RESIDENTS’ ENVIRONMENTALLY RESPONSIBLE BEHAVIOR AND THE CONCEPTS
OF CONNECTEDNESS TO COASTAL RESOURCES
By
Jenni Lee

A THESIS
Submitted to
Michigan State University
in partial fulfillment of the requirements
for the degree of
MASTER OF SCIENCE
Community, Agriculture, Recreation and Resource Studies
2012

ABSTRACT
RESIDENTS’ ENVIRONMENTALLY RESPONSIBLE BEHAVIOR AND THE CONCEPTS
OF CONNECTEDNESS TO COASTAL RESOURCES
By
Jenni Lee
Most coastal communities are highly densely populated areas and simultaneously popular
tourism and recreation destinations. Accordingly, coastal residents cause negative impacts on the
environment through residence and recreation activities. Residents’ environmentally responsible
behavior (ERB) may be considered trivial but should be thought of as a fundamental step to
minimizing negative impacts on the environment. Therefore, it is critical to find important
factors that can promote ERB. The purpose of this study is to explore how several attitudinal and
psychological variables, such as place attachment, attitudes toward tourism development
(ATTD), and beach satisfaction are related to the facilitation of residents’ ERB.
For data collection, a mail survey was conducted using a stratified random sample of
South Carolina coastal residents. The residents were from three coastal counties known as
popular tourism and recreation destinations. Of 3000 households receiving questionnaires, 697
returned them. Factor analysis was conducted first for data reduction and then multiple
regression analysis was conducted.
The findings suggest that place attachment and ATTD were significant determinants
promoting ERB. However, beach satisfaction and socio-demographic variables were not
important. The study’s results suggest that resource managers and policy makers need to provide
more community participatory programs so residents become more attached to local natural
resources and ultimately get more actively involved in ERB. Future research will be beneficial to
generalize these findings to other coastal areas.

ACKNOWLEDGEMENTS

First of all, I would like express my appreciation to my faculty advisor, Dr. Chi-Ok Oh,
for his support and suggestions throughout this thesis. Without his guidance, I would never have
been able to finish my thesis. I would also like to thank my guidance committee members, Dr.
Dennis Propst and Dr. Stephen Gasteyer, whose suggestions and guidance improved the quality
of my thesis.
Also, while I have not named them all, I would like to say thank you to all of my friends
with whom I shared my life as a master’s student.
Lastly, thanks and much love to my parents, Jong-Young Lee and Mee-Ran Oh, and
sisters Soo-Jeong Lee and Soo-Bin Lee. My parents have always supported my dream and
encouraged me to overcome my struggles. Without their support, I would not be standing on this
stage.

iii

TABLE OF CONTENTS

LIST OF TABLES .......................................................................................................................... v
LIST OF FIGURES ...................................................................................................................... vii
I. INTRODUCTION ....................................................................................................................... 1
1. Introduction ............................................................................................................................. 1
2. Purpose of the Study and Research Questions ........................................................................ 9
II. LITERATURE REVIEW ......................................................................................................... 12
1. Environmentally Responsible Behavior (ERB) .................................................................... 12
2. Place Attachment and Environmentally Responsible Behavior (ERB) ................................ 12
3. Attitudes toward Tourism Development and ERB ............................................................... 16
4. Place Satisfaction and ERB ................................................................................................... 22
5. Socio-demographic Variables and ERB ................................................................................ 26
6. Place Segmentation Analysis ................................................................................................ 29
III. METHODOLOGY ................................................................................................................. 33
1. Introduction ........................................................................................................................... 33
2. Setting and Data Collection.................................................................................................. 33
3. Measurement Instruments ..................................................................................................... 34
4. Data Analysis ........................................................................................................................ 39
IV. RESULTS ............................................................................................................................... 41
1. Introduction ........................................................................................................................... 41
2. Descriptive Statistics ............................................................................................................. 42
3. Exploring the Structure of Variables ..................................................................................... 52
4. Hypotheses Testing ............................................................................................................... 58
5. Place Segmentation Analysis ................................................................................................ 60
V. DISCUSSION .......................................................................................................................... 67
1. Discussion ............................................................................................................................. 67
2. Limitations ............................................................................................................................ 67
3. Future Research ..................................................................................................................... 78
APPENDICES .............................................................................................................................. 82
APPENDIX A: Survey Scales................................................................................................... 88
APPENDIX B: Correlation of Items and Scree Plots ............................................................... 92
REFERENCES ............................................................................................................................. 98

iv

LIST OF TABLES

Table 1 Measurement Instruments ................................................................................................ 37
Table 2 Comparison of Demographic Characteristics .................................................................. 41
Table 3 Frequency of ERB Categories ......................................................................................... 43
Table 4 Frequency Distribution of ERB Items (1=Rarely to 5=Usually) ..................................... 43
Table 5 Frequency of Place Attachment Categories ..................................................................... 45
Table 6 Frequency Distribution of Place Attachment Scale Items ............................................... 45
Table 7 Frequency of Attitudes toward Tourism Development Categories ................................. 47
Table 8 Frequency Distribution of Attitudes toward Tourism Development ............................... 47
Table 9 Frequency Distribution of Place Satisfaction .................................................................. 49
Table 10 Frequency Distribution of Length of Residence ............................................................ 49
Table 11 Frequency Distribution of Distance to Closest Beach Access ....................................... 50
Table 12 Frequency Distribution of Gender ................................................................................. 50
Table 13 Frequency Distribution of Age ...................................................................................... 51
Table 14 Frequency Distribution of Income ................................................................................. 51
Table 15 Total Variance Explained .............................................................................................. 52
Table 16 Communalities ............................................................................................................... 52
Table 17 Pattern Matrix ................................................................................................................ 52
Table 18 Total Variance Explained .............................................................................................. 55
Table 19 Communalities ............................................................................................................... 55
Table 20 Pattern Matrix ................................................................................................................ 55
Table 21 Total Variance Explained .............................................................................................. 57
v

Table 22 Communalities ............................................................................................................... 57
Table 23 Rotated Component Matrix ........................................................................................... 58
Table 24 Coefficients Table .......................................................................................................... 60
Table 25 Comparison of Demographic Information across the three counties Using ANOVA .. 62
Table 26 Multiple Regression Coefficients with Dummy Variables (place of residence) ........... 62
Table 27 Beaufort County Coefficients Table .............................................................................. 63
Table 28 Charleston County Coefficients ..................................................................................... 64
Table 29 Horry County Coefficients............................................................................................. 65
Table 30 Satisfaction Scale ........................................................................................................... 88
Table 31 Place Attachment Scale ................................................................................................. 88
Table 32 Attitudes toward Tourism Development Scale .............................................................. 89
Table 33 Environmentally Responsible Behavior Scale ............................................................... 90
Table 34 Correlations of Items in the Environmentally Responsible Behavior Scale .................. 92
Table 35 Correlations of Items in the Place Attachment Scale .................................................... 94
Table 36 Correlations of Items in the Attitudes toward Tourism Development Scale ................. 96

vi

LIST OF FIGURES

Figure 1 Conceptual Model .......................................................................................................... 11
Figure 2 Scree plot for ERB.......................................................................................................... 93
Figure 3 Scree Plot for Place Attachment ..................................................................................... 95
Figure 4 Scree Plot for Attitudes toward Tourism Development ................................................. 97

vii

I. INTRODUCTION

1. Introduction
Typically, population growth negatively impacts natural resources and the environment.
The natural environment that encompasses residential districts is likely to be especially
vulnerable to degradation (Wenner, Sanger, Upchurch, & Thompson, 2010). Coastal areas, in
particular, are densely populated and easily impacted by human residence as well as recreation
and leisure activities. One of the consequences of such densely populated areas is humaninduced marine and coastal pollution. This comes about through waste waters released from
human settlements and from poor trash disposal practices (Bird, 1998). Also, a variety of
recreation and leisure activities that occur in these areas renders the environment more
vulnerable to human-induced impacts (Marafa, 2008). Especially, uncontrolled visitation and
overuse of beaches degrade beach conditions and destroy some of the flora and fauna (Kreag,
2001; Priskin, 2003).
These negative human impacts are caused not only by tourists but by coastal residents.
Particularly, a majority of coastal residents, who are often avid recreationists, can negatively
impact the environment through their residential and recreational activities. Thus, if coastal
resources are to be used sustainably, coastal residents are also responsible for conserving natural
resources and the environment (Bird, 1998). As individual behavior can either impair or
ameliorate environmental problems, it is important for individuals, particularly residents, to
engage in environmentally responsible behavior (ERB) as an important step in preserving natural
resources and the environment.
ERB refers to an individual’s actions that are intended to obviate harmful effects on the
environment (Saunders, 2003). To promote residents’ ERB, it is important to understand the
1

mechanism of how an individual adopts ERB. Identifying important factors that lead to longterm behavioral change can effectively contribute to more environmental conservation. For
example, if residents’ attitudes toward ERB become more positive, they could reveal more
consistent ERB. Thus, it is important to find factors that can lead to more consistent ERB. In the
present study, several factors are identified as predictors of ERB, including place attachment,
attitudes toward tourism development, place satisfaction, and socio-demographic variables.
Conceptual Framework
To explain the formative process of ERB, a number of studies have focused on exploring
various factors that contribute to residents’ ERB (De Young, 2000; Kaplan, 2000; Nordlund &
Garvill, 2002; Vaske & Kobrin, 2001). These include altruism, personal-norm, intrinsic
satisfaction, and socio-demographic variables. All of thesecan be classified largely into internal
and external factors. Internal factors indicate psychological variables that are based on values,
beliefs, and attitudes (Clark, Kotchen, & Moore, 2003). External factors refer to individuals’
external conditions, such as socio-demographic variables (Van Liere & Dunalap, 1980; Vaske &
Kobrin, 2001). It has been suggested that rather than focusing on the effects of these factors
separately, internal and external factors ought to be integrated into one research framework. This
kind of research framework could help researchers examine which determinants are important in
facilitating the adoption of ERB (Guagnano, Stern, & Dietz, 1995). Accordingly, this study
explores the formative process of ERB using two different sets of explanatory variables, internal
and external factors. The internal factors included are place attachment, attitudes toward tourism
development, and place satisfaction. The external factors included are socio-demographic
variables.
Using a study population of coastal residents living in popular tourism destinations, this
2

thesis intends to assess how the concept of ERB is empirically associated with these internal and
external factors. It is expected that each concept of place attachment and attitudes toward tourism
development has a positive relationship with ERB. However, it is hypothesized that residents
who are more satisfied with place will show less ERB. Finally, it is expected that different sociodemographic variables also influence residents’ ERB.
The study identifies place attachment as an important internal predictor. Place attachment
can be defined as an emotional or affective bond between a person and a particular place (Vaske
& Kobrin, 2001). The psychological structure of place attachment is complex. It is composed of
self-referent cognitions, emotions, and behavioral commitment, causing its structure to vary
(Jorgensen &Stedman, 2001). However, place attachment is most commonly measured by two
sub-dimensions of place attachment: place identity and place dependence. Place identity is
understood as belonging to the cognitive process that expresses emotional attachment to a place.
Place dependence is understood as representing functional attachment to a place, formed by
experiences with that place (Jorgensen & Stedman, 2001; Williams & Roggenbuck, 1989). This
study tries to explain ERB utilizing this two-dimensional approach.
Many studies have demonstrated that place attachment can lead to more robust ERB
(Halpenny, 2010; Moore & Graefe, 1994; Scannell &Gifford b, 2010; Vaske & Kobrin, 2001).
For example, Vaske and Kobrin’s study (2001) found that children participating in communitybased natural-resources based programs were more attached to their local natural environment
and more likely to behave environmentally friendly. This relationship can be supported by
Fishbein and Ajzen’s theory of reasoned action (TRA) (1977). According to TRA, individual
behavior can be explained by several important antecedents, such as attitudes toward the
behavior, subjective norm, and behavioral intention (Eagly & Chaiken, 1993, p. 169). Place

3

attachment is also considered to be reflective of environmental attitudes, as it consists of
cognitive and affective components (Halpenny. 2006; Jorgersen & Stedman, 2001). Thus,
because this attitudinal concept is an antecedent to behavior, it is expected that place attachment
is closely connected with ERB. As residents are attached to the natural environment around the
community they live in, they are more likely to be concerned about and sensitive to negative
environmental impacts (Kyle, Graefe, Manning, & Bacon, 2004; Scannell & Gifforda, 2010;
Schultz, Jennifer, Tabanico, & Khanzian, 2004). Likewise, residents benefitting directly or
indirectly from resources and the environment, such as local beaches, are more likely to become
attached to those places (Moore & Graefe, 1994). And, attached residents, in turn, are more
likely to become concerned about beach conditions and, ultimately, behave in a manner that
protects the beaches (Vorkinn & Riese, 2001).
As another internal factor of ERB, this study includes attitudes toward local tourism
development. This factor consists mainly of the degree to which residents agree about the
positive and negative impacts of local tourism development. Attitudes toward local tourism
development deal with two themes: personal and community benefits from local tourism
development and concern over local tourism development. The first theme gauges residents’
attitudes toward personal and community benefits reaped from local tourism development. The
second theme, concern over tourism development, gauges residents’ attitudes toward negative
impacts caused by local tourism development.
Attitudes toward tourism development and ERB seem to be connected. Indeed, TRA
implies that one of the best predictors of behavior is attitude toward that behavior (Eagly &
Chaiken, 1993). This study, however, identifies attitudes toward target (i.e., attitudes toward
tourism development) rather than attitudes toward behavior (i.e., ERB). This relationship can be

4

supported by Fazio’s relationship of attitudes toward target and behavior (1986). Fazio (1986)
applied Ajzen and Fishbein’s TRA (1977) to demonstrate how behavior was influenced by
attitudes toward the target. Fazio’s main framework (1986) explains the relationship between
attitudes and behavior in the following order: attitude toward target (here, tourism development)
→behavioral beliefs → attitudes toward behavior → behavior. If we apply Fazio’s framework
(1986) to this study, we might assume that residents’ attitudes toward tourism development can
influence their beliefs about ERB. In turn, these beliefs can predict their attitudes toward ERB
and, ultimately, affects ERB.
The process between attitudes toward target and behavioral beliefs can be further
strengthened by Stern’s value-belief-norm (VBN) theory (2000). Stern (2000) introduced three
value-based environmental concerns—egoistic, social-altruistic, and biospheric. Egoistic values
are related to personal benefits derived when a person, threatened by environmental problems,
acts to protect the environment. Social-altruistic values are related to the concern a person feels
through their own moral norms, for other people (neighbors and community members). If other
people suffer from any negative impacts, a person such as this is more likely to take action on
their behalf. Biospheric values are based on the concept that all living things are valuable.
Anyone holding such values is likely to behave more environmentally responsibly. Each of these
values can be connected to residents’ attitudes toward tourism development.
Based on the positive attitude items related to personal and community benefits, it can be
anticipated that local tourism development produces, in terms of economic and community
impacts, benefits for residents. When egoistic and social-altruistic values are applied, it can be
assumed that, to maintain such benefits, residents will act out of a deep concern for the local
beaches. These positive attitudes toward tourism development should result in behavioral beliefs

5

that the environment ought to be protected to ensure residents’ personal and social-altruistic
value. Their beliefs can result in generally positive attitudes toward ERB. Several authors have
shown that individuals who have positive attitudes toward tourism development develop a deeper
concern for nature-based tourism resources (e.g., Lepp, 2007; Walpole & Goodwin, 2001; Byrd,
Cardenas, & Dregalla, 2009).
Second, we contend that residents will also show negative attitudes as a result of negative
consequences derived from tourism development. These concerns revolve around environmental
and community issues. When biospheric values are applied, we expect that residents’ concerns
will be positively associated with ERB. A person is more likely to show ERB if he or she holds
biospheric values-showing concerns over negative environmental impacts caused by tourism
development. Thus, it can also be postulated that negative attitudes about local tourism
development will influence residents’ behavioral beliefs that ERB is necessary if their
community’s natural resources and environment are to be protected. And, that belief could result
in positive attitudes toward ERB and ultimately more ERB.
Therefore, it can be seen that within Fazio’s applied TRA framework, the relationship
between attitudes toward target and behavioral beliefs can be supported by Stern’s VBN theory
(2000). Both negative and positive attitudes toward tourism seem to impact the behavioral beliefs
that the natural environment (the local beach) needs to be protected. Such an impact is followed
by positive attitudes toward ERB and more ERB.
The last internal variable included is place satisfaction. Place satisfaction mainly
indicates the degree of overall satisfaction a resident experiences during a visit to a local beach.
Compared to place attachment and attitudes toward tourism development, place satisfaction may
be more representative of visitors than of residents. Also, place satisfaction is limited to the

6

judgment of the perceived quality of a setting. Unlike the other two predictors, place satisfaction
is not based on personal values (Stedman, 2002). These facts suggest residents could not have a
personally close relationship with place. Thus, the relationship between place satisfaction and
ERB may be negative. It is possible that a high degree of place satisfaction with local beaches
may indicate less ERB. According to Stedman (2002), high-satisfaction may lead to
complacency, the belief that no action is needed to improve beach conditions and the
environment. Therefore, this study assumes that a person highly satisfied with the local beaches
is less likely to be concerned about the environment and thus less interested in ERB.
In addition to these three internal variables, this study includes as external variables
socio-demographic factors. These factors include income, age, gender, distance, and length of
residence. A vast amount of the literature is dedicated to identifying socio-demographic variables
as important predictors of ERB. The results, however, are not always consistent (Barr, 2003;
Cottrell, 2003; Korfiatis, Hovardas, & Pantis, 2004; Larson, Whiting, & Green, 2011; Vaske,
Donnelly, Williams, & Jonker, 2001).
While these internal and external factors are identified as predictors of ERB, in this study,
general ERB is included rather than site-specific ERB. Typically, site-specific ERB is expected
to have carry-over effects (also known as spill-over effects) on general ERB. This implies that if
individuals engage in ERB at a specific site, they are more likely to become interested in other
types of ERB. Several studies have already demonstrated the existence of carry-over effects
when they tested the relationships between various types of ERB. Such behavior includes
environmentally friendly behavior and employee’s eco-friendly purchase behavior (Rashid,
2007), recycling and packaging waste prevention behavior (Thogersen, 1999), and different
types of environmentally friendly consumer behavior (Thogersen & Olander, 2003).

