s greater awareness of destination environmental responsibility increases their
adaptation of environmental behavior. Research needs to be done to examine whether the event
attendees’ consistence or inconsistence between at-home and at-event environmental behavior is
influenced by event attendees’ perceptions about destination environmental responsibility.
Also, event attendees’ environmental behavioral differences between in daily life and
event tourism contexts are affected by event attendees’ characteristics. Event attendees or
recreationists are not homogeneous in terms of their travel/recreation behavior. Sport event
attendees include local residents who live near by the destination, who are members of the
destination’s community (e.g., students on college football game days), and tourists who traveled
from outside the local area to the destination. A local resident’s or destination traveler’s
community may or may not differ from what one experiences as a tourist. Accordingly,
differences in environmental behavior between daily life and event settings may be moderated by
types of event attendees. Event attendees or recreationists differ in their psychological
connections to a particular recreational activity (e.g., perceived personal importance of tailgating
7

activity) and behavioral connections to a tourism/recreation place (e.g., prior traveling history to
a place). Researchers have conceptualized those connections as recreation involvement (Kyle,
Absher, Hammitt, & Cavin, 2006; Lee, 2011), and experience-use history (Hammitt &
McDonald, 1983; Ong & Musa, 2012), respectively. Literature has addressed such
tourist/recreationist characteristics to help to understand recreationists’ environmental behavior
in destinations (e.g., Lee, 2011), yet little research has examined the potential moderating roles
of recreation involvement and experience-use history on differences in pro-environmental
behavior in daily life and in the tourism setting.

Problem Statement
The problem underlying this study is the absence of a comprehensive model for
explaining sport event attendees’ pro-environmental behavior both in daily-life and tourism
contexts. Accordingly, this study aims to (1) examine whether and how psychological constructs
of environmental behavior—facilitators (i.e., Personal moral norms, descriptive social norms,
injunctive social norms, hedonic goals, and gain goals) and inhibitors (i.e., perceived behavioral
difficulty)—influence sport event attendees’ pro-environmental behavior in daily life versus in
the context of sport event tourism; (2) examine whether event attendees’ pro-environmental
behavior in daily life differs from their behavior in the sport event tourism context; and (3) assess
whether the difference of pro-environmental behavior between in daily life and event setting is
differently affected depending on event attendee’ characteristics—i.e., event attendee perceived
destination environmental responsibility, event attendee types, recreation involvement, and
experience-use history—in a sport event tourism context.

8

Purpose of Study
The primary purpose of this study is to provide helpful information to event managers
and policy makers about how to develop or advance environmental campaigns at event tourism
destinations. If the underlying mechanism of pro-environmental behavior in daily life differs
from the mechanism of such behavior in an event tourism context, then event organizers and
policy makers need to develop environmental campaigns that focus more on identified predictors
and less on insignificant factors when implementing such campaigns in event tourism contexts.
An additional purpose of this study is to help event organizers to target different sub-groups of
event attendees with tailored environmental programs in a sport event tourism context. Together,
it is hoped that the information about differentiated roles of predictors of pro-environmental
behavior in daily life versus in a tourism context, and about the moderating role of sport event
attendees’ characteristics in understanding pro-environmental behavior would assist in designing
more effective environmental campaigns in a sport event tourism context.

Conceptual Model
This study empirically tests the effects of psychological constructs of environmental
behavior (i.e., personal moral norms, descriptive social norms, injunctive social norms, hedonic
goals, gain goals, perceived behavioral difficulty) on sport event attendees’ pro-environmental
behavior in their daily lives and in a tourism setting. It also tests whether
consistency/inconsistency of environmental behavior between daily life and the tourism setting is
affected by event attendees’ characteristics (i.e., event attendee perceived destination
environmental responsibility, event attendee types, recreation involvement, and experience-use

9

history). This study proposes a conceptual model to show the relationships between key variables
(see Figure 1).

Psychological Constructs of
Pro-environmental Behavior
Self-reported
Pro-environmental
Behavior

Personal
Moral Norms

Descriptive
Social Norms

Event Attendees’
Characteristic

Destination
Environmental
Responsibility

Daily
Life

Injunctive
Social Norms

Event Attendee
Type

RQ2

RQ1
Hedonic
Goals
Tourism
Context

Recreation
Involvement

Gain
Goals
Experience
Use
History

Perceived
Behavioral
Difficulty

Figure 1 Conceptual model

10

Research Questions and Hypotheses
The following two research questions (RQ) and 17 hypotheses (H) are developed:

RQ1. Do the predictive roles of psychological constructs of environmental behavior (i.e.,
personal moral norms, descriptive social norms, injunctive social norms, hedonic goals,
gain goals, and perceived behavioral difficulty) differ in predicting sport event attendee’s
pro-environmental behavior in daily life versus in the context of sport event tourism?”

H1.1 Sport event attendees who have higher level of personal moral norms (H1.1a),
descriptive social norms (H1.1b), injunctive social norms (H1.1c), hedonic
goals (H1.1d), gain goals (H1.1e), and have a lower level of perceived
behavioral difficulty (H1.1f) show a significantly higher level of engagement
in pro-environmental behavior in daily life.
H1.2. Sport event attendees who have a higher level of personal moral norms
(H1.2a), descriptive social norms (H1.2b), injunctive social norms (H1.2c),
hedonic goals (H1.2d), gain goals (H1.2e), and have a lower level of
perceived behavioral difficulty (H1.2f) show a significantly higher level of
engagement in pro-environmental behavior in an event setting.

RQ2. Are sport event attendee’s differences in pro-environmental behavior between in daily life
and in an event setting affected by the event attendees’ characteristics (i.e., event attendee
perceived destination environmental responsibility, event attendee types, recreation
involvement, and experience-use history)?
11

H2.1 Sport event attendees’ engagement in pro-environmental behavior decreases
in an event setting compared to in daily life setting.
H2.2. Degree of decrease in sport event attendees’ pro-environmental behavior
from daily life to an event setting is different depending on the level of event
attendees perceived destination environmental responsibility.
H2.3. Degree of decrease in sport event attendees’ pro-environmental behavior
from daily life to an event setting is different depending on event attendee
types.
H2.4. Degree of decrease in sport event attendees’ pro-environmental behavior
from daily life to an event setting is different depending on the level of event
attendee’ recreation involvement.
H2.5. Degree of decrease in sport event attendees’ pro-environmental behavior
from daily life to an event setting is different depending on event attendee’
experience-use history.

Definitions of Terms
The following terms are defined to clarify their use in this study.
Sport event tourism: A sub-set of tourism at the nexus of sport tourism and event tourism (Jago
& Fredline, 2004), where sport tourism can be defined as leisure-based travel that takes
individuals temporarily outside of their home communities to participate in physical activities, to
watch physical activities, or to venerate attractions associated with physical activities (Gibson,

12

1998), and event tourism incorporates tourism-oriented events including all planned events such
as special events, hallmark events, mega-events, and specific types of events (Getz, 2008).
Pro-environmental behavior: environmental behavior that changes the availability of materials or
energy from the environment or alters the structure and dynamics of ecosystems or the biosphere
in environmentally responsible way (Stern, 2000).
Personal moral norms: An individual’s sense of personal moral obligation to engage in proenvironmental behavior (Schwartz, 1977).
Descriptive Social Norms: An individual’s perceptions about what others actually do (Cialdini,
Reno, & Kallgren, 1990).
Injunctive social norms: An individual’s perceptions about what others approve of doing
(Cialdini, Reno, & Kallgren, 1990).
Hedonic goals: An individual’s goals that promise to improve the way one feels in a particular
situation (e.g., enjoyment) (Lindenberg & Steg, 2007).
Gain goals: An individual’s goals that promise to increase ones’ personal resources (e.g., saving
cost) (Lindenberg & Steg, 2007).
Perceived behavioral difficulty: An individuals’ perceived amount of effort required to perform
the behavior (Kollmuss & Agyeman, 2002).
Destination environmental responsibility: A set of practices that a destination incorporates
environmental sustainability into its management and operations (Laing & Frost, 2010).
Tourist: Individual travelers who are not locals or, those who are neither host community nor
local residents in the vicinity of the destination.
Recreation involvement: The degree to which an individual is psychologically engaged in a
particular recreation activity (Kyle et al., 2006).
13

Experience-use history: The amount of total experience a recreationist has within a particular
recreational activity or setting (Hammitt & McDonald, 1983).

Delimitations
This study is delimited to the following:
1.

Study participants are sport event attendees who are tailgaters at one or more of the first
three MSU football games in fall 2013. As this study is conducted only on the first three
games during the season, participants of this study would not be representative of all
tailgaters at MSU home football games throughout the season.

2.

Study participants are intercepted by survey administrators during the tailgating period
prior to the games. Those event attendees who are not intercepted or do not agree on
participating in the study are excluded in this study. This would limit examining the
potential difference between those who participate in the study and those who do not.

3.

On-site surveys are conducted throughout all tailgating areas on MSU campus, except the
most densely student-populated tailgating area, where administering on-site survey is
challenging. In this zone, students are densely packed and listen to loud music, and
frequently involved in consuming large amounts of alcoholic beverages. Due to these
factors that created some risk for survey administrators, it is judged that this set of event
attendees are not appropriate for hypothesis testing by conducting on-site survey. Thus,
this study does not include event attendees who participate in tailgating in the student
populated area. This limits generalizability of the study findings into whole tailgating
population.

14

4.

Pro-environmental behavior in this study includes recycle behaviors with specific
materials, which is included in the recycling scheme developed by the MSU Office of
Campus Sustainability. Recycling behavior is comparable behavior between daily life
and a tourism context, yet recycling behavior is one of categories of pro-environmental
behavior. Findings of this study are delimited to recycling behavior, which would be
different from other types of pro-environmental behavior.

5.

Pro-environmental behavior in daily life is delimited only to the behavior at home. Proenvironmental behavior in the workplace or at school would be considered daily life, yet
would not be same as the behavior at home.

Sport event tourism clearly generates positive social and economic benefits however
negative environmental impacts are also present. To minimize such negative environmental
impacts, event destinations have implemented campaigns to motivate sport event attendees to
engage in pro-environmental behavior. However, it is challenging to mobilize individuals’
environmental concerns and transform them into action while they are travelling or attending
events. The following chapter describes literature relevant to sport event tourism and the
theoretical background of pro-environmental behavior to inform the conceptual model of this
study.

15

CHAPTER 2
LITERATURE REVIEW

This chapter includes the literature review under the following sections: (1) Sport Event
Tourism; (2) Pro-environmental Behavior and Underlying Psychological Constructs and (3)
Difference of Environmental Behavior in Daily Life versus Tourism Context.

Sport Event Tourism
This section begins by defining key terms such as tourism, sport, event tourism, and sport
tourism, and then narrows down to the context for this research, and sport event tourism.

Tourism Definitions
Defining tourists or tourism is a difficult proposition. Dissimilar definitions have been
found in a variety of literature, with inconsistencies among the practitioners. Throughout the
definition criteria, distance traveled is most often used to identify tourists. In the United States,
the National Tourism Resources Review Commission (1973) defined a tourist as one who travels
away from home for a distance of at least 50 miles (one way) for business, pleasure, personal
affairs, or any other purpose except to commute to work, whether he or she stays overnight or
returns the same day. Although distance traveled seems to be the dominant determinant of a
tourist, the mileage of the distance is debatable, as the ranges differ when destination marketing
organizations formulate the definition (Masberg, 1998). McIntosh, Goeldner and Ritchie (1995)
took a more systems-based approach to understanding the tourist and tourism. They defined
tourism as “the sum of phenomena and relationships arising from the interaction of tourists,
16

business supplies, host governments, and host communities in the process of attracting and
hosting these tourists and other visitors” (p.10). This definition includes the potential impacts
that tourists may have upon the host community, which, until recently, was a neglected
component of the definition process. A number of authors have reached a consensus on how to
approach to understand tourism: that it is necessary to view tourism as an integrated system of
multiple components, ranging from the tourists themselves, to the tourism industry, the host
community or destination, and the impacts upon both the destination area and the tourists (e.g.,
Gunn, 1974; Leiper, 1979). Keeping in mind the multiple components to understand tourism,
subjects of this study include tourists, who specifically are identified followed by the local
Convention and Visitors’ Bureau’s (CVB) definition. That is, tourists refer to individual travelers
who are NOT residents in the vicinity of the destination (i.e., non-residents of the Greater
Lansing area defined as Eaton, Clinton, or Ingham County).

Sport Definitions
There is considerable controversy over efforts to define sport. Some insist that an allencompassing definition is impossible because sport is a socially constructed activity that has
varied across historical eras, societies, and cultures, whereas others hold that sport has specific
and timeless characteristics, such as being goal-oriented, competitive and a forum for the
creation of winners and losers (Paddick, 1975). Among the various definitions, Coakley (2001)
provided a fairly typical working definition of organized sport: ‘Sport is an institutionalized
competitive activity that involves vigorous physical exertion or the use of relatively complex
skills by individuals whose participation is motivated by a combination of intrinsic and extrinsic
factors’ (p. 8). Yet, this view may be unnecessarily rigid, and the definition does not address the
17

idea that sporting activities may be either formal or recreational. There is sport-as-competition,
which is tightly structured, with goal-oriented matches and tournaments; there is also sport-asplay, which is comprised of recreational sporting activities (Gruneau, 1980). In terms of sport
tourism, sport tourists include those who participate in sports to play for recreational purposes,
those who are spectators at games, and those who attend sport events.

Event Tourism
Event tourism is generally recognized as being inclusive of all planned events in an
integrated approach to tourism (Getz, 2008). Yet, the term ‘event tourism’ was not widely used
until the late 1900s; rather, it was normal to speak of special events, hallmark events, megaevents, and specific types of events. In the 1960s and 1970s, the events sector was not
recognized as an area of separate study within leisure, recreation, or tourism. Only limited
attention was paid to how hallmark events could combat the seasonality of tourism demand (e.g.,
Ritchie & Beliveau, 1974) in this era. In the 1980s and 1990s, research on event tourism and
event management expanded dramatically (Getz, 1991; Hall, 1992). A number of studies in event
tourism have advanced the research by helping us to understand motivation among event tourists
(e.g., Crompton & McKay, 1997; Uysal, Gahan, Martin, 1993), the importance of events in
generating tourism demand (Bos, 1994), and the economic impacts of event tourism (e.g.,
Crompton & McKay, 1994; Gartner & Holecek, 1983; Ritchie, 1984). Although the social and
cultural impacts of event tourism were addressed in this era (e.g., Hall, 1989; Ritchie, 1984),
most of the attention was focused on the economic dimensions. In the 2000s, scholars sought
more balance in understanding event tourism, including the positive and negative economic,
social, and environmental dimensions.
18

Although not all events need to be tourism-oriented, and there is no real justification for
considering event tourism as a separate field of studies (Getz, 2008), it is necessary to understand
the interrelationships between tourism and event management for assisting professional practices,
as well as between tourism studies and event studies for spurring theoretical advancement. Getz
(2008) illustrated the set of interrelationships that occur at the nexus of tourism and event studies
(see Figure 2), and specifically categorized event tourism based on event types: i) business
events and tourism; ii) sport events and tourism; and iii) festivals and other cultural events and
tourism.

TOURISM
MANAGEMENT
AND TOURISM
STUDIES
- Developing and
promoting tourism
- Understanding travel
and tourists,
including event tourists

EVENT
TOURISM

EVENT
MANAGEMENT
AND EVENT
STUDIES
- Design, production,
and management of events
- Understanding planned event
experiencing and
the meanings attached

Figure 2 Event tourism at the nexus of tourism and event studies
(Source: Getz, 2008)

19

Sport Tourism
Sport tourism refers to “all forms of active (e.g., scuba diving and skiing) and passive
(e.g., attending sporting events and visiting sports museums) involvement in sporting activity,
participated in causally or in an organized way for non-commercial or business/commercial
reasons that necessitate travel away from home and work locality (Standevan & DeKnop, 1999,
p.12).” Gibson (1998) defined sport tourism in more travel-related terms: “leisure-based travel
that takes individuals temporarily outside of their home communities to participate in physical
activities, to watch physical activities, or to venerate attractions associated with physical
activities (p. 49).” Researchers proposed a typology of sport tourism that can be used to provide
a clearer understanding of the term sport event tourism. Gammon and Robinson (1997) classified
sport tourists as either ‘hard’ or ‘soft’ participants. A ‘hard’ sport tourist is a person who travels
for either active or passive involvement in a competitive sport; hence, their prime motivation for
travel is sport. The ‘soft’ sport tourist is someone who is primarily involved in a recreation or
leisure purpose, rather than in competitive activity (Gammon & Robinson, 1997). Gibson (1998)
suggested three categories of sport tourism: active sport tourism (i.e., travelling to take part in a
sport); sport event tourism (i.e., travelling to watch a sporting event); and nostalgia sport tourism
(i.e., visiting sports museums, famous sports venues, and sports themed cruises).

Sport Event Tourism
The concepts of sport tourism and event tourism have become more prominent in the last
few years, both as an academic field of study and as an increasingly popular tourism product
(Gibson, 1998). Yet, there are still inconsistencies in defining sport event tourism.

20

Researchers in such fields as sports management, event management, and tourism
management, who have legitimate claims to the subject areas, have not bridged the artificial
academic divide between these different disciplines frequently (Gibson, 1998; Getz, 1989).
Consequently, there are distinct communities of discourse. Some researchers reviewed planned
events and tourism as the origins and evolution of event tourism, and saw sport event tourism as
a sub-category of event tourism (e.g. Getz, 1989; 2008). Other researchers viewed sport-related
activity and tourism as the nexus of sport tourism, and saw sport event tourism as a sub-category
of sport tourism (e.g., Gibson, 1998). Recently, Deery, Jago, and Fredline (2004) questioned
whether sport tourism and event tourism are the same things. Their conceptualization showed
sport tourism as being at the nexus of event tourism and sports, with both sport tourism and event
tourism being sub-sets of tourism in general. This study follows this definition of sport event
tourism.

Pro-environmental Behavior and Underlying Psychological Constructs

Pro-environmental Behavior
Human behavior is influenced by both direct and indirect controls. Direct controls
regulate individual behavior and specify punishment if regulations are not followed, whereas
indirect controls guide individuals to choose certain behaviors voluntarily. In the context of
tourism, two strategies—direct and indirect management— may be applied to manage
inappropriate tourist behavior at destinations. Direct management strategies include enforcing
regulations, physically closing areas, and implementing permit-based systems and fees. Indirect
management strategies include persuasive messages, interpretive displays and presentations,
21

environmental education, and informational campaigns. Gramann, Christensen, and Vander
Stoep (1992) stated that direct controls are more appropriate than indirect controls if tourist visits
result in critical damage to cultural or natural resources, or endanger the personal safety of
tourists. In contrast, indirect controls may be more effective if tourist-induced damages can be
reduced by persuading them to follow desirable or appropriate behaviors.
There is a growing effort among tourism destinations to implement environmental
practices and promote pro-environmental behavior among tourists. Although individuals may
have positive environmental attitudes, and engage in pro-environmental behavior in their
everyday lives, they may not or are less likely to engage in such behavior while they are
traveling (e.g., Miao & Wei, 2013). Understanding why some individuals choose to engage in
pro-environmental behavior and then comparing this across contextual situations—living daily
life vs. being a tourist—is critical to destination managers’ efforts to minimize negative
environmental impacts. This section explores existing theories and models of pro-environmental
behavior, and identifies factors influencing such behavior.

Personal Moral Norms: Norm Activation Model and Value-Belief-Norm Theory
Among various theories for explaining pro-environmental behavior, Schwartz’s (1977)
Norm Activation Model (NAM), and Stern’s (2000) Value-Belief-Norm theory have been
studied extensively to explain various types of pro-environmental behavior. Originally, NAM
was developed to explain altruistic behavior, and was later applied in an environmental context
(Schwartz, 1977). According to NAM, behavior occurs in response to personal moral norms or to
feeling morally obligated. Personal moral norms are activated when individuals are aware that
certain behaviors may cause adverse consequences to others or to the environment (i.e.,
22

Awareness of Consequences: AC) and when they think they cannot avert personal responsibility
(i.e., Ascription of Responsibility: AR).
The NAM was later extended into the Value-Belief-Norm (VBN). Stern and his
colleagues (Stern, 2000; Stern, Dietz, Abel, Guagnano, & Kalof, 1999) proposed the VBN theory,
which links Schwartz’s (1992, 1994) value theory, Dunlap’s and Van Liere’s (1978) New
Environmental Paradigm (NEP) perspective, and Schwartz’s (1977) Norm-Activation Model
through a causal chain of variables—personal values, NEP, AC and AR beliefs, and personal
moral norms—leading to behaviors (see Figure 3).

Beliefs

Values
Biospheric
Altruistic
Egoistic

Proenvironmental
Personal Norms

Ecological
Worldview
(NEP)

Adverse
consequences
for valued
objects
(AC)

Perceived
ability to
reduce
threat
(AR)

Sense of
obligation to
take proenvironmental
actions

Behaviors
Activism
Non-activist
pubic-sphere
behaviors
Private-sphere
behaviors
Behaviors in
organizations

Figure 3 Schematic representation of variables in the VBN theory
(Source: Stern, 2000)

23

Both NAM and VBN theories focus on personal moral norms or, a sense of moral
obligation to explain pro-environmental behavior, which require individuals to restrain their
egoistic tendencies in order to benefit others and the environment. An extensive literature shows
the important roles of personal moral norms in understanding pro-environmental behavior. For
example, Nordlund and Garvill (2002) reported on the significant roles played by personal moral
norms in predicting several types of pro-environmental behavior, including recycling/reusing,
environmentally responsible consumption, energy conservation, and transportation behavior.
Other research has also supported the applicability of the theory/model and the important roles of
moral norms in explaining pro-environmental behaviors such as household energy saving (Black
et al., 1985), and curbside recycling (Guagnano, Stern, & Dietz 1995). Based on the
understanding gained from the theory, the model, and empirical studies, it is expected that
personal moral norms have a positive effect on pro-environmental behavior.

Descriptive and Injunctive Social Norms: Social Norm Approach
Researchers have studied the role of specific environmental attitudes, or personal moral
norms, in inducing pro-environmental behavior based on the notion that people may become
environmentally friendly if they are concerned about others (Schwartz, 1977) or about
ecosystems (Dunlap & Van Liere, 1978). Research showed that such environment-related factors
(e.g., personal moral norms) have significant effects on pro-environmental behavior. Yet,
considering only environment-related factors may limit understanding of pro-environmental
behavior. Another potential influential factor is social norms.
Individuals have various reasons or motives for engaging in pro-environmental behavior.

24

The Social Norm Approach (SNA; Perkins & Berkowitz, 1986) helps to explain how the
perceptions about what we think others believe and do influences each of us as an individual.
The norms in VBN theory differ from the norms in SNA. The former, personal moral norms,
refers to a feeling of moral obligation, whereas the latter, social norms, refers to perceptions
about others’ moral code and behaviors. Researchers discriminate between the “is” and the
“ought” meaning of social norms, which correspond to descriptive and injunctive norms
(Cialdini, Reno, & Kallgren, 1990; Cialdini et al., 2006). A descriptive norm refers to a person’s
beliefs about what other people actually do. An injunctive norm refers to a person’s beliefs about
what other people approve of doing. This distinction becomes crucial when developing normbased interventions to change behavior. When communicators seek to persuade an audience to
behave according to existing norms, and want their campaigns to be successful, they must
recognize how different types of norms affect behavior in different ways.
SNA describes how the dominant or most typical attitudes and behaviors guide group
members’ actions. Social norms can be powerful agents of choosing behavior as individuals have
strong tendencies to conform to group patterns and expectations. For example, Goldstein,
Cialdini, and Griskevicius (2008) demonstrated that the message employing social norms (e.g.,
“the majority of guests reuse their towels”) had more effect on hotel guests’ behavior than using
the standard environmental message (e.g., “help save the environment”). Accordingly, it is
expected that social norms have a positive effect on pro-environmental behavior to a significant
degree.

25

Hedonic and Gain Goals: Goal-framing Theory
Kollmuss and Agyeman (2002) pointed out that although many researchers have explored
the direct and indirect influential factors of pro-environmental behavior, there has been a limited
discussion of how individuals’ personal desires, such as comfort, may play an important role in
shaping pro-environmental behavior. Several studies support the importance of nonenvironmental factors, in addition to environment-related factors, in affecting pro-environmental
behavior.
Recently, Lindenberg and Steg (2007) proposed a goal-framing theory, and explained
that individuals’ decisions about pro-environmental behavior are influenced by three different
motivational goal frames: normative (i.e., to act appropriately), gain (i.e., to guard and improve
one’s resources), and hedonistic (i.e., to feel better right now). The goal-framing theory explains
the significance of each type of motivational goal, and how it differs across situations and
individuals. For example, in financially or contextually difficult situations, the effects of
normative motivations (e.g., personal moral norms) on behavior become weak, so individuals
may not choose pro-environmental behavior.
Pro-environmental behavior is probably best viewed as a mixture of self-interest and a
concern for whole ecosystems (Bamberg & Möser, 2007). Researchers who view proenvironmental behavior primarily as pro-socially or pro-environmentally motivated behavior
often use the Norm Activation Model (Schwartz, 1977) or the Value-Belief-Norm theory (Stern,
2000). However, researchers who consider multiple motives for engaging in pro-environmental
behavior (e.g., Lindenberg & Steg, 2007) suggest that gain and hedonic goals are additional
influencing factors. This is particularly important when examining and comparing individuals’
pro-environmental behavior at daily life versus event settings. When individuals are tourists, they
26

are escaping their normal lives and seeking enjoyment. Then, hedonic motives and related
behavior is more likely to occur when one is on vacation than in normal life.

