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dissertation entitled

ACTIVITY-BASED MARKET SEGMENTATION IN A RURAL
TOURISM DESTINATION: A CASE STUDY OF WEST-
CENTRAL MICHIGAN

presented by

Nai-Kuan Yang

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“—— ,iﬂh— —

 

ACTIVITY-BASED MARKET SEGMENTATION IN A RURAL
TOURISM DESTINATION: A CASE STUDY OF WEST-CENTRAL
MICHIGAN

By

Nai-Kuan Yang

A DISSERTATION

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

DOCTOR OF PHILSOPHY

Park, Recreation and Tourism Resources

2009

ABSTRACT

ACTIVITY-BASED MARKET SEGMENTATION IN A RURAL TOURISM
DESTINATION: A CASE STUDY OF WEST-CENTRAL MICHIGAN

By
Nai-Kuan Yang

The primary purpose of this study is to ﬁll the gap between rural tourism and
activity-based segmentation by applying activity-based segmentation to proﬁle rural
tourists in the west-central Michigan region. Both a priori (the type and number of
segments are decided before data collection) and post hoc (segments are divided in terms
of a set of deﬁned characteristics after the data are collected) approaches are applied in
the study. Three samples were drawn from transient, overnight and potential visitors.

The results of this study lend support to previous studies in the following two
ways. First, the ﬁndings prove that activity is a valuable segmentation base from either
the a priori or the post hoc approach. In the a priori approach, signiﬁcant activities such
as boating, festival/events, hiking/walking, shopping, and visiting a federal/state park
were found among all three populations sampled. In the post hoc approach, latent class
analysis identiﬁed three to four classes (segments) among the samples with light activity
(including both outdoor and general) participants, no preference activity participants, and
cultural tourists making up the largest segments. Second, the socio-demographic, trip
related, travel expenditures, lodging choices, motivations, perceptions of destination
attributes, and knowledge of attractions variables were found to be helpful for proﬁling
segments in both the a priori and post hoc approaches. Tourists’ knowledge of attractions
is an especially useful variable for proﬁling segments since activity participation relates

to the type of resources at attractions such as the Pere Marquette River and others. For

example, the study area is known for abundant water resources for boating activities.
Surprisingly, there were still many respondents who said they enjoyed boating but did not
know about or were aware of but had not visited these attractions. Therefore,
understanding the relationship between attractions and activity participation can directly
assist tourism planners to improve efforts to promote their areas.

The post hoc approach involved advanced statistical analysis (latent class
analysis), which has not yet been commonly used in tourism research. But, the approach
proved useful for identifying activities from a broad range that a planner might promote
that would most appeal to the target segment. For example, outdoor tourists tend to
participate in a range of outdoor activities such as boating and camping. Compared with
the post hoc approach, the a priori might be an easier approach for tourism planners to
use to ﬁnd interesting segments and then proﬁle them. But, the a priori approach alone
might lead a planner to promote only boating and miss tourists with other outdoor
interests. Because a priori and post hoc analysis could be applied to the same survey
derived data set with only a marginal increase in the cost, employing both is generally
advisable. Adding the post hoc approach could help the planner focus promotions to draw
the attention of tourists who would most enjoy the attractions available. This study
demonstrated that combining the two approaches yields the best information for
promoting an area. In this study area speciﬁcally, light general activity participants were
the largest segment, so the advertising for the area should address the variety of activities
offered but also highlight speciﬁc activities such as boating and hiking/walking that were

identiﬁed as signiﬁcant in the a priori ﬁndings.

To my family and friends, especially Ting-Chieh and Martina.

iv

ACKNOWLEDGEMENTS

I would like to thank my dissertation committee members, Dr. Sarah Nicholls
(chair), Dr. Donald Holecek, Dr. Scott Loveridge, and Dr. Richard Paulsen. Dr. Nicholls,
thank you for your guidance. Dr. Holecek, thank you for constant intellectual discussions.
Dr. Loveridge, thank you for willing to step in and guide me when Dr. Thum leﬁ. Dr.
Paulsen, thank you for your support and mentoring, you are a role model in being a great
scholar. Dr. Thum, thank you for assisting with research methods even after you stepped
down as a committee member.

Ting-Chieh and Martina, thank you for your love, support and encouragement.
We learn to face challenges together and you are my motivation to live on. Jamie, a
heartfelt thank you for standing by me with wise advice in any difﬁcult situation. Rita,
thank you for taking care of my kids with emergent requests. Yaya, thank you for
cheering me on, being patient and supporting me. Daddy, and grandmas, I am humble
and generous with your spirits rooted inside me.

John, thank you for mentoring me for years. Bob, Joanne, David, Scott, and Janet,
thank you for working intensively with me to improve my writing. Charlotte, Joy, Louise,
and friends from Edgewood United Church, thank you for inspiring me and helping me to
ﬁnd my goals for my next journey. There are so many people that have been here from
the beginning that I feel thankful for. There is a Taiwanese saying, “There are so many
people to appreciate so we thank God.” Indeed,

THANK GOD!!

TABLE OF CONTENTS

LIST OF TABLES ........................................................................................................... viii
LIST OF FIGURES ......................................................................................................... xiii
CHAPTER 1 INTRODUCTION ........................................................................................ 1
Statement of the Problem ................................................................................................ 1
Theoretical Framework ................................................................................................... 4
Purpose of the Study ....................................................................................................... 7
Research Questions and Hypotheses ............................................................................... 8
Signiﬁcance ................................................................................................................... 12
Delimitations ................................................................................................................. 1 3
Limitations .................................................................................................................... 14
Study Organization ........................................................................................................ 14
CHAPTER 2 LITERATURE REVIEW ........................................................................... 16
Introduction ................................................................................................................... 1 6
Tourism Development in Rural Areas ........................................................................... 16
Theoretical Framework of Market Segmentation ......................................................... 23
Summary of the Literature Review ............................................................................... 49
CHAPTER 3 METHODOLODY ..................................................................................... 51
Study Area ..................................................................................................................... 51
Population and Sample .................................................................................................. 57
Survey Instruments ........................................................................................................ 60
Data Collection .............................................................................................................. 65
Nonresponse Study ........................................................................................................ 68
Imputation for Survey Sampling ................................................................................... 69
Data Analysis ................................................................................................................ 70
CHAPTER 4 RESULTS ................................................................................................... 75
Examination of the Representativeness of the Potential Visitor Sample ...................... 75
Nonrespondent Bias Study ............................................................................................ 77
Description of the Sample ............................................................................................. 81
Testing of the Study Hypotheses ................................................................................... 95
CHAPTER 5 DISCUSSION AND CONCLUSIONS .................................................... 152
Summary of Results and Discussion ........................................................................... 152
Implications ................................................................................................................. 1 73
Limitation of Findings ................................................................................................. 176
Future Research ........................................................................................................... 176

vi

APPENDIX A: Descriptive Statistics for Activity Participation in the Study ............... 180

APPENDIX B: Logistic Regression Model Results--without Signiﬁcant Findings ...... 188
APPENDIX C: Transient Visitor Survey ....................................................................... 192
APPENDIX D: Overnight Visitor Survey ...................................................................... 195
APPENDIX E: Potential Visitor Survey ......................................................................... 203
BIBLIOGRAPHY ........................................................................................................... 21 1

vii

LIST OF TABLES

Table 2-1: Common Classiﬁcation of Methods Used in Market Segmentation ............... 34
Table 2-2: General Market Segmentation Bases Used in Tourism Research ................... 36
Table 2-3: Popular Market Segmentation Bases Used in Tourism Research ................... 38
Table 2-4: Activity-based Segmentation Research in Tourism Studies ........................... 41
Table 3-1: The Rural Settings in Each Study Area County .............................................. 53
Table 3-2: Percentage Employment in Selected Industry Types by Gender .................... 53
Table 3-3: The Attractions in Each County ...................................................................... 54
Table 3-4: County Covered and Number of Households in Six DMAs ........................... 59
Table 3-5: Low Response Rate Studies in Tourism Research .......................................... 60
Table 3-6: Variables Used for Analysis in the Surveys--Activity .................................... 61
Table 3-7: Variables Used for Analysis in the Surveys--Trip-related Characteristics ..... 62
Table 3—8: Variables Used for Analysis in the Surveys--Pull and Push Factors .............. 64
Table 3-9: Variables Used for Analysis in the Surveys--Socio-demographic
Characteristics ........................................................................................... 65
Table 3-10: Transient Visitor Survey Response Counts ................................................... 67
Table 3-11: Overnight Visitor Survey Response Rates .................................................... 67
Table 3-12: Potential Visitor Survey Response Rates ...................................................... 68
Table 4-1: Activities that Rural Tourists Like to Participate in ........................................ 76
Table 4-2: Comparison of Potential Visitor Sample and Michigan Travel Market Sampl;6
Table 4-3: Comparison of Place of Residence of Potential Visitors, Overnight Visitors,
and Transient Visitors ............................................................................... 77