7

These relationships are supported by two theories: Schwartz's norm-activation theory
(1977) and Festinger’s theory of cognitive dissonance (1957). Schwartz's norm-activation theory
(Schwartz, 1977) implies that beliefs about the consequences of environmental degradation (here,
degradation of beach conditions) can encourage residents to participate in conservation behavior
in a specific site and sequentially to engage in other types of ERB. Festinger’s theory of
cognitive dissonance (1957) indicated that to reduce the psychological conflict from holding two
or more incompatible beliefs simultaneously, one tries to maintain one’s own beliefs. Based on
this theory, it can be assumed that individuals who carry out one type of ERB engage in other
types of ERB to achieve consonance with their own beliefs that natural resources and the
environment need to be conserved. This is so called spill-over effects (Rashid, 2007).
While this study does not explicitly consider residents’ beach-specific ERB, through their
close relationship with their local beaches, it is logically deduced that residents’ general ERB
originates from their intention to conserve the beach areas. Based on residents’ agreement about
their attachment to local beaches and attitudes toward local tourism development, it can be seen
how closely they are associated with local beaches. Thus, this assumption suggests that highly
attached residents with strong attitudes toward local tourism development will reveal beachspecific ERB and, ultimately, general ERB.
Lastly, to test whether residents in different coastal areas show heterogeneous processes
of developing ERB, this study also conducts a segmentation analysis by residential area.
Residents of South Carolina were selected as the population of the study. Three coastal tourism
destinations, in particular, were chosen: Beaufort, Charleston, and Horry Counties. While all are
popular destinations, each presents a different coastal destination type. Beaufort County was
chosen because it includes Hilton Head Island, an upscale tourism destination and retirement

8

community. Charleston County is well known as a destination providing historical, cultural, and
natural recreation opportunities for residents and tourists. Lastly, Horry County is famous for
Myrtle Beach, representing lower income and family oriented coastal tourism destination. Each
county shows a heterogeneous degree of tourism and/or economic development as well as
residents’ demographic characteristics. As a result, these distinctive features in each region are
expected to result in different degrees of place attachment, residents’ attitudes toward tourism
development, and satisfaction with the local beach. Further, the influences of these variables on
ERB will vary by region.
To sum up, this study conducts two main analyses. First of all, it demonstrates the
hypothesized relationships between ERB and predictors place attachment, attitudes toward
tourism development, place satisfaction, and socio-demographics. Secondly, it tests whether,
depending on residential place, each predictor could have different impacts on ERB.

2. Purpose of the Study and Research Questions
The main purpose of this study is to identify important factors that can facilitate the
adoption of ERB by coastal residents. Place attachment, attitudes toward tourism development,
place satisfaction, and socio-demographic variables are included as important explanatory
variables that predict variations in ERB.
ERB may be thought of as a fundamental step to improving environmental conditions. To
improve environmental conditions, a large number of people need to change their behavior to
improve environmental conditions (Saunders, 2003). Hence, it is sensible and useful to find an
effective way to promote community-wide ERB. This study, in several ways, differs from
previous ones. First, while several studies have focused on examining visitors’ ERB in the
context of nature-based tourism destinations (Halpenny, 2010; Vaske & Kobrin, 2010), this
9

study mainly targets the ERB of residents who live in nature-based tourism destinations. Second,
compared to other studies focused solely on place attachment (Budruk et al., 2009; Gosling &
Williams, 2010; Scannell & Gifford b, 2010), this study additionally includes other important
variables such as residents’ attitudes toward tourism development and place satisfaction. Because
Beaufort, Charleston, and Horry Counties are popular nature-based tourism destinations,
residents’ attitudes toward tourism development are expected to have a close relationship with
their ERB. Also, residents can be considered as active recreationists by visiting their local
beaches and using other natural resources in their communities. Thus, the level of residents’
satisfaction with to local beaches is also expected to have significant impacts on ERB. Finally,
this study makes use of a segmentation approach to assess whether residents of each destination
show different patterns of adopting ERB.
Given this, the study is guided by five research questions: (1) what is the predictive
relationship between residents’ place attachment and ERB, based on the two dimensions of place
attachment—place identity and place dependence?; (2) how are the two sub-dimensions derived
from attitudes toward tourism development associated with ERB?; (3) what is the relationship
between place satisfaction and ERB?; (4) what are the effects of socio-demographic variables
(age, distance, length of residence, gender, and income) on ERB? (5) do coastal residents of
different tourism destinations show dissimilar patterns of predictive relationships between the
predictors and ERB? Hence, the specific hypotheses can be stated as follows:

10

Place Attachment

+

Attitudes toward
Tourism Development

+
ERB

Place Satisfaction
?
Socio-demographic

Figure 1 Conceptual Model

H1: Residents who are emotionally and functionally attached to local beaches are more
likely to behave environmentally responsibly. Further, place identity has more effect on
ERB than does place dependence.
H2: Residents who are concerned about the negative impacts of local tourism development
on the community are more likely to engage in ERB.
H3: Residents who consider local tourism development as a beneficial tool for their
community are more likely to engage in ERB.
H4: Residents who are highly satisfied with recent visits to their local beaches are less likely
to participate in ERB.
H5: Socio-demographic variables will have different impacts on ERB.
H6: Depending on where residents live, each predictor will have different impacts on ERB.
11

II. LITERATURE REVIEW

1. Environmentally Responsible Behavior (ERB)
Many researchers have studied ERB as a strategy for improving environmental quality
(De Young, 2000; Kaplan, 2000; Nordlund & Garvill; 2002; Thapa, 1999; Thogersen, 2004).
ERB—also referred to as pro-environmental behavior or environmentally significant behavior—
can be defined as any behavior that can contribute to environmental conservation, and can result
in sustainable natural ecosystems and healthy environmental conditions (Sintov, Desario, &
Prescott, 2010). Several studies (e.g., Barr, 2007; Smith-Sebasto & D’Acosta, 1995; Whitmarsh
& O’Neill, 2010) have asserted that promoting ERB can contribute to the mitigation of human
impact on the natural environment. Some research has focused specifically on predicting a single
type of ERB, such as recycling or waste management (Barr, 2007; Schultz, Oskamp, & Mainieri,
1995). Others have measured multiple types of ERB on one scale (Smith-Sebasto & D’Acosta,
1995; Thapa, 1999). Smith-Sebasto and D’Acosta (1995) developed the environmentally
responsible behavior index (ERBI). ERBI consists of different types of environment-related
behaviors. These behaviors are known to fall under four main themes—consumerism, activism,
education, and recycling. Thapa (1999) adopted Smith-Sebasto and D’Acosta’s ERBI scale
(1995). Using the New Ecological Paradigm Scale (NEP scale) as environmental attitudes, Thapa
(1999) found that undergraduate students reported a relatively high rate of environmental
attitudes but a lower rate of participation in ERB. Among the four main ERB types, students
showed that the highest level of ERB consisted of recycling and the lowest consisted of activism.
While Thapa’s study (1999) indicated a low rate of ERB, it suggested that Smith-Sebasto and
D’Acosta’s ERBI (1995) is also applicable for study when measuring individuals’ multiple types
of ERB.
12

Various studies adopted varying structures of ERB. However, the main purpose of such
studies was to identify factors that could promote ERB. Many researchers have made an effort to
find factors of ERB in their specific study context (Clark et al., 2003; Cottrell, 2003; De Young,
2000; Larson et al., 2011; Vaske, Donnelly, Williams, & Jonker, 2001). Several researchers
included external factors as predictors of ERB, such as socio-demographic or economic
characteristic variables (Cottrell, 2003; Larson et al., 2011; Vaske et al., 2001).
More commonly, studies on ERB include internal variables. Such variables might
originate from values, beliefs, and attitudes toward a particular action (Clark et al., 2003;
Nordlund & Garvill, 2002; Nordlund & Garvill, 2003; Proshansky, Fabian, & Kaminoff, 1983;
Schultz, 2001; Stern, 2005; Van Liere & Dunlap, 1978). Nordlund and Garvill’s study (2002)
was based on value-oriented variables. Their study was mainly based on Schwartz’s normactivation theory (1977). This theory implies that an important determinant of ERB is the
activation of a personal norm. A personal norm can be activated when something an individual
values is threatened by environmental conditions. This means that an individual is aware of what
the consequences are of a poorly protected environment. If an individual places a great deal of
importance on general and environmental values, a more personal norm will be activated. Such a
norm represents moral obligations to engage in environmentally responsible behavior when an
individual starts to feel the environment is being threatened. Thus Nordlund and Garvill (2002)
hypothesized that an individual’s general and environmental values, indicated by selftranscendence and eco-centrism, will positively predict awareness of environmental problems
and that such values will influence one’s perceived moral obligations and finally ERB.
Similarly, Stern’s value-belief-norm (VBN) theory (2000) was developed based on a
value-oriented concept. VBN theory mainly comprises three types of values—egoistic, social-

13

altruistic, and biospheric. Egoistic values are related to personal benefits; social-altruistic values
are related to human concerns about other people, such as the neighborhood and community;
biospheric values are based on the concept that all living things are valuable. Personal norms can
be activated when an individual becomes aware of adverse effects on individual values caused by
violating ERB. Through a sequential process, this activated personal norm can result in ERB.
Thus, to promote ERB, Schwartz’s norm-activation theory (1977) and Stern’s VBN
theory (2000) point to the importance of considering awareness of consequence. Nordlund and
Garvill’s study (2003) applied both of these theories. The main purpose of their study was to
reduce personal car use. The authors found that car owners who knew the environmental
consequences of car use were more willing to reduce their use of it. In conclusion, in assessing
relationships between different determinants and ERB, value-based theory appears to be highly
applicable.
The ultimate goal of studying ERB is to contribute to the sustainability of the natural
environment. Previous studies illustrated numerous types of ERB with their own specific themes,
such as recycling (Berger, 1997). The best result of an ERB study, however, is one where
individuals have positive attitudes toward one type of ERB, which encourages another type of
ERB. Recently, several researchers have begun examining whether one type of ERB can predict
other types of ERB (Rashid, 2007; Thgersen, 1999; Thogersen, 2004; Thogersen & Olander,
2003; Whitmarsh & O’Neill, 2010). When one type of behavior that leads to the adoption of
other, more environmentally beneficial behaviors, it is called a catalyst behavior, also known as
the spill-over or carry-over effect. Whitmarsh and O’Neill (2010) reported on catalyst behavior,
stating that if people adopt new behavior- in their case recycling behavior- they will also take on

14

other types of ERB. Seven different types of ERB factors were identified. Correlation analysis
showed that some similar ERB were correlated, indicating the possibility of catalyst behavior.
Some other studies (Rashid, 2007; Thgersen, 1999; Thogersen, 2004; Thogersen &
Olander, 2003) applied Festinger’s cognitive dissonance theory (1957) to demonstrate ERB spillover effect. Cognitive dissonance theory (Festinger, 1957) is based on the idea that when
encountering dissonance, an individual tends to try to maintain a balance between their beliefs.
Thus, one is uncomfortable about behaving inconsistently. Based on cognitive dissonance theory,
Rashid (2007) demonstrated the spill-over effect. Rashid looked at employees of an organization
that operated an environmental management system. Such employment affected their attitudes
and norms, consequently, resulting in displays of ERB outside the organization. Thogersen (2004)
also demonstrated the tendency to behave consistently across different types of ERB (in this case,
buying organic and recycling). It can thus be postulated that an individual who conducts one type
of ERB will also behave responsibly in other types of ERB.
As predictors of ERB, this study identified one external variable, socio-demographic
factors, and three internal variables—place attachment, attitudes toward tourism development,
and place satisfaction. This thesis, for study sites, makes use of coastal tourism destinations. As
Schwartz’s norm-activation theory (1977) and Stern’s VBN theory (2000) have been applied to
diverse ERB studies within the context of the natural environment, applying these theories to
coastal areas could help explain relationships between various predictors and ERB. ERB, in this
study, indicates more general types of ERB rather than place-specific ERB (for instance, beach
protective action). While, all internal variables are related to local beaches, they are still expected
to predict general ERB. Residents’ close relationships to their local beach could reveal more

15

intention to conserve coastal areas and that might influence general ERB. In other words, as
found in the literature, this study expects to find a spill-over effect.

2. Place Attachment and Environmentally Responsible Behavior (ERB)
Place attachment is widely used by environmental psychologists. This overarching
concept encompasses a vast array of place-related literature (e.g., Kyle et al., 2003; Trentelman,
2009). Shumaker and Taylor (1983) defined place attachment as “a system of interlocked
attitudes and behaviors that refer to the home and the household and reflect the intimacy of
strength of the individual’s tie to the locale” (p. 220). Of the numerous studies on place
attachment (Gosling & Williams, 2010; Halpenny, 2010; Kyle et al. 2003; Vorkinn & Riese,
2001), each measures this multi-dimensional concept with a different approach. Researchers base
their various approaches on a certain facet of the concept. Some use a single dimension, place
attachment (Casakin & Kreitler, 2008; Stedman, 2002; Vorkinn & Riese, 2001). Others include,
in addition to place attachment, similar place concepts, such as connectedness to nature (Gosling
& Williams, 2010) and place identity (Hernandez, Martin, Ruiz, & Gidalgo, 2010).
Recently, however, researchers have used a two-dimensional approach developed by
Williams and Roggenbuck (1992), an approach consisting of place identity and place
dependence. Based on this approach, many studies zero in on the fields of leisure and recreation
(Budruck, White, Wordrich & Van Riper, 2008; Budruck, Thomas, & Tyrrell, 2009; Bricker &
Kerstetter, 2000; Hailu, Boxall, & McFarlane, 2005; Moore & Grafe, 1994; Kyle, Graefe,
Manning, & Bacon, 2004; Vaske & Kobrin, 2001; White, Virden, & Riper, 2008). Though they
look similar, place identity and place dependence differ in meaning. Kyle et al. (2010) defined
place identity as an “individual’s cognitions, beliefs, perceptions or thoughts that the self is
invested in [a] particular setting” (p. 1081). On the other hand, place dependence is more
16

concerned with “how well a setting serves goal achievement given an existing range of
alternatives” (Jorgensen & Stedman, 2001, p. 234). White et al. (2008) used these two
dimensions of place attachment to assess the relationship with visitors’ perception of recreational
impacts. Place identity and place dependence were positively predicted by prior experience, but
perceptions of recreational impact were not.
Hailu et al.’s study (2005) also demonstrated that visitors’ place attachment is
significantly influenced by previous trips. It implies that the attachment of visitors predicted by
the experience of previous trips could result in greater travel demand on recreational areas.
Moore and Graefe’s research (1994) mainly aimed at finding predictors of place identity and
place dependence within a recreational setting. The best predictor of place identity was length of
association with the trail. The best predictor of place dependence was distance from home and
frequency of visits to the trail. It is apparent that the leisure and recreation studies actively used
these two dimensions of place attachment as important variables to explain factors related to
leisure and recreation.
There have been numerous research efforts that examine a relationship between place
attachment and ERB (e.g., Budruk et al., 2009; Gosling & Williams, 2010; Halpenny, 2010;
Kyle, Absher, & Graefe, 2003; Kyle, Graefe, Robert, & James, 2004; Payton, Fulton, &
Anderson, 2005; Raymond, Brown, & Robinson, 2011; Scannell & Giffordb, 2010; Vaske &
Kobrin, 2001; Vorkinn & Riese, 2001). These scholars applied diverse dimensions of place
attachment, and also, depending on a study’s context, employed different types of ERB. Some
researchers focused on place-specific ERB (Gosling & Williams, 2010; Kyle et al., 2003; Kyle et
al., 2004; Payton et al., 2005; Raymond et al., 2011; Vorkinn & Riese, 2001); others examined

17

general ERB (Budruk et al., 2009; Halpenny, 2010; Scannell & Gifford, 2010; Vaske & Kobrin,
2001).
Place-specific ERB refers to behavior that is particularly focused on the conservation of a
specific place. Raymond et al. (2011) measured ERB that related to the conservation of native
vegetation. The authors found no direct relationship with ERB. Instead the authors looked at how
four dimensions of place attachment (place identity, place dependence, family bonding, and
friend bonding) moderated the relationship between awareness of consequence (AC; specific
belief) and personal norm (PN). PN, at last, directly predicted ERB. Also, they found that place
attachment was an important concept that moderated this relationship between AC and PN and
finally ERB.
Vorkinn and Riese (2001) examined how environmental concerns about the
development of a hydropower plant were predicted by place attachment. Place attachment, they
found, was significantly related to the subjects’ concerns, exposing a negative attitude about the
proposed hydropower development. Kyle et al. (2003) measured different types of recreationuser fees as an indicator of concern for the environment conditions. They identified two
dimensions of place attachment- place identity and place dependence. Place identity had a
positive relationship with fees supporting conservation and restoration of natural resources. Place
dependence also had a positive relationship with fees supporting facility development in the
natural resource settings. These results suggest that place-identified visitors are more likely to be
concerned about environmental conditions.
Kyle et al. (2004) also confirmed that place identity was more strongly associated with
ERB than was place dependence. Their study did not specifically represent ERB or concern for
the natural environment. Rather, it identified perceptions of social and environmental conditions

18

of the Appalachian Trail in the United States. Visitors with strong place identity felt that the
Appalachian Trail was more problematic while visitors with place dependence did not. Such a
finding suggests that visitors having place identity are more likely than those having place
dependence to exhibit narrow latitude about disturbing environmental conditions. It seems that in
predicting ERB, place identity outweighs place dependence. Payton et al.’s study (2005)
supports this idea. Place identity was significantly related to civic actions, which indicated
behavior that supported the conservation of a national wildlife refuge.
Most studies scrutinize place-specific ERB. Some, however, have revealed significant
relationships with general ERB (Budruk et al., 2009; Halpenny, 2010; Scannell & Gifford, 2010;
Vaske & Kobrin, 2001). Vaske and Kobrin’s study (2001) identified two dimensions of place
attachment to natural settings—place identity and place dependence. Unlike other studies, ERB
was composed of different types of general ERB, such as recycling behaviors. The authors
empirically showed that place identity significantly mediated a positive relationship between
place dependence and ERB. While no direct relationship between place dependence and ERB
was shown, the study indicated that ERB was significantly influenced by place identity. In this
study, respondents were children intercepted at a youth, natural-resource-based community work
program that also provided environmental education. Vaske and Kobrin (2001) concluded that in
addition to environmental education, place attachment assumed a significant role in contributing
to ERB.
Scannell and Gifford (2010) tested the relationship between place attachment and proenvironmental behavior using two settings: civic and nature place attachment. In their study,
nature place was known as outdoor recreation and tourism areas. Their study also included
general ERB. The results showed that people attached to a natural environment exhibited more

19

engagement in ERB; people attached to a civic place showed no such pattern. The disparity may
have been caused by the dominant values of each group. People attached to a natural
environment are more sensitive about possible harmful impacts (Kyle, Graefe, Manning, &
Bacon, 2004).
Budruk et al. (2009) examined the relationship between residents’ attachment to their
living place and their attitudes toward urban green space (pro-environmental attitudes). Proenvironmental attitudes were measured using the new environmental paradigm (NEP) scale. The
authors identified three particular dimensions of NEP: balance with nature, anthropocentrism,
and ecological limits. While the place of attachment was urban green spaces, the NEP scale
indicated no relation with this area. Likewise, place attachment was measured via place identity
and place dependence. The results show that place identity was strongly related to balance with
nature and weakly related to anthropocentrism. However, place dependence was not significantly
associated with any dimensions of NEP.
Lastly, Halpenny intended her research (2010) to demonstrate the relationship between
place attachment and general ERB. Like the aforementioned studies, Halpenny (2010) found that
general ERB was significantly connected with place identity only. Halpenny (2010) also
observed place-specific ERB (park protective behavior) and found that all the dimensions of
place attachment (place identity, place dependence, and place affect) were significant in
explaining the park-protective behavior. Both models place-specific ERB and general ERB
indicated the importance of place identity, representing broader applicability as a means of
promoting ERB.

