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AN EXAMINATION OF FACTORS PREDICTING RESIDENTS'
SUPPORT FOR TOURISM DEVELOPMENT

presented by

Pavlina Latkova

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AN EXAMINATION OF FACTORS PREDICTING RESIDENTS’ SUPPORT FOR
TOURISM DEVELOPMENT

By

Pavlina Latkova

A DISSERTATION

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

DOCTOR OF PHILOSOPHY
Department of Park, Recreation and Tourism Resources

2008

ABSTRACT

AN EXAMINATION OF FACTORS PREDICTING RESIDENTS’ SUPPORT FOR
TOURISM DEVELOPMENT

By

Pavlina Latkova

Previous research has demonstrated that support of residents is essential for
successful sustainable tourism development. To achieve community support for the
tourism industry, tourism developers need to understand how residents formulate their
perceptions of tourism impacts. Most research on residents’ perceptions and attitudes has
been conducted in one or a few communities, and a small number of studies have
examined “a wide range of communities located within close proximity of each other in
aggregate” (McGehee & Andereck, 2004). Building on the model by Perdue, Long, and
Allen (1990), this study attempted to examine residents’ perceptions of tourism impacts
in several Midwest communities at different stages of tourism and economic
development. A theoretical framework consisting of social exchange theory (Skidmore,
1975) and Butler’s (1980) destination life cycle model was used to guide the study.
Residents’ attitudes toward tourism were tested with thirteen hypotheses. Independent
variables included tourism knowledge, perceived power, community attachment,
economic role of tourism, personal benefits from tourism, and residents’ characteristics.
The dependent variables included positive and negative impacts of tourism, support for
future tourism development, support for restrictions on tourism development, and
residents’ outlook on the future of their community which was the ultimate dependent

variable.

Data were collected using a mail questionnaire across three geographical regions
at different stages of tourism and economic development. A total of 3,008 households
constituted the population and twenty—eight percent (28 %) of the surveys were returned.
After the mail data collection was completed, a non-response survey was sent out to
assess any bias in the dataset. The results obtained from the non-respondents were found
to be relatively the same as from the main study results. The questionnaire was developed
based on a literature review and input obtained from several county officials and tourism
professionals as well as three tourism researchers from different universities.

Residents’ attitudes toward tourism were tested utilizing a series of multiple
regression analyses and one-way ANOVA. Results of the study further validated the
model by Perdue at al. (1990). In addition, residents who perceived tourism as an
important economic development strategy were more supportive of tourism development.
Support for social exchange theory was mixed because personal benefits from tourism
failed to predict support for restrictions on future tourism development. The results do not
support previous studies which suggest that attitudes toward tourism become more
negative with higher levels of tourism. Implications for tourism planners and developers
included the need to educate residents about both costs and benefits associated with
tourism development. The more tourism industry officials can demonstrate how
individuals benefit from tourism in the area, the more support the industry is likely to
enjoy from local residents. Future studies should improve measurements of the economic

role of tourism, restrictions on future tourism development and community future.

This dissertation is dedicated to my family.
Your love, patience and encouragement are the foundation of my life’s successes.

iv

ACKNOWLEDGMENTS

I would like to thank my dissertation committee members, Dr. Christine Vogt (chair),
Dr. Donald Holecek, Dr. Richard Paulsen, and Dr. Ingrid Steglitz; whom I have had the
privilege of working with over the past four years.

Dr. Vogt, thank you for all your support and guidance that has brought me to the
finish line. I admire your work ethic, wisdom, energy, and compassion for your students. I
hope to be an inspiration to my students as you have been to me. Dr. Holecek, “jak se mas”,
you have provided me with a constructive criticism that has made me a better researcher. Dr.
Paulsen, you were there for me when I found myself at the lowest point of my Ph.D. journey.
Thank you for being a great listener and a mentor. Dr. Steglitz, thank you for your
encouragement, support, new ideas, and the expertise you brought to my committee.

Chtela bych podekovat moji mame Pavline, tatovi Janovi, a sestre Zdence za jejich
celozivotni lasku, trpelivost, podporu a porozumeni. Nase pristi spolecna dovolena bude v
San Franciscu!

Victoria, thank you for being there for me during good times and bad times. Victoria,
your love, help, support, encouragement, and caring for me have been the foundation of my
success. I have been very fortunate to have met you, moje lasko.

Ariel, Jamie, and Paige— I will forever be indebted to you, my Ph.D. buddies. You
have consistently supported me, pushed me to the higher level of performance, made me
laugh and cry, and provided me with strength to keep on going.

Geraldine, Christine, Leona, Brodie and Thamsyne, thank you for your support,

friendship, and believing in me. Maciku, diky za Tvoji celozivotni podporu a pratelstvi.

TABLE OF CONTENTS

LIST OF FIGURES ........................................................................ xi

CHAPTER I. INTRODUCTION ...................................................... 1

Tourism Attitudes ..................................................................... 2

Statement of the Problem ............................................................ 3

Theoretical Framework ............................................................ 4

Doxey’s Index of Irritation “Irridex” ......................................... 4

Butler’s Tourism Area Life Cycle (TALC) Model ........................ 5

Social Exchange Theory ....................................................... 5

Perdue, Long and Allen’s Model of Residents’ Tourism Perceptions 6

Research Questions ................................................................. 7

Hypotheses ........................................................................... 8

Delimitations ........................................................................ 1 1

Limitations ........................................................................... 1 1

Definitions ........................................................................... 11

Study Organization ................................................................. 12

CHAPTER II. LITERATURE REVIEW .............................................. 14

Tourism Development in Rural Areas ........................................... 14

Theoretical Framework ............................................................ 16

Overview of Theoretical Perspectives on Residents’ Attitudes ......... 16

Doxey’s Index of Irritation “Irridex” ......................................... 23

Butler’s Tourism Area Life Cycle (TALC) Model ........................ 25

Social Exchange Theory ....................................................... 33

Perdue, Long and Allen’s Model of Residents’ Tourism Perceptions 35

Factors Influencing Residents’ Attitudes toward Tourism ............... 42
Proposed Comprehensive Model of Residents’ Tourism Perceptions

and Support for Tourism Development ..................................... 50

Summary of the Literature Review ............................................... 55

CHAPTER III. METHODOLOGY ..................................................... 60

Study Area ........................................................................... 60

Population and Sample ............................................................. 61

Data Collection ...................................................................... 62

Non-Respondent Study ............................................................ 65

Survey Instrument .................................................................. 66

Reliability Test ...................................................................... 71

Data Analysis ........................................................................ 74

vi

Multiple Regression Analysis ................................................
Multiple Regression Models Used in the Study ............................
Analysis of Variance ...........................................................
Statistical Procedures of Data Analysis ..........................................

CHAPTER IV: RESULTS ...............................................................

Description of the Sample .........................................................
Socio-Demographic Profile ...................................................
Community Attachment .......................................................
Level of Objective Knowledge of Tourism .................................
Level of Subjective Knowledge of Tourism ................................
Perceived Power ................................................................
Perceived Economic Role of Tourism .......................................
Personal Benefits from Tourism .............................................
Positive Impacts of Tourism ..................................................
Negative Impacts of Tourism .................................................
Support for Future Tourism Development ..................................
Support for Restrictions on Future Tourism Development ...............
Perceived Community Future .................................................

Testing of the Study Hypotheses ..................................................
Regression Model 1: Hypothesis 1 ...........................................
Regression Models 2 and 3: Hypotheses 2 and 3 ...........................
Regression Models 4 and 5: Hypotheses 4, 5 and 6 ........................
Regression Models 6, 7 and 8: Hypotheses 7, 8 and 9 .....................
Relationship between Residents’ Attitudes and the Stage of Tourism
Development: Hypotheses 10, 11, 12 and 13 ...............................

CHAPTER V: CONCLUSION ..........................................................
Summary of Results and Discussion .............................................
Social Exchange Theory Relationships .....................................
Perceptions of Tourism across Different Levels of Tourism and
Economic Development ......................................................
Implications ..........................................................................
Limitation of Findings ..............................................................
Future Research .....................................................................
Final Thoughts .......................................................................

APPENDICES ..............................................................................
Appendix A: Descriptive Statistics for Items Used in the Study .............
Appendix B: First and Second Wave Cover Letters ...........................
Appendix C: Reminder Postcard ..................................................
Appendix D: Survey Instrument and Non-Respondent Study

BIBLIOGRAPHY .........................................................................

vii

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190

Table 1:

Table 2:

Table 3:

Table 4:

Table 5:

Table 6:

Table 7:

Table 8:

Table 9:

Table 10:

Table 1 1:

Table 12:

Table 13:

Table 14:

Table 15:

Table 16:

Table 17:

Table 18:

Table 19:

LIST OF TABLES

Theoretical Frameworks Used to Study Residents’ Perceptions of
Tourism .....................................................................

Strength and Weaknesses of Theoretical Frameworks ...............
An Overview of Measurements Used to Test Butler’s Model .......
Support for Social Exchange Theory ....................................

Previously Identified Factors Influencing Residents’ Attitudes
toward Tourism ............................................................

Characteristics of Geographic Areas under Study .....................
Response Rates .............................................................
Variables and Sources for Scale Items Used for Measurements

Support for Testing Demographic Characteristics of the Proposed
Model ........................................................................

Cronbach Alpha Coefficients for Composite Scales Used in the
Study .........................................................................

Regression Analysis of the Relationships between Variables .......
Socio-Demographic Profile of Homeowners by County .............
Community Attachment by County .....................................
Contribution of Tourism and Recreation to County’s Economy

Used as the Original Variable of Level of Objective Knowledge of
Tourism by County .........................................................

Level of Objective Knowledge of Tourism by County Determined
as Distance from the “Target” ............................................

Level of Subjective Knowledge of Tourism by County ..............
Perceived Power by County ..............................................
Perceived Economic Role of Tourism by County .....................

Personal Benefits from Tourism by County ...........................

viii

20

22

27

35

49

61

64

69

71

73

78

85

86

87

87

88

89

89

90

Table 20:

Table 21:

Table 22:

Table 23:

Table 24:

Table 25:

Table 26:

Table 27:

Table 28:

Table 29:

Table 30:

Table 31:

Table 32:

Table 33:

Table 34:

Positive Impacts of Tourism by County ..............................
Negative Impacts of Tourism by County ...............................
Support for Future Tourism Development ...........................

Support for Restrictions on Future Tourism Development by
County .......................................................................

Perceived Community Future by County ...............................

Correlations, Means, and Standard Deviations among Regression
Model 1 Variables by County ............................................

Regression Analysis of Model 1 Relationships between
Attachment, Objective and Subjective Knowledge, Power,
Economic Role and Personal Benefits from Tourism .................

Correlations, Means, and Standard Deviations among Regression
Models 2 and 3 Variables by County ...................................

Regression Analysis of Model 2 Relationships between Residents
Characteristics and Positive Impacts from Tourism Controlling for
Personal Benefits ...........................................................

Regression Analysis of Model 2 Relationships between Residents
Characteristics and Negative Impacts from Tourism Controlling
for Personal Benefits ......................................................

Correlations, Means, and Standard Deviations among Regression
Models 5 and 6 Variables by County ....................................

Regression Analysis of Model 4 Relationships between Objective
and Subjective Knowledge, Power, Economic Role, and Personal
Benefits on Positive Impacts from Tourism ............................

Regression Analysis of Model 5 Relationships between Objective
and Subjective Knowledge, Power, Economic Role, and Personal
Benefits on Negative Impacts from Tourism ...........................

Correlations, Means, and Standard Deviations among Regression
Models 6, 7 and 8 Variables by County ..............................

Regression Analysis of Model 6 Relationships between Personal

Benefits from Tourism, Positive and Negative Impacts, and
Support for Future Tourism Development ..............................

ix

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92

93

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100

102

104

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109

111

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118

120

Table 35:

Table 36:

Table 37:

Table 38:

Table 39:

Table 40:

Table 41:

Table 42:

Table 43:

Table 44:

Table 45:

Regression Analysis of Model 7 Relationships between Personal
Benefits from Tourism, Positive and Negative Impacts, and

Restrictions on Future Tourism Development .........................

Regression Analysis of Model 8 Relationships between Personal
Benefits from Tourism, Positive and Negative Impacts,
Restrictions on Future Tourism Development, and Community

Future .......................................................................

County Market Share of Pleasure Travelers in Michigan (1996-

2000) ..........................................................................

Population Trends (1960-2010) ........................................

Michigan Tourism Spending by County in 2000 ......................

Contribution of Tourism and Recreation to the Local Economy in

2007 ..........................................................................

Proportion of Seasonal Homeowners (1990-2000) ..................

Average Number of Jobs in Tourism-Related Businesses
(1977-1987) ................................................................

Total Economic Activity ..................................................

One-Way Analysis of Variance Examining Differences in
Perceptions of Positive and Negative Impacts, Support and
Restrictions on Future Tourism Development, and Community
Future based on Area’s Level of Tourism and Economic

Development ...............................................................

Summary of Hypotheses 10-13 Testing .................................

121

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Figure I:
Figure 2:

Figure 3:

Figure 4:

Figure 5:

Figure 6:

Figure 7:

Figure 8:

Figure 9:

Figure 10:

Figure l 1:

Figure 12:

Figure 13:

Figure 14:

LIST OF FIGURES

Doxey’s Index of Irritation “Irridex” ....................................
Butler’s Tourism Area Life Cycle (TALC) Model ...................

Perdue, Long and Allen’s Model of Residents Tourism
Perceptions ..................................................................

Proposed Comprehensive Model of Residents’ Tourism
Perceptions and Support for Tourism Development ..................

Proposed Comprehensive Model of Residents’ Tourism
Perceptions and Support for Tourism Development with
Hypotheses .................................................................

Two-Way Interaction between Age and Personal Benefits
Predicting Positive Impacts of Tourism in Emmet County ..........

Two-Way Interaction between Age and Personal Benefits
Predicting Negative Impacts of Tourism in Emmet County .........

Two-Way Interaction between Age and Personal Benefits
Predicting Negative Impacts of Tourism in Tuscola County ........

Two-Way Interaction between the Economic Role of Tourism and
Personal Benefits Predicting Positive Impacts of Tourism in
Emmet County .............................................................

Two-Way Interaction between the Economic Role of Tourism and
Personal Benefits Predicting Positive Impacts of Tourism in
Saginaw County ...........................................................

Two-Way Interaction between the Economic Role of Tourism and
Personal Benefits Predicting Positive Impacts of Tourism in

Tuscola County ............................................................

County Market Share of Pleasure Travelers in Michigan (1996—
2000) .......................................................................

Trends in State of Michigan Lodging Use Tax Collections for
Emmet, Saginaw, and Tuscola Counties (1983-1989) ................

Trends in State of Michigan Lodging Use Tax Collections for
Emmet, Saginaw, and Tuscola Counties (1990-1995) ...............

xi

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26

37

52

95

104

106

107

112

112

113

124

128

128

Figure 15: Residents’ Attitudes by Level of Tourism and Economic
Development ............................................................... 1 32

Figure 16: Summary of Hypotheses 1-9 Testing ..................................... 135

xii

Chapter I
INTRODUCTION
Over the past twenty years, rural communities in the United States have
experienced economic hardship due to decline in traditional industries (Perdue et al.,
1987). To mitigate economic difficulties, many rural communities have adopted tourism
as a new economic development strategy. Tourism is associated with economic,
environmental, and socio-cultural benefits which can contribute to revitalization of
communities and enhancement of residents’ quality of life (Andereck & Vogt, 2000;
McCool & Martin, 1994). However, just like any other industry, tourism may bring
changes to communities which will negatively affect residents’ lives. Therefore, before a
community begins to develop tourism resources, it is crucial to understand residents’
opinions regarding fixture development.

Previous research in the field of tourism has demonstrated that support of tourism
by residents is essential for successful sustainable development of tourism (Andereck &
Vogt, 2000; Jurowski & Gursoy, 2004). Residents’ perceptions of tourism are shaped by
numerous factors. Some may perceive tourism development as economically beneficial
with its potential to create jobs, generate income, and enhance infrastructure.
Alternatively, others may view tourism development in a negative light because of its
potential negative impact on the environment and local culture. Understanding how
residents formulate their perceptions of tourism impacts and their attitudes toward
tourism can help mitigate negative attitudes towards tourism which often influence
destination attractiveness and hence, the number of visitor arrivals (Gursoy & Rutherford,

2004; Murphy, 1985). High tourist satisfaction has been linked to positive host

perceptions and attitudes (Andriotis & Vaughan, 2003). Therefore, to achieve community
support for the tourism, community leaders and developers should view tourism as a
“community industry” (p.181) and involve residents in local tourism planning (Murphy,
1985). “Through the tourism planning process, the community can collectively assess its
own potential and that of the surrounding area of tourism” (Fridgen, 1991, p. 213).
Community participation in tourism planning is a key to successful tourism development
with the overall goal of improving the community.

Understanding of residents’ perceptions and attitudes has been shown to be an
important factor in achieving successful sustainable tourism development. As a result,
many studies have investigated residents’ perceptions of tourism impacts and attitudes
towards tourism development in communities worldwide, including those in developed
(e.g., Ap, 1992; Andereck & Vogt, 2000; Jurowski & Gursoy, 2004; Snaith & Haley,
1999) and less developed countries (e.g., Belisle & Hoy, 1980; Kayat, 2002; Smith &
Krannich, 1998). Most available research has been conducted in one or a few
communities (Perdue, Long & Allen, 1990; McGehee & Andereck, 2004), and only a
small number of studies have examined several communities (McGehee & Andereck,
2004) at different stages of economic and tourism development. To address this gap in
the literature, the current study examined residents’ attitudes toward tourism in several

rural Midwestern communities at different stages of economic and tourism development.

Tourism Attitudes
Residents’ attitudes toward tourism have been investigated for over forty years.

Early studies on resident attitudes tended to focus on tourism impacts, especially positive

economic impacts (Ap & Crompton, 1998). In the 1970’s, several studies noted negative
impacts associated with tourism, however; it was not until the 1980’s and 1990’s when a
more balanced and systematic perspective enabled evaluation of both positive and
negative impacts of tourism evolved (Andereck & Vogt, 2000; Ap & Crompton, 1998).
Numerous theories have been applied and models developed to examine
residents’ attitudes towards tourism and to determine the key variables influencing
attitudes toward tourism. While some studies have found support for their theoretical
frameworks and predictors of tourism, others have been inconclusive. However, these
studies suggest that tourism impacts on host communities are multifaceted. While tourism
can improve residents’ quality of live and contribute to revitalization of communities
experiencing economic hardship, successful sustainable tourism development is

dependent on local residents’ support (Jurowski, Uysal, & Williams, 1997).

Statement of the Problem

The problem of the study was to examine residents’ perceptions of tourism
impacts and attitudes toward the existing tourism industry and future tourism
development in several small rural Midwestern communities at different stages of
economic and tourism development. The research also tested the influence of residents’
attitudes toward tourism on support of future tourism development. The study extended
the model developed by Perdue, Long and Allen (1990) and utilized social exchange
theory (Skidmore, 1975), and Butler’s (1980) destination life cycle model as the

theoretical framework to guide the study.

To address this research problem, this study provides an insight to residents’
perceptions of tourism impacts and attitudes toward future tourism development in this
collection of rural communities. The results of the study will enhance knowledge and
understanding of residents’ attitudes towards future tourism development and assist
tourism planners and developers in developing appropriate policies and strategies to
alleviate resident concerns and issues, as well as minimize problems, and optimize

benefits associated with tourism development.

Theoretical Framework
Over the past thirty years, several models have been developed to predict the
impact of tourism development, changes in residents’ attitudes towards tourism, and to
determine the key variables behind a destination’s life cycle and variables influencing

attitudes toward tourism.

Doxey ’3 Index of Irritation: “Irridex ”

Doxey’s (1975) “Irridex” model was one of the first models that identified the
collective effect of tourism development on residents’ attitudes. Doxey’s model
suggested a unidirectional, predictable sequence of changes in residents’ attitudes.
According to Doxey, as the tourism industry increases, residents’ attitudes change and
become more negative, moving from euphoria to apathy, annoyance, and then final stage

of antagonism where residents openly express their irritation toward tourists.

Butler '3 Tourism Area Life Cycle ( T ALC) Model

Extending Doxey’s work, Butler ( 1980) developed a tourism area cycle of
evolution model implementing the product cycle concept. According to Butler (1980) a
resort cycle moved through five stages; exploration, involvement, development,
consolidation and stabilization, decline, or rejuvenation (depending on efforts to improve
the adverse effects). Over these distinct stages, there were noteworthy changes in the
types of visitors, the available infrastructure, the marketing and advertising strategies, the
natural and built environment, and local people’s attitude towards tourism. These changes
cumulated over time and resulted in one of the alternative scenarios of the post-stagnation
stage. If growth was unplanned, the area would reach its carrying capacity causing the
attractiveness of the area and the number of visitors to decline. In contrast, appropriate
planning can have not only prolong the tourism flow over a long period of time, but also
instigate a new life cycle by changing attractions or introducing new facilities (e.g.,
casino, golf course) (Stansfield, 1978).

Earlier studies focusing on residents’ perceptions and attitudes toward tourism
have been criticized for being primarily exploratory or lacking in theoretical background
(Ap, 1992). To address the atheoretical nature of residents’ attitudes studies, later studies

integrated new theories, social exchange theory (e.g., Ap, 1992) being one of them.

Social Exchange Theory
Social exchange has been the dominant theoretical framework in research
regarding residents’ perceptions and attitudes toward tourism development. Social

exchange theory is concerned “with understanding the exchange of resources between

individuals and groups in an interaction situation”, ..., where “actors” supply one another
with valued resources” (Ap, 1992, 668). The main premise of social exchange theory is
that individuals evaluate an exchange based on the costs and benefits associated with that
exchange. Hence, people will engage in an exchange if the exchange is likely to produce
valued rewards, and the perceived costs do not exceed perceived rewards (Skidmore,
1975). Essentially, if residents perceive an exchange to be beneficial to their well—being,
they will evaluate that exchange positively. However, if they perceive costs from an
exchange, rather than benefits, they will evaluate that exchange negatively. In terms of
tourism, residents who perceive to benefit from tourism are likely to have positive
attitudes toward tourism development than those who do not perceive themselves as
benefiting from tourism.

The current study utilized the above mentioned theoretical framework to examine
residents’ attitudes toward tourism development in several rural Midwestern communities
building on the model developed by the Perdue, Long, and Allen (1990). This model was
used to analyze residents’ support for tourism in diverse rural Colorado communities,
with varying levels of tourism development. Testing a modified Perdue, Long, and
Allen’s (1990) in rural Michigan communities (resembling those in Colorado) further

validated and improved the model.

Perdue, Long, and Allen ’3 Model of Residents ’ Tourism Perceptions
Perdue, Long, and Allen (1990) developed a conceptual model to analyze
quantitative data collected from several small mral communities in Colorado. The model

was used to test rural residents’ perceptions of tourism impacts. Moreover, the following

relationships were examined: (a) relationship between perceived impacts and residents’
support for additional tourism development, (b) relationship between residents’ support
for additional tourism development and restrictive tourism policies and special tourism
taxes, and (c) relationship between residents’ support for additional tourism development

and the perceived future of the community.

Research Questions
Building on the modified Perdue, Long, and Allen (1990) model, and utilizing
social exchange theory, and the tourism area life cycle model, nine research questions

were formulated:

1. To what extent are level of objective and subjective knowledge of tourism, power,
perceived economic role of tourism, and community attachment related to
personal benefits from tourism?

2. To what extent are residents’ characteristics related to positive (negative)

perceptions of impacts when controlling for personal benefits from tourism?

3. To what extent are level of objective and subjective knowledge of tourism, power,
perceived economic role of tourism, and community attachment related to
positive (negative) perceptions of impacts when controlling for personal benefits

from tourism?

4. To what extent are personal benefits from tourism related to perceived positive

(negative) impacts of tourism?

5. To what extent are personal benefits from tourism, perceived positive impacts of
tourism, and perceived negative impacts of tourism related to support for future
tourism development?

6. To what extent are personal benefits from tourism, perceived as positive impacts
of tourism, and perceived negative impacts of tourism related to support for future
restrictions on tourism development?

7. To what extent are personal benefits from tourism, perceived positive impacts of
tourism, perceived negative impacts of tourism, support for future tourism
development, and support for future restrictions on tourism development related
to perceived community future?

8. Do residents’ perceptions of tourism impacts differ based on their area’s level of
tourism and economic development?

9. Do residents’ perceptions of community future differ based on their area’s level of

tourism and economic development?

Hypotheses
Based on the proposed comprehensive model, the study theory (i.e., social
exchange theory (SET), and tourism area life cycle (TALC) model were tested with the

following hypotheses:

H1: Level of objective and subjective knowledge, power, economic role of tourism,

community attachment are positively related to personal benefits from tourism

(SET).

H2:

H3:

H4:

H5:

H6:

H7:

H8:

When controlling for personal benefits from tourism, there is a positive (negative)
relationship between residents’ characteristics and residents’ positive (negative)
tourism impact perceptions (SET).

Personal benefits from tourism will moderate the relationship between residents’
characteristics and residents’ positive (negative) tourism impact perceptions
(SET).

Level of objective and subjective knowledge of the tourism industry, power,
perceived economic role of tourism, and community attachment are positively
related to perceived positive impacts of tourism and negatively related to
perceived negative impacts of tourism when controlling for personal benefits from
tourism (SET).

Personal benefits from tourism will moderate the relationships between level of
objective and subjective knowledge of the tourism industry, power, perceived
economic role of tourism, and community attachment and residents’ positive
(negative) tourism impact perceptions (SET).

There is a positive (negative) relationship between personal benefits from tourism
and positive (negative) perceived impacts of tourism (SET).

Differences will be found among personal benefits from tourism and perceptions
of positive (negative) impacts of tourism regarding their ability to predict support
for future tourism development (SET).

Differences will be found among personal benefits from tourism and perceptions
of positive (negative) impacts of tourism regarding their ability to predict support

for future restrictions on tourism development (SET).

H9:

H10:

H11:

H12:

H13:

Differences will be found among personal benefits from tourism, perceptions of
positive (negative) impacts of tourism, support for future tourism development,
and support for future restrictions on tourism development regarding their ability
to predict perceived community future (SET).

Residents from communities with low economic and low tourism development
and those with high economic and high tourism development will perceive greater
positive and smaller negative impacts of tourism than residents from communities
with low tourism development and high economic development (TALC).
Residents from communities with low economic and low tourism development
and those with high economic and high tourism development will be more
supportive of future tourism development than residents from communities with
low tourism development and high economic development (TALC).

Residents from communities with low economic and low tourism development
and those with high economic and high tourism development will be less
supportive of restrictions on future tourism development than residents from
communities with low tourism development and high economic development
(TALC).

Residents from communities with low tourism development and high economic
development and those with high economic and high tourism development will be
more optimistic about the firture of their county than residents from communities

with low economic and low tourism development (TALC).

10

Delimitations
This study was delimited to a random sample of permanent and seasonal
homeowners drawn from sampling frames representing the three areas under study. The
data used in this study were collected as part of a larger municipal recreation and tourism
master plan needs assessment. As such, only a limited number of questions specific to the

study problem was included in the survey instrument. Next, the study was delimited to
testing social exchange variables that have shown a significant relationship to residents’

perceptions in earlier studies. Lastly, the study was delimited to a quantitative data
C 0 l l ecti on.

Limitations
The study was limited by the following factors: (1) a low survey response rate due
t0 the length of the survey and declining mail survey rates; (2) samples drawn from
general population who may not be as knowledgeable about the tourism industry as

Individuals involved in tourism development (i.e., tourism business owners, civic

leaders); and (3) the geographic regions under study were not selected using stratified

random sample, thus they may not be representative of other tourism destinations at

Similar development stages.
Definitions

Attitude: “Attitudes are intellectual, emotional, and behavioral responses to events,

things, and persons which people can learn over time” (F ridgen, 1991, p.43).

Community Attachment: “the social bond and local sentiment residents express toward

11

their community” (Jurowski, 1998, p. 31), and attachments “ to biophysical or
landscape features of place” (Brehm et al., 2006, p. 146).

Level of Tourism Development: Level of tourism development will be determined based
on Butler’s (1980) destination life cycle model suggesting destinations may
proceed through the following stages: exploration, involvement, development,
consolidation and stabilization, decline, or rejuvenation, depending on efforts to
improve the adverse effects.

Length of Residence: Length of residence refers to the total number of years a permanent
or seasonal resident has resided in a given study area.

Local Community: “A complex system of friendship and kinship networks and formal
and informal associational ties rooted in family life and on-going socialization
process” (Kasarda & Janowitz, 1974, p. 329).

Perceived Power (Involvement in Decision Making): Power is considered a major
variable in the social exchange relation (Ap, 1992; Emerson, 1972; Madrigal,
1995). “Power refers to the ability of one actor to influence decision outcomes
that will affect others” (Madrigal, 1995, p. 338)

Tourist: A person who travels outside their home community during their leisure time for
any purpose (e. g., vacation, visiting friends and family, conducting business,

exploring retirements areas) (Jones, 2003).

Study Organization

The current study is organized into five chapters. Chapter 1 consists of a general

background and introduction to the theoretical framework used to guide the study.

12

Chapter 2 provides an in-depth overview of the study’s theoretical concepts and
justification of constructs explored in relation to residents’ attitudes toward future
tourism development. Consistencies and inconsistencies found by previous residents’
attitudes toward tourism studies are also discussed.

Chapter 3 discusses the research methodology used to obtain and analyze
information for this study. First, the sample and population are described. Next, the data
collection techniques and research instrument, including the scale development, are
discussed. Finally, the statistical tests used for data analysis are explained.

An overview of data analysis is provided in Chapter 4. First, description of the
sample focusing on socio-demographics and key variables used in the conceptual model
are reported. Then, results of hypotheses testing using standard multiple regression and
one-way ANOVA with post-hoc tests are presented.

Chapter 5 provides a summary and discussion of the key results in two sections
addressing theoretical frameworks used to guide the study. Next, implications for tourism
industry official are presented. Finally, limitations of study findings and

recommendations for future research are discussed.

l3

Chapter II
LITERATURE REVIEW
The purpose of this chapter is to provide an overview of empirical studies and
theoretical frameworks that emphasize and support the importance of understanding
residents’ attitudes in achieving successful tourism development. The literature overview
is presented under the following topics: (1) tourism development in rural areas, (2)

theoretical framework, and (3) factors influencing attitudes toward tourism development.

Tourism Development in Rural Areas

Over the past thirty years, rural areas in the United States have experienced a
decline in traditional industries (e. g., agriculture, logging, and heavy manufacturing). As
a result, rural communities throughout the United States have begun to look for new
forms of economic development, tourism being one of them (Long et al., 1990; Allen et
al., 1993; Allen et al., 1988; Perdue et al., 1987). Tourism development is often
associated with economic growth due to its ability to provide new local employment and
tax revenues and to diversify rural communities’ economies.

Tourism in rural areas is typically characterized by small-scale and emphasis on
entrepreneurial independent operators (Madrigal, 1993). Rural areas have a special appeal
to tourists because they offer a diverse tourism product based on the area’s distinct
environment, culture, history, and geographic character. Many tourists visit rural areas
because of the uniqueness and attractiveness of rural communities and surrounding areas
(Betz & Perdue, 1993). Conlin and Baum (1995) suggested that increasing urbanization

will cause an increase interest in visitation of rural settings. Unfortunately, tourism

l4

outcomes are more evident in rural areas than urban areas, and thus have a great impact
on rural residents (Madrigal, 1993).

In comparison to other economic development strategies in rural areas, tourism is
less costly and easier to establish because it allows rural communities to build on already
existing assets in the area, typically natural and cultural resources. Despite these benefits,
rural tourism also has some disadvantages, specifically employment in the tourism
industry is often low paying, without benefits and seasonal. However, rural tourism
remains a viable economic option which can assist communities in diversifying their
local economies (Wilson et al., 2001).

In general, tourism development should enhance residents’ quality of life by
addressing the economic, socio-cultural, and environmental benefits of tourism. In rural
areas, local government is the most important authority in terms of tourism development
planning and policies. In an effort to revitalize rural communities, local governments
often focus on optimization of economic benefits with little regard for the negative social
and environmental impacts associated with tourism and residents’ opinions about tourism
development. Tourism development brings changes which influence residents’ lives and
can result in residents’ negative attitudes toward tourists. Since local people frequently
interact with tourists, their willingness to be hospitable hosts is imperative to successful
tourism development. Therefore, tourism needs to be perceived as a “community
industry” (Murphy, 1985, p. 181) where tourism planning not only attempts to optimize
economic impacts of tourism, but also addresses and minimizes tourism negative social

and environmental impacts while involving local people in the planning process.