Table 4-4: Comparison of First and Second Wave Respondents in the Overnight Visitor
Survey ....................................................................................................... 78

viii

Table 4-5: Comparison of First and Second Wave Respondents in the Potential Visitor

Survey ....................................................................................................... 80
Table 4-6: Transient Visitors’ Socio-demographic Proﬁle ............................................... 82
Table 4-7: Transient Visitors’ Trip—related Characteristics .............................................. 83
Table 4-8: Transient Visitors’ Knowledge of Attractions ................................................ 84
Table 4-9: Overnight Visitors’ Socio-demographic Proﬁle .............................................. 86
Table 4-10: Overnight Visitors’ Trip-related Characteristics ........................................... 87
Table 4-11: Overnight Visitors’ Knowledge of Attractions ............................................. 89
Table 4-12: Overnight Visitors’ Perceptions of Destination Attributes ........................... 91
Table 4—13: Overnight Visitors’ Travel Expenditures ...................................................... 92
Table 4—14: Potential Visitors’ Socio-demographic Proﬁle .............................................. 93
Table 4-15: Potential Visitors’ Trip-related Characteristics ............................................. 94
Table 4-16: Potential Visitors’ Travel Motivations .......................................................... 95

Table 4-17: Types of Activities that Interested Transient Visitors on Their Current Trip 97

Table 4-18: Types of Activities Overnight Visitors and Family Participated in during
their Trips to West-central Michigan ........................................................ 99

Table 4-19: Types of Activities Potential Visitors Participated in Most Often during
Pleasure Trips .......................................................................................... 101

Table 4-20: Logistic Regression Model Results-Boating by Socio-demographic
Characteristics for Transient Visitors ..................................................... 105

Table 4-21: Logistic Regression Model Results--Festival/event by Socio-demographic
Characteristics for Transient Visitors ..................................................... 106

Table 4-22: Logistic Regression Model Results--Hiking/walking by Socio-demographic
Characteristics for Overnight Visitors .................................................... 106

Table 4—23: Logistic Regression Model Results--Shopping by Socio-demographic
Characteristics for Overnight Visitors .................................................... 107

Table 4-24: Logistic Regression Model Results--Visiting a F ederal/state Park by Socio-
demographic Characteristics for Potential Visitors ................................ 108

ix

Table 4-25:

Table 4-26:

Table 4-27:

Table 4-28:

Table 4-29:

Table 4-30:

Table 4-31:

Table 4-32:

Table 4-33:

Table 4-34:

Table 4-35:

Table 4-36:

Table 4-37:

Table 4-38:

Table 4-39:

Table 4-40:
Table 4-41:

Logistic Regression Model Results--Visiting a Federal/State Park by Travel
Motivations for Potential Visitors ........................................................... 109
Logistic Regression Model Results--Shopping by Trip-related Characteristics
for Overnight Visitors ............................................................................. 111
Logistic Regression Model Results--Hiking/walking by Travel Expenditures
for Overnight Visitors ............................................................................. 112
Logistic Regression Model Results--Shopping by Travel Expenditures for
Overnight Visitors ................................................................................... 112
Logistic Regression Model Results--Hiking/walking by Travel Sources Used
by Overnight Visitors .............................................................................. 113
Logistic Regression Model Results--Shopping by Travel Sources Used by
Overnight Visitors ................................................................................... l 14
Logistic Regression Model Results--Visiting a Federal/state Park by Travel
Sources Used by Potential Visitors ......................................................... 115
Logistic Regression Model Results--Hiking/walking by Accommodation
Type for Overnight Visitors .................................................................... 1 16
Logistic Regression Model Results--Boating by Attractions for Transient
Visitors .................................................................................................... 118
Logistic Regression Model Results--Festival/event by Attractions for
Transient Visitors .................................................................................... 119
Logistic Regression Model Results--Hiking/walking by Attractions for
Overnight Visitors ................................................................................... 120
Logistic Regression Model Results--Shopping by Attractions for Overnight
Visitors .................................................................................................... 121
Logistic Regression Model Results-~Shopping by Destination Attributes for
Overnight Visitors ................................................................................... 122
Comparison of Model Fit Indicators for the Transient Visitor Sample ...... 125
Probability of Activity Participation across Latent Classes among Transient
Visitors .................................................................................................... 125
Comparison of Model Fit Indicators for the Overnight Visitor Sample ..... 127

Probability of Activity Participation across Latent Classes among Overnight
Visitors .................................................................................................... 129

Table 4-42:

Table 4-43:

Table 4-44:

Table 4-45:

Table 4-46:

Table 4-47:

Table 4-48:

Table 4-49:

Table 4-50:

Table 4-51:

Table 4-52:

Table 4-53:

Table 4-54:

Comparison of Model Fit Indicators for the Potential Visitor Sample ....... 131
Probability of Activity Participation across Latent Classes among Potential
Visitors .................................................................................................... 134
Socio-demographic Characteristics across Latent Classes among Transient
Visitors .................................................................................................... l 3 8
Socio-demographic Characteristics across Latent Classes among Overnight
Visitors .................................................................................................... 139
Socio-demographic Characteristics across Latent Classes among Potential
Visitors .................................................................................................... 140
Trip-Related Characteristics across Latent Classes among Transient Visitors
................................................................................................................. 141
Trip-related Characteristics across Latent Classes among Overnight Visitors
................................................................................................................. 143
Trip-related Characteristics across Latent Classes among Potential Visitors
................................................................................................................. 144
Travel Expenditures across Latent Classes among Overnight Visitors ...... 145
Travel Motivations across Latent Classes among Potential Visitors .......... 146

Knowledge of Attractions across Latent Classes among Transient Visitorsl47

Knowledge of Attractions across Latent Classes among Overnight Visitors
................................................................................................................. 148

Perceptions of Destination Attributes across Latent Classes among Overnight
Visitors .................................................................................................... 151

Table 5-1: Summary of Results Based on the A Priori Approach .................................. 161

Table 5-2: Summary of Results Based on the Post Hoc Approach--Activity Participation

................................................................................................................. 170

Table A-l: Transient Visitors’ Socio-demographic Proﬁle--Participation in Boating and

Festival/event .......................................................................................... 1 80

Table A-2: Transient Visitors’ Trip-related Characteristics--Participation in Boating and

F estival/event .......................................................................................... 180

Table A-3: Transient Visitors’ Knowledge of Attractions--Participation in Boating and

Festival/event .......................................................................................... 1 8 1

xi

 

Table A-4: Overnight Visitors’ Socio-demographic Proﬁle--Participation in

Hiking/walking and Shopping ................................................................ 182
Table A-5: Overnight Visitors’ Trip-related Characteristics on their Most Recent Trip to
WCMI--Participation in Hiking/walking and Shopping ......................... 183
Table A-6: Overnight Visitors’ Knowledge of Attractions--Participation in
Hiking/walking and Shopping ................................................................ 184
Table A-7: Overnight Visitors’ Perceptions of Destination Attributes--Participation in
Hiking/walking and Shopping ................................................................ 185
Table A—8: Overnight Visitors’ Travel Expenditures--Participation in Hiking/walking and
Shopping ................................................................................................. l 85
Table A-9: Potential Visitors’ Socio-demographic Proﬁle -- Visiting a Federal/state Park
................................................................................................................. 1 86
Table A-10: Potential Visitors’ Trip-related Characteristics--Visiting a Federal/state Park
................................................................................................................. 186
Table A-1 1: Potential Visitors’ Travel Motivations--Visiting a F ederal/state Park ....... 187

Table B-1: Logistic Regression Model Results--Boating by Trip-related Characteristics
for Transient Visitors .............................................................................. 188

Table B-2: Logistic Regression Model Results--Festival/event by Trip-related
Characteristics for Transient Visitors ..................................................... 188

Table B-3: Logistic Regression Model Results--Hiking/walking by Trip-related
Characteristics for Overnight Visitors .................................................... 189

Table B-4: Logistic Regression Model Results--Visiting a F ederal/state Park by Trip-
related Characteristics for Potential Visitors .......................................... 189

Table B-5: Logistic Regression Model Results--Boating by Travel Sources for Transient
Visitors .................................................................................................... 190

Table B-6: Logistic Regression Model Results--Festival/event by Accommodation Type
for Transient Visitors .............................................................................. 190