20

In sum, as with place-specific ERB, general ERB has been found to have an important
relationship with place attachment. As a result, it is plausible to reason that attachment to natural
environment could positively influence not only place-specific ERB but general ERB.
Not all studies, however, have shown a significant relationship between proenvironmental concepts and place attachment (Gosling & Williams, 2010; Hernandez, Marti,
Ruiz, & Hidalgo, 2010; Uzzell, Pol, & Badenas, 2002). Gosling and Williams’ study (2010)
reported that place attachment failed to significantly predict conservation behaviors.
Conservation behaviors consisted of two types: on-farm replanting and remnant vegetation
protection behavior. Gosling and Williams (2010) assumed that place attachment would not
predict these two behaviors, as they designed the measurement items of conservation behaviors
so specifically; ERB only indicated farm-related behaviors, and not general environmental
behaviors.
Based on the literature, place attachment is identified as an important variable in
promoting ERB. Specifically, if the attached place is the natural environment, people are more
likely to behave in environmentally responsible ways (Scannell & Giffordb, 2010). For both
place-specific and general ERB, several articles identified place identity as being more important
than place dependence. Finally, as with place-specific ERB, general ERB has a positive
relationship with place attachment. It can be assumed that place attachment can result in the
carry-over effect (spill-over effect) of ERB. Hence, in this study, as residents are attached to a
natural environment (the local beach), it is expected that their place attachment can positively
predict ERB. Also, based on other studies’ findings, place identity is expected to play a more
important role than place dependence.

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3. Attitudes toward Tourism Development and ERB
Residents living in or around the natural environment feel the impact of tourism
development. After all, natural resources are often the source of such development, and coastal
beach areas are no exception. Residents in tourism destinations wish, of course, to maximize the
benefits from tourism development, but it is also important to protect the natural environment.
When these two goals are pursued properly, the desirable result is sustainable tourism
development (Choi & Sirakaya, 2006). Much of the literature on sustainable tourism
development emphasizes the importance of including community (Gursoy, Jurowski, & Uysal;
2002; Jurowski, Uysal, & Williams, 1997; Zhang & Lei, 2009). Residents can improve beach
conditions by behaving more environmentally responsibly. Whether residents are willing to do
so may depend on their attitudes toward tourism development.
To measure the residents’ attitudes toward tourism development, Lankford and Howard
(1993) developed tourism impact attitude scale (TIAS). Based on the literature, the authors
developed numerous predictors that appear to influence attitudes toward tourism development.
The scale included such items as economic dependency on tourism, resident involvement in
tourism decision making, perceived impacts on local outdoor opportunities, and others. Based on
these items, Lankford and Howard (1993) conducted a factor analysis, extracting two main
factors. Factor 1 indicated personal and community benefits. Factor 2 indicated concern for local
tourism development.
A number of articles have measured residents’ attitudes toward tourism (e.g., Andereck
Valentine, Knopf, & Vogt, 2005; Andriotis & Vaughan, 2003; Jurowski et al., 1997; Liu,
Sheldon, & Var, 1987; Wang, Pfister, & Morais, 2006). Some other articles, demonstrated
whether individuals’ attitudes toward tourism development influenced their support of local
22

tourism development (Andereck & Nyaupane, 2011; Choi & Murray, 2010; Gursoy et al., 2002).
However, no studies have aimed to examine a direct relationship between residents’ attitudes
toward tourism development and their ERB. Bearing the strongest resemblance to the present
study is one by Choi and Murray (2010). These authors pose, however, a different hypothesized
relationship. Choi and Murray (2010) mainly demonstrate whether the positive and negative
impacts residents felt from tourism were related to their support for tourism development. In
addition, the authors also identified other factors that influenced residents’ positive or negative
attitudes to tourism development. One of these was attitudes toward environmental sustainability.
Residents’ attitudes toward environmental sustainability mainly imply eco-centric values. Thus,
the main research question was whether residents’ higher eco-centric values predicted positive or
negative attitudes toward tourism. However, the results indicated this relationship was not
significant.
While no studies included positive and negative attitudes together as predictors of ERB,
two studies did look at the relationship between positive attitudes toward tourism development
and behaviors that conserve tourism resources (Lepp, 2007; Walpole & Goodwin, 2001). Based
on Lankford and Howard (1993), positive attitudes were related to perceived personal and
community benefits from local tourism development. And these personal and community
benefits usually accrued from the economic effects of tourism (e.g., employment, investments,
and profitable local businesses; Andereck et al., 2005). It is commonly known that people,
through their activities pursuing economic development, usually have a negative impact on the
natural environment (Nordlund & Garvill, 2003). Typically, tourism development is
accompanied with negative impacts on the environment. Andereck and Nyaupane’s study (2011)
showed that residents regard tourism development as a beneficial economic tool but also as

23

facilitating the conservation of natural and cultural resources. This seeming paradox appears
quite possible as, these days, sustainable tourism development has been highly recommended by
the tourism industry and academic researchers (Goodwin, 1996). Residents and the industry were
aware that to benefit economically from tourism development, the natural environment would
have to be well maintained. Residents and the industry would thus strive to protect the natural
environment so as to continue to benefit from it.
This logic can be supported by Stern’s VBN theory (2000). When people try to protect
the natural environment to secure their own benefits, it is considered, of VBN theory’s three
types of value, an egoistic value. It may also be considered social-altruistic for considering
community benefits. Two studies (Lepp, 2007; Walpole & Goodwin, 2001) demonstrated this
value-based relationship. Lepp (2007) conducted his study in the rural village of Bigodi, Uganda,
where the primary tourism resources are forested wetland. Most Bigodi residents indicated that
they received communal benefits, and half of them cited personal benefits from the tourism
development. Briefly, residents had positive attitudes about tourism development and wanted to
conserve their natural environment. Walpole and Goodwin’s study (2001) also demonstrated the
relationship between positive attitudes toward tourism springing from tourism development and
intention to support conservation. The study site was Komodo National Park, located in the
Lessar Sunda Islands of Indonesia. Overall, residents’ positive attitudes were significantly
predicted by benefits from tourism.
It is plausible to suppose that residents’ ERB can also be predicted by negative attitudes
toward tourism development caused by environmental degradation. Despite various efforts to
minimize any negative impacts, tourism development can still cause significant environmental
damage to the environment (Goodwin, 1996). Such damage includes water pollution, wildlife

24

destruction, deforestation, as well as destruction of wetlands, soil and beaches (Andereck et al.,
2005). One study (Zhang & Lei, 2009) shared the current study’s assumption that greater ERB
could result from negative attitudes toward nature-based tourism development.
Zhang and Lei (2009) measured residents’ environmental attitudes toward tourism
development and their ERB. The development was occurring in the Beimen coastal wetland area
in Taiwan. Environmental attitudes were developed by the New Environmental Paradigm (NEP)
and three types of respondents were identified using a cluster analysis: strong, moderate, and
weak environmental attitudes toward tourism development. Environmentally related behaviors
consisted of three main factors, willingness to accept change, intention to be volunteers, and
participation and empowerment in tourism development. They found that residents with strong
and moderate environmental attitudes expressed a greater intention to be volunteers. While their
study’s structure differed from the present one, what could correspond with this study are the
environmental attitudes they discovered toward tourism development.
Some other studies (Gursoy et al., 2002; Jurowski et al., 1997) measured the relationship
between residents’ biospheric value and attitudes toward tourism development. While Jurowski
et al.’s study (1997) measured strong eco-centric values, the residents did not oppose tourism
development. However, if one disregards their support for tourism development, it can be
concluded that residents within a nature-based tourism site usually have strong biospheric values.
Lastly, Byrd et al. (2009) found that in five North Carolina counties (i.e., Alamance, Tyrell,
Guilford, Wayne, and Stokes), residents and business owners both opposed tourism development
if it threatened any negative impacts to the natural environment. Applying Stern’s VBM theory
(2000), it can be hypothesized that individuals having biospheric values will behave so as to

25

maintain their biospheric values, more environmentally responsibly. Thus, this study expects
residents having biospheric values to demonstrate greater ERB.
In conclusion, we expect ERB to be closely connected with both positive attitudes
derived from tourism benefits and negative attitudes caused by tourism development. Overall,
those who perceive more personal and community benefits and those expressing greater concern
about tourism development are expected to have greater ERB.

4. Place Satisfaction and ERB
Place satisfaction has been defined as how one judges the perceived quality of a setting
(Mesch & Manor, 1998). If the place meets certain basic needs such as sociability, services, and
physical needs, then individuals will be satisfied (Fried, 1982; Guest & Lee, 1983; Stedman,
2002). Several studies (e.g., Fried, 1984; Stedman, 2002) have asserted a positive relationship
between place satisfaction and place attachment. Theodori (2000) pointed out the similarity in
the meanings of place satisfaction and place attachment. At a fundamental level, however, these
two concepts differ. Satisfaction is more inclined to be a utilitarian value, as an indicator of an
evaluated factor. Attachment is more inclined to be an emotional feeling or a sense of rootedness,
based on sentiment (Theodori, 2000).
In contrast to the numerous studies demonstrating the relationship between place
attachment and ERB, only three studies explored the relationship between place satisfaction and
ERB (Halpenny, 2010; Stedman, 2002; Uzzell et al., 2002). Stedman (2002) included place
attachment and place satisfaction as predictors of ERB (here a willingness to protect a lake). The
research was conducted mainly in Vilas County, Wisconsin, a tourism-intensive and recreational
home landscape. The study targeted primarily property owners. Stedman (2002) found that
property owners were highly attached to their lake and highly satisfied with its quality.
26

Stedman’s findings (2002) supported the hypotheses that highly attached property owners would
exhibit a higher level of ERB intention and that highly satisfied property owners would exhibit a
lower level of ERB intention. His postulation was that people typically start to feel concerned
about the environment when they perceive threats to a place; once that happens, people then take
different protective actions. Property owners highly satisfied with lake quality would thus feel
little need to restore lake conditions.
A similar finding came from Uzzell et al.’s study (2002). Stedman’s regression model
(2002) treated place attachment and place satisfaction as separate predictors of ERB. Uzzell et al.
(2002) designed a different structure based on structural equation models (SEM). Place identity
was used as a latent variable, which was explained by place satisfaction, as well as by place
identification and social cohesion. In this case, place identification was represented with a
different meaning different to place identity. Place identification indicated the attributes of the
place that gave it a distinctive identity in the minds of residents. The authors hypothesized that
this latent variable, place identity, is an important predictor of ERB. Residents in two
neighborhoods in Guildford, England—Onslow village and Stoughton—were interviewed. For
Stoughton village, the results indicated that social cohesion and place identification contributed
to place identity. Satisfaction also contributed to place identity, though the relationship was
found to be weaker. Place identity, however, strongly contributed to environmental sustainability.
As for Onslow village, place identification and place satisfaction were important predictors of
place identity. A weak negative relationship was found for cohesion. Residents in Onslow
differed from those in Stoughton village in that for them place identity had a weak negative
relationship with sustainability. Clear differences then were evidenced in the two neighborhoods
regarding place satisfaction and ERB. Though no direct relationship existed between place

27

satisfaction and environmental sustainability, it’s plausible to suppose that strong place
satisfaction results in less ERB.
Finally, Halpenny’s study (2006) also demonstrated the relationship between place
satisfaction and ERB. Halpenny’s study (2006) mainly targeted visitors of Canada’s Point Pelee
National Park. This study was inspired by Stedman’s (2002) study that mainly developed the
structural model with the concepts such as place satisfaction, place attachment, and ERB
intention. As with Uzzell et al.’s study (2002), multiple indicators for this latent variable, place
satisfaction, were identified. They included natural, social, and an activity-inductive environment.
Also, Halpenny’s study (2006) included two types of ERB, park-specific and general. The result
showed that place attachment was positively related to both types of ERB, though a relationship
was stronger with park-specific ERB. Place satisfaction, however, was found to have no direct
relationships with either type of ERB. However, when place satisfaction positively predicted
place attachment, it then had an indirect effect on both types of ERB. Each indicator of place
satisfaction—natural, social, and activity-conducive environments—was also tested to
demonstrate the relationship between place attachment and ERB.
Not all the indicators had a direct relationship with the two types of ERB. There were,
however, indirect effects, through place attachment, on park-specific behavior and general ERB.
Overall, it is reasoned that place satisfaction may not have a direct relationship with ERB. Its
indirect relationships, however, allow one to assume that an important mediating variable in
promoting ERB is place attachment. Halpenny (2006) concluded that place satisfaction was not
significant because satisfaction was measured as that of visitors rather than that of residents.
While Stedman (2002) and Uzzell et al. (2002) measured residents’ satisfaction, Halpenny (2006)
included visitors’ satisfaction, which reflected a lack of a close relationship with the park. This

28

might be why the satisfaction was not significant. She also assumed, because most visitors
expressed strong satisfaction, that it was not significant.
The present study, like Stedman’s (2002), includes residents—coastal residents. It is
expected that the study will find a significant negative relationship between place satisfaction
and ERB.

5. Socio-demographic Variables and ERB
Socio-demographic variables are considered important factors for predicting
environmental value orientations (Larson et al., 2011). The vast majority of ERB studies have
included socio-demographic variables as predictors of ERB. Age, income, and gender are the
most commonly used socio-demographic variables (Cottrell, 2003; Korfiatis, Hovardas, & Pantis,
2004; Larson et al., 2011). Some studies, however, have included length of residence (Sheldon &
Var, 1984; Vaske et al., 2001) or residential distance from a particular place (Halpenny, 2006;
Moore & Graefe, 1994).
However, these socio-demographic variables still fail to lead to consistent results.
Cottrell (2003) revealed that younger people are more likely to behave in an environmentally
responsible way. Korfiatis et al. (2004) and Ngo, West, and Calkins (2009) revealed that older
people are more likely to exhibit environmentally responsible behavior. The income variable also
has mixed results, in that some studies have shown it to have a negative relationship with ERB
(Cottrell, 2003; Korfiatis et al., 2004); other studies indicate a positive relationship (Ngo et al.,
2009; Vaske et al., 2000). Gender, however, has consistently shown that women are more likely
to demonstrate ERB (Barr, 2003; Ngo et al., 2009; Zelezny, Chua, & Aldrich, 2000).
Regarding length of residence, Vaske et al. (2000) demonstrated that individuals who
have lived in a place longer are more likely to reveal an anthropocentric attitude. This means
29

they are less likely to behave in an environmentally responsible manner. The authors of that
study assumed that this was because long-term residents were typically engaged in agriculture
and ranching, which would make them more anthropocentric than bio-centric. Finally, Halpenny
(2006) hypothesized that shorter distances would reveal more ERB, yet her study findings
showed that visitors residing far from Point Pelee national park were more likely to reveal ERB.
The author surmised that this result was due to respondents already being inclined to trying to
protect the environment.
Although evidence exists for a demographic basis of ERB, each variable has yielded
inconsistent results. Also, whereas Halpenny’s (2006) study of the distance variable focused on
visitors’ ERB, this study will explore residents’ distance from a natural area and ERB.
Consequently, different results are expected from this study. Accordingly, this study will
examine how coastal beach residents’ socio-demographic variables are associated with ERB.

6. Place Segmentation Analysis
The present study will carry out, based on the model hypothesized above, a segmentation
analysis. Multiple-regression analyses will be conducted for each segmented county- Beaufort,
Charleston, and Horry. This step will be taken because each county has unique characteristics
regarding economic and tourism development as well as socio-demographic conditions. This fact
could result in different degrees of ERB and thus probably produce different predicted
relationships among the variables. Several studies have borne out the assumption that predicted
relationships will differ according to the place (Ngo et al., 2009; Scannell & Gifford, 2010;
Uzzell et al., 2002).
Ngo, et al.’s study (2009) mainly aimed to identify the determinants of ERB for
greenhouse gas reduction. The study identified as the dependent variable two types of ERB:
30

indoor ERB and environmentally responsible automobile behavior. As determinants, the study
identified four main themes of predictors: attitude-, value-, knowledge-, and behavior-related
variables; policy opinion variables; automobile-related variables; and socio-demographic
variables. The results indicated that a mixed relationship was identified; that is, some
relationships were significant while others were not. One of the socio-demographic variables,
places of residence (Prairie, Atlantic, and the provinces of Quebec or Ontario in Canada), was
coded a dummy variable. Ngo and his colleagues found that of these three regions, households in
Quebec and Ontario revealed a higher level of indoor ERB than those of the prairie or the
Atlantic. As for environmentally responsible automobile behavior, the prairie province revealed
a higher level of ERB than the other two provinces. The authors speculated that, because
residents of the Prairie Provinces were accustomed to driving cross-country as part of the
agricultural business, they had more of a notion of demonstrating automobile behavior that was
environmentally responsible.
Scannell and Gifford’s study (2010) also assumed that the relationship between place
attachment and ERB differed according to whether residents lived in the city or the country. The
authors found that while attachment to a place in nature revealed ERB, civic attachment failed to
explain ERB.
Overall, it can be seen that the hypothesized relationship between variables and ERB can
vary depending on place characteristics, such as whether it is rural or urban, and the degree of
economic development or type of tourism development and socio-demographic conditions. In
this study, each county (Beaufort, Charleston, and Horry) differs in economic development, type
of tourism development and residents’ socio-demographic variables. It is thus expected that each

31

county will have a different relationship between ERB and the predictors (place attachment,
attitudes toward tourism, place satisfaction, and socio-demographic variables).

32

III. METHODOLOGY

1. Introduction
This chapter primarily describes the methodology employed to assess any relationships
between ERB and the following explanatory variables: place attachment, attitudes toward
tourism development, place satisfaction, and socio-demographic variables. The first part
describes both data collection, method and study sites. Measurement instruments, the second part,
give an account of the types of scales with the specific items used. The chapter concludes with a
description of the data analysis.