Perceived Behavioral Difficulty: Contextual Influence
Researchers agree that environmental attitudes have a positive effect on proenvironmental behavior (e.g., Stern [2000] Value-Belief-Norm theory). Yet, researchers also
found an environmental attitude-behavior gap (e.g., Lindenberg and & Steg’s [2007] goalframing theory). Findings from previous studies suggest that the strength of correlations between
environmental attitudes and behavior vary, depending on contextual effects such as situational
constraints (e.g., Diekmann & Preisendörfer, 2003).
Ajzen’s (1985) Theory of Planned Behavior (TPB), which is one of the most widely
studied models for explaining individuals’ behavior, argued that situational variables (i.e.,
perceived behavioral control) are additional noteworthy behavioral determinants. Kollmuss and
Agyeman (2002) discussed a low/high cost model, and claimed that people choose proenvironmental behavior consistent with their environmental attitude only when demands for such
behavior cost less. In this context, cost refers to economics, time, effort, and comfort, which
individuals value when taking an action. Similarly, Corraliza and Berenguer (2000)
demonstrated that a strong feeling of moral obligation determines pro-environmental behavior
only when environmental attitude does not conflict with other favorable situations. In addition,
Nilsson, von Borgstede, and Biel (2004) showed that willingness to accept climate change policy
(e.g., taxes and charges to reduce the use of non-environmentally friendly alternatives) is
predicted by personal moral norms in the public sector, but not in the private sector. This is

27

because personal benefits, not environmental concerns, are the dominant private sector
motivation of behavior.
When comparing pro-environmental behavior in daily life versus in event tourism setting,
it is critical to taking contextual influential factor into account. Dolnicar (2010) identified two
indicators—income and moral norms—which tourism destinations can use to identify potential
tourists who will consistently engage in pro-environmental behavior while travelling. Yet, at the
same time, the author admitted that there would be different contextual influences between being
at home and travelling that were not measured in the study, yet would highly be associated with
the result. At home, individuals can arrange conditions in ways that enable them to engage in
pro-environmental behavior if they so choose. On the other hand, at a tourism destination,
conditions of infrastructure are beyond their control. That is, behavioral control or perceived
behavioral difficulty should be controlled for, as discussed in earlier sections, if research aims to
explain the inconsistencies in pro-environmental behavior between these two settings.
Accordingly, this study includes perceived behavioral difficulty in daily life and event setting in
understanding pro-environmental behavior in each of the settings.

Difference of Environmental Behavior in Daily Life and Tourism Contexts
A few studies have reported on the differences in pro-environmental behavior between
daily-life and tourism contexts. Dolnicar (2010) showed that a large proportion of individuals
(about 70%) behave in an environmentally responsible way at home, and also behave in an
environmentally responsible way on vacation. Other researcher yet indicated that proenvironmental behavior is more pervasive in the daily-life context, compared to the tourism
context (e.g., Dolnicar, 2010; Miao & Wei, 2013). While on-going research is being conducted, a
28

question unanswered is why some individuals engage in pro-environmental behavior consistent
with their positive environmental attitude, even when constraints become higher, and others do
not? In other words, under which conditions or event attendees’ characteristics, do gaps of proenvironmental behavior between at home and when travelling become smaller or larger? The
following section discusses potential moderating factors in the difference of pro-environmental
behaviors between two settings.

Sport Event Attendees
One potential factor which is associated with the difference of individuals’ environmental
behavior between in daily life and event setting is event attendee types. Although all tailgaters
are sport event attendees, there are different sub-types of event attendees. Specifically in this
study context, the MSU campus, as a sport event tourism destination during football season,
invites current MSU community members, local residents, and tourists. These sub-types of event
attendees differ in their traveling behavior, and in experiencing out-of-routine activities at event
destinations. Accordingly, it is expected that the degree of behavior change from daily life to
event setting differs depending on sub-types of event attendees.
Alternatively, however, it may not be necessary different in terms of the nature of the
experience or, experiencing a temporary difference of everyday routines, among sub-types of
event attendees. The meanings attached to events and to event tourism experiences are important
components in understanding sport event attendees. A starting point can be the concept of
liminality, or shared “ritual process” (Turner, 1969). The liminality aspect of the ritual process,
as it is often related to tourism, has been viewed as depicting an experience in tourism whereby
the usual order of things is changed. Specifically, Jafari (1987) saw “tourist culture” as being
29

shared by people who willingly travel to, or enter into, an event-specific place for a defined
period of time, to engage in activities that are out of the ordinary, and to have experiences that
transcend the ordinary. Similarly, Cohen (1979) and Smith (1978) emphasized an understanding
of tourists based on their experience that is a temporary difference of everyday activities, which
is beyond the definitions of tourism by the outside observer. Therefore, behavioral change from
daily life to sport event setting may or may not be influenced by sub-types of event attendees.

Destination Environmental Responsibility
Another influential factor that helps to explain behavioral consistencies and
inconsistencies between daily life and an event setting is event attendee perceived destination
environmental responsibility. There is growing recognition of the need for environmental
sustainability at an event destination, and in destinations’ environmental practices in greening
events, yet there is a lack in the event literature about whether destinations’ environmental
responsibility contribute to encouraging event attendees to engage in pro-environmental behavior.
Sport event tourism has unique imperatives in the realm of environmental responsibility, given
that sport facilities and places are visited by large numbers of people in a confined space over a
relatively small time period (Trendafilova, Babiak, & Heinze, 2013). The issue of destination
environmental responsibility is linked to greater awareness of environmental operations such as
waste management or the installation of energy efficient facilities, which can make a substantial
positive impact on the environment. However, developing or organizing a green event involves
more than just implementing such operational systems. Environmental operations that have been
adopted by a destination can also be used to promote event attendees’ pro-environmental
behavior, if the information that ultimately fits with the green theme of the event is shared
30

among event attendees. In this sense, researchers emphasize that destinations’ environmental
responsibility should be engage with the local community, tourists, and other stakeholder groups
(Laing & Frost, 2010). Not all event goers attending green events are committed to
environmental behavior in a similar scope. There is a need to examine whether event attendees’
perceptions about destination environmental responsibility affect the gap between how green
they are at home and how green they are at an event.

Recreation Involvement
Event attendees differ in the degree to which an individual is engaged in a particular
recreation activity. Researchers in tourism and leisure studies have conceptualized recreationists’
psychological connection of a particular recreation activity as recreation involvement (Kyle et al.,
2006; Lee, 2011). To be specific, recreation involvement refers to an enduring involvement in an
activity that is considered personally relevant, to the extent that a recreationist perceives it to be
self-related or in some way instrumental in achieving their personal goals (Kyle et al., 2006).
Recreation involvement has been applied to understanding various recreational activities such as
camping (e.g., McIntyre & Pigram, 1992), and sports (e.g., Wiley, Shaw, & Havitz, 2000).
Tourism and leisure studies have also investigated the outcomes of recreation involvement. For
example, studies showed that a high level of recreation involvement by recreationists increases
sensitivity to their assessment of a recreational experience (e.g., Gross & Brown, 2006);
increases attachment to specific settings (Kyle, Graefe, Manning, & Bacon, 2003); and increases
the likelihood of behavioral changes (Dawson, Havitz, & Scott, 2011). Together, the above
studies addressed recreation involvement as an important factor to consider when one aims to
understand recreationists’ behavior.
31

Although there is little empirical research available for examining the relationship
between recreation involvement and the pro-environmental behavior of recreationists, several
leisure and tourism studies have provided insights on this potential relationship. For example,
Lee (2011) showed that a high level of recreation involvement by recreationists visiting wetlands
increased individuals’ conservation commitment, such as a willingness to donate money to
environmental organizations, and also increases the likelihood of engaging in pro-environmental
behavior in their daily life, such as carpooling. Although Lee’s (2011) study measured generic
behaviors rather than pro-environmental behaviors specifically related to a recreational activity
or a destination, the findings of this study imply that differences or similarities in proenvironmental behavior at home and at a destination are related to recreation involvement. Sport
event attendees differ in the degree of their recreation involvement of a particular recreational
activity (i.e., tailgating), and it is expected that recreation involvement can help to explain the
gap of pro-environmental behaviors between at home and at tourism contexts.

Experience-Use History
Experience-use history is expected to play a moderating role in explaining the behavioral
difference between in daily life and in event setting among sport event attendees. Past experience
is defined as the sum of accumulated experience a recreationist has within a particular
recreational activity or setting (Hammitt & McDonald, 1983; Virden, 1992). Researchers have
examined past experiences with a recreation setting by implying the terminology, experience-use
history, and measuring it, for example, through the total number of previous visits to an area
(Hammitt & McDonald, 1983; Ibitayo & Virden, 1996). Several researchers addressed the
influences of past experiences on an individual recreationist’s present travel decisions (Williams,
32

Schreyer, & Knopf, 1990); perceptions about the recreation environment (Hammitt & McDonald,
1983); perceptions about the recreationists’ depreciative behavior (Ibitayo & Virden, 1996;
White, Virden, & van Riper, 2008); and actual pro-environmental behavior at the destination
(Ong & Musa, 2012). To be specific, Hammitt and McDonald (1983) suggested that past
experiences influence how recreationists perceive a recreation environment. White et al. (2008)
showed the effect of a visitor’s prior experiences with the setting on their perceptions of
environmental impacts on the recreation area. Ibitayo and Virden (1996) segmented park visitors
into high- and low-experienced groups, based on the number of past park visits, and showed that
the level of past experience was related to the perception of depreciative behaviors such as
littering, water pollution, noise, and vandalism. More recently, Ong & Musa (2012) showed that
past experience was the most important factor in explaining scuba divers’ responsible underwater
behavior. Experienced divers were more likely to engage in responsible behavior, such as
avoiding touching and standing on coral, and inspecting regulator readings, than inexperienced
divers. Based on the existing literature discussed above, it is expected that experience-use history
can additionally help to explain the gap of pro-environmental behaviors between at home and at
tourism contexts.

33

CHAPTER 3
METHODS

This study assesses whether and how sport event attendees are motivated to engage in
pro-environmental behaviors differently in their daily lives, compared with at a sport event
tourism setting. Specifically, this study investigates two research questions: (1) “Do predictive
roles of psychological constructs of environmental behavior (i.e., personal moral norms,
descriptive social norms, injunctive social norms, hedonic goals, gain goals, and perceived
behavioral difficulty) differ in predicting sport event attendee’s pro-environmental behavior in
daily life versus in the context of sport event tourism?” and (2) Are sport event attendee’s
differences in pro-environmental behavior between in daily life and in an event setting affected
by the event attendees’ characteristics (i.e., event attendee perceived destination environmental
responsibility, event attendee types, recreation involvement, and experience-use history)?
The study employed a quantitative research method approach. On-site intercept surveys
were administered to target sport event attendees, or tailgaters, followed by online and mail
follow-up surveys. Self-administered surveys were conducted to measure tailgaters’
demographic and tailgating characteristics, underlying psychological constructs of environmental
behaviors, and self-reported pro-environmental behavior in daily life and in an event setting. A
cross-sectional survey design was used to examine the extent to which psychological constructs
predict pro-environmental behavior at home and at event settings. Statistical analyses were
employed to investigate the research questions and hypotheses.
This chapter includes following sections: (1) Study Site and Study Participants; (2)
Research Design; (3) Survey Instrument Development; and (4) Preparation for Data Analysis.
34

Study Site and Study Participants

Sport Event Tourism Destination: Michigan State University
Collegiate football games in the United States are a growing sector of sport event tourism.
College campuses, as sport event tourism destinations, are visited by a huge number of fans on
football game days. Every fall, Michigan State University attracts large numbers of tailgaters
from across Michigan and beyond. According to the Michigan State Athletics Department (2013),
63,831 season tickets were sold for the 2012 football season. MSU has ranked among the
NCAA's top 25 in football attendance over the last 56 years. In 2011, MSU ranked 18th in the
NCAA in total football attendance, as 518,545 fans went through the turnstiles for seven home
games (NCAA, 2012).
While these sport events clearly generate positive social and economic benefits, the
Environmental Protection Agency (EPA) estimated that an average college football game
produces 50 to 100 tons of waste, and releases 188 to 376 metric tons of carbon dioxide into the
environment (EPA, 2012). In response to this negative environmental consequence, MSU
adopted the EPA-implemented ‘Game Day Challenge’ initiative to promote a ‘Zero Waste’
program on game days. In an effort to reduce the amount of waste accumulated during football
games, MSU makes an effort to motivate football fans to engage in pro-environmental behavior
while attending sporting events (i.e., watching games and participating in tailgating). MSU’s
environmental effort has been extended to make recycling opportunities accessible throughout
the campus on event days.

35

Study Participants: Football Tailgaters
This study sought to compare sport event attendees’ pro-environmental behavior in daily
life and in an event setting, which may provoke two different underlying mechanisms of such
behavior. “Sport event attendees” refers to tailgaters at collegiate football games. Study
participants are tailgaters at MSU football games. Unlike spectators who pass through a
controlled entrance at the stadium, there is no controlled entrance for tailgaters. Also, not all
spectators participate in tailgating activities, and vice versa. In addition, there has been no
previous data reported for the study area on tailgaters’ profiles, nor, to the researcher’s
knowledge, for the host areas of U.S. collegiate football games. For this reason, it was
challenging to draw a random sample from the tailgater population. To recruit a wide range of
study participants, a systematic sampling with a spatial zoning plan was developed. The
following section describes the research design, including the sampling method and sample
frame.

Research Design

Sampling Days
An on-site sampling strategy was employed for the first three MSU home game days in
the fall of 2013 (August 30, September 7, and 14) to target tailgaters. Three sampling days were
chosen within the study scope (e.g., budgetary and time constraints). On each game day,
sampling activity started one hour after the tailgating lots opened, which allowed tailgaters to
set-up their tents and other tailgating facilities before being sampled. Sampling activity was
discontinued before the game started. Table 1 shows the sampling schedule for the three game
36

days; specific descriptions of the sampling and sample frame are presented. To comply with
MSU and federal regulations, Institutional Review Board (IRB) approval was obtained for this
research (see Appendix A for the IRB exempt letter).

Table 1 Sampling schedule

Date

Day 1

Day 2

Day 3

MSU vs.
Western Michigan

MSU vs.
South Florida

MSU vs.
Youngstown State

Aug. 30 (Fri.)

Sep. 7 (Sat.)

Sep. 14 (Sat.)

Tailgate lots open

3:00 p.m.

7:00 a.m.

9:00 a.m.

Kick-off time

8:00 p.m.

12:00 p.m.

2:00 p.m.

Sampling hours

4:00 p.m.-7:30 p.m.
(3.5 hrs)

8:00 a.m.-11:30 p.m.
(3.5 hrs)

10:00 a.m.-1:30 p.m.
(3.5 hrs)

Systematic Sampling
As described in the ‘Study Participants’ section, it was impossible to identify the
population of football tailgaters and draw a random sample, so a systematic sampling strategy
was used, intercepting tailgaters on site. Tailgaters were contacted on an individual level, not at
the group level, because individuals within the group were not considered to be homogeneous.
Tailgaters were intercepted using systematic sampling procedures. The procedure was to start at
a set location within a designated tailgating zone and request that the four closest adults complete
a questionnaire. Once that was completed, to move a distance of five-footsteps, following a preset route, and again select the four closest adult tailgaters. The purpose of having the interval of
five-footsteps was to move approximately five steps to the next group of tailgaters. At times,
37

more than five steps were necessary because sometimes tailgaters were farther apart due to large
vehicles or other obstacles being in the path.
To further facilitate the systematic sampling activity, a sampling log-sheet (see Appendix
B) was developed. This log-sheet was designed for survey administrators to record their
sampling activity by following a time frame. Specifically, it was expected that each administrator
would spend approximately five minutes at each data collection point, and one minute in
approaching the next data collection point. Thus, 10 data collection points, or 10 cells on the log
sheet, were expected per hour. At each data collection point, survey administrators compiled the
number of completed on-site questionnaires and the number of rejections at each cell. This
practice helped survey administrators continue targeting tailgaters in a systemic way.
Intercepted tailgaters were asked to complete a self-administered on-site questionnaire.
Each of the on-site questionnaires had a unique survey ID number. Those who completed the onsite survey were automatically assigned a unique ID number, which was pre-printed by the
researcher. This respondent ID number provided participants with confidentiality and a record of
their participation, disconnected from their identity. If intercepted tailgaters refused to fill out the
questionnaire, survey administrators thanked the person and moved to the neighboring tailgater.
Survey administrators targeted tailgaters who appeared to be 18 years or older. Survey
administrators confirmed the age of intercepted tailgaters if it was unclear that they were over 18.
Upon completion of the on-site questionnaire, the participants’ email or mailing address was
requested for a follow-up online or mail survey. If intercepted tailgaters refused to participate in
the follow-up, survey administrators noted the reason why (e.g., no interest, busy, etc.) if
identifiable.

38

To administer the on-site survey, six paid survey administrators were recruited. To
comply with MSU and federal regulations, all survey administrators completed IRB training.
IRB approval for each survey administrator was obtained before contacting study participants. In
addition, the survey administrators were informed about the study’s background and purpose,
and trained in the sampling procedures before conducting the on-site survey (see Appendix C for
the 1st pre-meeting agenda for survey administrators). Pre-meetings were held before each
sampling activity, and follow-up meetings were also called at the end of each sampling day to
gather feedback from the survey administrators. On the first sampling day, the survey
administrators introduced themselves and explained the research purpose to intercepted tailgaters.
On the following sampling days, the survey administrators followed the same protocol, with the
addition of confirming that the intercepted tailgaters had not been previously contacted. If they
had, the survey administrators thanked the repeat-tailgaters and approached the next tailgaters.

Spatial Zoning Plan
The spatial zoning plan was incorporated with the systematic sampling procedure to
obtain study participants from the entire population of tailgaters at the study site. Six zones were
identified for sampling activities, with one of the six survey administrators assigned to each.

39

Zone 1
Zone 2
Zone 3

Zone 6

Zone 5
Zone 4
S

Zone 4

Zone 5A

Figure 4 Sampling zone
40

On the first and second game days, on-site surveys were administered in Zones 1 through
6 (see in Figure 4). Zones 1 through 6 were identified based on observations from the previous
year’s football season (i.e., the 2012 season): tailgating locations were found throughout the
campus, and they were also designated MSU tailgating environmental campaign sites. The
researcher observed spatial patterns of tailgating activities at the study site during the 2012
football season. The researcher also consulted with staff from the MSU Office of Sustainability
at the MSU Surplus Store & Recycling Center, which administered the recycling campaign
entitled “Game Day Challenge” on a single football game day at MSU. On the campaign day,
volunteers contacted individual tailgaters, provided information about MSU’s recycling activity,
and collected recyclable materials from tailgaters. Based on observations and opinions of
personnel from the Office of Sustainability, sampling areas from Zones 1 through 6 were
identified as areas where tailgaters were observed, and where the recycling campaign was
conducted. On the third game day, on-site surveys were administered in Zones 1 through 6,
excluding Zone 5 and adding Zone 5A (see Figure 4). Zone 5 was excluded because it was
learned, after the second game day, that this zone was heavily populated by repeat tailgaters. As
an alternative to Zone 5, a newly identified Zone 5A was added, which also had considerable
tailgating activity. Accordingly, the six survey administrators were assigned into each of the six
zones on the third game day.
Although the identified sampling zones covered the majority of the designated tailgating
areas throughout the MSU campus, one tailgating area was excluded from the sampling activity.
There was an area in the southern part of the campus (see zone S in the Figure 4). This zone was
mainly populated with MSU students who were densely packed, often listening to loud music,
and frequently involved in consuming large amounts of alcoholic beverages. Due to these factors,

41

which created some risk for survey administrators in addition to the fact that many of these
students lived on campus, and were not just attending an event on campus, it was judged that this
set of event attendees was not appropriate for testing the hypothesis. Therefore, the tailgating
area populated mainly by MSU students was excluded from the sampling activity.
In each assigned zone, the survey administrator followed a pre-drawn sampling route,
which was developed by the researcher. It helped survey administrators to target tailgaters evenly
in the space within an assigned zone during the given sampling time frame. Survey
administrators were requested to move forward by following a route. Sometimes, there were
additional incoming tailgaters after a survey administrator had moved to the next data collection
point. Yet, survey administrators were required to NOT go backwards. Survey administrators
provided the actual routes they followed after completing the sampling activity. When a planned
route within a zone was not fully covered during the first sampling day, the assigned survey
administrator started the second game day sampling activity from where she/he stopped on the
first sampling day.

Sample Size Determination
If the population variance is known, a commonly used formula for determining a sample
size statistically is:

where
n is the required sample size;
z is the z-value associated with the desired confidence interval;
is the population variance; and
42

E is the maximum acceptable difference (maximum error) or margin of error that can also be
used to refer to sampling error in general. However, because there was no information available
about population variance for this study’s population, the study determined the sample size based
on other researchers’ suggestions from their studies.
Researchers offered various approaches for deciding on the appropriate sample size.
Some researchers generally suggested 100 to 150 as the minimum sample (Anderson & Gerbing,
1988), and 200 to 400 (Boomsma & Hoogland, 2001) as an acceptable sample size. Others
specifically suggested a ratio of sample size to number of observed variables, ranging from 1:5
(Bentler & Chau, 1987) through 1:10 (Hair, Black, Babin, Anderson, & Tatham, 2006). This
study used 35 items to assess the relationships between psychological constructs of
environmental behavior and recycling behavior in daily life and event settings, and event
attendees’ characteristics (See the section on Survey Instrument Development for a description
of the measurement items). According to the suggested ratio for the sample, a required sample
size for this study would range between 175 (35 x 5) and 350 (35 x 10). Another approach to
determine an appropriate sample size is to follow researchers’ suggestions of a sample size for a
specific analysis. One of the main analyses used in this study is exploratory factor analysis.
Guadagnoli and Velicer (1988) suggested a minimum sample size of 150 for exploratory factor
analysis, as long as item inter-correlations are reasonably strong. Another of the main analyses
used in this study is multiple regression analysis. Tabachnick and Fidell (2007) suggested that
the sample size for multiple regressions can be calculated by using the equation N ≥ 60 + 8m
(where m is the number of independent variables). This study has six psychological constructs
and three control variables as independent variables in multiple regression, thus a sample size of
150 is an acceptable sample size for regression analysis. Based on the aforementioned

43

approaches, this study expected to achieve a sample size ranging from 150 to 350 completed
questionnaires.

Sample Frame
Data collection was initially designed to be implemented on-site, with follow-up surveys
administered after the first and second game days. According to the systematic sampling
procedure, it was expected that one survey administrator would intercept a maximum of 40
tailgaters per hour. If six survey administrators continued sampling activities for 3.5 hours for
one sampling day, they were expected to intercept about 840 tailgaters per game day. Sampling
activities continued for two game days during the first phase of surveys, so administrators were
expected to intercept a maximum of 1,680 tailgaters. The researcher assumed that about half of
the intercepted tailgaters would provide contact information for follow-up, and, again, that half
of those contacted tailgaters would follow-up. Accordingly, the expected sample size through the
first phase of the survey was about 400 completed questionnaires, although the actual sample
size was smaller than expected due to some tailgaters not being willing to complete the initial onsite survey, and a lower response rate to the subsequent mail or email survey. Accordingly, a
third game day was conducted for on-site sampling, with a greater proportion of the questions,
which were necessary for the hypothesis tests, shifted to the on-site questionnaire, did not need
to a follow-up survey for this study. The first and the second phases of sampling activities are
described as follows.

44

First Phase of Survey on Days 1 and 2
For the first phase of sampling activity on the first and second game days, a one-page onsite questionnaire was administered (see Appendix D for survey items). Questions were asked
about study participants’ tailgating characteristics, followed by asking whether intercepted
tailgaters were interested in participating in the follow-up online and mail surveys. A total of
1,497 tailgaters were intercepted and 1,105 (74%) tailgaters agreed to complete the on-site
questionnaire on the two game days. A total of 575 questionnaires were collected on Day 1 and
530 questionnaires were collected on Day 2. Out of the 1,105 tailgaters who completed the onsite questionnaires, 516 (47%) tailgaters provided their contact information for a follow-up
survey, with 283 (49%) tailgaters on Day 1, and 233 (44%) on Day 2. Identifiable reasons for
refusal to participate in the follow-up survey included: “not interested,” felt “participating in onsite and follow-up surveys was duplicated and time-consuming,” and “did not want to give
personal information.” Table 2 shows the on-site sampling frame for the first phase on the first
two game days.

Table 2 On-site sampling frame at first phase
Day 1

Day 2a

Total

On-site survey
777
720
1,497
# of intercepted tailgaters
575
530
1,105
# of on-site questionnaires collected
74%
74%
74%
% of participation in on-site survey
Willingness to follow-up
575
530
1,105
# of on-site questionnaires collected
283
233
516
# of email/mail collected
251
203
454
# of email addresses collected
32
30
62
# of mail addresses collected
49%
44%
47%
% of willingness to follow-up
a
Note: Repeated intercepted tailgaters (n=99) were excluded in the sampling frame in Day 2.

45

For the follow-up survey in the first phase, the online and mail follow-up survey was
conducted using a modified Dillman Total Design Survey Method (2000). A consent form (see
Appendix E for the consent form for the follow-up survey) and the follow-up questionnaire (see
Appendix F for survey items for the follow-up questionnaire) were sent individually to each of
the consenting tailgaters.
Mail surveys were used for those who provided a regular mailing address for the followup survey. All regular mail surveys were sent by first-class mail. The initial mailing included: the
questionnaire, cover letter, and postage paid business reply envelope. It was sent out on the first
business day after the game day (September 3, 2013 for Day 1, and September 9, 2013 for Day
2). For each of the Day 1 and 2 surveys, one week after the first mailing, the second mailing was
sent only to those who had not yet responded. A third and final attempt was made one week after
the second mailing, if there was still no response. The second and the third mailing attempts
included a reminder slip (see Appendix G) with the full package of the survey. Because of
expected time lag for returning the questionnaires, the reminder slip included a thank you and
apology message for those who had already responded with a completed questionnaire that had
not yet reached the researcher.
The online survey was conducted with the same protocol as the mail survey. The online
survey was developed through Qualtrics.com. A customized link was created and sent to each of
the participants via email. The customized link only allowed the targeted tailgater to participate
in the follow-up survey once. The initial emailing (September 5, 2013 for Day 1, and September
11, 2013 for Day 2), included a cover email message (see Appendix H) with the survey link, and
was sent two days after the mail survey mailing. There was a two-day interval between mail and
online survey distributions so that targeted tailgaters received their follow-ups in a similar time

46

frame. One week after the first emailing, a second emailing was sent, which included a reminder
message with the survey link. Like the mail survey, a third and final attempt was made one week
after the second emailing. For the online survey, reminders were sent only to those who had not
yet responded. Both online and mail survey collection were closed two weeks after the last
reminders were sent.
Out of 516 tailgaters who completed the on-site survey and provided contact information
for the follow up, 454 (88%) provided their email addresses, whereas 62 (12%) provided their
regular mail addresses. Of 251 email surveys distributed for Day 1, 30 (12%) emails failed, and
out of 203 email surveys distributed for Day 2, 27 (13%) emails failed. After distributing 397
surveys to valid online addresses, 121 online surveys (30% response rate) were completed. Out
of the 57 valid mail surveys distributed, there were 5 invalid mailing addresses. Of the remaining
52 valid addresses, 30 (53%) mail surveys were completed and returned. In total, 151
questionnaires were collected from the first phase of survey (see Table 3 for the Follow-up
Sample Frame at First Phase).