15

A majority of studies prior to the 1960’s focused on the positive economic
benefits of tourism. In the 1970’s, several studies noted negative issues associated with
tourism, but it was not until the 1980’s and 1990’s that tourism research started to
evaluate positive and negative impacts (Ap & Crompton, 1998). Host communities have
been recognized to play an important role in the tourism development process. As a
result, in recent years numerous studies have examined residents’ perceptions and
attitudes toward tourism development as an important planning, policy and promotion

consideration for successful sustainable tourism development.

Theoretical Framework
Several models and theories have been utilized to address the impacts of tourism
development, to estimate changes in residents’ attitudes toward tourism in relation to
tourism development, and to determine the key variables influencing attitudes toward

tourism.

Overview of Theoretical Perspectives on Residents ’ Attitudes

The concept of a tourism area life cycle has evolved over several decades.
Christaller’s (1963) research was the starting point in tourism area life cycle theory as he
considered three stages of tourism area life cycle (discovery, growth, and decline), while
examining changes of Mediterranean communities that had become popular tourists’
destinations. According to Christaller, painters are the first people to discover a future
resort area while they are looking for untouched unusual places to paint. Slowly, the
place develops into an artist colony; poets, and other artistically inclined people then start

to follow the artists. The area becomes fashionable as the new and modern infrastructure

16

replaces existing buildings. More people visit the area as more package tours are
arranged. The destination becomes overcrowded causing the artists and their followers to
go elsewhere.

A decade later, Cohen (1972) categorized visitors into four types of tourists (i.e.,
drifter; explorer; individual mass tourist; and organized mass tourist) based on their
willingness to venture beyond their comfort zones. Each of these categories embodies
unique characteristics that influence visitors’ likelihood to interact with the host culture
or alternatively, remain within the tourist “comfort zone” created by the tourism industry
providers. Drifters try to escape from their home environments and their own culture.
These travelers do not associate themselves with the tourism industry. They have no
arrangements for transportation and accommodations. They tend to take work when they
need money, and desire to blend in with the host culture residents while sharing their
shelter, food and habits. Explorers also have no itinerary; however, they like to stay in
more comfortable accommodation and seek more reliable transportation. Their goal is to
interact with locals as much as possible and to learn their language. Individual mass
tourists do not arrange their own trip; instead they use services of a tourist agency. They
live in “tourist enclaves” which resemble their home country. Occasionally, they deviate
from the pre-planned program and take a trip on their own within the realms of the
familiar territory. Unlike the individual mass tourists, the organized mass tourists’ trip is
totally organized. They are the least adventurous of all since they travel with groups of
people from their own culture and stay within a “tourist bubble” that creates the image of

their culture keeping them isolated from the local community.

17

Another view of the resort cycle was taken by Plog (1974) who related patterns of
tourism development to personal characteristics of individuals. Plog (1974) segmented
tourists into three types based on a psychographic continuum ranging from the
psychocentric at one end to the allocentric at the other. Psychocentrics are conservative
and passive tourists who choose common, popular and safe destinations and activities,
while allocentrics are individualistic, active and adventurous travelers. Mid-centrics are
intermediate between the previous two and according to Plog (1974) constituted the most
common tourist type. Plog’s (1974) and Cohen’s tourist typologies were later associated
with a specific stage of Butler’s tourism area life cycle (TALC) model which suggests
that a resort cycle moves through five stages of exploration (characterized by Plog’s
allocentrics and Cohen’s explorers), involvement, development (characterized by Plog’s
mid-centrics and Cohen’s institutionalized tourists), consolidation, stagnation
(characterized by Plog’s psychocentrics and Cohen’s organized mass tourists), and
stabilization, decline, or rejuvenation, depending on efforts to improve the adverse
effects.

Another approach was taken by Doxey (1975) whose “Irridex” model identified
the collective effect of tourism development on residents’ attitudes. According to Doxey,
as the tourism industry increases, residents’ attitudes change and become more negative,
moving from euphoria to apathy, annoyance, and then final stage of antagonism where
residents openly express their irritation with tourists. The concept of changing
relationship between tourists and residents was later used by Butler (1980) as one of the

indicators to identify the stage of the tourism area life cycle.

18

Building on previous observations by resort cycle theorists, Miossec (1976)
proposed a model that conceptualized the spatial and temporal evolution of a destination
with respect to physical change. Miossec’s (1976) model examined the interplay of the
four key elements that influence the evolution of a resort: the destination and its
characteristics, transportation, tourist behavior, and attitudes of decision makers and
residents. In the initial phase, the region is isolated, there is minimal or no development
and developers make tourists aware of the destination. The development moves into the
second phase as pioneer resorts are being developed. Noticeable changes begin in the
third phase where system of resorts established to serve tourists’ expands, and
infrastructure and transportation networks are constructed. Residents’ attitudes evolve
from an initial state of acceptance of tourism to an adoption of planning control, and even
rejection of tourism. In the last stage, the area becomes gradually more saturated with
further development. Tourists become more aware of what the regions have to offer with
some specialization occurring where the system of facilities catering to specific market
segments is established. Tourists themselves begin to attract new visitors to the
destination rather than the original attractions. This change results in some tourists
moving onto other areas.

Early studies concerning residents’ perceptions and attitudes toward tourism have
been criticized for being primarily exploratory, descriptive, and lacking theoretical
background and operational definitions (Ap, 1992). To address the atheoretical nature of
residents’ attitudes studies, the early use of Doxey’s and Butler’s tourism development
models as a theoretical basis has been expanded to integrate new theories such as social

exchange theory (e. g., Ap, 1992), social representation theory (e. g., Pearce et al., 1996),

19

community attachment theory (e. g., Jurowski, 1998), growth machine theory (e.g.,
Madrigal, 1995), personal construct theory (Lawton, 2005), power theory (e. g., Kayat,
2002), and social carrying capacity (e.g., Allen et al., 1988).

In an effort to synthesize various perspectives in addressing residents’ perceptions
of tourism, Faulkner and Tideswell (1997) suggested two large dimensions of tourism
development: extrinsic and intrinsic. The extrinsic dimension refers to tourist
destinations’ characteristics (e.g., stage of a tourism life cycle); while the intrinsic
dimension considers characteristics of residents of the host community that can
potentially affect the impacts of tourism on a given community. Theoretical frameworks
that have been utilized to examine residents’ attitudes toward tourism in prior studies are

summarized in Table 1.

Table 1: Theoretical Frameworks Used to Study Residents’ Perceptions of Tourism

 

 

 

Study Theog
Akis et al. (1996) Butler’s TALC model & Doxey’s Irridex model
Allen et al. (1988) Social carrying capacity

 

 

 

 

13) (1992) Social exchange theory
Andereck et al. (2005) Social exchange theory
Andriotis (2005) Social exchange theory
Andriotis & Vaughan (2003) Social exchange theory

Social Representation theory

 

Besculides et al. (2002)

Benefits based framework

 

Bryant & Napier (1981)

Social exchange theory

 

Carmichael et al. ( 1986)

Social exchange theory
Social caLrying capacity

 

 

 

 

 

 

Cavus & Tanrisevdi (2003) Doxey’s Irridex model

Choi & Sirakaya (2005) Sustainability subjective indicators framework
Choy (l 992) Butler’s TALC model

Deccio & Baloghu (2002) Social exchange theory

D0flet al. (2003) Butler’s TALC model

Doglas (1997) Butler’s TALC model

 

Faulkner & Tideswell (1997)

Butler’s TALC model & Doxey’s Irridex model
Social exchange theory

 

Fredline & Faulkner (2000)

Social representation theog

 

Getz (1994)

Social exchange theory

 

 

Gursgy et al. (2002)

 

Social exchange theory

 

20

 

Continued Table 1: Theoretical Frameworks Used to Study Residents’ Perceptions of

 

 

 

Tourism
Study Theory
Gursoy & Rutherford (2004) Social exchange theory
loannides (1992) Butler’s TALC model

 

Hernandez et al. (1996)

Butler’s TALC model & Doxey’s Irridex model
Social exchange theory

 

 

 

 

 

 

 

 

 

Hovinen (1981) Butler’s TALC model

Hovinen (2002) Butler’s TALC model

Jurowski (1994) Social exchange theory

Jurowski et al. (1997) Social exchange theory

Jurowski & Gursoy (2004) Social exchange theory

Kayat (2002) Social exchange theory + power variable

Ko & Stewart (2002) Model of residents’ tourism perceptions (Perdue et al.,1990)
Lawton (2005) Personal construct thecfl

Lee & Back (2006) Social exchange theory

Social canying capacity

 

Long et a1. (1990)

Social canyingcapacity

 

 

 

 

 

 

Madrigal (1993) Social exchange theory

Madrigal (1995) Growth machine theory

Martin (1996) Growth machine theory

Mason & Cheney (2000) Butler’s TALC model & Doxey’s Irridex model
Meyer-Arendt (1985) Butler’s TALC model

McGehee & Andereck (2004) Social exchange theory

 

Perdue et al. (1990)

Social exchange theory

 

Perdue et al. (1999)

Social exchange theory

 

 

Social cagying capacity
Pearce et al. (1996) Social representation theory
Russell & Faulkenr (2004) Butler’s TALC model

 

Ryan et al. (1998)

Butler’s TALC model & Doxey’s Irridex model

 

Sirakaya et al. (2002)

Social exchange theory

 

 

 

 

 

 

Smith & Krannich (1998) Tourism dependence framework

Stansfield (I978) Resort cycle

Teye et al.(2002) Social exchange theory

Tooman (1997) Butler’s TALC model

Weaver (1990) Butler’s TALC model

Weaver & Lawton (2001) Butler’s TALC model & Doxey’s Irridex model Faulkner &

Tideswell

 

 

Yoon et al. (2001)

 

Social exchange theory

 

 

To address both extrinsic and intrinsic dimensions of tourism development, this
study will utilize social exchange theory (intrinsic dimension) and the destination life
cycle (extrinsic dimension) model as the theoretical framework to guide the study.
Strengths and weaknesses of the theoretical frameworks utilized in this study are

summarized in Table 2.

21

Table 2: Strengths and Weaknesses of Theoretical Frameworks

 

Butler’s
Destination
Lifecycle Model
and Doxey’s
Irridex

Strengths

Focus on extrinsic dimension of tourism development

Identifies levels of tourism development reflecting the
relationship between demand and supply

Emphasizes the economic, social and physical factors that
influence the destination’s ability to absorb tourists and tourism
infrastructure

Can be utilized as a forecasting tool for determining the
destination’s carrying capacity limits, and as a descriptive guide
for tourism product planning, development and management

 

Weaknesses

Does not consider intrinsic dimension, as such assumes
homogeneity and uni-directionality of communities

Ignores the idea of developing coping mechanisms/adaptation,
as well as relatively stable urban communities, as such would
fail to predict later stages of development

- Ignores influences of internal and external factors
0 Does not identify the key forces behind the life cycle

 

Social Exchange
Theory

Strengths

Considers heterogeneity of communities, thus explains why
there are different attitudes within the same community
Explains both positive and negative effects of a phenomenon
under study

Can examine relationships at both individual and collective
levels

Provide understanding of exchange of social, environmental,
cultural and economic resources; thus the evaluation of the
exchange process is complex and dynamic

lndividual’s evaluation of social, environmental, cultural and
economic benefits/costs differs based on personal benefit/cost
measures

Individuals evaluate a range of interacting costs and benefits
before they make a rational decision

Based on individual interactions, community
outcomes/reactions can be understood

 

 

Weaknesses

 

Assumes the decision-making process always ends in gaining,
there are no losers only winners

Not all people who enter exchange have complete or correct
information

Assumes individual’s knowledge is a result of direct experience
rather than socially and historically derived

Suggests those who perceive benefits will have higher positive
impacts and also perceive lower negative impacts-it does not
consider the nature of interaction, stage of development

0 Residents vary in the degree to which they benefit/bear the costs
0 Assumes that every individual has an equal influence on policy

and planning

 

22

Doxey 's Index of Irritation: “Irridex "

Doxey’s Irridex model (1975) was one of the first models concerning residents’
attitudes toward tourism. Based on two resort region studies, one on Barbados and the
other on Niagara-on-the-lake, Doxey (1975) suggested that with the increasing influx of
tourists, residents’ attitudes change and become more negative, moving from euphoria to
apathy, annoyance, and then antagonism. The stage of euphoria is associated with the
early stages of the development of the tourism in a destination where visitors and
investors are still welcomed. There is a little planning and no tourism marketing. During
the apathy period tourists are taken for granted, relations between tourist and residents is
purely commercial. Planning, marketing, and advertising are deliberately targeted
towards tourists. The annoyance stage signifies that the area has reached the carrying
capacity of the destination (the saturation point). As residents’ needs become more
neglected, they begin to question the presence of tourists and the need for a tourism
industry in their communities. Despite of that, the authorities are mainly concerned with
increasing the level of infrastructure in the area. Finally, the area enters the stage of
antagonism, where residents openly express their irritations against tourists. In summary,
Doxey’s Irridex suggests that increased levels of tourism development lead to increased
residents’ irritation with tourists and overall negative attitudes towards tourism

development (Figure l).

23

Figure 1. Doxey’s Index of Irritation: “Irridex”

 

Euphoria
e E7
Apathy
e e
\/

 

 

 

 

Annoyance

ee
\/

 

 

Antagonism

<7

Source: Williams (1998, p. 158).

 

 

 

While several researchers have used Doxey’s Irridex in conjunction with Butler’s
model as a theoretical framework to guide their studies (e.g., Akis et al., 1996; Hernandez
et al., 1996; Faulkner & Tideswell, 1997; Mason & Cheyne, 2000; Ryan et al., 1998),
only one study (Cavus & Tanrisevdi, 2003) developed specific measures to test Doxey’s
Irridex rather than using analogies drawn from Butler (1980) and Doxey (1975). Cavus
and Tanrisevdi (2003) developed a set of statements to measure the threshold level of
residents’ toward tourism development in Kusadasi, Turkey. Results of their study
revealed that residents were in the annoyance stage. Residents felt that they were not
included in the city’s planning process and problems caused by tourism have not been
resolved by formal authorities. Cavus and Tanrisevdi (2003) suggested including local
residents in the long-term development of the city to avoid possible antagonistic

residents’ attitudes toward visiting tourists.

24

Butler 's Tourism Area Life Cycle (TALC) Model

Based on Doxey’s work, Butler (1980) developed a tourism area cycle of
evolution model implementing the product life cycle concept. Butler (1980) proposed that
a resort cycle moved through five stages of exploration, involvement, development,
consolidation and stabilization, decline, or rejuvenation, depending on efforts to mitigate .
the adverse effects. Over these distinct stages, there were noteworthy changes in the types
of visitors, the available infrastructure, the marketing and advertising strategies, the
natural and built environment, and local residents’ attitudes towards tourism. These
changes accumulated over time and resulted in one of the alternative scenarios of the
post-stagnation stage. If growth was unplanned, the area would reach its carrying
capacity causing the attractiveness of the area and the number of visitors to decline. On
the other hand, appropriate planning may have not only prolonged the tourism flow over
a long period of time but also instigated a new life cycle by changing attractions or
introduction of new facilities, e. g., casinos (Stansfield, 1978) (Figure 2). Butler (1980)
was aware of his model’s limitations where the extent to which the stages of the cycle
were experienced varied for different areas “reflecting variations in such factors as rate of
development, numbers of visitors, accessibility, government policies, and numbers of
similar competing areas” (p.1 1).

While the majority of tourist destination evolution research has stressed the
changing nature of tourism markets and the motives of travelers, Butler (1980)
emphasized the economic, social and physical factors that impact the destination’s ability
to absorb tourists and tourist facilities (Hall, 2003). The significance of Butler’s model is

emphasized by “the synergistic relationship between the marketplace (demand) and the

25

destination (supply) in that changes in one have effects on the other” (Hall, 2003, p. 39).
Butlers’ model enables examining the evolution of tourism in an area, specifically the
development of a destination through time and space by identifying endogenous and
exogenous developmental factors accountable for each stage of tourism evolution

(loannides, 1992).

Figure 2: Butler’s Tourism Area Life Cycle (TALC) Model

A

Rejuvenation

    
  
  
    
  
  

Reduced growth

p------------------—---------- -------------------

' _ Stagnation
Consolidation Stabilization

Decline

Immediate decline

Number of tourists

Development

Involvement

 

 

Exploration .
> Time

Source: Butler (1980, p. 7).

Several models of tourism area development have been proposed to describe
destinations’ evolution; however, Butler’s (1980) model has been discussed, applied, and
tested the most. Previous studies have utilized Butler’s (1980) model at micro (e. g.,
resorts, natural attractions, counties) and macro (e.g., island, countries) level case studies

using different temporal scales and methodologies (Karplus & Krakover, 2004).

26

Qualitative studies typically utilized decadal date to apply Butler’s (1980) model (e. g.,
Douglas, 1997; Tooman, 1997). On the other hand, qualitative studies usually depend on
annual arrivals or annual bed occupancy data (e. g., Choy 1992, Hovinen, 2002). Table 3

provides for an overview of measurements used to test the tourism area’s life cycle stage.

Table 3: An Overview of Measurements Used to Test Butler’s Model

 

Study

Measurement

 

Akis et al. (1996)

Decadal data examining factors influencing the
destination’s life cycle throughout time

 

 

 

 

Debbage (1990) Decadal data examining factors influencing the
destination’s life cycle throughout time

Choy (1992) Annual tourist arrivals

Cooper &Jackson (1989) Decadal data examining factors influencing the
destination’s life cycle throughout time

Douglas (1997) Decadal data examining factors influencing the

destination’s life cycle throughout time

 

Dong et al. (2003)

Annual tourist arrivals

 

Getz (1992)

Decadal data examining factors influencing the
destination’s life cycle throughout time
Interviews

Field observations

 

Hovinen (1981)

Annual tourist arrivals

 

Hovinen (2002)

Annual tourist arrivals
Percent change in gross sales by tourism sectors
Changes in available tourism infrastructure

 

loannides (1992)

Annual tourist arrivals
Bed occupancy
Tourist accommodatiorgrowth

 

Faulkner & Tideswell (1997)

Level of tourism development
Tourist/resident ratio

Type of tourist

Seasonality

 

Foster & Murphy (1991)

Annual tourist arrivals

 

Hernandez et al. (1996)

Decadal data examining factors influencing the
destination’s life cycle throughout time

 

 

Karplus & Krakover (2004) Longitudinal monthly bed-night data, investment in
infrastructure, and security indicators
Lundtrop & Wanhill (2001) Annual tourist arrivals

 

Meyer—Arendt ( l 985)

Decadal data examining factors influencing the
destination’s life cycle throughout time

 

 

 

 

 

Russell & Faulkner (2004) Type of entrepreneurial activities

Tooman (1997) Decadal data examining factors influencing the
destination’s life cycle throughout time

Weaver (1990) Decadal data examining factors influencing the
destination’s life gcle throughout time

Weaver & Lawton (2001) Decadal data examining factors influencing the

 

destination’s life cycle throughout time

 

27

 

Regardless of methodologies used to test Butler’s (1980) model, support for this
model has been mixed. Hovinen (1981, 2002) applied Butler’s model to Lancaster
County, Pennsylvania. He concluded that Lancaster County showed significant
departures from Butler’s proposed stages of exploration consolidation, and stagnation; as
well as the postulated S-shaped logistic curve of growth of number of tourists. While the
model’s later stages of consolidation and stagnations did not fit well the evolution of
tourism in Lancaster County, the author concluded that Butler’s model was a useful
conceptual framework in suggesting the potential decline of a tourist destination if
destination specific problems were not addressed. Hovinen (2002) suggested that future
studies should consider the role of entrepreneurs in creating conditions that change the
course of the evolutionary cycle, as well as multiple tourism products each with its own
cycle. Similarly to Hovinen, Getz (1992) found that Niagara Falls’ stages of exploration
and involvement diverted from Butler’s model. He concluded that Niagara Falls appeared
to be in a long state of maturity comparable to Lancaster County.

Foster and Murphy (1991) examined Butler’s model suitability in explaining
tourism development with particular reference to its relationship to retirement. Similarly
to Hovinen (1981, 2002), their study supported the model’s applicability to the early
tourism development trends but failed to explain the later stages of the resort
communities’ evolution. However, the study also revealed that these communities
became more appreciative of the retirement sector when they experienced the first major
decline in tourism. A decade later, Weaver and Lawton (2001) examined residents’
perceptions of tourism on Tamborine Mountain in the Gold Coast using analogy drawn

from Butler’s (1980) model. Their study revealed that each stage of tourism development

28

was accompanied by a bell-curve of residents’ perceptions, as opposed to observing the
S-shaped logistic curve throughout the whole tourism life cycle (Butler, 1980). As a
result, Weaver and Lawton (2001) concluded that the TALC model was not adequate in
describing resident attitudes toward tourism development. Dong et al. (2003) indicated
that Butler’s model provided limited explanation in understanding how ethic tourism
development occurred in Yunnan. The model failed to explain the geographic progression
of ethic tourism to remote area of Yunnan province. Additionally, the level of local
residents’ involvement did not follow the proposed cycle of tourism development.

In contrast, Meyer-Arendt (1985) confirmed that different stages of the evolution
of the Grand Isle seaside resort in Louisiana were linked to Butler’s model. loannides
(1992) analyzed the role of local attributes and the effectiveness of national government
policies within the context of Butlers’ model. Similarly to Meyer-Arendt (1985),
loannides (1992) concluded that the growth of tourism industry followed Butler’s tourist
area lifecycle. Cooper and Jackson (1989) confirmed that TALC model was a useful
descriptive tool for analyzing the development of tourism destinations as well as the
evolution of tourism markets. A study by Karplus and Krakover (2004) re—examined
Butler’s model using statistical testing. They utilized a regression model which
incorporated monthly bed-night data as a measure of demand. In addition, investment in
infrastructure (number of hotel rooms available) was applied as an endogenous variable
and a security indicator (wars and cycles of violence) was incorporated as an exogenous
variable. Karplus and Krakover (2004) concluded that the Dead Sea development

conformed to the patterns of Butler’s (1980) model.

29

Choy (1992) applied destination life cycle model (Butler, 1980) to several Pacific
Island destinations. Based on the tourism arrival growth patterns, Choy (1992) concluded
that Butler’s (1980) model was not applicable to most Pacific Island designations because
it was unable to explain large variations in grth patterns across various destinations in
the region. Weaver (1990) applied Butler’s model to the evolution of tourism in Grand
Cayman Island in the Caribbean. This study was an example of a resort which skipped
the first stage of the proposed cycle. Similarly to Choy (1992), Weaver (1990) found a
distinctive deviation from the cycle with respect to local versus non-local control in the
development stage. Local participation actually increased in the development stage from
government policies designed to encourage tourism development as well as to place
limitations on the growth. Hernandez et al. (1996) examined resident’s attitudes toward a
proposed “enclave” resort in Isabela, Puerto Rico, building on social exchange theory,
tourism development cycle theories, and the segmentation approach. Based on the
assessment of tourism infrastructure, employment in tourism, and role of tourism in local
economy, Isabela was classified as pre-resort, experiencing pro-developmental
(exploration) stage. Results of their study indicated Isabela residents’ attitudes toward
proposed tourism development were ambivalent, thus not supportive of Butler’s model.

Faulkner and Tideswell (1997) suggested two large dimensions of tourism
development (i.e., extrinsic and intrinsic) to assess impacts of tourism development of the
Gold Coast. The extrinsic dimension referred to the progression of residents’ attitudes
toward tourists and the stage of a tourism life cycle measured by tourist destinations’
characteristics (i.e., level of tourism development, tourist/resident ratio, type of tourist,

and seasonality). The intrinsic dimension considered characteristics of residents of the

30

host communities. Contrary to the propositions by Butler (1980) and Doxey (1975),
Faulkner and Tideswell (1997) concluded that residents in large scale mature tourist
destinations did not become antagonistic towards tourism.

Similarly to Faulkner and Tideswell (1997), Russell and Faulkner (2004)
proposed an alternative conceptual framework to examine tourism development process.
They combined chaos theory and entrepreneurship with Butlers’ model as vital factors
influencing tourism development in the Gold Coast. Their results showed that each stage
of the tourism area life cycle was linked to a specific entrepreneurial activity. Russell and
Faulkner (2004) concluded that entrepreneurial activities disturb the existing equilibrium
creating conditions for change in the evolutionary cycle.

Several studies have applied Butler’s model utilizing decadal data to examine
tourism development. Akis et al. (1993) utilized Butler’s model to study Greek and
Turkish Cypriots residents’ perceptions of coastal tourism development. Different stages
of Butler’s model were determined based on a review of historical data (e. g., increase of
number of beds, property prices, and tourists’ infrastructure). Results of their study
supported Butler’s proposition in that residents who had little exposure to tourism were
more supportive of tourism than residents with a more intensive exposure to tourism. On
the other hand, residents who had more exposure to tourism also perceived their
interaction with tourists as positive rather than negative. The authors concluded that
results indicated that these destinations were still in the stage of development rather than
consolidation. Douglas (1997) utilized Butler’s model as a conceptual tool to build
comparative profiles of historical development in several tourists’ destinations in

Melanesia. The level of a specific destination’s life-cycle was determined through

31

analysis of historical data (i.e., total population, total visitor arrivals, occupancy, earnings
from tourism, and employment in tourism for 1993) as well as comprehensive literature
review focusing on type of tourists, promotional effort and governmental involvement in
tourism development. Similarly to Douglas (1997), Tooman (1997) applied the model as
a conceptual framework to evaluate social welfare changes as tourism evolved over time
in the area of Smokey Mountains. Different stages of Butler’s model were determined
based on literature review of historical data between 1830 and 1995 (e. g., changes in
tourist infrastructure, economic depression).

Though the testing of Butler’s model has resulted in mixed findings, the literature
appears to suggest that the model is a very usefirl conceptual framework for examination
of the tourism development patterns from different perspectives while allowing the use of
various spatial settings, temporal scales and methodologies (Karplus & Krakover, 2004).
It is important to note that the majority of these studies focused on application of the
model to beach resorts and other new tourist products (e. g., Douglas, 1997; Meyer-
Arendt, 1985). However; little attention has been given to urban tourism, particularly
heritage tourism destinations, e. g. Venice, Salzburg, Toledo (Garrod & Fyall, 2000).
Some researchers have argued that the lack of the model’s application to urban tourism is
due to the difficulty to observe and analyze examples of complete cycles of urban tourism
development. The decline stage of the destination cycle is difficult to observe in heritage
destinations whose tourism product builds on cultural assets inherited from the past. The
cultural and historical uniqueness makes substitutability between heritage destinations
very limited and the decline stage an unexpected outcome of tourism development cycle.

The number of tourists visiting heritage destinations is steadily increasing with

32

excursions being the most popular way to visit these destinations. The challenge for
urban tourism destinations is to serve the needs of increasing visitor flows while
managing the growth of the tourism industry and protecting their cultural heritage—the
foundation of their tourism product. While visitor management remains to be one of the
main concerns for city officials, urban tourism development and management continues

to be a relatively unexplored area of tourism research (Russo, 2002).

Social Exchange Theory

Social exchange has been a dominant theoretical framework for research
regarding residents’ perceptions and attitudes toward tourism development. Social
exchange theory is concerned “with understanding the exchange of resources between
individuals and groups in an interaction situation”, ..., where “actors” supply one another
with valued resources” (Ap, 1992, p. 668). Social exchange theory provides a conceptual
framework for understanding the exchange of tangible or intangible resources between
individuals and groups. Thus, social exchange theory enables evaluation of the exchange
process which is complex and dynamic and can be utilized to explain both positive and
negative attitudes (Ap, 1992). The main premise of social exchange theory is that
individuals evaluate an exchange based on the costs and benefits associated with that
exchange. Hence, people will engage in an exchange if the exchange is likely to produce
valued rewards, and the perceived costs do not exceed perceived rewards (Skidmore,
1975). Essentially, if residents perceive an exchange to be beneficial to their well-being,
they will evaluate that exchange positively. However, if they perceive costs from an

exchange, rather than benefits, they will evaluate that exchange negatively. In terms of

33

tourism, residents who perceive they benefit from tourism are likely to have more
positive attitudes toward tourism development than those who do not perceive themselves
as benefiting from tourism. Based on social exchange theory, “expressed support for
tourism development is considered as a willingness to enter into an exchange” (Gursoy,
Jurowski & Uysal, 2002, p. 82).

Social exchange differs from solely economic exchange because obligations
involved in a social exchange are not identified (Kayat, 2002). Social exchange theory
encompasses three points of view, economic, environmental, and socio-cultural, that can
assist in explaining why people behave the way they do. From the economic perspective,
residents who can profit economically from tourism will be in favor of firrther tourism
development. On the other hand, residents may value environmental factors more than
economic benefits because they can benefit from more outdoor recreation in natural
areas. Lastly, residents may perceive tourism as a means of preserving their traditions and
culture as well as an opportunity for more cultural experience for residents. The way
residents perceive the economic, environmental, and socio-cultural factors of the
exchange process will determine how they respond to tourism development (Andriotis &
Vaugham, 2003).

Social exchange theory has been proven to be a suitable theoretical framework for
analyzing residents’ perceptions and attitudes toward tourism (Ap, 1992; Getz, 1994;
Jurowski et al., 1997; Perdue et al., 1990). Over the past decade, several models have
been developed using social exchange theory as a theoretical base (e. g., Andereck et al.,
2005; Ap, 1992; Gursoy et al., 2002; Jurowski et al., 1997; Gursoy & Rutherford, 2004;

Perdue et al., 1990) (Table 4).

34

Table 4: Support for Social Exchange Theory

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Study Support for Social Exchange Theory

Ap (1992) Yes
Andereck et al. (2005) Inconclusjve*
Andriotis (2005) Inconclusive*
Andriotis & Vaughan (2003) Yes

Bryant & Napier (1981) Yes
Carmichael et al. (1996) Yes

Deccio & Baloghu (2002) Yes

Faulkner & Tideswell (1997) Yes

Getz (1994) Yes

Gursoy et al. (2002) Yes

Gursoy & Rutherford (2004) Yes
Hernandez et 3141996) Yes

Jurowski et al. (1997) Yes

Jurowski & Gursoy (2004) Yes

Lee & Back (2006) Yes

Kayat (2002) Yes (added “power” variable)
Madrigal (1993) Yes
MCGChCC & Andereck (2004) Inconclusive*
Perdue et al. (I 990) Yes

Sirakaya et al. (2002) Yes

Teye Ct al.(2002) Inconclusive*
Yoon et al. (2001) Yes

 

 

3"Not all social enhance theory variables were found to be related to support for additional tourism
development

Perdue, Long, and Allen '5 Model of Residents ’ Tourism Perceptions

Perdue, Long, and Allen (1990) developed a conceptual model to analyze
quantitative data collected from 16 rural communities in Colorado utilizing social
exchange theory. The model tested rural residents’ perceptions of tourism impacts and
support for tourism development. Moreover, the model examined the following
relationships: (a) relationship between perceived impacts and residents’ support for
additional tourism development, (b) relationship between residents’ support for additional

tourism development and restrictive tourism policies and special tourism taxes, and (c)

35

relationship between residents’ support for additional tourism development and the

perceived future of the community (Figure 3).

36

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37

Perdue et al. (1990) hypothesized that residents’ characteristics would be
unrelated to perceived impacts of tourism, and support for additional tourism
development would be positively related to the positive impacts and negatively related to
the negative impacts when controlling for personal benefits from tourism. In accordance
with tourism perception as a “doomsday development strategy”, Perdue et al. (1990) also
hypothesized that support for additional tourism development will be negatively related
to the community’s perceived future. Lastly, it was hypothesized that support for
additional tourism development would be negatively related to support for restrictive
tourism development and special tourism user fees and additional taxes.