2. Setting and Data Collection
The target population for this study is coastal residents of South Carolina. According to
Census Bureau data, South Carolina is one of the fastest-growing states in the US (United States
Census, 2011). Population growth invariably leads to various human-induced environmental
problems, including nonpoint-source pollution, resource depletion, and habitat loss. Such
problems are of concern to coastal zone managers, such as in Department of Natural Resources
or Department of Environmental Quality (Wenner, Sanger, Upchurch, & Thompson, 2010).
These managers need to be better equipped to handle environmental problems by understanding
how South Carolina coastal residents think about their coastal areas and how/when these
residents will behave in an environmentally responsible way.
Particularly, this study targeted residents in three coastal counties (i.e., Beaufort
Charleston, and Horry) recognized for being popular coastal tourism and recreation destinations.
Using stratified random sampling, a total of 3,000 household addresses were purchased from

33

Survey Sampling International; 50% were from Charleston, 30% from Horry, and 20% from
Beaufort County. Further, 65% of the sample in each county was drawn from zip codes closer to
coastal areas and the remaining 35% were drawn from inland zip codes.
Beaufort County
Beaufort County includes Hilton Head Island, known as one of the most popular coastal
tourism destinations in the state. Hilton Head Island features 19 km of beachfront on the
Atlantic Ocean and is an upscale coastal tourism destination. It is also known as a retirement
community. Primary attractions are beach vacations, golf, dining, shopping, and beach recreation.
The year-round population is 162,233, though at its peak during summer vacation it can, as of
2010, balloon to 275,000. Over the past decade, the island's population growth rate was 32%
(United States Census, 2010).
Charleston County
The major city in Charleston County is Charleston, located south of the mid-point of
South Carolina’s coastline. Charleston is the second largest city in South Carolina and is also
well known as a popular tourism destination. It provides residents and tourists alike, historical,
cultural, and natural recreation opportunities for residents and tourists. The city’s historic district
is an attraction in its own right, featuring colonial architecture, cobblestone streets, and horsedrawn carriage tours. Also, a variety of accommodations are available, including luxury hotels,
hotel chains, inns, and bed and breakfasts. Charleston County’s population is 350,209. The
population of Charleston County has, over the past decade, gradually increased (United States
Census, 2010).

34

Horry County
Horry County it home to one of America’s major tourist destinations- Myrtle Beach. It
attracts over 14.6 million visitors annually. Myrtle Beach represents a lower and middle-income
and family-oriented coastal tourism destination. The city offers a variety of outdoor recreation
opportunities, entertainment attractions, golf courses, and restaurants. Thus, Myrtle Beach's
economy is dominated by the tourist industry, which brings in millions of dollars each year. 2010
census data indicates that the county's population is 269,291. Over the past decade, its population
has been rapidly increasing (United States Census, 2010).
Residents in Beaufort, Charleston, and Horry Counties seemed appropriate for studying
residents’ ERB. They were chosen as the study population for several different reasons. First,
people who reside near or within a natural environment, in this case coastal areas, value natural
resources (Scannell & Gifford, 2010). Second, residents as well as recreationists who tend to be
compared to visitors who stay for a short while become attached to their coastal areas and
resources (Hay, 1998). Third, it is important to examine how the attitudes toward tourism of
residents, as inhabitants of major coastal tourism destinations, are associated with their
environmental attitudes and behaviors. Finally, because residents live close to coastal resources
such as local beaches, their experience in those areas can be also connected with their
environmental attitudes and behaviors.

3. Measurement Instruments
To increase survey response rates, the study utilized a modified Dillman’s total survey
method with a self-administered questionnaire (Rosenbaum & Lidz, 2007).
ERB was measured with the “Environmentally Responsible Behavior Index Scale”

35

(ERBI), developed by Smith-Sebasto & D’Acosta (1995). The original ERBI scale consists of 24
items utilizing a 5-point, Likert-type scale response format (from 1 = rarely to 5 = usually). In
here, 11 items which were considered more appropriate in the context of coastal tourism and
recreation were used.
The scale used to measure place attachment was originally developed by Williams and
Roggenbuck (1989). Their items mainly consisted of place identity and place dependence
dimensions and have also been used by numerous researchers (Kyle, Graefe, Manning, & bacon,
2004; Moore & Graefe, 1994; Vaske & Korbrin, 2001). In the survey, the place attachment scale
is referred to as the “Agreement of Beach Meaning.” It is composed of a five-point, Likert-type
scale ranging from 1 = strongly disagree to 5 = strongly agree.
Residents’ satisfaction with local beaches was measured on a five-point, Likert-type scale,
ranging from 1 = not at all satisfied to 5 = extremely satisfied. Place satisfaction was derived
from a single item that asked about overall satisfaction with local beaches.
Attitudes toward tourism development items were derived from the tourism impact
attitudes scale (TIAS) developed by Lankford & Howard (1994). Designed to measure residents’
attitudes toward tourism development, the original TIAS had 27 items. This study, however,
adopted 12 items which were considered more appropriate in the context of coastal tourism
development. The TIAS consists of mainly two categories—concern for local tourism
development and personal and community benefits. The questionnaire consisted of a five-point,
Likert-type scale ranging from 1 = strongly disagree to 5 = strongly agree).

36

Table 1 Measurement Instruments
Scale
Definition
Any behaviors
that can
contribute to
environmental
conservation
and can result
Environmentally in sustainable
natural
Responsible
ecosystems
Behavior
and
Index
healthy
scale(ERBI)
environmental
(Dependent
conditions
variable)
(Sintov,
Desario, &
Prescott,
2010).

Place
attachment is
William &
defined as
Roggenbuck’s
emotional
(1989)
bond between
Place
a person and a
attachment scale
particular
(Independent
place
variable)
(Vaske &
Kobrin, 2001).
Place
satisfaction
(Independent
variable)

Tourism impact
attitudes scale
(TIAS)
(Independent
variable)

Items
I tried to find out what I can do to help the
environment
I talked to others about environmental
issues
I watched TV programs about
environmental issues
I read articles about current environmental
issues
I donated money/member of conservation
group
I joined a community clean effort
I switched to environmentally safe brand
items
I read labels to see if items are
environmentally safe
I separated out recyclable items from trash
I recycled newspapers
I recycled glass bottles, jars, or aluminum
cans
No other beach can compare to this one
I feel this beach is part of me
I go to this beach because it is close by
This beach means a lot to me
I wouldn’t substitute another beach for this
one
I am more satisfied visiting this beach than
any other
Visiting this beach says a lot about who I
am
I am very attached to this beach

Overall degree
of satisfaction
with South
Carolina local
beach
Residents’
attitudes
toward South
Carolina local
tourism
development

SmithSebasto &
D’Acosta,
1995,
Thapa, 1999

Williams &
Roggenbuck
(1989),
Vaske &
Kobrin
(2001),
Kyle,
Graefe,
Maning, &
Bacon
(2004)

Overall, how satisfied are you with your
most recent visit to your local beach?
Tourism has led to more spending in our
community
Our standard of living has increased
considerably because of tourism
Tourism has changed our precious
traditional culture
Local residents have suffered from living in
a tourism-destination area
I reschedule activities to avoid tourists

Studies

37

Lankford &
Howard
(1994)

Table 1 (cont’d)
I enjoy having tourism in my town
Meeting tourists from other regions is
a valuable experience to better
understand their culture and society
Tourism results in positive impacts on
the cultural identity of our community
Benefits of tourism are greater than
costs to people in the community
Governments should control tourism
development
Construction of tourist facilities have
destroyed the natural environment
Tourism has resulted in traffic
congestion, noise and pollution
Tourism provides more parks and
recreational areas for local residents
Improving public transit facilities is a
waste of tax payer money

38

4. Data Analysis
To assess the mechanism of how residents become more interested in ERB in the context
of coastal tourism and recreation destinations, this study used the following explanatory
variables: place attachment, attitudes toward tourism, place satisfaction, and various sociodemographic variables. Multiple-regression analysis was employed to examine the relationships
between those independent variables and the dependent variable of ERB. A multiple-regression
analysis was deemed suitable, for it could determine whether a set of independent variables
included could explain a considerable amount of variation in a dependent variable. Moreover, it
could provide information on which of these independent variables were more important in
explaining variation in the dependent variable (Field, 2009). Before conducting a multipleregression analysis, the author conducted an exploratory factor analysis (EFA) with principle
component analysis (PCA) (Field, 2009). EFA facilitates data reduction; it can lessen a group of
interrelated items to a smaller set of factors that explain the maximum amount of common
variance (Field, 2009).
PCA is a technique of reducing data by identifying groups or clusters of variables. The
concepts such as ERB, place attachment, and attitudes toward tourism development cannot be
measured directly. Thus, each concept is considered as a latent variable and needs to be
measured on a scale consisting of multiple items. However, a regression model consisting of so
many items as independent variables would make estimation results hard to interpret. It would
also increase the concern about a high level of multicollinearity. A high level of multicollinearity
would make it difficult to distinguish individual influences of each independent variable on ERB
(Warner, 2007). Thus, for these three variables, data reduction is necessary.

39

With these factors, a multiple-regression analysis was conducted to identify the
predictive relationships between a variety of independent variables (i.e., place attachment,
attitudes toward tourism development, place satisfaction, and socio-demographic variables) and
the dependent variable, ERB.
In addition, a place-segmented multiple regression analysis was conducted. It examined
whether the degree of tourism development or level of economic development in each county
was important in affecting residents’ engagement in ERB. The original sample data were
segmented into the three coastal areas by county—Beaufort, Charleston, and Horry. Each county
was expected to show a different degree of tourism development, level of economic development,
and socio-demographic conditions. Thus, the segmented regression analysis was to demonstrate
whether differences in the three counties affected the relationships between the independent
variables and ERB. All of these analyses, including EFA, multiple regression analysis, and
ANOVA were conducted using IBM SPSS statistics 19.

40

IV. RESULTS
1. Introduction
Out of 3,000 questionnaires mailed out, 697 were returned. The gross response rate was
23.2%. The majority (95.5%) of respondents were permanent residents and rests of them were
seasonal (4.5 %).
Table 2 Comparison of Demographic Characteristics
2007 Census (%)
Gender
48.7
Male
51.3
Female
Age
26.9
Under 5-under 18
13.6
18-29
13.1
30-39
14.4
40-49
13.5
50-59
9.4
60-69
5.6
70-79
3.4
80+
Income
12.5
<$10,000
35.4
$10,000 -49,999
30.2
$50,000-99,999
21.9
$100,000 and above

2007 Study (%)
57.3
42.7

2.8
10.6
12.9
19.4
31.1
17.6
5.6
1.0
24.1
41
34.1

To check the degree of generalizability, the data collected here were compared to 2007
census data. Overall, there were some differences between these two data. First, with respect to
the variable of gender, the study data showed that the percentage of males (57.3%) was
somewhat higher than that of females (42.7%). However, census data showed that this was
different with a higher percentage of females than males (51.3% vs. 48.7% respectively). In term
of age, the study data contained a considerably higher percentage of individuals who were 60
years or older (54.4%) compared to the census data (18.4%). Lastly, there was a difference in
income. Compared to the general population (21.9%), our respondents (34.1%) generally
41

reported a higher household income. Accordingly, caution is needed in interpretation of study
results.
Based on these questionnaires, this chapter summarizes the data analysis. The following
section described the descriptive characteristic of each variable—ERB, place attachment,
attitudes toward tourism development, place satisfactions, and socio-demographic variables.
Exploratory factor analysis was first used to explore the underlying structure of a latent concept.
With respect to ERB, two factors—general ERB and recycling behaviors were identified. Place
attachment also had two dimensions—place identity and place dependence. Attitudes toward
tourism consisted of two main themes—concern for tourism development and benefits from
tourism development. Lastly, multiple-regression analysis was conducted to assess the
relationships between these independent variables and ERB. Segmented multiple regression
analysis was also conducted based on the respondents’ residential counties—Charleston,
Beaufort, and Horry.

42

2. Descriptive Statistics
1) Environmentally Responsible Behavior (ERB)
Table 3 Frequency of ERB Categories
Values given are Percentages (Frequency)
17.5 (111)
13.2 (84)
21.6 (137)
22.5 (144)
25.0 (160)

1 (Rarely)
2 (Occasionally)
3 (Sometimes)
4 (Frequently)
5 (Usually )

Table 4 Frequency Distribution of ERB Items (1=Rarely to 5=Usually)
Mean
Values given are Percentages
Items
n
(S.D.)
(Frequency)
1
2
3
4
5
General ERB
I tried to find out what I can do to
help the environment
I talked to others about
environmental issues
I watched TV programs about
environmental issues
I read articles about current
environmental issues
I donated money/member of
conservation group
I joined a community clean effort
I switched to environmentally safe
brand items
I read labels to see if items are
environmentally safe
Recycling Behavior
I separated out recyclable items
from trash

635
638
636
641
631
623
636
636

642

I recycled newspapers

639

I recycled glass bottles, jars, or
aluminum cans

642

Mean

636

3.31
(1.22)
3.17
(1.16)
3.28
(1.13)
3.42
(1.09)
2.45
(1.32)
2.03
(1.25)
2.97
(1.23)
3.02
(1.35)

11.2
(71)
10.5
(67)
8.8
(56)
6.1
(39)
34.1
(215)
49.9
(311)
14.5
(92)
20.0
(127)

12.0
(76)
15.5
(99)
14.0
(89)
13.7
(88)
19.2
(121)
17.8
(111)
22.3
(142)
15.3
(97)

29.6
(188)
34.2
(218)
31.6
(201)
28.1
(180)
24.6
(155)
17.7
(110)
26.3
(167)
22.2
(141)

29.1
(185)
26.2
(167)
32.1
(204)
36.5
(234)
12.5
(79)
8.3
(52)
25.5
(162)
28.1
(179)

18.1
(115)
13.6
(87)
13.5
(86)
15.6
(100)
9.7
(61)
6.3
(39)
11.5
(73)
14.5
(92)

4.01
(1.37)
4.02
(1.45)
4.00
(1.44)
3.24
(1.27)

10.9
(70)
14.1
(90)
12.9
(83)

6.2
(40)
4.5
(29)
5.6
(36)

8.6
(55)
6.3
(40)
8.6
(55)

19.2
(123)
16.0
(102)
14.5
(93)

55.1
(354)
59.2
(378)
58.4
(375)

43

Participants were asked their views on environmental issues, using a five-point, Likerttype scale called Environmentally Responsible Behavior Index (ERBI). One (1) indicated they
rarely participated in ERB and five (5) indicated they usually participated in it. The results
indicated a mean score of 3.24 (SD = 1.27, n = 636) for ERB. More than one-fifth (21.6%) of the
respondents indicated that they sometimes participated in ERB. Similarly, about half (47.5%) of
the respondents reported that they have frequently or usually participated in ERB.
The frequency table of ERB items revealed that the lower levels of ERB were “I joined a
community clean effort” (M = 2.03, SD = 1.25) and “I donated money/member of conservation
group” (M = 2.45, SD = 1.32). The table revealed that the higher levels of ERB were: “I
separated out recyclable items from trash” (M = 4.01, SD = 1.37), “I recycled newspapers”
(4.02), and “I recycled glass bottles, jars, or aluminum cans” (M = 4.00, SD = 1.44). Performing
the lower levels of ERB items usually requires greater effort and more time. In contrast, higher
levels of ERB items, such as recycling behaviors, suggest that people can participate in these
activities more easily. Therefore, these results were consistent with a priori expectations.

44

2) Place Attachment
Table 5 Frequency of Place Attachment Categories
Values given are Percentages (Frequency)
1 (Strongly Disagree)
5.2 (31)
2 (Disagree)
18.2 (107)
3 (Neutral)
29.3 (173)
4 (Agree)
28.6 (168)
5 (Strongly Agree )
18.5 (109)
Table 6 Frequency Distribution of Place Attachment Scale Items
Values given are Percentages
Mean
(Frequency)
Items
n
(S.D.)
1
2
3
4
5
Place Identity
3.48
3.7
13.9
31.0
33.2
18.2
I feel this beach is part of me
588
(1.05)
(22)
(82)
(182) (195) (107)
4.08
0.7
2.4
18.7
44.4
33.8
This beach means a lot to me
588
(0.82)
(4)
(14)
(110) (261) (199)
Visiting this beach says a lot about
3.21
5.8
21.0
35.3
22.4
15.6
590
who I am
(1.11)
(34) (124) (208) (132)
(92)
3.52
4.4
13.4
28.6
33.2
20.3
I am very attached to this beach
590
(1.09)
(26)
(79)
(169) (196) (120)
Place Dependence
No other beach can compare to this
2.88
9.5
32.7
29.1
17.5
11.1
587
one
(1.14)
(56) (192) (171) (103)
(65)
I wouldn’t substitute another beach
3.15
6.5
24.9
30.8
22.7
15.2
587
for this one
(1.14)
(38) (146) (181) (133)
(89)
I am more satisfied visiting this
3.27
5.8
19.6
32.2
27.1
15.3
587
beach than any other
(1.11)
(34) (115) (189) (159)
(90)
3.37
588
Mean
(1.06)
(1=Strongly Disagree to 5=Strongly Agree)
The intensity of place attachment was based on the meaning of their local beach to them.
On a five-point, Likert-type scale, one (1) indicated strongly disagree and five (5) indicated
strongly agree (most highly attached to the local beach). The results revealed that the mean score
of place attachment items was 3.37 (SD = 1.06). And, almost half of respondents (47.1%)
possessed a high level of attachment to the local beach. Overall, it can be said that most of the
respondents were fairly attached to their local beach.
45

The highest level of place attachment was the item, “This beach means a lot to me” (M =
4.08, SD = 0.82); the lowest was “No other beach can compare to this one” (M = 2.88, SD =
1.14). Of the two dimensions of place attachment, place identity had a slightly higher mean score
(M = 3.57, SD = 0.83) than place dependence (M=3.13, SD = 1.13). These results indicated that
the respondents were slightly more emotionally attached to their local beaches than they were
functionally attached.