Table B-7: Logistic Regression Model Results--Shopping by Accommodation Type for
Overnight Visitors ................................................................................... 190

Table B-8: Logistic Regression Model Results--Hiking/walking by Destination Attributes
for Overnight Visitors ............................................................................. 191

xii

 

LIST OF FIGURES

Figure 2-1: Steps in Segmentation, Targeting, and Positioning ....................................... 25
Figure 2-2: Steps in A Priori Segmentation ...................................................................... 29
Figure 2-3: Steps in Post Hoc Segmentation .................................................................... 33
Figure 3-1: Study Area Map ............................................................................................. 52

Figure 4-1: Probability of Activity Participation by Latent Classes among Transient
Visitors .................................................................................................... 128

Figure 4-2: Probability of Activity Participation by Latent Classes among Overnight
Visitors .................................................................................................... 132

Figure 4-3: Probability of Activity Participation by Latent Classes among Potential
Visitors .................................................................................................... 136

xiii

CHAPTER 1 INTRODUCTION

Statement of the Problem

Michigan is well known as a rural tourism destination with an abundance of
resources including a variety of agricultural, nature-based, and cultural amenities. In
general, the whole state of Michigan is perceived as a rural tourism destination and a
peaceful and relaxing place to visit (Adkins, 2009). The new “Pure Michigan” campaign
successfully promotes the state with activities and attractions in both rural and urban
areas. Although there is a full list of things to do and destinations related to various
activities in the “Pure Michigan” website, it does not help to create local destination
images to draw tourists’ attention to different regions.

Lane (1994b) indicated that rural tourism is multifaceted. Thus, the perception of
rural tourists as a homogenous group may not offer useful information for tourism
planners when promoting rural destinations. For tourism planners in Michigan to ensure
suitable services and recreational opportunities that can meet tourists’ interests in rural
destinations, it is important to understand the tourists’ needs. Moreover, to develop
marketing plans that will attract the tourists who enjoy the variety of cultural and natural
activities that Michigan can offer, it is crucial for tourism planners to obtain more
information related to rural tourists’ needs.

Frochot (2005) stated that rural tourism is an extremely diverse market with a
wide variety of visitor needs and expectations. Molera and Albaladejo (2007) also
addressed the need to discover different segments in the rural tourist market. Following
from these statements, it is reasonable to assume that segmentation techniques could be

particularly appropriate for understanding this diverse market. Sharpley (1996) also

pointed out that market segmentation could be used as an important tool for
understanding and promoting rural tourism.

Market segmentation studies in rural tourism are still limited. Kastenholz, Davis,
and Paul (1999) studied tourism in rural communities in North and Central Portugal and
identiﬁed four different tourist groups based on beneﬁt segmentation: want-it-all,
independent, traditional, and environmental. Molera and Albaladejo (2007) segmented
tourists in rural areas of south-eastem Spain by beneﬁt segmentation. The ﬁve segments
identiﬁed in their study included family rural, relax rural, active rural, rural life, and rural
accommodation. Park and Yoon (2009) adopted motivation as a base to segment rural
tourists in Korea. Four clusters were found in their study: family togetherness, passive
tourists, want-it-all, and learning and excitement. All of these researchers used the post
hoc approach to segment the rural destinations, which means that subgroups of tourists
are obtained by data-driven segmentation on a multivariate basis aﬁer data are collected
and analyzed.

Keyser (2002) once mentioned that destinations included many elements that are the
“reasons” why the tourism industry exists. All the elements such as attractions (for
example, natural resources), tourist facilities and services (for example, accommodations
and restaurants), infrastructure (for example, roads and water supply), transportation (for
example, boats and coaches) and hospitality (for example, services) must be provided for
a destination to function effectively. Of those elements, attractions at destinations are the
most powerful elements and usually form the foundation of the destination image.
Tourism planners could use these destination images to create market awareness of an

area.

Goeldner and Ritchie (2003) also addressed the importance of attractions in the
tourism system. Attractions are one of the most important motivators for travel. Without
attractions, there would be little need to build other tourism services such as
transportation systems, lodging facilities, etc. Mainly, attractions can be classiﬁed into
cultural, natural, events, recreation, and entertainment attractions. Across those categories,
the major component of tourism is the recreation activity engagement in a destination.
Thus, considerable thought and effort should be devoted to the type of activities in which
visitors tend to participate. In order to attract people and also guide advertising sectors to
promote the area efﬁciently, it is useful to discover which activities are more popular in
an area.

According to Towner (1996) and Roberts and Hall (2001), activities have long
been recognized as strongly associated with rural areas. Additionally, the literature has
shown that identifying tourists’ activity preferences could be an efﬁcient approach to
segment markets (Jang, Morrison, & O'Leary, 2004; McKercher, Ho, du Cros, & So-
Ming, 2002). However, there is no reference in the literature to activity-based
segmentation being used in segmenting rural tourists.

Since it has been suggested that the main attraction of rural areas comes from
offering a variety of activities, it is important to investigate the beneﬁt of applying
activity-based market segmentation in rural tourism markets. Also, market segmentation
studies have been applied in Asia and Europe but not yet in the United States (US).
Considering cultural and geographical differences, those results may not be generalized

to the North American continent. Market segmentation research by activity-based

segmentation in US rural destinations can help to understand rural tourists in the US
cultural context.
Theoretical Framework

Since Smith (1956) introduced the concept of market segmentation, it has become
a central theme in marketing. The most popular deﬁnition of market segmentation was
suggested by Kotler (1978) as follows:

Market segmentation is the subdivision of a market into distinct subsets of

customers, where any subset may conceivably be selected as a target market to be

reached with a distinct marketing mix (p. 249).

Early in the twentieth century, the Fordist industrial development led to a focus on
mass marketing. Since marketing and production processes have become more efﬁcient,
marketing strategies have changed from being product-oriented to consumer-oriented,
and from a mass market to a target market focus. When marketing started to focus on
diverse customers, market segmentation divided customers into different groups based on
certain criteria while also identifying the characteristics that differed between groups
(Dickson & Ginter, 1987).

Kotler (2000) stated that the advantage of market segmentation is that companies
usually cannot attract all buyers because the buyers are too varied in their needs. Also, for
companies with limited skills and resources, it is not efﬁcient to design different products
to satisfy all customers. In this regard, market segmentation is the best strategy for
looking into the market in depth, and it can lead to promoting products efﬁciently. In the
context of tourism, segmentation would help to understand target markets so that

marketers can more efﬁciently promote their destination. Kotler, Bowen, and Makens

(2005) addressed the procedure for market segmentation, which includes the following

elements:

1. Requirement for effective segmentation: There are many ways to segment a market,
and not all are equally efﬁcient. Therefore, in order to constructively segment a market,
market segments should share some common characteristics: (a) Measurability: The
segment’s size and total demand are measurable; (b) Accessibility: The segments can
be accessed so services can reach them; (c) Substantiality: The segments are large
enough to create a proﬁtable and long-lasting business; and (d) Actionability:
Successful plans can be designed for serving target segments.

2. Evaluating market segments: There are three factors that need to be evaluated in
different segments: (a) Segment size and growth; (b) Segment attractiveness (potential
members, competitors, substitutes, suppliers); and (c) Company goals and resources.

Market Segmentation Approaches
Market segmentation can be categorized as a priori (commonsense) and post hoc

(data-driven) (Bassi, 2007; Dolnicar, 2004; McKercher, 2008; Wind, 1978). A priori

(commonsense segmentation with prior knowledge) has long been studied in tourism

research. Under this approach, the grouping criteria are identiﬁed in advance. Dolnicar

(2004) found that at least 53% of the segmentation studies published in the Journal of

Travel Research between 1989 and 2004 divided tourists by commonsense information

and portrayed the result as a priori segments. In the post hoc approach, subgroups of

tourists can be determined by data-driven segmentation on a multivariate basis.
Based on Kotler (1978), the deﬁnition of market segmentation included two

procedures: (a) The choice of market segmentation bases, and (b) Development of

proﬁles of resulting segments. The theoretical framework for this study is discussed in
the following two main themes.
Market Segmentation Bases

In market segmentation research, the selection of a set of variables or
characteristics to partition customers is a crucial step because market segmentation
requires researchers to choose a battery of variables that can be used as a “differentiable”
base that deﬁnes the segments. The choice of market segmentation bases has been
broadly discussed in the tourism literature. Lowyck, Van Langenhove, and Bollaert (1990)
reviewed the earliest publications for tourist segmentation and identiﬁed three major
segmentation bases: demographic, socioeconomic, and psychographic. Andereck and
Caldwell (1994) reviewed and addressed tourist segmentation bases as socio-
demographics, geographic, personality, participation patterns, seasonality, and
motivations. Tsiotsou (2006) further reviewed the most recent market segmentation
studies where segmentation bases included trip characteristics, leisure activities, life-style,
destination choice, loyalty, and satisfaction. In addition, many tourism studies have
employed market segmentation to examine speciﬁc themes such as beneﬁts bases
(Canever, van Trijp, & van der Lans, 2007; F rochot, 2005; Kelley, Strother, Blouin, &
Crouch, 1986; Loker & Perdue, 1992; Rong & Emine, 2007; Shim & Bickle, 1994), and
motivation bases (Andreu, Kozak, Avci, & Ciﬁer, 2005; Bieger & Laesser, 2002;
Mehmetoglu, 2005; Sangpikul, 2008).