Table 3 Follow-up sample frame at first phase
Online survey distribution
# of email addresses collected
# of emails failed
# of online surveys distributed
# of online surveys collected
Response rate
Mail survey distribution
# of mail address collected
# of mailings failed
# of mail surveys distributed
# of mail surveys collected
Response rate
47

Day 1

Day 2

Total

251
30
221
63
29%

203
27
176
58
33%

454
57
397
121
30%

32
3
29
16
55%

30
2
28
13
50%

62
5
57
30
53%

A non-response check was performed with the sample collected at the first phase of
survey. Responses were categorized into three groups. GROUP 1 included intercepted tailgaters
who completed an on-site survey, but did not provide contact information for a follow-up survey.
GROUP 2 included tailgaters who completed an on-site survey and provided contact information
for a follow-up survey, but did not participate in the follow-up survey. GROUP 3 included
tailgaters who completed both the on-site and follow-up surveys. By comparing characteristics
among the three groups, it was learned that those who completed the follow-up surveys were
more likely to be older, female, better aware of the recycling program at MSU, and more active
in pro-environmental behavior at home (see Appendix I). Yet, there were no significant
differences in tailgating characteristics, perceptions about the destination’s responsible behavior,
and general environmental behavior at tailgating. This finding was not unexpected because
previous literature showed that age and gender were associated with individuals’ conservation
efforts (e.g., Nolan et al., 2008). It is also reasonable to expect that those tailgaters who had a
more environmental focus were somewhat more likely to spend their time and efforts on the
follow-up survey compared to those who did not. This finding implies that more environmentally
responsible tailgaters may be moderately overrepresented in further statistical analysis.

Second Phase of Survey on Day 3
During the collection of follow-up questionnaires in the first phase, it became clear that
the expected number of completed questionnaires would not meet the required sample size.
Another concern was that study participants who completed the on-site survey but not the follow
up may challenge a potential non-response bias in further analysis. Accordingly, the second

48

phase was developed with additional sampling implemented on the third game day. Unlike the
first phase of the survey, tailgaters were intercepted and asked to fill out the extended version of
the on-site questionnaire (see Appendix J) in the second phase of the study. The second version
of the on-site questionnaire was a combined version of the original on-site and follow-up
questionnaires from the first phase of surveys. It was now expected that each survey
administrator would spend approximately ten minutes, instead of six minutes, for one data
collection point (including one minute for approaching the next data collection point). Also, each
survey administrator intercepted five tailgaters, instead of four, at each data collection point. By
implementing these changes into the second phase of the sampling procedure, it was expected
that one survey administrator would intercept a maximum of 30 tailgaters per hour. If six survey
administrators continued the sampling activities for 3.5 hours, for one sampling day, it was
expected they would intercept about 640 tailgaters per one game day. Another change was that a
new zone, Zone 5A, was added, while, Zone 5 was dropped, as described above in ‘Spatial
Zoning Plan.’ During the second phase of surveys, a total of 581 tailgaters were intercepted and
218 (38%) tailgaters were not willing to participate in the on-site survey. As a result, 363 (62%)
tailgaters agreed to participate and completed an on-site questionnaire on the third game day (see
Table 4).

Table 4 On-site sampling frame at second phase
Day 3a
On-site survey
581
# of intercepted tailgaters
363
# of on-site questionnaires collected
218
# of rejections
62%
% of participation in on-site survey
Note: a Repeated intercepted tailgaters (n=109) were excluded in the sampling frame for Day 3.

49

From the first phase of data collection 151 surveys were collected, and from the second
phase 363 surveys were collected, accordingly a total of 514 surveys were collected.

Survey Instrument Development
Two steps were taken to develop the survey instrument for this study. First, the initial
survey instrument was created based on previous studies. Second, the instrument was reviewed
by dissertation committee members. The instrument was also reviewed by five survey
administrators to confirm the readability and clarity of the questions. The purpose of this process
was to detect any fault in the design of the questionnaire. Some minor changes were made to the
items following comments from committee members and recruited survey administrators, prior
to conducting the actual survey. In this section, the organization of the survey, and development
of the survey instrument are described.

The Questionnaires
The first phase of surveys utilized two questionnaires—on-site and follow-up surveys.
The on-site questionnaire allowed the researcher to: (1) develop a profile of intercepted event
attendees; and (2) understand potential non-response bias. The on-site questionnaire (see
Appendix D) measured study participants’ MSU affiliation, residence, age, gender, game
attendance, travel distance, tailgating group size and type. The on-site questionnaire also
measured study participants’ awareness/perception of MSU’s recycling program and event
attendees’ engagement in general environmental behavior both at home and at the tailgate setting.
The follow-up online and mail survey (see Appendix F) measured theoretical constructs in the

50

conceptual framework of this study: event attendees recycling behaviors in daily life and in event
settings, situational constraints of such behaviors and underlying psychological constructs of
recycling behavior; and event attendees’ psychological and behavioral involvement in tailgating
activities. The second phase of the study utilized only the on-site survey, which combined a full
version of the on-site questionnaire, with the key portion of the follow-up questionnaire from the
first phase (see Appendix J) included in the on-site questionnaire.

Pro-environmental Behavior
Pro-environmental behavior was measured with four items, and each of the items was
measured for both daily life and a sport event setting. As a primary concern of this research is to
compare behavioral differences in the two settings, the measurement items for pro-environmental
behavior were identical for both settings. Accordingly, each of the study participants responded
to eight total behavioral items. Four material-specific recycling items were developed by
adapting previous literature (Klöckner & Oppedal, 2011; Knussen & Yule, 2008). These focused
on generally accepted recyclable items in both the household setting and in the recycling scheme
for game days developed by the MSU Office of Campus Sustainability. The items included: (1) I
recycle non-deposit glass containers; (2) I recycle non-deposit aluminum cans; (3) I recycle
plastic (e.g., bottle, cup, grocery bags); and (4) I recycle paper (e.g., mixed paper, boxboard).
Items were scored on a 5-point Likert-type scale from 1 = Never, to 5 = Always. In addition to a
5-point scale, a “don’t use” option was given to respondents for each of the items.

51

Perceived Behavioral Difficulty
Perceived behavioral difficulty was used to measure situational influences to recycling
behavior. Perceived behavioral difficulty was also measured for both daily life and event settings.
Instrumentation for perceived behavioral difficulty was adapted from previous literature (GreenDemers, Pelletier, & Ménard, 1997; Klöckner & Oppedal, 2011). The purpose of
operationalizing perceived behavioral difficulty was to measure the amount of perceived
difficulty one encountered while engaging in each of the material-specific recycling behaviors in
the two settings. Accordingly, four behavioral difficulty items were derived from the four
material-specific recycling behavior items: (1) Recycling non-deposit glass containers; (2)
Recycling non-deposit aluminum cans; (3) Recycling plastic (e.g., bottle, cup, grocery bags); and
(4) Recycling paper (e.g., mixed paper, boxboard). The items of behavioral difficulty are
identical to the items of recycling behavior, excluding the measurement scale. The sole
difference between behavioral difficulty and behavior was that perceived behavioral difficulty
was measured by asking respondents to rate the level of difficulty for each recycling behavior on
a 5-point scale of 1 = not difficult at all to 5 = very difficult, instead of the 5-point frequency
scale of never to always. Like behavior measurement, a “don’t use” option for a specific item
(e.g. glass containers) was provided for each item.

Psychological Constructs of Environmental Behavior
Five underlying psychological constructs of environmental behavior—personal moral
norms, descriptive social norms, injunctive social norms, hedonic goals, and gain goals—were
operationalized in more general terms, not specifically operationalized for each setting (i.e., in
daily life and event settings). Goal-framing theory (Steg, 2007), in conjunction with the Norm

52

Activation Model (Schwartz, 1977) and the Social Norms Approach (Perkins & Berkowitz,
1986), guided the development of psychological construct measurement items. All psychological
construct items were rated on a 5-point Likert-type scale ranging from 1=strongly disagree to
5=strongly agree.
Personal moral norm items were adapted from previous literature (Andersson & von
Borgstede, 2010; Dolnicar & Leisch, 2008; Kaiser, 2006). Study participants were asked to
indicate the extent to which they agreed with the following statements: (1) I feel morally
obligated to engage in recycling; (2) I should do anything I can do to recycle; and (3) I feel guilty
when I waste recyclable materials.
Descriptive social norms and injunctive social norms were distinguished in this study.
Descriptive social norm items reflected the extent to which the study participant perceived the
popularity of recycling behavior, and injunctive social norm items measured the extent to which
the study participant perceived social approval of recycling behavior. Social norm variables were
constructed by taking theoretical conceptualization into account (Cialdini, Reno, & Kallgren,
1990; Perkins & Berkowitz, 1986) and by adapting previous literature in different behavioral
domains such as with hotel customers (Goldstein et al., 2008; Park & Smith, 2007; Thøgersen,
2006). Three items were used to measure descriptive social norms: (1) I think that a majority of
people in the United States engage in recycling; (2) I think that a majority of people at tailgating
areas at MSU engage in recycling; and (3) I think that a majority of people in my community
engage in recycling. Three items were used to measure injunctive social norms: (1) A majority of
people in the United States should engage in recycling; (2) A majority of people at tailgating
areas at MSU should engage in recycling; and (3) A majority of people in my residential
community should engage in recycling.

53

Hedonic goals and gain goals were distinguished in this study. The hedonic goal items
reflected the extent to which the study participant perceived emotional benefits by engaging in
recycling behavior. Gain goal items measured the extent to which the study participant perceived
personal resource benefits by engaging in recycling behavior. Hedonic and gain goal variables
were constructed by taking theoretical conceptualization into account (Lindenberg & Steg, 2007),
and by adapting previous literature in different behavioral domains such as green electricity
programs (Clark, Kotchen, & Moore, 2003), green product consumption (Hartmann & ApaolazaIbáñez, 2008), and pro-environmental behavior at hotels (Miao & Wei, 2013). Three items were
used to measure hedonic goals: (1) I recycle because of the enjoyment that I feel; (2) I take
satisfaction in recycling; and (3) I recycle because I enjoy learning new skills/techniques. Three
items were used to measure gain goals: (1) One of the best things about recycling is that it helps
lower costs to society; (2) We should recycle materials and protect the environment to benefit
people; (3) My health and the health of my family may improve because of recycling.

Destination Environmental Responsibility
Destination environmental responsibility was used to measure event attendees’
perceptions about the destination’s practices in greening events. Items were constructed by
taking into account conceptualization (Laing & Frost, 2010; Mair & Laing, 2013), and by
modifying items from previous literature in different domains such as hotels (Holcomb,
Upchurch, & Okumus, 2007). Also, with respect to the study site’s green practices, three items
with different aspects of the destination’s environmental practices were developed: (1) MSU has
visible communications about its green practices at tailgating; (2) MSU has established active
recycling programs where I am tailgating; and (3) MSU has established systems to reduce

54

food/material waste during tailgating. Items were rated on a 5-point Likert-type scale ranging
from 1=strongly disagree to 5=strongly agree.

Recreation Involvement
Items for recreation involvement were based on the previous literature (Lee, 2011;
McIntyre, 1989; Kyle, Absher, Norman, Hammitt, & Jodice, 2007). The original recreation
involvement scale developed in previous literature consisted of attraction (measured using five
items), centrality (four items), social bonding (three items), identity affirmation (three items),
and identity expression (three items) dimensions (Kyle et al., 2007). This study used a reduced,
five-item version of the recreation involvement scale. The five items represented each of the five
dimensions of recreation involvement. The five items selected had the highest Cronbach’s alpha
reliability scores based on the previous literature. Items included in this study are: (1) Tailgating
is one of the most enjoyable things I do; (2) I find a lot of my life is organized around Tailgating;
(3) I enjoy discussing Tailgating with my friends; (4) When I participate in Tailgating, I can
really be myself; and (5) I can tell a lot about a person by seeing them Tailgating. Items were
rated on a 5-point Likert-type scale ranging from 1=strongly disagree to 5=strongly agree.

Experience-use History
Experience-use history was measured using four questions, based on previous research
(Hammitt, Backlund, & Bixler, 2004). Specific questions asked event attendees’ years and
frequency of engaging in tailgating at both the study site and other locations. These four items
were: (1) What was the first year you tailgated at MSU?; (2) What was the first year you
tailgated at other universities/colleges?; (3) How many times did you tailgate at MSU during the

55

years 2010, 2011 and 2012?; and (4) How many times did you tailgate at other
universities/colleges during the years 2010, 2011 and 2012?

Socio-demographics and Tailgating Characteristics
Study participants were asked about their socio-demographic information, which was
used to understand the respondents’ profiles, and to create sub-groups of study participants for
further analysis. Respondents were asked their age, gender, five-digit zip code for their principal
home, and current MSU affiliation. By utilizing the zip code and MSU affiliation, event
attendees were categorized into two types—tourists and non-tourists. Tourists refer to those
tailgaters who are neither local residents living in the vicinity of the destination (i.e., Greater
Lansing area defined as Eaton, Clinton, or Ingham County) nor those who are MSU community
members (i.e. students, faculty, staff, or alumni). This definition is also used by the Greater
Lansing Convention and Visitors Bureau.
In addition to demographic characteristics, tailgating characteristics were also gathered.
Questions included: (1) Are you here as a fan of MSU, visiting, or other teams?; (2) Are you
attending the game today?; (3) Are you the leader of the tailgate at this site?; (4) How many
miles did you travel from your principal home to East Lansing for this experience?; (5) How
many adults (18 years and older including you) and children (under 18) came with you to this
tailgate?; and (6) Who came with you to this tailgate (e.g., family or friends)?
Finally, three items were developed to measure general environmental behavior both in
daily life and at sport event settings. Items measured three commonly recognized categories of
environmental behavior: reduce, reuse, and recycle. These are general environmental behavior
categories discussed in previous studies (Barr, 2007; De Young, 1986; Miao & Wei, 2013). Also,

56

the study site currently uses a three R’s scheme: “Reduce, Reuse, and Recycle” to help protect
natural resources and reduce the amount of waste. Three items were included: (1) I look for ways
to reduce food/material waste; (2) I look for ways to reuse things; and (3) I recycle materials.
The three items were used to identify potential differences between respondents and nonrespondents.

Preparation for Data Analysis

Data Cleaning
After distributing questionnaires, 514 surveys were collected. Prior to analyzing the data,
eight responses were dropped because these respondents did not complete the majority of the
questionnaire. In addition, 101 surveys were dropped because they were found to be
inappropriate for analysis; they did not provide comparable responses to sport event attendees’
recycling behaviors in daily life and in event settings. For example, although an answer choice
option “don’t use” was intentionally included in behavior measurement scales to reduce the
measurement error, those responses were not appropriate when comparing paired responses.
Accordingly, responses with “don’t use” selected for recycling behavior measurement items in
either setting were removed using a list-wise deletion method. After deleting incomplete
responses, a total of 405 useable responses were available for further analysis. Before analyzing
the data, the researcher examined the potential differences in profiles between all tailgaters who
provided an on-site response in either Phase I or II, and the sub-set of tailgaters who completed
the follow-up in Phase I or the full on-site survey in Phase II. Of the study participants’

57

characteristics used for further statistical analysis (n=405), there were no substantial deviations
from the on-site intercepted tailgaters (n=1,468) (see Table 5).

Table 5 Comparison between intercepted tailgaters and study participants
Intercepted
tailgaters
(n=1,468)

Demographic Characteristic

Study
Participants
(n=405)

Age
16.0%
17.2%
18-24
22.5%
19.7%
25-34
16.6%
16.0%
35-44
22.7%
19.0%
45-54
16.3%
21.4%
55-64
5.9%
6.7%
65 or older
Mean
41.7
42.9
Standard Deviation
15.0
15.9
Gender
Male
44.9%
50.7%
Female
55.1%
49.3%
Affiliation
MSU Employee
2.9%
3.2%
MSU Student
9.7%
11.9%
Not MSU Employee or Student
87.4%
84.9%
If Not MSU Employee or Student, MSU Alumni?
Yes
44.5%
49.4%
No
55.5%
50.6%
Residence
Out of State of Michigan
6.8%
6.7%
In State of Michigan
93.2%
93.3%
If Residents of State of Michigan, residents in Eaton, Clinton, or Ingham County?
Yes
28.2%
33.9%
No
71.8%
66.1%

58

Table 5 Comparison between intercepted tailgaters and study participants (Cont’d)
Tailgating Characteristic
Attending Game
Yes
No
Leader of Tailgating
Yes
No
Travel Miles
0-49 miles
50-99 miles
100-149 miles
150-199 miles
200 or more
Adult Group Size
1-5 adults
6-10 adults
11 or more
Have Child in Group
Yes
No
Tailgating Group Type
By myself
Family only
Friends only
Family and Friends
Club/Organization
Other

Intercepted
tailgaters
(n=1,468)

Study
Participants
(n=405)

84.7%
15.3%

78.5%
21.5%

28.2%
71.8%

27%
73%

37.0%
45.6%
8.2%
2.3%
6.9%

43.1%
41.4%
6.5%
1.5%
7.5%

66.2%
19.8%
14.0%

63.1%
22.8%
14.1%

24.2%
75.8%

21.3%
78.7%

2.9%
17.4%
19.6%
57.7%
1.6%
0.8%

2.7%
15.6%
18.1%
60.6%
2.7%
0.2%

Reliability and Validity Tests
Two major criteria for assessing measurement are reliability and validity. Both were
evaluated for the measurement constructs in this study. Reliability means repeatability—the
ability to yield consistent results over time from several measurements made in the same way

59

(Babbie 2010). Internal consistency reliability has been the most commonly used reliability
method in developing measurement scales. Cronbach’s alpha test was used for internal
consistency reliability. A coefficient score of 0.7 or higher demonstrates an acceptable level of
reliability coefficient (Nunnally, 1978). Validity is the validation for measurement, and refers to
the accuracy or truthfulness of a measurement. That is, validity is the extent to which differences
found with a measuring tool reflect true differences among respondents being studied (Babbie
2010). Two forms of validity were used in this study: content validity and construct validity.
Content validity is the degree to which the content of the items adequately represents the
universe of all relevant items under study. It is evaluated by examining the subjective agreement
among professionals that a scale logically appears to accurately reflect what it is intended to
measure. Construct validity is the degree to which a measure relates to other variables as
expected within a system of theoretical relationships. Two categories of construct validity are
convergent validity (i.e., the degree to which a measure is correlated with other measures with
which it is theoretically predicted) and discriminant validity (i.e., the degree to which the
operationalization does not correlate with other operationalizations with which it should not
theoretically correlate). Construct validity is evaluated by examining the item loadings and their
associated t-values, as well as item-total correlation. If factor loadings for the indicators under
the same construct were higher than 0.5, this supported the convergent validity of the constructs.
Discriminant validity is assessed by looking at correlations between items in different constructs.
Low correlation provides evidence of discriminant validity. Results of reliability and validity
tests are presented in Chapter 4.

60

Data Analysis Plan
This study employed several statistical analysis tools to describe the sample and test the
hypotheses associated with two research questions in the proposed model. The computer
software used for the analyses was the Statistical Package for the Social Sciences (SPSS) version
20.0 for Windows.
Descriptive statistics were obtained to describe study participants’ profiles. Study
participants’ demographic information (e.g., age, gender, affiliation, and residence), and
tailgating characteristics (e.g., game attendance, group size, and travel distance) were presented
by using frequencies, percent, mean, and standard deviation. Normality assumptions for
parametric tests employed in this study were examined in terms of skewness and kurtosis.
Skewness within a range between -3 and 3, and a kurtosis score of between -10 and 10 are
considered acceptable (Kline, 1998). All the variables in the study were within these acceptable
ranges (see Appendix K).
In answering Research Question 1, an exploratory factor analysis (EFA) followed by a
series of multiple regression analysis was employed. The EFA was expected to retain factors,
which were measured by multiple items. For example, underlying psychological constructs of
environmental behavior were retained through EFA, and entered as independent variables in
multiple regression analysis.
EFA was employed to assess the dimensionality of measurement items. A factor analysis
is a useful tool for developing operational representatives for a theoretical construct (Gorsuch,
1983), and is an important tool for questions of validity of the measurement of psychological
constructs (Nunnally, 1978). Exploratory factor analysis is used over confirmatory factor
analysis in this study. Although both exploratory and confirmatory approaches seek to account

61

for as much variance as possible in a set of observed variables with a smaller set of latent
variables, exploratory factor analysis is particularly appropriate because it is used for scale
development or when there is little empirical basis for specifying a priori the number and
patterns of common factors (Ford, MacCallum, & Tait, 1986). In EFA, various rules of thumb
are used by researchers for the retention of factors. Kaiser’s (1960) recommendation of an
eigenvalue over 1 was used as a criterion for factor retention. Then, the acceptability of factor
analysis results of all scales was measured by using the Kaiser-Meyer-Olkin (KMO) measure of
sampling adequacy, which should be larger than the recommended value of 0.6 (Tabachnick &
Fidell, 2007). A factor loading of .40 was used as a minimum cutoff for an item to be retained.
To ensure that each attribute loaded only on one factor, items which had factor loadings lower
than .40 or cross-loaded on more than one factor were eliminated (Hair et al., 2006).
Multiple regression analyses were employed to assess the effects of independent
variables (i.e., psychological constructs of environmental behavior, and perceived behavioral
difficulty) on a dependent variable (i.e., pro-environmental behavior). Variables used in multiple
regressions were constructed by calculating composite scores with multiple measurement items,
which were derived from the EFA results. Separate multiple regression analysis was conducted
for each dependent variable (e.g., predicting behavior in a daily life setting, and predicting
behavior in an event setting). By comparing significantly influential factors in each of the
regression models, this study answered the first research question, “Do predictive roles of
psychological constructs of environmental behavior differ in predicting sport event attendee’s
pro-environmental behavior in daily life versus in the context of sport event tourism?”
In answering Research Question 2, paired sample t-tests and mixed between-within
subjects analyses of variance (ANOVAs) were employed. Paired sample t-tests were used to test

62

whether there were statistically significant differences between pro-environmental behaviors in
daily life and in event settings. A finding of no significant difference between mean scores
indicates that the behavior in daily life was consistent with the behavior in the event setting,
whereas a significant difference indicates a lack of consistency in the behavior between the two
settings.
Mixed between-within subject ANOVAs were then conducted to assess the impacts of
event attendees’ characteristics (e.g., event attendee types) on pro-environmental behavior across
the two settings (daily life and event setting). Mixed between-within subjects ANOVA employs
both types of ANOVA—between-groups ANOVA and within-subjects ANOVA—which is used
when the study participants encounter repeated measures (i.e., the same subjects are used and
measured under different settings). The within-subjects design should only be used when the two
sets of scores represent measures of exactly the same thing. Also, mixed between-within subject
ANOVAs introduced a concept of interaction, which refers to the way in which a category of one
independent variable (e.g., tourists) combines with a category of another independent variable
(e.g., at event setting) to produce an effect on the dependent variable (e.g., recycling behavior)
that goes beyond the sum of the separate effects. By testing whether behavioral differences
across settings vary depending on event attendees’ characteristics, this study answered the
second research question, “Is a sport event attendee’s differences in pro-environmental behavior
in daily life and in an event setting affected by the event attendees’ characteristics? Assumptions
that have to be met in each of the analyses were assessed before conducting statistical tests, and
are presented in the following chapter.
The following chapter describes the procedures of statistical analyses, tests the
hypotheses, and reports the results.

63

CHAPTER 4
RESULTS

The primary purpose of this study is to identify differences in the underlying mechanisms
of pro-environmental behavior in daily life and in an event setting. To achieve this purpose, a
self-administered survey was conducted and statistical analyses were employed to answer the
research questions. This chapter consists of three subsections: (1) Description of the Study
Participants; (2) Examination of Research Question 1; and (3) Examination of Research Question
2.

Description of the Study Participants

Socio-demographic Profile
The socio-demographic characteristics of the study participants included MSU affiliation
and residential area, which were used for identifying sub-types of event attendees (see Table 6).
In addition, age, gender, game attendance, leader of tailgating group, travel distance, tailgating
group size, and group type were included to understand the profile of football tailgaters (see
Table 7).
As shown in Table 6, study participants’ (n=405) affiliations were ‘MSU employee’
(3.2%), ‘MSU student’ (11.9%), and ‘not MSU employee or student’ (84.9%). Of 344
respondents who were not an MSU employee or student, about one half of them (49.4%) were
‘MSU alumni.’ The majority of study participants (93.3%) were residents of the State of

64

Michigan. Of 378 Michigan residents, about one third (33.9%) were residents in the greater
Lansing area, which is comprised of Clinton, Eaton, and Ingham counties.

Table 6 Affiliation and residential area of study participants
Variable

Frequency

Affiliation (n=405)
MSU Employee
MSU Student
Not MSU Employee or Student
If Not MSU Employee or Student, MSU Alumni?
Yes
No
Residence (n=405)
Out of State
Michigan
Michigan Resident of Clinton, Eaton, or Ingham County?
Yes
No

Percent (%)

13
48
344

3.2
11.9
84.9

170
174

49.4
50.6

27
378

6.7
93.3

128
250

33.9
66.1

Table 7 shows the socio-demographic characteristics of the study participants. Study
participants ranged in age from 18 to 83 years, and the average age was 43 years. The majority of
study participants (78.5%) planned to attend the game in the stadium. Twenty-seven percent of
study participants indicated that they themselves were the leaders of the tailgating groups. Most
respondents traveled less than 100 miles from their principal home to East Lansing for tailgating,
with 43.1% traveling less than 50 miles, 41.4% traveling between 50 and 99 miles and 15.5%
traveling 100 miles or more. About two-thirds (63.1%) of the study participants came to the
tailgating event with a group of 1 to 5 adults, and 21.3% of the study participants had children in
their groups. Study participants’ tailgating group types are: ‘by myself’ (2.7%), ‘family only’
(15.6%), ‘friends only’ (18.1%), ‘family and friends’ (60.6%), and ‘club/organization’ (2.7%).