46

3) Attitudes toward Tourism Development
Table 7 Frequency of Attitudes toward Tourism Development Categories
Values given are Percentages (Frequency)
Concerns
Benefits
1 (Strongly Disagree)
2.86 (20)
3.07 (21)
2 (Disagree)
16.72 (116)
12.77 (89)
3 (Neutral)
21.86 (119)
29.84 (208)
4 (Agree)
32.22 (224)
36.07 (251)
5 (Strongly Agree )
22.36 (124)
9.78 (68)

Table 8 Frequency Distribution of Attitudes toward Tourism Development
Values given are Percentages
Mean
(Frequency)
Items
n
(S.D.)
1
2
3
4
5
Concerns for Tourism Development
Tourism has changed our precious
3.41
1.9
13.8
32.6
31.3
34.2
637
traditional culture
(0.96)
(13) (96) (227) (218) (83)
Local residents have suffered from
3.08
4.7
27.8
25.4
22.4
11.3
639
living in a tourism-destination area
(1.11)
(33) (194) (177) (156) (79)
3.73
2.0
12.5
16.6
37.7
23.1
I reschedule activities to avoid tourists
641
(1.05)
(14) (87) (116) (263) (161)
Construction of tourist facilities have
3.24
4.3
23.0
23.8
26.4
13.6
635
destroyed the natural environment
(1.12)
(30) (160) (166) (184) (95)
Tourism has resulted in traffic
4.02
1.4
6.5
10.9
43.3
29.6
639
congestion, noise and pollution
(0.93)
(10) (45)
(76) (302) (206)
3.49 (1.03)
Mean
Benefits from Tourism Development
Tourism has led to more spending in
639
4.08
0.4
3.0
10.2
53.2
24.8
our community
(0.74)
(3)
(21)
(71) (371) (173)
Our standard of living has increased
3.31
2.9
17.1
30.7
30.4
10.2
639
considerably because of tourism
(1.00) (20) (119) (214) (212) (71)
3.27
3.0
11.8
40.3
30.4
6.2
I enjoy having tourism in my town
639
(0.88) (21) (82) (281) (212) (43)
Meeting tourists from other regions is a
3.35
3.2
10.0
36.6
35.0
6.9
valuable experience to better
639
(0.89) (22) (70) (255) (244) (48)
understand their culture and society
Tourism results in positive impacts on
3.31
3.0
14.1
33.6
33.9
7.2
639
the cultural identity of our community
(0.93) (21) (98) (234) (236) (50)
Benefits of tourism are greater than
3.25
4.7
15.9
30.0
32.9
7.5
634
costs to people in the community
(1.00) (33) (111) (209) (229) (52)
Tourism provides more parks and
3.24
4.3
17.5
27.5
36.7
5.7
640
recreational areas for local residents
(.98)
(30) (122) (192) (256) (40)
3.40 (0.91)
Mean
(1=Strongly Disagree to 5=Strongly Agree)
47

Residents were asked what they thought of tourism development in their local community.
The scale for attitudes toward tourism development included two overarching themes: concern
for tourism development and benefits from tourism development. A five-point, Likert-type scale
was designed where one (1) indicated strongly disagree and five (5) indicated strongly agree. For
the items mainly representing concern about tourism development, the mean score was 3.49 (SD
= 1.03). Over half of the respondents (54.58%) answered they either agreed or strongly agreed.
For items representing benefits from tourism development, the mean score was 3.40 (SD = 0.91).
Nearly half of residents (45.85%) either agreed or strongly agreed. The residents seemed on
average to be neutral in terms of their concern about tourism development and benefits from
tourism development
The respondents reported the highest mean score on the following item: “Tourism has
resulted in traffic congestion, noise, and pollution” (M = 4.02, SD = 0.93). They reported the
lowest mean score on: “Local residents have suffered from living in a tourism-destination area”
(M = 3.08, SD = 1.11). This could imply that residents were concerned about traffic congestion,
noise, and pollution as major drawbacks derived from tourism development. The item of highest
regard about benefits from tourism development was “Tourism has led to more spending in our
community” (M = 4.08, SD = 0.74). The lowest item was “Tourism provides more parks and
recreational areas for local residents” (M = 3.24, SD = 0.98). It appears that residents considered
tourism a tool that could contribute positively to community economic development.

48

4) Place Satisfaction
Table 9 Frequency Distribution of Place Satisfaction
Values given are Percentages (Frequency)
Mean
Item
n
(S.D.)
1
2
3
4
5
Satisfaction
574
3.8 (0.78)
0.5
3.5
29.4
49.0
17.6
with beach
(3)
(20)
(169)
(281)
(101)
(1=Not at all satisfied to 5=Extremely satisfied)
For place satisfaction, residents were asked how satisfied they were with their most
recent visit to the local beach. A five-point, Likert-type scale was designed with one (1)
representing not at all satisfied and five (5) representing extremely satisfied. The mean score was
3.8 (SD = 0.78) and the largest portion of responses selected 4, very satisfied. The results would
suggest that residents were fairly satisfied with their local beach.
5) Socio-demographic variables
This study included socio-demographic characteristics: length of residence,
distance from residential areas to closest beach, gender, age, and income.
Table 10 Frequency Distribution of Length of Residence
Length of Residence
Absolute Frequency (Percent)
Less than 4 years
124 (19.0)
4-6 years
121 (18.6)
7-10 years
123 (18.9)
11-15 years
92 (14.1)
16-20 years
72 (11.1)
21-30 years
61 (9.4)
31-40 years
33 (5.1)
41-50 years
11 (1.7)
More than 50 years
14 (2.2)
No response
46
TOTAL
651 (100.0)
Mean (S.D.)
13.11 (12.21)

49

The average length of residence was over 13 years. About one-fifth (19.0%) of the
residents reported that they had lived in their community for less than four years; 18.6% for 4 to
6 years and 18.9 % for 7 to 10 years. Thus, over half the residents had lived in their community
for less than 10 years.
Table 11 Frequency Distribution of Distance to Closest Beach Access
Distance to Closest Beach Access
Absolute Frequency (Percent)
Less than 2 mile
233 (35.6)
2-4.9 miles
150 (22.9)
5-10 mile
187 (28.5)
More than 10 miles
85 (13.0)
No response
42
TOTAL
697 (100.0)

A little over a third of the residents (35.6%) indicated they lived less than two miles from
the nearest beach; 22.9% reported living between 2-4.9 miles, 28.5% lived between 5-10 miles,
and 13.1% over 10 miles. Thus, over half the residents lived within five miles of the nearest
beach.
Table 12 Frequency Distribution of Gender
Gender
Female
Male
No response
TOTAL

Absolute Frequency (Percent)
278 (42.7)
373 (57.3)
46
651 (100.0)

Over half the samples (57.3%) were male; 42.7% were female.

50

Table 13 Frequency Distribution of Age
Age
18-29
30-39
40-49
50-59
60-69
70-79
80+
No response
TOTAL
Mean (S.D.)

Absolute Frequency (Percent)
18 (2.8)
68 (10.6)
83 (12.9)
125 (19.4)
200 (31.1)
113 (17.6)
36 (5.6)
54
697 (100.0)
58.59 (14.34)

The mean age of the respondents was 58.5 years and nearly a third (31.3%) reported
being in the age category of 60-69. The breakdowns of ages consist of: 18-29 (2.8%), 30-39
(10.6%), 40-49 (12.9%), 50-59 (19.4%) and 70-79 (17.6%). The youngest age was 24 and the
oldest 94.
Table 14 Frequency Distribution of Income
Household income
<$10,000
$10,000-19,999
$20,000-29,999
$30,000-39,999
$40,000-49,999
$50,000-59,999
$60,000-69,999
$70,000-79,999
$80,000-89,999
$90,000-99,999
$100,000 and Above
No response
TOTAL

Absolute Frequency (Percent)
6 (1.0)
19 (3.3)
26 (4.5)
50 (8.6)
45 (7.7)
63 (10.8)
50 (8.6)
46 (7.9)
52 (8.9)
28 (4.8)
199 (34.1)
113
697 (100.0)

Over a third of the respondents (34.1%) reported household income of over $100,000; the
median category was between $70,000 to 79,999.

51

3. Exploring the Structure of Variables
1) Environmentally Responsible Behavior
Table 15 Total Variance Explained
Component

Initial Eigenvalues
Total

1
2

5.63
2.01

% of
Variance
51.18
18.43

Cumulative %
51.18
69.61

Extraction
Sums of
Squared
Loadings
Total
% of
Variance
5.63
51.18
2.03
18.43

Rotation
Cumulative %

Total

51.18
69.61

5.29
3.51

Table 16 Communalities
Items
A. I tried to find out what I can do to help the environment
B. I talked to others about environmental issues
C. I watched TV programs about environmental issues
D. I read articles about current environmental issues
E. I donated money/member of conservation group
F. I joined a community clean effort
G. I switched to environmentally safe brand items
H. I read labels to see if items are environmentally safe
I. I separated out recyclable items from trash
J.I recycled newspapers
K. I recycled glass bottles, jars, or aluminum cans

Initial
1.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000

Extraction
.674
.742
.676
.718
.546
.422
.593
.567
.933
.885
.903

Table 17 Pattern Matrix
Items
B. I talked to others about environmental issues
C. I watched TV programs about environmental issues
D. I read articles about current environmental issues
A. I tried to find out what I can do to help the environment
E. I donated money/member of conservation group
G. I switched to environmentally safe brand items
H. I read labels to see if items are environmentally safe
F. I joined a community clean effort
I. I separated out recyclable items from trash
K. I recycled glass bottles, jars, or aluminum cans
J.I recycled newspapers
Cronbach’s
52

Component
1
2
.867
.845
.842
.809
.752
.704
.699
.675
.959
.953
.936
.91
.95

A principle component analysis (PCA) was conducted on the 11 ERB items with oblique
rotation. Oblique rotation was used because it was possible the underlying factors were
correlated. An ERB scale was composed of different types of behaviors; however, each behavior,
at a fundamental level, had the same aim, conservation of natural environment. Hence, the
factors may be correlated.
Before factor extraction, preliminary analysis was conducted to check the assumptions of
factor analysis. The first assumption was that items needed to be correlated fairly well; however,
the correlation coefficients should not be greater than 0.9 so as to alleviate the concern of
multicollinearity (Field, 2010). The correlation matrix (Table 29, see Appendix) for ERB
indicates that all items were fairly well correlated. Also no correlation coefficients surpassed 0.9.
While the sample size is also important for testing the reliability of factor analysis, scholars
usually recommend a sample size of 300 or more (Tabachnick and Fidell, 2007). A sample total
of over 600 in this study met the minimum sample requirement. Usually, however, for a more
precise measure of sampling adequacy, researchers use the Kaiser-Meyer-Olkin measure of
sampling adequacy (KMO) and recommend a value of over 0.5. The KMO measure (KMO = .86)
verified the sampling adequacy for the analysis. All KMO values for individual items were
greater than 0.78, well above the acceptable limit of 0.5. Barlett’s test of sphericity was used
mainly to reject the null hypothesis that the original correlation matrix was an identity matrix.
This would result in all correlation coefficients being zero (Field, 2010). Barlett’s test of
sphericity (

(55) = 5191.215, p < .001) was significant, meaning the null hypothesis was

rejected. It also indicated that correlation between items was sufficiently large for PCA.
For the factor extraction, an initial analysis was run to obtain eigenvalues for each
component. To determine the important particular vector, based on Kaiser’s criterion,

53

eigenvalues greater than one were retained (Field, 2010). Two components had eigenvalues over
Kaiser’s criterion of 1 and each explained 51.17% and 18.43% of variance. The average
communality (7.659/11 = 0.696) also met the recommended value of greater than 0.6 (when the
sample size was over 250). These results also demonstrated that each of the two factors
explained a considerable amount of variance. Lastly, the scree plot (Figure 2, see Appendix)
showed inflexions, which also justified the two factors.
The items that cluster around the same component suggest that Component 1 represents
general environmentally responsible behavior and Component 2 represents recycling behavior.
Lastly, to measure internal consistency, a reliability test was conducted for each
component. Cronbach’s alpha value, between 0.7 and 0.8 is considered acceptable. Both subscales of ERB, the general environmentally responsible behavior and recycling behavior, had
high reliability scores ( = .91 and a = .95).

54

2) Place Attachment
Table 18 Total Variance Explained
Component

Initial Eigenvalues
Total

1
2

4.66
0.86

% of
Variance
66.63
12.26

Cumulative %
66.63
78.89

Extraction Sums of
Squared Loadings
Total
% of
Variance
4.66
66.63
0.86
12.26

Rotation
Cumulative %

Total

66.63
78.89

4.66
0.86

Table 19 Communalities
Items
A. No other beach can compare to this one
B. I feel this beach is part of me
C. This beach means a lot to me
D. I wouldn’t substitute another beach for this one
E. I am more satisfied visiting this beach than any other
F. Visiting this beach says a lot about who I am
G. I am very attached to this beach

Initial
1.000
1.000
1.000
1.000
1.000
1.000
1.000

Extraction
.759
.759
.754
.818
.844
.753
.835

Table 20 Pattern Matrix
Component
C. This beach means a lot to me
G. I am very attached to this beach
B. I feel this beach is part of me
F. Visiting this beach says a lot about who I am
A. No other beach can compare to this one
E. I am more satisfied visiting this beach than any other
D. I wouldn’t substitute another beach for this one
Cronbach’s

1
.967
.831
.742
.719
.

.86

2

-.893
-.887
-.849
.88

A principle component analysis (PCA) was conducted on the seven place-attachment
items with oblique rotation. Originally, place attachment items were all eight items, but one item,
“I go to this beach because it is close by,” was dropped, since it showed low-correlation and the
content was not appropriate for the place attachment scale.

55

Prior to factor extraction, a preliminary analysis was conducted to check the assumptions
of factor analysis. A correlation matrix (Table 30, see Appendix) demonstrated that the overall
place attachment was fairly well correlated, with no variable being highly correlated (r > 0.9).
The sample size (over 500) for the factor analysis also met Tabachnick and Fidell’s (2007)
recommended sample size of over 300. For more accuracy, the KMO measure verified the
sampling adequacy for the analysis (KMO = .89), and all KMO values for individual items were
greater than 0.85, well above the acceptable limit of 0.5. Barlett’s test of sphericity (

(21) =

2816.433, p < .001), indicated that correlation between items were sufficiently large for PCA.
An initial analysis was run to obtain eigenvalues for each component. While the ERB
scale obtained eigenvalues based on Kaiser’ criterion of one, place attachment followed the
Jolliffe’s recommendation to retain eigenvalues of over 0.7. Based on a scree plot (Figure 3, see
Appendix), two inflections were found, while one eigenvalue was lower than the other. It was
thus decided that the place attachment scale would follow Jolliffe’s criterion of 0.7. Two
components were extracted with a variance of 66.62% and 12.25%. It is reasonable to have two
components, as several studies (Halpenny, 2006; Kyle et al., 2004) already specified the twodimensionality of place attachment. The average communality (5.522/7 = 0.788) also met the
recommended value of it being greater (when the sample size is over 250) than 0.6. This also
demonstrated that each of the two factors explained a considerable amount of variance.
Accordingly, it was decided that place attachment would retain two components.
A pattern matrix shows the factor loading after rotation. The pattern matrix table only
displayed an absolute value greater than 0.4, which, for interpretation purposes, explained around
16% of the variance in the variable. The items that cluster around the same component suggest
that Component 1 represents place identity and Component 2 represents place dependence.

56

Lastly, to measure internal consistency, a reliability test was conducted for each
component. Place identity and place dependence both had high reliabilities (a = .86 and a = .88).
2) Attitudes toward Tourism Development
Table 21 Total Variance Explained
Component

Initial Eigenvalues
Total

1
2

4.57
1.69

% of
Variance
38.04
14.06

Cumulative
%
38.04
52.10

Extraction Sums of Squared
Loadings
Total
% of
Cumulative
Variance
%
4.57
38.04
38.04
1.69
14.06
52.10

Rotation
Total
4.57
1.69

Table 22 Communalities
Items
A. Tourism has led to more spending in our community
B. Our standard of living has increased considerably because of tourism
C. Tourism has changed our precious traditional culture
D. Local residents have suffered from living in a tourism-destination
area
E. I reschedule activities to avoid tourists
F. I enjoy having tourism in my town
G. Meeting tourist from other regions I as valuable experience to better
understand their culture and society
H. Tourism results in positive impacts on the cultural identity of our
community
I. Benefits of tourism are greater than costs to people in the community
J. Construction of tourist facilities have destroyed the natural
environment
K. Tourism has resulted in traffic congestion, noise and pollution
L. Tourism provides more parks and recreational areas for local
residents

57

Initial
1.000
1.000
1.000

Extraction
.407
.521
.579

1.000

.606

1.000
1.000

.520
.598

1.000

.443
.585
.491
.532
.585
.386

Table 23 Rotated Component Matrix
Items

Component
1
2

K. Tourism has resulted in traffic congestion, noise
and pollution
C. Tourism has changed our precious traditional culture
J. Construction of tourist facilities have destroyed the natural environment
D. Local residents have suffered from living in a tourism-destination area
E. I reschedule activities to avoid tourists
B. Our standard of living has increased considerably because of tourism
H. Tourism results in positive impacts on the cultural identity of our community
G. Meeting tourist from other regions I as valuable experience to better
understand their culture and society
I. Benefits of tourism are greater than costs to people in the community
F. I enjoy having tourism in my town
L. Tourism provides more parks and recreational areas for local residents
A. Tourism has led to more spending in our community
Cronbach’s

.761
.761
.704
.704
.683
.719
.699
.631
-.489

.81

.618
.599
.589
.588
.79

A principle component analysis (PCA) was conducted on the 12 attitudes–towardtourism-development items with orthogonal rotation. The original 14 items were reduced to 12
because two showed low-correlation—“Government should control tourism development” and
“Improving public transit facilities is a waste of taxpayer money.”
Preliminary analysis was conducted also to check the assumptions of the factor analysis.
A correlation matrix (Table 31, see Appendix) revealed that items were somewhat correlated and
that none were highly correlated (r > 0.9). The sample size (over 600) for the factor analysis also
met Tabachnick and Fidell’s (2007) recommended sample size of over 300. The KMO measure
verified the sampling adequacy for the analysis (KMO = .87), and all KMO values for individual
items were > .76, well above the acceptable limit of .5. Barlett’s test of sphericity (

(78) =

2453.466, p < .001), indicated that the correlation between items were sufficiently large for PCA.
An initial analysis was run to obtain eigenvalues for each component of the data. Two
components had eigenvalues over Kaiser’s criterion of one. Each component explained 38.04%
and 14.05% of the variance. A scree plot (Figure 4, see Appendix) once more confirmed that the
58

two factors could be extracted. Even the average communality (6.253/12 = 0.521) was lower
than Kaiser’s recommended value of 0.6 (when the sample size is over 250). As the sample size
of this research (over 600) is larger than that of Kaiser’s criterion (250), it may be concluded that
items for each factor explained a considerable amount of variance. Overall, it can be concluded
that two factors can be extracted from the attitudes toward tourism development scale.
The rotated component matrix shows the factor loading after rotation. The items that
cluster around the same component suggest that Component 1 represents concerns about tourism
development and Component 2 represents regard for the benefits that come from tourism
development.
Lastly, a reliability test was conducted for each component to measure internal
consistency. Concerns about tourism development and regard for benefits that come from
tourism development all had high reliabilities (a = .81 and a = .79).