Development of Proﬁles of Resulting Segments
Andereck and Caldwell (1994) stated that proﬁling the characteristics of the

segments may help to design and promote products more efﬁciently. With respect to

tourists’ characteristics, the most commonly used factor for segment proﬁling is socio-
economic (Bargeman, Joh, Timmermans, & Van der Waerden, 1999). Mazanec (1992)
addressed the value of using additional characteristics such as geographic, demographic,
behavioral, and life-style. He also stated that a marketing strategy would tend to fail if the
segments were not described using those characteristics.

Purpose of the Study

The purpose of this study is to assess the effectiveness of activity-based
segmentation of the rural tourism market. Assessment of the effectiveness of market
segmentation depends on whether the segments can be identiﬁed by activity participation
and statistical differences can also be found between segments while proﬁling the
resulting segments. Five counties (Lake, Manistee, Mason, Newaygo, and Oceana)
located in the west-central portion of Michigan (hereafter abbreviated as the WCMI
region) serve as the study area.

Most previous studies have only considered one of the two approaches, but
Dolnicar (2004) suggested using a priori and post hoc approaches in combination to
more creatively segment tourist markets. The segments developed from different
approaches can offer tourism planners more information to use in deciding which are the
most suitable subgroups of tourists to target. Therefore, the framework of this study is
completed with two approaches: the a priori and the post hoc approach.

The a priori approach is used to ﬁnd segments corresponding to the criterion that
is set for the different samples. The post hoc approach indentiﬁes segments with bundles
of activities that are determined by the data after they have been collected and analyzed.

The number of segments is unknown until the data are analyzed.

After deciding the segments in each approach, the study examines the segments
based on the attributes that could be used to proﬁle the segments. These attributes, which
include socio-demographic, trip characteristics, travel motivations, and travel
expenditures, have been identiﬁed in the literature and found helpﬁrl in explaining the
segments. The study also includes attributes of the study area such as attraction attributes
and destination attributes. These factors could help to understand the tourists not only
from the demand side but also from the supply side. Finally, marketing strategies for each
segment are suggested. The study then also evaluates these two approaches by
comparing the effectiveness of dividing and proﬁling segments.

Research Questions and Hypotheses

The study framework and the hypotheses examined are presented in two sections:
a priori and post hoc approaches. The design of the questionnaire on activity participation
in each sample is slightly different. Transient visitors were asked about their activity
participation on their current trip to the WCMI region. Overnight visitors were asked
about their activity participation in general on their trips to the WCMI region. Potential
visitors were asked abou their activity participation on their pleasure trips in other areas.

A Priori Approach--Individual Activity
Choice of Market Segmentation Bases

A priori approach is known for setting the criteria before segmenting. The criteria
set in this study differ in each sample. The transient visitors sample is divided into two
groups, those whose primary destination on this current trip was the WCMI region and
those whose primary destination on this current trip was not the WCMI region. The

overnight visitors sample is divided into two groups, those who spent two nights or less

on their most recent overnight visit to the WCMI region and those who spent at least
three nights on their most recent overnight visit to the WCMI region. The potential
visitors sample is divided into two groups, those who have never visited the WCMI
region and those who have visited the WCMI region.
Research Question 1.1: Is there any significant difference between the two groups in
each sample with respect to their participation in each activity?

H1.1: There is a signiﬁcant difference between the two groups in each sample
with respect to their participation in each activity. (Forty-ﬁve individual
activities are compared between the groups.)

Development of Proﬁles of Resulting Segments
After ﬁnding out if there are signiﬁcant differences between the groups with
respect to activity preferences, those activities that showed higher participation and
signiﬁcantly differentiated the two groups are chosen to examine their relationship with
other variables. The criterion set for distinguishing the two groups in each sample acted
as the controlling variable in every logistic regression model.
Research Question 1.2: Can socio-demographic characteristics be used to predict
respondents ’ activity participation while controlling the variable?

H1.2: Socio-demographic characteristics can be used to predict activity
participation while controlling the variable.

Research Question [.3: Can travel motivations be used to predict respondent‘s’
activity participation while controlling the variable?

H. 1 .3: Travel motivations can be used to predict activity participation while

controlling the variable.

Research Question 1.4: Can trip characteristics be used to predict respondents’
activity participation while controlling the variable?
H.1.4: Trip characteristics can be used to predict activity participation while
controlling the variable.
Research Question 1.5: Can travel spending be used to predict respondents ’ activity
participation while controlling the variable?
H. 1 .5: Travel spending can be used to predict activity participation while
controlling the variable.
Research Question 1.6: Can the type of travel information sources utilized be used to
predict respondents ’ activity participation while controlling the variable?
H. 1 .6: The type of travel information sources utilized can be used to predict
activity participation while controlling the variable.
Research Question 1.7: Can the choice of lodging type be used to predict respondents’
activity participation while controlling the variable?
H. 1 .7: The choice of lodging type can be used to predict activity participation
while controlling the variable.
Research Question 1.8: Can WCMI attraction visits be used to predict respondents’
activity preferences while controlling the variable?
H. 1 .8: WCMI attraction visits can be used to predict activity preferences while
controlling the variable.
Research Question 1.9: Can perception of W C MI destination attributes be used to

predict respondents ’ activity preferences while controlling the variable?

10

H. 1 .9: Perception of WCMI destination attributes can be used to predict activity
preferences while controlling the variable.
Post Hoc Approach--Bundle of Activities
Choice of Market Segmentation Bases
The ﬁrst step of market segmentation is to identify bases for segmenting the
market. The questions and hypotheses to be examined are listed below:
Research Question 2.1: Are there any classes that can be identified using an activity-
based approach?
H2.1: There are classes that can be identiﬁed using an activity-based approach.
Development of Proﬁles of Resulting Segments
The second step of market segmentation is to develop proﬁles of resulting
segments. The main goal in this step is to ﬁnd variables that can be used to further
characterize these segments. The research questions and hypotheses are tested under ﬁve
dimensions:
Research Question 2.2: Are there signiﬁcant diﬂerences between classes with respect
to respondents ’ socio-demographic characteristics?
H22: There are signiﬁcant differences between classes with respect to
respondents’ socio-demographic characteristics.
Research Question 2.3: Are there significant diﬂerences between classes with respect
to respondents’ trip-related characteristics?
H2.3: There are signiﬁcant differences between classes with respect to

respondents’ trip-related characteristics.

11

Research Question 2.4: Are there significant differences between classes with respect
to respondents’ travel expenditures?
H2.4: There are signiﬁcant differences between classes with respect to
respondents’ travel expenditures.
Research Question 2.5: Are there significant differences between classes with respect
to respondents’ travel motivations?
H2.5: There are signiﬁcant differences between classes with respect to
respondents’ travel motivations.
Research Question 2.6: Are there signiﬁcant differences between classes with respect
to respondents’ knowledge of WC M1 attractions?
H2.6: There are signiﬁcant differences between classes with respect to
respondents’ knowledge of WCMI attractions.
Research Question 2. 7: Are there signiﬁcant diﬂerences between classes with respect
to respondents ’ perceptions of W C M1 destination attributes?
H2.7: There are signiﬁcant differences between classes with respect to
respondents’ perceptions of WCMI destination attributes.
Significance

The signiﬁcance of this study is discussed from a theoretical and a practical

perspective.

Theoretical Significance

This study is the ﬁrst to use activity-based market segmentation to better

understand market segments in rural tourism. The study includes a priori and post hoc

approaches. Most studies apply only one of these approaches. The comparison of two

12

approaches can explain which approach is more applicable to market segmentation when
applied to a regional rural destination.

Also, latent class analysis is applied in the post hoc approach. It is not commonly
used in tourism study. This approach could be more broadly applied to market
segmentation of categorical variables in future studies.

The study includes more sets of factors than previously used in tourism market
segmentation. For example, the attributes include socio-demographic, travel motivations,
trip related characteristics, travel expenditures, attractions, and destination attributes. The
study consider not only demand-side but also supply-side factors.