In the Perdue et al. (1990) study, a series of multiple regressions were utilized to
explore these relationships. The researchers found that, when controlling for personal
benefits from tourism, perceptions were unrelated to residents’ characteristics with the
exception education and gender. Perdue et al. (1990) suggested that while further
research is needed, it would be better focused on determining actors which influence
attitudes especially of those residents who are unlikely to benefit from tourism
development. Support for additional tourism development was positively (negatively)
related to the perceived positive (negative) impacts of tourism when controlling for
personal benefits from tourism. Perdue et al. (1990) emphasized the importance of
improving tourism image with local residents, particularly those residents who are
unlikely to benefit from tourism development. Furthermore, support for additional
tourism development was negatively related to the perceived future of the community.
Perdue et al. (1990) referred to this occurrence as a “doomsday” phenomenon where

residents are more likely to support tourism development when the rural economy is

38

deteriorating. However, the popularity of tourism as a rural economic development
strategy may decrease when other sectors of the local economy improve. Lastly, support
for additional tourism development was negatively related to restrictive tourism policies;
however, special tourism taxes were unrelated to support for additional tourism
development. Support for restrictive tourism policies and special tourism taxes were
positively related to negative impacts and perceived community future. These results
suggest that residents who perceive negative impacts from tourism are more supportive of
restrictive tourism policies and special tourism taxes. However, support for special
tourism taxes will differ based on the perception of tax revenue use. For example, if the
tax revenues were to be used for tourism promotion, residents who perceive negative
impacts from tourism would be less likely to support taxes on tourism. Perdue et al.
(1990) noted that results of their study regarding support for restrictions on tourism
development and special tourism taxes may have been different had their study specified
how restrictions would be administered and how tourism tax revenues would be used.
Perdue et al. (1990) expressed the need for further research in other rural
communities to validate and improve their model. While the study by Perdue et al. (1990)
was a significant contribution to resident attitudes research that mostly examined
communities heavily dependent on tourism, the study utilized a nonrandom set of
Colorado communities which were not representative of all rural Colorado communities.
Perdue et al. (1990) suggested that future research should examine a random sample of
rural communities and attempt to measure current levels of tourism development in these

communities.

39

Perdue, Long, and Allen’s (1990) model has been validated and improved by
several studies (i.e., Ko & Stewart, 2002; Madrigal, 1993; McGehee & Andereck, 2004;
Snaith & Haley, 1994). Madrigal (1993) examined residents’ perceptions of tourism
development in two Arizona cities with different levels of tourism development. He
found social exchange variables (i.e., economic reliance, balance of power) to be better
predictors of perceptions than residents’ characteristics. Additionally, while personal
economic reliance (defined as respondent’s income’s dependency on the tourism
industry) was significantly related to positive perceptions of tourism, it was not found to
be related to the negative perceptions that were more dependent on the level of tourism
development (represented by city of residence).

Snaith and Haley (1994) applied the conceptual framework developed by Perdue
et al. (1990) to a large urban area—York, the United Kingdom. They found economic
reliance to be a significant predictor of positive perceptions of tourism, and negative
perceptions of tourism to be significant predictors of support for local government control
of tourism. Also, older respondents, respondents with a greater household income and
those with positive perceptions of tourism were more supportive of local tax levies.
However, homeowners were less supportive of tax levies to support tourism
development. Their findings contradicted earlier research (e.g., Belisle & Hoy, 1980;
Davis et al., 1988, Perdue et al., 1990) which had suggested that residents’ characteristics
had no or little effect on their perceptions of tourism development. These contradictions
in findings may be because Snaith and Haley’s (1994) study examined populations in a
historically established urban area. It can be argued urban areas have been able to

develop coping mechanisms to accommodate tourism related inconveniences better than

40

rural areas (Rothman, 1978), suggesting residents’ attitudes change throughout time
(Cavus & Tanrisevdi, 2003).

K0 and Stewart (2002) tested the Perdue et al. (1990) model adding a new
construct—overall community satisfaction. In contrast to Perdue et al. (1990), they found
no significant relationship between personal benefits from tourism development and
perceived negative impacts of tourism. Additionally, no relationship was found between
personal benefits from tourism development and overall community satisfaction.
Perceived positive (negative) tourism impacts were positively (negatively) related to
overall community satisfaction.

McGehee and Andereck (2004) extended the Perdue et a1. (1990) work by adding
a community level of tourism dependence variable. Community dependence on tourism
item was not measured by the questionnaire. The variable was developed using several
rural tourism experts’ opinions because of the lack of community-specific economic data
in the area under study. Four rural tourism experts were asked to rank each community on
a five-point scale where 1 = not at all tourism dependent and 5 = extremely tourism
dependent. In contrast to Perdue et al. (1990), McGehee and Andereck (2004) found a
relationship between age and perceptions of tourism impacts. Additionally, having lived
in a community as a child variable (not previously tested by Perdue et al. (1990), was
found to be a significant predictor of perceptions of tourism impacts. Consistent with
other studies (e. g., Allen et al., 1988; Smith & Krannich, 1998) community tourism
dependence was found to be a significant predictor of perceptions of tourism impacts.
Compared to Perdue et al. (1990), both negative perceptions of tourism impacts and

support for additional tourism predicted tourism planning.

41

Factors Influencing Residents 'Attitudes toward Tourism

Studies examining residents’ attitudes towards tourism development have
identified several factors that influence residents’ attitudes (Table 5). Socio-demographic
characteristics have been commonly used by researchers to explain differences in the
perceived impacts of tourism. Socio-demographic variables include age, income,
education, ethnicity, gender, length of residents, and employment in tourism. The
research found these socio—demographic characteristics explain very little variation in
residents’ attitudes toward tourism development (Perdue et al., 1990).

In contrast with Perdue et al. (1990), McGehee and Andereck (2004) found a
relationship between age and perceptions of tourism impacts. Older residents were less
likely to agree with negative impacts of tourism. The results suggested that opportunities
to benefit from tourism increase with age because older residents have more experience
necessary for tourism managerial positions or establishment of their own tourism-related
business (McGehee & Andereck, 2004). Snaith and Hailey (1994) found that older
respondents were more supportive of local tax levies. Another study found older residents
to be as supportive of tourism as younger residents (Tomljenovic & Faulkner, 1999). In
addition, older residents were less concerned about tourism negative impacts. Cavus and
Tanrisevdi (2002) found the older residents perceived greater negative perceptions of
tourism. Another study conducted by Brougham and Butler (1981) revealed that older
residents were less positive about tourism.

Regarding gender, women have been found to be more opposed to tourism
development than men due to perceived negative impacts such as crime, noise, and

increased traffic (Harrill & Potts, 2003; Mason & Cheyne, 2000). Iroegbu and Chen

42

(2001) found that male respondents, respondents who had college education and urban
residents with incomes over $25,000 per year were most likely to support tourism
development. Snaith and Hailey (1994) found that respondents with a greater household
income were more supportive of local tax levies. On the other hand, homeowners viewed
tourism more negatively and were less supportive of tax levies for tourism development.
Additionally, having lived in a community as a child variable (not previously tested by
other studies), was found to be a significant predictor of perceptions of tourism impacts
in the study conducted by McGehee and Andereck (2004). Respondents who lived in a
community as a child were less likely to agree with negative impacts. It was implied that
residents with long-term connection to the community were more concerned about the
communities’ future.

An early study by Pizam (1978) found a significant relationship between
economic reliance on the tourism industry and residents’ attitudes. He concluded that
local residents who were economically dependent on tourism had more favorable
attitudes toward tourism than those who did not depend on tourism. Other studies have
also tested this relationship and found similar results (Deccio & Baloghu, 2002; Jurowski
et al. 1997; Liu et al., 1987, Sirakaya et al., 2002). Similarly to these studies, McGehee
and Andereck (2004) concluded that as communities become more dependent on the
tourism industry, negative impacts become more apparent, and will overtake the positive
impacts of tourism. As mentioned before, the community dependence variable was
created post hoc by several rural tourism experts (McGehee & Andereck, 2004).

Building on previous studies (e.g., Butler, 1980; Doxey, 1975, Perdue et al.,

1990), Madrigal (1993) included personal economic reliance on tourism and the level of

43

tourism development variables as predictors of residents’ attitudes toward tourism in his
hierarchical regression models. The level of tourism development was measured using
three variable: (1) sales (the annual hotel/motel and restaurant/bar sales as a percentage
of total retail sales), (2) growth (the disparity in sales between fiscal years 1985 and
1989); and (3) rooms (the total number of hotel/motel rooms as a percentage of total
residents). Madrigal (1993) concluded that residents whose livelihood was more
dependent on tourism were more likely to perceive positive impacts of tourism. However,
no differences were found in residents’ assessment of negative impacts. Madrigal (1993)
argued that perceptions of negative impacts were more dependent on an area’s “level of
tourism development than residents’ economic reliance on the tourism industry” (p. 350).
Allen, Long, Perdue, and Kieselbach (1988) examined tourism impacts in 20 rural
communities in Colorado. Their findings suggested that there was a carrying capacity
threshold for tourism. Residents’ perceptions of tourism impacts became less positive as
the level of tourism development (calculated as a percentage of retail sales) in a
community increased (1988). In contrast, Allen et al. (1993) argued that the relationship
between the level of tourism development and residents’ attitudes was not as strong as
other studies reported. To address the influence of tourism development and total
economic activity on residents’ attitudes, Allen et al. (1993) developed two per capita
ratios: (1) the level of tourism development was measured as a ratio with tourism receipts
in the numerator and community population in the denominator; (2) the total economic
activity was measured as a ratio with all retail sales in the numerator and community
population in the denominator. Using the two ratios, the 10 communities under study

were categorized into four community types: low-low, low—high, high-low, and high-

44

high. They found that communities with low tourism development and low total
economic activity as well as communities with high tourism development and high total
economic activity viewed tourism development more favorably than communities with
low tourism and high economic activity and communities with high tourism development
and low economic activity. Allen et al. (1993) suggested the total level of economic
activity in a community along with the level of tourism development to be considered to
gain a more precise insight into residents’ attitudes toward tourism development. Long,
Perdue, and Allen (1990) examined the relationship between a community’s level of
tourism development (defined as dependence on tourism and measured as a percentage of
retail sales) and residents’ attitudes. Long et al. (1990) concluded that initially residents’
attitudes towards tourism are enthusiastic, but as costs outweigh benefits of tourism
development, their attitudes reach a threshold afier which their support for tourism
declines. In their study, the threshold was achieved when approximately 30% of the
community‘s retail sales were derived from tourism (Long et al., 1990). Smith and
Krannich (1998) defined level of tourism development as a community’s level of
economic dependence on tourism. The economic dependence on tourism variable was
measured as a ratio with per capita receipts in the numerator and per capita income in the
denominator. Consistent with Long etal., (1990), Smith and Krannich (1998) found
residents’ in tourism dependent communities perceived more negative impacts than
residents in less tourism dependent communities.

Another variable that has been examined in relation to residents’ attitudes is
community attachment, often measured as length of residence and/or growing up in a

community as a child. Um and Crompton (1987) found that the more attached residents

45

were to the community, the less positively they perceived impacts of tourism. Consistent
with the findings by Brougham and Butler (1981) and Lankford and Howard (1994),
McCool and Martin (1994) found that long-term residents perceive tourism less
positively than did short-term residents. These results were consistent with findings of a
study conducted by Lankford and Howard (1994). Snaith and Hailey (1994) observed
that while short-term residents had more positive perceptions of tourism, both short-term
and long-term residents recognized the benefits and impacts associated with tourism
development. Inconsistent with these findings, studies by Allen et al. (1993) and Perdue
et al. (1990) did not reveal a relationship between length of residence and residents’
attitudes toward tourism development.

Limited research on power and residents’ perceptions of tourism impacts exists
(Kayat, 2002; Madrigal, 1993). Kayat (2002) operationalized power as any resources
owned by residents that could be used in an exchange for benefits from tourism. The
study found power to have an indirect influence on residents’ perceptions of tourism
impacts. The influence of power was found to be moderated by residents general values
(e.g., religion, culture), their dependence on tourism, and ability and/or willingness to
adapt. Madrigal (1993) operationalized power as resident’s perceptions of their ability
and tourism related businesses’ ability to influence decisions related to tourism
development. He found power to be the strongest predictor of tourism impacts.
Respondents who believed they had more personal influence perceived positive impacts
from tourism more than residents who felt they had no or little influence. In addition,

respondents who believed that tourism related business had less influence on decisions

46

related to tourism development perceived positive impacts from tourism more than
residents who felt tourism related businesses had too much influence.

Level of knowledge is another variable that has been examined as a predictor of
residents’ attitudes toward tourism. A study by Andereck et al. (2005) revealed that
respondents who perceived greater knowledge about the tourism industry also perceived
greater positive impacts of tourism in relation to community life, image, and economy. In
addition, respondents who reported greater knowledge about the tourism industry showed
no differences in perceptions of tourism impacts with respect to community problems,
environment, and services (Andereck et al., 2005). Lankford and Howard (1994) found
that perceived (subjective) knowledge of tourism was positively related to perceptions of
positive impacts of tourism. Davis et al. (1988) used five questions to assess residents’
general level of knowledge of the tourism industry. They found that residents who were
more knowledgeable perceived greater positive impacts of tourism on the economy and
overall were more appreciative of the tourism industry.

Other studies have shown that residents who placed a great emphasis on tourism
playing a major role in the economic development (Andereck et al., 2005; Huh & Vogt,
2008) and residents who perceived greater benefits from tourism (Andereck et al., 2005;
McGehee & Andereck, 2004, Perdue et al. 1990) had more positive attitudes toward
tourism while recognizing some of the negative impacts introduced by tourism
development to their communities (Snepenger et al., 2001).

Other factors that may influence residents’ evaluation of tourism impacts are
use/lack of use of recreational resources that attract tourists (Jurowski, 1994), level of

contact with tourists (Andereck et al., 2005; Brougham & Butler, 1981) and distance

47

from the tourism zone (Belisle & Hoy, 1980; Hanill & Potts, 2003; Jurowski & Gursoy,
2004; Snaith & Hailey, 1994; Williams, 1998). However, another study conducted by
Rothman (1978) implied that communities that had a long-term experience with tourism
were able to develop mechanisms to cope with the negative aspects of tourism and adjust
to changing conditions, thus allowing for residents’ attitudes to change over the time.
These residents did not perceive tourism as undesirable because they had been involved

in the tourism industry for a long time and have managed to adapt.

48

Table 5: Previously Identified Factors Influencing Residents’ Attitudes toward Tourism

 

Factors Influencing Residents’ Attitudes
toward Tourism

Study

 

Age

Brougham & Butler (1981)
Cavus & Tanrisevdi (2003)
McGehee & Andereck (2004)
Perdue et al. (1990)

Snaith & Haley (1994)
Tomljenovic & Faulkner (1999)

 

Education

Iroegbu & Chen (2001)

 

Gender

Harrill & Potts (2003)
Iroegbu & Chen (2001)
Mason & Cheyne (2000)

 

Income

Iroegbu & Chen (2001)
Snaith & Haley (1994)

 

Community attachment

Brougham & Butler (1981)
McCool & Martin (1994)
Lankford & Howard (1994)
Snaith & Haley (1994)

Um & Crompton (1987)

 

Contact with tourists

Andereck et al. (2005)
Brwham & Butler (1981)

 

Distance from tourism zone

Belisle & Hoy (1980)
Harrill & Potts (2003)
Jurowski & Gursoy (2004)
Snaith & Haley (1994)
Williams @998)

 

Economic role of tourism

Andereck et al. (2005)
Huh & Vogt (2008)

 

Economic reliance on tourism

Deccio & Baloghu (2002)
Jurowski et al. (1997)

Liu et al. (1987)

Madrigal (1993)

McGehee & Andereck (2004)
Pizam (1978)

Sirakaya et al. (2002)

 

Involvement in decision making

Kayat (2002)
Madrigal (1993)

 

Knowledge about tourism

Andereck et al. (2005)
Davis et al. (1988)
Lankford & Howard (1994)

 

Level of tourism development

Allen et al. (1988)
Long et al. (1990)
Smith & Krannich (1998)

 

Lived in a community as a child

McGehee & AndereckJ2004)

 

Personal benefits from tourism

Andereck et al. (2005)
McGehee & Andereck (2004)
Perdue et al. (1990)

 

Use/lack of use of recreational resources

 

Jurowski (1994)

 

49

Proposed Comprehensive Model of Residents ' Tourism Perceptions and Support for
Tourism Development

In summary of the literature review, Perdue et al. (1990) developed a model that
examined the relationship between residents’ perceptions of tourism impacts and their
support for additional tourism development. They used social exchange theory as a
theoretical framework to guide their study. Following the logic of social exchange theory,
several studies have concluded that people who receive greater economic benefits
typically perceive less socio-cultural and environmental impacts from tourism than
people who do not benefit from tourism (Ap, 1992; Lankford & Howard, 1994; Perdue et
al., 1990). As mentioned before, social exchange theory considers heterogeneity between
communities and as such can explain why different attitudes occur within the same
community. Social exchange provides an understanding of the exchange of socio-
cultural, environmental, and economic resources, thus the evaluation of the exchange is
complex and dynamic. Personal costs and benefits are the key components of social
exchange theory which enables explanation of both positive and negative impacts of
tourism. As a result, an individual’s evaluation of socio-cultural, environmental, and
economic benefits and costs differs based on personal benefits and costs measures. As a
result, people are likely to participate in exchange as long as the perceived benefits from
exceed perceives costs associated with tourism (Ap, 1992). Cost and benefits associated
with tourism influence peoples’ perceptions of tourism impacts which in turn support
additional tourism development or restrictions on future tourism development.

To be consistent with Perdue et al. (1990), this study also utilized social exchange
theory to examine the relationship between residents’ perceptions of tourism and support

for additional tourism development and restrictions on future tourism development.

50

Figure 4 shows the proposed model of residents’ tourism perceptions and support for
tourism development. To extend the Perdue, Long, and Allen (1990) model, four
community characteristics variables will be added to the comprehensive model and tested
as predictors of residents’ perceptions of tourism development: community attachment,
level of knowledge, power, and economic role of tourism (Figure 4). The selection of
these independent variables was based on suggestions and empirical testing by a number
of researchers (Gursoy & Rutherford, 2004; Jurowski et al., 1997; Lankford & Howard,

1994; Madrigal, 1993; McCool & Martin, 1994; Huh & Vogt, 2008; Yoon et al., 2001).

51

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Community attachment has been shown to be an important factor in residents’
quality of life (Brehm et al., 2004). McCool and Martin (1994) emphasized the
importance of community attachment consideration in planning and developing
community-based tourism in rural United States. Jurowski (1997) suggested that many
elements that create an image of a community are affected by tourism. Residents’
assessment of tourism impacts on communities’ resources will affect their community
attachment which in turn will shape residents’ perceptions of the tourism industry
impacts. Studies that have explored the influence of community attachment on residents’
perceptions of tourism impacts measured attachment as length of residence, having been
born, and/or grown up here (Davis et al., 1988; McCool & Martin 1994; McGehee &
Andereck, 2004; Andereck et al., 2005). However, results suggesting the relationship
between attachment and residents’ perceptions of tourism have not been consistent.
McCool and Martin (1994) suggested that newcomers showed a higher level of
attachment to their community than long-term residents and had the tendency to be
attached to the natural features of a place, as opposed to social networks. To build on
findings by McCool and Martin (1994), the relationships between social and
environmental attachment and residents’ perceptions of tourism impacts were explored in
this study.

Level of knowledge has shown to be a significant predictor of residents’
perceptions of tourism impacts. Keogh (1990) concluded that residents who were more
knowledgeable about the positive and negative aspects of tourism development viewed
tourism development in their community more favorably than those who were less

informed. Andereck et al. (2005) suggested that the level of knowledge tourism is an

53

indicator of residents’ level of engagement with the industry and tourists. While several
studies examined subjective knowledge of tourism (Andereck et al., 2005; Lankford &
Howard, 1994), only one study examined objective knowledge of tourism (Davis et al.,
1988). To extend the existing research concerning the relationship level of knowledge of
tourism and residents’ perceptions of tourism impacts, this study investigated the
influence of both subjective and objective knowledge of tourism on residents’
perceptions of tourism impacts.

Power has been recognized as a central component of the social exchange theory
and defined as “the ability of one actor to influence the outcome of another actor’s
behaviors or experience” (Madrigal, 1993, p. 338). Power within a community is
determined by access to resources (e.g., economic), position held in a community (e. g.,
officer), and skills. Balance of power exists when people’s ability to personally influence
decisions is perceived as equitable (Emerson, 1962). So far, results suggesting
relationship between power and residents’ perceptions of tourism impacts have been
mixed. While power was found to be the strongest predictor of residents’ perceptions in
the study conducted by Madrigal (1993), Kayat (2002) found power to have an indirect
influence on residents’ perceptions of tourism impacts. For the purpose of this study,
power was operationalized as: (1) level of personal influence on decisions related to
tourism development and (2) level of involvement in tourism development.

Based on social exchange theory, those who view the tourism industry a main
development strategy also perceive greater benefits from tourism and in turn are more
likely to perceive tourism positively. To confirm this proposition, the economic role of

tourism variable was tested as a predictor of residents’ attitudes toward tourism.

54

Consistent with social exchange theory, several studies have found a positive
relationship between personal benefits from tourism and positive impacts (Andereck &
Vogt, 2000; Jurowski, 1997; McGehee & Andereck, 2004; Perdue et al., 1990). However,
inconsistent with social exchange theory, several studies have shown that people who
perceived benefits from tourism did not perceive lower levels of negative impacts of
tourism. Respondents who perceived benefits from tourism showed no differences from
others in terms of tourism negative impacts (Andereck et al., 2005; Madrigal 1993).
Researchers have suggested that the lack of the relationship may be due to low levels of
tourism development (Madrigal, 1993; K0 & Stewart, 2002), and/or viewing tourism
industry as means of improving local economy (Gursoy et al., 2002). To further explore
the relationship between personal benefits from tourism and residents’ attitudes toward
tourism, this relationship was tested across three different communities each representing

different levels of tourism and economic development.

Summary of the Literature Review

Social exchange theory suggests that individuals are likely to participate in an
exchange if they believe costs will not exceed benefits. In terms of tourism, residents who
perceive tourism to be personally valuable to them and believe that the costs associated
with tourism do not exceed the benefits will support tourism development. Perdue et al.
(1990) developed a model to understand residents’ attitudes toward tourism by applying
social exchange theory. While Perdue et al. (1990) found support for the relationship
between perceived benefits and support for future tourism development, they expressed

the need for further research in other rural communities to validate and improve their

55

model. Perdue et al. (1990) suggested that future research should examine a random

sample of rural communities and attempt to measure current levels of tourism

development in these communities. In addition, Perdue et al. (1990) expressed the need

for future studies to focus on determining the factors which particularly influence tourism

perceptions by residents who are unlikely to benefit from tourism.

Building on the Perdue et al. (1990) conceptual model, a comprehensive

residents’ attitudes toward tourism literature review, and utilizing social exchange theory

(Skidmore, 1975), the following hypotheses were proposed for this study:

H1:

H2:

H3:

H4:

H5:

Level of objective and subjective knowledge, power, economic role of tourism,
community attachment are positively related to personal benefits from tourism.
When controlling for personal benefits from tourism, there is a positive (negative)
relationship between residents’ characteristics and residents’ positive (negative)
tourism impact perceptions.

Personal benefits from tourism will moderate the relationship between residents’
characteristics and residents’ positive (negative) tourism impact perceptions.
Level of objective and subjective knowledge of the tourism industry, power,
perceived economic role of tourism, and community attachment are positively
related to perceived positive impacts of tourism and negatively related to
perceived negative impacts of tourism when controlling for personal benefits from
tourism.

Personal benefits from tourism will moderate the relationships between level of

objective and subjective knowledge of the tourism industry, power, perceived

56

H6:

H7:

H8:

H9:

economic role of tourism, and community attachment and residents’ positive
(negative) tourism impact perceptions.

There is a positive (negative) relationship between personal benefits from tourism
and positive (negative) perceived impacts of tourism.

Differences will be found among personal benefits from tourism and perceptions
of positive (negative) impacts of tourism regarding their ability to predict support
for future tourism development.

Differences will be found among personal benefits from tourism and perceptions
of positive (negative) impacts of tourism regarding their ability to predict support
for future restrictions on tourism development.

Differences will be found among personal benefits from tourism, perceptions of
positive (negative) impacts of tourism, support for future tourism development,
and support for future restrictions on tourism development regarding their ability

to predict perceived community future.

Butler (1980) proposed a model in which there is an inverse relationship between

level of tourism development and residents’ attitudes toward tourism. According to this

model, residents’ attitudes toward tourism are positive at the first stages of tourism

development; however, with increasing tourist arrivals and tourism infrastructure,

destinations reach a point of saturation signified by negative attitudes of local people

toward tourism. Allen et al. (1993) suggested that a better understanding of residents’

attitudes will be gained when both total economic activity and tourism development are

57

considered. Building on Butler’s (1980) destination life cycle model and a study by Allen

et al. (1993), the following hypotheses were proposed:

H10:

H11:

H12:

H13:

Residents from communities with low economic and low tourism development
and those with high economic and high tourism development will perceive greater
positive and smaller negative impacts of tourism than residents from communities
with low tourism development and high economic development.

Residents from communities with low economic and low tourism development
and those with high economic and high tourism development will be more
supportive of future tourism development than residents from communities with
low tourism development and high economic development.

Residents from communities with low economic and low tourism development
and those with high economic and high tourism development will be less
supportive of restrictions on future tourism development than residents from
communities with low tourism development and high economic development.
Residents from communities with low tourism development and high economic
development and those with high economic and high tourism development will be
more optimistic about the future of their county than residents from communities

with low economic and low tourism development.

Chapter 2 provided an in-depth overview of the study’s theoretical concepts and

justification of constructs explored in relation to residents’ attitudes toward future

tourism development. The study’s proposed comprehensive model and hypotheses were

58

also discussed. The following chapter will discuss the research methodology used to
obtain and analyze information for this study consisting of the sample and population
description, the data collection techniques, non-respondents assessment, research

instrument development (including reliability testing), followed by statistical procedures.

59

Chapter III
METHODOLOGY
The research methodology used to obtain and analyze information for this study is
discussed in this chapter. First, the sample and population are described. Next, the data
collection techniques and research instrument, including the scale development, are

discussed. Finally, the statistical tests used for data analysis are explained.

Study Area

Residents’ attitudes toward the existing tourism industry and future tourism
development were evaluated across several small rural Michigan communities at different
stages of tourism development. Specifically, the geographical regions under study
included three counties: Emmet, Saginaw and Tuscola. Emmet County is located in the
upper part of lower peninsula and is a combination of urban and rural areas. Saginaw
County is located in the central part of lower peninsula and is a combination of urban and
rural areas. Tuscola County is situated on the east side of lower peninsula and is the least
developed of the three counties. Among these geographical regions under study, Emmet
County represents a high level of tourism and economic development, Saginaw County
represents a low level of tourism development and high level of economic development,
and Tuscola County represents a low level of tourism development (since tourism
industry in the county is almost non-existent) and low level of economic development

(see Table 6 for more details).

60

Table 6: Characteristics of Geographic Areas under Study

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Emmet County Saginaw County Tuscola County

Mix of urban and rural Mix of urban and rural Primarily rural

Population: 31,437 Population: 210,039 Population: 58,266

Total area: 468 mil2 Total area: 816 mil2 Total area: 914 mil2

Water area: 414 mil2 Water area: 7 mil2 Water area: 101 mil2
Housing Units: 18,554 Housing Units: 85,505 Housing Units: 23,378
Renter-occupied housing units: Renter-occupied housing units: Renter-occupied housing units:
3,075 21,040 3,417

Owner—occupied Owner-occupied Owner-occupied

housing units: 9,502 housing units: 59, 390 housing units: 18,037
Seasonal use: 5,032 Seasonal use: 301 Seasonal use: 724

Median household income: Median household income: Median household income:
$40,222 $38 ,637 $40,174

Median house value: Median house value: Median house value:
$131,500 $85,200 $87,100

Retail sales receipts in Retail sales receipts in Retail sales receipts in
2002 : 2002: 2002 :

$1,529,549,000 $1 1,140,523,000 $984,159,000

Total economic activity per Total economic activity per Total economic activity per
capita in 2002: capita in 2002: capita in 2002:

$47,057 $53,088 $16,889

 

 

Source: US. Census Bureau, 2000.
Note: Total economic activity per capita = all retail sales receipts in 2002/population in 2002.

Population and Sample

Permanent and seasonal residents who were homeowners in Emmet, Saginaw, and
Tuscola counties comprised the population in this study. Three sampling frames
consisting of residents listed by the tax assessors departments in winter 2006 were used to
represent the population of the three counties. A random sample was drawn from each
sampling frame. Addresses were sorted and any doubles eliminated (in case of multiple
properties, only one was kept) to assure one household received only one survey.

The random sample size included permanent and seasonal homeowners, condos,
and farms with a homestead. All eligible properties had a state equalization value (SEV)
of $25,000 and more. The following were excluded from the random sample: renters,

businesses, trusts, lawyers, bankers, real estate, and multiple properties. Thus, the desired

61

sampling unit was an individual household. The total of 3,008 households (Emmet
County, N=1,008; Saginaw County = N=1,000; Tuscola County=l,000) were randomly
selected.

When determining sample size the following factors were considered: sampling
error, population size, desired precision, heterogeneity of population (Dillman, 2000;
Singleton et al., 1988), resources available (Alreck & Settle, 2004) and expected response
rate. Given the population size, the final size of 381 per county was deemed appropriate
at a 95% confidence level with a margin of error of i 5% (Dillman, 2000; Di Grino,
1986). In previously studies conducted on residents’ attitudes towards tourism that used
Dillman’s method of data collection (Jurowski et al., 1997; Choi & Sirakaya, 2005),
response rates ranged from 42% to 58%. Thus, a random sample of 1000 (after rounding
up to the nearest hundred) individuals in this study is based on the assumption of a 42%
response rate. Additionally, the selected sample size satisfies Cohen’s suggestion for
acceptable level of power of 0.80 (a = 0.05, d = 0.50) (Hair et al., 1998) and the desired
level of 20 observations for each independent variable as recommended for multiple
regression analysis, though the minimum ratio is 5 observations to 1 independent variable

(Hair et al., 1998; Tabachnick & Fidell, 1996).

Data Collection
Self-administered questionnaires were mailed and data collected throughout May
of 2007. The majority of previous studies of residents’ perceptions and attitudes toward
tourism development had used survey-based methods for collecting data. Survey research

is a popular social research method used for descriptive, exploratory and explanatory

62

purposes. Using probability sampling allows the survey administrator to collect data from
a group of respondents whose characteristics reflect those of a population which may be
too large to observe directly. The main strengths of surveys are economic feasibility, the
amount of data collected, the change to sample large population, collection of
standardized and reliable data, and the provision of anonymity and privacy to encourage
responses to sensitive issues (Alreck & Settle, 2004; Babbie, 1998). Additionally, surveys
are valid instruments for measuring attitudes (Babbie, 1998). However, the main
weaknesses of surveys are low rates of response, potentially high costs, as well as lack of
interaction with respondents (Alreck & Settle, 2004).

A multi-method approach was used to develop the survey instrument. First, a
review of existing literature dealing with residents’ attitudes towards tourism
development was conducted to develop a master list of attributes which theoretically
measured residents’ perceptions of tourism development and other constructs to be tested
in the study model. To ensure content validity of the questionnaire and clarity of
questions and instructions, a draft of the survey was shared with several county officials
and tourism professionals as well as three tourism researchers from different universities.
Next, the questionnaire was modified as a result of a feedback from tourism experts and
the finalized version of the questionnaire was administered using strategies adopted from
Dillman’s (2000) total design method for mail surveys. The questionnaire was sent out
along with a cover letter and a pre-stamped reply envelope. The cover letter included a
statement guaranteeing respondents’ data confidentiality and protection of their privacy.
One week later, a postcard reminder was sent, followed by a revised second letter and a

replacement questionnaire in another two weeks to reduce non-response rate. To increase

63

response rate, incentives to respond were provided by the counties and included: outlet
mall certificates, local restaurant certificates, camping passes, and passes to a county fair.
Once the data collection period ended, a non-response study was conducted to assess any
biases in the dataset. The survey instruments, cover letters, and reply envelope used in the
study are provided in Appendices B-D.

From the 3,008 homeowner population, 90 surveys were undeliverable, and 809
were returned and completed for an overall response rate of 28%. Please refer to Table 7
for response rates by county. Given the response rate, there was a high probability that
the sample might be different from the study population. A Chi-square goodness of fit
test was employed to test whether the observed frequencies differed from their expected

values obtained from the US. Census Bureau (2000). Results showed the sample to be
significantly different from the population by residential status [)6 (1) =9.727, p=.002,

fewer permanent homeowners than expected]. To avoid bias in the estimate obtained
from the sample data, (i.e., statistical procedures would have given greater weight to
those people over-sampled, in this case seasonal homeowners); two weights (>1 for
permanent, <1 for seasonal) were created so the groups would more closely resemble the
proportions in the p0pulation. Thus, 11 (sample size) reported throughout the study is the
actual number of surveys received but reported statistics were weighted for population
estimate

Table 7: Response Rates

 

 

 

 

 

 

 

 

 

 

County Sample Size Undeliverable Returned Response Rate
Emmet 1,008 37 343 35.3%
Saginaw 1,000 14 224 22.7%
Tuscola 1,000 39 242 25.2%
Total 3,008 90 809 27.7%

 

 

64

Non-Respondent Study

After the mail data collection was completed, a non-response survey was sent out
to assess any bias in the dataset. The non-response survey consisted of several key
variables used in the study to determine differences in responses between the main study
participants and non-respondents. A total of 300 non-response surveys were mailed in
June 2007. From the 300 non-response surveys, 5 surveys were undeliverable, and 51
were returned and completed for an overall response rate of 18%. The two-page long
questionnaire included: level of subjective knowledge, perceived personal benefits from
tourism, support for tourism having a vital role in the county, support for restrictions on
tourism development, support for encouragement of non-residents to develop tourism
business in the county, and power. Independent sample t-tests were computed to test for
differences between the main study and non-respondent study.