65

Table 7 Socio-demographic characteristics of study participants
Variable
Age (n=401, mean=43 S.D.=15.9)
18-19
20s
30s
40s
50s
60 or older
Gender (n=405)
Male
Female
Attending Game (n=404)
Yes
No
Leader of Tailgating (n=404)
Yes
No
Travel Miles (n=401)
0-49 miles
50-99 miles
100-149 miles
150-199 miles
200 or more
Adult Group Size (n=404)
1-5 adults
6-10 adults
11 or more
Have Child in Group (n=404)
Yes
No
Tailgating Group Type (n=404)
By myself
Family only
Friends only
Family and Friends
Club/Organization
Other

66

Frequency

Percent (%)

17
96
66
58
98
66

4.2
23.9
16.5
14.5
24.4
16.5

182
223

44.9
55.1

317
87

78.5
21.5

109
295

27.0
73.0

173
166
26
6
30

43.1
41.4
6.5
1.5
7.5

255
92
57

63.1
22.8
14.1

86
318

21.3
78.7

11
63
73
245
11
1

2.7
15.6
18.1
60.6
2.7
0.2

Tailgating Experience Profile
Tailgate experience characteristics included the respondent’s recreation involvement (see
Table 8) and experience-use history (see Table 9).
Recreation involvement was measured with five items, using a 5-point Likert-type scale
that ranged from 1 for strongly disagree to 5 for strongly agree. As shown in Table 8, the mean
scores ranged from 2.4 to 3.8. Among the five items, “Tailgating is one of the most enjoyable
things I do” reached the highest level of agreement (mean=3.79), followed by
“When I participate in Tailgating, I can really be myself” (mean=3.65), “I enjoy discussing
Tailgating with my friends” (mean=3.40), “I can tell a lot about a person by seeing them
Tailgating” (mean=3.06). The item, “I find a lot of my life is organized around Tailgating”
(mean=2.41) had the lowest level of agreement among the items.

Table 8 Recreation involvement profile
Mean

Std.
Deviation

Tailgating is one of the most enjoyable things I do.

3.79

1.01

I find a lot of my life is organized around Tailgating.

2.41

1.14

I enjoy discussing Tailgating with my friends.

3.40

1.16

When I participate in Tailgating, I can really be myself.

3.65

1.15

I can tell a lot about a person by seeing them Tailgating.

3.06

1.14

Recreation Involvement a (N=405)

Note.
a: Measurement item scale ranged from 1=“strongly disagree” to 5=“strongly agree”

67

Table 9 Experience-use history profile
Variable
Frequency
Percent (%)
First year you tailgated at MSU (N=370)
1944-1979
57
15.4
1980-1989
54
14.6
1990-1999
87
23.5
2000-2009
107
28.9
2010-2013
65
17.6
First year you tailgated at other universities/colleges (N=254)
1959-1979
32
12.6
1980-1989
49
19.3
1990-1999
44
17.3
2000-2009
96
37.8
2010-2013
33
13.0
Frequency of tailgate at MSU during the years 2010, 2011 and 2012 (N=370)
0 time
32
8.6
1-5 times
77
20.8
6-10 times
66
17.8
11-15 times
69
18.6
16-20 times
75
20.3
21 times or more
51
13.8
Frequency of tailgate at other universities/colleges during the years 2010, 2011 and 2012
(N=279)
0 times
106
38.0
1-5 times
140
50.2
6-10 times
14
5.0
11-15 times
6
2.2
16-20 times
5
1.8
21 times or more
8
2.9

Experience-use history was measured with four items (see Table 9). Among the
respondents, 15.4% indicated the first year of their tailgating at MSU was between the years
1944 and 1979; 14.6% indicated the 1980s; 23% indicated the 1990s; and 46.5% indicated that
their first year of tailgating at MSU was between 2000 and 2013. Of their tailgating experiences
at other universities/college, approximately half of respondents (50.8%) indicated that their first
year of tailgating at other places was between 2000 and 2013. Among experienced tailgaters,

68

8.6% did not attend tailgating during the years 2010, 2011 and 2012; 38.6% attended between 1
and 10 times; 38.9% attended 11 to 20 times; and 13.8% attended tailgating 21 or more times
during the last three years. Frequencies of study participants’ tailgating at other
universities/colleges during the years 2010, 2011, and 2012 are: 38% never attended; 55.2%
attended 1 to 10 times; and 6.9% attended tailgating events 11 or more times.

Pro-environmental Behavior and Related Psychological Constructs Profile
Study participants’ pro-environmental behavior, perceived behavioral difficulty, and
psychological constructs (i.e., personal moral norms, descriptive social norms, injunctive social
norms, hedonic goals, and gain goals) and event attendee perceived environmental responsibility
of destinations were measured with multiple items using a 5-point Likert-type scale (see Tables
10 through 13).
Table 10 presents a profile of study participants’ pro-environmental behavior at home and
at the tailgate event. Three items were used to measure respondents’ self-reported general
environmental behavior both at home and in tailgating settings. Study participants engaged in
more environmental behaviors at home, compared to their behavior at tailgating: “I recycle
materials” at home (mean=4.25) versus at tailgating (mean=3.25); “I look for ways to reuse
things” at home (mean=4.13) versus at tailgating (mean=3.09); and “I look for ways to reduce
food/material waste” at home (mean=4.05) versus at tailgating (mean=3.18). Four items were
used to measure respondents’ self-reported material-specific recycling behavior both at home
and at tailgating settings (see also Table 10). Different patterns in material-specific recycling
behavior were observed across settings. At home, the mean value of recycling non-deposit
aluminum cans (mean=4.30) is highest, followed by recycling plastic (mean=4.29), recycling
paper (mean=4.20), and recycling non-deposit glass containers (mean=4.18). At the tailgate
69

event, the mean value of recycling non-deposit aluminum cans (mean=3.21) is highest, followed
by recycling non-deposit glass containers (mean=3.18), recycling plastic (mean=3.06), and
recycling paper (mean=2.83). Generally, study participants recycled more at home than they did
at the tailgate event: “I recycle non-deposit glass containers” at home (mean=4.18) versus at
tailgating (mean=3.18); “I recycle non-deposit aluminum cans” at home (mean=4.30) versus at
tailgating (mean=3.21); “I recycle plastic” at home (mean=4.29) versus at tailgating
(mean=3.06); and “I recycle paper” at home (mean=4.20) versus at tailgating (mean=2.83).

Table 10 Pro-environmental behavior profile
At Home
(N=405)
Mean

Std.
Deviation

At Tailgating
(N=405)
Mean

Std.
Deviation

General pro-environmental behavior a
I recycle materials.

4.25

1.13

3.25

1.31

I look for ways to reuse things.

4.13

1.06

3.09

1.34

I look for ways to reduce food/material
waste.

4.05

1.02

3.18

1.27

I recycle non-deposit glass containers.

4.18

1.31

3.18

1.47

I recycle non-deposit aluminum cans.

4.30

1.24

3.21

1.48

4.29

1.16

3.06

1.42

4.20

1.25

2.83

1.45

Material-specific recycling behavior a

I recycle plastic
(e.g., bottle, cup, grocery bags).
I recycle paper
(e.g., mixed paper, boxboard).

Note.
a: Measurement item scale ranged from 1=“strongly disagree” to 5=“strongly agree”

70

Perceived behavioral difficulty was measured using four items (see Table 11). The mean
scores of the four items in the home setting ranged from 2.31 to 2.40, whereas the mean scores of
the same four items in the tailgating setting ranged from 3.01 to 3.10. Study participants
perceived recycling behavior at home as less difficult than recycling at tailgating: “Recycling
non-deposit glass containers” at home (mean=2.40) and at tailgating (mean=3.05); “Recycling
non-deposit aluminum cans” at home (mean=2.34) and at tailgating (mean=3.03); “Recycling
plastic” at home (mean=2.31) and at tailgating (mean=3.01); and “Recycling paper” at home
(mean=2.35) and at tailgating (mean=3.10).

Table 11 Perceived behavioral difficulty profile
At Home
(N=405)
Mean

Std.
Deviation

At Tailgating
(N=405)
Mean

Std.
Deviation

Perceived behavioral difficulty a
Recycling non-deposit glass containers.

2.40

1.64

3.05

1.37

Recycling non-deposit aluminum cans.

2.34

1.65

3.03

1.37

2.31

1.59

3.01

1.32

2.35

1.55

3.10

1.37

Recycling plastic
(e.g., bottle, cup, grocery bags).
Recycling paper
(e.g., mixed paper, boxboard).

Note.
a: Measurement item scale ranged from 1=“not difficult at all” to 5=“very difficult”

The underlying psychological motivation of recycling behavior was measured using five
constructs with 15 items: personal moral norms, descriptive social norms, injunctive social
norms, hedonic goals, and gain goals (see Table 12). The mean scores of personal moral norms
(ranged from 3.85 to 4.04), injunctive social norms (ranged from 4.27 to 4.31), and gain goals

71

(ranged from 3.77 to 4.30) were higher than the mean scores of descriptive social norms (ranged
from 2.69 to 3.37) and hedonic goals (ranged from 2.96 to 3.90).

Table 12 Psychological constructs of recycling behavioral profile
N=405

Mean

Std.
Deviation

4.04
4.03
3.85

1.03
1.03
1.18

2.88

1.00

2.69

1.08

3.37

1.00

4.31

0.94

4.27

0.92

4.31

0.94

3.43
3.90
2.96

1.21
1.13
1.26

3.77

1.13

4.30

0.99

3.90

1.13

a

Personal Moral Norms (PMN)
I feel morally obligated to engage in recycling. (PMN1)
I should do anything I can do to recycle. (PMN2)
I feel guilty when I waste recyclable materials. (PMN3)
Descriptive Social Norms a (DSN)
I think that a majority of people in the United States engage in
recycling. (DSN1)
I think that a majority of people at tailgating areas at MSU engage
in recycling. (DSN2)
I think that a majority of people in my community engage in
recycling. (DSN3)
Injunctive Social Norms a (ISN)
A majority of people in the United States should engage in
recycling. (ISN1)
A majority of people at tailgating areas at MSU should engage in
recycling. (ISN2)
A majority of people in my residential community should engage
in recycling. (ISN3)
Hedonic Goals a (HG)
I recycle because of the enjoyment that I feel. (HG1)
I take satisfaction in recycling. (HG2)
I recycle because I enjoy learning new skills/techniques. (HG3)
Gain Goals a (GG)
One of the best things about recycling is that it helps lower costs
to society. (GG1)
We should recycle materials and protect the environment to
benefit people. (GG2)
My health and the health of my family may improve because of
recycling. (GG3)

Note.
a: Measurement item scale ranged from 1=“strongly disagree” to 5=“strongly agree”

72

Event attendee perceived destination environmental responsibility was measured using
three items (see Table 13). The mean scores of the three items were: “MSU has visible
communications about its green practices at tailgating” (mean=3.24), “MSU has established
active recycling programs where I am tailgating” (mean=3.16), and “MSU has established
systems to reduce food/material waste during tailgating” (mean=3.08).

Table 13 Event attendee perceived destination environmental responsibility
N=405

Mean

Std.
Deviation

3.24

1.21

3.16

1.17

3.08

1.10

Destination Environmental Responsibility a (DER)
MSU has visible communications about its green practices at
tailgating. (DER1)
MSU has established active recycling programs where I am
tailgating. (DER2)
MSU has established systems to reduce food/material waste during
tailgating. (DER3)

Note.
a: Measurement item scale ranged from 1=“strongly disagree” to 5=“strongly agree”

73

Research Question 1
As discussed in the literature review, there has been little empirical research to examine
whether and how underlying mechanisms of pro-environmental behavior differ between daily
life and in event settings. This section answers Research Question 1, “Do predictive roles of
psychological constructs of environmental behavior (i.e., personal moral norms, descriptive
social norms, injunctive social norms, hedonic goals, gain goals, and perceived behavioral
difficulty) differ in predicting sport event attendee’s pro-environmental behavior in daily life
versus in the context of sport event tourism?” To address this research question, these steps were
taken:
1.

Dependent and independent variables were constructed using exploratory factor
analyses. The factor loadings for each item were examined, and internal consistency
for each of the items was tested. Poor items were removed based on the factor
analysis. The mean values for each of the identified factors was calculated and used
as a new variable for a reduced model. Descriptive statistics (mean, standard
deviation, and correlation coefficients) of newly constructed variables were
calculated before conducting multiple regression analysis.

2.

A series of multiple regression analyses was conducted. Two separate multiple
regression analyses were conducted to examine the effects of psychological
constructs on recycling behavior at home, and those same effects on recycling
behavior at tailgating.

74

Exploratory Factor Analysis
Three separate factor analyses were conducted. First, the 15 items measuring underlying
psychological constructs of recycling behavior were factor analyzed to examine the
dimensionality of the scale, and to form setting-general independent variables to be used in
regression analysis. Second, the eight items measuring perceived behavioral difficulty were
factor analyzed to examine a two-dimensional scale representing behavioral difficulty at both
home and tailgating settings, and to form setting-specific independent variables used for the
subsequent regression analysis. Third, the eight items measuring recycling behavior were factor
analyzed to examine a two-dimensional scale of recycling behavior at home and tailgating
settings, and to form setting-specific dependent variables used for regression analysis.

EFA for Psychological Constructs
First, principle component exploratory factor analysis was conducted to identify the
dimensions of study participants’ psychological constructs that influence recycling behavior. A
factor analysis with an oblique rotation method was utilized. In exploratory factor analysis,
rotation of the factor dimensions identified in the initial extraction of factors allows the
researcher to obtain simple and interpretable factors (Field, 2005). Rotation is chosen differently
depending upon whether the factors are believed to be correlated (i.e., oblique rotation methods)
or uncorrelated (i.e., orthogonal rotation methods). With an oblique rotation, it is possible for
factors to be correlated with each other and variables to be correlated with more than one factor.
This increases the factor structure complexity, but it does allow for more complex relationships
that are certainly a part of the intricate world of human behavior. Oblimin rotation, as the oblique
rotation method that is widely used in social science studies (Field, 2005), was utilized in this

75

factor analysis because the underlying psychological constructs of pro-environmental behaviors
are somewhat correlated (Lindenberg & Steg, 2007; Thøgersen, 2006). Oblimin rotation
generates a simple-structured solution by fining a rotation of the initial extracted factors that
minimizes the cross products of the factor loading (Robins, Fraley, & Krueger, 2009).
As a result of the first EFA, items that were either below the 0.40 loading or were crossloaded on more than one factor were deleted (Hair et al., 2006). One item, “We should recycle
materials and protect the environment to benefit people,” was dropped because of low factor
loading (< 0.4). No items were cross-loaded. A five-factor model emerged with the remaining 14
items. The Kaiser-Meyer-Olkin (KMO) measure of sampling adequacy was 0.901, which
exceeded the acceptable value of 0.6 (Tabachnick & Fidell, 2007). The sample also met the
criteria for factor analysis evidenced by Bartlett’s test of sphericity (

=3,647.95, p < 0.001).

Both measures indicated that factor analysis was appropriate for the data. The results of the
factor analysis are reported in Table 14. They show that the factors appear to represent the five
underlying psychological constructs of recycling behavior with significant loadings ranging from
0.58 to 0.94. Table 14 also showed the presence of five factors with an Eigenvalue exceeding 1.0,
and a cumulative 79.52% of variance explained by this factor solution. Cronbach’s alpha
coefficients were calculated to test the internal consistency reliability of the data set. The
Cronbach’s alpha for each factor ranged from 0.70 to 0.93, suggesting strong internal
consistency (see also Table 14).

76

Table 14 Exploratory factor analysis: Psychological constructs
Factor
loading
Factor 1: Injunctive Social Norms (ISN)
ISN1

0.943

ISN2

0.935

ISN3

0.892

Factor 2: Descriptive Social Norms (DSN)
DSN1

0.809

DSN2

0.799

DSN3

0.693

Factor 3: Hedonic Goals (HG)
HG1

0.853

HG2

0.575

HG3

0.757

Factor 4: Personal Moral Norms (PMN)
PMN1

0.801

PMN2

0.746

PMN3

0.793

Factor 5: Gain Goals (GG)
GG1

0.79

GG3

0.73

Eigenvalue

Variance
explained

Cronbach's α

6.7

47.89

0.93

3.86

13.29

0.70

2.13

8.04

0.83

1.44

5.33

0.87

0.92

4.597

0.81

Note.
1) ISN1=A majority of people in the United States should engage in recycling; ISN2=A majority of people at
tailgating areas at MSU should engage in recycling; ISN3=A majority of people in my residential community
should engage in recycling; DSN1=I think that a majority of people in the United States engage in recycling;
DSN2=I think that a majority of people at tailgating areas at MSU engage in recycling; DSN3=I think that a
majority of people in my community engage in recycling; HG1=I recycle because of the enjoyment that I feel;
HG2=I take satisfaction in recycling; HG3=I recycle because I enjoy learning new skills/techniques; PMN1=I
feel morally obligated to engage in recycling; PMN2=I should do anything I can do to recycle; PMN3=I feel
guilty when I waste recyclable materials; GG1=One of the best things about recycling is that it helps lower costs
to society; GG3=My health and the health of my family may improve because of recycling.
2) Extraction method: principle components analysis with Oblimin rotation and Kaiser normalization; KMO
measure of sampling adequacy = 0.901; Bartlett’s test of sphericity, (91) = 3,647.95, p < 0.001.

77

Factor 1 represented “injunctive social norms” with three items: A majority of people in
the United States should engage in recycling (ISN1); a majority of people at tailgating areas at
MSU should engage in recycling (ISN2); and a majority of people in my residential community
should engage in recycling (ISN3). Factor 2 represented “descriptive social norms” with three
items: I think that a majority of people in the United States engage in recycling (DSN1); I think
that a majority of people at tailgating areas at MSU engage in recycling (DSN2); and I think that
a majority of people in my community engage in recycling (DSN3). Factor 3 represented
“hedonic goals.” Three items were loaded on this dimension: I recycle because of the enjoyment
that I feel (HG1); I take satisfaction in recycling (HG2); and I recycle because I enjoy learning
new skills/techniques (HG3). Factor 4 represented “personal moral norms,” with three items on
this dimension: I feel morally obligated to engage in recycling (PMN1); I should do anything I
can do to recycle (PMN2); and I feel guilty when I waste recyclable materials (PMN3). Finally,
factor 5 represented “gain goals.” Two items were loaded on this dimension: One of the best
things about recycling is that it helps lower costs to society (GG1); and my health and the health
of my family may improve because of recycling (GG3). In summary, the interpretation of the
first EFA, with a five-factor solution, was consistent with the initially proposed five-factor
psychological constructs in the research framework. Following the EFA, items under each factor
were averaged to form five setting-general independent variables in a further regression analysis.

EFA for Perceived Behavioral Difficulty
The second EFA was conducted to reduce the number of measurement items to a smaller
number of factors, prior to regression analysis. The eight items measuring perceived behavioral
difficulty were factor analyzed to examine whether the four items represented perceived

78

behavioral difficulty at home, and another four items represented perceived behavioral difficulty
at the tailgating event setting. An oblique rotation (i.e., Oblimin rotation) was utilized by
assuming the two factors were correlated; an orthogonal rotation method (i.e., Varimax rotation)
yielded the same two-factor solution. The factor analysis was appropriate for the data (KMO
measure of sampling adequacy = 0.825, Bartlett’s test of sphericity [

=4,103.57, p < 0.001]),

and two factors loaded by all eight items were extracted (see Table 15). The results showed that
the factors appear to represent the perceived difficulty of recycling behavior at home and at
tailgating, with significant loadings ranging from 0.93 to 0.97. The results also showed the
presence of two factors with an Eigenvalue exceeding 1.0, explaining 48.15% and 42.84% of the
variance, respectively, and demonstrating a cumulative 90.99% of the variance explained. The
Cronbach’s alpha values for each factor were 0.96 and 0.97, and revealed a strong internal
consistency among items for each factor. Factor 1 represented “perceived behavior difficulty at
home” and Factor 2 represented “perceived behavior difficulty at tailgating.” Following the EFA,
items under each factor were averaged to form two setting-specific independent variables in
further regression analysis.

79

Table 15 Exploratory factor analysis: Perceived behavioral difficulty
Factor

Factor
loading

Factor 1: Perceived behavioral difficulty
at home
non-deposit glass containers
non-deposit aluminum cans
plastic
paper
Factor 2: Perceived behavioral difficulty
at tailgating
non-deposit glass containers
non-deposit aluminum cans
plastic
paper

Eigenvalue

Variance
explained

Cronbach's α

3.852

48.153

0.972

3.472

42.837

0.959

.959
.963
.965
.956

.951
.938
.965
.930

Note.
Extraction method: principle components analysis with Varimax rotation; KMO measure of sampling adequacy =
0.825; Bartlett’s test of sphericity, = 4,103.57, p < 0.001.

EFA for Pro-environmental Behavior
The third EFA was conducted to reduce the number of measurement items to a smaller
number of factors, prior to regression analysis. The eight items measuring recycling behavior
were factor analyzed to examine whether the four recycling items represented environmental
behavior in a daily life setting, and the other four items represented the same behavior in an
event setting. An oblique rotation (i.e., Oblimin rotation) was utilized by assuming the two
factors were correlated; an orthogonal rotation method (i.e., Varimax rotation) yielded the same
two-factor solution. As expected, factor analysis was appropriate for the data, and two factors
loaded by all eight items were extracted (KMO measure of sampling adequacy = 0.785, Bartlett’s
test of sphericity [

=3,039.85, p < 0.001]). The results of factor analysis are reported in Table

16. The results showed that the factors appear to represent recycling behavior at home and at
tailgating, with significant loadings ranging from 0.88 to 0.95. The results also showed the
presence of two factors with an Eigenvalue exceeding 1.0, explaining 56.31% and 26.47% of the
80

variance, respectively, demonstrating a cumulative 82.78% of variance explained. The
Cronbach’s alphas for each factor were 0.92 and 0.94, suggesting strong internal consistency.
Factor 1 represented “recycling behavior at home” and Factor 2 represented “recycling behavior
at tailgating.” Following the EFA, items under each factor were averaged to form two settingspecific dependent variables in further regression analysis.

Table 16 Exploratory factor analysis: Recycling behavior
Factor

Factor
loading

Factor 1: Recycling behavior at home
non-deposit glass containers
non-deposit aluminum cans
plastic
paper

Eigenvalue

Variance
explained

Cronbach's α

4.505

56.308

0.915

2.118

26.471

0.944

.881
.914
.877
.901

Factor 2: Recycling behavior at tailgating
non-deposit glass containers
non-deposit aluminum cans
plastic
paper

.918
.943
.945
.895

Note.
Extraction method: principle components analysis with Oblimin rotation; KMO measure of sampling adequacy =
0.785; Bartlett’s test of sphericity, = 3,039.85, p < 0.001.

Multiple Regression Analysis
In order to assess Research Question 1 with the 12 hypotheses, two separate multiple
regression analyses were conducted to examine and compare the underlying mechanism of proenvironmental behavior in daily life (Model 1), and the mechanism of pro-environmental
behavior in a sport event setting (Model 2).

81

Variable Description
Variables used in two regression models are presented in Table 17. Five psychological
constructs (i.e., personal moral norms, descriptive social norms, injunctive social norms,
hedonic goals, and gain goals) were used as independent variables for hypothesis tests (H1.1a
through H1.1e; and H1.2a through H1.2e), and two demographic variables (i.e., age and gender)
were included as control variables. These seven variables were setting-general variables that
were used in both Model 1 and Model 2. Perceived behavioral difficulty at home (H1.1f) and
perceived behavioral difficulty at tailgating (H1.2f) was used as a setting-specific independent
variable for hypotheses tests in each model. And, habitual environmental behavior at each
setting was included in each counterpart model as an additional control variable. Habitual
environmental behavior at home (or at tailgating) was constructed by averaging two items, “I
look for ways to reuse things at home (or at tailgating)”, and I look for ways to reduce
food/material waste at home (or at tailgating).” These two variables—perceived behavioral
difficulty and habitual environmental behavior—were setting-specific variables that were used in
Models 1 and 2. Age, gender, and habitual environmental behavior were included as control
variables in regression analyses because there was a potential bias in these variables among this
study’s participants (see non-response check in Chapter 3).

82

Table 17 Variables used in regression models
Variable (Hypothesis)
Dependent variable
recycling behavior at home
recycling behavior at tailgating
Independent variable-setting–general
personal moral norms (H1.1a, H1.2a)
descriptive social norms (H1.1b, H1.2b)
injunctive social norms (H1.1c, H1.2c)
hedonic goals (H1.1d, H1.2d)
gain goals (H1.1e, H1.2e)
age
gender
Independent variable-setting–specific
perceived behavioral difficulty at home (H1.1f)
perceived behavioral difficulty at tailgating (H1.2f)
habitual environmental behavior at home
habitual environmental behavior at tailgating

Model 1
at home

Model 2
at tailgating

þ
o

o
þ

þ
þ
þ
þ
þ
þ
þ

þ
þ
þ
þ
þ
þ
þ

þ
o
þ
o

o
þ
o
þ

Since gender is a categorical variable with two different categories, one dummy variable
was created to be included in the regression model. A dummy variable, MALE, was created and
assigned a value of one when the survey participant’s gender was male, and a value of zero when
the participant gender was female. Before performing the regression analyses, the researcher
examined associations among variables. Pearson’s correlations were examined to assess the
association between pro-environmental behaviors, with the variables measured by a Likert-type
scale: personal moral norms (PMN), descriptive social norms (DSN), injunctive social norms
(DSN), hedonic goals (HG), gain goals (GG), perceived behavioral difficulty (PBD), and
habitual environmental behavior (HABIT), and the continuous variable of AGE. With the
dummy variable, MALE, a group comparison was conducted.