4. Hypotheses Testing
Multiple-regression analysis was conducted based on the factors extracted by factor
analysis. Regarding ERB factors, only general ERB was used as a dependent variable because it
was deemed to be more closely related to the independent variables used here in the context of
coastal tourism and recreation destinations than was the other factor, recycling behavior. For the
place attachment variable, place identity and place dependence factors were included as subdimensions of the place attachment variable. The attitudes toward tourism development variable
was also represented by including two dimensions—concerns about tourism development and
regard for its benefits. Also included in the regression model were place satisfaction and sociodemographic variables. The main research question is whether these variables can predict ERB.
The following equation is the main regression model in this study:
59

Table 24 Coefficients Table
t
Place attachment
Place identity
Place dependence
Attitudes toward tourism development
Concerns for tourism development
Benefits from tourism development
Place satisfaction
Income
Length of residence
Distance
Age
Gender
R square
F-ratio
Significance p<.001*, p<0.05**, p<0.1***

0.28
0.14

4.40*
1.71**

0.22
0.09
0.09
-0.01
0.01
-0.01
-0.00
-0.06
0.133
6.16*

4.51*
1.76***
0.21
-0.74
1.17
-0.15
-0.50
-0.56

Multiple regression analysis was conducted. The model summary indicated that R2
was .133, indicating this model accounted for 13.3% of the variation in ERB. Next, an ANOVA
table represented whether the model significantly predicted the outcome. F-ratio was 6.16 (p
< .001), which can be interpreted that the model significantly predicted the outcome variable,
ERB. The coefficients table demonstrated that ERB was significantly predicted by the variables
of place attachment, attitudes toward tourism.
Both dimensions of place attachment—place identity and place dependence—revealed
that they significantly predicted ERB. The positive value of place identity ( = 0.28, p < .001)
indicated that more emotionally attached residents are more likely to behave environmentally

60

responsibly. The positive value of place dependence ( = 0.14, p < .1) also revealed that
functionally attached residents also behave more environmentally responsibly. Comparing these
two factors, the coefficient magnitude of place identity ( = 0.28) was greater than that of place
dependence ( = 0.14), suggesting that place identity has a greater impact on ERB than place
dependence.
Both dimensions of attitudes toward tourism development (concerns about tourism
development and regard for its benefits) showed a significant relationship with ERB. The
positive value of concerns about tourism development ( = 0.22, p < .001) implies that those
residents more concerned about tourism development are more likely to behave environmentally
responsibly. The positive value of benefits from tourism development ( = 0.09, p < .1) also
implies that residents feeling positive about tourism development are more likely to behave
environmentally responsibly. While both were significantly related to ERB, the coefficients
value of concerns about tourism development ( = 0.22) was higher than that of regard for its
benefits ( = 0.09). It can be said that ERB is more affected by concerns about tourism than
regard for the benefits that tourism development bring.
The place satisfaction variable was not significantly related to ERB. Also, no sociodemographic variables (income, length of residence, distance to beach, age, and gender) were
significantly related to ERB.
In conclusion, these results supported the research hypotheses related to the variables of
place attachment and attitudes toward tourism development. They did not, however, support
those hypotheses related to place satisfaction and socio-demographic variables.

61

5. Place Segmentation Analysis
Table 25 Comparison of Demographic Information across the three counties Using ANOVA
Chi-Square
Sig.
Length of Residence
5.04
0.01**
Distance
30.00
0.00*
Gender
1.36
0.26
Age
28.50
0.00*
Income
24.24
0.00*
Significance *p<.001, p<0.05**, p<0.1***

Table 26 Multiple Regression Coefficients with Dummy Variables (place of residence)
t
Place Attachment
Place identity
Place dependence
Attitudes toward Tourism Development
Concerns for tourism development
Benefits from tourism development
Place satisfaction
Income
Length of residence
Distance
Age
Gender
Charleston vs Horry
Charleston vs Beaufort
R square
F-ratio
Significance *p<.001, p<0.05**, p<0.1***

0.26
0.11

4.34*
1.76***

0.24
0.10
0.04
-0.03
0.00
-0.08
0.00
-0.02
-0.55
-0.39
0.182
7.43*

4.99*
2.12**
0.56
-1.47
0.51
-1.51
0.69
-0.17
-4.62*
-3.04**

First of all, to see the difference in the socio-demographic variables of the three counties,
ANOVA analysis was conducted. It was found that aside from gender, all variables were
significantly different across the three counties.
In addition, to see whether each county revealed a statistically different level of ERB,
multiple regression was conducted with dummy variables based on place of residence. This
model revealed that residents in different counties show a heterogeneous degree of ERB.
62

Charleston County was used as a reference group. The negative coefficients of those dummy
variables indicated that residents of Charleston County were significantly less interested in
behaving environmentally responsible compared to those of the other two counties.
Three separate multiple regression analyses were conducted to see whether each variable
had a different impact on ERB. The original sample data were segmented according to the
residents’ county and multiple regression analyses were conducted for each county using the
same regression model.
Beaufort County
Table 27 Beaufort County Coefficients Table
t
Place Attachment
Place identity
Place dependence
Attitudes toward Tourism Development
Concerns for tourism development
Benefits from tourism development
Place satisfaction
Income
Length of residence
Distance
Age
Gender
R square
F-ratio
Significance p<.001*, p<0.05**, p<0.1***

0.38
0.29

2.21**
1.56***

0.12
0.25
-0.16
0.06
-0.02
0.04
0.02
0.02
0.211
1.96**

0.96
1.83***
-0.80
1.09
-0.99
0.37
2.06**
0.10

The model summary for Beaufort County showed that R2 was .211 indicating this model
accounted for 21.1% of the variation in ERB. An ANOVA table addressed whether the model
was acceptable. The F-ratio was 1.96 (p < .05), showing the model significantly predicted the
outcome variable, ERB. Consistent with the aggregate model above, both dimensions of place
attachment—place identity ( = 0.38, p < .05) and place dependence ( = 0.29, p < 0.1)—were
significantly related to ERB. Thus, the same interpretation can be applied; emotionally attached
63

residents behave more environmentally responsibly ( = 0.38) than do functionally attached ones
(

). However, for the attitudes-toward–tourism-development variable, only one factor

was significantly related to ERB—benefits from tourism development (

= 0.25, p < 0.1). Based

on this result, it can be assumed that residents of Beaufort County, who perceived tourism as a
beneficial tool, are more motivated to behave environmentally responsibly. Neither was place
satisfaction significant in Beaufort County. As for the socio-demographic variables, age ( =
0.02, p < 0.05) was significant, suggesting that older residents are more likely behave
environmentally responsibly.
Charleston County
Table 28 Charleston County Coefficients
t
Place Attachment
Place identity
Place dependence
Attitudes toward Tourism Development
Concerns for tourism development
Benefits from tourism development
Place satisfaction
Income
Length of residence
Distance
Age
Gender
R square
F-ratio
Significance p<.001*, p<0.05**, p<0.1***

0.16
-0.00

1.87***
-0.01

0.25
0.08
0.13
-0.04
0.00
-0.16
0.00
-0.13
0.156
3.69*

3.49*
1.03
1.32
-1.48***
0.68
-2.07**
0.37
-0.91

The model summary for Charleston County indicated that R2 was .156, indicating this
model accounted for 15.6% of the variation in ERB. An ANOVA table addressed whether the
model was acceptable. The F-ratio was 3.69 (p < .001), showing the model significantly
predicted the outcome variable, ERB. For residents of Charleston County, only place identity
64

( = 0.16, p < 0.1) was significantly related to ERB. This result demonstrated that only
emotionally attached residents behaved environmentally responsibly. In contrast to residents of
Beaufort County, Charleston County residents’ ERB had a positive relationship to the factor
addressing concerns about tourism development ( = 0.25, p < .001). Based on this result, it can
be assumed that residents of Charleston County who perceive the negative impacts of tourism on
their community are more likely to behave environmentally responsibly. Again, the place
satisfaction variable was not significant in Charleston County either. Among socio-demographic
variables, residents of a lower income level ( = -.0.05, p < 0.1) were more likely to behave
environmentally responsibly. In Charleston County, the distance variable ( = -0.16, p < 0.1)
was also significant, implying that residents who lived close to a local beach participated in more
ERB.
Horry County
Table 29 Horry County Coefficients
t
Place attachment
Place identity
Place dependence
Attitudes toward tourism development
Concerns for tourism development
Benefits from tourism development
Place satisfaction
Income
Length of residence
Distance
Age
Gender
R square
F-ratio
Significance *p<.001, p<0.05**, p<0.1***

0.30
0.20

3.05**
2.19**

0.24
0.06
-0.05
-0.06
0.01
-0.08
-0.01
0.07
0.295
4.52*

3.31*
0.85
-0.42
-2.30**
1.24
-1.08
-0.96
0.44

The model summary for Horry County indicated that R2 was .295, indicating it accounted
for 29.5% of the variation in ERB. The ANOVA table addressed whether the model significantly
65

predicted the outcome. The F-ratio was 4.52 (p < .001), showing the model significantly
predicted the outcome variable, ERB. Positively related to ERB were the two dimensions of
place attachment—place identity ( = 0.30, p < 0.05) and place dependence ( = 0.20, p < 0.05).
Thus, like the aforementioned models, place identity ( = 0.30) was more closely connected with
ERB than was place dependence ( = 0.20). Like residents of Beaufort County, Horry County
residents holding negative attitudes toward tourism development were more likely to behave
environmentally responsibly ( = 0.24, p < 0.001). Place satisfaction was unrelated to ERB.
Regarding socio-demographic variables, a resident’s level of income ( = -.0.06, p < 0.05) was
negatively related to ERB.
While these three place-segmented models indicated that they were significantly
predicting ERB, each variable, depending on the county, offered different explanations. Place
attachment, place identity, and place dependence were significant in Beaufort and Horry
Counties. In Charleston County, however, only place dependence was significant. The benefits from tourism development factor was an important variable in Beaufort County but in the other
two counties the concerns about tourism development factor was significant. Regarding sociodemographic variables, it was indicated that in Beaufort County, age was positively related with
ERB. In Charleston County, it was revealed that income and distance were both negatively
related to ERB. Lastly, ERB in Horry County was also negatively related to income.

66

V. DISCUSSION
1. Discussion
The aim of this study was to explore factors that can promote ERB. In the aggregate
model, several factors such as place attachment, and attitudes toward tourism development were
significant. However, none of socio-demographic variables were significantly associated with
ERB. For place-segmented analysis, in addition to place attachment and attitudes toward tourism
development, several socio-demographic variables, including age, income and distance, were
significant.
First of all, concerning the variables of place attachment, over two-thirds of the residents
indicated that they were strongly attached to their local beaches. This result was congruent with
other place attachment studies—that a certain place can be special to people (Budruk, 2008;
Budruk, 2009; Halpenny, 2010; Kyle et al., 2003; Kyle et al., 2004; Raymond et al., 2011; Vaske
& Kobrin, 2001; Vorkin & Riese, 2001). According to Raymond et al. (2011), a human’s
connection with nature is highly influenced by their relationship with the surrounding natural
world. Depending on the types and attributes of a place, people will be impacted physically or
symbolically by that place. Such impacts can nourish strong connections with nature, represented
by place attachment. In the literature, however, what kinds of impacts and factors nourish place
attachment is still a controversial topic.
Explanatory factor analysis (EFA) was conducted with place attachment and two factors,
place identity and place dependence, were extracted. Such a result demonstrated that place
identity and place dependence carried different implications. While the main purpose of this
study was to determine the effects of place identity and place dependence on ERB, study results
indicated that both were positively connected with ERB. This means that both groups, people

67

who are highly place-identified and highly place-dependent, are more likely to behave in an
environmentally responsible manner. The findings mirror results reported in the literature—place
attachment functions to promote behavior related to environmental conservation (Kyle et al.,
2004; Payton et al., 2005; Raymond et al., 2011; Vorkin & Riese, 2011).
Why do place attachment and ERB have a significant relationship? Reasons could vary.
One might be attributed to Ajzen and Fishbein’s (1977) theory of reasoned action (TRA).
According to TRA, the best predictor of behavior is individuals’ attitudes. Attitudes are typically
measured with cognitive, affective, and behavioral dimensions. The measurement of place
attachment also taps into these elements. Hence, in this framework, place attachment could
possibly be used as a proxy for attitudes. In addition, as those who are attached to the natural
environment are usually more concerned about its surrounding conditions, this positive
relationship seems to correspond well with the theory (Raymond et al., 2010).
It was also hypothesized that place-identified people would show greater ERB than placedependent people. The finding was also consistent with this hypothesis. Emotionally attached
people were more likely to demonstrate ERB than functionally attached people. Several studies
have indicated the importance of place identity related to conserving the environment
(Hernandez et al., 2010; Kyle et al., 2003; Kyle et al., 2004; Payton et al., 2005; Vaske & Kobrin,
2001). In particular, Raymond et al. (2010) stated that emotional attachment towards nature can
be a more powerful predictor of ERB than ordinary place identity. People usually try to protect
what they love. Thus, emotional attachment toward nature can bring out a stronger desire to
conserve the natural environment. This could be why residents emotionally attached to their local
beaches exhibit greater ERB than functionally attached residents.

68

In conclusion, place attachment is effective in promoting ERB. Such a finding implies
that planners and managers who are in charge of protecting natural resources can improve the
effectiveness of their coastal management plan by enhancing residents’ attachment to their local
beaches. Also, given that a good number of residents are recreationists in different coastal areas,
recreation and tourism resource managers will need recreation and tourism programs that can
encourage residents to build strong place attachment to natural resources in the community
(Bricker & Kerstetter, 2000; Hwang, Lee, & Chen, 2005; Kyle et al., 2003). In particular, place
identity was more highly related with ERB than was place dependence. Functional attachment
could be enhanced by giving incentives that encourage more visitations, such as lowering the
price of annual passes. This could also promote emotional attachment (Oh, Lyu, & Hammitt,
2012) and more ERB.
Secondly, South Carolina residents’ attitudes toward tourism development were
measured according to two main themes: negative attitudes based on concerns about tourism
development and positive attitudes based on the perceived personal and community benefits.
Regression analysis was conducted to demonstrate whether the two factors positively predicted
ERB. The results show that residents, who were concerned about tourism development in coastal
areas, are more likely to behave in an environmentally responsible manner. Also, residents, who
perceived benefits from local tourism development, are likely to behave environmentally
responsibly. A number of reasons may explain the results.
First of all, for the factor related to residents’ concerns, it is rational from an
environmental perspective, that coastal residents would be highly concerned about local tourism
development. Such development could result in negative impacts on the natural environment
(Nordlund & Garvill, 2003). In this study, a good number of respondents indicated that traffic,

69

congestion, noise, and pollution resulting from tourism development have become greater
concerns. And, their concern did lead to stronger intention to behave environmentally
responsibly. This finding is consistent with Byrd et al.’s study (2009) that revealed residents of
North Carolina would, if it negatively impacted the environment, oppose tourism development.
Put another way, residents who have higher eco-centric values place more importance on their
local environment and become more concerned about harmful environmental impacts caused by
tourism development (Zhang & Lei, 2009). Such concern stimulates coastal residents’ sensitivity
to environmental issues caused by tourism development and helps them engage more in ERB.
For the other factor, perceived personal and community benefits, one plausible
interpretation is as follows. Residents might engage in ERB to maintain their personal and
community benefits that arise from local tourism development. This finding is consistent with
several other studies. In Lepp’s study (2007), for example, residents benefited from local tourism.
Such personal and community benefits motivated residents to conserve their local resources.
Walpole and Goodwin (2001) also found that residents’ positive attitudes toward local tourism
resulted in a motivation to conserve local resources. Thus, it is reasoned that residents’ perceived
personal and community benefits could provide them additional incentives to conserve their
community resources including local beaches.
In addition, Fazio’s applied framework of TRA (1986) and Stern’s VBN theory (2000)
could further explain the significant relationship between the two attitudes and ERB. Residents’
attitudes toward the target, here tourism development, did predict behavior. Every resident would
have his or her own specific value, such as egoistic, altruistic or/and bio-centric value, and any
harmful impacts on such value, caused by deteriorating beach conditions, would motivate the
residents to conserve their local beaches.

70

While both factors significantly predicted ERB, concerns about tourism development
explained more about ERB than did the benefits from tourism development. Less ERB could be
caused by residents’ lack of awareness of a connection between conservation of local beaches
and their personal and community benefits. The positive relationship of perceived benefits and
ERB could be achieved based on the premise that residents are aware of the importance of the
relationship between conservation and local tourism development. If they are unaware of it, less
ERB might result. Zhang and Lei (2012) indicated that residents who have a high level of
environmental knowledge are more likely to reveal eco-tourism attitudes and are ultimately more
likely to participate in eco-tourism-related programs. Thus, less ERB from perceived benefits
could be caused by lack of awareness regarding the important relationship between conservation
and local tourism development.
In conclusion, as both attitudes were significantly related to ERB, several implications
present themselves. First of all, regarding residents’ concerns about tourism development, a
positive relationship with ERB could imply that residents were worried about their natural
environment. Resource managers and planners could establish public involvement opportunities
by including these residents (Lankford & Howard, 1994). Since residents are actively involved in
various ERB including protecting community resources, their attitudes and preferences must be
considered important to be integrated into sustainable tourism and recreation resource
development. Second, residents who perceive positive tourism benefits are more likely to behave
environmentally responsibly. This implies that to conserve community resources more
effectively, resource managers and planners can provide more campaigns, workshops, and events
or monitoring programs to help residents understand this relationship. Numerous researchers
have underscored the importance of providing environmental knowledge to residents and other

71

stakeholders, especially in the tourism development process (Choi & Murray, 2010; Choi &
Sirakaya, 2006; Lankford & Howard, 1994). Thus, sustainable tourism development could be
helped along by supporting education programs for residents’ greater awareness of the
connection between their benefits and environmental conservation.
As indicated above, place attachment and attitudes toward tourism development were
positively related to ERB. The connection of these concepts with ERB can be further explained
using carry-over (spill-over) effects. Unlike many studies that used place-specific ERB (Gosling
& William, 2010; Kyle et al., 2004; Raymond et al., 2011), this study used general ERB. It can
be assumed that as residents become more attached to certain community resources such as local
beaches, they gradually develop stronger environmental responsibility. As a result, site-specific
environmental attitudes and ERB will spread to general ERB. This logic has been supported by
prior studies (Rashid, 2007; Thogersen, 1999; Thogersen & Olander, 2003), indicating that one
type of ERB predicts another.
The carry-over effects can be explained by Schwartz’s norm-activation theory. This
theory asserts that an awareness of the consequences of negative impacts of resource
development positively influences beliefs about ERB, leading to strong engagement in and
diffusion through broader types of ERB. In addition, Festinger’s theory of cognitive dissonance
(1957) indicated that to reduce the psychological conflict from holding two or more incompatible
beliefs simultaneously, they try to maintain their own beliefs. According to this theory,
individuals who carry out one type of ERB, such as beach-specific ERB here in this study, are
more likely to engage in other types of ERB (i.e. general ERB). In other words, people tend to
achieve consonance of their own beliefs that natural resources and the environment need to be
conserved. This is so called spill-over effects (Rashid, 2007).