No significant differences were found between the main study and non-
respondent study in terms of subjective knowledge, support for tourism having a vital
role in the county, support for encouragement of non-residents to develop tourism
business in the county, and power in all three counties. An independent sample t-test
showed significant differences between the main study and non-respondent study in
terms of perceived personal benefits from tourism in Emmet County (I = -2.158, df= 325,
p < 0.05). Non-respondents believed they could benefit from additional tourism more
than main study participants. Additionally, the t—test found significant differences
between the main study and non-respondent study in terms of restrictions on tourism
development in Saginaw County (t =3.57, df = 208, p < 0.05). Main study participants

were more agreeable with restrictions on future tourism development than non-

65

respondents. Overall, the results obtained from the non-respondents were found to be

relatively the same as from the main study results, thus there were believed to be no

major concerns regarding measurement errors in the studyl.

Participants in the non-respondent survey were also asked why they did not
complete and returned the original survey. Multiple responses included: the survey was
“too busy” (27.1%), “too long” (14.6%), “did no receive the survey” (6.3%), “not
interested” (6.3%), and “have little knowledge about tourism development” (4.2%).
Single responses included: “lost the survey,” “1 was out of town,” and “I did not think my

opinion mattered.”

Survey Instrument
The survey instrument was comprised of a series of attitude items based on
previous work by Lankford and Howard ( 1994) and Perdue, Long, and Allen (1990) that
had been previously tested for internal consistency reliability2 and convergent validity3.
A Likert scale where 1 equaled strongly disagree and 5 equaled strongly agree was used
for each attitudinal item, as recommended by Maddox (1995) for tourism impact research

due to its better validity (convergent and discriminant).

 

Due to lrrmted space in the non-response questionnaire layout, incomplete number of questions was
included. Thus, t-tests results show comparison of selected items representing construct rather then
composite scales.

2 Internal consistency examines reliability within a similar set of items on a test. Cronbach’s internal
consistency reliability (expressed as a correlation coefficient ranging from 0 to 1), has been most widely
used reliability method in studies developing scales for measurement residents’ attitudes towards tourism.
A score of 0.7 or higher is an acceptable reliability coefficient (Nunnally & Bernstein, 1994).

3 . . . . . .

Convergent validity “examines the extent to which the measure correlates With other measures desrgned
to measure the same thing” (Ap & Crompton, 1998, p.128); this confirms that measures that should be
related are in fact related.

66

Additional items pertaining to independent measures included: perceived personal
benefits from tourism, community attachment, power, economic role of tourism, and
subjective knowledge of tourism were based on previous work by Brehm et al. (2004),
Madrigal (1993), McCool and Martin (1994), McGehee and Andereck (2004), and Huh
and Vogt (2008).

Objective knowledge of tourism was measured by items developed by MSU
researchers in collaboration with County Extension Council officials and Saginaw and
Emmet tourism departments.

Tourism development level was determined using secondary data regarding: (1)
county market share of pleasure travelers in Michigan, 1996-2002, (2) Michigan tourism
spending by county in 2000, (3) contribution of tourism and recreation to the local
economy in 2007, (4) proportion of seasonal homeowners for 1990 and 2000, (5) lodging
use tax (1983-1995), and (6) annual average number of jobs in tourism-related businesses
(1977-1987).

The estimated county market share of pleasure travelers in Michigan, 1996-2002
was obtained from the Travel, Tourism, and Recreation Resource Center at Michigan
State University. Estimates were based on results form a telephone survey conducted by
the Travel, Tourism, and Recreation Resource Center at Michigan State University. A
pleasure trip was defined as an overnight or a day trip to places at least 50 miles from
respondents’ homes. The regions under study included Illinois, Indiana, Michigan,
Minnesota, Ohio, Wisconsin, and Ontario. Market share was defined as the percentage of
pleasure trips to Michigan that originate in a given region and had a specific county as its

main destination.

67

Michigan tourism spending by county in 2000 was calculated by Dr. Stynes, a
researcher at Michigan State University, using the Tourism Spending Model. The
Tourism Spending Model estimated spending by tourists to each county in Michigan
considering five lodging segments: motels, campgrounds, seasonal homes, visiting
friends and family, and day trips. Spending for each segment was estimated using as a
mix of secondary data (e. g., lodging room use tax, inventories) and parameters from a
number of surveys.

Contribution of tourism and recreation to the local economy in 2007 (county’s
dependency on the tourism industry) was calculated by Dr. Stynes using the Michigan
Economic Impact Model (MITEIM). The MITEIM model estimates economic impact as:
visits * spending * multiplier.

Information regarding proportion of seasonal homeowners for 1990 and 2000 was
obtained from the US Census. Statistics on lodging use tax (1983-1989) and annual
average number of jobs in tourism-related businesses (1977-1987) by county were
obtained from the “Travel and Tourism in Michigan: A Statistical Profile” (1991).
Statistics on lodging use tax (1990-1995) were derived from County Tourism Profiles
developed by the Travel, Tourism, and Recreation Resource Center at Michigan State
University in 2001.

Level of economic development was determined as a ratio using all retail sales
receipts (total economic activity) in the numerator and community population in the
denominator.

The study variables, scale items used for measurements and sources of the

measurements can be found in Table 8.

68

Table 8: Variables and Sources for Scale Items Used for Measurement

 

 

 

 

 

 

 

 

Variable Measurement Study
Community In terms of your community attachment, how important are the Adopted from Brehm
attachment following aspects: et al. (2004)

(Q12) 0 family ties
0 friends close by
0 local culture and traditions
0 opportunities to be involved in community projects
0 natural landscapes/views
0 presence of wild life
0 opportunities for outdoor recreation
(not important at all—very important)
Level of o How would you describe your level of knowledge about the Adopted from:
Subjective tourism industry? (not at all knowledgeable— moderately,- McGehee & Andereck
Knowledge slightly, very knowledgeable) (2004)
(Q9)
Level of 0 Which response best represents the % of tourism and Developed by
Objective recreation bring to your county’s economy? researchers at MSU
Knowledge in collaboration with
(Q8) County Extension
Council officials and
Saginaw and Emmet
tourism departments
Perceived o What level of personal influence have you had on decisions Modified from:
Power related to tourism development in the county? Madrigal (1993)
(Involvement c What level of involvement have you had in tourism
in DCCiSiOD development in the county?
Making) (not at all -very little-some-quite a bit-a lot)
(016)
Economic 0 Compared to other economic sectors, how important role Adopted from:
Role of do you think tourism and recreation should have in the Huh & Vogt (2008)
Tourism county? (no role-minor role-role equal to other economic
(Q6) sectors-dominant role)
Perceived o I would personally benefit from more tourism development Adopted from:
Personal in my community.(strongly agree-agree-unsure-disagree— Perdue et al. (1990)
Benefits strongly disagree) McGehee & Andereck
From 0 Amount I feel 1 benefit personally from tourism in my (2004)
Tourism community.(not at all-very little-some—quite a bit-a lot)
(Q10, Q14)
Positive 0 Increasing the number of tourists visiting an area improves Modified from:
Tourism the local economy. Perdue et. al (1990)
Impacts 0 Tourism development increases the number of recreational Lankford & Howard
(Q14) opportunities for local residents. (1994)
0 Tourism development increases the quality of life in an McGehee & Andereck
area. (2004)

 

Tourism provides highly desirable jobs for local
residents.
(strongly agree-agree- unsure-disagree—strongly disagree)

 

 

69

Continued Table 8: Variables and Sources for Scale Items Used for Measurement

 

 

 

 

 

 

 

Variable Measurement Study
Negative Tourism development increases the traffic problems of an Modified from:
Tourism area. Perdue et. al (1990)
Impacts 0 Tourism results in more litter in an area. Lankford & Howard
(Q14) 0 Tourism development increases the amount of crime in the (1994)

area. McGehee & Andereck
Tourism results in an increase of the cost of living. (2004)
Tourism causes communities to be overcrowded.
0 An increase in tourists in my community will lead to
friction between residents and tourists.
0 Tourism related jobs are low paying.
0 Tourism development unfairly increases property taxes.
0 Tourism encourages more private development (e.g.,
housing, retail)
(strongly agree-agree-unsure-disagree—strongl y disagree)
SUpport for The community should try to attract more tourists. Adopted from:
FUNIC 0 Tourism can be one of the most important economic PCT due et. 31 (1990)
Tourism developmental option for an area. Lankford & Howard
Development Additional tourism would help this community grow in the (1994)
(014) aright direction. McGehee & Andereck
1 support tourism having a vital role in this community. (2004)
(strongly agree-agree-unsure-disagree—strongly disagree)
Support for 0 Local government should control tourism development. Adopted from:
Restrictions 0 Nonresidents should be allowed to develop tourism Perdue et. 31 (1990)
on Future attractions in an area. Lankford & Howard
Tourism Local government should restrict tourism development. (1994)
giljjopmem (strongly agree-agree-unsure-disagree—strongly disagree)
Perceived The future of my community looks bright. Adopted from:
Community (strongly agree-agree-unsure-disagree—strongly disagree) Perdue et. al (1990)
Future Lankford & Howard
(Q13) (1994)
McGehee & Andereck
(2004)

 

Demographics and other residents’ characteristics of respondents that have shown

a significant relationship to residents’ perceptions in earlier studies were also tested and

included age, annual income, education, and length of residence (Table 9).

70

Table 9: Support for Testing Independent Variables of the Proposed Model

 

 

 

 

 

Differences in Perceptions of
Independent Variable Study Additional Tourism Development
Age Andereck et al. (2005) No
Brougham & Butler (1981) Yes
Cavus & Tanrisevdi (2003) Yes
Madrigal ( 1993) No
McGehee & Andereck (2004) Yes
Perdue et al. (1990) No
Snaith & Haley (1994) Yes
Tomljenovic & Faulkner (1999) Yes
Annual Income Iroegbu & Chen (2001) Yes
Madrigal (1993) No
Snaith & Haley (1994) Yes
Education Iroegbu & Chen (2001) Yes
Madrigal (1 993) No
Perdue et al. (1990) No
Length of residency Brougham & Butler (1981) Yes
Cavus & Tanrisevdi (2003) Yes
Madrigal (1993) No
Perdue et al. (1990) No

 

 

 

In accordance with Michigan State University and federal regulations, the resident
survey instrument was submitted to the University Committee on Research Involving
Human Subjects (UCRIHS) for their review (to satisfy IRGP requirements) before data
collection began. Additionally, after modification of the instrument was completed, the

finalized instrument was resubmitted for UCRIHS final approval.

Reliability Test
Reliability indicates the internal consistency of a scale (i.e., the degree to which
the scale items measure the same underlying attribute). Cronbach’s internal consistency
reliability (expressed as a correlation coefficient ranging from 0 to 1), has been most
widely used reliability method in studies developing scales for measurement residents’

attitudes towards tourism. A score of 0.7 or higher is an acceptable reliability coefficient

(Nunnally & Bernstein, 1994).

71

To test the consistency of multiple item scales, reliability was computed using
Cronbach Alpha Coefficient (Nunnally & Bernstein, 1994) and corrected item-to-total
correlation (recommended correlations is 0.30 and above) (Parasuranam et al., 1988).

Overall, all the composite scales met the Cronbach Alpha Coefficient requirement
of 0.70 with some exceptions. The perceived personal benefits from tourism scale
(consisting of two items) had a Cronbach Alpha Coefficient of 0.68 in Saginaw County
and 0.55 in Tuscola County; however, the corrected item-to-total correlation was higher
than 0.30, which is a general criterion for acceptable reliability. Therefore, the study used
the perceived personal benefits from tourism scale without deleting any items. Perceived
negative impacts of tourism scale (consisting of nine items) had a Cronbach Alpha
Coefficient of 0.69 in Tuscola County. The “tourism encourages more private
development (e. g., housing, retail)” item had corrected item-to-total correlation lower
than 0.30, thus was deleted which increased the Cronbach Alpha Coefficient to 0.75.
Support for restrictions on tourism development scale (consisting of three items) had a
Cronbach Alpha Coefficient of 0.49 in Emmet County, 0.43 in Saginaw County, and 0.36
in Tuscola County. The items “local government should control tourism development”
and “nonresidents should be allowed to develop tourism attractions in an area” had
corrected item-to-total correlation lower than 0.30, thus were deleted reducing the
support for restrictions on tourism development scale to a single item measurement.
Table 10 provides a summary of the Cronbach Alpha Coefficients for composite scales

used in this study.

72

Table 10. Cronbach Alpha Coefficients for Composite Scales Used in the Study

 

 

Cronbach Alpha Coefficient

 

 

 

 

Variable Measurement Items .
Emmet Saginaw Tuscola
Perceived - I would personally benefit from more .71 .68 .55
Personal tourism development in my community
Benefits
from 0 Amount I feel I benefit personally from
Tourism tourism in my community
Community In terms of your community attachment, how .74 .85 .77
Attachment important are the following aspects:
0 family ties
0 friends close by
0 local culture and traditions
0 opportunities to be involved in community
projects
0 natural landscapes/views
0 presence of wild life
0 opportunities for outdoor recreation
Power 0 What level of personal influence have you .88 .87 .92
(Involvement had on decisions related to tourism
in Decision development in the county?
Making) 0 What level of involvement have you had in
tourism development in the county?
Negative 0 Tourism development increases the traffic .76 .78 .75
Tourism problems of an area.
Impacts 0 Tourism results in more litter in an area.
0 Tourism development increases the amount
of crime in the area.
0 Tourism results in an increase of the cost of
living.
0 Tourism causes communities to be
overcrowded.
0 An increase in tourists in my community
will lead to friction between residents and
tourists.
0 Tourism related jobs are low paying.
0 Tourism development unfairly increases
property taxes.
Support for 0 The community should try to attract more .88 .88 .91
Future tourists.
Tourism 0 Tourism can be one of the most important

Development

 

economic developmental options for an
area.

Additional tourism would help this
community grow in the aright direction.

I support tourism having a vital role in this
community.

 

 

73

Continued Table 10. Cronbach Alpha Coefficients for Composite Scales Used in the
Study

 

Cronbach Alpha Coefficient

 

 

Variable Measurement Items .
Emmet Sailnaw Tuscola
Positive 0 Increasing the number of tourists visiting .89 .89 .91
Tourism an area improves the local economy.
Impacts 0 Tourism development increases the number
of recreational opportunities for local
residents.

0 Tourism development increases the quality
of life in an area.

0 Tourism provides highly desirable jobs for
local residents.

0 Shopping, restaurants, and entertainment
options are better as a result of tourism.

0 Tourism encourages more public
development (e.g., roads, public facilities)

0 Tourism helps preserve the cultural identity
and restoration of historical buildings.

0 Tourism contributes to income and standard
of living.

0 Tourism development improves the
physical appearance of an area.

0 Tourism provides incentives for new park
development. '

0 Tourism provides incentives for protection
and conservation of natural resources.

0 Tourism provides incentives for purchase
of open space.

 

 

 

Data Analysis

In previous studies, researchers have focused on examining socioeconomic factors
(Cavus & Tanrisevdi, 2002; Williams & Lawson, 2001); distance from the tourism area
(Belisle & Hoy, 1980; Gursoy & Jurowski, 2002); economic dependency (Akis et al.,
1996; Lankford & Howard, 1994); resident and community typologies (Ap & Crompton,
1993; Smith & Krannich, 1998); level of tourism development (Faulkner & Tideswell,
1997; Long et al., 1990; Madrigal, 1993), community attachment (Jurowski et al., 1997;
McCool & Martin, 1994), involvement in decision making (Lankford & Howard, 1994;

Madrigal, 1993), knowledge about tourism (Andereck et al., 2005; Davis et al., 1998),

74

level of contact with tourist (Andereck et al., 2005; Lankford & Howard, 1994), type and
form of tourism (Butler, 1980; Cohen, 1972), and development of measurements of
residents’ attitudes (Ap & Crompton, 1998; Choi & Sirakaya, 2005; Lankford & Howard,
1994)

Methodological approaches to measuring residents’ attitudes have used the
following statistical techniques: chi-square (e. g., Cavus & Tanrisevdi , 2003), t-tests and
ANOVA (e.g., Andriotis & Vaughan, 2003); MANOVA (e.g., Andereck & Vogt, 2000),
ANCOVA (e.g., Allen et al., 1993), MANCOVA (e.g., Madrigal, 1995), correlations
(e.g., Deccio & Baloghu, 2002), multiple regression (e.g., McGehee & Andereck, 2002),
cluster analysis (e. g., Andriotis & Vaughan, 2003); factor analysis (e.g., Cavus &
Tanrisevdi, 2003); and structural equation modeling (e.g., Jurowski & Gursoy, 2004).

In this study, demographic characteristics (e. g., age, annual income, education,
and length of residence), personal benefits from tourism, community attachment, power,
economic role of tourism, level of subjective knowledge, level of objective knowledge,
and attitudinal responses were first analyzed using descriptive statistics. Secondly,
building on the model developed by Perdue, Long, and Allen (1990), a series of multiple
regression analyses were performed to explore the relationships among the variables.
Thirdly, one-way ANOVA test was employed to examine the relationship between

residents’ perceptions and their area’s level of tourism development.

Multiple Regression Analysis

Multiple regression is a statistical method employed to explore the relationship

between predictor/ independent variables and the criterion/dependent variables. Multiple

75

regression establishes the effectiveness of a set of independent variables in explaining a

proportion of the variance in a dependent variable through a significance test of R2. By

comparing beta weights, multiple regression determines which independent variables are

the strongest predictors of dependent variables (Cohen & Cohen, 1983). In general,

multiple regression can answer the following questions (Pallant, 2005):

How well a set of variables is able to predict a particular outcome?
Which variable in the set is the best predictor of an outcome?
Is a particular predictor variable still able to predict an outcome when the effects

of another variable are controlled for?

The major assumptions of multiple regression are:

Sample size required for standard multiple regression is “about 15 subjects per
predictor” (Tabachnik & F idell, 2001, p. 117).

Multicollinearity occurs when there are high intercorrelations among a set of
predictor variables. To assure multicollinearity does not exist among predictors, a
correlation matrix and tolerance need to be examined (Leech et al., 2005).
Outliers (i.e., very high and very low scores) have a great influence on regression
results and thus should be deleted or rescored to reduce their influence. Outliers
can be identified fi'om the standardized residual plot (Tabachnik & Fidell, 2001).
Normality assumes that residuals are normally distributed‘ about the predicted
dependent variable scores. Linearity suggests the residuals have a straight-line
relationship with predicted dependent variable scores. Homoscedasticity assumes

that the variance of the residuals about predicted dependent variable scores are the

76

same for all predictors. Outliers, linearity and homoscedasticity assumptions can
be checked through examination of residuals scatterplots (Pallant, 2005).
With each model tested in this study, the following statistics were reported:

0 Standardized beta coefficients which determine (through a significance test)
whether a specific predictor variable is significantly contributing to the prediction.

0 The F-distribution which indicates (through a significance test) that a combination
of specific independent variables predicts the dependent variable.

0 R Square (R2) which indicates what proportion of the variance in the dependent

variable can be explained by a combination of specific independent variables.

Multiple Regression Models Used in the Study

To validate and further extend the model developed by Perdue et al. (1990),
several standard multiple regression models were tested (Table 11). In the standard
multiple regression approach all the independent variables are entered into the regression
simultaneously. Each independent variable is evaluated in terms of its predictive power,
i.e., what it adds to the prediction of the dependent variable above the predictability
offered by all the other independent variables (Tabachnik & Fidell, 2001). To confirm
that the relationship between an independent variable and a dependent variable was not
moderated by the level of another independent variable, simultaneous multiple
regressions with interactions (Cohen & Cohen, 1983) were employed. Specifically, the
effect of “personal benefits” on the relationship between residents’ characteristics and
perceived impacts of tourism, as well as the relationship between levels of knowledge,
power, economic role of tourism, community attachment and perceived positive and

negative impacts of tourism was tested.

77

Table 11: Regression Analysis of the Relationship between Variables

 

Independent Variables

Dependent Variables

 

Model I

 

(H1)

Level of objective knowledge

Personal benefits from tourism

 

Level of subjective knowledge

 

Power

 

Economic role of tourism

 

Community attachment

 

Model 2

 

(H2, H3)

Age

Tourism positive impacts

 

Income

 

Education

 

Length of residence/ownership

 

Personal benefits from tourism

 

Model 3

 

(H2, H3)

Age

Tourism negative impacts

 

Income

 

Education

 

Length of residence/ownership

 

Personal benefits from tourism

 

Model 4

 

(H4, H5, H6)

Level of objective knowledge

Touristositive impacts

 

Level of subjective knowledge

 

Power

 

Economic role of tourism

 

Communig attachment

 

Personal benefits from tourism

 

Model 5

 

(H4, H5, H6)

Level of objective knowledge

Tourism negative impacts

 

Level of subjective knowledge

 

Power

 

Economic role of tourism

 

Community attachment

 

Personal benefits from tourism

 

Model 6

 

(H7)

Personal benefits from tourism

Support for future tourism development

 

Tourism positive impacts

 

Tourism negative impacts

 

Model 7

 

(H8)

Personal benefits from tourism

Support for restrictions on future tourism
develppment

 

Tourism positive impacts

 

Tourism negative impacts

 

Model 8

 

(H9)

Personal benefits from tourism

Perceived community future

 

Tourism positive impacts

 

Tourism negative impacts

 

Supmm for fiiture tourism development

 

 

 

Support for restrictions on future
tourism development

 

 

78

 

Regression model I was used to test the relationship between objective level of
knowledge, subjective level of knowledge, power, economic role of tourism, community
attachment and personal benefits from tourism. Regression models 2 and 3 were used to
test the relationship between residents’ characteristics and the positive and negative
impacts of tourism while controlling for personal benefits from tourism. Regression
models 2 and 3 were also used to test moderating effect of personal benefits from tourism
on the relationship between residents’ characteristics and residents’ positive (negative)
tourism impact perceptions. Regression models 4 and 5 were used to test the relationship
between objective level of knowledge, subjective level of knowledge, power, economic
role of tourism, community attachment and positive (negative) impacts of tourism while
controlling for personal benefits from tourism. Additionally, regression models 4 and 5
were used to test moderating effect of personal benefits from tourism on the relationships
between objective level of knowledge, subjective level of knowledge, power, economic
role of tourism, community attachment and residents’ positive (negative) tourism impact
perceptions. Regression models 4 and 5 were also used to test the relationship between
personal benefits from tourism and positive (negative) perceived impacts of tourism.
Regression model 6 was used to test the differences among personal benefits from
tourism, tourism positive impacts, and tourism negative impacts regarding their ability to
predict support for future tourism development. Regression model 7 was used to test the
differences among personal benefits from tourism, tourism positive impacts, and tourism
negative impacts regarding their ability to predict support for restrictions on future
tourism development. Regression model 8 was used to test the differences among

personal benefits from tourism, tourism positive impacts, tourism negative impacts,

79

support for future tourism development, and support for restrictions on future tourism

development regarding their ability to predict perceived community future.

Analysis of Variance

One-way analysis of variance (ANOVA) is used when more than two population
means are compared to identify significant differences among them. Analysis of variance
compares the variance between the different groups with the variability within each
group. A significant F test indicates that the population means are not equal, meaning
there is more variability between the groups (caused by the independent variables) than
there is within each group (due to a chance, an error term). To indicate which of the
groups differ, a post-hoc test needs to be conducted (Tabachnik & F idell, 2001).

One-way ANOVA was used to test the relationship between residents’
perceptions and their area’s level of tourism development (H10-H13). The dependent
measures tested included positive and negative impacts of tourism development, support

for tourism development, and support for restrictions on tourism development.

Statistical Procedures of Data Analysis
The overall statistical analysis included: (1) descriptive statistics focusing on
residents’ socio-demographic profile and key variables used in the conceptual model (i.e.,
knowledge, power, economic role of tourism, community attachment, personal benefits,
positive and negative impacts of tourism, support for additional tourism, support for
restrictions on tourism development, and community future); (2) standard multiple

regression to examine the relationships among variables and multiple regression with

80

interactions to determine the moderating effect of “personal benefits” on other
independent variables; and (3) one-way ANOVA to test the relationship between
residents’ perceptions of and the stage of tourism and economic development, followed
by post-hoc tests to examine expected differences on residents’ perceptions based on their
area’s level of tourism and economic development. All of the aforementioned statistical
procedures were performed at a county level to assure generalizability of the results and
to assess the destinations’ tourism lifecycle position. Data were analyzed using Statistical

Package for Social Sciences (SPSS 15.0).

81

Chapter IV
RESULTS

The problem addresses in this study was to evaluate residents’ perceptions of
tourism impacts and attitudes toward the existing tourism industry and future tourism
development in communities (i.e., counties) at different stages of tourism development.
The research also tested the influence of residents’ attitudes toward tourism on support of
future tourism development. A number of hypotheses were stated regarding residents’
attitudes in three communities with different levels of tourism development. In this
chapter, the following topics will be reported: (1) description of the sample focusing on
socio-demographics and key variables used in the conceptual model (i.e., community
attachment, knowledge, power, economic role of tourism, personal benefits, positive and
negative impacts of tourism, support for additional tourism, support for restrictions on
tourism development, and community future); (2) analysis of the study hypotheses using
standard multiple regression to examine the relationships among variables (HI-H9); and
(3) one-way ANOVA to test the relationship between residents’ perceptions of tourism
and the stage of tourism and economic development (H10-H13). Following the one-way
ANOVA, post-hoe tests are presented to examine expected differences on residents’
perceptions based on their area’s level of tourism and economic development.

First, statistical tests (i.e., descriptive statistics and standard multiple regressions)
were employed for each county to determine consistencies or inconsistencies in the
relationships between independent and dependent variables. Secondly, one-way

ANOVAs were used to determine differences in residents’ perceptions of tourism

82

between the counties based on an attempt to assess the destinations’ tourism lifecycle

position.

Description of the Sample

Socio-Demographic Profile

The socio-demographic profile of the respondents representing the three counties
under study is offered in Table 12. In Emmet County, the average age was 60 years old.
The highest earned level of education was an advanced degree (38.4%) or college degree
(37.9%). The majority of respondents had household income of $100,000 and over
(50.5%). Approximately sixteen percent (15.5%) of the respondents were employed
directly or indirectly in the tourism industry. The respondents lived or had an ownership
in the county 23 years on average. Initially, fifty-seven percent (57.0%) of the
respondents were permanent residents and forty-three percent (43.0%) were seasonal
residents. To assure that the sample represented the study population, the data were
weighted creating a population mix of sixty-five percent (65.3%) of permanent residents
and thirty-five percent (34.7%) of seasonal residents (values matching those obtained from
the US. Census Bureau, 2000).

For Saginaw County, the average age was 53 years old. The highest earned level
of education was a high school degree (26.1%) or college degree (21.2%).
A sizable proportion of respondents had household income of $50,000 or less (43.3%).
Approximately ten percent (10.1%) of the respondents were employed directly or

indirectly in the tourism industry. The residents lived or had an ownership in the county

83

34 years on average. The majority of the respondents (94.7%) were permanent residents
and five percent (5.3%) of the respondents were seasonal residents.

For Tuscola County, the average age was 58 years old. The highest earned level
of education was a high school degree (31.6%) or some college (26.7%).
A sizable proportion of respondents had household income of $50,000 or less (47.6%).
Only two percent (1.8%) of the residents were employed directly or indirectly in the
tourism industry. The residents lived or had an ownership in the county 37 years on
average. The majority of the respondents (88.6%) were permanent residents and eleven
percent (1 1.4%) of the respondents were seasonal residents.

In all counties, residents between the ages 19 through 39 were believed to be
underrepresented because they were not homeowners. This could have resulted in

relatively high levels of education and high annual income reported by respondents.

84

Table 12: Socio-Demographic Profile of Homeowners by County

 

 

 

 

 

Socio-Demographic Variables Emmet Saginaw Tuscola
Age n=318a n=194a n=219a
18 and under 0.0% 0.0% 0.0%
19-29 1.1 4.6 0.5
30-39 5.3 9.8 9.6
40-49 15.4 29.9 16.0
59-59 26.6 26.3 30.1
60-69 25.3 18.0 22.8
Over 69 26.3 1 1.3 21.0
Higher Education n=321 a n=203 a n=225 a
Less than high school 0.0 1.0 4.9
High school graduate 7.0 26.1 31.6
Technical school degree 3.1 8.9 7.1
Some college 13.7 21.2 26.7
College degree 37.9 28.1 20.0
Advanced degree 38.4 14.8 9.8
Income n=294 a n=l87 a n=212 3
Less than $49,999 15.2 43.3 47.6
$50,000-$99,999 34.3 41.7 39.6
$ 100,000 or more 50.5 15.0 12.7
Employment in Tourism Industry =315 a n=l99 a n=22 a
Employed 15.5 10.1 1.8
Not employed 84.5 89.9 98.2
Length of Residency or Home Ownership n=318 a n=204 a n=228 a
Under 10 years 24.6 11.3 11.4
10 years or more 75.4 88.7 88.6
Residential Status n=321 a n=206 “ n=228 a
Permanent resident 65.3 94.7 88.6
Seasonal resident 34.7 5.3 1 1.4

 

it IS the actual number of surveys received but statistics were weighted for population estimate.

Community Attachment

Community attachment was measured using two dimensions: social (measured

using four items) and environmental (measured using three items).

As shown in Table 13, Emmet County residents perceived the environmental

dimension of community attachment (mean score range from 4.28 through 4.61) to be

highly important. Emmet residents indicated the highest level of importance for

“environmental landscapes/views” (mean =4.6I) aspect. Saginaw County residents

85

perceived the social dimension of community attachment (mean score range from 3.29
through 4.30) to be highly important. Saginaw residents indicated the highest level of
importance for “family ties” (mean =4.30) aspect. Tuscola County residents perceived
both social (mean score range from 3.17 through 4.35) and environmental (mean score
range from 3.73 through 4.13) dimensions of community attachment to be important.
Tuscola residents indicated the highest level of importance for “family ties” (mean =4.35)
aspect in the social dimension and “presence of wildlife” (mean =4.13) in the

environmental dimension.

Table 13: Community Attachment by County

 

 

 

 

 

 

Emmet Sagi_naw Tuscola
Community Attachment Mfl Std. Dev. Mean Std. Dev. M1 Std. Dev.
Environmental Dimension n=315 n=203 n=222
Natural landscapes/views 4.61 a 071 3.59 1.23 3.73 1,20
Opponl‘mt‘es f“ °“‘d0°’ 4.43 0.85 3.81 1.14 3.97 1.04
recreation
Presence ofwildlife 4.28 0.93 3.68 1.18 4.13 1.00
Social Dimension n=3 1 3 n=205 n=225
Family ties 3.91 1.48 4.30 1.13 4.35 1.13
Friends close by 3.88 1.18 4.15 1.01 4.01 1.11
Local culture and traditions 3.83 1.04 3.29 1.16 3.38 1.15
Qppommnfs ‘0 be ‘nVF’lvid 3.55 1.17 3.41 1.18 3.17 1.17
in comqu or orgamzatrons

 

3 Scale ranged from “1=not important at all” to “5=very important.”

Level of Objective Knowledge of Tourism

Residents’ objective knowledge of tourism in their county was measured with a
question: “Which response best represents the percentage tourism and recreation bring to
your county’s economy?” Respondents were provided with five categories of percentages

(i.e., 0-20%, 21-40%, 41-60%, 61-80%, 81-100%) to select an answer from (Table 14).

86

This scale was designed to measure factual knowledge, that is, there was a correct answer

and respondent’s answers were scored correct or incorrect.

Table 14: Contribution of Tourism and Recreation to County’s4 Economy Used as the

Original Variable of Level of Objective Knowledge of Tourism by County

 

 

 

Emmet Saginaw Tuscola
Objective Knowledge (n=321) (n=206) (n=228)
% % %
0-20% 0.9 33.2 a 62.3 ‘
21-40% 14.2 a 45.7 27.8
41-60% 27.0 15.6 7.1
61-80% 45.9 5.0 2.8
81-100% 12.0 0.5 0.0

 

 

 

 

a . . . .
Percentage of respondents who provrded the correct answer about the actual contribution of tourism and
recreation to county’s economy.