83

Table 18 Descriptive statistics of variables in regression analysis
Variable (Descriptive statistics)

Relationship
Pearson’s correlation coefficient
Recycling at home
Recycling at tailgating
.503**
.245**
.221**
.367**
**
.388
.200**

PMN
DSN
ISN

(mean=4.19, S.D.=0.85)
(mean=2.98, S.D.=0.81)
(mean=4.29, S.D.=0.93)

HG

(mean=3.43, S.D.=1.04)

.451**

.307**

GG

(mean=3.84, S.D.=1.04)

.369**

.241**

PBD_H
PBD_T
HABIT_H
HABIT_T
AGE

(mean=2.38, S.D.=1.53)
(mean=3.09, S.D.=1.25)
(mean=4.09, S.D.=0.96)
(mean=3.13, S.D.=1.24)
(mean=42.88, S.D.=15.93)

-.139**

.062

.200
.514**

-.072
.218**

.171**
.052

.569**
.066

MALE

(44.9%)

t-value
Recycling at home
Recycling at tailgating
-1.45
-1.38

Note.
1) PMN=personal moral norms; DSN=descriptive social norms; ISN=injunctive social norms; HG=hedonic goals,
GG=gain goals, PBD_H=perceived behavioral difficulty at home, PBD_T=perceived behavioral difficulty at
tailgating, HABIT_H=habitual environmental behavior at home, HABIT_T=habitual environmental behavior at
tailgating. All variables listed were measured with a 5-point Likert type scale.
2) MALE has a value of 1 for males and 0 for females
3) **Significance level of 1% (2-tailed).

Results of correlation analyses revealed the presence of relationships between recycling
behavior at home with independent variables PMN, DSN, ISN, HG, GG, PBD_H and HABIT.
Similarly, correlations between recycling behavior at tailgating with independent variables PMN,
DSN, ISN, HG, GG, and HABIT were significant, while the correlation between the recycling
behavior at tailgating and PBD_T was not statistically significant. There were no significant
correlations between recycling behavior and AGE (see Table 18). To investigate group
differences of the MALE variable, an independent t-test was conducted. The results showed no
significant difference (p-value was greater than 0.05). Although the mean difference of recycling
behaviors was not statistically significant, patterns of the mean difference were observed by
84

looking at t-values (see Table 18). MALE has a value of 1 for males and 0 for females, and the
negative t-value means that the males’ mean values of recycling at home (mean=4.15) and
recycling at tailgating (mean=2.97) were lower than females’ mean values of recycling at home
(mean=4.31) and recycling at tailgating (mean=3.15).

Assumptions Test
The assumptions assessed for conducting multiple regression analysis were: normality,
linearity; homoscedasticity; independence of errors; and absence of multicollinearity. The
assumptions were met for regression analysis:
The normality assumption was examined in terms of skewness and kurtosis. Skewness
within the range -3 to 3, and a kurtosis score of -10 to 10 are considered acceptable (Kline, 1998).
All items measuring the independent and dependent variables used in the analyses of this section
were determined to be within these acceptable ranges, denoting that there were no significant
indications of non-normality (see Appendix K). The assumption of linearity refers to a straightline relationship between two variables, and linearity was diagnosed from bivariate scatterplots
between pairs of study variables (Tabachnick & Fidell, 2007). All the variables entered in the
analyses showed linearity. The assumption of homoscedasticity means that the variance of the
residual terms should be constant at each level of the predictor variable(s) (Field, 2005).
Homoscedasticity can be checked by case-wise diagnostics. In case-wise diagnostics, it is
expected that 95% of the cases in the data will have standardized residuals within ±2.5. In this
study, fewer than 5% of the cases were outside of standardized residuals ±2.5. The assumption of
independent errors means that the residual terms should be uncorrelated for any two
observations. Independence of errors can be checked with the Durbin-Watson test, which tests

85

for serial correlation between errors. A test statistic between 0 and 4 means that the residuals are
uncorrelated (Fields, 2005). In this study, the values of a Durbin-Watson test ranged between 1
and 3, which met the assumption for independent errors. Finally, the assumption of an absence of
multicollinearity is that there is no strong correlation between two or more predictors in a
regression model (Field, 2005). Existence of multicollinearity could produce unstable estimates
in regression analysis. The assumption of an absence of multicollinearity was assessed by
obtaining correlation coefficients for each of the predictor variables. Correlation coefficients of
above 0.8 indicate the possible existence of multicollinearity. The correlation coefficients among
all the predictors in this study are below 0.8, which met this assumption. The variance inflation
factor (VIF) of all the variables was also assessed. All VIF values ranged between 1.02and 2.88,
which is below the commonly recommended cut-off threshold of 5-10. Accordingly, the
assumption of absence of multicollinearity was met.

Hypothesis Test
Using the independent variables described above, two separate multiple regression
analyses were conducted to examine the underlying mechanism of recycling behavior at home
(Model 1), and the mechanism of recycling behavior at tailgating (Model 2). Results of the two
regression models are presented in Table 19.

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Table 19 Results of regression full models for recycling behavior at home and at tailgating
Model 1
Standardized coefficients
Variable
PMN
DSN
ISN
HG
GG
PBD
HABIT
MALE
AGE

β
.183**
.057
-0.13
.144*
.026
-.151***
.352***
.010
-.010

t-value
p-value
2.672
.009
1.256
.210
-.216
.829
2.387
.017
.433
.665
-3.525
.000
7.122
.000
.247
.805
-.241
.809
Model Fit:
= .370, adjusted
= .355
F = 24.452, p < .001

Model 2
Standardized coefficients
β
-.040
.175***
.010
.080
.034
-.044
.503***
-.058
.012

t-value
p-value
-.606
.545
3.914
.000
.155
.996
1.371
.171
.599
.550
-1.064
.288
11.317
.000
-1.406
.161
.289
.773
Model Fit:
= .385, adjusted
= .370
F = 26.249, p < .001

Note.
1) PMN=personal moral norm; DSN=descriptive social norms; ISN=injunctive social norms; HG=hedonic goals;
GG=gain goals; PBD=perceived behavioral difficulty; HABIT=habitual environmental behavior; MALE=study
participant’s gender is male, AGE=study participant’s age
2) Significance levels: * < .05, ** < .01, *** < .001

Model 1 explained approximately 36% of the variability in study participants’ recycling
behavior at home (

= .370, adjusted

= .355, F = 24.452, p < .001). For testing the

hypotheses in Model 1, personal moral norms (H1.1a: β = .183, t = 2.672, p < .01), and hedonic
goals (H1.1d: β = .144, t = 2.287, p < .05) positively influenced, and perceived behavioral
difficulty (H1.1f: β = -.151, t = -3.525, p < .001) negatively influenced sport event attendees’
recycling behavior at home. Out of six hypotheses in Model 1, three hypotheses, H1.1b (effect of
descriptive social norms on recycling behavior at home), H1.1c (effect of injunctive social norms
on recycling behavior at home), and H1.1e (effect of gain goals on recycling behavior at home)
were rejected.
Model 2 explained approximately 37% of the variability in study participants’ recycling
behavior at tailgating (

= .385, adjusted

= .370, F = 26.249, p < .001). For testing
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hypotheses in Model 2, descriptive social norms (H1.2b: β = .175, t = 3.914, p < .001) positively
influenced study participants’ recycling behavior at tailgating. Out of the six hypotheses, the
remaining five hypotheses, H1.2a (effect of personal moral norms), H1.2c (effect of injunctive
social norms), H1.2d (effect of hedonic goals), H1.2e (effect of gain goals), and H1.2f (effect of
perceived behavioral difficulty) were not supported.
Among the control variables, habitual environmental behavior had a significant influence
on recycling behavior both at home (β = .352, t = 7.122, p < .001) and at tailgating (β = .503, t =
11.317, p < .001). Yet, gender and age did not have a significant influence on recycling behavior
either at home or at tailgating.
This study also performed multiple regressions, including only the significant variables:
PMN, HG, PBD, and HABIT in Model 1 (see Table 20), DSN, and HABIT in Model 2 (see Table
21). The results were unchanged in terms of the model significance. The adjusted R-square was
slightly increased in both Model 1 (adjusted
reduced model), and Model 2 (adjusted

= .355 in full model, adjusted
= .370 in full model, adjusted

= .362 in

= .376 in reduced

model). Thus, for any practical application, the reduced models may be recommended over the
full models.

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Table 20 Results of reduced regression model for recycling behavior at home
Model 1
Standardized coefficients
Variable
PMN
HG
PBD
HABIT
Model Fit:

= .368, adjusted

β
t-value
3.394**
.191
3.303**
.164
-3.358**
-.138
7.373***
.354
= .362, F = 55.667, p < .001

p-value
.001
.002
.001
.000

Note.
1) PMN=personal moral norms, HG=hedonic goals, PBD=perceived behavioral difficulty, HABIT=habitual
environmental behavior.
2) Significance levels: * < .05, ** < .01, *** < .001

Table 21 Results of reduced regression model for recycling behavior at tailgating
Model 2
Standardized coefficients
Variable
DSN
HABIT
Model Fit:

= .379, adjusted

β
t-value
5.491***
.227
12.341***
.509
= .376, F = 121.562, p < .001

p-value
.000
.000

Note.
1) DSN=descriptive social norms, HABIT=habitual environmental behavior.
2) Significance levels: * < .05, ** < .01, *** < .001

In summary, the results of the two separate regression models indicated that the
underlying mechanism of recycling behavior in daily life was differentiated from the same
recycling behavior in an event setting. Higher levels for moral norms and hedonic goals, with
lower levels of perceived behavioral difficulty suggested a higher level of behavioral
engagement for recycling in a daily life setting. However, those variables predicting recycling
behavior in daily life did not contribute to predicting recycling behavior in an event setting. In a
sport event setting, higher levels of descriptive social norms suggested sport event attendees’
have higher levels of engagement in recycling behavior.

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Research Question 2
The purpose of this section is to answer Research Question 2, “Are sport event attendee’s
differences in pro-environmental behavior between in daily life and in an event setting affected
by event attendees’ characteristics (i.e., event attendee perceived destination environmental
responsibility, event attendee type, recreation involvement, and experience-use history)? To
answer the research question, this section addresses the following:
1. Dependent and independent variables were constructed. The assumptions for
conducting analyses were assessed. The mean and standard deviation values were
calculated and compared before conducting the analyses.
2. A paired-sample t-test was conducted to examine the first hypothesis, which
assesses whether sport event attendees’ engagement in pro-environmental
behavior decreases in an event setting, compared to their behavior in their daily
life setting.
3. A mixed between-within subjects ANOVA was employed, which combines a
between-subjects ANOVA and repeated measures ANOVA into one analysis.
Four separate mixed between-within subjects ANOVAs were conducted to
examine the rest of the four hypotheses under Research Question 2.

Paired-sample t-test
As described in the measurement section in Chapter 3, pro-environmental behavior (PEB)
was measured with four items of recycling in a daily life setting, and also repeatedly measured
with the four items of recycling in the sport event setting. Four separate paired-sample t-tests
were conducted with the four repeatedly measured items. One-tailed tests were performed to

90

examine whether the means of PEB at home were larger than the means of PEB at tailgating. The
normality assumption for paired-sample t-tests was examined in terms of skewness and kurtosis.
There were no significant indications of non-normality with the eight dependent variables used
in the analyses (see Appendix K).
Results showed that study participants’ mean score with the item of “recycling nondeposit glass” was smaller in an event setting (Mean = 3.18, S.D. = 1.47) compared to their
behavior in a daily life setting (Mean = 4.18, S.D. = 1.31), t (404) = 12.553, p < 0.001 (onetailed), Cohen’s d = 0.713. An effect size of Cohen’s d is a measure that describes the magnitude
of the difference between two means, and Cohen (1988) proposed rules of thumb for interpreting
effect size of Cohen’s d: a “small” effect size is .20, a “medium” effect size is .50, and a “large”
effect size is .80.The first t-test result indicated that mean score of pro-environmental behavior
decreased in the event setting compared to in daily life setting with a medium effect size.
between two settings were Study participants’ mean score with the item of “recycling
non-deposit aluminum cans” was smaller in an event setting (Mean = 3.21, S.D. = 1.48)
compared to their behavior in a daily life setting (Mean = 4.30, S.D. = 1.24), t (404) = 14.177, p
< 0.001 (one-tailed), d = 0.800. The mean score for the item “recycling plastic” was also smaller
in an event setting (Mean = 3.06, S.D. = 1.42) compared to their behavior in a daily life setting
(Mean = 4.29, S.D. = 1.60), t (404) = 16.463, p < 0.001 (one-tailed), Cohen’s d = 0.974. Finally,
the mean score with the item “recycling paper” was smaller in an event setting too (Mean = 2.83,
S.D. = 1.45) compared to their behavior in a daily life setting (Mean = 4.20, S.D. = 1.25), t (404)
= 17.566, p < 0.001 (one-tailed), Cohen’s d = 1.019. Together, the paired-sample t-tests
supported hypotheses 2.1.

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Mixed between-within subjects ANOVA
Mixed between-within subjects ANOVA was employed to examine whether recycling
behavior scores changed across at home and at tailgating settings differently, depending on the
event attendees’ characteristics.

Variable Description
Study participants’ pro-environmental behavior (PEB) was a dependent variable in the
mixed between-within subjects ANOVA. A 2 x 2 mixed between-within subjects ANOVA
(mixed ANOVA hereafter) is one that has two levels between a factor (e.g., event attendee type:
Locals and Tourists) and two levels within a factor (e.g., setting: home and tailgating). Four
separate mixed ANOVAs were conducted. The dependent variable and the within factor
remained the same across all four analyses. Four between factors (i.e., event attendee perceived
destination environmental responsibility, event attendee type, recreation involvement, and
experience-use history) were used for each of the mixed ANOVAs. Ancillary variables (i.e., age
and gender as the demographic characteristics, personal moral norms as the psychographic
characteristic) were examined to look at any potential associations between these variables and
recycling behavior before conducting the main analyses.
Dependent Variable and Within-Factor. Pro-environmental behavior (PEB) was
constructed and used as a dependent variable. As described in the measurement section in
Chapter 3, PEB was measured with four items of recycling in the daily life setting, and also
repeatedly measured with the four items of recycling in the sport event setting. To reduce the
number of measurement items to a smaller number of factors, the eight items were factor
analyzed (see the results of the third EFA in the section on Research Question 1 in this chapter).

92

As a result of the EFA, two factors loaded by all eight items were extracted: Factor 1 represents
“recycling behavior at home” and Factor 2 represents “recycling behavior at tailgating.” Items
under each factor were averaged to form a dependent variable measured repeatedly across two
settings (SET), and used in further analyses.
Between-Factor: Destination Environmental Responsibility (DER). DER was
constructed and used as a between-factor for the first mixed ANOVA. As described in the
measurement section in Chapter 3, DER was measured with three items. The three items were
originally measured using a Likert-type scale, and were then converted into a categorical variable
for the analysis. The primary purpose of this analysis was to assess whether behavioral
inconsistency across settings was associated with DER. For purposes of analysis, study
participants were divided into two groups. Specifically, study participants were split into “Low”
and “High” groups using a median split. A median split is one method for turning a continuous
variable into a categorical one. The three items, measured on a 5-point Likert-type scale, were
first averaged. Then, study participants who scored at or below the median value (=3.0) were put
in the category “Low,” while participants who scored above the median were categorized in the
“High” group. As a result of dichotomization, a between-factor with two groups, Low DER
(n=228) and High DER (n=176), was constructed and used in the first 2 x 2 mixed ANOVA.
Between-Factor: Sport Event Attendee Type (TYPE). TYPE was constructed and used as
a between-factor for the second mixed ANOVA. As described in the measurement section in
Chapter 3, study participants were categorized into two groups—tourist and non-tourist—
utilizing zip codes and MSU affiliation. “Tourists” refers to those tailgaters who were neither
local residents living in the vicinity of the destination (i.e., Clinton, Eaton, or Ingham County),
nor MSU community members; all others were categorized as “Non-tourist.” A between-factor

93

with two groups, Tourist TYPE (n=124) and Non-tourist TYPE (n=281) was constructed and
used in the second 2 x 2 mixed ANOVA.
Between-Factor: Recreation Involvement (RI). RI was constructed and used as a
between-factor for the third mixed ANOVA. As described in the measurement section in Chapter
3, RI was measured with five items using a Likert-type scale. Like the DER variable construction
described above, the five items were averaged and converted into a categorical variable by
following the median-split process. Study participants who scored at or below the median value
(=3.2) were put in the category “Low,” while participants who scored above the median were
categorized in the “High” group. As a result of dichotomization, a between-factor with two
groups, Low RI (n=216) and High RI (n=188), was constructed and used in the third 2 x 2 mixed
ANOVA.
Between-Factor: Experience-use History (EUH). EUH was constructed and used as a
between-factor for the fourth mixed ANOVA. As described in the measurement section in
Chapter 3, EUH was measured with four items. Following the suggestion by Hammitt and his
colleagues (2004), study participants were categorized into four groups—Veterans, Locals,
Visitors, and Beginners. Specific steps included: (1) summing the years of activity at the study
site with the frequency of the last three years’ activity at the study site, then divided into “Low”
and “High” groups on the median value of the sum. Medians are used as the bases of
segmentation rather than means because of some outlier values for some extremely experienced
recreationists; (2) following the same procedure for activity at other sites; and (3) identifying
four combinations of EUH groups (see Figure 5).

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Experience at study site

Experience
at other sites

High

Low

High

Veterans

Visitors

Low

Locals

Beginners

Figure 5 Four groups in experience-use history

As a result, a between-factor with four groups, Veterans EUH (n=86), Locals EUH
(n=89), Visitors EUH (n=45), and Beginners EUH (n=145), was constructed and used in the
fourth 4 x 2 mixed ANOVA. Variables used in four mixed between-within subjects ANOVAs
are presented in Table 22.

Table 22 Variables in mixed between-within subjects ANOVAs
Analysis
(Hypothesis)

Dependent
variable

Withinsubject factor

Analysis 1
(H2.2)
Analysis 2
(H2.3)
Analysis 3
(H2.4)

Between-subject factor
DER: Low and High group

Self-reported
proenvironmental
behavior (PEB)

Setting (SET):
at-home and
at-tailgating

TYPE: Tourist and Non-tourist
RI: Low and High group
EUH: Veterans, Locals, Visitors, and
Beginners

Analysis 4
(H2.5)

Note.
DER=destination environmental responsibility; TYPE=event attendee type; RI=recreation involvement; and
EUH=experience-use history

95

Assumptions Test
The assumptions that were assessed for conducting mixed between-within ANOVAs
were: normality, homogeneity of variance, and homogeneity of covariance. The assumptions
were met for the analysis.
The normality assumption was examined in terms of skewness and kurtosis of the
dependent variable. A skewness within the range -3 to 3 and a kurtosis score of -10 to 10 are
considered acceptable (Kline, 1998). Dependent variables used in the analyses in this section
were determined to be within these acceptable ranges, indicating there were no significant
indications of non-normality (see Appendix K). Levene’s Test of Equality of Variances was
conducted to assess that the variances of each variable are equal across groups (homogeneity of
variance). If Levene’s test is significant, with p < 0.05 as a criterion, this means that the
assumption is violated (Field, 2005). If the assumption of homogeneity of variance is violated,
the researcher could not be confident that the observed mean differences were attributable to the
group categories. For all employed analyses, the Levene’s tests were not significant with a p >
0.05, suggesting the assumptions of equal variance were met. Box’s Test of Equality of
Covariance Matrices (Box’s M) was performed to check the assumption of homogeneity of
covariance across the groups, using p < 0.001 as a criterion. For all analyses of this section,
none of the Box’s M were significant (all ps > 0.001), suggesting that the assumptions of
homogeneity of covariance were met.

Preliminary Analysis
Preliminary analyses were conducted to examine: whether study participants’
demographic (i.e., age and gender), psychographic (i.e., personal moral norms), and behavioral

96

(recycling behavior at home) profiles differed between groups in each of the between-subject
factors (see Table 23.). If study participants in one group (e.g., High DER) did not differ from the
participants in another group (e.g., Low DER) in their profiles, then the research would draw a
conclusion from the results of the mixed ANOVA: that the amount of variability in the
dependent variable can be explained by the independent variable, and would not be affected by
other factors such as age and gender.

Table 23 Demographic, psychographic, and behavioral profiles between groups

Age (Mean)

Male (%)

Mean (S.D.)

Recycling
behavior
at home
Mean (S.D.)

43
43

44%
46%

3.90 (1.00)
4.06 (0.91)

4.18 (1.21)
4.32 (0.96)

49
40

46%
42%

4.02 (0.93)
3.87 (1.03)

4.28 (1.08)
4.16 (1.17)

43
43

40%
51%

3.91 (0.97)
4.05 (0.96)

4.17 (1.16)
4.32 (1.04)

53
47
43
36

63%
43%
44%
46%

3.80 (1.03)
3.97 (1.06)
4.14 (0.76)
4.03 (0.88)

4.28 (1.17)
4.28 (1.00)
4.44 (1.05)
4.28 (1.05)

Demographics

DER
Low (n=228)
High (n=176)
TYPE
Tourist (n=124)
Non-tourist (n=281)
RI
Low (n=216)
High (n=188)
EUH
Veterans (n=86)
Locals (n=89)
Visitors (n=45)
Beginners (n=145)

Personal
moral norms

Note.
DER=destination environmental responsibility; TYPE=event attendee type; RI=recreation involvement; and
EUH=experience use history

First, the means of age between/among groups were compared by employing independent
sample t-tests (two-tailed) for two group comparisons, and a one-way ANOVA for four group
comparisons. The results showed that study participants’ age was not significantly different

97

between Low and High DER (t [398] = 0.126, p = 0.900) or between Low and High RI (t [398] =
0.024, p = 0.981); yet the means of age were significantly different between Tourist and Nontourist TYPE (t [399] = -5.559, p < .05), and among EUH groups (F [3, 357] = 26.736, p < .05).
Second, percentages of male participants between/among groups were compared by employing
chi-square tests (two-tailed). The results showed that gender profiles were not significantly
different between Low and High DER groups (
Tourist and Non-tourist TYPE (

[2, N=404] = 0.119, p = 0.730), or between

[2, N=405] = 0.651, p = 0.420); the percentages of gender

profile were significantly different, however, between Low and High RI (
p < 0.05), and among EUH groups (

[2, N=404] = 4.668,

[4, N=365] = 12.783, p < 0.05). Third, the means of

personal moral norms between/among groups were compared. The results showed that study
participants’ personal moral norms scores were not significantly different: between Low and
High DER (t [399] = -1.578, p = 0.115); between Tourist and Non-tourist TYPE (t [400] = 1.468,
p = 0.143); between Low and High RI (t [399] = -1.385, p = 0.167); and among EUH groups (F
[3, 359] = 1.529, p = 0.207). Fourth, the means of recycling behavior at home between/among
groups were compared. The results showed that study participants’ recycling behavior scores in
daily life were not significantly different: between Low and High DER (t [402] = -1.223, p =
0.222); between Tourist and Non-tourist TYPE (t [403] = 0.974, p = 0.331); between Low and
High RI (t [402] = -1.407, p = .160 and among EUH groups (F [3, 361] = 0.487, p = 0.691).
Result of the preliminary analyses showed that study participants between groups with
four between-subjects factors were homogeneous in terms of their personal moral norms, and
recycling behavior at home. However, those study participants who were tourists (i.e., Tourist
TYPE) and who were experienced tailgaters (i.e., Veterans EUH) were slightly older compared to
non-tourists and less experienced tailgaters. Also, more male participants were observed in the
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high involvement group (i.e., High RI) and as experienced tailgaters (i.e., Veterans EUH). From
these results, age and gender were known to potentially influence the dependent variable.
Accordingly, the researcher controlled for the influence of age and gender by including them as a
covariance in the main analyses.

Hypothesis Test
Four mixed between-within subjects ANOVAs were conducted to test the hypotheses
H2.2 through H2.5. For each model of the mixed ANOVAs, Wilk’s Lambda (Λ) test was
employed to assess the model fit. Partial eta squared (

was reported as a measure of effect

size for group mean differences to show the proportion of variance in the dependent variable
explained by the independent variable. A commonly used guideline for interpreting effect size
was applied, where .01 indicated a small effect, .06 indicated a moderate effect, and .14 indicated
a large effect (Cohen, 1988). The interaction effect was assessed to test whether the impact of
within-subject factors (i.e., SET) on recycling behavior are influenced by between-subject factors
(e.g., DER), after controlling for the effects of participants’ age and gender. If no significant
interaction effects were observed, then the main effects of the between-subject factor and the
within-subject factor would be assessed. If significant interaction effects were observed, the
researcher can conclude that event attendees are less likely to engage in pro-environmental
behavior at an event setting compared to an in-home setting. However, the degree of such change
is different, depending on attendee characteristics such as their perception about destination
environmental responsibility.

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The first 2 x 2 (DER x SET) mixed between-within subjects ANOVA was conducted to
evaluate the impact of Destination Environmental Responsibility (DER: Low and High group) on
study participants’ recycling behavior scores across SET (at home and at tailgating), after
controlling for the effects of age and gender. Results showed that the main effect of DER (F [1,
396] = 34.410, p < 0.001,

= 0.080) and the main effect of SET (F [1, 396] = 34.272, p < 0.001,

= 0.080) were significant. The results also revealed a significant interaction between DER and
SET with a moderate effect size (Λ = 0.914, F [1, 396] = 37.367, p < 0.001,

= 0.086). There

was no effect of age and gender on behavioral change across settings. As illustrated in Figure 6,
study participants’ recycling behavior scores at tailgating decreased compared to their behavior
at home, and the rates of decrease were different between the Low and High DER groups.

Figure 6 Recycling scores at home and at a tailgating setting between DER groups

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The second 2 x 2 (TYPE x SET) mixed between-within subjects ANOVA was conducted
to evaluate the impact of Sport Event Attendee Type (TYPE: Tourist and Non-tourist) on study
participants’ recycling behavior scores across SET (at home and at tailgating), after controlling
for the effects of age and gender. Results showed that there was no main effect of TYPE on
recycling behavior (F [1, 397] = 0.045, p = 0.882), although the main effect of SET (F [1, 397] =
23.039, p < 0.001,

= 0.055) was significant. There was a significant interaction between TYPE

and SET with a moderate effect size (Λ = 0.987, F [1, 397] = 5.311, p < 0.05,

= 0. 13). Age

and gender had no effect on behavioral change across settings. As illustrated in Figure7, study
participants’ recycling behavior scores at tailgating decreased compared to their behavior at
home, and the rates of decrease were different between the Tourist and Non-tourist groups.