72

Hence, even though beach-specific ERB was not explicitly measured, we can say that,
through its close connectedness with the local beaches, beach-specific ERB is expected to
influence general ERB.
The last internal factor was place satisfaction. A negative relationship was expected and
found, but the result was not significant. This result was contrary to other studies showing
negative relationships (Stedman, 2002; Uzzell et al., 2002). This variable was not significant for,
perhaps, a couple of reasons. First, a majority of residents were highly satisfied with their recent
visits to the local beaches. Perhaps, this skewed level of satisfaction resulted in non-significance.
In addition, while previous studies (e.g., Halpenny, 2006; Stedman, 2002; Uzzell, 2002) typically
measured place satisfaction with multiple items, this study used a single item—overall
satisfaction with the local beaches. The use of a single item to measure place satisfaction might
not capture the multi-dimensional aspects of the concept appropriately. A satisfaction scale with
multiple items might have provided a more comprehensive picture of place satisfaction and the
effects of this concept may have been different.
For an external factor, we measured socio-demographic variables. The results indicated
that all socio-demographic variables (income, length of residence, distance, age, and gender)
were not significant. First of all, income was not significant. Usually, researchers hypothesize
that income is positively related with ERB, as environmental conservation is considered a luxury
activity (Larson et al., 2011; Ngo et al., 2009; Scott & Willits, 1994; Van Liere & Dunlap, 1980;
Vaske et al., 2001). However, in this study, no relationship was found. Neither was length of
residence significant. According to Sheldon and Var (1984), lifelong residents feel more
sensitive to environmental changes than do short-term residents. Within the current study, the
average length of residence was only 13 years, and a large portion of residents had lived there for

73

less than 10. This could be why length of residence was not a significant predictor of ERB.
Another non-significant variable was distance from residential area to local beach.
Halpenny’s study (2006) found no direct relationship between distance and ERB. However, they
found that place attachment was an important mediating variable that connected these two
variables and distance showed a negative indirect relationship with ERB. In other words, closely
residing residents would behave environmentally responsibly when they were attached to a place.
However, in this study, distance and place attachment were included together with no testing of
mediation effects. Distance itself may not be an important predictor of ERB.
Age was not a significant predictor either. Numerous studies have included age as a
predictor of ERB. Some have found a negative relationship (Cottrell, 2003; Scott & Willts, 1994;
Van Liere & Dunlap, 1980) and others a positive one (Barr, 2003; Korfiatis et al., 2004; Ngo et
al., 2009). The mixed results indicate that the influence of age may depend on the context. Lastly,
gender was found to be not significant. Most studies have found that women are more likely to
behave in an environmentally responsible way (Barr, 2003; Mobley, Vagias, & DeWard, 2010;
Scott & Willits, 1994; Zelezny et al., 2000). However, no such finding was detected here.
Place Segmentation Analysis
First of all, ANOVA analysis was conducted to find whether socio-demographic
characteristics were different across the three counties. Variables, including income, length of
residence, distance, and age, differed significantly from one another. Because of these
differences, the three counties were dummy coded to help determine whether each county had its
own pattern of predicting ERB. The results indicated that Charleston County behaved in the least
environmentally responsible way. Lastly, three place-based segmented analyses were conducted
to demonstrate whether each factor had a different impact on ERB. All three models showed a

74

relatively high explanatory power of predicting ERB. However, each factor had a different result
according to place of residence.
Residents in Beaufort County (Hilton Head Island)
In Beaufort County, both place identity and place dependence significantly predicted
ERB. Further, the coefficient magnitude of place identity was greater than that of place
dependence. These results indicated that residents who were emotionally attached were more
likely to behave environmentally responsibly than those who were functionally attached. These
results were consistent with the hypotheses related to place attachment mentioned above as well
as with other place attachment studies (Kyle et al., 2004; Payton et al., 2005; Raymond et al.,
2011; Vorkin & Riese, 2011).
For attitudes toward tourism development, only one factor—benefits from tourism
development—was significantly related to ERB. This may be because Beaufort County is known
as an exclusive resort town. Residents are most likely well-off retirees who own upscale condos
or use up-scale time-shares. As they perceived having a great benefit from local beach tourism
development, they may have well been aware of the importance of beach quality and followed up
with more ERB. Lastly, as with the original model, place satisfaction was not significant here.
Among the socio-demographic variables, only age significantly explained ERB. Age
was not significant in the aggregate model; in Beaufort County, however, older residents were
more likely to behave environmentally responsibly. Beaufort County was, on average, the oldest
of the three counties. According to Linden and Thelander (2001), concern about environmental
problems were higher among people aged 55-60. Ngo et al. (2009) also stated that older people
were more likely to have such social values as a conservative life style. That could be one reason
why age was significant here.

75

Residents in Charleston County
Residents in Charleston County showed that only place-identified residents were
significantly related to ERB. In contrast to the aggregate model, place dependence was not
significant in Charleston County. This implies that residents who were emotionally attached to
local beaches were more likely to engage in ERB but those who were functionally attached were
not interested in ERB.
For the attitudes toward tourism development variable, only one factor—concern about
tourism development—was significant. It is plausible, though, that such a significant relationship
exists. Residents’ concerns for environmental issues could intensify their belief that the
environment needed to be protected and that could lead to more ERB. Place satisfaction was not
significant here either.
Among socio-demographic variables, income was significant. It was indicated that lower
income residents were more likely to behave in an environmentally responsible way. Distance
was also a significant predictor of ERB. Residents who lived close to local beaches were more
likely to participate in ERB. This might imply that Charleston residents who lived close to the
local beach had a stronger notion about the need to behave in more environmentally responsible
ways.
Residents in Horry County (Myrtle Beach)
The model with residents in Myrtle Beach showed that place identity and place
dependence were significantly related to ERB. Emotionally and functionally attached residents
were more inclined to engage in ERB. Similar to the aggregate model, emotionally attached
residents were more likely to participate in ERB than were functionally attached residents.
However, only concern about tourism development was significant. Myrtle Beach,

76

located in Horry County, is a major tourist destination and one of the fastest growing areas in the
US (United States Census, 2010). Active tourism development and population growth,
unsurprisingly, raise residents’ concerns about the quality of local beaches, leading to higher
ERB. Fitting the pattern up to now, place satisfaction was not an important variable.
Among socio-demographic variables, income was an important predictor. Less affluent
residents were more likely to behave environmentally responsibly. These results were consistent
with Charleston County.
The results can provide several important implications related to the policy-making and
management of coastal tourism destinations and communities. Resource managers and planners
associated with resource management agencies (e.g., Department of Natural Resources,
Department of Environmental Quality) could have a better idea of which county behaves less
environmentally. The study’s finding indicated that residents in Charleston County were less
likely than those of the other two counties to exhibit ERB. Resource managers and planners
could promote more ERB, targeting this county in particular. Specifically, by identifying which
factors are contributing to a lower level of ERB, they could establish different types of
management plans catered to each county. Charleston County revealed a non-significant
relationship with place dependence and benefits from tourism development. Resource managers
and planners may need to develop programs that encourage residents to become functionally
attached to a place. Also, providing more information regarding the relationship of residents’
perceived benefits from tourism development and conservation could encourage more ERB.

77

2. Limitations
It is worth noting several limitations of this study. First of all, the independent
variables—place attachment, attitudes toward tourism development, and place satisfaction—
were measured based on residents’ experiences at local beaches. Place-specific ERB was not
included in this study; instead, general ERB was used as a final dependent variable. Placespecific ERB could play an important role in mediating relationships between other independent
variables such as place attachment and general ERB. It is reasoned that as coastal residents
repeatedly use coastal resources and thus become attached to those resources and sites, they are
highly connected with the natural environment and gradually engage in place-specific ERB and
eventually general ERB.
Second, this study applied a value-based theory suggested by Stern (2000) to explain the
relationship between attitudes toward tourism development and ERB. However, the current study
did not explicitly determine the residents’ specific types of values. Stern’s VBN theory (2000)
was applied here and three values—egoistic, social-altruistic, and biospheric—were assumed
based on residents’ attitudes toward tourism development. Thus, it was postulated that based on
their close relationship with the local beach, they would have one of these types of value toward
the local beach and thus try to behave environmentally responsibly to protect that value.
Third, one other limitation is related to the measurement of attitudes toward tourism
development. While, within the TIAS scale, residents’ concerns mainly consisted of different
types of negative impacts from tourism development, this study mainly focused on
environmental issues as the main concern regarding tourism development. Nevertheless, as
residents’ highest agreement of concern was on environmental issues, it seems plausible to
interpret that as being the main concern. Likewise, while there were different types of items that

78

indicated positive impacts from tourism development, the interpretation of study results were
focused on the economic aspect. Among those items, most of them with high mean scores were
related with economic benefits. Thus, when interpreting the relationship between residents’
perceived benefits and ERB, it would be plausible that their perceived benefits were largely
influenced by economic benefits derived from local tourism development.
Lastly, while these conceptual relationships were explained by Fishbein and Ajzen’ TRA,
this study did not include behavioral intentions. According to TRA, the best predictor of
behavior is behavioral intentions. Antecedents of behavioral intentions are attitudes toward the
behavior and subjective norms (Eagly & Chaiken, 1993, p. 169). In this study, an individual’s
observable responses are used because actual behavior encompasses intentions to behavior
(Eagly & Chaiken, 1993). Thus, as place attachment also can be considered to be reflective of
environmental attitudes, it is expected that place attachment is closely connected with ERB.

3. Future Research
The findings from this study suggest several possibilities for future research. First of all,
regarding place attachment, it would be better to distinguish between place attachment and
community attachment. The two ideas are distinct, but as they appeared to be similar, they were
both used without distinction (Trentelman, 2009). Place attachment is more likely to focus on a
particular place, while community attachment reflects individuals’ rootedness to community.
Banks’ study (2010) entangled the two. These two concepts may affect ERB differently. Thus, in
a future study, it would be useful to highlight their differences and see whether they impact ERB
differently.
Second, this study was conducted in popular coastal tourism destinations in South
Carolina and future studies will be required to generalize study findings in other tourism
79

destinations. It is possible that those three variables, in other tourism destinations, could have
different impacts on ERB (Jorgensen & Stedman, 2006).
Third, it is recommended to use multiple items to measure place satisfaction. Place
satisfaction was, in this study, measured with only a single item and the result was not significant.
However, previous studies with the use of multiple items have shown a significant relationship
with ERB (Stedman, 2002; Uzzell et al., 2002). It will, in future studies, be beneficial to use a
more comprehensive scale of place satisfaction.
Fourth, it is also suggested to include beach-specific ERB to better explain carry-over
effects (spill-over effects). This study only measured general ERB and residents’ place-specific
ERB was implicitly assumed to be related to general ERB. It is expected that beach-specific ERB
will be an important mediating factor that connects other independent variables and general ERB.
Thus carry-over effects (spill-over effects) will be explained more accurately. In addition, as
coastal residents are highly influenced by local tourism development, beach-specific ERB in the
context of tourism can provide better implications to understand residents’ ERB within popular
tourism destination.
Also, to test the formative process of ERB, it would be beneficial to employ a more sophi
sticated method than multiple-regression, say, structural equation modeling (SEM). SEM is a use
ful tool that helps examining more complex causal relationships among variables (Halpenny, 200
6, Kyle et al., 2003). In this study, place attachment, attitudes toward tourism development, place
satisfaction, and socio-demographic variables were considered independent predictors of ERB. H
owever, it is reasoned that these variables are closely interconnected and jointly predict the proce
ss of residents’ engagement in ERB. Some studies have considered place satisfaction as an antec
edent to place attachment (Fried, 1984; Lee, 2001; Mesch & Manor, 1998). Other studies have c

80

onsidered place attachment as an antecedent to attitudes toward tourism development (Gursoy &
Rutherford, 2003; McCool & Martin, 1994; Williams, McDonald, Riden, & Uysal, 1995). Also,
place attachment is related to different socio-demographic variables, such as length of residence,
distance, and gender (Moore & Grafe, 1994; Rollero & De Piccoli, 2010). While such studies as
well as this one examined these multifaceted relationships independently, the use of SEM can hel
p examine these relationships in an integrated study framework. Thus, future studies should inclu
de all the relevant factors that explain ERB while using more advanced analytical methods such
as SEM.
Lastly, as this study only included quantitative data through survey methods, it was
difficult to provide more in-depth information such as why and how residents become more
attached to community resources. Thus, for future research, the use of qualitative methods such
as focus groups or in-depth interviews will be beneficial to provide a better understanding of
why residents are attached and lead to more ERB.

81

APPENDICES

82

APPENDIX A: Survey Scales

Residents’ Overall Satisfaction Scale

Overall, how satisfied are you with your most recent visit to your local beach?
(Please Circle one)
Table 30 Satisfaction Scale
Not at all
Slightly
Satisfied
Satisfied
1
2

Moderately
Satisfied
3

Very
Satisfied
4

Extremely
Satisfied
5

Residents’ Place Attachment Scale
Please rate your agreement with the following statements based on your most recent visit to
your local beach
Table 31 Place Attachment Scale
Strongly
Disagree
No other beach can compare to this
one
I feel this beach is part of me
I go to this beach because it is close
by
This beach means a lot to me
I wouldn’t substitute another beach
for this one
I am more satisfied visiting this
beach than any other
Visiting this beach says a lot about
who I am
I am very attached to this beach

Disagree

Neutral

Agree

Strongly
Agree

1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

88

Residents’ Attitudes toward Tourism Development Scale

Please indicate your level of agreement with each of the following statements regarding tourism
in your local community.
Table 32 Attitudes toward Tourism Development Scale
Strongly
Disagree
Disagree
Tourism has led to more spending in
1
2
your community
Our standard of living has increased
1
2
considerable because of tourism
Tourism has changed our precious
1
2
traditional culture
Local residents have suffered from
1
2
living in a tourism destination area
I reschedule activities to avoided
1
2
tourists
I enjoy having tourism in my town
1
2
Meeting tourist from other region is a
valuable experience to better
1
2
understand their culture and society
Benefits of tourism are greater than
1
2
costs to people in the community
Tourism results in positive impacts
1
2
on cultural identity of our community
Government should control tourism
1
2
development
Construction of tourist facilitates
have destroyed the natural
1
2
environment
Tourism has resulted in traffic
1
2
congestion, noise and pollution
Tourism provides more parts and
1
2
recreational areas for local residents
Improving public transit facilities is a
1
2
waste of tax payer money

89

Neutral

Agree

Strongly
Agree

3

4

5

3

4

5

3

4

5

3

4

5

3

4

5

3

4

5

3

4

5

3

4

5

3

4

5

3

4

5

3

4

5

3

4

5

3

4

5

3

4

5

Residents’ Environmentally Responsible Scale

Please circle the number corresponding to your opinion about the following statements.

Table 33 Environmentally Responsible Behavior Scale
Rarely Occasionally
I tried to find out what I can do
1
2
to help the environment
I talked to others about
1
2
environmental issues
I watched TV programs about
1
2
environmental issues
I read articles about current
1
2
environmental issues
I donated money/member of
1
2
conservation group
I joined a community clean
1
2
effort
I switched to environmentally
1
2
safe brand items
I read labels to see if items are
1
2
environmentally safe
I separated out recycle items
1
2
from trash
I recycled newspaper
1
2
I recycled glass, bottles, jars, or
1
2
aluminum cans

90

Sometimes

Frequently

Usually

3

4

5

3

4

5

3

4

5

3

4

5

3

4

5

3

4

5

3

4

5

3

4

5

3

4

5

3

4

5

3

4

5

Socio-demographic Variables Scale

Approximately how long have you lived at this residence? (Years) _____________

Which of the following best describes the distance from your residence to the closest beach
access?
 Less than 2 miles
 2-4.9 miles
5-10 miles
 More than 10 miles

What is your age?

________________________

Are you:
 Female
 Male

What is your approximate annual household income before taxes?

 <$10,000
 $10,000-19,999
 $20.000-19,999
 $30,000-39,999
 $40,000-49,999
 $50,000-59,999

 $60,000-69,999
 $70,000-79,999
 $80,000-89,999
 $90,000-99,999
 $100,000 and above

91

APPENDIX B: Correlation of Items and Scree Plots

Table 34 Correlations of Items in the Environmentally Responsible Behavior Scale
A
B
C
D
E
F
1.000
A. Helpenvieo
.783
1.000
B.Talkenviriss
.621
.690
1.000
C.TVenviron
.622
.702
.804
1.000
D. Readenviro
.488
.570
.500
.576
1.000
E. Donatmone
.481
.477
.399
.422
.572
1.000
F. Joincommu
.543
.530
.505
.547
.491
.413
G. Switchenvi
.541
.552
.510
.548
.434
.346
H. Seeelables
.330
.317
.262
.342
.265
.191
I. Recyleitems
.318
.307
.252
.327
.256
.209
J. Recyclenew
.325
.305
.252
.311
.239
.187
K. Recyglass

92

G

1.000
.798
.381
.355
.344

H

1.000
.360
.332
.312

I

1.000
.871
.896

J

1.000
.833

K

1.000

Figure 2 Scree plot for ERB

93

Table 35 Correlations of Items in the Place Attachment Scale
A
B
C
D
1.000
A. Nootherbeach
.571
1.000
B. Beachmypart
.386
.613
1.000
C. Meansalot
.664
.582
.479
1.000
D. Wouldnotsubsti
.681
.578
.443
.807
E. Moresatisfied
.536
.720
.571
.586
F. SaywhoIam
.520
.737
.681
.618
G. Attachedbeach

94

E

F

G

1.000
.617
.611

1.000
.770

1.000

Figure 3 Scree Plot for Place Attachment

95

Table 36 Correlations of Items in the Attitudes toward Tourism Development Scale
A
B
C
D
E
F
G
1.000
A. TourismCom
.338
1.000
B. TourisStandr
.096
-.070
1.000
C. Tourchancult
-.089
-.287
.532
1.000
D. ResideNssuffe
-.029
-.177
.397
.462
1.000
E. Reschedulacti
.175
.385
-.335
-.502
-.471 1.000
F. Enjoytourism
.144
.279
-.149
-.286
-.279
.470
1.000
G. Meettourisval
.172
.379
-.242
-.392
-.335
.530
.647
H. Tourpositicul
.224
.430
-.249
-.425
-.340
.497
.300
I. Benefigreatcos
-.054
-.238
.429
.516
.400
-.333
-.218
J. Tourismdestro
.059
-.154
.410
.426
.493
-.393
-.189
K. Tourispollut
.184
.379
-.146
-.308
-.244
.352
.295
L. Tourismpark

96

H

1.000
.436
-.296
-.266
.382

I

J

1.000
-.310
-.260
.355

K

1.000
.535
-.270

1.000
-.198

L

1.000

Figure 4 Scree Plot for Attitudes toward Tourism Development

97

REFERENCES

98

REFERENCES

Ajzen, I., & Fishbein, M (1977). Attitude-behavior relations: A theoretical analysis and review of
empirical research. Psychological Bulletin, 84(5), 888-918.
Andereck, L. K., & Nyaupane, P. G. (2011). Exploring nature of tourism and quality of life
perceptions among residents. Journal of Travel Research, 50(3), 248-260.
Andereck, L. K., Valentine, M. K., Knopf, C. R., & Vogt, A. C. (2005). Residents’ perceptions of
community tourism impacts. Annals of Research, 32(4), 1056-1076.
Andriotis, K., & Vaughan, D. R. (2003). Urban residents’ attitudes toward tourism development:
the case of Crete, Journal of Travel Research, 42, 172-185.
Banks, E. C. (2010). Disentangling the influence of community and place attachment on
resident attitudes toward tourism development. Unpublished doctoral dissertation, North
Carolina State University, Raleigh, NC.
Barr, S. (2003). Strategies for sustainability: citizens and responsible environmental behavior. Area,
35(3), 227-240.
Barr, S. (2007). Factors influencing environmental attitudes and behaviors: A U.K. case study of
household waste management. Environment & Behavior, 39(4), 435-473.
Berger, I. (1997). The demographics of recycling and the structure of environmental behavior.
Environment & Behavior, 29, 515-531.
Bird, A. (1998). Marine trash: a coastal threat to Southern California. Retrieved from
http://www.dbc.uci.edu/~sustain/suscoasts/amybird.html
Bott, S., Cantrill, G. J., & Myers, E. O. (2003). Place and the promise of conservation psychology.
Human Ecology Review. 10(2), 100-112.
Bricker, K.S., & Kerstetter, D. L. (2000). Level of specialization and place attachment: an
exploratory study of whitewater recreationists. Leisure Science, 22(4), 233-257.
Budruk, M., White, D. D., Wodrich, A. J., & Van Riper, J. C. (2008). Connecting visitors to people
and place: visitors’ perception of authenticity at Canyon de Chelly National Monument,
Arizona. Journal of Heritage Tourism, 3(3), 185-202.
Budruk, M., S., Schneider, E. I., & Anderson, H. D. (2011). Differentiating place attachment
dimensions among proximate and distant visitors to two water-based recreation areas,
Society & Natural Resources, 24(9), 917-932.
Budruk, M., Thomas, H., & Tyrrell, T. (2009). Urban green spaces: a study of place attachment
and environmental attitudes in India. Society & Natural Resources, 22(9), 824-839.