This measurement was recoded to determine how many units away the respondents ’-
answer was from the right answer (the “target”). The four new response categories were:
“target” (the right answer), 1 unit away from the target, 2 units away from the target, and
3 units away from the target (Table 10). As shown in Table 15, Tuscola residents were
most knowledgeable and Emmet residents were least knowledgeable (they overestimated)
about the actual contribution of tourism and recreation to the county’s economy.

Table 15: Level of Objective Knowledge of Tourism by County Determined as Distance
from the “Target”

 

 

 

Emmet Saginaw Tuscola
Objective Knowledge Q=302) (n=l99) (n=212)
°/o % %
“Target” 14.2 33.2 62.3
1 unit away 27.9 45.7 27.8
2 units away 45.9 15.6 7.1
3 units away 12.0 5.5 2.8

 

 

 

 

 

4 Contribution of tourism and recreation to county’s economy (Emmet, 25%; Saginaw, 7%; Tuscola, under
20%) was estimated by Dr. Daniel Stynes, a researcher at MSU, using the Michigan Economic Impact
Model (MITEIM) in 2007.

87

Level of Subjective Knowledge of Tourism

Respondents were also asked to describe their perceived level of knowledge about
the tourism and recreation industry in their county. As shown in Table 16, Emmet
residents felt they were “somewhat knowledgeable” (mean=3.25), whereas Saginaw
residents (mean=2.63) and Tuscola residents felt they were “slightly knowledgeable”

about the tourism and recreation industry in their county.

Table 16: Level of Subjective Knowledge of Tourism by County

 

 

 

Emmet Saginaw Tuscola
(n=313) (n=205) (n=224)
Mean Std. Dev. Mean Std. Dev. Mean Std. Dev.
Subjective Knowledge 325 a 1.08 2.63 1.05 2.31 1.12

 

 

 

 

a Five—point scale wherel=not at all, 2=slightly, 3=somewhat, 4=moderately, 5=very knowledgeable.

Perceived Power

The two items measuring power (involvement in tourism development (TD)
decision making) included “personal influence on TD decision making” and
“involvement in TD.” As shown in Table 17, residents in all three counties under study
felt they had “very little” personal influence on tourism development decision making
[mean=1 .76 (Emmet); mean=1.39 (Saginaw); mean=l .47 (Tuscola)], as well as
minimally involved in tourism development in their county [mean=1.74 (Emmet);

mean=l .47 (Saginaw); mean=l .47 (Tuscola)].

88

Table 17: Perceived Power by County

 

 

 

Emmet Saginaw Tuscola
Power (n=31fl (n=20l) (n=225)
Mean Std. Dev. Mean Std. Dev. Mean Std. Dev.
Personal influence on TD decision 1.76 a 0.87 1.39 0.67 1.47 0.79
making
Involvement in TD 1.74 0.90 1.47 0.78 1.47 0.77

 

 

 

 

a
Scale ranged from “1=none” to “5=a lot.”

Perceived Economic Role of Tourism

Residents were asked to indicate how important a role they thought tourism and

recreation should have in their county compared to other economic sectors. One variable

measured economic role of tourism. As shown in Table 18, Emmet residents felt tourism

and recreation should have a dominant role (mean=3.53) in their county compared to

other economic sectors (such as agriculture, manufacturing, services, etc.). Saginaw

residents (mean=2.95) and Tuscola residents (mean=2.86) felt tourism and recreation

should have a role equal to other economic sectors in their county.

Table 18: Perceived Economic Role of Tourism by County

 

 

 

Emmet Saginaw Tuscola
(n=301) (n=20m (n=212)
Mean Std. Dev. Mean Std. Dev. Mean Std. Dev.
Economic Role of Tourism 353 a 0.53 2.95 0.61 2.86 0.65

 

 

 

 

a F our-point scale where 1=no role, 2=minor, 3=equal, 4=dominant was used.

Personal Benefits from Tourism

Two variables measured residents’ benefits from tourism: “How much do you

personally benefit from tourism in your county?” and “I would personally benefit from

more tourism.” As show in Table 19, Emmet residents perceived “some” benefits from

89

tourism (mean=2.64) whereas Saginaw residents (mean=1.92) and Tuscola (mean=1.60)
perceived “very little” benefit from current tourism industry in their county. Residents in
all three counties under study felt they would receive “some” benefits from additional
tourism activities in their counties [mean=2.73 (Emmet); mean=2.75 (Saginaw);

mean=2.59 (Tuscola)].

Table 19: Personal Benefits from Tourism by County

 

 

 

Emmet Saginaw Tuscola
Personal Benefits from Tourism (n=307) (n=196) (n=223)
Mean Std. Dev. Mean Std. Dev. Mean Std. Dev.
Personal benefit from current 2.64 a 1.28 1.92 0.95 1.60 0.81
tourism
Personal benefit from more tourism 2.73 b 1.21 2.75 1.10 2, 59 1.06

 

 

 

 

a Scale ranged from “1=no at all” to “5=a lot.”

Scale ranged from “1=strongly disagree” to “5=strong1y agree.”

Positive Impacts of Tourism

Twelve indicator variables were used to examine residents’ perceptions of
positive impacts of tourism. As shown in Table 20, residents in all three counties under
study reported the highest level of agreement with the statement “increasing the number
of tourists visiting an area improves the local economy” [mean=4.17 (Emmet);
mean=4.06 (Saginaw); mean=4.08 (Tuscola)]. In addition, Emmet residents also agreed
the most with the statement “shopping, restaurants, entertainment options are better as a

result of tourism” (mean=4.17).

90

Table 20: Positive Impacts of Tourism by County

 

 

 

Emmet Saginaw Tuscola

Positive Impacts of Tourism n=315 E97) (n=225)

M211 Std. Dev. M_eap Std. Dev. Mean Std. Dev.
Increasing the number of tourists
visiting an area improves the local 4.17 a 0.83 4.06 0.75 4.08 0.75
economy
Shopping, restaurants, entertainment
options are better as a result of 4.17 .75 3.96 0.80 3.93 0.82
tourism
Tourism encourages more public
development (e.g., roads, public 3.98 0.78 3.91 0.76 3.89 0.81
facilities)
mm“ °°”.‘“.b“‘es ‘0 “mm and 3.89 0.83 3.63 0.88 3.69 0.87
standard of livmg
Tourism provrdes desuable jobs for 3.80 0.96 3.73 0.92 3.79 0.82
ocal homeowners
Tounsm provrdes incentives for new 3.79 0.82 3.70 0.80 3.79 0.77
park development
Tourism development increases the
number of recreational opportunities 3.73 0.82 3.78 0.80 3.68 0.82
for local homeowners
Tourism provides incentives for
protection and conservation of 3.63 0.93 3.47 0.90 3.51 0.94
natural resources
Tourism provrdes incentives for 3.59 0.92 3.51 0.7 6 3.55 . 0.81
purchase of open space
Tourism helps preserve the cultural
identity and restoration of historical 3.48 0.89 3.78 0.76 3.57 0.88
buildings
””1““ devebpmem ‘mpmves the 3.41 1.03 3.84 0.80 3.68 0.87
physrcal appearance of an area
Tourism development increases the 3.31 0.94 344 0.85 3.26 0.88

uali of life in an area

 

 

 

 

a Scale ranged from “l=strongly disagree" to “5=strongly agree.”

Negative Impacts of Tourism

Eight indicator variables were used to examine residents’ perceptions of negative

impacts of tourism. As shown in Table 21, residents in all counties under study seemed to

be mostly concerned about the following statements: “tourism development increases the

traffic problems of an area” [mean=4.43 (Emmet); mean=3.79 (Saginaw); mean=3.84

(Tuscola)], and “tourism related jobs are low paying” [mean=3.67 (Emmet); mean=3.59

(Saginaw); mean=3.55(Tuscola)]. Emmet residents (mean=3.79) and Tuscola residents

91

(mean=3.60) were also concerned about tourism resulting in more litter in the area. In
addition, Emmet residents were concerned about tourism increasing the cost of living

(mean=3.76).

Table 21: Negative Impacts of Tourism by County

 

 

 

Emmet Saginaw Tuscola

Negative Impacts of Tourism (n=314) (n=197) (n=225)
Mean Std. Dev. Mean Std. Dev. Mean Std. Dev.

Tourism development increases the a
traffic problems 0 fan area 4.43 0.71 3.79 0.85 3.84 0.81
Iggnsm results in more litter in an 3.79 0.91 3.34 0.96 3.60 0.95
“mm. “ism“ “‘ a“ ”crease “the 3.76 0.88 3.19 0.83 3.33 0.83
cost of lrvrng
Tourism related jobs are low paying 3.67 0.89 3.59 0.84 3.55 0.81
Tourism causes communities to be 3.36 0.95 2.68 0.87 2.8 6 0.89
over-crowded
Tour‘s” deve'OPme‘“ ““3”” 3.35 1.06 3.05 0.86 3.38 0.94
increases property taxes
“mm “Eminent ‘ncreases the 3.25 0.98 2.82 0.89 3.07 0.81
amount of crime 1n the area
An increase in tourists in the county
will lead to friction between 3.00 0.98 2.49 0.86 2.86 0.92
homeowners and tourists

 

 

 

 

 

 

 

8 Scale ranged from “l=strongly disagree” to “5=strongly agree.”

Support for Future Tourism Development

Four indicator variables were used to evaluate residents’ support for additional
tourism development. As shown in Table 22, residents in all three counties under study
reported a high level of support for additional tourism development in their county.
Emmet residents indicated the highest level of agreement with “tourism can be one of the
most important economic developmental option for an area” statement (mean=3.95).
Saginaw residents (mean=3.83) and Tuscola residents (mean=3.68) agreed the most with

the statement “the county should attract more tourists.”

92

Table 22: Support for Future Tourism Development by County

 

Support for Future Tourism
Development

 

 

Tourism can be one of the most
important economic developmental
option for an area

The county should try to attract more
tourists

Additional tourism would help the
county grow in the right direction

I support tourism having a vital role
in this county

 

Emmet Saginaw Tuscola
(n=313) (n=197) (n=225)
Mean Std. Dev. Mean Std. Dev. Mean Std. Dev.

3,95 3 0.84 3.53 0.90 3.50 0.96
3.53 0.92 3.83 0.84 3.68 0.93
3.47 0.94 3.74 0.93 3.61 0.93
3.79 0.86 3.76 0.87 3.60 0.98

 

 

 

 

 

 

a Scale ranged from “l=strongly disagree” to “5=strongly agree.”

Support for Restrictions on Future Tourism Development

One indicator variable was used to assess residents’ support for restrictions on

future tourism development. As shown in Table 23, Saginaw residents (mean=2.36) and

Tuscola residents (mean=2.37) felt that local government should not restrict tourism.

Emmet residents (mean=2.62) reported a neutral opinion about restrictions on tourism

development.

Table 23: Support for Restrictions on Future Tourism Development by County

 

 

 

Emmet Saginaw Tuscola
Restrictions on Future Tourism (n=309) (n=194) (n=225)
Development Mean Std. Dev. Mean Std. Dev. Mean Std. Dev.
Local government should restrict 2.62 a 1.00 2.36 0.95 237 0.94

future tourism development

 

 

 

 

a Scale ranged from “l=strongly disagree” to “5=strongly agree.”

Perceived Community Future

One indicator variable was used to examine residents’ opinions about their

community’s future. As shown in Table 24, Emmet residents (mean=3.34) were more

93

likely to agree with “the future of my county looks bright” statement than Tuscola

(mean=2.53) and Saginaw (mean=2.19) residents.

Table 24: Perceived Community Future by County

 

 

 

 

 

 

Emmet Saginaw Tuscola
Community Future (n=311) (n=205) (n=219)
Mean Std. Dev. Mean Std. Dev. Mean Std. Dev.
The future of my county looks bright 3.34 a 0-93 2.19 0.94 2.53 1.02

 

a Scale ranged from “l=strongly disagree” to “5=strongly agree.”

Testing of the Study Hypotheses

The Perdue, Long, and Allen (1990) model utilized regression analysis to

examine relationships among the model variables. To extend the model, four community

characteristics variables were added to the conceptual model and tested as predictors of

residents’ perceptions of tourism development: community attachment, level of

knowledge, power, and economic role of tourism (Figure 5). The selection of these

independent variables was based on suggestions by a number of researchers (Gursoy et

al., 2002; Gursoy & Rutherford, 2004; Jurowski et al., 1997; Madrigal, 1993; McCool &

Martin, 1994; Huh & Vogt, 2008). In keeping with the Perdue et al. (1990) conceptual

model, this study also employed a series of multiple regression analysis (see Chapter 3,

Table 11 for a detailed description of regression models tested in the study) to explore the

relationships among the extended conceptual model variables.

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Regression Model 1: Hypothesis 1

H1: Level of objective and subjective knowledge, power, economic role of
tourism, community attachment are positively related to personal benefits
from tourism.

To examine the nature and the strength of the relationships among regression

model 1 variables, Pearson correlation tests were conducted for each county. Table 25
shows correlations between the independent variables and personal benefits from tourism
variable (DV) by county. Significant correlations were found between the independent
variables and personal benefits in all three counties with three exceptions. Objective
knowledge and personal benefits from tourism were not correlated in Emmet and
Saginaw counties. In addition, attachment was uncorrelated with personal benefits from
tourism in Tuscola County.

Correlations between independent variables ranged from 0.001 to 0.64. High
correlations between social attachment and environmental attachment (r—=0.37 [Emmet],
r=0.64 [Saginaw], r=0.50 [Tuscola]) suggested possible multicollinearity between
predictors. Follow up collinearity diagnostics test revealed low tolerance level (<l-R")
which confirmed multicollinearity existed between the two predictors. As a result, the
social attachment and environmental attachment variables were combined into one
variable, attachment. The high correlations between social attachment and environmental

attachment found in this study are inconsistent with correlations (r=.021) reported by

Brehm et al. (2004).

96

Table 25: Correlations, Means, and Standard Deviations among Regression Model 1
Variables by County

 

Emmet County

 

1. Objective
Knowledge a

 

2. Subjective
Knowledge b

—.225**

 

b9
3. Power c

-.095

.424**

 

4. Economic Role a

-.247**

.093

.051

 

5. Social
Attachment

,C

-.137*

.222**

.206**

.056

 

6. Environmental
b, c
Attachment

-.202**

J65**

.054

.103

.371**

 

7. Benefits

. b c
From Tourism

-.085

.313**

.302**

J75**

.146*

.137*

 

Mean

3.50d

15.18e

13.34f

5.36d

 

Std. Dev.

 

 

 

1.68

 

 

3.54

 

1.94

 

2.20

 

 

Saginaw County

 

1. Objective
Knowledge 3

 

2. Subjective
Knowledge b

-.112

 

13,
3. Power C

-.149*

.233**

 

4. Economic Role a

-.325**

.240**

.129*

 

5. Social
b, c
Attachment

.001

.015

.026

.231**

 

6. Environmental
b, c
Attachment

.030

-.015

-.003

.209*

.642**

 

7. Benefits
. b
From Tourrsm c

-.118

.254**

.331**

.340**

.201**

.065

 

Mean

2.86d

15.13"

14.79f

4.68 d

 

Std. Dev.

 

 

 

1.37

 

 

3.44

 

3.99

 

1.80

 

 

Tuscola County

 

1. Objective
Knowledge a

 

2. Subjective
Knowledge b

-.275**

 

,C

3. Power

-187**

.279**

 

4. Economic Role a

—.304**

.010

.171*

 

 

5. Social b
Attachment ’ c

 

-.063

 

.119

 

.106

 

.124

 

 

 

 

97

 

Continued Table 25: Correlations, Means, and Standard Deviations among Regression
Model 1 Variables by County

 

 

 

 

 

 

 

 

 

 

 

 

 

 

e- ATuscolaiCounty 1 2 3 4 _5 _._ _ ,, 6 7
6. Environmental
Attachment b‘ c -.104 .014 .027 .198* .504** 1
7. Benefits
From Tourism " -.204** .265** .291 ** 308* .141 * -.005 1
Mean - _ 2.94d - 14.93" 15.79f 4.18a
Std. Dev. — — 1.50 _ 3.19 3.37 1.56

 

 

a Ordinal data, b Interval data, C Refer to chapter 3 for reliability scores. Summed scale ranged from 2-10d
4-20 e, and 3-15 f. *Correlations is significant at the 0.05 level, ** 0.01 level.

5

Simultaneous multiple regression was conducted to test the relationships between
objective knowledge of tourism, subjective knowledge of tourism, power, economic role
of tourism, and community attachment; and personal benefits from tourism (Table 26).

In Emmet County, regression model 1 significantly predicted personal benefits
from tourism, F (5,279) = 10.29,p < 0.001, R2 =.14; with subjective knowledge of
tourism (B=.207, t=3.2, p <0.05), power (B=.200, t=3.2, p <0.05), and economic role of
tourism (B=.146, t=2.6, p <0.05), statistically contributing to the prediction. The more
knowledgeable respondents felt about the tourism and recreation industry in their county,
the more they perceived personal benefits from tourism. The more power respondents felt
they had in tourism development decision making, the more personal benefits they
perceived from tourism. The more respondents believed tourism should have a dominant
role in the county’s economy, the more personal benefits they perceived from tourism.

In Saginaw County, regression model 1 significantly predicted personal benefits
from tourism, F (5,185) = 10.46, p < 0.001, R2 =.20; with power ([3=.272, t=4.0, p
<0.001), and economic role of tourism (B=.263, t=3.6, p <0.001), statistically

contributing to the prediction. The more power respondents felt they had in tourism

98

development decision making, the more they perceived personal benefits fi'om tourism.
The more respondents felt tourism should be a dominant development strategy, the more
they perceived personal benefits from tourism.

In Tuscola County, regression model 1 significantly predicted personal benefits
from tourism, F (5,192) = 6.87, p < 0.001, R2 =.13; with subjective knowledge of tourism

(B=.187, t=2.4, p <0.05), power (13:. 148, t=2.l, p <0.05), and economic role of tourism
(0:232, t=3.2, p <0.05), statistically contributing to the prediction. The more
knowledgeable respondents felt about the tourism and recreation industry in their county,
the more they felt they personally benefited from tourism. The more power respondents
believed they had in tourism development decision making, the more personal benefits
they perceived from tourism. The more respondents felt tourism should play a major
economic role in their county, the more personal benefits they perceived from tourism.

A positive significant relationship was found between subjective knowledge and
personal benefits from tourism in Emmet County and Tuscola County. Significant
relationship in a positive direction was found between power and personal benefits from
tourism, and the economic role of tourism and personal benefits from tourism in all three
counties. Insignificant relationship was found between objective knowledge and personal
benefits, and community attachment and personal benefits in all three counties. As a

result, hypothesis 1 was partially supported by the data from all three counties.

99

Table 26: Regression Analysis of Model 1 Relationships between Attachment, Objective
and Subjective Knowledge, Power, Economic Role and Personal Benefits from Tourism

 

 

 

 

 

 

 

 

 

 

 

 

 

M d 11 Emmet Saginaw Tuscola
° ° (n=279)a (n=185)a (n=192)a
Model Statistics 13 t p B t p B t p
Objective
Knowledge .007 0.1 ms .014 0.2 us -.058 -0.8 ns
sublecm’e .207 3.2 <.05 .108 1.6 ns .178 2.4 <.05
knowledge
Power .200 3.2 <.05 .272 4.0 <.001 .148 2.1 <.05
Economic role .146 2.6 <.05 .263 3.6 <.001 .232 3.2 <.05
Attachment .042 0.7 ns .1 l l 1.6 us .022 0.3 ns
F= 10.29,p <.001, F = 10.46,p <.001 F= 6.87,p <.001
Adjusted R2 =.14 Adjusted R2 :20 Adjusted R2 = .13

 

a . . . .
n mdrcates the total number of cases used m the analysrs

Note: Initially, the relationship between residents’ characteristics and personal benefits was tested and was
found to be insignificant.

Regression Models 2 and 3 .' Hypotheses 2 and 3

Simultaneous multiple regression was used to test the relationships between
residents’ characteristics, personal benefits from tourism and the positive and negative
impacts of tourism.

H2: When controlling for personal benefits from tourism, there is a positive
(negative) relationship between residents’ characteristics and residents’
positive (negative) tourism impact perceptions.

H3: Personal benefits from tourism will moderate the relationship between
residents’ characteristics and residents’ positive (negative) tourism impact

perceptions.
Pearson correlation tests were conducted for each county to examine the nature
and the strength of the relationships among regression models 2 and 3 variables. Table 27

shows correlations between the independent variables and positive (negative) impacts of

100

tourism variables (DV) by county. In Emmet County positive significant correlations
were found between age and positive impacts, and between education and positive
impacts. Next, in Saginaw County positive significant correlations were found between
education and positive impacts, and between education and negative. In Tuscola County,
income and positive impacts were positively significantly correlated. Positive (negative)
significant correlations were found between personal benefits from tourism and positive
(negative) impacts of tourism in all three counties. Correlations between independent

variables ranged from 0.02 to 0.38 (Table 27).

101

Table 27: Correlations, Means, and Standard Deviations among Regression Models 2 and

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

3 Variables by County
__Emmet County 1 2 3 4 5 6

1. Age a l

2. Education F -.081* * 1

3. Income —.263* * .323** 1

4. Benefit from Tourism 3' c -.320* * -,024 .028 1

5. Positive Impacts 3’ ° 094* .133** -.024 .283** 1

6. Negative Impacts a’ c -.025 -.061 -.090 4228* * —.351*"‘ 1

Me“ 60.25 - - 5.36 d 45.03 e 28.64 f

Std. Dev. 13.21 - — 2.20 6.96 4.48
Saginaw County I 2 3 4 5 6

1. Age a 1 1

2. Educationb -.255* 1

3. Income -.289** .349** 1

4. Benefit from Tourism 3’ ° -.124 .212** .188* 1

5. Positive Impacts 3' c -.103 .148“* .027 .474* 1

6. Negative Impacts 8’ c ,127 -.198** -,143 -.406** -.333** 1

Me“ 52.59 - - 4.68 d 44.76 e 24.96 f

Std. Dev. 13.61 — — 1.80 6.60 4.32
Tuscola County 1 2 3 4 5 6

1. Age 3 1

2. Educationb —.227** 1

3. Income -.260** .384** 1

4. Benefit from Tourism a‘ ° .018 0.056 -.045 1

5. Positive Impacts a: c -.052 -022 .136* .377** 1

6. Negative Impacts 3' c -.049 -.018 -.105 -.l97** -.l73""'l I

Mean 57.79 - - 4.18 d 44.40e 26.56 f

Std. Dev. 13.29 - - 1.56 7.32 4.24

 

 

 

a Interval data, b Ordinal data, (

Regression model 2 was used to test the relationships between residents’

Refer to chapter 3 for reliability scores. Summed scale ranged from 2-10 ,
12-60 e, and 8-40 f. *Correlations is significant at the 0.05 level, ** 0.01 level.

characteristics and the positive impacts of tourism, while controlling for personal benefits

from tourism. First, second order effects (interactions between independent variables)

102

 

were tested. Next, second order effects which were found to be insignificant were
removed from the model, and the main effects were retested (Table 28).

In the case of Emmet County, regression model 2 significantly predicted
perceptions of positive impacts of tourism, F (5,280) = 9.32, p < 0.001, R2 = .13; with age
(13:.144, t=2.3, p <0.05), education (13:.168, t=2.9, p <0.05), personal benefits from
tourism (13:.329, t=5.6, p <0.001), and a two-way interaction between age and personal
benefits (B=.130, t=2.2, p <0.05) statistically contributing to the prediction. The older the
residents, the more they agreed with statements regarding positive impacts of tourism.
Respondents with higher levels of education agreed more with tourism positive impacts.
The more respondents benefited from tourism, the more they perceived tourism to have
positive impacts. Older residents who benefited more from tourism perceived positive
impacts of tourism more than younger residents who benefited more from tourism
(Figure 6).

Regression model 2 significantly predicted perceptions of tourism positive
impacts in Saginaw County, F (5,173) = 9.52, p < 0.001, R2 = .20; with personal benefits
from tourism (13:.452, t=6.0, p <0.001) having a positive significant relationship with
tourism positive impacts.

1n the case of Tuscola County, regression model 2 significantly predicted
perceptions of positive impacts of tourism, F (5,198) = 8.13, p < 0.001, R2 = .15; with
education (B=-.164, r=-2.3, p <0.05), income (B=.211, t=2.9, p <0.05), and personal
benefits from tourism (0:.375, t=5.7, p <0.001) statistically contributing to the

prediction. Respondents with lower levels of education agreed more with statements

regarding positive impacts of tourism. Residents with a higher annual income were more

103

likely to perceive positive impacts of tourism. The more respondents benefited from

tourism, the more they agreed with tourism positive impacts.

Table 28: Regression Analysis of Model 2 Relationships between Residents
Characteristics and Positive Impacts from Tourism Controlling for Personal Benefits

Model 2

<
<

<

A *P 1
go ersona 22 <_05 -0,011 -0.1 ns 0.028

p< = p =
R2-13 R2=20

n indicates the total number of cases used in the analysis

 

Figure 6: Two—Way Interaction between Age and Personal Benefits Predicting
Positive Impacts of Tourism in Emmet County

 

 

 

 

 

 

 

 

 

 

5
4 a
‘3
a 3 —
15 —O——-Younger
2 Residents
IE!
8 2 ‘ -------01der
9' Residents
1 s
0 .
Low Personal Benefits High Personal Benefits

 

 

 

104

Regression model 3 was used to test the relationships between residents’
characteristics and the negative impacts of tourism while controlling for personal benefits
from tourism. First, second order effects (interactions) were tested. Next, second order
effects which were found insignificant were removed from the model and the main
effects were retested (Table 29).

In the case of Emmet County, regression model 3 significantly predicted
perceptions of negative impacts of tourism, F (5,280) = 5.43, p < 0.001, R2 = .07; with
personal benefits from tourism (13:-.246, t=-4.0, p <0.001), and a two-way interaction
between age and personal benefits (13:-.146, t=-2.4, p <0.05) statistically contributing to
the prediction. The less respondents benefited from tourism, the more they agreed with
negative impacts of tourism. Older residents who benefited more from tourism perceived
negative impacts of tourism less than younger residents who benefited more from tourism
(Figure 7).

Regression model 3 significantly predicted perceptions of tourism negative

impacts in Saginaw County, F (5,173) = 7.83, p < 0.001, R2 = .17; with personal benefits

from tourism (B=-.361, t=-4.7, p <0.001) having a negative significant relationship with
tourism negative impacts.

In the case of Tuscola County, regression model 3 significantly predicted
perceptions of negative impacts of tourism, F (5,198) = 3.31, p < 0.05, R2 = .06; with
personal benefits from tourism (13:-.142, t=-2.0, p <0.05), and a two-way interaction
between age and personal benefits (13:.195, t=2.7, p <0.05) statistically contributing to
the prediction. The less respondents benefited from tourism, the more they agreed with

the negative impacts of tourism. Younger residents who perceived lower benefits from

105

tourism agreed more with negative impacts of tourism than older residents who perceived

lower benefits from tourism (Figure 8).

Table 29: Regression Analysis of Model 3 Relationship between Residents
Characteristics and Negative Impacts from Tourism Controlling for Personal Benefits

Emmet
I

Model 3

< <

Age’II Personal
-2.4 <.05 0.023 0.3 0.195 2.7 <.05

p< = P = P
R’=.07 . R2=.06

 

11 indicates the total number of cases used in the analysis

Figure 7: Two-Way Interaction between Age and Personal Benefits Predicting Negative
Impacts of Tourism in Emmet County

 

 

 

 

 

 

 

 

 

5

4 s
8 -------- 1
a 3 ~
5 + Younger
E Residents
$02 . ---I---Older
2 Residents

l 4

0 1

Low Personal Benefits High Personal Benefits

 

 

 

106

Figure 8: Two-Way Interaction between Age and Personal Benefits Predicting
Negative Impacts of Tourism in Tuscola County

 

 

 

 

   
 
 

  

 

 

 

5 _ , _ _.V 7,,
4 _
M
‘5'
s 3 -
E + Younger
2 Residents
g 2 ---l---Older
2 Residents
1 _
0 1
Low Personal Benefits High Personal Benefits

 

 

 

Positive significant relationships were found between age and positive impacts of
tourism in Emmet County. Positive significant relationships were found between
education and positive impacts in Emmet and Tuscola counties, and between income and
positive impacts of tourism in Tuscola County. As a result, hypothesis 2 was partially
supported by results from Emmet and Tuscola counties and not supported in Saginaw
County.

Personal benefits from tourism were found to moderate the relationship between
age and residents’ positive tourism impact perceptions in Emmet County and between
age and residents’ negative tourism impact perceptions in Emmet and Tuscola counties.
As a result, hypothesis 3 was partially supported by results from Emmet and Tuscola

counties and not supported in Saginaw County.

107

Regression Models 4 and 5 .' Hypotheses 4, 5 and 6

H4: Level of objective and subjective knowledge of the tourism industry,
power, perceived economic role of tourism, and community attachment
are positively related to perceived positive impacts of tourism and
negatively related to perceived negative impacts of tourism when
controlling for personal benefits from tourism.

H5: Personal benefits from tourism will moderate the relationships between
level of objective and subjective knowledge of the tourism industry,
power, perceived economic role of tourism, and community attachment
and residents’ positive (negative) tourism impact perceptions.

H6: There is a positive (negative) relationship between personal benefits from

tourism and positive (negative) perceived impacts of tourism.

Pearson correlation tests were conducted for each county to examine the nature
and the strength of the relationships among models 4 and 5 variables. Table 30 shows
correlations between the independent variables and positive (negative) impacts of tourism
variables (DV) by county. Positive significant correlations were found between objective
knowledge and positive impacts, and between subjective knowledge and positive impacts
in Emmet County. Power and positive impacts were positively significantly correlated in
Tuscola County. Positive significant correlations were found between economic role and
positive impacts, and personal benefits and positive impacts in all three counties.
Negative significant correlations were found between subjective knowledge and negative

impacts in Emmet and Saginaw counties. Power and negative impacts were negatively

108

significantly correlated in Saginaw County. Negative significant correlations were found

between the economic role of tourism and negative impacts in Emmet and Saginaw

counties, and between personal benefits from tourism and negative impacts in all three

counties. Correlations between independent variables ranged from 0.001 to 0.42 (Table

30).

Table 30: Correlations, Means, and Standard Deviations among Regression Models 5 and

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

6 Variables by County
Benefits
Emmet Objective Subjective Economic from
County Knowledge a Knowledge a Power a Role 3 Attachment a Tourism 3
Positive
Impacts a' b —.123* .164** .061 .408** .144** .283**
Negative
Impacts a’ .089 -.167** -.072 -.337** .027 -.228**
Mean - - 3.50c - 28.53d 5.36 c
Std. Dev. - - 1.68 - 4.66 2.20
Benefits
Emmet Objective Subjective Economic from
County Knowligeia Knowledge a Power a Role 8 Attachment a Tourism a
Positive
Impacts 2” b .021 .131 .103 .451** .289** .474"
Negative
Impacts 3’ -037 -.259** -.144** —.175** -.029 -.405*
Mean _ - 286° - 26.17 4.68 c
Std. Dev. _ _ 1.37 - 5.90 d 1.80
Benefits
Emmet Objective Subjective Economic from
County Knowledge 8 Knowledge a Power a Role 3 Attachment 8 Tourism a
Positive
Impacts 3’ b -.125 .075 .168* .423** .298** .377**
Negative
Impacts 3" -.052 .003 -.042 -.105 .060 -.197"‘*
Mean - - 2.94c - 26.80d 418°
Std. Dev. - - 1.50 — 4.97 1.56

 

 

 

 

 

 

 

a
Interval data,

 

Refer to chapter 3 for reliability scores. Summed scale ranged from 2—10 G, 7-35 d.

*Correlations is significant at the 0.05 level, ** 0.01 level.

109

 

Regression model 4 was used to test the relationships between objective
knowledge of tourism, subjective knowledge of tourism, power, economic role of
tourism, and attachment; and the positive impacts of tourism while controlling for
personal benefits from tourism. First, second order effects (interactions between
independent variables) were tested. Next, second order effects which were found
insignificant were removed from the model and the main effects were retested (Table 31).

In the case of Emmet County, regression model 4 significantly predicted
perceptions of positive impacts of tourism, F (7,279) = 12.74, p < 0.001, R2 = .23; with
economic role of tourism (B=.371, t=6.7, p <0.001), personal benefits from tourism
(B=.191, t=3.3, p <0.05), and a two-way interaction between economic role of tourism
and personal benefits (13:-.179, r=-3.3, p <0.05) statistically contributing to the
prediction (Figure 9).