Figure 7 Recycling scores at home and at a tailgating setting between Tourist and Non-tourist
groups

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The third 2 x 2 (RI x SET) mixed between-within subjects ANOVA was conducted to
evaluate the impact of Recreation Involvement (RI: Low and High group) on study participants’
recycling behavior scores across SET (at home and at tailgating), after controlling for the effects
of age and gender. Results showed that there was no main effect of RI on recycling behavior (F
[1, 396] = 0.989, p = 0.321), although the main effect of SET (F [1, 396] = 36.754, p < 0.001,
= 0.085) was significant. The result revealed no significant interaction between RI and SET (Λ =
0.998, F [1, 396] = 0.827, p = .364). As illustrated in Figure 8, study participants’ recycling
behavior scores at tailgating decreased compared to their behavior at home, yet the decreasing
rates were not different between the Low and High RI groups.

Figure 8 Recycling scores at home and at a tailgating setting between RI groups

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The fourth 4 x 2 (EUH x SET) mixed between-within subjects ANOVA was conducted to
evaluate the impact of Experience-use History (EUH: Veterans, Locals, Visitors, Beginners) on
study participants’ recycling behavior scores across SET (at home and at tailgating), after
controlling for the effects of age and gender. Results showed that there was no main effect of
EUH on recycling behavior (F [3, 355] = 1.393, p = 0.245), although the main effect of SET (F
[3, 355] = 37.513, p < 0.001,

= 0.096) was significant. The result revealed no significant

interaction between EUH and SET (Λ = 0.987, F [3, 355] = 1.516, p = .210). As illustrated in
Figure 9, study participants’ recycling behavior scores at tailgating decreased compared to their
behavior at home, yet the decreasing rates were not different among EUH groups.

Figure 9 Recycling scores at home and at a tailgating setting between EUH groups

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In summary, results of the paired-sample t-tests, and four separate mixed between-within
subjects ANOVAs indicated that study participants’ self-reported recycling behavior scores
decreased from home to the tailgating setting. Further, they decreased more dramatically among
those participants who had a low level of perceived destination environmental responsibility, and
among those who were in the Non-tourist group. However, the decreasing rate of recycling
behavior scores from home to a tailgating setting did not differ depending on recreation
involvement and experience-use history.

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CHAPTER 5
DISCUSSION AND CONCLUSION

This chapter presents five sections: (1) Summary of the Study; (2) Discussion of Key
Findings and Theoretical Contributions; (3) Managerial Implications; (4) Limitations and Future
studies; and (5) Conclusion.

Summary of the Study
The main purpose of the study was to develop and test a model to understand whether
and how sport event attendees are motivated to engage in pro-environmental behavior in their
daily lives versus in a tourism setting. The study employed a quantitative research method
approach. On-site intercept surveys were administered on the first three MSU home football
game days in the fall of 2013 to target sport event attendees, or tailgaters. This was followed by
online and postal follow-up surveys. Self-administered surveys were utilized to measure
tailgaters’ demographic and tailgating characteristics, underlying psychological constructs of
environmental behaviors, and self-reported pro-environmental behavior in daily life and in event
settings. A total of 405 responses were used for statistical analyses and two research questions
with 17 hypotheses were examined.
To answer the first research question, this study examined the linear relationships among
personal moral norms, descriptive social norms, injunctive social norms, hedonic goals, gain
goals, perceived behavioral difficulty, and recycling behavior in daily life and in a sport event
setting. Two regression models were analyzed to examine different effects of psychological
constructs on environmental behavior across different settings. The results support the idea that

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underlying mechanisms of environmental behavior differ between two settings. Specifically, the
first regression model suggested that personal moral norms (H1.1a), hedonic goals (H1.1d), and
perceived behavioral difficulty (H1.1f) have effects on pro-environmental behavior in daily life,
and these effects were independent of any effects of age, gender, and habitual environmental
behavior, as controlled for in the analysis. There were not significant effects of descriptive social
norms (H1.1b), injunctive social norms (H1.1c), and gain goals (H1.1e) on pro-environmental
behavior in daily life. The second regression model suggested that descriptive social norms
(H1.2b) as an underlying motive contributed significantly to the variance of environmental
behavior in the sport event setting, after controlling for age, gender, and habitual environmental
behavior. There were no significant effects of personal moral norms (H1.2a), injunctive social
norms (H1.2c), hedonic goals (H1.2d), gain goals (H1.2e), and perceived behavioral difficulty
(H1.2f). The two separate regression models together answered the first research question. Study
findings suggest that predictive roles of select psychological constructs of environmental
behavior differ in predicting sport event attendee’s pro-environmental behavior in this context.
An additional purpose of the present study was to examine whether sport event attendees’
characteristics (i.e., event attendee perceived destination environmental responsibility, event
attendee types, recreation involvement, and experience-use history) moderate the behavioral
inconsistency across two different settings. This study assessed the behavioral discrepancy
between daily life and a sport event setting by employing paired sample t-tests. The study
further examined the interaction effects of event attendees’ characteristics and behavioral
settings on discrepancies in pro-environmental behavior in both settings by employing mixed
between-within subjects ANOVAs. The results revealed that attendees’ engagement in proenvironmental behavior decreases in an event setting compared to that of their daily life setting

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(H2.1). Furthermore, the degree of decrease in sport event attendees’ pro-environmental behavior
from daily life to the event setting is different depending on event attendees’ perception of
destination environmental responsibility (H2.2) and event attendee types (H2.3). Recreation
involvement (H2.4) and experience-use history (H2.5) were not found to have moderate effects
on the behavioral discrepancy between in daily life versus in a tourism context. These findings
suggested that sport event attendees were less likely to engage in pro-environmental behavior in
event settings compared to their behavior in daily life, and this behavioral discrepancy was
moderated by event attendees’ characteristics.

Discussion of Key Findings and Theoretical Contributions

Psychological Constructs of Environmental Behavior
The first regression analysis indicated that personal moral norms and perceived
behavioral difficulty were underlying motives of pro-environmental behaviors in daily life. This
finding is consistent with those reported by a number of other studies (e.g., Andersson & von
Borgstede, 2010; Do Valle, Rebelo, Reis, & Menezes, 2005; Ramayah, Lee, & Lim, 2012;
Sidique, Lupi, & Joshi, 2010 for recycling behavior). Together with previous literature this study
suggests that the Norm Activation Model and Value-Belief-Norm theory appear to be successful
in explaining the role of personal moral norms as a mechanism of pro-environmental behavior in
a daily life setting. Another stream of previous research explains that the explanatory power of
personal moral norms in environmental behavior engagement becomes insignificant when
behavior change is costly in terms of effort, inconvenience, money, or time (e.g., Guagnano et al.,

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1995; Diekmann & Preisendörfer, 2003). Where recycling at home is generally considered a
“low-cost” behavior (Diekmann & Preisendörfer, 2003), this same behavior in a tourism context
may require increased effort and become a “high-cost” action. This study finding still supports
the previous literature addressing that personal moral norms and perceived behavioral difficulty
together are underlying mechanisms of environmental behavior in the context of daily life.
However at the same time, these two underlying motives become insignificant when predicting
the same behavior in a tourism context. Therefore, the previous research is supported that
personal moral norms and perceived behavioral difficulty are underlying mechanisms of
environmental behavior when the behavior requires low cost.
Another finding from the first regression analysis is that hedonic goals also play an
important role in predicting environmental behavior in individuals’ daily life. In the
environmental behavior research, relationships between affect and environmental behavior have
been addressed in a few studies (e.g., De Young, 1986; Hartmann & Apaolaza-Ibáñez, 2008;
Vining & Ebreo, 1990). Yet, empirical study findings about the hedonic goals as underlying
mechanisms of environmental behavior are inconclusive. Some researchers found that the
emotional motive is significantly related to environmental behavior, even when moral norms are
controlled for (e.g., Smith, Haugtvedt, & Petty, 1994). Other researchers discuss that emotional
benefit has effects on environmental behavior only when the behavior is difficult to undertake
(see Lindenberg & Steg, 2007 for discussion). On a continuing discussion about the role of
hedonic goals in predicting environmental behavior, this finding provides support that they are
important underlying motives of pro-environmental behavior in daily life, even after controlling
for moral norms and perceived behavioral difficultly. The present study provides strong
empirical evidence that individuals’ decisions about pro-environmental behavior are influenced

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by different underlying motives as the Goal-framing theory addresses. This study expands upon
previous literature on environmental behavior studies by integrating and testing multiple
motivational goal frames in a single model.
Furthermore, the first regression result suggests that subjects internalize norms. In the
first regression model, hedonic goals explain variance in pro-environmental behavior in daily life
in addition to personal moral norms. This finding suggests that personal moral norms and
hedonic goals are distinct motives yet lie on the continuum of norm internalization. Deci and
Ryan (1985) provide insight with their self-determination theory. This theory suggests that an
individual’s motives underlying a behavior progress from intrinsic to extrinsic along a continuum.
An intrinsically motivated behavior refers to behavior that is performed for the sole pleasure and
satisfaction derived from its practice. An extrinsically motivated behavior is done for
instrumental reasons, which is again further specified from self-determined extrinsic motivation
(e.g., personal moral norms) to non-self-determined extrinsic motivation (e.g., regulation,
incentives). Applying the logic of this continuum to environmental behavior domain, easy proenvironmental behaviors are predicted by relatively low level of self-determination (i.e., motives
toward the intrinsic end), whereas more difficult behaviors were predicted by higher levels of
self-determination (i.e., motives toward the extrinsic end) (Green-Demers, Pelletier, & Ménard,
1997; Pelletier et al., 1998). This study finding offers a plausible explanation to the continuum of
norm internalization. In other words, it is logical to expect that predictive powers of both
personal moral norms and hedonic goals on environmental behavior are likely to be observed
together if recycling behavior at home is determined by motives at the intrinsic end on a
continuum.

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The results of the second regression suggest that descriptive social norms significantly
influence environmental behavior in sport event settings yet do not affect environmental
behavior in daily life settings. Alternatively, personal moral norms, hedonic goals, and perceived
behavioral difficulty do not have significant effects on environmental behavior in event settings.
This finding is a noteworthy because different types of norms have distinct roles in predicting
environmental behavior across two settings. Researchers recognized different types of norms
(Deci & Ryan, 1985; Park & Smith, 2007; Perkins & Berkowitz, 1986; Thøgersen, 2006), and
different norms have been studied in separate behavioral and contextual domains. Some
researchers focus on personal moral norms to explain environmental behavior (e.g., Nordlund &
Garvill, 2002; Schwartz, 1977; Stern, 2000; Turaga, Howarth, & Borsuk, 2010) whereas others
focus on social norms to understand environmental behavior (e.g., Cialdini et al., 2006;
Goldstein et al., 2008; Nolan et al., 2008; Perkins & Berkowitz, 1986). This study generated
additional understanding of environmental behavior by integrating multiple motives which were
adapted by the Goal-framing theory and the Social Norm Approach. Empirical support was
provided to support that different types of norms exist together within individuals yet take
different roles in predicting their environmental behavior depending on settings where the action
is taken.

Different Mechanisms of Environmental Behavior in Daily Life versus Event Setting
Individuals are influenced by different mechanisms of pro-environmental behavior in
their daily lives versus in a tourism setting. Arguably the most significant finding from the
present study is that the three constructs—personal moral norms, hedonic goals, and perceived
behavioral difficulty—were found to have significant effects on environmental behavior in the

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daily life setting, whereas they were found not to have significant effects on environmental
behavior in the sport event setting. This suggests that underlying mechanisms of environmental
behavior across settings differs. Previous literature has discussed the inconsistent predictive roles
of psychological constructs (e.g., personal moral norms) to explain environmental behavior may
be partially due to different types of environmental behavior. However, a few studies have
attempted to compare the predictive roles of such constructs in explaining the same behavior but
in different settings. As this study first attempts to explore different mechanisms of the same
environmental behavior between daily life and sport event settings, further discussion will
involve possible explanations of this finding based on existing relevant theories rather than
comparing and contrasting previous empirical research findings.
First, the Goal-frame theory, which is one of the theoretical backgrounds of this study,
helps better understand environmental behavior. The central idea of the theory is that goals
govern or “frame” which knowledge and attitudes become cognitively most accessible. As
regression analyses showed, descriptive social norms are dominant in influencing environmental
behavior in the event setting, whereas personal moral norms and hedonic goals are dominant in
affecting environmental behavior in daily life setting. When event attendees attend a sport event,
this setting will influence what they think of at the moment, what information they are sensitive
to, what action alternatives they perceive, and how they act. Researchers who explored
motivations of football tailgating participants addressed that social interaction is one important
driver of individual participation of tailgating (e.g., Drenten, Peters, Leigh, & Hollenbeck, 2009;
James, Breezeel, & Ross, 2001; Snelgrove, Taks, Chalip, & Green, 2008). Attaching
socialization meaning to tailgating activity, individuals in such sport event settings may make
certain associations about their descriptive social norms and environmental behavior in their

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focal attention on “social frame.” Under this frame in sport event settings, tailgaters may have
weak association with their moral norms and environmental behavior and act accordingly.
Second, different motivational mechanisms of environmental behavior between two
settings are most likely a result of different routes of cognitive process in an individual’s
decision making in their daily life compared to that of event settings. An individual would search
their memory or knowledge for cues to answer the question: what behavior would be appropriate
in this situation? Social psychologists have developed dual-processing models such as the
Elaboration Likelihood Model or ELM (Petty & Cacioppo, 1981) and the Heuristic-Systematic
Model or HSM (Chaiken, 1980) and explained that information is processed along two routes.
Specifically, individuals process information along central and peripheral routes (according to
ELM) or systematic and heuristic routes (according to HSM) depending on whether an
individual is motivated and capable of processing the logical arguments presented in the
information. The central or systematic route is more likely to be taken when individuals are
involved in the arguments based on provided information; otherwise, the peripheral or heuristic
route is more likely to be taken. The former route induces an enduring attitude, whereas the latter
route induces a less enduring attitude as individuals follow simple inferences or heuristic cues.
For example, individuals in daily life take the central route of persuasion by accessing personal
moral norms that is shaped from knowledge about the consequences of behavior as the Norm
Activation Model and the Value-Belief-Norm theory suggested. However, individuals as event
attendees have less involvement with the consequence of environmental behavior–at least in the
short term, and make behavioral decisions by taking the peripheral route, such as following the
external cues or “what others do.”

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Third, the different mechanisms of environmental behavior across settings could further
be explained by Festinger’s (1957) cognitive dissonance theory. This study deals with the issue
of inconsistency in pro-environmental behavior across two settings. This is probably because
individuals simply do not perceive environmental behavior in event settings in the same way as
they do in daily life settings, even though pro-environmental behaviors are seemingly the same.
The cognitive dissonance theory suggests that most individuals desire to behave consistently
except in cases where there is an influence of idiosyncratic conditions on behavior consistency.
Thøgersen (2004) further explains that individuals have the desire to behave consistently if they
perceive two different types of environmental behaviors share similarity. Accordingly, if the
seemingly same behaviors are perceived as different, individuals may show inconsistency in
their behaviors. This theoretical notion supports that the underlying mechanism of a certain
environmental behavior is not necessarily the same as the mechanism of the seemingly same
behavior in different setting. The dissimilarity between two seemingly same behaviors in
different settings was reflected in the factor analysis. Eight behavioral items revealed two factors,
which distinctively represent behaviors in daily life and behaviors in the event setting. Such
theoretical and empirical discussions help to better understand why individuals’ underlying
motives for environmental behavior in daily life are not the same as the motives they have for
environmental behavior in event settings.

Moderating Effects of Event Attendees’ Characteristics on Environmental Behavior
Discrepancy between Daily Life and Event Setting
The results of the second research question provided partial support for the hypothesized
relations of event attendees’ characteristics and their discrepancy in pro-environmental behavior

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between two settings. As hypothesized, event attendees perceived destination environmental
responsibility and the type of event attendee significantly moderated behavioral discrepancy
between daily life and the event setting. Contrary to expectations, recreation involvement and
experience-use history did not exert a significant moderating effect on behavioral discrepancy
across settings.
Study participants’ environmental behaviors across two settings were found to be
different. Sport event attendees’ engagement of pro-environmental behavior in the sport event
setting decreased from daily life to the event setting, yet behavioral discrepancy between the two
settings was smaller among those who perceived that the sport event destination took
environmental responsibly by implementing green practices (e.g., communication, and waste
management systems), than those who did not. As the present study sought to identify
significant moderators that explain behavioral consistency/discrepancy across daily life and
event settings, it is worth noting the findings with a focus on the spillover effects of
environmental behavior. In the past, studies have been carried out to understand how to
encourage a specific environmental behavior and recently researchers have begun to explore the
mechanisms of one type of environmental behavior spilling over into other types of
environmental behavior (Thøgersen & Ölander, 2006; Whitmarsh & O'Neill, 2010). The present
study first attempted to understand how environmental behavior in a setting is transferred to
another setting. It was found that one of the most prominent mechanisms of such a spillover
effect is the destination’s environmental responsibility. This finding proposes that individuals’
spillover of pro-environmental behavior across settings is a function of the surroundings in
which they are found. In the present study, this includes the event destination environment,
where event-goers spend their leisure time interacting with others and the event setting.

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Individuals’ perceptual understanding of the venue’s environmental responsibility influences the
attendee’s own effort to be consistent in their environmental behavior between daily life and
event settings. This finding also implies that the perception that an individual event-goer is
helping the destination achieving a positive outcome eventually reduces their discrepancies of
pro-environmental behavior.
In relation to whether environmental behavior in daily life is transferred to the sport event
setting, the present study suggests that the moderating role of event attendee types on behavioral
discrepancy between the two settings. Although both tourist and non-tourist types of event
attendees’ engagement in environmental behavior in the sport event setting was less than in daily
life, behavioral discrepancy between the two settings is smaller among tourists than non-tourists.
In other words, tourists are less likely to change their pro-environmental behavior at the event
setting, whereas non-tourists’ (i.e., local residents, MSU students and employees) behavior was
more dramatically decreased from daily life to the event setting. Student respondents within the
non-tourists group probably contribute to the difference in behavior, yet student participants
make up only 12% of study participants. Although assessing why such behavioral difference
exists between groups is observed is beyond the scope of this study, previous literature about
tourists versus locals provides insight to understand that event attendee type is another
mechanism of spillover of environmental behavior across settings.
Tourists differ from locals in their perception of the destination (Jutla, 2000), their
interpretation of place (Kaltenborn & Williams, 2002), and their uses of space (Snepenger,
Murphy, O’Connell, & Gregg, 2003). As previous literature suggested, sport event attendees
who travel specifically to attend the event have substantially higher identification with the
subculture of sport or sport event as well as higher fan motivation (e.g., Snelgrove et al., 2008).

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And this is probably because tourists make a greater effort to travel to the host destination in
order to attend the sport event than non-tourists. A subculture can be understood as a subgroup of
society composed of individuals who come together to share a common facet, such as sport or
event activity, and who therefore develop distinctive attitude, beliefs, and values (Gelder, 2007).
It has been shown that involvement with a particular sport or sport event can socialize an
individual into the certain subculture, and such association renders a degree of identification with
the subculture. This identification may result in the person describing themselves, and being
described by others in relation to the group (Wheaton, 2000). In comparison to locals, tourists
tend to develop identification among themselves as a subculture of tailgaters or football fans.
Such association between tourists, oneself, and the subculture group helps explain their relatively
consistent pro-environmental behavior. Such understanding of subculture is not limited to sport
event tourists, but is also applicable to tourists in general. Although defining a tourist often
involves physical criteria (e.g., travel distance), understanding tourists—specifically sport event
tourists, researchers suggest understanding tourists requires a focus on the nature of the
experience. Tourists typically involve a stage of liminality (Jafari, 1987), in which tourists find
themselves in the transitory stage between two established social statuses—being individuals
living daily life versus being tourists where the usual order of things is changed (Jafari 1987;
Turner, 1969). Tourists willingly travel to, or enter into, a specific place for a defined period of
time, and experience a temporary difference of everyday activities (Cohen, 1979; Smith, 1978).
Therefore, the present study concludes that the subculture or liminality of tourists versus nontourists could help to better understand the consistency/discrepancy of environmental behavior
between daily life and event settings.

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Results from the moderating effects tests, contrary to expectations, showed recreation
involvement and experience-use history did not exert significant an effect on behavioral
discrepancy across settings. Previous studies showed that recreationists’ environmental behaviors
tended to differ based on their recreation involvement and experience-use history (e.g., Gross &
Brown, 2006; Lee, 2011; Ong & Musa, 2012; White et al., 2008). Specifically, those researchers
explained that recreation involvement and previous recreation experience increase the connection
between a recreationist and a recreation activity which affects environmental behavior. If
recreationists are more involved in certain activity and have more experience, they are more
sensitive to deteriorated conditions in recreation settings, and evaluate depreciative behavior
more negatively (White et al., 2008). The present study finding however, did not show such a
moderating effect of recreation involvement and experience-use history on environmental
behavioral change between daily life and the event setting. One possible explanation of such
different results is that the previous studies focused on vandalism or littering behavior that
directly brings negative consequences on the recreational area or travelling site, which in turn
affects quality of recreational activity. On the other hand, this study focuses on proenvironmental behavior that does not directly threaten recreational settings and activities.
Another potential reason is that tailgating as recreational activity is so called “casual leisure”,
which differs from “serious leisure”. Those participants in the current study who have higher
psychological and longer behavioral involvement to tailgating activity are still not aligning extra
cognitive and behavioral efforts to continuing such casual recreational activity. Contrary to
casual leisure, serious leisure such as fishing and skiing requires development of skills and
knowledge, accumulation of experience and the expending of effort (Stebbins, 1982). This
understanding provides a an explanation that, regardless of the psychological and behavioral

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involvement to tailgating activity, study participants do not put extra effort in engaging in proenvironmental behavior in the event setting as this is a casual leisure activity.

Managerial Implications
The present study offers some important managerial implications to sport event tourism
practitioners who want their events and destinations to become greener. Based on the results of
this research, the following are suggestions to induce greater pro-environmental behavior from
event attendees.
Policy makers and event managers in sport event tourism destinations need to understand
that event attendees’ underlying mechanism of environmental behavior is different from that of
daily life. Event managers who are able to determine the critical factors that increase proenvironmental behavior in event setting will be able to advance their efforts in greening an event
by inducing pro-environmental behavior among event attendees. In terms of environmental
policy and campaign interventions, the most promising way to increase the overall level of
recycling at sport events may be to better utilize descriptive social norms. Event attendees may
not always be accessible to the association between certain behaviors and associated
environmental impact, which brings moral obligation into influencing behavior. If social norms
influence behavior, the norms should be recognized. In other words, event attendees have to be
aware that the majority of other attendees in the same surroundings are actually engaging in proenvironmental behavior. There are a couple of suggestions to enforce social norms and to ease
the translation of the norms into behavior via environmental campaign efforts in event
destinations.

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In addition to locating recycling bins, additional options need to be provided for tailgaters
such as “bring your own recycling bags,” “use recycling bags provided by the university,” or
“use recycling bag dispensers located on campus.” Such options not only allow tailgaters to
bring or pick up recycling bags and engage in recycling in more convenient way, but they also let
tailgaters be aware of what others do. Specifically, if volunteers circulate throughout the parking
lots handing out recycling bags so tailgaters can easily recycle at their own tailgate location, and
tailgaters can see others’ recycling at their location. This ensures a sign of social norms showing
that other tailgaters actually engage in recycling. Also, it is recommended to initiate a student
internship program to support the on-site recycling education program at sport events. Volunteer
students can be recruited by the for example, the “Office of Campus Sustainability” for the
football season. Currently, on-site recycling education is mostly run by volunteers of the current
student body at universities including MSU, and they are recruited only for a single or limited
game event. However, by recruiting student interns for a season term helps ensure continuous
improvement of such a recycling campaign. It is also expected to have a more consistent
recycling education program throughout the season. Such an on-site recycling campaign can be
enhanced by inviting volunteers from actual tailgaters who include not only students but also
local residents and alumni, etc. Inviting tailgater volunteers, having relatively flexible and short
schedule, from actual tailgaters can be beneficial to increase the effects of social norms on proenvironmental behavior. Individuals are more likely to follow behavior of others if the others are
similar to them (e.g., similar age group, similar social status, etc.). On each game day, and at the
beginning and the end of the football season, student interns and volunteers should be honored
and thanked through public announcement. Together these implications are recommended
actions to show tailgaters how other tailgaters are taking responsibility to recycling on game days.