99

Byrd, T. E., Cardenas, A. D., & Dregalla, E. S. (2009). Differences in stakeholder attitudes of
tourism development and the natural environment. e-Review of Tourism Research, 7(2), 3951.
Casakin, H. P., & Kreitler, S. (2008). Place attachment as a function of meaning assignment.
Open Environmental Sciences, 2, 80-87.
Choi, C. H., & Murray, I. (2010). Resident attitudes toward sustainable community tourism.
Journal of Sustainable Tourism, 18(4), 575-594.
Choi, C. H., & Sirakaya, E. (2006). Sustainability indicators for managing community tourism.
Tourism Management, 27 (6), 1274-1289.
Clark, F. C., Kotchen, J. M., & Moore, R. M. (2003). Internal and external influences on proenvironmental behavior: participation in a green electricity program. Journal of
Environmental Psychology, 23, 237–246.
Cottrell, P. S. (2003). Influence of socio-demographics and environmental attitudes on general
responsible environmental behavior among recreational boaters. Environment & Behavior,
35(3), 347-375.
De Young, R. (2000). Expanding and evaluating motives for environmentally responsible behavior.
Journal of Social Issues,56(3), 509-526.
Eagly, A., & Chaiken, S. (1993). The psychology of attitudes. Fort Worth, TX: Harcourt Brace
Jovanovich College Publishers.
Environmental Engineering Solutions (2008). Marine pollution due to growing of coastal
population.Retrieved from http://environmentengineering.blogspot.com/2008/06/marinepollution-due-to-growing-of.html
Festinger, L. (1957). A theory of cognitive dissonance. Stanford, CA: Stanford University Press.
Fazio, R. H. (1986). How do attitudes guide behavior? In R. M. Sorrentino E. T. Higgins (Eds.),
The handbook of motivation and cognition: Foundations of social behavior (pp. 204-243).
New York: Guilford Press.
Festinger, L. (1957). A theory of cognitive dissonance. Evanston, IL: Row, Peterson.
Field, A. (2009). Discovering statistics using SPSS. Thousand Oaks, CA: SAGE publications.
Fried, M. (1982). Residential attachment: sources of residential and community satisfaction.
Journal of Social Issues, 38(3), 107-119.
Fried, M. (1984). Residential attachment: Sources of residential and community satisfaction.
Journal of Social Issues, 38(3), 107-119.
Goodwin, H. (1996). In pursuit of ecotourism. Biodiversity and Conservation, 5, 277-291.

100

Gosling, E., & Williams, J. H. K. (2010). Connectedness to nature, place attachment and
conservation behavior: Testing connectedness theory among farmers. Journal of
Environmental Psychology. 30, 298-304.
Guagnano, G. A., Stern, P. C., & Dietz, T. (1995). Influences on attitude–behavior relationships: A
natural experiment with curbside recycling. Environment & Behavior, 27(5), 699–718.
Guest, M. A., & Lee, A. B. (1983). Sentiment and evaluation as ecological variables. Sociological
Perspectives, 26(2), 159-184.
Gursoy, D., Jurowski, C., and Uysal, M. (2002) Resident attitudes: A Structural Modelling
Approach. Annals of Tourism Research, 29(1), 79-105.
Gursoy, D., & Rutherford, G. D. (2004). Host attitudes toward tourism: an improved structural
model. Annals of Tourism Research, 31(3), 495-516.
Halpenny, W. (2006). Environmental behavior, place attachment and park visitation: a case study
of visitors to Point Pelee National Park. Unpublished doctoral dissertation, University of
Waterloo, Waterloo, ON, Canada.
Halpenny, W. (2010). Pro-environmental behaviors and part visitors: the effect of place attachment.
Journal of Environmental Psychology. 30, 409-421.
Hay, R. (1998). Sense of place in development context. Journal of Environmental Psychology, 18,
5-29.
Hernandez, B., Marti, M. A., Ruiz, C., & Hidalgo, C. M. (2010). The role of place identity and
place attachment in breaking environmental protection laws. Journal of Environmental
Psychology, 30, 281-288.
Hailu, G., Boxall, C. P., & McFarlane, L. B. (2005). The influence of place attachment on
recreation demand. Journal of Economic Psychology, 26, 581-598.
Hwang, S., Lee, C., & Chen, H. (2005). The relationship among tourist’s involvement, place
attachment and interpretation satisfaction in Taiwan’s national parks. Tourism Management,
26, 143-156.
Jorgensen, S. B., & Stedman, C. R. (2001). Sense of place as an attitude: Lakeshore owners
attitudes toward their properties. Journal of Environmental Psychology, 21, 233-248.
Jurowski, C., Uysal, M., & Williams, R. D. (1997). A theoretical analysis of host community
resident reactions to tourism. Journal of Travel Research, 36, 3-11.
Kaplan, S. (2000). Human nature and environmentally responsible behavior. Journal of Social
Issues, 56(3), 491-508.
Korfiatis, J. K., & Hovardas, T., & Pantis, D. J. (2004). Determinants of environmental behavior in
societies in transition: evidence from five European countries. Population and Environment,
25(6), 563-583.
Kreag, G. (2001). The impacts of Tourism. Duluth, MN: University of Minnesota.
101

Kyle, G. T., Absher, J. D., & Graefe, A. R. (2003). The moderating role of place attachment on the
relationship between attitudes toward fees and spending preferences. Leisure Sciences, 25(1),
33-50.
Kyle, G., Gerafe, A., Manning, R., & Bacon, J. (2003). An examination of the relationship between
leisure activity involvement and place attachment among hikers along the Appalachian Trail.
Journal of Leisure Research, 35(3), 249-273.
Kyle, G., Graefe, A., Manning, R., & Bacon, J. (2004). Effects of place attachment on users’
perception of social and environmental conditions in a natural setting. Journal of
Environmental Psychology, 24, 213-225.
Kyle T. G., Theodori, L. G., Absher, D. J., & Jun, J. (2010). The influence of home and community
attachment on Firewise behavior. Society & Natural Resources, 23(11), 1075-1092.
Lankford, V. S., & Howard, R. D. (1994). Developing a tourism impact attitude scale. Annals of
Tourism Research, 21, 121-139.
Larson, R. L., Whiting, W. J., & Green, T. G. (2011). Exploring the influence of outdoor recreation
participation on pro-environmental behavior in a demographically diverse population. Local
Environment, 16(1), 67-86.
Lepp, A. (2007). Residents’ attitudes towards tourism in Bigodi village, Uganda. Tourism
Management, 28, 876-885.
Linden, A. & Thelander, A. (2001). Environmental consumption as a social project. The European
Science Foundation Programme: Consumption, Everyday Life, and Sustainability.
LancasterUniversity, UK.
Liu, Sheldon, & Var (1987). Resident perception of the environmental impacts of tourism. Annals
of Tourism Research, 14,17-37.
McCool, F. S., & Martin, R. S. (1994). Community attachment and attitudes toward tourism
development. Journal of Travel Research, 32, 29-34.
Marafa, M. L. (2008). Integrating sustainable tourism development in coastal and marine zone
environment. Retrieved from http://etudescaribeennes.revues.org/1373
Mesch, S. G., & Manor, O. (1998). Social ties, environmental perception, and local attachment.
Environment & Behavior, 30, 504-519.
Mobley, C., Vagias, W., & DeWard, S. (2010). Exploring additional determinants of
environmentally responsible behavior: the influence of environmental literature and
environmental attitudes. Environment & Behavior, 42, 420-447.
Moore, L. R., & Graefe, R. A. (1994). Attachments to recreation settings: The case of rail-trail
users. Leisure Sciences, 16(1), 17-31.

102

Ngo, A., West, E. G., & Clakins, H. P. (2009). Determinants of environmentally responsible
behaviours for greenhouse gas reduction. International Journal of Consumer Studies, 33,
151-161.
Nordlund, M.A., & Garvill, J. (2002). Value structures behind pro-environmental behavior.
Environment & Behavior, 45(6), 740-756.
Nordlund, M. A., & Garvill, J. (2003). Effects of values, problem awareness, and personal norm on
willingness to reduce personal car use. Journal of Environmental Psychology, 23, 339-347.
Oh, C., & Lyu, S., & Hammitt, E. H. (2012). Predicting linkage between recreation specialization
and place attachment. Journal of Leisure Research, 44(1), 70-87.
Payton, A. M., Fulton, C. D., & Anderson, H. D. (2005). Influence of place attachment and trust on
civic action: a study at Sherburne National Wildlife Refuge, Society & Natural Resources,
An International Journal, 18(6), 511-528.
Pelletier, G. L., & Sharp, E. (2008). Persuasive communication and proenvironmental behaviors:
how message tailoring and message framing can improve the integration of behaviors
through self-determined motivation. Canadian Psychology, 49(3), 210-217.
Priskin, J. (2003). Tourist perceptions of degradation caused by coastal nature-based recreation.
Environmental Management, 32(2), 189-204.
Proshansky, H., Fabian, A., & Kaminoff, R. (1983). Place-identity: physical world socialization of
the self. Journal of Environmental Psychology, 3, 57-83.
Rashid, N. (2007). Employee involvement in ems/iso14001and its spillover effect in creating
consumer environmentally responsible behavior. Unpublished doctoral dissertation,
University Sains Malaysia, Malaysia.
Raymond, M. C., Brown, G., & Robinson, M. G. (2011). The influence of place attachment and
moral and normative concerns on the conservation of native vegetation: a test of two
behavioral models. Journal of Environmental Psychology, 31, 323-335.
Roe, B., Teisl, F., Levy, A., & Russell, M. (2001). US consumers' willingness to pay for green
electricity. Energy Policy, 29, 917-925,
Rollero, C., & De Piccoli, N. (2010). Place attachment, identification and environment perception:
an empirical study. Journal of Environmental Psychology, 30, 198-205.
Rosenbaum, J., & Lidz, W. C. (2007). Maximizing results of internet survey. Center for Mental
Health Services Research, 4(2).
Saunders, D. Carol. (2003). The emerging field of conservation psychology. Human Ecology
Review, 10(2), 137-149.
Scannell, L., & Gifford, R. (2010)a. Defining place attachment: A triple organizing framework.
Journal of Environmental Psychology, 30, 1-10.

103

Scannell, L., & Gifford, R. (2010)b. The relations between natural and civic place attachment and
pro-environmental behavior. Journal of Environmental Psychology, 30, 289-297.
Schultz, W. P. (2001). The structure of environmental concern: concern for self, other people, and
the biosphere. Journal of Environmental Psychology, 21, 327-339.
Schultz, W. P., Jennifer, C., Tabanico, J., & Khanzian, M. A. (2004). Implicit connection with
nature. Journal of Environmental Psychology, 30, 289-297.
Schultz, W. P. Oskamp, S., & Mainieri, T. (1995). Who recycles and when? A review of personal
and situational factor. Journal of Environmental Psychology, 15, 105-121.
Schwartz, H. S. (1977). Normative influences on altruism. Advances in experimental social
psychology, 10, 221-279.
Scott, D., & Willits, K. F. (1994). Environmental attitudes and behavior: a Pennsylvania survey.
Environment & Behavior, 26, 239-260.
Sheldon, J. P., & Var, T. (1984). Resident attitudes to tourism in North Wales. Tourism
Management,5(1), 40-47.
Shumaker, A., & Taylor, B. R. (1982). Toward a clarification of people-place relationships: a
model of attachment to place. In F. R. Nickolaus & E. S. Geller (Eds.), Environmental
Psychology (1st eds., pp. 219-256). New York: Praeger Publishers.
Sintov, D. N., Desario, G., & Prescott, A. C. (2010). Effective of a competition-based intervention
in promoting pro-environmental behavior in a university residential setting. American
Council for an Energy Efficient Economy (ACEEE) Summer Study on Energy Efficiency in
Buildings Asilomar Conference Grounds, Pacific Grove, CA.
Slade, D. (2008). S.C. population growth in top 10: In-migration gives state biggest boost. The Post
and Courier. Retrieved from
http://www.postandcourier.com/news/2008/dec/23/s_c_population_growth_top65942/
Smith-Sebasto, N. J., & D’Acosta, A. (1995). Designing a likert-type scale to predict
environmentally responsible behavior in undergraduate students: A multistep process.
Journal of Environmental Education, 27(1).
Stedman, C. R. (2002). Toward a social psychology of place: predicting behavior from place-based
cognitions, attitude, and identity. Environment & Behavior, 34, 561-581.
Stern, P. (2000). Toward coherent theory of environmentally significant behavior. Journal of
Social Issues, 56(3), 407-424.
Stern, P. (2005). Understanding individuals’ environmentally significant behavior. The
Environmental Law Reporter, 10785-10790.
Thapa, B. (1999). Environmentalism: The relation of environmental attitudes and environmentally
responsible behaviors among undergraduate students. Bulletin of Science Technology &
Society, 19(5), 426-437.
104

Theodori, L. G. (2000). Level of analysis and conceptual clarification in community attachment
and satisfaction research: connection to community development. Journal of Community
Development Society. 31(1), 35-58.
Trentelman, K. C. (2009). Place attachment and community attachment: A primer grounded in the
lived experience of a community sociologist. Society & Natural Resources, 22(3), 191-210.
Thogersen, J. (1999). Spillover process in the development of a sustainable consumption patter.
Journal of Economic Psychology, 20, 53-81.
Thogersen, J. (2004). A cognitive dissonance interpretation of consistencies and inconsistencies in
environmentally responsible behavior. Journal of Environmental Psychology, 24, 93-103.
Thogersen, J., & Olander, F. (2003). Spillover of environment-friendly consumer behavior.
Journal of Environmental Psychology, 23, 225-236.
United Nations Environment Programme (2001). Environmental impacts of tourism. Retrieved
from http://www.gdrc.org/uem/eco-tour/envi/index.html
United States Census (2010). South Carolina. Retrieved from http://2010.census.gov/2010census/
Uzzell, D., Pol, E., & Badenas, D. (2002). Place identification, social cohesion, and environmental
sustainability. Environment & Behavior, 34, 26-53.
Van Liere, D. K., & Dunlap, E. R. (1978). Moral norms and environmental behavior: an
application of Schwartz’s norm-activation model to yard burning. Journal of Applied Social
Psychology,8(2), 174-188.
Van Liere, K., & Dunlap, R. (1980). The social bases of environmental concern: A review of
hypotheses, explanations, and empirical evidence. Public Opinion Quarterly, 44, 181–197.
Vaske, J.J., Donnelly, P. M., Williams, R. D., & Jonker, S. (2001). Demographic influences on
environmental value orientations and normative beliefs about national forest management.
Society and Natural Resources, 14, 761-776.
Vaske, J.J., & Kobrin, C. K. (2001). Place attachment and environmentally responsible behavior.
The Jouranl of Environmental Education. 32(4), 16-21.
Vorkinn M., & Riese, H. (2001). Environmental concern in a local context: the significance of
place attachment. Environment & Behavior, 33(2), 249-263.
Walpole, J. M., & Goodwin, J. H. (2001). Local attitudes towards conservation and tourism around
Kmodo National Park, Indonesia. Environmental Conservation, 28(2), 160-166.
Wang, Y., Pfister, E. R., & Morais, B. D. (2006). Residents’ attitudes toward tourism development:
a case study of Washington, NC. Proceedings of the 2006 Northeastern Recreation Research
Symposium (pp. 411-418). Bolton Landing, NY.
Warner, R. (2008). Applied Statistics. Thousand Oaks, CA: Sage Publications.

105

Wenner, E., Sanger, D., Upchurch, S., & Thompson, M. (2011). Ashepoo-Combahee-Edisto
(ACE) Basin, South Carolina. Retrieved from http://www.nerr. noaa. gov/siteprofile
White, D. D., Virden, J. R., & Riper, J. C. (2008). Effects of place identity, place dependence, and
experience-use history on perceptions of recreation impacts in a natural setting.
Environmental Management, 42, 647-657.
Whitmarsh, L. & O’Neil, S. (2010). Green identity, green living? The role of pro- environmental
self-identity in determining consistency across diverse pro- environmental behaviors.
Journal of Environmental Psychology, 30, 305-314.
Williams, R. D., McDonald, D.C., Riden, M.C., & Uysal, M. (1995). Community attachment,
regional identity and resident attitudes toward tourism. Proceedings of the 26th Annual
Travel and Tourism Research Association Conference proceedings (pp. 424-428). Wheat
Ridge, CO: Travel and Tourism Research Association.
Williams, R. D., & Roggenbuck, W. J. (1989). Measuring Place Attachment: Some Preliminary
Result. Session on Outdoor Planning and Management NRPA Symposium on Leisure
Research, San Antonio, Texas, October 20-22, 1989.
Zhang, H., & Lei, S. (2009). Residents’ environmental attitudes and behavioral intention of
tourism development in Beimen Coastal Wetland area, Taiwan. Annual of International
Tourism Journal, 2552.
Zelezny, C. L., Chua, P., & Aldrich, C. (2000). Elaborating on gender differences in
environmentalism. Journal of Social Issues, 56(3), 443-457.

106