Regression model 4 significantly predicted perceptions of tourism positive
impacts in Saginaw County, F (7,185) = 15.49, p < 0.001), R2 = .35; with objective
knowledge (13:.176, t=2.8, p <0.05), with economic role of tourism (B=.289, t=4.0, p
<0.001), attachment (B=.123, t=2.0, p <0.05), personal benefits from tourism (0:383,
t=5.7, p <0.001), and a two-way interaction between economic role of tourism and
personal benefits (13:-.167, t=-2.6, p <0.05) statistically contributing to the prediction
(Figure 10).

In the case of Tuscola County, regression model 4 significantly predicted
perceptions of positive impacts of tourism, F (7,192) = 13.92, p < 0.001, R2 = .32; with
economic role of tourism (B=.240, r—=3.0, p <0.05), attachment (B=.218, t=3.6, p <0.001),

personal benefits from tourism (13:.29, t=4.6, p <0.001), and a two-way interaction

110

between economic role of tourism and personal benefits (13:-.201, t=-2.9, p <0.05)
statistically contributing to the prediction (Figure 11).

The more respondents benefited from tourism, the more they perceived tourism to
have positive impacts in all three counties. Next, respondents who believed tourism
should have a dominant economic role in their county agreed more with positive impacts
of tourism in all three counties. Residents who were more attached to their communities
in Saginaw and Tuscola counties agreed more with positive impacts of tourism. The more
accurate respondents’ knowledge about tourism contribution to Saginaw County’s local
economy, the more respondents agreed with statements about tourism positive impacts.
Lastly, residents who believed tourism should have a dominant economic role in their
county and perceived less benefits from tourism agreed more with positive impacts of
tourism than residents who believed tourism should have no/minor role in their county

and perceived less benefits from tourism in all three counties.

Table 31: Regression Analysis of Model 4 Relationship between Objective and
Subjective Knowledge, Power, Economic Role, and Personal Benefits on Positive
Impacts from Tourism

 

 

 

 

 

 

 

 

 

 

 

 

 

 

M d 14 Emmet Saginaw Tuscola
° ° (n=279)a (n=185)a (n=192)’l
Model Statistics 13 t J B t p B t p
01’1““ 0.035 0.6 ns 0.176 2.8 <.05 0.041 0.6 ns
knowledge
SUbJecm’e 0.101 1.6 ns —0.023 -04 ns 0.026 0.4 ns
knowledge
Power -0.003 -0.1 ns -0.025 -0.4 ns -0.015 -0.2 as
Economic role 0.371 6.7 <.001 0.289 4.0 <.001 0.240 3.2 <.05
Attachment 0.034 0.6 ns 0.123 2.0 <.05 0.218 3.6 <.001
Personal benefits 0.191 3.3 <.05 0.383 5.7 <.001 0.295 4.6 <.001
- 111
E°°n°m'° 1°"? -0179 -33 <.05 -O.l67 -2.6 <.05 -0201 -29 <.05
Personal benefits
F= 12.74, p <.001, F= 15.49, p <.001, F= 13.92, p <.001,
Adjusted 112 :23 Adjusted R2 =35 Adjusted R2= .32

 

a . . . .
n Indicates the total number of cases used In the analysrs

111

 

Figure 9: Two-Way Interaction between the Economic Role of Tourism and Personal
Benefits Predicting Positive Impacts of Tourism in Emmet County

 

 

 

 

 

 

 

 

 

 

5

4 — . ............................... .
m / —O—No/minor
E 3 a economic
a. role
5 ----l-- Major
€- economic
E 2 H role
Art

1 a

0 I

Low Personal Benefits High Personal Benefits

 

Figure 10: Two-Way Interaction between the Economic Role of Tourism and Personal
Benefits Predicting Positive Impacts of Tourism in Saginaw County

 

 

 

 

 

 

 

 

 

5

4 a
m —O—No/minor
E 3 economic
E i role
2 ---l---Major
I": 2 _ economrc
g role
A11

1 _

0 I

 

 

Low Personal Benefits High Personal Benefits

 

112

 

 

Figure 11: Two-Way Interaction between the Economic Role of Tourism and Personal
Benefits Predicting Positive Impacts of Tourism in Tuscola County

 

 

 

 

 

 

 

u + No/minor
3 economic
W 3
E- role
0 ------l Major
‘E economic
'g 2 A role
A-

1 r

0

 

 

 

Low Personal Benefits High Personal Benefits

 

 

 

Regression model 5 was used to test the relationships between objective
knowledge of tourism, subjective knowledge of tourism, power, economic role of
tourism, and attachment; and the negative impacts of tourism while controlling for
personal benefits fiom tourism. First, second order effects (interactions between
independent variables) were tested. Next, second order effects which were found
insignificant were removed from the model and the main effects were retested (Table 32).

In the case of Emmet County, regression model 5 significantly predicted
perceptions of negative impacts of tourism, F (6,279) = 8.11, p < 0.001, R2 = .13; with
economic role of tourism (B=-.294, t=-5. l , p <0.001), and personal benefits from tourism

(B=-. 184, t=-3.0, p <0.05) statistically contributing to the prediction.

113

Regression model 5 significantly predicted perceptions of tourism negative
impacts in Saginaw County, F (6,185) = 6.60, p < 0.001), R2 = .15; with personal benefits
from tourism ([3=-.339, t=-4.4, p <0.001) being the only significant predictor.

Regression model 5 significantly predicted perceptions of tourism negative
impacts in Tuscola County, F (6,192) = 2.20, p < 0.05), R2 = .04; with personal benefits
from tourism (B=-.161, r=-2. 1 , p <0.05) statistically contributing to the prediction.

The less respondents benefited from tourism in all three counties, the more they
perceived tourism to have negative impacts. Emmet County residents who felt that
tourism should have no or a minor economic role in their county, agreed more with

negative impacts of tourism.

Table 32: Regression Analysis of Model 5 Relationship between Objective and
Subjective Knowledge, Power, Economic Role, and Personal Benefits on Negative
Impacts from Tourism

 

 

 

 

 

 

 

 

 

 

 

 

 

M d I 5 Emmet Saginaw Tuscola
° 9 (n=279)‘ (n=185)‘ (n=192)a
Model Statistics 0 t p B t p 13 t p
Object‘ve 0.014 0.2 ns -0.1 1 1 -1 .6 ns -0135 -1.7 ns
knowledge
sublecm’e -0077 -12 ns -O.138 -1.9 ns 0.039 0.5 ns
knowledge
Power 0.040 0.6 ns -0.004 -0.1 as -0.089 -1 .2 as
Economic role O-.294 -5.l <.001 -0.083 -1 .1 ns -0.l 11 -l .4 ns
Attachment .083 l .4 ns 0.047 0.7 ns 0.070 1.0 ns
Personal benefits -0.184 -30 <.05 -0.339 4.4 <.001 -0. 161 —2.1 <.05
F=8.ll,p<.001, F=6.60,p<.001, F=2.20,p <05,
Adjusted R2 =. 13 Adjusted R2 =. 15 Adjusted R2 = .04

 

 

a n indicates the total number of cases used in the analysis

A positive significant relationship was found between objective knowledge and
positive impacts of tourism in Saginaw County only. An insignificant relationship was

found between objective knowledge and negative impacts in all three counties. Next, no

114

significant relationships were found between subjective knowledge and positive
(negative) impacts, and power and positive (negative) impacts in all counties. Significant
positive (negative) relationships were found between economic role of tourism and
positive (negative) impacts in Emmet County. In addition, economic role predicted
positive impacts but failed to predict negative impacts in Saginaw and Tuscola counties.
Attachment had a statistically significant relationship in a positive direction with positive
impacts of tourism in Saginaw and Tuscola counties but did not predict positive impacts
in Emmet County and negative impacts in all three counties. As a result, hypothesis 4
was partially supported by the data in all three counties. Personal benefits from tourism
predicted positive and negative impacts in all three counties. Therefore, hypothesis 6 was

supported by the data from homeowners in all counties.

Regression Models 6, 7 and 8: Hypotheses 7, 8 and 9

H7: Differences will be found among personal benefits from tourism and
perceptions of positive (negative) impacts of tourism regarding their
ability to predict support for future tourism development.

H8: Differences will be found among personal benefits from tourism and
perceptions of positive (negative) impacts of tourism regarding their
ability to predict support for future restrictions on tourism development.

H9: Differences will be found among personal benefits from tourism,
perceptions of positive (negative) impacts of tourism, support for future

tourism development, and support for future restrictions on tourism

115

development regarding their ability to predict perceived community
future.

Pearson correlation tests were conducted for each county to examine the nature
and the strength of the relationships among model 6, 7 and 8 variables. Table 33 shows
correlations between the independent variables and the dependent variables by county.
Positive significant correlations were found between personal benefits from tourism and
support for future tourism, and between positive impacts of tourism and support for
future tourism in all three counties. Negative significant correlations were found between
negative impacts of tourism and support for future tourism in all three counties.

Personal benefits from tourism had a statistically significant relationship in a
negative direction with support for future restrictions on tourism development in Emmet
and Saginaw counties. Negative significant correlations were found between positive
impacts of tourism and support for future restrictions on tourism development in all three
counties. Positive significant relationships were found between negative impacts of
tourism and support for future restrictions on tourism development in all three counties.

Positive significant correlations were found between personal benefits from
tourism and community future and between restrictions on tourism development and
community future in Tuscola County but not Saginaw and Emmet counties. Positive
impacts of tourism had a statistically significant relationship in a positive direction with
community future in Emmet and Saginaw counties. Negative significant correlation was
found between negative impacts of tourism and community future tourism in Emmet
County. Support for future tourism development was significantly positively correlated

with community future in Saginaw County.

116

Correlations between independent variables ranged from .17 to .78 (Tables 30 and
33). High correlations between positive impacts of tourism and support for future tourism
development (r=.70 [Emmet], r=.78 [Saginaw], r=.76 [Tuscola]) suggested possible

multicollinearity between predictors in the model 9. Follow up collinearity diagnostics
test revealed low tolerance level (<1-R2) which confirmed multicollinearity existed
between these two predictors. Due to support for future tourism development variable
having unsatisfactory tolerance level (<1-R’), support for future tourism development

variable was excluded from further testing of the model 8.

117

Table 33: Correlations, Means, and Standard Deviations among Regression Models 6, 7,
and 8 Variables by County

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Benefits Support for Restrictions
Emmet from Positive Negative future on Future Community
County Tourism 3’ b Impacts 8’ b Impacts a’ tourism a, Tourism 8’ b Future a’ b
Support for
Future
Tourism 474* * .701 * * -.496** 1
Restrictions
on Future
Tourism -. 179* * -.388** .433** -.481 1
Community
Future .007 239* * -.214** .052 .064 1
Mean 5.36 c 45.03 d 28.64 c 14.72 2.62 3.84
Std. Dev. 2.20 6.96 4.48 304 1.00 .93
Benefits Support for Restrictions
Saginaw from Positive Negative future on Future Community
County Tourism a' b Impacts 2" b Impacts a, b tourism a’ Tourism a, b Future a, b
Support for
Future
Tourism 471* * .778* * -424** 1
Restrictions
on Future
Tourism -.308* * -.409* * .453** -.466** 1
Community
Future 0.016 145* .052 .145* -.034 1
Mean 4.68 c 44.76 d 24.96 c 14.84 2.36 2.19
Std. Dev. 1.80 6.60 4.32 3.04 .95 .94
Benefits Support for Restrictions
Tuscola from Positive Negative future on Future Community
County Tourism 3, b Impacts 8’ b Impacts 51' b tourism 3, Tourism a, b Future a’ b
Support for
Future
Tourism 436* * 763* * -.318** 1
Restrictions
on Future
Tourism -073 -.344* * 373* -.291* * 1
Community
Future .165* .101 .025 .053 .293** 1
Me“ 4.18 c 44.40 d 26.56 c 14.40 f 2.37 2.53
Std. Dev. 1.56 7.32 4.24 3.40 .94 1.02

 

 

 

 

 

 

 

 

 

a Interval data, b Refer to chapter 3 for reliability scores. Summed scale ranged from 2-10 c, 12-60 d,
840 e, and 4-20 f. *Correlations is significant at the 0.05 level, ** 0.01 level.

118

Regression model 6 was used to test the relationships between personal benefits
from tourism, positive and negative impacts of tourism; and support for future tourism

development. In the case of Emmet County, model 6 significantly predicted support for
future tourism development, F (3,306) = 148.61, p < 0.001; R2 = .59; with personal

benefits from tourism (0:204, t=5.3, p <0.001), positive impacts (0:.552, r=13.7, p
<0.001), and negative impacts (13:-.249, t=-6.3, p <0.001), statistically contributing to the
prediction.

In Saginaw County, regression model 6 significantly predicted support for future

tourism development, F (3,195) = 115.44, p < 0.001; R2 = .64; with positive impacts

(0:.668, t=13.9, p <0.001), and negative impacts (13:-.167, t=-3.5, p <0.05), statistically
contributing to the prediction.

In the case of Tuscola County, regression model 6 significantly predicted support
for future tourism development, F (3,271) = 120.84, p < 0.001; R2 = .62; with personal

benefits from tourism (B=.141, t=3.1, p <0.05), positive impacts (B=.679, t=15.0, p
<0.001), and negative impacts (13:-.148, r=-3.5, p <0.05), statistically contributing to the
prediction.

The more respondents benefited from tourism, the more they supported additional
tourism development in Emmet and Tuscola counties. Next, respondents who perceived
tourism to have positive impacts were more supportive of additional tourism
development while residents who felt tourism had negative consequences were less

supportive of additional tourism development in all three counties (Table 34).

119

Table 34: Regression Analysis of Model 6 Relationships between Personal Benefits from
Tourism, Positive and Negative Impacts, and Support for Future Tourism Development

 

 

 

 

 

 

 

 

 

 

 

Emmet Saginaw Tuscola
Model 6 a a a
(n=306) (n=195) (n=221)

Model Statistics [3 t p B t p B t p
Personal Benefits 0.204 5.3 <.001 0.078 1.5 ns 0.141 3.1 <.05
Positive Impacts 0.552 13.7 <.001 0.668 13.9 <.001 0.679 15.0 <.001

Mauve Impacts -0.249 -6.3 <.001 -0.l67 -3.5 <05 -0. 148 -3.5 <.05
F= 148.61,p <.001, F=115.44,p <.001, F=120.84,p <.001,
Adjusted R2 =.59 Adjusted R2 =.64 Adjusted R2= .62

 

a . . . .
n indicates the total number of cases used in the analy51s.

Regression model 7 was used to test the relationships between personal benefits
from tourism, positive and negative impacts of tourism; and support for future restrictions
on tourism development. In the case of Emmet County, model 8 significantly predicted
support for future restrictions on tourism development, F (3,302) = 32.18, p < 0.001; R2 =
.24; positive impacts (13:-.247, t=-4.5, p <0.001), and negative impacts (13:.334, t=6.l, p
<0.001), statistically contributing to the prediction.

Similarly in Saginaw County, regression model 7 significantly predicted support
for future restrictions on tourism development, F (3,192) = 23.90, p < 0.001; R2 = .26;
positive impacts (13:-.273, t=-3.8, p <0.001), and negative impacts (0:.341, r=5.0, p
<0.001), statistically contributing to the prediction.

Also in Tuscola County, regression model 7 significantly predicted support for
future restrictions on tourism development, F (3,221) = 19.97, p < 0.001; R2 = .20; with
positive impacts having a negative relationship (B=-.299, t=-4.6, p <0.001), and negative
impacts having a positive relationship (B=.327, t=5.2, p <0.001), with support for future

restrictions on tourism development.

120

Residents who perceived tourism to have positive impacts were less supportive of
restrictions on future tourism development while residents who felt tourism had negative
consequences were more supportive of restrictions on future tourism development in all

three counties (Table 35).

Table 35: Regression Analysis of Model 7 Relationships between Personal Benefits from
Tourism, Positive and Negative Impacts, and Restrictions on Future Tourism
Development

 

 

 

 

 

 

 

 

 

 

Emmet Saginaw Tuscola
Model 7 a a a
(n=302) (n=192) (n=221)

Model Statistics [3 t p [3 t J p t p
Personal Benefits -0.033 -0.6 as -0.042 ~0.6 ns 0.064 1.0 ns
Positive Impacts -0.247 -4.5 <.001 -0.273 -3.8 <.001 -0.299 -4.6 <.001
Negative Impacts 0.334 6.1 <.001 .341 5.0 <.001 .327 5.2 <.001

F=32.l,p<.001, F=23.90,p<.001, F= 19.97,p<.001,
Adjusted R2 :24 Adjusted 122 :26 Adjusted R2 = .20

 

 

a . . . .
11 indicates the total number of cases used in the analysrs.

Regression model 8 was used to test the relationships between personal benefits
from tourism, positive and negative impacts of tourism, support for future restrictions on
tourism development; and perceived community fiiture. In the case of Emmet County,
model 8 significantly predicted community future, F (4,298) = 11.66, p < 0.001; R2 = .13;
with positive impacts (0:263, t=4.2, p <0.001), negative impacts (13:-.258, t=-4. l,p
<0.001), and restrictions on future tourism development (0:265, t=4.3, p <0.001),
statistically contributing to the prediction.

In Saginaw County, positive impacts (B=.l90, t=2.2, p <0.05), significantly

predicted community future, however the overall model 8 was not significant (F (4,191)

= l.6l,p>0.05;R2=.01).

121

In the case of Tuscola County, model 8 significantly predicted community future,
F (4,216) = 9.50, p < 0.001; R2 = .14; with positive impacts (fir—“.181, t=2.5, p <0.05), and
restrictions on future tourism development (B=.389, t=5.4, p <0.001), statistically
contributing to the prediction.

Respondents who perceived tourism to have positive impacts were more
optimistic about their community’s future in all three counties. Emmet County residents
who felt tourism had negative consequences were less optimistic about their community’s
future. Emmet and Tuscola counties residents who believed local government should

restrict tourism development perceived their community’s future to be bright (Table 36).

Table 36: Regression Analysis of Model 8 Relationships between Personal Benefits from
Tourism, Positive and Negative Impacts, Restrictions on Future Tourism Development,
and Community Future

 

 

 

 

M d 18 Emmet Saginaw Tuscola
o
e (n=298)a (n=i9i)' (n=216)a

- ' b 13 t p B t p B t 9
Model Statistics
Personal benefits -0.077 —1.4 ns -0.038 -0.4 ns 0. 127 1 .8 ns
Positive Impacts 0.263 4.2 <.001 0.190 2.2 <.05 0.181 2.5 <.05

 

Negative Impacts -0.258 -4.1 <.001 0.107 1.3 ns -0.049 -O.7 ns

Restrictions on
Future Tourism 0.265 4.3 <.001 -0.017 -0.2 ns 0.389 5.4 <.001

Development

 

 

F= ll.66,p<.001, F=1.61,p>.05, F=9.50,p<.001,
Adjusted R2 =. 13 Adjusted R2 =01 Adjusted R2 = .14

a . . . .
11 indicates the total number of cases used in the analys1s.

 

 

 

 

 

 

b . . .
Due to unsatisfactory tolerance level, support for future tourism variable was excluded from the model 8.

No differences were found among positive and negative impacts of tourism
regarding their ability to predict support for future tourism development in all three
counties. Personal benefits from tourism predicted support for future tourism in Emmet

and Tuscola counties but failed to predict support for tourism in Saginaw County. As a

122

result, hypothesis 7 was supported by the data in Saginaw County but not in Emmet and
Tuscola counties.

In all three counties positive (negative) impacts of tourism predicted support for
future restrictions on tourism development, but personal benefits from tourism failed to
predict support for future restrictions on tourism development. Therefore, hypothesis 8
was partially supported by study results in all three counties.

Positive impacts predicted community future in all three counties. Negative
impacts predicted community future in Emmet County only. Support for future
restrictions on tourism development predicted community future in Emmet and Tuscola
counties, but not in Saginaw County. Personal benefits from tourism failed to predict
community future in all thee counties. As a result, hypothesis 9 was partially supported

by the data in all three counties.

Relationship between Residents ’ Attitudes and the Stage of Tourism Development:
Hypotheses 10, 11, 12 and 13

Residents’ attitude toward the existing tourism industry and future tourism
development were evaluated across three Michigan counties at different stages of tourism
development. Secondary data were utilized to determine the level of tourism development
in each: (1) county market share of pleasure travelers in Michigan, 1996—2002, (2)
Michigan tourism spending by county in 2000, (3) contribution of tourism to the local
economy in 2007, (4) proportion of seasonal homeowners for 1990 and 2000, (5) lodging
use tax (1983-1995), and (6) annual average number of jobs in tourism-related

businesses (1977-1987) by county.

123

Figure 12 and Table 37 depict the percentage of market share of the overall
Michigan pleasure traveler market between 1996 and 2002 for Emmet, Saginaw, and
Tuscola counties. As previously mentioned (Chapter 3), these data were calculated by
researchers at Travel, Tourism, and Recreation Resource Center at Michigan State
University. Based on their calculations, Emmet and Saginaw counties fall within the first

quadrant of estimated tourism market share by county, while Tuscola falls in the fourth

quadrant. Considering each county’s tourism market share per capita5 (see Table 37 and
Table 38), it can be argued that Emmet County is at the highest level of tourism

development followed by Saginaw and Tuscola counties.

Figure 12: County Market Share of Pleasure Travelers in Michigan (1996-2002)

 

 

County Market Share of Pleasure Travelers
Michigan 1996-2002

Percent

50q_w::::\\.h“‘1h————I\\\\
4.0 N
+ Emnet County

3.0 / —I— Saginaw County

2 0 A + Tuscola County
1.0 —/ /
011a——‘==!-t-=:=:!:=;::I€/;‘4‘r ; . ;

1996 1997 1998 1999 2000 2001 2002

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

5Emmet County’s population accounted for 14% of Saginaw County’s population and 50% of Tuscola
County’s population between 1990 and 2000, while Emmet County’s tourism market share accounted for
40% of Saginaw County’s tourism market share and also is 18 times greater than Tuscola County’s tourism
market share.

124

Table 37: County Market Share of Pleasure Travelers in Michigan (1996-2002)

 

 

 

Year Emmet County in %1 ’2 Saginaw County in%l’2 Tuscola County in %l"2
1996 0.9 5.6 0.1
1997 2.9 5.1 0
1998 1.9 4.8 0.2
1999 0.2 4.8 0
2000 2.1 3.7 0.1
2001 2.1 4.1 0
2002 3.3 3.9 0
1996-2002 1.8 4.5 0.1

 

 

 

 

data obtained from the Travel, Tourism, and Recreation Resource Center at Michigan State UniverSity.

2 . . . . .
numbers represent percentages of the total estimated tourism market share in Michigan by county.

Table 38: Population Trends (1960-2010)

 

 

Year
County 1960 1970 1980 1990 2000 2010
Emmet 15,904 18,331 22,992 25,040 31,437 33,393
Saginaw 190,752 219,743 228,059 21 1,946 210,039 202,268
Tuscola 43,305 48,603 56,961 55,498 5 8,266 56,805

 

 

 

 

 

 

 

Source: US. Census Bureau, 2000.

Table 39 portrays Michigan tourism spending by county in 2000 calculated using

the Tourism Spending Model developed by Dr. Stynes. Considering Michigan tourism

spending per capita, it can be argued that Emmet County represents a high level of

tourism development because of the highest tourism spending per capita, followed by

Saginaw and Tuscola counties with lower tourism spending per capita.

Table 39: Michigan Tourism Spending by County in 2000

 

 

Total Tourism Spletéding 3 Tourism Spending per
County (in Millions) ' County Population Capita
Emmet $121.9 31,437 $3,878
Saginaw 191.2 210,039 910
Tuscola 23.0 58,266 345

 

 

 

 

lTotal Tourism Spending includes the following tourism industry segments: motels, campgrounds, seasonal

homes, visiting family and friends, and day trips. 2Data obtained from Michigan Spending by County

Report prepared by Dr. Stynes.

3 Source: US Census Bureau, 2000.

125

Table 40 presents contribution of tourism and recreation to the local economy
(county’s dependency on the tourism industry) in 2007 calculated by Dr. Stynes using his
Michigan Tourism Economic Impact Model (MITEIM). It was estimated that tourism
contributes 25% to the Emmet County’s economy, 7% to the Saginaw Emmet County’s
economy, and less than 1% to the Tuscola County’s economy. Considering a county’s
dependency on the tourism industry, it can be argued that Emmet Count represents the

highest level of tourism development in comparison to Saginaw and Tuscola counties.

Table 40: Contribution of Tourism and Recreation to the Local Economy in 2007

 

 

 

 

 

 

Tourism and Recreation Contribution to
County Local Economy*
Emmet 25%
Saginaw 7%
Tuscola Under 1%

 

 

* data for Emmet and Saginaw counties were calculated by Dr. Stynes using Michigan Tourism Economic
Impact Model (MITEIM), data for Tuscola County was determined as a ratio using county’s total tourism
activity in the numerator and total economic activity in the denominator.

Table 41 provides an overview of the proportion of seasonal versus permanent
residents. Nearly thirty-five percent of the Emmet homeowners were seasonal residents
(34.7%). The majority of Saginaw homeowners (99.5%) and Tuscola homeowners
(96.1%). were permanent residents. Considering the proportion of seasonal versus
permanent residents, it can be argued that Emmet County represents a high level of
tourism development, while Saginaw and Tuscola counties represent a low level of

tourism development.

126

Table 41: Proportion of Seasonal Homeowners (1990-2000)

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

2000
Proportion of
County Permanent Seasonal Total Seasonal
Homeowners Homeowners Homeowners Homeowners
Emmet 9,502 5,039 14,541 34.7%
figinaw 59,390 301 59,691 0.5%
Tuscola 18,037 724 18,761 3.9%
1990
Proportion of
County Permanent Seasonal Total Seasonal
Homeowners Homeowners Homeowners Homeowners
Emmet 7,057 4,382 11,439 38.3%
LS_aginaw 55,304 202 55,506 0.4%
Tuscola 15,817 743 16,560 4.5%

 

Source: US. Census Bureau, 2000.

Figures 13 and 14 show trends in State of Michigan lodging use tax collections
for Emmet, Saginaw, and Tuscola counties between 1983 and 1995. Estimated lodging
use tax collections per capita in 1995 was $43 in Emmet County, $4 in Saginaw County,

and $.40 in Tuscola County (estimates were calculated using unadjusted lodging use tax

collections in 1995 [Source: County Tourism Profiles, 2001] and average population

between 1990 and 2000 [Source: U. 8. Census Bureau, 2000]). Considering estimated

lodging use tax collections per capita in 1995 by county, it can be argued that Emmet

County represents a high level of tourism, while Saginaw and Tuscola counties represent

a low level of tourism development.

127

 

Figure 13: Trends in State of Michigan Lodging Use Tax Collections for
Emmet, Saginaw, and Tuscola Counties (1983-1989)

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Trends in State of Michigan Lodging Use Tax Collections
1983-1989
$600,000
$500,000 //
$400’000 V + Emmet County
$300,000 / + Saginaw County
$200 000 W/ +Tuscola County
$100,000
$' ‘ 1 _ r 1 1 1
1983 1984 1985 1986 1987 1988 1989

 

 

 

Note: Data not adjusted for inflation were obtained from the “Travel and Tourism in
Michigan: A Statistical Profile” (1991).

Figure 14: Trends in State of Michigan Lodging Use Tax Collections for
Emmet, Saginaw, and Tuscola Counties (1990-1995)

 

Trends in State of Michigan Lodging Use Tax Colections
1990-1995
$500,000
$450,000
$400,000
$350,000
$300,000
$250,000
$200,000
$150,000
$100,000
$50,000
3-

 

+ Emmet County
—-— Saginaw County
+ Tuscola County

 

 

 

 

1990 1991 1992 1993 1994 1995

 

 

 

Note: Data adjusted for inflation were obtained from County Tourism Profiles developed by the Travel,
Tourism, and Recreation Resource Center at Michigan State University.

128

Table 42 shows the average number of tourism related jobs in Emmet, Saginaw,
and Tuscola counties between 1977 and 1987. Using the average number of tourism
related jobs between 1977 and 1987, number of tourism related jobs for calculated per
capita. It was estimated that between 1977 and 1987, the tourism industry employed 5 out
of 100 people in Emmet County, 3 out of 100 people in Saginaw County, and 1 out of

100 people in Tuscola County.

Table 42: Average Number of Jobs in Tourism-Related Businesses (1977-1987)

 

 

 

Year Emmet County* Sagrinaw County* Tuscola County*
1977 1,172 6,112 586
1978 1,347 6,400 677
1979 1,226 6,138 602
1980 1,098 6,286 513
1981 1,048 6,235 509
1982 1,024 6,066 463
1983 1,009 6,009 522
1984 1,014 6,407 514
1985 1,139 6,568 592
1986 1,118 6,951 585
1987 1,007 7,472 629
1977-1987 12,202 70,644 6,192

 

 

 

 

* data obtained from the “Travel and Tourism in Michigan: A Statistical Profile” (1991).

Note: Tourism-related businesses included eating and drinking places, lodging establishment, gasoline
services, miscellaneous amusement and recreation services, boating-related businesses, and camping
related businesses.

In summary, level of economic development was determined as a ratio using all
retail sales receipts (total economic activity) in the numerator and community population
in the denominator (Table 43). Considering total economic activity per capita, it was
determined that Emmet and Saginaw counties represent a high level of economic

development and Tuscola County represents a low level of economic development.

129

Table 43: Total Economic Activity Per Capita in 2002

 

Retail Sales Receipts Population Total Economic Activity
County 2002 ($1,000) 2002 Per Capita (8)
Emmet 1,529,549 32,504 47,057
Saginaw 1 1,140,523 209,851 53,088
Tuscola 984,159 58,272 16,339

 

 

 

 

Source: US. Census Bureau, 2000.

Using the level of tourism development and level of economic development, three
types of communities were identified: (1) low tourism — low economy (Tuscola County),
(2) low tourism - high economy (Saginaw County), and (3) high tourism— high economy
(Emmet County).

One-way ANOVA was employed to test the relationships between residents’
perceptions of current tourism impacts, support of future tourism development and
restrictions on future tourism development, and the stage of tourism and economic
development.

H10: Residents from communities with low economic and low tourism
development (i.e., Tuscola County) and high economic and high tourism
development (i.e., Emmet County) will perceive greater positive and
smaller negative impacts of tourism than residents from communities with
low tourism development and high economic development (i.e., Saginaw
County).

H11: Residents from communities with low economic and low tourism
development (i.e., Tuscola County) and high economic and high tourism
development (i.e., Emmet County) will be more supportive of future
tourism development than residents from communities with low tourism

development and high economic development (i.e., Saginaw County).

130

H12:

H13:

Residents from communities with low economic and low tourism
development (i.e., Tuscola County) and high economic and high tourism
development (i.e., Emmet County) will be less supportive of restrictions
on future tourism development than residents from communities with low
tourism development and high economic development (i.e., Saginaw
County).

Residents from communities with low tourism development and high
economic development (i.e., Saginaw County) and high economic and
high tourism development (i.e., Emmet County) will be more optimistic
about the future of their county than residents from communities with low

economic and low tourism development (i.e., Tuscola County).

Results of one-way ANOVA suggested that perceptions of tourism negative

impacts F (2, 736) =43.29, p <.001), restrictions on future tourism development F (2,
728) =6.17, p <05), and community future (F (2, 735) =99.38 p <.001) differed based on
an area’s level of tourism and economic development. Emmet County residents (high
level of tourism and economic development) were more concerned about negative
impacts of tourism than Saginaw County residents (low level of tourism development and
high level of economic development) and Tuscola County residents (low level of tourism
and economic development). In addition, Tuscola County residents were more concerned
about negative impacts of tourism than Saginaw County residents. Next, Emmet County
residents were more supportive of restrictions on future tourism development than

Saginaw and Tuscola counties residents. Lastly, Emmet County residents were more

131

optimistic about their community’s future than Saginaw and Tuscola counties residents.
Further, Tuscola County residents were more positive about their community’s future

than Saginaw County residents (Figure 15 and Table 44).