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By understanding the explanation of dual process models in reference to the discrepancy
of environmental behavior across settings, such different behavior is possible because individuals
in the event setting do not take a central route of cognitive process (e.g., access to their existing
moral norms to behavioral decision) when they are traveling. When policy makers and
communicators design environmental campaigns and construct persuasive messages to promote
pro-environmental behavior among event attendees, it is important to ensure that event attendees
are motivated and able to process the argument contained in campaigning messages. In an event
setting, it can be more effective to use peripheral cues in motivating individuals to engage in proenvironmental behavior when they are not in a central focus of logical argument of the behavior.
For example, locational information of recycling facilities/bins can be an easy cue to decide to
participate in the recycling program. Fundamentally, recycling bins should be placed throughout
parking lots in visible ways—e.g., clearly labeled, using clear, consistent, eye catching signage.
In addition, locations for placement of recycling bins should be informed to tailgaters in advance
and during the event. Announcing such information through season ticket holders, students,
alumni email circulations, and through websites can be a cue to follow when tailgaters make
decisions about recycling. Locations need be consistent throughout the seasons (and on-going
season) so tailgaters know where to expect them, which becomes an easier cue to follow. Colocation, by having a recycling bin next to each trash can when at all possible, can be one method
to use as an easily accessible cue for following.
As individuals experience cognitive dissonance when their behavior is inconsistent
between at home and at the event, following managerial implications are suggested. When
individuals experience cognitive dissonance, they try to resolve the dissonance by adding
information or by changing behavior (Festinger, 1957). Therefore, one approach to encourage

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spillover of pro-environmental behavior from home to the event is to provide additional
cognitive elements that resolve or minimize the dissonant. Thus, it is recommended to develop
an environmental campaign focusing on reduced perceived dissimilarity of the behaviors across
two settings. For example, a destination manager or campaign planners could target home game
fans and develop a slogan conveying a theme “doing a good job at home AND home games.”
This helps tailgaters connect recycling at home to recycling at tailgating. Further, a campaign
suggested earlier, “bring your own recycling bags” can also help tailgaters to connect in their
cognition that recycling at home is not different from recycling at tailgating, while providing
them an alternative easy option for recycling at an event setting. Such slogan needs to be
publicized through multiple channels such as emails, websites, newsletters, etc. One
recommended channel is to put an eye-catching slogan on a window cling for tailgate vehicles so
the driver and passengers could attend.
The spillover effect of pro-environmental behavior from daily life to the sport event
setting may be facilitated via shared effort from the venue or destination. Event attendees’
perceptions about the destinations’ environmental sustainability efforts is a crucial factor that
emphasizes the important role of the institution in the spillover phenomenon in greening sport
event tourism. Accordingly, the environmental campaign can be advanced by informing
attendees of the destination’s greening efforts. Policy makers and event managers should actively
educate individuals through environmental campaigns or the tools of informal education (e.g.,
brochures, exhibitions, and seminars). More importantly, event attendees must be able to “see”
that the destination is committed to minimizing the event induced negative environmental
impacts. The tools include budget allocation for their greening efforts, non-financial resources
they are using, and on-going feedback from such efforts etc. For example, event managers can

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advertise the tailgating recycling program and report recycling statistics on video boards,
websites, campaign brochures, etc. Also, numbers and lists of student interns and tailgater
volunteers for an on-site recycling campaign can be another source of information to shows the
efforts from the event destination. Improved recycling logistics and advanced waste management
systems specifically for the event setting need be publicly announced and shared among
tailgaters. One additional recommendation to publicize the destination’s effort in greening the
event is to utilize social media. For instance, event mangers can let the game day recycling
interns create a Facebook event to spread awareness of greening event efforts, and recruit
volunteers for game days. Even managers can also use a Flickr photo steam to share photos of
recycling efforts and outcomes. These visualized actions taken by event destinations would
increase individuals’ awareness of destination’s effort in greening the event, that in turn enhance
event attendees’ motivation of taking environmental responsibly, being consistent in their proenvironmental behavior across settings. Also, it is recommended to develop a recycling contest
for game days with different campus groups (e.g. fraternities, sororities, clubs, etc.) competing
for most non-deposit recycling by type such as plastic, paper, non-deposit aluminum, nondeposit glass, non-deposit plastic. By working the MSU Athletics and Office of Campus
Sustainability to do recognition (e.g., tickets or some similar rewards), it is expected to have
shared efforts from destinations and tailgaters.
A final suggestion is that greening sport event tourism could benefit by formulating
environmental campaigns differently for tourists than for locals. For example, communications
directed outside the locale highlight opportunities for event attendees to strengthen and celebrate
their identification with the sport’s subculture and the institution. On the other hand, campaigns

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within the locale highlight the entertainment and achievements that the green event offers.
Summary of managerial implications are presented in Figure 10.

Utilize social norms
Ø Show tailgaters how other tailgaters are taking responsibility to recycling on game days
Ø Facilitate tailgaters to recycle at their location
Ø Launch “student internship program”
Ø Invite tailgater volunteers
Use peripheral cues rather than central focus of logical argument
Ø Provide locational information of recycling facilities/bins
Ø Place recycling bins in visible ways
Ø Place recycling bins at the same locations throughout the seasons
Ø Co-locate a recycling bin next to each trash can
Ø Use multiple channels (e.g., websites, newsletters, a window cling for tailgate vehicles)
Reduce cognitive distance between at home and at the event
Ø Reduce perceived dissimilarity of the recycling behaviors between two settings
Ø Launch “Bring your own recycling bags” program
Ø Develop slogan such as “Doing a good job at home AND on home games”
Shared efforts from destinations and tailgaters
Ø Inform tailgaters of the destination’s greening efforts
Ø Educate tailgaters through environmental campaigns or informal education (e.g.,
brochures, exhibitions, and seminars)
Ø Let tailgaters see destinations’ environmental commitment (e.g., report recycling
statistics, budget allocation for their greening efforts)
Ø Get coaches/players visibly supporting recycling in website, score board, etc.
Ø Publicize Improved recycling logistics and advanced waste management system
Ø Utilize social media (e.g., create a Facebook event to spread awareness of greening
event efforts, use a Flickr photo steam to share photos of recycling efforts and
outcomes)
Ø Develop a recycling contest for game days with different campus groups (e.g.
fraternities, sororities, clubs, etc.)
Ø Work with MSU Athletics and Office of Campus Sustainability to
do recognition, tickets or some similar rewards
Target different types of tailgates with tailored approach
Ø Target tourists and highlight sport subculture
Ø Target locals and highlight enjoyment and achievement from the green event
Figure 10 Summary of managerial implications

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Limitations and Future Studies
The present study presents promising contributions. However, the findings presented here
need to be qualified in the light of several limitations. The first limitation is related to the sample
used in this study. A limitation of this quantitative study is the inability to determine the entire
study population. Also, a nonprobability systematic sampling strategy was used in this study.
Although nonprobability sampling approach is an acceptable technique in tourism studies
especially when identifying the entire population is almost impossible, the sampling approach of
the present study limits generalizability of the study findings. It is recommended to replicate the
study in the same destination and other sport event destinations in the future. Also, it is
recommended to conduct a study with season ticket holders and utilize probability sampling in a
future study. This would include a number who don't tailgate, and allows the researchers group
comparisons (e.g., spectator only group, tailgater only group). But also, the findings from ticket
holder sample would be generalizable across all of college football in U.S. for season ticket
holders whose population is known.
Also, the present study context is a collegiate football tailgate event. A future study is
recommended to target different types and sizes of sport events (e.g., active sport event attendees
such as a marathon or sport-fishing event, and spectators to a large scale sport event such as the
Olympic Games) and examine whether the present study findings are consistently observed. Also,
this study context is a public tourism destination where green initiatives are a stated concern of
the institution. Another suggestion for future research is to employ the present study’s model to
examine the response of visitors to green hospitality businesses. Resorts or hotels, which seek to
go green in their practices (e.g., adopting energy efficient products, promoting recycling and
energy saving among customers) offer opportunities to reduce negative environmental

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consequences. Yet at the same time, such businesses seek to increase market competitiveness
and business profits by adapting green initiatives. Accordingly, understanding customers’
perception and behavior toward green hospitality businesses are key interests for green
hospitality businesses not only because of environmental sustainability but also their economic
sustainability. Under this difference between a public tourism destination and a hospitality
business, expanding the present study into green hospitality businesses will bring meaningful
contributions to sustainable tourism.
The second limitation is related to the measurement issues. This study used self-reported
rather than observed pro-environmental behavior because of the scope of the dissertation project
in terms of time and cost. Therefore, it is possible that some individual responses may be
overestimated, because environmental behavior conveys social desirability (Mick, 1996). To
address this, researchers acknowledge the conceptual restrictions need to be placed on
interpreting “what individuals say they do”, which is different from “what they actually
undertake” (Corral-Verdugo, Bernache, Encinas, & Garibaldi, 1994; Corral-Verdugo, 1997).
Nevertheless, challenges with observational studies also raise concerns if the observers are
identifiable: Socially desirable behavior is more likely to occur; and observation can also create a
negative image for the institution if subjects think they are being observed. Although there would
be a discrepancy between self-reported and observed behavior, this type of methodology (i.e.,
measuring self-reported behavior over observing actual behavior) is common in field research.
Also, although there is likely to be an over-estimation by respondents concerning their actual
behavior, researchers addressed that self-reported behavior measures are likely to be
proportionally accurate (Vining & Ebreo, 1992; Warriner, McDougall, & Claxton, 1984), and
social desirability bias is not a major issue on environmentally-related research (Milfont, 2009).

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Future studies measuring the same behavior at each of the different settings are necessary to
confirm what the present study found.
Additionally, the present study assessed cross-sectional data with repeatedly measured
environmental behavior. That is, the present study respondents reported environmental behaviors
in both settings in a single set of the questionnaire. Therefore, there is a concern that the
respondents would tend to report a similar level of behavioral engagement at both settings.
Nevertheless, the present study findings—i.e., examined spillover effect of environmental
behavior from one to another setting—have a shared understanding with previous literature
findings (e.g., Thøgersen, 2004; Whitmarsh & O'Neill, 2010)—i.e., examined the spillover
effects between different types of environmental behaviors. For example, environmental attitude
or personal moral norms were found to be a significant determinant of one type of environmental
behavior yet not of another type of behavior. One suggestion for future research is to conduct a
longitudinal study or to utilize separate sets of questionnaires to examine and compare the
environmental behaviors across settings. Another potential expansion of the present study is to
conduct an experimental study to measure effectiveness of different types of message or
information on environmental behavior across settings. Even though importance of information
and campaigns to promote environmental behavior among event attendees has been discussed in
previous literature as well as in the present study, the relative effectiveness of different types of
messaging is less empirically tested. Messages can convey either personal moral norms or
descriptive social norms and such different types of message would have different effects on
individuals’ environmental behavior if one type of message is cognitively more relevant or
accessible in a certain setting (i.e., event setting).

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Lastly, one limitation is that this study did not control for the actual difficulty of
recycling behavior at home and in tailgating areas. Although the questionnaire included the
questions to measure study participant perceived behavioral difficulty, which was controlled in
the analyses, recycling opportunities especially in tailgating areas were not measured. Obviously,
recycling opportunities were not evenly distributed to all tailgating participants throughout the
parking lots, and recycling should be more favorable in the area closer to the stadium where
recycling facilities were relatively well established. Therefore, it is necessary to design a study
where different levels of recycling opportunity are available to a homogenous population and see
how recycling rate differs among varying levels of opportunity, while all other factors being
equal. It is recommended in future study to utilize GPS to document and explain spatial
distribution of actual recycling facilities, and look at how pro-environmental behavior varies
with the spatial dimensions, and whether such spatial dimensions additionally explain behavioral
discrepancy between in daily life and in sport event tourism context.

Conclusion
Sport event tourism is an increasingly popular type of tourism which provides
recreational and tourism opportunities to event attendees and provides economic and social
benefits to host communities. Yet, although this type of tourism interacts with the environment in
a number of ways, in the past environmental concerns in sport event tourism had little attention
in practice and literature. Recently, it is starting to be recognized that sports events have a
significant impact on the environment. Common ways in which sport event tourism affect the
environment have been identified as development of fragile or scarce land types, soil erosion,
noise and light pollution, consumption of renewable and non-renewable resources, creation of

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greenhouse gases by consuming electricity and fuel, waste sent to landfill, etc. (UNEP, 2014).
Policy makers and event planners are now paying closer attention to building and managing sport
event tourism in greener ways that can limit negative environmental impacts. Multiple attempts
have been made to green events including design, construction, and operation of
destinations/venues, transport, recycling, and waste management etc. (EPA, 2012; UNEP, 2014).
One effort at greening sport event tourism in recent years is to motivate event attendees to
engage in pro-environmental behavior while they are enjoying their recreation activity in
destinations. To aid the sport event tourism destinations in going greener and enhancing
environmental sustainability, the present study concludes three domains need to be understood
and integrated as shown in Figure 11.

Destination
Environmental
Responsibility

Environmental
Psychology

Tourist
Characteristic/Behavior

Figure 11 Three domains for greening sport event tourism

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The first domain is environmental psychology. In early literature on environmental
behavior, most approaches focused on moral norms that play a key role in promoting proenvironmental behavior. In the last decade, researchers argued that environmental attitudes or
moral norms were only one set of the factors affecting behavior, and in many cases, not the most
important one. Although some scholars explicitly acknowledge that behavior may result from
multiple motives, little is known about how multiple motives become significant influential
factors in some cases, yet not significant factors in other cases (e.g., in tourism context). This
notion becomes increasingly important when tourism destinations take environmental
responsibility by implementing multiple practices including environmental campaigns targeting
travelers or event attendees. In addition, destinations’ greening efforts expect not only direct
effects (e.g., reducing CO2 emission by adopting energy efficient products or implementing
waste management system) but also indirect effects (e.g., reducing CO2 emission by letting event
attendees engage in recycling). The latter effort could become more effective if event attendees
perceive destination’s greening activity. Accordingly, the second domain of destination
environmental responsibility needs to be integrated for the betterment of environmental
sustainability of sport event tourism. The third domain is tourist characteristics and behavior.
Individuals living in daily life take a temporal role of being tourists or experiencing the unique
mindset of other than daily living when they attend events or travel to destinations. While being
tourists, individuals differ in terms of their behavior and attention. Such distinction is a unique
arena of tourism studies that needs to be integrated when adapting behavioral and cognitive
theories and models developed from other disciplines. The present study compared how multiple
motives of environmental behavior differently affect pro-environmental behavior between in
daily life versus in sport event tourism contexts. By addressing the behavioral discrepancy and

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different underlying mechanisms of environmental behavior between two settings, the present
study provides additional understanding about the situation where environmental sustainability
behavior occurs at sport event tourism destinations.

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APPENDICES

131

Appendix A IRB Approval Letter

132

Appendix B Sampling Log Sheet

On-site Sampling Log Sheet (8/30/13)
Survey Administrator’s Name: __________________________

Hour: 4-5PM
Stops by
# Completed
Time
Surveys
:00
:06
:12
:18
:24
:30
:36
:42
:48
:54
Total
Hour: 5-6PM
Stops by
# Completed
Time
Surveys
:00
:06
:12
:18
:24
:30
:36
:42
:48
:54
Total

Hour: 6-7PM
Stops by
# Completed
Time
Surveys
:00
:06
:12
:18
:24
:30
:36
:42
:48
:54
Total

# Declined

Hour: 7-8PM
Stops by
# Completed
Time
Surveys
:00
:06
:12
:18
:24
:30
:36
:42
:48
:54
Total

# Declined

Total completed # __________
Total rejected #

__________

Total contacted # __________

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# Declined

# Declined

Appendix C Survey Administrator Pre-meeting Agenda

“Going Green on Game Days”
Reducing Waste and Increasing Recycling on Football Game Days

Survey Administrator Pre-meeting
Work Schedule
· August 30 (Fri.) 3:30 p.m. - 8:15 p.m. (Game starts at 8 pm)
· September 7 (Sat.) 7:30 a.m. - 12:15 p.m. (Game starts at 12 pm)
Meeting Place
· Meet at 3:30 pm (Aug 30) and7:30 am (Sep 7) at Room 149 in NR Building
(Call me if you do not have an access to the Building)
Sampling Times
· August 30, 2013: 4:00 – 7:30PM then return to 149 NR. Depart by 8:15PM
· September 7, 2013: 8:00 – 11:30 AM then return to 149 NR. Depart by 12:15PM
Main Activity
· Follow the sampling protocol and conduct an on-site survey
· Collect completed questionnaires
Protocol
1. Walk to the assigned tailgating area from 149 NR
2. Begin at agreed upon entry point in your area at start time.
3. Target four nearest tailgaters at your entry point
4. Introduce yourself, explain research purpose, ask their participation in a survey, and
collect completed questionnaires.
5. If one or more refuse, request that the next closest individuals complete the survey so you
have four taking the survey.
6. Thank the survey participants, and move five steps by following the route (you will have
a route map that you need to follow on Fri.)
7. Repeat no. 4-6, and continue with an interval of five-steps
Study Purpose
· Purpose of this study is to help MSU and other universities go greener at football game
days by understanding game day visitors’ tailgating experience and recycling behavior.
Sample Frame
· Spend about 5 minutes for one stop, and spend 1 minute for moving to the next stop
· Expect to have about 30 completed questionnaire collected for an hour per person
· Expect to have about 100 completed questionnaire collected during one-day working
hours per person
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Tips
· This research is being partially funded by the MSU Office of Campus Sustainability. It is
under the direction of Dr. Charles Nelson, Department of Community Sustainability and Ju
Hyoung, Ph.D. Candidate, Department of Community Sustainability.
· Please contact tailgaters in a formal way, but in friendly mood. Please keep in mind your
positive energy and smile motivates people to participate in this study and increases the
response rate.
· Those sampled are to remain confidential. Your need to articulate that their contact
information and responses remains strictly confidential by MSU regulation. This research
is approved by the MSU Institutional Review Boards (IRB).
· Those providing an email or regular mail address will ONLY be contacted by Dr. Nelson
and Ju Hyoung for the research purpose of better understanding tailgating and recycling
behavior. They will receive a mail or online survey (based on their preference) on
Wednesday after the game. This follow-up questionnaire will take about 15 minutes.
· We are working to be representative of all tailgaters in our sampling. This includes MSU
students, faculty, employees, local residents, or travelers. They can be fans of MSU or fans
of the opposing team.
· The one exception for an adult is if you believe a person is drunk, under the influence of
drugs or in some way threatening or belligerent, do not attempt to sample such a person.
· We are not targeting children. Please contact those who are 18 years or older.
· If tailgater(s) choose not to participate, thanks to them and, move to the next person. Do
not take any rejection personally. This is a completely voluntary survey. You can ask their
participation, but respect their “not to participate” decision. Please take note how many
tailgaters choose “not to participate.”
· While tailgaters are filling out the questionnaire, you may want to mention, “There are no
right or wrong answers. We are interested in your experience and perceptions.”
· There are no financial or other incentives for study participants. Don’t mention incentives.
This is very unlikely to come up.
· You may be getting tired at some point. Please take your time for re-filling your energy, be
sure to use the restroom as needed, carry/drink water and some light snacks.
· You may have food/drink shared by tailgaters, but do not drink any alcoholic beverage.
I want you to have a fun time, and get some experience for your future research. I truly
appreciate your commitment and contribution to this research project. Thanks.
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Appendix D On-site Questionnaire Used at the First Phase Survey
(Fit into one page)

MSU Game Day Survey

Dear Tail Gate Participant:
Researchers from the Department of Community Sustainability at MSU are working to better
understand the relationship between one’s attitudes regarding recycling in daily life and when
one attends a major sporting event. This research is funded in part by the MSU Office of Campus
Sustainability. It will be used to help guide future recycling efforts on campus for major events.
Please take the 4 minutes or so needed to complete this survey. You indicate your voluntary
agreement to participate by completing and returning this survey. You will be asked to
participate in a follow-up survey by providing your email or mailing address. However, if you
choose not to complete all or part of the questions, you will not suffer any penalty. You are free
to discontinue your participation at any time. Your responses will be kept confidential and your
privacy will be protected to the maximum extent allowable by law. Thanks! Ju Hyoung Han,
Doctoral Candidate and Dr. Chuck Nelson, Associate Professor, Department of Community
Sustainability, 131 Natural Resources Building, MSU. For further information contact
hanju@msu.edu or nelsonc@msu.edu.
1. Are you best described currently as (check one)
r An MSU Employee
r An MSU Student
r Not MSU Student or Employee
1a. If you are not a current MSU student or employee, check those that apply. Are you
r An MSU Alumnus
r A family member of an MSU student
r A family member of an MSU employee
__________

2. What is your five-digit zip code?
3. In what year were you born?
4. What is your gender?

__________

r Male

5. Are you here as a fan of (check one)

r Female
r MSU team r Visiting team r Other: _______
136

r Yes r No

6. Are you attending the game today?

7. Are you the leader of the tailgate at this site? r Yes r No
8. How many miles did you travel from your principal home to East Lansing for this
experience? ___________# MILES (One-way)
9. How many people came with you to this tailgate?
#______ adults 18 and older (incl. you) & #_____ children under 18
10. Which one best describes those who came with you to this tailgate? (please check one)
r By myself
r Family and Friends
r Other _____________

r Family only
r Club/Organization

r Friends only

11. I was aware that MSU is operating recycling programs in tailgating areas today before
receiving this questionnaire. r Yes r No
12. I know where to recycle near my tailgating location today. r Yes r No
13. Please circle the one response that best describes your behaviors at your home and at
MSU football tailgating.

1
1
1

AT HOME
1 = Never to
5 = Always
2
3
4
2
3
4
2
3
4

5
5
5

I recycle materials.
I look for ways to reuse things.
I look for ways to reduce
food/material waste.

AT TAILGATING
1 = Never to
5 = Always
1
2
3
4
5
1
2
3
4
5
1
2
3
4
5

14. Please circle the one response that best describes your perceptions of MSU’s recycling
activity at football tailgating. (1 = Strongly Disagree to 5 = Strongly Agree)
MSU has visible communications about its green practices at
tailgating.
MSU has established active recycling programs where I am
tailgating.
MSU has established systems to reduce food/material waste
during tailgating.

137

1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

15. I am interested in sharing additional information about tailgating and recycling in a
follow-up survey.
(You will ONLY be contacted for this purpose by the researcher.)
Name:______________________________________
Email Address:

Mailing Address:

OR _________________________________
___________________________________
(Street/PO)
_________________________________
(City)
(State)
(Zip code)
Thank you very much for your help today!

For researcher use only

If refused, why

ID #

138

Appendix E Consent Form for Follow-up Mail Survey Used at the First Phase Survey
(Fit into one page)
September, 2013
Dear Tailgating Participant:
Thank you for completing a questionnaire during game day tailgating at MSU regarding
recycling. As was discussed by the survey administrator when you completed the initial on-site
survey, researchers from the Department of Community Sustainability at MSU are working to
better understand the relationship between one’s attitudes regarding recycling in daily life and
when one attends a major sporting event. This research is funded in part by the MSU Office of
Campus Sustainability. It will be used to help guide future recycling efforts on campus during
major events and improve communications with event attendees.
As the survey administrator discussed with you and you provided your contact information for
this purpose, this is the second questionnaire of the two in this study. It is focused on your
attitudes regarding recycling and tailgating. It allows us to better understand your attitudes,
opinions and behaviors related to recycling when you are tailgating and in everyday life.
Your participation in this survey will take approximately 15 minutes. As you will probably
notice, there is a code number which is only to allow us to cross out your name from a second
mailing of the questionnaire if we receive your response so we don’t bother you with more
mailings. Once your response is received, we will disassociate your name/code number with
your responses and they will be strictly confidential. Your identity will not be linked to the data
you provide. The research data will be kept on the campus of Michigan State University in a
locked file cabinet or password protected computer for three years after the close of the research
and only the appointed researchers and the Institutional Review Board (IRB) will have access to
the data. There are no anticipated risks associated with participation. You consent to voluntarily
participate in this study by completing this survey. You may choose not to participate at all, may
choose not to answer specific questions, or may discontinue at any time without penalty. This
study is intended for research purposes only. Findings will be published in academic journals,
presented at professional meetings and used to assist the Office of Campus Sustainability to
improve recycling opportunities, but the identities of all research participants will remain
confidential.
If you have any questions or concerns about this research, please contact the researchers below:

139

· Charles Nelson, Ph.D., Associate Professor in the Department of Community Sustainability
at Michigan State University by mail: 480 Wilson Road, 131 Natural Resources Building,
East Lansing, MI 48824, phone: (517) 432-0272, or e-mail: nelsonc@msu.edu
· Ju Hyoung Han, Doctoral Candidate in the Department of Community Sustainability at
Michigan State University by mail: 480 Wilson Road, 131 Natural Resources Building, East
Lansing, MI 48824, phone: (517) 449-5273, or e-mail: hanju@msu.edu
If you have any questions or concerns about your role and rights as a research participant, would
like to obtain information or offer input, or would like to register a complaint about this research
study, you may contact, anonymously if you wish:
· Michigan State University Human Research Protection Program, by mail: 408 W. Circle
Drive, Room 207 Olds Hall, MSU, East Lansing, MI 48824, phone: (517) 355-2180, or email: irb@msu.edu.
Thank you very much for your participation.
Sincerely,

Ju Hyoung Han, Doctoral Candidate
Department of Community Sustainability

Dr. Charles Nelson, Associate Professor
Department of Community Sustainability

140

Appendix F Survey Items for Follow-up Mail Survey Used at the First Phase Survey
(Fit into three pages)

ID # _______________

SECTION I. ENVIRONMENTAL BEHAVIOR
1. Please describe your behaviors at your home and at MSU football tailgating. Please circle the
one response that best describes you for each situation at home and tailgating using the 0-5
scale below.

0
1
Don’t Use Never

0
0
0
0

AT HOME
1 2 3 4
1 2 3 4
1 2 3 4
1 2 3 4

5
5
5
5

2

3

4

5
Always

I recycle non-deposit glass containers.
I recycle non-deposit aluminum cans.
I recycle plastic (e.g., bottle, cup, grocery bags).
I recycle paper (e.g., mixed paper, boxboard).

AT TAILGATING
0 1 2 3 4 5
0 1 2 3 4 5
0 1 2 3 4 5
0 1 2 3 4 5

2. Please describe the level of difficulty concerning recycling for each situation at home and at
MSU football tailgating. Please circle the one response that best describes you for each
situation at home and tailgating using the 0-5 scale below.

0
Don’t Use

0
0
0
0

AT HOME
1 2 3 4
1 2 3 4
1 2 3 4
1 2 3 4

1
2
Not Difficult At All

3

4

5
Very Difficult

5
Recycling non-deposit glass containers.
5
Recycling non-deposit aluminum cans.
5 Recycling plastic (e.g., bottle, cup, grocery bags).
5 Recycling paper (e.g., mixed paper, boxboard).

141

AT TAILGATING
0 1 2 3 4 5
0 1 2 3 4 5
0 1 2 3 4 5
0 1 2 3 4 5

3. Please share your general perceptions of recycling practices in regards to non-deposit glass
and cans, as well as plastic and paper.
Strongly
Disagree
I feel morally obligated to engage in recycling.
I should do anything I can do to recycle.
I feel guilty when I waste recyclable materials.
I think that a majority of people in the United States engage in
recycling.
I think that a majority of people at tailgating areas at MSU engage in
recycling.
I think that a majority of people in my community engage in
recycling.
A majority of people in the United States should engage in recycling.
A majority of people at tailgating areas at MSU should engage in
recycling.
A majority of people in my residential community should engage in
recycling.
I recycle because of the enjoyment that I feel.
I take satisfaction in recycling.
I recycle because I enjoy learning new skills/techniques.
One of the best things about recycling is that it helps lower costs to
society.
We should recycle materials and protect the environment to benefit
people.
My health and the health of my family may improve because of
recycling.