Practical Signiﬁcance

The study provides marketing suggestions combining and evaluating the results
from the two approaches. The implications of this study can also assist tourism managers
to provide more in-depth knowledge of target markets that will facilitate more accurate
target marketing resulting in enhanced visitor satisfaction and revenue for area businesses.

Delimitations

This study is delimited to a quantitative research design and data collection
techniques. Visitors’ activity preferences were assessed based on a dichotomous response
indicating whether the study participant likes to participate in each activity during their
leisure time or not. The intensity of preferences or actual participation in each listed
activity was not assessed and was beyond the scope of this study. In future studies, it
might be worth investigating activity preferences from a qualitative perspective. The

study is delimited to market segmentation analysis in a rural area of the United States of

13

America, speciﬁcally, Lake, Manistee, Mason, Oceana, and Newaygo Counties in
Michigan.
Limitations

The proposed study is limited by the following factors: (a) Mail survey response
rates were low; (b) Potential visitor surveys were not drawn proportionally from the six
designated marketing areas (DMAs) selected for analysis; (0) The transient visitor survey
was scheduled on both weekdays and weekends, but only conducted during summer
months.

To improve study quality, the study compared results from three data sets and
examined whether consistent trends could be found across similar groups. Non-response
bias and the non-proportional drawing sample issue are discussed in chapter 3. For
potential visitors, if respondents were found to not be representative of the population,
then adjustments were processed and evaluated.

In the potential visitor survey, general activity preferences were elicited.
Therefore, the market segmentation result from the potential visitor survey can be applied
generally to other rural destinations similar to the study area in Michigan. However, the
overnight and transient surveys asked about activity preferences in the study area, thus,
the results from those data may not be applied to other destinations due to the uniqueness
of the characteristics of the resources in the study area.

Study Organization

This study is organized into ﬁve chapters. Chapter 1 presents the general context

of the study and the theoretical framework discussed in more detail later. Chapter 2

provides an overview of rural tourism destinations and the study’s theoretical foundation

14

in market segmentation. Activity-based segmentation research in the tourism industry is
also introduced.

Chapter 3 discusses the research methodology used to obtain and analyze
information for this study. First, the study area, sample, and population are described.
Second, data collection techniques and research instruments are introduced. Third, non-
response bias and survey sampling issues are discussed. Finally, the statistical tests
adopted for data analyses are explained.

Chapter 4 reports the results of data analyses. First, the representativeness of the
sample and non-respondent study are described. Second, there is a description of the
three samples with respect to activity preferences and socio-demographic characteristics.
Finally, the results of hypothesis testing using chi-square, latent class analysis, logistic
regression, and one-way ANOVA are presented.

Chapter 5 provides a summary and discussion of the results. Implications for
tourism managers and theory are presented. Finally, limitations of the study and

recommendations for future research are discussed.

15

CHAPTER 2 LITERATURE REVIEW

Introduction

The purpose of this chapter is to review deﬁnitions and studies of rural tourism
and to provide an overview of practical studies and theoretical frameworks that support
the importance of the usefulness of market segmentation in efﬁciently targeting markets.
The literature review is presented under the following topics: (a) Tourism development in
rural areas; (b) Theoretical framework of market segmentation.

Tourism Development in Rural Areas

In this section, the deﬁnition of rural tourism is examined, the development of
rural tourism in the United States is brieﬂy introduced, and recent studies in rural tourism
are examined. The ﬁnal element of this section discusses one of the most important
components of rural tourism — activities — and how this component (activities) can be
critically adapted in market segmentation.

Definition of Rural Tourism

It is difﬁcult to clearly deﬁne rural tourism (George, Mair, & Donald, 2009;
Molera & Albaladejo, 2007). The concept of rural tourism has been confused with other
concepts such as farm, green, ecotourism, and nature tourism (Frochot, 2005). In general,
rural tourism can be deﬁned as tourism that takes place in the countryside. However,
Lane (1994b) emphasized the importance of the continuum concept for rural tourism
which allows tourism planners to adjust the deﬁnition to ﬁt alternate research scenarios.
Deﬁnitions from policy and research perspectives are discussed below.

According to the United Nations (2006), the deﬁnition of rural areas in the United

States is “Agglomerations of 2,500 or less inhabitants, generally having population

16

densities of less than 1,000 persons per square mile or more.” This deﬁnition is not a
world-wide standard. For example, Canada’s criterion of rural is “the population living
outside places of 1,000 people or more or; outside places with densities of 400 or more
people per square kilometer.” (du Plessis, Beshiri, & Bollman, 2002).

According to the US Census Bureau:

"rural" consists of all territory, population, and housing units located outside of
urbanized areas and urban clusters. The rural component contains both place
and non place territory. Geographic entities, such as census tracts, counties,
metropolitan areas, and the territory outside metropolitan areas, often are

"split" between urban and rural territory, and the population and housing units
they contain often are partly classiﬁed as urban and partly classiﬁed as rural.
(US Census Bureau, 2000)

Based on the United Nations World Tourism Organization (UNWTO, 2002), the
term “rural tourism” is deﬁned as tourism in which the rural culture is a key component
of the recreational product. Tourism planners focus on creating opportunities for visitors
to experience the atmosphere and products of the countryside. In practice, rural tourism
mainly focuses on small-scale forms of recreation and stays in small-scale
accommodations such as family-owned hotels, country estates, campgrounds, or
individual second homes (United Nations World Tourism Organization, 2002). This
concept excludes some forms of tourism, such as winter sports, found in intensively
visited areas, which are not considered part of rural tourism.

Lane (1994b) has indicated that rural tourism is multifaceted, which makes

deﬁning rural tourism even more challenging. However, his deﬁnition of “pure” rural

l7

tourism is that: (a) It has to be situated in rural areas; (b) It has the special characteristics
of the rural environment (open space, heritage, etc.); (o) It is small-scaled in terms of
facilities and services; ((1) The tourism business is mainly controlled by locals, and
development is concerned with long-term development instead of short-term economic
returns; and (e) It considers not only the location but also the area’s environment,
economy, and history.

Getz and Page (1997) pointed out that rural recreation is particularly connected to
the wilderness. The combination of environment, unique experiences, and tourists’
expectations thus create rural tourism as a special circumstance. Alexander and McKenna
(1998) and F rochot (2005) stated that rural tourism can be related to outdoor activities,
natural environments, wildemess, and rustic lifestyles. Therefore, rural tourism cannot be
conﬁned to farm tourism or nature-based tourism but should take account of all aspects of
tourism in rural areas, including physical, social, and historical dimensions.

From these studies, the most common deﬁnition of rural tourism is “tourism that
takes place in the countryside.” F rochot (2005) even referred to “tourism in rural areas”
as rural tourism in order to avoid any confusion. The deﬁnitions of rural tourism
generally suggest that tourists can enjoy the rural areas in many different ways and that
any activities occurring in rural settings should be included in rural tourism.

For this study, Frochot’s (2005) deﬁnition (i.e., toruism in rural areas) was
adopted. Therefore, rural tourism is deﬁned as “tourism that takes place in the
countryside.” Under this deﬁnition, the understanding of countryside as rural areas is

based on the US Census Bureau’s classiﬁcation. Activities in rural areas, including

18

nature-based, agriculture, heritage, and most other kinds of activities, are all considered
rural tourism.
Rural Tourism Development and Trends

Interest in the countryside can be traced back to the nineteenth century, when
people searched for relief of stress from the rapidly industrializing cities. In Europe, after
the Industrial Revolution, urbanization gradually regenerated an interest in rural life; at
that time, the rural image was perceived as green ﬁelds decorated with hay bales, or
spring ﬂowers or animals (e. g., Sue, 2004). Even now, rural tourism is an important
segment of European tourism and still plays a very essential role as an economic source
(Veer & Tuunter, 2005).

Each country evolves unique rural development patterns, as a result of its cultural
and geographical variations. In the United States, rural tourism destinations used to refer
to the towns that prospered with the expansion of the railroads. People were encouraged
to move from the East Coast to western rural areas in order to work in the mineral
industries (Ragatz, 1969). Gradually, as mineral product prices fell, the mineral industries
declined; at the same time, agricultural research led to more intensive farming. As rural
areas in the United States encountered a decline in traditional industries (e. g., logging,
and mining), these changes forced people to move out of rural areas and into increasingly
(sub) urbanized areas. Rural towns began to shrink, and rural communities were left
struggling and searching for new opportunities to ensure survival (Blaine, Mohammad, &
Var, 1993; Oppennann, 1996; Wilson, Fesenmaier, Fesenmaier, & van Es, 2001).