Figure 15: Residents’ Attitudes by Level of Tourism and Economic Development

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Residents' Attitudes by Level of Tourism and Economic
s"°"9'y Development
Agree
5
—I-— Pesitive
irrpacts
4
q, —-—- Negative
% inpacts
Z 3 dr— Support for
3 future tourism
2
2 9 - Restrictions on
future tourism
+ Commnity
1 I 1 future
Strongjy Tus cola Saginaw Emmet
Disagree (low -Iow ) (low -high) (high-high) Counties

 

132

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No differences were found regarding perceptions of positive impacts based on an
area’s level of tourism and economic development. Emmet County residents (high level
of tourism and economic development) were more concerned about negative impacts of
tourism than Saginaw (low level of tourism and high level of economic development) and
Tuscola (low level of tourism and economic development) counties residents. These
results contradicted hypothesis 10, therefore hypothesis 10 was not supported by the data.
No differences were found regarding support of future tourism development based on an
area’s level of tourism and economic development. Thus, hypothesis 11 was not
supported by the data. Emmet County residents were more supportive of restrictions on
future tourism development than Saginaw and Tuscola counties residents. In addition,
Saginaw County residents were equally supportive of restrictions on future tourism
development to Tuscola County residents. These results contradicted hypothesis 12,
therefore hypothesis 12 was not supported by the data. As hypothesized, Emmet County
residents were more optimistic about the future of their community than Saginaw and
Tuscola counties residents. However, Saginaw County residents were less positive about
their community’s future than Tuscola County residents, thus hypothesis 13 was partially
supported by the data.

Figure 16 provides an overview of results for regression analyses performed to
explore the relationships among the proposed conceptual model variables for each of the
three samples (hypotheses 1-9). Table 45 shows a summary of results for one-way
ANOVA employed to test the differences among residents’ perceptions of tourism based

on their area’s levels of tourism and economic development (hypotheses 10-13).

134

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Table 45: Summary of Hypotheses 10-13 Testing

 

Hypotheses Tested Results

 

H 10 Residents’ positive (negative) perceptions of tourism
impacts differ based on area’s level of tourism and Rejected
economic development.

 

H 11 Residents’ support for future tourism development differs

 

based on area’s level of tourism and economic Rejected
development.

H 12 Residents’ support for restrictions on future tourism
development differs based on area’s level of tourism and Rejected

economic development.

 

H 13 Residents’ perceptions of community’s future differ based

on area’s level of tourism and economic development. Partially supported

 

 

 

Chapter 4 provided an overview of data analysis including a description of the
sample and results of hypotheses testing using standard multiple regression and one-way
ANOVA with post-hoc tests. The following chapter presents a summary of results,
conclusions and discussion of key findings, implications and limitations of study

findings, as well as recommendations for future residents’ attitude research.

136

Chapter V
CONCLUSION

The focus of this study was to assess residents’ perceptions of tourism impacts in
several Midwest communities at different stages of tourism and economic development.
A theoretical framework consisting of social exchange theory (Skidmore, 1975) and
Butler’s (1980) destination life cycle model was used to guide the study. The study
extended Perdue et al.’s (1990) model which examined residents’ support for tourism in
several Western US. communities and tested an extended model across three different
communities each representing different levels of tourism and economic development.

Data were collected using a mail questionnaire across three geographical regions
at different stages of tourism and economic. A random sample of permanent and seasonal
residents was drawn from sampling frames representing the three areas under study. Only
homeowners who were listed in the 2006 winter property tax bill were eligible to be
included in the sample. Questionnaires were mailed and data collected using strategies
recommended by Dillman (2000) in May 2007. From the 3,008 homeowner population,
809 were returned and completed for an overall response rate of 28%. The chi-square
goodness of fit showed the sample to be significantly different from the population by
residential status. To assure the sample represents the study population, a set of weights
was created. After the mail data collection was completed, a non-response survey was
sent out in June, 2007 to assess potential bias in the dataset. Of the 300 non-response
surveys, 51 were returned and completed for an overall response rate of 18%. The results
obtained from the non-respondents were found to be indistinctive from the main study

results, thus there were assumed to be no major concerns regarding measurement errors

137

in the study. A questionnaire was developed based on a literature review of existing
studies addressing residents’ attitudes towards tourism development and modified based
on input obtained from several county officials and tourism professionals as well as three
tourism researchers from different universities.

The overall statistical analysis included: (1) descriptive statistics focusing on
residents’ socio-demographic profile and key variables used in the conceptual model (i.e.,
knowledge, power, economic role of tourism, community attachment, personal benefits
from tourism, positive and negative impacts of tourism, support for additional tourism,
support for restrictions on tourism development, and community future); (2) standard
multiple regression to examine the relationships among variables and multiple regression
with interactions to determine moderating effect of “personal benefits” on other
independent variables; and (3) one-way ANOVA to test the relationship between
residents’ perceptions of and the stage of tourism and economic development, followed
by post-hoe tests to examine expected differences on residents’ perceptions based on their
area’s level of tourism and economic development. All of the aforementioned statistical
procedures were performed at a county level to assess generalizability of the results and

to test attitudes across different levels of tourism and economic development.

Summary of Results and Discussion
The study sought to extend past research on rural residents’ perceptions of
tourism development within the theoretical framework of social exchange theory
(Skidmore, 1975). In addition, residents’ perceptions of tourism development were tested

across three different communities each representing different levels of tourism (Butler,

138

1980) and economic development (Allen et al., 1993). The key results of the study are

discussed in two sections addressing the theoretical frameworks used to guide the study.

Social Exchange Theory Relationships

Previous researchers have found a relationship between perceptions of tourism
impacts and support for tourism development (Madrigal, 1993; McGehee & Andereck ,
.2004; Perdue et al., 1990). Jurowski (1994) suggested that perceptions of tourism impacts
and willingness to enter into a tourism exchange are dependent on residents’ assessment
of perceived costs and benefits associated with the exchange. In keeping with social
exchange theory, this study examined several factors to determine which value elements
influence willingness to enter into a tourism exchange.

Consistent with social exchange theory residents who perceived tourism as a
development strategy in their county also perceived greater benefits from tourism than
did others. These outcomes were consistent across all three counties. Additionally, those
who felt they had more power regarding tourism decision making perceived greater
personal benefits from tourism in Emmet County and Saginaw County. These results are
consistent with social exchange theory suggesting those who have power also have the
ability to influence decisions (Emerson, 1972). Those who were more familiar with
tourism (knowledgeable) also perceived greater benefits from tourism in Emmet and
Tuscola counties. Inconsistent with social exchange theory, subjective knowledge did not
predict personal benefits from tourism in Saginaw County suggesting that residents’ may
not be as knowledgeable about the tourism industry in their county as they indicated

when surveyed.

139

Contradicting the results of study by Perdue et al. (1990), residents’
characteristics predicted positive impacts of tourism when controlling for personal
benefits from tourism in two of the three sampled regions. Consistent with the findings of
McGehee and Andereck (2004), the older the respondents in Emmet County the more
they agreed with positive impacts. Additionally, older residents in Emmet County who
benefited more from tourism perceived positive impacts of tourism more and negative
impacts of tourism less than younger residents who benefited from tourism. In
accordance with social exchange theory, people who perceive greater benefits from
tourism perceive greater positive and lower negative impacts of tourism. Tourism in
Emmet County has had a long tradition which has given residents the opportunity to
benefit from tourism throughout the years. Younger residents in Tuscola County who
perceived lower benefits from tourism agreed more with negative impacts of tourism than
older residents who perceived lower benefits from tourism. It could be argued that
younger residents have not had the opportunity to realize benefits associated with tourism
(i.e., mid-management jobs) that may contribute to the community’s overall quality of
life.

Education predicted positive impacts in Emmet and Tuscola counties; however,
the direction of the relationship was different in each. In Emmet County respondents with
a higher level of education agreed more with positive impacts, while Tuscola County
residents with lower level of education were more agreeable with positive impacts of
tourism. A possible explanation might be that tourism in Emmet County is well
established, and residents have recognized that positive benefits can outweigh tourism

negative impacts. In Tuscola County, agriculture is the pre-dominant economic activity,

140

and thus residents with a lower level of education might perceive tourism as an
opportunity for employment due to tourism potential to employ people with low as well
as high skills. Tuscola County residents with higher annual income agreed more with
tourisms’ positive impacts. A possible explanation might be that tourism offers more
leisure opportunities for local people and as such contributes to overall quality of life.

Residents who felt that tourism should play a major or equal role to other
economic sectors perceived greater positive impacts and lower negative impacts of
tourism in all three counties. These results are consistent with a previous study conducted
by Huh and Vogt (2008) and social exchange theory suggesting that those who perceive
tourism as a development strategy have more positive views of tourism because they
perceive greater benefits from tourism (Andereck et al., 2005).

Inconsistent with other studies (Lankford & Howard, 1994; McCool & Martin,
1994), highly attached respondents in Saginaw and Tuscola counties perceived tourism more
positively. McCool and Martin (1994) suggested that the relationship between
community attachment and positive impacts occurs among newcomers living in
communities with high levels of tourism development. This is not the case in this study.
In the areas under study, community attachment may be more related to the traditional
rural lifestyle and landscape which has developed over a long period of time. It can be
speculated that, due to economic hardship experienced by rural communities, residents
with a strong sense of community tend to be more concerned about their communities’
future while recognizing the potential of tourism to diversify their declining economy.

Inconsistent with other studies (Andereck et al., 2004; Davis et al. 198 8)

subjective knowledge did not predict positive and negative impacts of tourism. More

141

knowledgeable residents (subjective knowledge was self-reported) would appear to be
more aware of ways tourism can improve the local economy and overall quality of life.
However, this was not the case in this study. It can be argued that residents’ subjective
knowledge may not necessarily be reflective of the tourism industry reality. Inconsistent
with social exchange theory and Madrigal’s (1993) findings, power (the strongest
predictor of tourism impacts in Madrigal’s study) was not found to be a significant
predictor of positive or negative impacts of tourism. These results suggest that perceived
influence over tourism related decisions as well as involvement in the tourism industry do
not necessarily guarantee that a person will see solely the positive or negative side of the
tourism industry. It should be noted that Madrigal (1993) included a single-item
measurement to test the influence of personal influence on decision outcomes related to
tourism development. The current study included a “personal power” variable in the final
model measured with a two-item scale.

Economic role of tourism failed to predict perceptions of negative impacts of
tourism in Saginaw and Tuscola counties. This is inconsistent with social exchange
theory as those who felt that tourism should not be a major development strategy and
those who perceived lower benefits did not perceive higher negative impacts of tourism.
A possible explanation for the lack of relationship between perceived economic role of
tourism and negative impacts is that Saginaw County has a diversified economy resulting
in lower dependence on tourism development. The lack of a significant relationship
between benefits from tourism and perceived negative impacts Tuscola County may be

due to low levels of tourism development (Ko & Stewart, 2002).

142

Residents who perceived tourism to have positive impacts were more supportive
of additional tourism development while respondents who felt tourism had negative
consequences were less supportive of additional tourism development in all counties. In
addition, residents who benefited from tourism were also more supportive of additional
tourism development in Emmet and Tuscola counties. Overall, these results were
consistent with previous studies (McGehee & Andereck, 2004; Perdue et al. 1990).

Those who perceived tourism to have positive impacts were less supportive of
restrictions on future tourism development while respondents who felt tourism had
negative consequences were more supportive of restrictions on future tourism
development in all counties. Personal benefits from tourism failed to garner support for
restrictions on future tourism development in all three counties. These findings were
consistent with the study by Perdue et al. (1990). Interestingly, the variable personal
benefit from tourism was not a significant predictor of support for restrictions on future
tourism development. Based on social exchange theory, if residents benefit from tourism
development, it would appear that they would want to see fewer restrictions on tourism
development. But this was not the case in this study. A possible explanation might be that
regardless of their benefit from tourism, all residents believe that tourism development
should be restricted to some extent (McGehee & Andereck, 2004).

Positive impacts predicted community future in all three counties. Respondents
who perceived tourism to have positive impacts were more optimistic about their
community’s firture. Negative impacts predicted community future in Emmet County
only. Emmet residents who felt tourism had negative consequences were less optimistic

about their community’s future. Support for future restrictions on tourism development

143

predicted community future in Emmet and Tuscola counties, but not in Saginaw County.
Emmet and Tuscola counties residents who believed local government should restrict
tourism development perceived their community’s future to be bright. In other words,
respondents who were most optimistic about their community’s future were also those
who perceived positive impacts of tourism but also who recognized the need for
restrictions on tourism development. Personal benefits from tourism failed to predict the
assessment of a community’s future in all three counties. A possible explanation might be
that regardless of their benefit from tourism, residents are not aware of the potential of
the tourism industry to improve communities’ overall quality of life (Ap & Crompton,

1998).

Perceptions of Tourism across Different Levels of Tourism and Economic Development

The second part of the study focused on differences among residents’ perceptions
of tourism based on their area’s levels of tourism and economic development.
Inconsistent with Allen et al. (1993), no differences were found regarding perceptions of
positive impacts and support of future tourism development based on an area’s level of
tourism and economic development. In other words, regardless of level of tourism and
economic development, residents perceived tourism favorably and supported additional
tourism development. Since economic activities are not distributed equally across these
counties, specifically residents living in rural areas might perceive tourism as an
important economic development strategy. Additionally, tourism in Saginaw County has
built on existing assets (e.g., culture, natural resources, and history) and developed

tourism and recreational infrastructure which serves the needs of local people and

144

visitors. As a result, residents’ perceptions of tourism in Saginaw might be more positive
than they would be if the nature of tourism product were different.

Inconsistent with Allen et al. (1993), Emmet County residents (high level of
tourism and economic development) were more concerned about negative impacts of
tourism and were more supportive of restrictions on future tourism development than
Saginaw (low level of tourism and high level of economic development) and Tuscola
(low level of tourism and economic development) counties residents. A possible
explanation might be that tourism in Emmet County has had a long history of natural
resource tourism (e.g., skiing, fishing, boating, golfing) which has resulted in Emmet
County’s high dependence on the tourism industry. Over the years, residents in Emmet
County have realized various benefits associated with tourism development. Yet,
increasing awareness of negative impacts of tourism may have led to residents’ desire to
restrict future tourism development. The results of the study did not support propositions
made by Allen et al. (1993). However, it is important to mention that the measurement
used to determine level of tourism development was inconsistent with measurement used
by Allen et al. (1993).

In terms of perceived community future, Emmet County residents were found to
be more optimistic about the future of their community than Saginaw and Tuscola
counties residents. Further, Tuscola County, residents were more positive about their
community’s future than Saginaw County residents. It would appear that Saginaw
residents would be more optimistic than Tuscola residents given their diversified
economy. It can be argued that Saginaw County residents’ perceptions might have been

influenced by the economic decline of manufacturing (particularly in the automobile

145

industry) experienced by the state of Michigan as a whole in recent years. Conversely,
Tuscola residents might have high hopes and expectations for future tourism
development.

Implications

This study has several implications for community tourism developers and local
government officials. Younger residents (Emmet County) and younger residents who
have not yet enjoyed benefits from tourism (Emmet and Tuscola counties) appeared to be
more concerned about the negative impacts of the tourism industry in their community.
Inviting younger residents to participate in the tourism planning process, listening to their
concerns and encouraging their leadership is strongly recommended.

Emmet County respondents with a lower level of education and Tuscola County
residents with higher level of education were less agreeable with positive impacts of
tourism. It appears that county officials should focus on building public relations that
reach out to residents regardless of their education level. In Emmet County, economic
opportunities (i.e., the potential of tourism to employ people with diverse skills) need to
be communicated to the greater public. In case of Tuscola County, in addition to the
traditional economic benefits associated with tourism, environmental and socio-cultural
benefits, and contribution of tourism to overall quality of life need to be promoted.

The results of the study supported the notion that residents who personally benefit
from tourism and who perceive tourism as development strategy View tourism more
positively and are more supportive of further tourism development. It can be argued that
the more tourism industry officials can demonstrate how individuals benefit from tourism

in the county, the more support the industry is likely to enjoy from local residents.

146

Highly place attached residents in Saginaw and Tuscola counties perceived tourism
more positively. What differentiates these highly attached residents from residents who
might be relatively new to these communities is a strong sense of community. It is in the
community’s best interest to involve all residents in their community’s life to give them
an opportunity to develop a strong sense of community which in turn will result in a more
common identity. The more common identity is felt by the community, the more likely it
is to make a constructive decision about the community’s future in terms of tourism
development (Ryan & Cooper, 2002).

In Saginaw County, residents who recognized the contribution of tourism and
recreation to the local economy were also aware of positive impacts associated with
tourism. Thus, it is important for tourism industry officials and decision makers to
continue to educate residents about both costs and benefits associated with potential
tourism development. Keogh (1990) suggested that residents who were more informed
about the positive and negative impacts associated with proposed tourism development
tended to have more favorable opinions toward tourism. Keogh (1990) concluded that
greater awareness about tourism projects might be achieved by providing residents with
information in a more readily understandable form, such as brochures or newsletters.
Another way to disseminate information within a community might be a press release, a
short article in a local newspaper, or an announcement on a local radio station.

The results of the study indicated that residents in all counties were fully aware of
positive and negative impacts associated with tourism suggesting heterogeneity within
these communities. In other words, while some residents might have negative attitudes

toward tourism, others may view tourism favorably. Rather than simply stating that

147

tourism is beneficial for local communities and assuming that residents’ expectations of
local government regarding development are indifferent, a more pro-active marketing
approach needs to be undertaken by local officials. Local officials should attempt to
address the needs of various groups that exist within the community through an internal
marketing process that will allow dividing residents into different market segments based
on their perceptions of tourism development (Madrigal, 1995). Identification of the
interest groups within a community will make is easier to assess the residents’
information needs, enable effective and sensitive development of tourism, as well as pre-
identify potential conflicts of interest in these communities (Snaith & Haley, 1999).

Based on the results of the study, everyone, regardless of their personal benefits
from tourism desired tourism development to be restricted to some extent to avoid
potential unmanaged growth. Again, it should be noted that residents’ concerns might be
a product of direct experience with the tourism industry as well as historically and
socially derived, therefore tourism needs to be restricted in different ways across different
communities. Residents need to be involved in the decision making process determining
restrictions on future tourism development. To be able to make informed decisions about
the type and the extent of restrictions on future tourism development, residents need to be
educated about tourism potential positive and negative impacts.

The results of this study revealed that residents’ perceptions of tourism had very
little or no influence on how residents perceived their community’s future. It can be
argued that either residents’ expectations of tourism development have not been met or
residents are not fully informed of tourism development benefits and contributions to the

overall quality of life. Again, local officials need to inform residents about what realistic

I48

outcomes can be anticipated from tourism development. Once these outcomes have been
actually realized, the general public needs to be informed about how individual residents

as well as the overall community have benefited from the tourism development.

Limitation of Findings

There are several limitations to the study. First, due to limited funds, the survey
instrument used in this study was shared with another PhD student. As a result, only a
limited number of questions (items) were included in the survey to measure constructs
pertinent to the study problem. This has resulted in several single item measurements
(i.e., level of objective and subjective knowledge, economic role of tourism, and
community future), poor reliability and validity of measures (i.e., restrictions of future
tourism development), as well as low R-squares.

Second, the study was limited to one empirical study (Allen et al., 1993) that
previously examined differences in residents’ attitudes across several communities with
different level of tourism and economic development. In addition, the current study was
not able to obtain data from four communities to represent the four community types
described by Allen et al. (1993). Therefore, a full comprehensive comparison of the study
results was not possible since a low level of economic and high level of tourism
development category was not represented in this study.

Third, since the study population consisted of seasonal and permanent
homeowners, it is believed that the age group 19-39 years was underrepresented as they
are more likely to live in apartments or with their parents. This could have resulted in

relatively high levels of education and high annual income reported by respondents.

149

Fourth, the study did not specifically define what and how restrictions on future tourism
development would be managed. Therefore, interpretation of what “tourism restrictions”
mean might have varied among respondents.

Fifth, measurements used to assess level of tourism development were not
comprehensive. The addition of other indicators (e.g., number of tourist arrivals over
time, changes in tourism infrastructure, and changes in total tourism receipts per capita)
may have changed the level of tourism development assessment. Sixth, the geographic
regions under study were not selected using stratified random sample, thus they are not
representative of other tomism destinations at similar development stages.

Next, the survey instrument used to assess biases in the dataset from non-
responders included a limited number of questions due to restricted funds and space
constraints in the questionnaire layout. As a result, several constructs specific to the study
problem as well as questions addressing demographics were not included in the non-
response survey. Lastly, the study was limited by a low survey response rate due to the

length of the survey and declining mail survey rates in the US.

Future Research
The study was somewhat exploratory in that it examined residents’ attitudes
toward current tourism and future tourism development in three rural communities at
different stages of tourism and economic development. Further research is needed to test
and validate the modified model in other communities with different levels of tourism
and economic development using a stratified random sample to obtain a better

representation of tourism destinations at different development stages.

150

Next, future studies should improve measurements of residents’ perception of
economic role of tourism, knowledge of tourism, restrictions on future tourism
development as well as community future.

Future researchers need to improve the measurement of levels of tourism
development. The current study used Butler’s (1980) destination life cycle as a guide to
determine the level of tourism development in communities under study. However, due to
the lack of accurate longitudinal tourism data at a county level (i.e., tourism sales totals
over time, tourism arrivals totals by sector and season), the study did not attempt to link
the level of tourism development to a specific stage of Butler’s (1980) model. Future
studies should consider multiple tourism products where each exhibits its own life cycle
(Agarwal, 1994), as well as an existence of a possible subset of the rejuvenation stage,
the so called “reinvention process”, which allows for extension of the life cycle due to a
creation of a new product line to reflect a new market demand (Baum, 1988). Lastly,
entrepreneurial activities should be incorporated because they create conditions for the
evolutionary cycle to move from one stage to another (Russell & Faulkner, 1999).

While the current study has demonstrated the positive relationship between
personal benefits from tourism and support for tourism, this study failed to answer what
specific benefits residents perceive from tourism. Therefore, future studies should include
collection of qualitative data that would provide more detailed information regarding
specific perceived benefits as reported by residents (McGehee & Andereck, 2004).

The current study drew study participants from the general population. They are
likely not to be as knowledgeable about the tourism industry as individuals involved in

tourism development (i.e., tourism business owners, civic leaders). To obtain a more

151

accurate understanding of perceptions of tourism development in a community, opinions
of all groups within the community (i.e., government officers, tourism business owners,
and residents) need to be examined.

Support for social exchange theory was inconsistent. Social exchange theory
assumes that the decision-making process always ends in gaining, suggesting there are no
losers only winners. In addition, social exchange theory presumes that all people who
enter into an exchange have complete and correct information. This may not always be
the case. In this study, it appears that residents’ knowledge of the tourism industry is
historically and socially derived rather than a product of direct experience. Application of
social exchange theory in conjunction with another theory (i.e., social representation
theory) might provide a better insight into residents’ attitudes toward tourism.

Social representations are instruments that enable individuals to understand the
surrounding world (Moscovici, 1988). While the term “social” suggests that
representations are shared by people in a given society, not all groups within a
community are homogenous. As a result, Moscovici (1988) proposed three levels of
consensus with regard to social representations: (1) hegemonic representations which are
accepted by the entire community, (2) emancipated representations where opinions
within the community vary to some extent and as a result small subgroups of peOple are
formed, and (3) polemic representations signifying a group conflict. Social
representations are established through communication within society, i.e., direct
experience, social interaction, and the media (Fredline & Faulkner, 2000). Identifying
commonality or consensus of residents’ perceptions within a community is instrumental

to identifying social representations. Social representations are particularly valuable in

152

identifying social conflicts within a community and providing a foundation for
community problem solving by examining residents’ attitudes toward a matter of social

interest (e.g., tourism development).

Final Thoughts

The current study further extended and tested the Perdue, Long, and Allen’s
(1990) model using data collected from several Michigan communities which may or
may not be concerned about local tourism opportunities. Studying multi-locations
enabled testing the model within different stages of tourism (Butler, 1980) and economic
development (Allent et al., 1993). Communities under study were not randomly selected;
therefore results of the study should not be generalized beyond the scope of this study.

In general, testing the model by Perdue at al. (1990) within different stages of
tourism and economic development further validated the model as well as addition of the
economic role of tourism development variable. Consistent with other studies (Madrigal,
1993; McGehee & Andereck , 2004; Perdue et al., 1990; Snaith & Haley, 1999)
regression models explaining perceived positive impacts and support for tourism
development performed better than those explaining negative impacts and support for
fiJture restrictions on tourism development I

Support for social exchange theory was mixed. For the most part, social exchange
theory was supported in suggesting that those who perceive tourism as a development
strategy have more positive views of tourism and support additional tourism development
because they perceive greater benefits from tourism. However, the variable personal

benefit from tourism was not a significant predictor of support for restrictions on future

153

tourism development. This finding was consistent with Perdue et a1. (1990) and McGehee
and Andereck (2004), but inconsistent with social exchange theory.

These findings do not support previous research which suggests that attitudes
toward tourism become more negative with higher levels of tourism (Allen et al., 1988,
Butler, 1980; Doxey, 1975; Long et al., 1990). In case of this study, residents at different
levels of tourism development perceived tourism positively and were favorable toward
additional tourism development. Even after considering the level of tourism development
in conjunction with the total economic activity, residents were supportive of future
tourism development regardless of county’s levels of tourism and economic
development. A possible explanation for support of tourism development across all three
regions under study might the difficult economic conditions experienced by the State of
Michigan as a whole. If the study had been conducted five years ago, the results might
have been different.

There is no doubt that tourism development will sooner or later play an important
development role in communities with transitional economies that are moving from
natural resources extraction to tourism development. Yet, the extent to which tourism
development will be sustained depends on the active involvement of the host
communities in the tourism development process. Some residents might perceive tourism
as the best economic opportunity, while others might be more skeptical. Thus, it is
important that the goals and strategies of tourism development reflect the views of all
resident groups (i.e., government officials, tourism business owners, residents) to ensure

community consensus on tourism development policies (Martin, 1996).

154

APPENDICES

155

APPENDIX A

Descriptive Statistics for Items Used in the Study

1. Residential status of homeowners:

 

 

 

n“ Permanent Seasonal
% %
Emmet 321 65.3 34.7
Saginaw 206 94.7 5.3
Tuscola 228 88.6 1 1.4

 

 

n 15 the actual number of surveys received but statistics were weighted for population estimate.

2. Age (in years) of homeowners:

Mean

 

 

 

n Median Std. Deviation Minimum Maximum
Emmet 318 60.2 60.0 13.4 28 95
Saginaw 194 52.6 52.0 13.6 23 86
Tuscola 219 57.8 58.0 13.3 29 90

 

 

 

 

 

it IS the actual number of surveys received but statistics were weighted for population estimate.

3. 2006 annual household income from all sources before taxes:

Less than $50,000

 

 

 

 

n $50,000-$99,999 $100,000 or more
% % %

Emmet 294 15.2 34.3 50.5

Saginaw 187 43.3 41.7 15.0

Tuscola 212 47.6 39.6 12.7

 

 

n rs the actual number of surveys received but statistics were weighted for population estimate.

4. Homeowners level of education:

 

High Technical
Less than school school Some College Advanced
n“ high school graduate degree college degree degree
% % % % % %
Emmet 321 0.0 7.0 3.1 13.7 37.8 38.4
Saginaw 203 1.0 26.1 8.9 21.2 28.1 14.8
Tuscola 225 4.9 31.6 7.1 26.7 20.0 9.8

 

 

 

 

 

 

 

 

n IS the actual number of surveys received but statistics were weighted for population estimate.

156

5. Number of years of residency/home ownership in the county:

 

n Mean Median Std. Deviation Minimum Maximum
Emmet 318 22.8 18.0 17.0 1 78
Saginaw 204 34.2 35.0 19.4 1 85
Tuscola 228 37.4 35.0 21.4 2 84

 

 

 

 

 

 

 

n 18 the actual number of surveys received but statistics were weighted for population estimate.

6. Perceived role of tourism and recreation in the county’s economy:

 

 

 

Equal to other
11‘ No role A minor role economic sectors A dominant role
% % % %
Emmet 301 0.6 2.6 39.5 57.3
Saginaw 200 2.0 15.0 69.0 14.0
Tuscola 212 1.9 23.1 63.3 12.7

 

 

 

 

n rs the actual number of surveys received but statistics were weighted for population estimate.

7. Contribution of tourism and recreation to the county’s economy (level of objective

 

 

 

 

 

 

 

knowledge):
n“ 0-20 21-40 41-60 61-80 81-100
% % % °/o %
Emmet 302 0.9 14.2 27.0 45.9 12.0
Saginaw 206 33.2 45.7 15.6 5.0 0.5
Tuscola 228 62.3 27.8 7.1 2.8 0.0

 

n 18 the actual number of surveys received but statistics were weighted for population estimate.

8. Level of subjective knowledge of the tourism and recreation industry in the county:

Not ‘

 

Slightly Somewhat Moderately Very
knowledge- knowledge- knowledge- knowledge- knowledge-
n“ able at all able able able able
% % % % %
Emmet 313 3.8 24.5 27.7 31.2 12.8
Saginaw 205 12.2 38.0 28.8 16.1 4.9
Tuscola 224 28.6 31.3 24.6 12.1 3.6

 

 

 

 

 

 

 

n rs the actual number of surveys received but statistics were weighted for population estimate.

157

9. Personal benefits from current tourism in the county:

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

"3 Not at all Very little Some Quite a bit A lot
% % % % %
Emmet 315 25.4 21.0 27.6 16.6 9.4
Saginaw 205 41.5 32.2 21.0 3.9 1.5
Tuscola 226 57.1 28.8 11.5 2.2 0.4
a n is the actual number of surveys received but statistics were weighted for population estimate.
10. Personal benefits from future tourism in the county:
Strongly Strongly
"3 disagree Disagree Neutral Agree agree
% % % % %
Emmet 313 17.6 28.2 27.0 18.3 8.9
Saginaw 197 13.7 29.4 29.9 21.8 5.1
Tuscola 225 15.1 36.0 27.6 17.8 3.6
a n is the actual number of surveys received but statistics were weighted for population estimate.
11. Power:
Very Quite a
“a None little Some bit A lot
Personal influence on TD decrsron % % % % %
making
Emmet 314 48.2 32.1 15.4 4.0 0.3
Saginaw 201 71.1 19.4 9.0 0.5 0.0
Tuscola 227 67.8 20.7 9.3 1.3 0.9
Involvement in TD
Emmet 316 50.0 31.8 13.1 4.5 0.6
Saginaw 201 68.2 19.4 10.4 1.5 0.5
Tuscola 225 65.8 24.4 7.6 1.3 0.9

 

 

 

 

 

 

 

 

n 15 the actual number of surveys received but statistics were weighted for population estimate.

158

12. Community attachment:

 

Not
important Slightly Somewhat Moderately Very
"9 at all important important important important
Natural % % % % ' %
landscapes/views
Emmet 314 0.4 1.9 5.4 20.6 71.7
Saginaw 198 8.1 10.6 23.7 24.3 28.3
Tuscola 218 4.6 13.3 20.6 27.1 34.4
Opportunities for
outdoor recreation
Emmet 314 0.7 3.2 9.6 25.2 61.3
Saginaw 204 4.9 7.8 22.5 30.9 33.8
Tuscola 222 3.6 3.6 23.9 31.5 38.5
Presence of
wildlife
Emmet 310 1.3 3.8 13.6 27.9 53.4
Saginaw 201 4.0 15.9 18.4 31.3 30.3
Tuscola 222 0.9 5.0 19.8 28.8 45.5
Family ties
Emmet 307 15.5 4.4 8.3 17.4 54.4
Saginaw 206 5.3 4.9 5.8 22.3 61.7
Tuscola 225 5.8 3.1 8. 16.9 66.2
Friends close by
Emmet 310 5.7 8.2 17.0 30.3 38.8
Saginaw 205 2.4 5.4 13.7 31.7 46.8
Tuscola 224 4.0 6.7 16.1 30.4 42.9
Local culture and
traditions
Emmet 313 2.1 8.9 24.1 33.5 31.4
Saginaw 199 8.0 16.6 29.1 30.7 15.6
Tuscola 220 5.5 16.4 33.6 23.6 20.9
Opportunities to be
involved in
community or
organizations
Emmet 310 6.2 12.5 26.4 29.8 25.1
Saginaw 204 8.3 13.7 25.0 34.3 18.6
Tuscola 222 9.1 20.0 30.0 26.8 14.1

 

 

 

 

 

 

 

n rs the actual number of surveys received but statistics were weighted for population estimate.