Strongly
Agree

1
1
1
1

2
2
2
2

3
3
3
3

4
4
4
4

5
5
5
5

1

2

3

4

5

1

2

3

4

5

1
1

2
2

3
3

4
4

5
5

1

2

3

4

5

1
1
1
1

2
2
2
2

3
3
3
3

4
4
4
4

5
5
5
5

1

2

3

4

5

1

2

3

4

5

4. I am aware that MSU is operating recycling programs in tailgating areas.

r Yes r No

5. Did you see/hear messages about recycling at tailgating areas as part of your MSU
experience? r Yes r No
If yes, from what source(s) (Please check all that apply)?
m Signs
m Message on MSU bus/vehicle
m Materials that came with ticket/tailgate information
m Person who provided questionnaire on the game day

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m MSU Website
m Talking to MSU employee
m Saw recycling containers
m Other _________________

6. I utilized MSU recycling facilities in tailgating areas on the day I was surveyed.
r Yes r No
6a. If no, why? (Please check all that apply)
m Recycling was difficult.
m Recycling was too time-consuming.
m Recycling took more effort to recycle than it is worth.
m Storing and sorting recyclable materials was unsanitary.
m Recycling was inconvenient.
m I did not know where to recycle near my tailgating location.
m I knew where to recycle but recycling drop-off centers were too far way.
m My participation in other activities was more important to me than recycling.
m The quality of my tailgating experience would have changed for the worse if I
recycled.
m Other _________________________________________________________
7. Do you have curbside recycling service where you currently live? r Yes r No
7a. If yes, do you utilize the service?
r Yes
r No à If no, why? (Please check all that apply)
m Recycling is difficult.
m Recycling is too time-consuming.
m Recycling takes more effort to recycle than it is worth.
m Storing and sorting recyclable materials is unsanitary.
m Recycling is inconvenient.
m I have more important things to do than recycling.
m Because of daily hassles, I often forget to recycle.
m The quality of my daily life would change for the worse if I recycled.
m Other _________________________________________________________
8. Do you have one or more recycling drop-offs facilities where you currently live?
r Yes r No
8a. If yes, do you utilize recycling drop-off facilities?
r Yes
r No à If no, why? (Please check all that apply)
m Recycling is difficult.
m Recycling is too time-consuming.
m Recycling takes more effort to recycle than it is worth.
m Storing and sorting recyclable materials is unsanitary.
m Recycling is inconvenient.
m I do not know where to recycle near my home.
143

m I know where to recycle but recycling drop-off centers are too far way.
m I have more important things to do than recycling.
m Because of daily hassles, I often forget to recycle.
m The quality of my daily life would change for the worse if I recycled.
m Other _________________________________________________________

SECTION II. TAILGATING EXPERIENCE

9. Are you an MSU football season ticket holder in 2013?

r Yes r No

10. Were you an MSU football season ticket holder in 2012? r Yes r No
11. Have you tailgated before today?
r YES (If YES, please answer the Questions 11a through 11d)
r NO (If NO, please skip ahead to Question 12)
11a. What was the first year you tailgated at MSU? _________
11b. What was the first year you tailgated at other universities/colleges? _________
11c. How many times did you tailgate at MSU during the years 2010, 2011 and 2012?
________ # TOTAL TIMES
11d. How many times did you tailgate at other universities/colleges during the years
2010, 2011 and 2012
_________ # TOTAL TIMES

12. How many day(s) did you spend away from home on your trip to MSU where you were
surveyed?
_____ TOTAL DAY(S)
13. Please check all of the following motivations you have for tailgating at MSU? Please check
ALL that apply.
m Food
m Beverages
m Fun

m Socialize
m Being outdoors on campus
m Being away from the daily
routine

144

m Support school/team
m Game day atmosphere
m Other: ____________________

14. For each statement, please circle the one number that best describes you in regard to the
statement about tailgating, whether at MSU or elsewhere.
Strongly
Disagree
Tailgating is one of the most enjoyable things I do.
Tailgating is very important to me.
Tailgating is one of the most satisfying things I do.
I find a lot of my life is organized around Tailgating.
Tailgating occupies a central role in my life.
To change my preference from Tailgating to another recreation activity
would require major rethinking.
I enjoy discussing Tailgating with my friends.
Most of my friends are in some way connected with Tailgating.
Participating in Tailgating provides me with an opportunity to be with
friends.
When I participate in Tailgating, I can really be myself.
I identify with the people and image associated with Tailgating.
When I’m Tailgating, I don’t have to be concerned with the way I look.
I can tell a lot about a person by seeing them Tailgating.
Participating in Tailgating says a lot about who I am.
When I participate in Tailgating, others see me the way I want them to
see me.

Strongly
Agree

1
1
1
1
1
1

2
2
2
2
2
2

3
3
3
3
3
3

4
4
4
4
4
4

5
5
5
5
5
5

1
1
1

2
2
2

3
3
3

4
4
4

5
5
5

1
1
1
1
1
1

2
2
2
2
2
2

3
3
3
3
3
3

4
4
4
4
4
4

5
5
5
5
5
5

15. Which ONE of the following best describes the highest level of education you have
completed? (Check one)
r Some high school or less
r High school diploma
r Some college/Technical school
r Bachelor’s degree
r Master’s/Professional degree
r Ph.D./Medical Doctor/Ed.D.
16. What is the one most important way for MSU to improve recycling and reduce waste during
tailgating.

Your assistance in this study is greatly appreciated. Please return your completed
questionnaire in the return envelope as soon as possible. Thank you!
145

Appendix G Reminder Messages

First Reminder:
Dear Tailgating Participant:
Recently, you provided us your name and address to help us better understand your tailgating
and recycling experiences. Unfortunately, while we have received a response from others, we
have not received one from you at this time. If you have already completed and returned the
questionnaire, please accept our thanks. If not, please take the 15 minutes necessary to respond
today. It is extremely important that you return your completed survey so that your views are
accurately represented. Your participation will help guide future recycling efforts on campus
during major events and improve communications with tailgaters.
Thank you very much for your participation.
Sincerely,
Ju Hyoung Han, Doctoral Candidate
Department of Community Sustainability

Dr. Charles Nelson, Associate Professor
Department of Community Sustainability

Second and Final Reminder:
Dear Tailgating Participant:
Thank you for completing a questionnaire during game day tailgating at MSU. This our final
reminder to encourage you to complete the follow-up survey about your tailgating experience.
While others we surveyed during tailgating have completed the follow-up, we have not
received one from you at this time. Please take the 15 minutes necessary to respond today.
Your participation will help guide future recycling efforts on campus during major events and
improve communications with tailgaters.
Thank you very much for your participation.
Sincerely,
Ju Hyoung Han, Doctoral Candidate
Department of Community Sustainability

Dr. Charles Nelson, Associate Professor
Department of Community Sustainability

146

Appendix H Email Message for Follow-up Online Survey Used at the First Phase Survey

Dear Tailgating Participant,
Thank you for completing a questionnaire during game day tailgating at MSU. As the survey
administrator discussed with you and you provided your contact information, this is the second
questionnaire to follow-up your tailgating and recycling experience. Your participation in this
survey will take approximately 15 minutes. Your participation will help guide future recycling
efforts on campus during major events and improve communications with tailgaters.
Please follow this link to the Survey: Take the Survey
Thank you very much for your participation.
Best regards,
Ju Hyoung Han

147

Appendix I Non-response Check for the First Phase Survey

Table I-1 Non-response check for Day 1 sample
Table I-2 Non-response check for Day 2 sample

Note 1)
GROUP 1 includes tailgaters who completed on-site survey, not provided contact information
for follow-up survey, GROUP 2 incudes tailgaters who completed on-site survey and provided
contact information for follow-up survey, yet did not participated in follow-up, and GROUP 3
includes tailgaters who completed both on-site and follow-up survey
Note 2)
DER1= MSU has visible communications about its green practices at tailgating; DER2 = MSU
has established active recycling programs where I am tailgating; DER3= MSU has established
systems to reduce food/material waste during tailgating; GEB1= I recycle materials; GEB2 = I
look for ways to reuse things; GEB3 = I look for ways to reduce food/material waste; Each of
GEBs measures tailgaters’ general environmental behavior both at home (H) and at tailgating
(T); All items are measured by 5-point Likert-type scale.

148

Table I - 1 Non-response check for Day 1 sample
GROUP 11)
N=292,
50.8%
Age: F (2, 568) = 9.884, p < .001
Mean (S.D.)
40.47 (14.58)

GROUP 21)
N=204
35.5%

GROUP 31)
N=79
13.7%

38.60 (13.46)

47.14 (16.92)

2

Gender: χ = (2, N = 570) = 1.70, p = .427
52.6%
55.4%
Male
47.4%
44.6%
Female
2
Affiliation: χ = (4, N = 575) = 9.31, p = .054
2.1%
4.4%
MSU Employee
11.0%
6.4%
MSU Student
87.0%
89.2%
Not MSU Employee or Student
MSU Alumni: χ2 = (2, N = 575) = 2.59, p = .274
42.1%
46.1%
Yes
57.9%
53.9%
No
2
Attending Game: χ = (2, N = 575) = 3.86, p = .145
84.9%
83.3%
Yes
15.1%
16.7%
No
2
Leader of Tailgating: χ = (2, N = 575) = 1.55, p = .460
28.8%
24.0%
Yes
No
71.2%
76.0%
2
Travel Miles: χ = (8, N = 575) = 14.03, p = .081
39.0%
33.8%
0-49 miles
44.2%
45.6%
50-99 miles
6.8%
11.3%
100-149 miles
2.4%
2.5%
150-199 miles
7.5%
6.9%
200 or more
Adult Group Size: χ2 = (4, N = 575) = 4.67, p = .323
65.1%
65.7%
1-5 adults
15.1%
19.6%
6-10 adults
19.9%
14.7%
11 or more
Having Child in Group: χ2 = (2, N = 575) = 2.25, p = .325
25.0%
26.0%
Yes
75.0%
74.0%
No
Tailgating Group Type: χ2 = (10, N = 574) = 12.23, p = .270
3.4%
3.4%
By myself
17.5%
12.8%
Family only
23.3%
20.7%
Friends only
54.1%
60.6%
Family and Friends
1.4%
1.5%
Club/Organization
0.3%
1.0%
Other

149

Potential
non-Response
Bias
Yes
No

46.8%
53.2%
No
7.6%
6.3%
86.1%
No
51.9%
48.1%
No
92.4%
7.6%
No
29.1%
70.9%
No
31.6%
36.7%
11.4%
7.6%
12.7%
No
64.6%
21.5%
13.9%
No
17.7%
82.3%
No
5.1%
27.8%
19.0%
46.8%
1.3%
0.0%

Table I - 1 (Cont’d)
GROUP 11)
N=292,
50.8%

GROUP 21)
N=204
35.5%

GROUP 31)
N=79
13.7%

Difference
Test
Statistics

Potential nonResponse
Bias

Awareness of Recycling Program: χ2 = (2, N = 573) = 2.18 p = .336
56.7%
57.6%
65.8%
Yes
43.3%
42.4%
34.2%
No

No

Awareness of Recycling Location: χ2 = (2, N = 573) = 7.89, p < .05
37.1%
36.0%
53.2%
Yes
62.9%
64.0%
46.8%
No

Yes

Destination Environmental Responsibility (mean, S.D.)
DER12)

3.25 (1.23)

3.31 (1.21)

3.42 (1.26)

DER22)

3.18 (1.18)

3.16 (1.20)

3.24 (1.31)

DER32)

3.03 (1.19)

2.98 (1.18)

2.97 (1.13)

F (2, 570) = 0.563,
p = .570
F (2, 570) = 0.134,
p = .874
F (2, 570) = 0.160,
p = .852

No
No
No

General Environmental Behavior at Home (mean, S.D.)
GEH_H1

2)

4.01 (1.25)

4.20 (1.06)

4.41 (0.95)

GEH_H22)

3.89 (1.20)

3.93 (1.12)

4.14 (1.03)

GEH_H32)

3.91 (1.11)

3.90 (1.11)

4.15 (1.00)

F (2, 558) = 4.124,
p < .05
F (2, 558) = 1.506,
p = .223
F (2, 558) = 1.739,
p = .177

Yes
No
No

General Environmental Behavior at Tailgating (mean, S.D.)
GEH_T1

2)

3.27 (1.40)

3.25 (1.36)

3.52 (1.28)

GEH_T22)

2.90 (1.46)

3.02 (1.37)

3.34 (1.27)

GEH_T32)

3.02 (1.39)

3.07 (1.35)

3.41 (1.22)

150

F (2, 564) = 1.214,
p = .298
F (2, 564) = 2.849,
p = .059
F (2, 564) = 2.524,
p = .081

No
No
No

Table I - 2 Non-response check for Day 2 sample
GROUP 11)
N=297,
56.0%
Age: F (2, 518) = 8.05, p < .001
Mean (S.D.)
43.78 (14.90)

GROUP 21)
N=161
30.4%

GROUP 31)
N=71,
13.6%

39.64 (13.44)

47.24 (12.82)

2

Gender: χ = (2, N = 525) = 12.25, p < .01
57.8%
50.0%
Male
42.2%
50.0%
Female
2
Affiliation: χ = (4, N = 530) = 0.81, p = .937
2.7%
1.9%
MSU Employee
7.7%
8.1%
MSU Student
89.6%
90.1%
Not MSU Employee or Student
MSU Alumni: χ2 = (2, N = 530) = 1.33, p = .515
46.5%
51.6%
Yes
53.5%
48.4%
No
2
Attending Game: χ = (2, N = 530) = 0.07, p = .965
91.2%
90.7%
Yes
8.8%
9.3%
No
2
Leader of Tailgating: χ = (2, N = 530) = 2.89, p = .236
30.0%
31.1%
Yes
70.0%
68.9%
No
Travel Miles: χ2 = (8, N = 530) = 5.30, p = .725
33.3%
29.2%
0-49 miles
44.8%
54.0%
50-99 miles
9.4%
8.1%
100-149 miles
2.7%
1.2%
150-199 miles
5.7%
7.5%
200 or more
2
Adult Group Size: χ = (4, N = 529) = 3.94, p = .414
70.3%
65.2%
1-5 adults
20.3%
23.0%
6-10 adults
9.5%
11.8%
11 or more
Having Child in Group: χ2 = (2, N = 529) = 0.34, p = .843
23.6%
26.1%
Yes
76.4%
73.9%
No
Tailgating Group Type: χ2 = (10, N = 529) = 16.68, p = .082
2.4%
3.7%
By myself
17.2%
23.0%
Family only
20.9%
11.8%
Friends only
58.1%
60.9%
Family and Friends
0.7%
0.0%
Club/Organization
0.7%
0.6%
Other

151

Potential
non-Response
Bias
Yes
Yes

35.2%
64.8%
No
2.8%
5.6%
91.7%
No
52.4%
48.6%
No
91.7%
8.3%
No
40.3%
59.7%
No
26.4%
54.2%
11.1%
4.2%
4.2%
No
65.3%
18.1%
16.7%
No
25.0%
75.0%
No
4.2%
20.8%
15.3%
55.6%
0.0%
4.2%

Table I - 2 (Cont’d)
GROUP 11)
N=297,
56.0%

GROUP 21)
N=161
30.4%

GROUP 31)
N=71,
13.6%

Difference
Test
Statistics

Potential nonResponse
Bias

Awareness of Recycling Program: χ2 = (2, N = 528) = 8.88 p < .05
51.9%
58.5%
70.8%
Yes
48.1%
41.5%
29.2%
No

Yes

Awareness of Recycling Location: χ2 = (2, N = 527) = 1.34, p = .511
38.4%
39.9%
45.8%
Yes
61.6%
60.1%
54.2%
No

No

Destination Environmental Responsibility (mean, S.D.)
DER12)

3.15 (1.21)

3.24 (1.17)

3.38 (1.27)

DER22)

3.04 (1.19)

3.12 (1.18)

3.32 (1.27)

DER32)

2.87 (1.16)

2.96 (1.16)

3.10 (1.20)

F (2, 516) = 1.064,
p = .346
F (2, 514) = 1.610,
p = .201
F (2, 514) 1.181,
p = .308

No
No
No

General Environmental Behavior at Home (mean, S.D.)
GEB_H1

2)

3.98 (1.25)

4.13 (1.10)

4.46 (0.95)

GEB_H22)

3.79 (1.16)

4.02 (1.00)

4.21 (0.99)

GEB_H32)

3.81 (1.11)

3.97 (1.10)

4.32 (0.92)

F (2, 522) = 5.039,
p < .01
F (2, 522) = 5.261,
p < .01
F (2, 522) = 6.615,
p < .01

Yes
Yes
Yes

General Environmental Behavior at Tailgating (mean, S.D.)
GEB_T1

2)

3.02 (1.32)

3.21 (1.31)

3.43 (1.43)

GEB_T22)

2.80 (1.30)

3.06 (1.26)

3.32 (1.33)

GEB_T32)

2.97 (1.33)

3.16 (1.31)

3.36 (1.34)

152

F (2, 522) = 3.032,
p = .05
F (2, 522) = 5.21,
p < .01
F (2, 522) = 2.975,
p = .053

No
Yes
No

Appendix J On-site Survey Used at the Second Phase Survey
(Fit into two pages, print both-sided)

MSU Game Day Survey

Dear Tail Gate Participant:
Researchers from the Department of Community Sustainability at MSU are working to better
understand the relationship between one’s attitudes regarding recycling in daily life and when
one attends a major sporting event. This research is funded in part by the MSU Office of Campus
Sustainability. It will be used to help guide future recycling efforts on campus for major events.
Please take the 10 minutes or so needed to complete this survey. You indicate your voluntary
agreement to participate by completing and returning this survey. You will be asked to
participate in a follow-up survey by providing your email or mailing address. However, if you
choose not to complete all or part of the questions, you will not suffer any penalty. You are free
to discontinue your participation at any time. Your responses will be kept confidential and your
privacy will be protected to the maximum extent allowable by law. Thanks! Ju Hyoung Han,
Doctoral Candidate and Dr. Chuck Nelson, Associate Professor, Department of Community
Sustainability, 131 Natural Resources Building, MSU. For further information contact
hanju@msu.edu or nelsonc@msu.edu.

1. Are you best described currently as (check one)
r An MSU Employee
r An MSU Student
r Not MSU Student or Employee
1a. If you are not a current MSU student or employee, check those that apply. Are you
r An MSU Alumnus
r A family member of an MSU student
r A family member of an MSU employee
__________

2. What is your five-digit zip code?
3. In what year were you born?
4. What is your gender?

__________

r Male

r Female

153

5. Are you here as a fan of (check one)

r MSU team r Visiting team r Other: _______
r Yes r No

6. Are you attending the game today?

7. Are you the leader of the tailgate at this site? r Yes r No
8. How many miles did you travel from your principal home to East Lansing for this
experience? ___________# MILES (One-way)
9. How many people came with you to this tailgate?
#______ adults 18 and older (incl. you) & #_____ children under 18
10. Which one best describes those who came with you to this tailgate? (please check one)
r By myself
r Family only
r Family and Friends r Club/Organization
r Other _____________
11. Have you tailgated before today?

r Friends only

r YES

r NO

If yes,
11a. What was the first year you tailgated at MSU? _________
11b. What was the first year you tailgated at other universities/colleges? _________
11c. How many times did you tailgate at MSU during the years 2010, 2011 and 2012?
_________ # TOTAL TIMES
11d. How many times did you tailgate at other universities/colleges during the years
2010, 2011 and 2012
_________ # TOTAL TIMES
12. For each statement, please circle the one number that best describes you in regard to
the statement about tailgating, whether at MSU or elsewhere.
Strongly
Disagree
Tailgating is one of the most enjoyable things I do.
I find a lot of my life is organized around Tailgating.
I enjoy discussing Tailgating with my friends.
When I participate in Tailgating, I can really be myself.
I can tell a lot about a person by seeing them Tailgating.

154

1
1
1
1
1

2
2
2
2
2

3
3
3
3
3

4
4
4
4
4

Strongly
Agree
5
5
5
5
5

13. I was aware that MSU is operating recycling programs in tailgating areas today before
receiving this questionnaire. r Yes
r No
14. I know where to recycle near my tailgating location today. r Yes

r No

15. Please circle the one response that best describes your perceptions of MSU’s recycling
activity at football tailgating.
Strongly
Disagree
MSU has visible communications about its green
practices at tailgating.
MSU has established active recycling programs where I
am tailgating.
MSU has established systems to reduce food/material
waste during tailgating.

Strongly
Agree

1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

16. Please circle the response that best describes your behaviors at your home and at MSU
football tailgating.
0
Don’t Use

1
2
Never

3

4

AT HOME

NA 1 2 3 4 5
NA 1 2 3 4 5
NA 1 2 3 4 5
0
1 2 3 4 5
0
1 2 3 4 5
0
1 2 3 4 5
0
1 2 3 4 5
NA: Not Applicable

5
Always
AT TAILGATING

I recycle materials.
I look for ways to reuse things.
I look for ways to reduce food/material waste.
I recycle non-deposit glass containers.
I recycle non-deposit aluminum cans.
I recycle plastic (e.g., bottle, cup, grocery bags)
I recycle paper (e.g., mixed paper, boxboard).

155

NA
NA
NA
0
0
0
0

1
1
1
1
1
1
1

2
2
2
2
2
2
2

3
3
3
3
3
3
3

4
4
4
4
4
4
4

5
5
5
5
5
5
5

17. Please circle the one response that best describes your perceived level of difficulty
concerning recycling at home and at MSU football tailgating.

0
Don’t Use

1
2
Not Difficult At All

3

4
5
Very Difficult
AT TAILGATING

AT HOME

0 1 2 3 4 5
0 1 2 3 4 5
0 1 2 3 4 5
0 1 2 3 4 5
NA: Not Applicable

Recycling non-deposit glass containers.
Recycling non-deposit aluminum cans.
Recycling plastic (e.g., bottle, cup, grocery bags).
Recycling paper (e.g., mixed paper, boxboard).

0
0
0
0

1
1
1
1

2
2
2
2

3
3
3
3

4
4
4
4

5
5
5
5

18. Please share your general perceptions of recycling practices in regards to non-deposit
glass and cans, as well as plastic and paper.
Strongly
Disagree
I feel morally obligated to engage in recycling.
I should do anything I can do to recycle.
I feel guilty when I waste recyclable materials.
Most people who are important to me think that I should engage in
recycling.
I think that a majority of people in the United States engage in recycling.
I think that a majority of people at tailgating areas at MSU engage in
recycling.
I think that a majority of people in my community engage in recycling.
A majority of people in the United States should engage in recycling.
A majority of people at tailgating areas at MSU should engage in
recycling.
A majority of people in my residential community should engage in
recycling.
I recycle because of the enjoyment that I feel.
I take satisfaction in recycling.
I recycle because I enjoy learning new skills/techniques.
One of the best things about recycling is that it helps lower costs to
society.
We should recycle materials and protect the environment to benefit
people.
My health and the health of my family may improve because of recycling.
Thank you very much for your help today!
156

Strongly
Agree
1
1
1
1

2
2
2
2

3
3
3
3

4
4
4
4

5
5
5
5

1
1

2
2

3
3

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Appendix K Skewness and Kurtosis of Measurement Items

Table K - 1 Skewness and kurtosis of measurement items
Measurement Items
Skewness
Kurtosis
(PMN1) I feel morally obligated to engage in recycling.
2.18
-4.85
(PMN2) I should do anything I can do to recycle.
1.91
-4.78
(PMN3) I feel guilty when I waste recyclable materials.
1.23
-5.33
(DSN1) I think that a majority of people in the United States
engage in recycling.
2.80
-1.37
(DSN2) I think that a majority of people at tailgating areas at
MSU engage in recycling.
3.06
-1.47
(DSN3) I think that a majority of people in my community
engage in recycling.
-2.14
-2.14
(ISN1) A majority of people in the United States should
engage in recycling.
-3.13
-6.68
(ISN2) A majority of people at tailgating areas at MSU
should engage in recycling.
-1.81
-7.00
(ISN3) A majority of people in my residential community
should engage in recycling.
-2.92
-6.71
(HG1) I recycle because of the enjoyment that I feel.
-2.62
-3.34
(HG2) I take satisfaction in recycling.
1.38
-5.18
(HG3) I recycle because I enjoy learning new
skills/techniques.
1.23
-3.82
(GG1) One of the best things about recycling is that it helps
lower costs to society.
-0.35
-5.20
(GG2) We should recycle materials and protect the
environment to benefit people.
-2.89
-6.37
(GG3) My health and the health of my family may improve
because of recycling.
1.35
-5.15
Note: PMN=personal moral norms; DSN=descriptive social norms; ISN=injunctive social
norms; HG=hedonic goals; GG=gain goals

157

Appendix K: Skewness and Kurtosis of Measurement Items (Cont’d)

Table K - 1 (Cont’d)
Measurement Items
(PBD_H1) Recycling non-deposit glass containers at home.
(PBD_H2) Recycling non-deposit aluminum cans at home.
(PBD_H3) Recycling plastic (e.g., bottle, cup, grocery bags)
at home.
(PBD_H4) Recycling paper (e.g., mixed paper, boxboard) at
home.
(PBD_T1) Recycling non-deposit glass containers at
tailgating.
(PBD_T2) Recycling non-deposit aluminum cans at
tailgating.
(PBD_T3) Recycling plastic (e.g., bottle, cup, grocery bags)
at tailgating.
(PBD_T4) Recycling paper (e.g., mixed paper, boxboard) at
tailgating.
(PEB_H1) I recycle non-deposit glass containers at home.
(PEB_H2) I recycle non-deposit aluminum cans at home.
(PEB_H3) I recycle plastic (e.g., bottle, cup, grocery bags) at
home.
(PEB_H4) I recycle paper (e.g., mixed paper, boxboard) at
home.
(PEB_T1) I recycle non-deposit glass containers at tailgating.
(PEB_T2) I recycle non-deposit aluminum cans at tailgating.
(PEB_T3) I recycle plastic (e.g., bottle, cup, grocery bags) at
tailgating.
(PEB_T4) I recycle paper (e.g., mixed paper, boxboard) at
tailgating.
(HABIT_H1) I look for ways to reuse things at home.
(HABIT_H2) I look for ways to reduce food/material waste at
home.
(HABIT_T1) I look for ways to reuse things at tailgating.
(HABIT_T2) I look for ways to reduce food/material waste at
tailgating.
AGE

Skewness
2.16
3.02

Kurtosis
-6.88
-6.66

2.66

-6.58

1.86

-6.66

-1.01

-4.21

-0.83

-4.33

-0.42

-3.89

-1.19
-3.12
-3.49

-4.27
-7.24
-5.71

-3.03

-6.74

-3.39
-1.74
-1.80

-7.11
-5.37
-5.43

-0.68

-5.06

1.33
-0.98

-5.21
-7.15

0.97
-0.65

-7.07
-4.59

-1.00
-3.98
1.14
-4.23
Note: PBD=perceived behavioral difficulty; PEB=pro-environmental behavior; HABIT=habitual
environmental behavior

158

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