During the late 19705 and early 19805, the tourism industry was found to be an

economic development tool for rural areas (Allen, Hafer, Long, & Perdue, 1993; Long,

19

Perdue, & Allen, 1990; Perdue, Long, & Allen, 1987). Many rural towns, originally built
by timber or mineral industries, became tourist destinations. These towns built in
Victorian-style architecture became the main exhibition for their economic salvation.
Tourism has been perceived as an incredible catalyst that accelerates economic grth by
increasing job opportunities, stabilizing populations, and promoting positive, ecological
friendly images (Derek, 2004). Therefore, more and more rural communities expect
toruism to be a panacea for their lack of economic growth. In addition, the desire to
possess a piece of one’s own rural land became a trend among rich people with a stressful
urban life. Seasonal home demand, especially in scenic areas (for example, areas with
mountains, lakes, and rivers), began to expand rapidly during this period. All these
factors have increased the attention on rural tourism development.

Most national tourism administrators and researchers agree that the demand for
rural tourism continues to grow (Lane, 1994b). Long and Lane (2000) identiﬁed a series
of factors that have impacted rural tourism development in recent decades. These factors
included interest in heritage and traditional rural life and the health beneﬁts from clean
air, simpler life styles, exercising, and relaxation from daily intensive work. Also, people
are tending to retire earlier and travel more. These factors have increased preferences for
traveling to rural areas. Some researchers also pointed out three directions that rural
tourism industries should focus on: recreational activities, the consciousness of
conservation, and authentic interest (Cawley, Gaffey, & Gillmor, 2002; Gannon, 1994).

Based on results of a travel poll by the Travel Industry Association of America
(TIA, wvwvztiaorg), Miller and Washington (2009b) stated that 62% of all US. adults

traveled to a small town or village in the US. in the past three years. The main reason for

20

traveling to a small town is to visit friends or relatives (44%). Douglass and Raento (2004)
stated that most rural areas are not popular destinations for international tourists
International tourists do not often travel outside of urban areas (Gartner & Lime, 2000).
For example, in Michigan, the primary tourism market remains domestic and, more
speciﬁcally, a regional market, in which 70 % of the tourists are Michigan residents
(Holecek, 2006). Given these results showing that visiting rural areas in the US. is very
popular among US. travelers, the remaining discussion of tourism in rural settings will
be focused on domestic tourists and target travelers who live close to the study area.
Studies of Rural Tourism

Each country and sub-region possesses its own particular characteristics
(Fesenmaier, Fesenmaier, & Van Es, 1995; Shaw, 1994). Both natural and cultural
resources provide the foundation for tourism operated through local businesses. In
addition, rural tourism can be developed with relatively little investment and can have
less impact on the environment than other industries, such as manufacturing.
Development need not depend on outside companies for investment or infrastructure
construction. Many articles discuss the beneﬁts of rural tourism development. Getz and
Charlsen (2000) claimed that there is high potential for local business development in
rural tourism development. Getz and Charlsen (2005) also found that because family
businesses often become the main attractions in rural communities such businesses can
shape the image of the tourism industry in those areas. Other studies have suggested that
tourism development may prevent rural areas from depopulation and other negative
impacts (Alexander & McKenna, 1998; Jenkins, Hall, & Troughton, 1998). Meanwhile,

the literature emphasizes some other beneﬁts of rural tourism development, such as the

21

building of cultural relations in the community (N ilsson, 2002), and shows that rural
tourism is increasingly conceived of and applied as a tool of socio-economic
development.

In addition, previous rural tourism research has also focused on examinations of
ways tourism destinations can be utilized to boost the economy (Aronsson, 1994; Crouch,
1994; David, 1999; Douglass & Raento, 2004; Gannon, 1994; Hall, 2004; Hjalager, 2004;
Huang & Stewart, 1996; Kneafsey, 2001; Lane, 1994a; Michael & John, 2001; Sharpley
& Roberts, 2004). Other studies have focused on certain activities that occur in rural
areas (Carmichael & Smith, 2004). For example, some studies of rural tourism have
focused on speciﬁc themes such as farm tourism (Oppennann, 1995), nature-based
tourism (Weaver & F ennell, 1997), and wine tourism (Hall, Sharples, Camboume, &
Macionis, 2000). Some discussions of rural tourism have mainly emphasized the
attractions and activities based on rural resources (Cloke, 1993; Getz & Page, 1997). It
has been suggested by Gartner (2004) that the demand for products in rural settings has
increased. However, studies of rural tourists to understand tourists’ travel behaviors are
still limited.

Rural Tourism Activities

It seems more and more people are seeking experiences found only in the
countryside (Backman, Backman, Potts, & Uysal, 1992). The literature suggests that the
attraction of rural tourism might be the pursuit of natural and man-made resources with a
peaceful atmosphere, such as rivers and lakes or tillage and pastoral scenery (Fleischer,
1997; Kieselbach & Long, 1990). Rural areas were found to be associated with providing

safe, peaceful, healthy experiences and activities (Roberts & Hall, 2001). Towner (1996)

22

indicated that rural areas have long been seen as locations for recreation and tourism
activity. “Traditional” activities, such as walking, picnicking and bird-watching, have
been explained as reﬂecting the need to escape from the stress of urban living. These
activities also represent Lane’s (1994b) “pure” rural tourism which involves leisure
pursuits in a place and setting where resources and tourists can blend pleasantly.

Hunting and ﬁshing were some of the earliest tourist activities to emerge in rural
areas. Over the years, many other activities have developed. Such activities include
picnicking, sightseeing, boating, horseback riding, fruit picking, and visiting historical
sites. However, Butler (1998), and Robert and Hall (2001) carefully noted that in some
cases, the location is far less important than the activity itself. For example, off-road
vehicle driving, survival games, and leisure shopping are almost irrelevant to the location.
Such activities are different from Lane’s “pure” form of rural tourism.

Overall, rural tourism is multifaceted, not just farm-based tourism. In addition to
farm-based trips, it encompasses a special interest in nature, focusing on activities such as
walking, hiking, climbing, bike and horseback riding, adventure, hunting and ﬁshing,
educational travel, heritage tourism, and so forth (Lane, 1994b). Fundamentally, the most
successful rural tourism is linked closely to the variety of activities an area can offer,
since activities have been seen as the major element of attractions, especially in rural
tourism.

Theoretical Framework of Market Segmentation

Market segmentation has been applied in tourism for years. This section

introduces the concept of and the approaches to market segmentation. Next, tourism

literature relating to market segmentation is reviewed and discussed in terms of two

23

major themes: (a) Identifying bases for market segmentation, with a focus on activity-
based applications in tourism research, and (b) Developing proﬁles of resulting segments,
with an examination of the factors that have been used to proﬁle segments and a
discussion of the potential factors that can be selected to proﬁle segments in this study.
The Concept of Market Segmentation

Market segmentation is one of the most essential concepts in marketing research
(Dibb & Stern, 1995; Wind, 1978), and it is a very well known method of increasing
proﬁts (Dolnicar & Leisch, 2003). The concept of market segmentation was developed to
reﬂect the differences existing among customers, showing that their desires were
different in terms of their preference for a product (Lessig, 1972). The earliest market
segmentation concept was developed as strategic orientation. As marketing employed
more complicated quantitative techniques, market segmentation was deﬁned as one type
of marketing technique (Plank, 1985). Schiffrnan and Kanuk (1978) suggested that
market segmentation was more than just an analytic technique; it also utilized resources
more efﬁciently, especially in advertising aimed at target groups. Market segmentation
was therefore perceived as a method of measurement and analysis, and the results of
identiﬁed segments were useful for distribution of resources (Wind, 1978). In such a
context, market segmentation was commonly perceived as a management strategy rather
than a measure of market conditions (Dickson & Ginter, 1987).

A popular and accepted deﬁnition of market segmentation was presented by
Kotler (1978) and Kotler and Armstrong (2008) and addressed in chapter 1. According to
this deﬁnition, market segmentation includes the processes of identifying segments,

examining the appropriateness of selecting segmentation bases (Bass, Tigert, & Lonsdale,

24

1968) and applying alternative multivariate techniques for segment identiﬁcation
(Blattberg & Sen, 1976). Market segmentation was considered to be a practical tool used
in market research, with results used for targeting marketing. Afterwards, the company
could focus on targeting markets to serve, which meant that market segmentation was
separated from market targeting. Kotler (1978) stated that market segmentation was the

ﬁrst major step in target marketing (see ﬁgure 2-1.).