159

13. Perceived positive impacts of tourism in the county:

 

Strongly Strongly
“3 disagree Disagree Neutral Agree agree
Increasing the number of tourists
visiting anarea improves the local % % % % %
economy ' '
Emmet 316 1.6 3.1 8.5 50.0 36.8
Saginaw 196 1.0 2.6 11.2 59.7 25.5
Tuscola .226 1.3 0.9 14.2 56.2 27.4
Shopping, restaurants, 7 , 7
entertainment options are better as
a result of tourism
Emmet 314 0.6 3.4 7.1 56.0 32.9
Saginaw 193 0.5 6.2 11.9 59.6 21.8
Tuscola 224 1.8 2.7 17.9 55.8 21.9
Tourism encourages more public
development (e. g., roads, public
facilities)
Emmet 313 0.7 4.8 13.0 59.2 22.3
Saginaw 197 0.5 4.1 18.3 57.9 19.3
Tuscola 225 1.8 4.0 16.0 60.0 18.2
Tourism contributes to income
and standard of living
Emmet 312 1.4 5.2 16.1 57.2 20.1
Saginaw 195 2.1 8.7 25.6 51.8 11.8
Tuscola 222 8 8.1 22.1 55.0 13.1
Tourism provides desirable jobs
for local homeowners
Emmet 315 2.1 8.6 19.9 46.2 23.2
Saginaw 197 . 7.6 21.8 50.8 17.3
Tuscola 225 1.3 4.9 23.1 54.7 16.0
Tourism provides incentives for
new park development
Emmet 308 0.7 7.0 20.1 56.5 15.7
Saginaw 197 1.5 6.6 22.3 59.9 9.6
Tuscola 224 1.8 1.8 25.9 56.7 13.8
Tourism development increases
the number of recreational
opportunities for local
homeowners
Emmet 311 1.4 7.7 19.4 59.2 12.3
Saginaw 194 1.0 7.2 17.0 61.9 11.7
Tuscola 221 2.7 5.4 21.7 61.5 8.6
Tourism provides incentives for
protection and conservation of
natural resources
Emmet 312 1.0 12.1 25.9 44.6 16.4
Saginaw 196 2.0 12.2 31.6 44.9 9.2
Tuscola 225 2.7 10.7 32.0 42.2 12.4
Tourism provides incentives for
purchase of open space
Emmet 309 2.2 8.8 31.7 42.6 14.7
Saginaw 197 0.5 5.6 45.7 39.1 9.1
Tuscola 224 1.3 5.4 41.5 40.6 11.2

 

 

 

 

 

 

13. Perceived positive impacts of tourism in the county continued:

Tourism helps preserve the
cultural identity and restoration of
historical buildings

Emmet

Saginaw

Tuscola
Tourism development improves
the physical appearance of an area

Emmet

Saginaw

Tuscola
Tourism development increases
the quality of life in an area

Emmet

Saginaw

Tuscola

 

311

.196

224

310
196
224

312
195
223

 

Strongly
disagree

2.0
1.0
2.3

3.1
1.0
1.3

3.6
3.6
4.5

 

Disagree

13.0
5.1
10.0

19.0
4.6
7.6

15.4
8.2
12.6

 

Neutral

27.5
20.9
26.2

23.9
21.3
27.7

34.3
33.8
38.6

 

Agree

49.8
61.2
52.0

41.7
55.8
487.

39.9
49.7
41.3

 

Strongly
agree

7.7
11.7
9.5

12.3

 

a . . . . . . .
It IS the actual number of surveys received but statistics were weighted for population estimate.

161

l4. Perceived negative impacts of tourism in the county:

 

 

 

 

 

 

 

Strongly Strongly
n“ disagree Disagree Neutral Agree agree
Tourism development increases
the traffic problems of an area % % % % %
Emmet 314 0.7 1.4 4.5 40.8 52.6
Saginaw 195 1.0 7.2 20.5 54.4 16.9
Tuscola 226 1.3 4.4 20.8 55.8 17.7
Tourism results in an increase of
the cost of living
Emmet 309 0.7 8.4 24.2 47.7 19.0
Saginaw 195 1.5 16.9 47.2 29.7 4.6
Tuscola 217 1.4 12.0 46.5 32.7 7.4
Tourism results in more litter in
an area
Emmet 314 0.4 9.3 23.8 44.1 22.4
Saginaw 197 1.5 20.3 30.5 38.1 9.6
Tuscola 225 1.3 12.9 26.2 44.0 15.6
Tourism related jobs are low
paying
Emmet 309 0.4 10.4 27.9 44.2 17.1
Saginaw 195 1.5 5.6 37.9 42.1 12.8
Tuscola 221 2.3 5.0 36.7 47.5 8.6
Tourism development unfairly
increases property taxes
Emmet 309 3.1 18.4 36.0 25.5 17.0
Saginaw 195 1.5 23. 49.7 19.0 6.2
Tuscola 222 0.9 16.2 40.1 29.3 13.5
Tourism causes communities to be
over-crowded
Emmet 311 1.0 19.3 33.6 34.9 11.2
Saginaw 195 5.1 40.5 39.0 12.3 3.1
Tuscola 222 2.7 35.6 38.3 19.8 3.6
Tourism development increases
the amount of crime in the area
Emmet 314 2.4 20.8 36.1 30.4 10.3
Saginaw 194 5.2 31.4 42.3 18.0 3.1
Tuscola 222 1.4 21.2 50.5 23.0 4.1
An increase in tourists in the
county will lead to friction
between homeowners and tourists
Emmet 314 3.6 30.1 37.0 21.7 7.6
Saginaw 195 9.2 46.2 32.3 10.8 1.5
Tuscola 222 3.6 34.7 38.3 19.8 3.6

 

a n is the actual number of surveys received but statistics were weighted for population estimate.

162

15. Perceived community future:

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Strongly Strongly
“3 disagree Disagree Neutral Agree agree
The future of the county looks % % % % %
bright
Emmet 309 4.3 11.2 38.0 38.7 7.8
Saginaw 205 26.3 38.0 26.3 8.8 0.5
Tuscola 219 14.6 37.4 33.3 9.6 5.0
a n is the actual number of surveys received but statistics were weighted for population estimate.
16. Support for additional tourism development in the county:
Strongly Strongly
n“ disagree Disagree Neutral Agree agree
Tourism can be one of the most
important economic % % % % %
developmental options for an area
Emmet 310 1.0 6.6 11.9 57.6 22.9
Saginaw 197 2.0 10.7 30.5 46.2 10.7
Tuscola 224 3.1 10.7 31.7 41.5 12.9
I support tourism having a vital
role in this county
Emmet 311 2.2 5.2 20.9 54.9 16.8
Saginaw 194 2.6 2.1 23.2 54.1 18.0
Tuscola 225 4.4 5.3 25.3 47.1 17.8
The county should try to attract
more tourists
Emmet 311 2.2 11.7 28.6 46.0 11.5
Saginaw 196 4.1 3.1 25.5 49.5 17.9
Tuscola 225 4.0 6.7 26.7 49.8 12.9
Additional tourism would help the
county grow in the right direction
Emmet 310 2.8 12.8 29.9 44.2 10.3
Saginaw 197 2.0 5.1 25.4 50.3 17.3
Tuscola 225 4.9 6.2 27.6 46.2 15.1
a n is the actual number of surveys received but statistics were weighted for population estimate.
17: Support for restrictions on tourism development in the county:
Strongly Strongly
n“ disagree Disagree Neutral Agree agree
Local government should restrict
tourism development
Emmet 309 12.4 35.3 34.1 14.5 3.7
Saginaw 194 19.6 37.1 33.5 7.7 2.1
Tuscola 225 19.6 34.2 38.2 5.8 2.2

 

n rs the actual number of surveys received but statistics were weighted for population estimate.

163

APPENDIX B

Cover Letters for the Survey Administration Approved by UCHRIS: IRB # XO7-312

First Wave Survey Cover Letter

May 4, 2007

Dear Name,

Michigan State University (MSU) is studying residents’ perceptions of tourism impacts
and attitudes toward future tourism development. We are interested in learning about
your opinion regarding current tourism impacts and future tourism development in
Emmet County. The research study will both fulfill a dissertation requirement and be
shared with the county tourism industry and planning officials.

The survey will take approximately 15 minutes to complete. You indicate your voluntary
agreement to participate by completing and returning this survey. However, if you choose
not to complete all or part of the questions, you will not suffer any penalty. You are free
to discontinue your participation at any time. Your responses will be anonymous and
your privacy will be protected to the maximum extent allowable by law.

As a thank you for taking the time to complete the survey, your name will be entered in a
drawing for four two-nights of camping at Camp Pet-o-se-ga and one package of four
passes to the 2007 Charlevoix/Emmet County Fair.

If you have any questions about this project at any time, please call Dr. Christine Vogt,
Associate Professor at MSU: (517) 432-0318 or contact her at vogtc@msu.edu. If you
have any questions or concerns regarding your rights as a study participant, or are
dissatisfied at any time with any aspect of this study, you may contact — anonymously,
if you wish, Peter Vasilenko, Ph. D., Director of the Human Research Protection
Programs (HRPP) at Michigan State University: (517) 355-2180, fax: (517)432-4503,
email: irb@msu.edu, or regular mail: 202 Olds Hall, East Lansing, MI 48824.

We greatly appreciate your cooperation!

Sincerely,

Christine A. Vogt, PhD
Michigan State University
vogtc@msu.edu

Enclosures: survey, postage paid envelope

164

Second Wave Survey Cover Letter

May 29, 2007
Dear Name,

In the last few weeks you should have received a letter and survey from Michigan State
University. I am doing research on home or property owners in several Michigan

counties. We have heard from many in the past few weeks, but haven’t received your input
yet. You were randomly selected to represent Emmet County home or property owners about
their views on community development, recreation and tourism.

If you have recently sent in your completed survey, I thank you. As a thank you for taking
the time to complete the survey, your name will be entered in a drawing for one of four two-
nights of camping at Camp Pet-o-se-ga or one package of four passes to the 2007
Charlevoix/Emmet County Fair. Our drawing will be on June 15’“. Please return your
completed survey soon.

If you are unable to complete the survey or you don’t think it applies to you, please e-mail,
call or send a note in the prepaid envelope. If the survey is addressed to someone that no
longer lives in the household, please have another adult or head of household complete it.
Otherwise I hope to receive your completed survey.

The survey will take approximately 15 minutes to complete. You indicate your voluntary
agreement to participate by completing and returning this survey. If you choose not to
complete all or part of the questions, you will not suffer any penalty. You are free to
discontinue your participation at any time. Your responses will be anonymous and your
privacy will be protected to the maximum extent allowable by law.

If you have any questions about this project at any time, call Dr. Christine Vogt, Associate
Professor at MSU: (517) 432-0318 or contact her at vogtc@msu.edu. If you have any
questions or concerns regarding your rights as a study participant, or are dissatisfied at any
time with any aspect of this study, you may anonymously contact Peter Vasilenko, Ph. D.,
Director of the Human Research Protection Programs (HRPP) at Michigan State University:
(517) 355-2180, fax: (517) 432-4503, email: irb@msu.edu, or regular mail: 202 Olds Hall,
East Lansing, MI 48824.

Thanks again for your time and effort!

Sincerely,

Christine A. Vogt, PhD
Michigan State University
vogtc@msu.edu or (517) 432-0318

Enclosures: survey, postage paid envelope

165

APPENDIX C
Reminder Postcard

Christine Vogt

Michigan State University

Park, Recreation and Tourism Resources
131 Natural Resources Building

East Lansing, MI 48824 -1222

 

    

.. .-..‘, -. .,; “31““: FT". , ‘_.__\__=N‘. 1:} ' ,.
. I 1‘. - 1 ”1% & -...- -: :11», .31: $2.61": ‘
=- 11-111. 1, iii 2. Mt_l.t.tt1,;;21tgtttt1‘tgitt .-

fl-:m:urmxaeemzsmaea;rmem ' '

     

 

Hello,

Recently, we sent you a survey about attitudes toward tourism. If you have already
returned the survey, thank you for your timely response. We appreciate your time and

effort.

If you have not yet sent the survey back in the prepaid envelope, please take some time
now to complete the survey. Your response is very important for the completion of this
study, accurate representation of vacation planning decision-making and will result in
recommendations to improve vacation planning information.

Once again, thank you for your help in completing this research. If you have any
questions, please do not hesitate to call me at 517-432-0318 or
e-mail me at vogtc@msu.edu. Thanks again for your help!

Sincerely,

Christine Vogt, Michigan State University

166

APPENDIX D

Surve Instrument and Non-Res ondent Stud

mwfigé‘h “DWARD TOURIS

 

in Emmet County

 

Thank you for agreeing to complete this survey. First, we would like to learn more
about out of town visitors that stay with you in Emmet County. We would also like to
know your opinions about tourism and recreation in Emmet County. You are one of a
small number of community residents who have been selected to take part in this study
so your responses are of great importance. The survey should take approximately 15
minutes to fill out. Afier you have completed the survey, please return it in the prepaid
envelope provided (or mail it to: Dr. Christine Vogt, Michigan State University, Dept. of
CARRS, 131 Natural Resources, East Lansing, MI 48824-1222).

Section 1. To better understand the economic impact of friends and/or relatives
visiting Emmet County, the first set of questions focuses on out of town visitors that
stayed with you rather than in paid accommodations in Emmet County.

1. How many out of town groups of guests stayed in your home (rather than in paid
accommodations) between May 2006 and April 2007? (please fill in a number)

Memorial Day and Labor Day, 2006 # of groups of guests
Labor Day and December 31, 2006 # of groups of guests
January 1, 2007 and April 30, 2007 # of groups of guests

2. Please provide information about three of the most recent groups of guests that
stayed in your home during May, 2006 to April, 2007.

167

Questions regarding three of the most recent

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

groups of guests: Group # 1 Group # 2 Group # 3
. 9 . .
What state did they come from. (please spectfv) state state state
. ‘ . . ‘. 9 .- J ) .)
Which month Clld they vrstt you. (please spur/ft) month month month
How long did they stay with you? (please fill in a # # #
number) days days days
# # #
What was the number of people per group? (please fill adults adults adults
in a number) # # #
children children children
Do you expect your guests will return to visit you in U 1;] U U U D
the next two years? yes no yes no ' yes no
. . . ,7 /
What was the purpose of their Vtsrt. (please all Group # 1 Group # 2 Group # 3
that apply)
Vacation [1 [:1 [3
Visiting friends Cl F1 (‘1
Exploring retirement areas [:1 3 :1
Exploring second home areas 1‘) H Ti
Conducting business/attend a convention [:1 [:1 C]
Conducting family/personal business (wedding,
. . H [1 D
funeral, medical, reunion, etc.)
Other purposes: [1 Cl [:1
Question 2 continued:
What type of activities did your guests participate in
during their visit? (please / all that apply) Group # 1 Group # 2 Group # 3
Bicycling E] {:1 1:]
Boating t‘; 1’.) F1
Camping [:1 D U

168

 

Conferences (.1 U L)
Dancing/ Nightclubbing C] C] [:1
Dining out 1:] D L]
Entertainment (theatre, movies, music, etc.) [3 D D
Festivals/exhibits E1 [:1 [:1
Fishing [3 [:1 [2]
Gaming 1:] El '3
Golfing El E1 E1
Hiking/walking [‘1 fl [1
Hunting E] Cl [3
Picnicking D [’1 [1
Picture taking [:1 D D
Scenic driving E] [:1 D
Shopping E] E! D
Skiing {:1 [:1 C]
Snowmobiling/ATV [:1 D [1
Swimming L] [J 1:1
Visiting city/state/national parks [3 t:- [:1
Visiting muscums/ Visiting cultural/historical areas Li [J [3
Watching wildlife/birds D C1 D
Other activities: 1:] D D

 

 

As a resident of Emmet County, what attraction(s) or sites do you recommend to
your guests to visit? (fill in your answer)

 

What specific local attraction(s) or sites did your guests visit while staying with you?
(fill in your answer)

 

169

Section 2. Next are some questions about economic development and tourism in
Emmet County.

5. The following are types of economic development options. Please indicate how
acceptable to you each of the following types of economic development is for Emmet
County. (circle one response for each type of development)

 

Type of Not Slightly Somewhat Moderately Very
development: Acceptable Acceptable Acceptable Acceptable Acceptable
Agriculture 1 2 3 4 5
HeaVy _ l 2 3 4 5
manufacturing
Higher 1 2 3 4 5
education
Light 1 2 3 4 5
manufacturing
Medical
and health 1 2 3 4 5
Retail and other 1 2 3 4 5
services
Towism/
Recreation 1 2 3 4 5
Other

9
types 1 “ 3 4 5

 

6. Compared to other economic sectors, how important a role do you think tourism and
recreation should have in Emmet County? (please / one)

S no role D a minor role
[1 a role equal to other economic sectors [:1 a dominant role

7. In your opinion, what place is most visited by tourists in Emmet County?
(fill in your answer)

 

170

Which response best represents the percentage tourism and recreation bring to
Emmet County’s economy? (please / one)

D 0-20% [1 21-40% L! 41-60% L] 61-80% C] 81-100%

How would you describe your level of knowledge about the tourism and recreation
industry in Emmet County? (please / one)

1:] not at all knowledgeable [:1 somewhat knowledgeable

D very knowledgeable D slightly knowledgeable
E] moderately knowledgeable

10. How much do you personally benefit from tourism in your community? (please \/
one)

I: not at all [:1 very little {:1 some (3 quite a bit 13 a lot
11. How much contact do you typically have with tourists visiting your community?
(please \/ one)

1] no contact at all [:1 some contact [:1 a large amount of contact

[:1 a little bit of contact U a moderate amount of contact

12. In terms of Emmet County, please indicate the level of importance for the following
aspects. (circle one response for each statement)

 

Not Slightly Somewhat Moderately Very
Important
Important Important Important Important
At All

Family ties l 2 3 4 5
Friends close by l 2 3 4 5
Natural . 1 2 3 4 5
landscapes/Views

Local. culture and 1 2 3 4 5
traditions

 

171

Presence of
wildlife

Opportunities to
be involved in
community or
organizations

Opportunities
for outdoor l ' 2 3 4 5
recreation

Opportunities for
economic growth

 

13. Please indicate your level of agreement with each of the following statements. (circle
one response for each statement)

 

Strongly Strongly
Disagree Disagree Neutral Agree Agree
If I had to move from Emmet
County, I would be very sorry to l 2 3 4- 5
leave.
I would rather live in Emmet
County than elsewhere. 1 2 3 4 5
The future of Emmet County
looks bright. l 2 3 4 5
Tourism holds great promise for l 2 3 4 5
the future of Emmet County.
Tourism wrll improve the 1 2 3 4 5

appearance of Emmet County.
Tourism creates more attractions
& activities for residents in l 2 3 4 5

Emmet County.

In most ways my life is close to

my ideal. I 2 3 4 5
The conditions of my life are 1 2 3 4 5
excellent.

I am satisfied with my life. I 2 3 4 5

 

I72

So far, I have accomplished what

I want in my life.

If I could live my life over, I
would change almost nothing.

Section 3. Next are some questions about your opinion on the impact of tourism in

Emmet County.

14. This set of questions asks your opinions regarding tourism in Emmet County.
Please indicate your level of agreement with each of the following statements. (circle
one response/or each statement)

Your opinions about tourism in

Emmet County:

Strongly Strongly
Disagree Disagree Neutral Agree Agree

 

Economic
impact:

 

Increasing

the number

of tourists
visiting an area
improves the
local economy.

Tourism
provides
desirable
jobs for local
residents.

Tourism
related jobs are
low paying.

Shopping,
restaurants,
entertainment
options are
better as

a result of
tourism.

Tourism
encourages
more private
development
(e.g., housing,
retail).

 

173

Tourism
encourages
more public
development
(e.g., roads,
public
facilities).

Tourism
contributes to
income and
standard of
living.

Tourism
results in an
increase of the
cost ofliving.

Tourism can
be one of the
most important
economic
developmental
option for an
area.

Tourism
development
unfairly
increases
property taxes.

 

Environmental
impact:

 

Tourism
development
improves

the physical
appearance of
an area.

Tourism
development
increases

the traffic
problems of an
area.

Tourism
results in more
litter in an
area.

 

174

Tourism
provides
incentives for
protection and
conservation
of natural
resources.

Tourism
provides
incentives
for new park
development.

Tourism
provides
incentives for
purchase of
open space.

 

Socio-cultural
impact:

 

An increase

in tourists

in Emmet
County will
lead to friction
between
residents and
tourists.

Tourism
development
increases the
quality of life
in an area.

Tourism helps
preserve

the cultural
identity and
restoration

of historical
buildings.

Tourism
causes
communities
to be over-
crowded.

 

175

Tourism
development
increases the
number of
recreational
opportunities
for local
residents.

Tourism
development
increases the
amount of
crime in the
area.

 

Other opinions:

 

I would
personally
benefit from
more tourism
development
in Emmet
County.

Local
government
should control
tourism
development.

The county
should try to
attract more
tourists.

Additional
tourism would
help Emmet
County grow
in the right
direction.

I support
tourism having
a vital role in
this county.

 

176

 

Section 3. Next are some questions about your opinion on the impact of tourism in

Emmet County.

15. The following are types of development that are part of the tourism industry. Please
indicate how acceptable you feel each of the following types of development is for
Emmet County. (circle one response for each type of development)

Nonresidents
should be
encouraged
to develop
tourism
businesses
in Emmet
County.

Local
government
should restrict
tourism
development.

 

 

Not Slightly Somewhat Moderately Very
Development of: Acceptable Acceptable Acceptable Acceptable Acceptable
Bars/tavems l 2 3 4 5
Beaches (public) 1 2 3 4 5
Eed and breakfast! 1 2 3 4 5
runs
Boating l 2 3 4 5
Campgrounds/RV
parks/clubs 1 2 3 4 5
Casinos l 2 3 4 5
Festrvals/farrs/ 1 2 3 4 5
events
Galleries/museums l 2 3 4 5
Historic/cultural 1 2 3 4 5

attractions

 

177

Hotels/motels
Land for hunting

Marinas/docks/
slips

Open space and
greenways

Parks with
developed areas

Piers (for fishing)
Resorts
Restaurants

Retail stores/
shopping district

Roads and
highways

Second homes/
condos

Trails-motorized

Trails-non-
motorized

Transportation
(public)

 

178

16. Tourism consists of tourism development (for example enhancement of existing
facilities, infrastructure, creating new facilities and businesses) and promotion
(e.g., advertising, public relations, brochures, TV ads, websites, billboards).
Please answer the following questions regarding your interest, personal influence
and involvement in tourism development and promotion. (circle one responsefor
each statement).

Very Quite
None Little Some A Bit A Lot

 

What level of personal influence have you had
on decisions related to tourism development 1 2 3 4 5
and promotion in Emmet County?

What level of involvement have you had in
tourism development and promotion in Emmet l 2 3 4 5
County?

What level of interest do you currently have
about tourism development and promotion in 1 2 3 4 5
Emmet County?

How much influence do tourism—related
businesses have in tourism development and l 2 3 4 5
promotion decision-making in Emmet County?

In the future, how willing are you to be
involved in tourism development and 1 2 3 4 5
promotion in Emmet County?

 

Section 5. Next are some questions regarding your opinion about tourism
promotions and marketing campaigns

17. Please evaluate the outcome of each of the following statements about promotions
and marketing campaigns (circle one response for each item)

 

Ve Neither Ve
Promotions and marketing ry Bad Bad or Good ry
. Bad Good
campaigns that: Good
Improve tourists‘ attitudes towards
1 2 3 4 5
Emmet County
Stimulate travel demand in Emmet 1 2 3 4 5
County

 

179

Attract tourists to Emmet County 1 2 3 4 5
Create a strong identity for Emmet 1 2 3 4 5
County

Improve the image of Emmet County 1 2 3 4 5
Make resrdents proud of Emmet 1 2 3 4 5
County

Make a good financial investment in l 2 3 4 5
Emmet County

 

18. How certain are you that promotions and marketing campaigns for tourism products
can bring benefits to Emmet County? Please indicate the level of mm with each
of the following statements. (circle one response for each item)

Promotions
and marketing
campaigns
can:

Not At All Slightiy Somewhat Moderately Very
Certain Certain Certain Certain Certain

 

Improve
tourists’
attitudes l 2 3 4 5
towards Emmet
County

Stimulate travel
demand in l 2 3 4 5
Emmet County

Attract tourists
to Emmet l 2 3 4 5
County

Create a strong
identity for l 2 3 4 5
Emmet County

Improve the
image of 1 2 3 4 5
Emmet County

Make residents
proud of Emmet l 2 3 4 5
County

 

180

Be a good
financial
investment in
Ermnet County

 

19. The following are types of tourism experiences or services in Emmet County. Which
of these tourism experiences or services do you support for tourism promotions and
marketing campaigns? (circle one response for each type of development)

 

5:32:30“ Not Slightly Somewhat Moderately Very
products: Acceptable Acceptable Acceptable Acceptable Acceptable
Bars/tavems 1 2 3 4 5
Beaches
(public) 1 2 3 4 5
Bed and
breakfast] inns I 2 3 4 5
Boating 1 2 3 4 5
Campgrounds/
RV parks/ 1 2 3 4 5
clubs
Casinos l 2 3 4 5
Festrvals/farrs/ l 2 3 4 5
events
Gallenes/ l 2 3 4 5
museums
Historic/
cultural l 2 3 4 5
attractions
Hotels/motels l 2 3 4 5
Lani f” 1 2 3 4 5
hunnng
Marinas/

.,
docks/slips l " 3 4 5
Open space 1 2 3 4 5
and greenways

 

181

Parks with

developed 1 2 3 4 5
areas

Piers (for

fishing) 1 2 3 4 5
Resorts 1 2 3 4 5
Restaurants 1 2 3 4 5

Retail stores/

shopping I 2 3 4 5
district

Roads and l 2 3 4 5
highways

Second homes/ I 2 3 4 5
condos

“3115'. 1 2 3 4 5
motorized

Trails-.non- 1 2 3 4 5
motorized

Transportation 1 2 3 4 5
(public)

 

20. Please indicate your level of agreement with each of the following statements. (circle
one response for each statement)

Strongly Strongly
Disagree Disagree Neutral Agree Agree

 

I would support promotions

and marketing campaigns that
address key “social issues " i 2 3 4 5
(e.g., climate change, reduction
of pollution) in Emmet County.

I would support cause-related
promotion and marketing
about tourism products (e.g., a l 2 3 4 5
donation, volunteer clean up)
in Emmet County.

 

I82

I would support tourism
promotions and marketing
campaigns that emphasize
sustainability of natural
amenities (e.g., scenic views,
forests) in Emmet County.

1 would support tourism
promotions and marketing
campaigns emphasize 1 2

sustainability of cultural 3 4 5
amenities (e.g., historic sites.

museums) in Emmet County.

A successful tourism economy

' n i n en

in Emmet Cou ty sdepe d t l 2 3 4 5

on promotion and marketing
campaigns.

 

21. How satisfied are you with the agencies/entities in tourism development and
promotion in Emmet County (e.g., Convention & Visitors Bureau, Chamber of
Commerce, Flaming Department, state and federal agencies).

 

Not Slightly Somewhat Moderately Very
Satisfied Satisfied Satisfied Satisfied Satisfied
At All
Tourism 1 2 3 4 5
development
T°"“S".‘ l 2 3 4 5
promotion

 

Section 6. Next are some final questions for classification purposes.
22. How much travel experience do you have? (please / one)

D none u very little 3 some 1: quiteabit 2 alot

23. How many times have you traveled out of state for vacation in the past 12 months?
(please fill in a number) # of times

24. How far do you live from a “tourist area” of Emmet County? (please fill in a number)
miles

183

25. How often do you participate in outdoor recreation activities in your area? (please \/
one)

C] everyday E] a couple times a week 13 a few times a month
[3 a few times a year [3 never

26. Did you live or visit Emmet County as a child? (please V one)

D lived D visited [3 neither lived or visited

27. How many years have you lived or owned a home in Emmet County? (please fill in a
number) years

28. Which statement best describes your residential status in Emmet County? (please /
one)

C 1 am a full-time, permanent, year-round resident who owns my home
[1 I am a part-time, seasonal resident who owns my home (please V all that apply)
L] I am retired and only reside at this residence during certain seasons/time periods
U I use the residence for vacation/weekend use

D Something else (please describe):

Cl None of the above describes my residential status (please describe):

 

 

29. What is your age? (please fill in a number) years old

30. Please indicate the highest level of education you have completed. (please / one)

D less than high school [:1 technical school degree
LJ college degree [3 high school graduate
[3 some college [3 advanced degree

31. Which of the following best describes your total 2006 annual household income from
all sources before taxes? (please \/ one)

D less than $50,000 [3 $50,000-$99,999 13 $100,000 or more

184

 

32. How much of your income comes from the tourism industry in Emmet County?
(please \/ one)

L] I am directly employed in the tourism industry in Emmet County

E] I am indirectly employed in the tourism industry in Emmet County (your work
place provides at least part of its products/services to tourism businesses)

[3 I am not employed in the tourism industry at all in Emmet County

Li I am employed in the tourism industry in another MI County, state or country

Please feel free to share any comments or thoughts you have regarding tourism and
recreation development in Emmet County.

Dr. Christine Vogt
Michigan State University
Dept. of CARRS
131 Natural Resources
East Lansing, MI 48824-1222

185

 

MICHIGAN STATE

UNIVERSITY

 

Hello,
June 18, 2007

Over the past two months you probably received a survey called “Attitudes toward
Tourism in Emmet County.”

I am writing you for a final time because we did not hear from you yet. 1 need

to accurately estimate the level of interest and support for recreation and tourism
development in Emmet County. This effort needs the full range of public input, including
your thoughts. Would you please consider answering these few questions and returning
this letter in the prepaid envelope? Your answer will be anonymous. This will be the last
contact with you. Thank you for your time. Please return your response in the pre—paid
envelope as soon as possible.

Sincerely,
Christine A. Vogt, Ph.D.
Principal Researcher

517-432-0318 or vogtc@msu.edu

1. Have you hosted out-of town guests in the past 12 months? (please \/ one)
F, no F yes, how many? number of out-town-guests

2. Please indicate your level of agreement with each of the following statements. (circle
one response. for each statement)

 

Strongly Strongly
Disagree Disagree Neutral Agree Agree
If I had to move
from Emmet 1 2 3 4 5

County, I would be
very sorry to leave.

I would rather live
in Emmet County 1 2 3 4 S
than elsewhere.

 

186

3. How would you describe your level of knowledge about the tourism and recreation
industry in Emmet County? (please / one)

L’ not at all knowledgeable
[J very knowledgeable

4. How much do you personally benefit from tourism in your community?

(please \/ one)

[J not at all C] very little

J slightly knowledgeable

CI some

[1 quite a bit

Dalot

5. This set of questions asks your opinions regarding tourism in Emmet County.
Please indicate your level of agreement with each of the following statements. (circle
one response for each statement)

Your opinions about
tourism in Emmet
County:

I support tourism having a
vital role in this county.

I would personally benefit
from more tourism
development in Emmet
County.

Nonresidents should be
encouraged to develop
tourism businesses in
Emmet County.

A successful tourism
economy in Emmet
County is dependent on
promotion and marketing
campaigns.

Local government
should restrict tourism
development.

 

Strongly
Disagree

Disagree

Turn over for a few more questions ...............

187

Strongly
Agree

 

 

6. Tourism consists of tourism development (for example enhancement of existing
facilities, infrastructure, creating new facilities and businesses) and promotion (e.g.,
advertising, public relations, brochures, TV ads, websites, billboards). Please answer
the following questions regarding your interest, personal influence and involvement
in tourism development and promotion. (circle one response for each statement).

 

Very Quite
None Little Some A Bit A Lot

What level of personal influence
have you had on decisions related

. 1 2 3 4 5
to tourism development and
promotion in Emmet County?
What level of involvement have
you had in tourism development 1 2 3 4 5
and promotion in Emmet County?
What level of interest do you
currently have about tourism 1 2 3 4 5

development and promotion in
Emmet County?

 

7. How certain are you that promotions and marketing campaigns for tourism products
can bring benefits to Emmet County? Please indicate the level of SEEN—Fm with each
of the following statements. (circle one response for each item)

Promotions
and
marketing
campaigns
can:

Not At All
Certain

Slightly
Certain

Somewhat
Certain

Moderately Very
Certain Certain

 

Stimulate
travel
demand 1 2
in Emmet
County

Improve
the image
of Emmet
County

 

188

8.

DUDEDD

\0

Make

residents
proud of 1 2 3 4 5
Emmet
County

Be a good
financial
investment 1 i , 2 3 4 _ . » 5.
inEmmet .7 . i'_:::;_.‘;.__ , _
County 7 7 .. ' '

 

Which statement best describes your residential status in Emmet County?
(please / one)

I am a full-time, permanent, year-round resident who owns my home

I am a part-time, seasonal resident who owns my home (please ~/ all that apply)
I am retired and only reside at this residence during certain seasons/time periods
1 use the residence for vacation/weekend use

Something else (please describe):
None of the above describes my residential status (please describe):

 

. To help us on surveys in the future, could you share with us why you didn’t return the

earlier surveys we sent you.

 

 

 

Thank you!
Return in prepaid envelope or to

131 Natural Resources Bldg.
E. Lansing, MI 48824-1222

189

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