 

 

Market Market Targeting Market Positioning

Segmentation 3. Develop 5. Develop

1. Identify Bases for Measures of Positioning for
Segrnenting the Segment Each Target
Market Attractiveness Segment

2. Develop Proﬁles of 4. Select the Target 6. Develop Marketing
Resulting Segments Segment(s) Mix for Each

Target Segment

 

 

 

 

 

 

 

 

Source: Kotler, Bowen, and Makens (2005, p. 263)

Figure 2-1 : Steps in Segmentation, Targeting, and Positioning

Market segmentation has been increasingly applied by tourism organizations and
researchers around the world to develop more effective marketing plans (Yannopoulos &
Rotenberg, 1999). It has become one of the main marketing strategies that help identify
homogeneous groups of tourists in order to satisfy their needs and increase marketing
effectiveness (Andereck & Caldwell, 1994; Tsiotsou, 2006). Nowadays, market
segmentation continues to be an essential marketing concept in both academic literature
and marketing practice (Grover & Srinivasan, 1987).

One general assumption of market segmentation research in travel and tourism
has been that tourists with particular travel behaviors are possibly unlike others engaged
in different activities (Jeffrey & Xie, 1995; Middleton & Clarke, 2001; Moscardo, Pearce,

Morrison, Green, & O'Leary, 2000). Market segmentation, therefore, is the process of

25

dividing visitors into subgroups based on common characteristics or behavior patterns
(Lawton, 2001; Middleton & Clarke, 2001 ). Ideally, the resulting segments should
contain tourists with similar needs, socio-demographic characteristics and behaviors who
can be identiﬁed and whose attributes match the characteristics and strengths of the
destination (Daerr, 2001). As a result, promotions, products and services can be designed
to ﬁt the needs of each target segment (Morrison, Hsieh, & O' Leary, 1994).

Therefore, market segmentation is perceived as a powerful tool in providing
tourism managers with a better understanding of individual markets and more precise
ideas for forming destination development (Formica & Uysal, 1998; Middleton & Clarke,
2001; Thurau, Carver, Mangun, Basman, & Bauer, 2007). With the use of market
segmentation, tourism managers can focus on selected groups of tourists and design and
offer services to meet their needs (Kotler, et al., 2005; Morrison, Pearce, Moscardo,
Nadkanri, & O' Leary, 1996). It is more efﬁcient to identify speciﬁc niche markets with
the highest returns rather than wasting unnecessary marketing investment and resources
on mass marketing (Sung, Morrison, & O’Leary, 2000). Tsiotsou (2006) emphasized the
advantages of a market segmentation strategy, which are to provide the base of target
marketing, to help develop more effective marketing mixes, to assist in differentiating
products, and to identify market opportunities and threats. McDonald and Dunbar (2004)
(as cited in Canever, et al., 2007) stated that the essential idea of marketing is to consider
both the customers’ needs (the demand side) and the companies’ capacity (the supply
side); therefore, from developing marketing strategies based on the results of market

segmentation, customers can be more satisﬁed and the company can be more proﬁtable.

26

Market Segmentation Approaches
Two principal types of segmentation have been discussed in the tourism literature:
a priori and post hoc segmentation (Dolnicar, 2004; Green, 1977; Hanlan, Fuller, &
Wilde, 2006; Wedel & Kamakura, 2000; Wind, 1978). The application of both
approaches in tourism research is represented in this section.
A priori Approach
If the type and number of segments are decided before data collection, the a priori
descriptive method is applied. For example, golfers are chosen as the segment tourism
managers try to understand. Respondents are classiﬁed as golfers or non-golfers. Those
two segments are then examined in terms of their socio-demographic or trip-related
characteristic differences. Wind (1978) explained that an a priori segmentation was
designed to help managers to discover consumers’ purchasing behaviors based on
product choices, loyalty, and so forth. Results could show the demographic,
socioeconomic, and other related characteristics of interest to product managers. The a
priori approach, referred to as proﬁling, has a long history of being applied in tourism
research (e.g., Bonn, Furr, & Susskind, 1999; Brown, 2001; Goldsmith & Litvin, 1999;
Lawton, 2001; MacKay, Andereck, & Vogt, 2002; Nicholson & Pearce, 2000; Oh, Parks,
& Demicco, 2002; So & Morrison, 2004).
The a priori approach can further be classiﬁed into two approaches (Chen,
2003a; Wedel & Kamakura, 2000):
1. A priori descriptive segmentation: the descriptor is identiﬁed before data collection.
For example, managers could be interested in exploring differences in characteristics

between people who are married and those who are unmarried. Hudson (2000) used

27

gender as a descriptor, and discussed the similarities and differences in the variables
of constraint for skiing.

2. A priori predictive segmentation: this is a two-stage approach. First, the segments
are selected based on descriptors and then the resulting segments are explained by a
set of independent variables. For example, gender (men and women) can be treated as
the ﬁrst descriptor to deﬁne the segments. A dependent variable (e.g., satisfaction in
visiting Michigan) and a set of independent variables (e.g., income, party size, etc.)
can be examined using a regression model to compare those two segments. If the two
segments yield signiﬁcantly different regression coefﬁcients, it means that the
independent variables that are signiﬁcantly different can be used to explain satisfaction
of visiting Michigan for the two segments.

Chen (2003b) stated that the a priori approach provides three distinctive
advantages: (a) It is based on the variables that could signiﬁcantly distinguish among
segments in the beginning. Sometimes it is impossible to know if the segments could be
signiﬁcantly different from each other in the post hoc segmentation approach; 0)) It is
easier to identify the segments using the interesting variables to explain the relationship
between variable and the segments. For instance, when “intent to travel” is deﬁned as a
dependent variable (criterion), researchers can determine which segment has the highest
potential to travel and thus choose it as the target segment; (c) It helps marketing
managers to understand new cases (customers) from the resulting segments. In summary,
the a priori segmentation approach can develop speciﬁc segments based on the
characteristics that managers know and have decided to set as descriptors before

analyzing the data and that reﬂect signiﬁcant difference in terms of other characteristics

28

among segments. In particular, the process could help to focus more on interpretation of
speciﬁc segments in marketing practices.

Dolnicar (2007) identiﬁed the a priori approach with a four step framework (see
ﬁgure 2-2). In this approach, the most common statistical analyses include t-tests (e. g.,
Goldsmith & Litvin, 1999; Lawton, 2001), chi-square tests (e. g., Smith & Carmichael,
2005; You & O' Leary, 2000), and binary logistic regression (e.g., de la Vina & Ford,
2001; Goldsmith & Litvin, 1999; So & Morrison, 2004). The appropriate analysis chosen
in Step 3 to differentiate the segments depends primarily on the measurement scale of the

data.

 

Selection of the segmentation criterion
(e. g., age, gender, $ spent, country of origin)

I

Grouping respondents into segments by assigning each
respondent to the respective segment

I

Proﬁling of segments by identifying in which personal
characteristics segments differ significantly

1

Managerial assessment of the usefulness of the market
segments (and formation of targeted marketing activities)

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Source: Dolnicar (2007)

Figure 2-2: Steps in A Priori Segmentation

Post Hoc Approach

In the post hoc method, consumers are divided in terms of a set of deﬁned
characteristics after the data are collected. Respondents are clustered according to
similarity in some characteristic such as purchasing behavior or attitude. After the

segments are determined, they are examined for differences in other characteristics such

29

as socio-demographic characteristics. The number of groups or clusters and their relative
size are not known until the cluster analysis has been completed (Green, 1977).
Compared to the a priori approach, post hoc studies were a later phenomenon.

Haley (1968) introduced the post hoc approach, stating that the a priori approach is

simply descriptive rather than being able to identify the actual cause of difference.

The post hoc approach can further be classiﬁed into two approaches (Chen, 2003a;

Wedel & Kamakura, 2000):

1. Post hoc descriptive segmentation: People are divided by cluster analysis into
mutually exclusive clusters. For example, a set of items related to beneﬁts sought by
tourists could form the variables used to divide them into several clusters.

In tourism research, the post hoc approach has been applied in many studies.

Some researchers used factor analysis as an approach to ﬁnd segments (e. g., Graham &

Wall, 1978; Hallab, 2006; Kim, Noh, & Jogaratnam, 2006). Cluster analysis, which

identiﬁes groups of residents who respond differently (similarly within groups,

differently between groups) to particular items (e. g., Davis, Allen, & Cosenza, 1988), and
which also can be seen as looking for heterogeneity among the respondents (Shappard,

1996), is the most popular approach in this type of segmentation (Dolnicar, 2002). The

combination of factor and cluster analysis is especially prevalent (e. g., F rochot, 2005;

Keng & Cheng, 1999; Loker & Perdue, 1992; Madrigal & Kahle, 1994; Mo, Havitz, &

Howard, 1994; Park, Yang, Lee, Jang, & Stokowski, 2002; Saunders, 1980).

Thurau, et al., (2007) explained the common procedures used in the factor-cluster
analysis approach: (a) Factor analysis (to determine the main categories based on a list of

items); (b) Cluster analysis (to decide market segments); and (c) Chi-square analysis (to

30

develop proﬁles for each segment and to determine the statistically signiﬁcant differences
among segments). Hair, Anderson, Tatharn, and Black (1998) emphasized that the
purpose of factor analysis was to identify the factors of items that obtained correlated
responses across respondents. Therefore, these variables can be explained regarding their
common dimensions.

However, based on the research of Dolnicar and Grun (2008), it appears that the
factor-cluster analysis developed in the early years of post hoc segmentation in tourism
research has been adopted by many researchers without questioning the procedure. While
the goal of factor analysis is to reduce the dimensionality of the questions, it may
eliminate the variables that are not well represented by the factor solution but may be
important to identify a market segment.

Ketchen and Shook (1996) noticed this problem and discussed the dangers of
using factor analysis in the ﬁrst stage of market segmentation, which included these: (a)
The relationships among variables could be changed and results could be different
because the data are converted and segments are identiﬁed based on the transformed
distance instead of original information; (b) Differences between segments could be
reduced. When doing factor analysis, more than half of the original information would be
discarded due to the typically explained variance of only 50% to 67%. When discarding
the variables with an Eigenvalue less than one, the most important pieces of information
for identifying the niche segments may be exclude unintentionally; and (c) Segments are
identiﬁed in a different space than originally located. Therefore, the interpretation of
segments may be debatable because the segments have been relocated in the space based

on factor score. Dolnicar and Grun (2008) reinforced that the “factor-cluster” analysis

31

approach performed signiﬁcantly less well than clustering the raw data and recommended
that researchers should use “cluster segmentation” directly from the raw data. That
conclusion was supported by other researchers such as Arabic and Hubert (1994),

Milligan and Cooper (1996) and Shappard (1996), who agreed that “factor-cluster

analysis” should not be used as a standard procedure in post hoc segmentation. Shappard

(1996) also claimed that cluster analysis on raw item scores may produce more accurate

or detailed segments while it preserves more of the original data.

2. Post hoc predictive segmentation: Post hoc predictive methods identify segments
through a causal model that consists of a dependent variable and a set of independent
variables. Chi-square automatic interaction detection (CHAID) (e.g. Hsu & Kang,
2007) and several other multivariate analyses, such as conjoint analysis, automatic
interaction detection (AID) (Assael, 1970), artiﬁcial neural network analysis (ANN)
(e.g., Kim, Wei, & Ruys, 2003) (Bloom, 2004), and classiﬁcation and regression tree
(CART) (Morwitz & Schmittlein, 1992), have been applied to post hoc predictive
segmentation. The listed methods are differentiated by their data distribution and
assumptions (e.g., parametric and non-parametric) and ntunber of variables in the
analysis (e. g., univariate, bivariate, and multivariate). Therefore, for a segmentation
research, a classiﬁcation algorithm (such as classiﬁcation and regression tree) can be
applied depending on the variable’s scale (e. g., continuous) and research design.

Dolnicar (2007) proposed a post hoc approach procedure (see Figure 2-3), which
clearly distinguishes it from the a priori approach, mainly in Step 1 and Step 2. In Step 1,
a set of items (market segmentation bases) related to the research question are designed

and chosen for analysis. At the second step, the appropriate algorithms should be decided.

32

The most dominant algorithms applied in tourism research are K-means and Ward’s
clustering, while a broad range of alternative clustering algorithms are also available,
such as alternative clustering algorithms (Everitt, Landau, and Leese 2001), neural
networks, bagged clustering (e. g., Dolnicar & Leisch, 2003, 2004), latent class analysis
(e.g., van der Ark & Richards, 2006) and ﬁnite mixture models (Wedel & Kamakura,
2000). The number of clusters that should be chosen still remains unresolved in market
research. While using the stability criterion to determine the number of clusters, repeated
computations are recommended to make sure that consistency across alternative solutions

is found.

 

Selection of the segmentation base
(e.g., travel motivations, vacation activities )

I

Grouping of respondents

 

 

 

 

 

 

 

Selection of segmentation algorithm(s)

 

Stability analysis

 

 

 

Computation of ﬁnal segmentation solution

I

Proﬁling (external validation) of segments by identifying in
which personal characteristics segments differ signiﬁcantly

I

Managerial assessment of the usefulness of the market
segments (and formation of targeted marketing activities)

 

 

 

 

 

 

 

 

 

Source: Dolnicar (2007)

Figure 2-3: Steps in Post Hoc Segmentation

In tourism research, both a priori and post hoc approaches are still commonly
applied to identify tourists with different perspectives. In some studies, a hybrid of a

priori and post hoc approaches is exercised. For example. customers could ﬁrst be

33

grouped according to favorite brand and then a clustering procedure could be used to
examine whether different segments have different needs (Green, 1977; Wedel &
Kamakura, 2000). The common statistical techniques used for segmentation are listed in
Table 2-1.

Table 2-1: Common Classification of Methods Used in Market Segmentation

 

 

A Priori Post Hoc
Descriptive Contingency tables, Clustering methods: Non-overlapping,
Log-linear models Overlapping, Fuzzy techniques,

ANN (artiﬁcial neural network),
Mixture models

 

Predictive Cross-tabulation, AID (automatic interaction detection),
Regression, CART (classiﬁcation and regression trees),
Logit analysis, ANN (artiﬁcial neural network),

Discriminant analysis Mixture models
Source: Wedel and Kamakura (2000)

 

Even though market segmentation has a slightly different deﬁnition and the
method has been divided into two approaches, most researchers agree that choosing a
segment base is a crucial step in market segmentation (Daerr, 2001). Based on Kotler’s
deﬁnition of market segmentation (see Figure 2-1), there are two major steps in
processing market segmentation. The next section covers the literature under this frame:
identifying bases for market segmentation and developing proﬁles of resulting segments.

Identifying Bases for Market Segmentation
Market Segmentation Bases

Choffray and Lilien (1980) deﬁned a segmentation base as one criterion or a set
of criteria used to segment customers and explained a segment descriptor as a variable or
characteristic that could be associated with segment members and be used to design
marketing strategy as well. Frank, Massy and Wind (1972) introduced the concept of
observable and unobservable bases in order to build market segmentation bases.

Observable bases were deﬁned as ethnic, geographic, demographic, and socio-economic

34

variables. Unobservable bases included psychographics, values, personality, and life style.
Kotler (1994) stated that the major variables for market segmentation purposes are
geographic, demographic (e.g., age, gender, family status, income), psychographic (e.g.,
life style, personality characteristics, activity level preferences, and personal values), and
behavioral (e.g., beneﬁts, frequency of use, loyalty). The general segmentation bases that

have been commonly examined in the tourism literature are listed in Table 2-2.

35

Table 2-2: General Market Segmentation Bases Used in Tourism Research

 

Broad type of base

Study

Speciﬁc examples of characteristics
used in each study

 

Geographic

Etzel & Woodside, I982

Near-home and distant travelers

 

Geographic

Reid & Reid, 1997

Five geographic markets (USA, UK,
Canada, Germany and Trinidad) to
Barbados

 

Geographic

Obenour, Lengfelder, & Groves,
2005

Tourists from six dominant market
areas (Detroit, MI; Columbus, OH;
Pittsburgh, PA; Cincinnati, OH,
Chicago, IL and Indianapolis, IN) to a
nature-based destination (Lake Erie
coastal region, OH) on destination
imrﬂassessment

 

Demographics

Anderson & Langmeyer, 1982

Under-50 and over-50 travelers on
needs and expgctations of vacations

 

Demographics

Fodness, 1992

Family life cycle on decision making

 

DemograLhics
Demographics

Hudson, 2000
(Smith & MacKay, 2001)

Gender on constraint differences
Age (younger: 18-25 years old; older:
60-75years old) on destination visuals

 

Psychographics

Kau & Lee, 1999

Singapore vacationers were clustered
into four groups: culture dissimilarity
seekers, destination novelty seekers,
novelty seekers, and familiarity
seekers.

 

Psychographics

(Kim, Crompton, & Botha, 2000)

Three constraint clusters and three
beneﬁt clusters.

 

Psychographics

Ekinci & Chen, 2002

British holidaymakers to Turkey were
segmented into two groups: agenda
achiever and relationship seekers

 

Behavior

Jurowski, Uysal, & Noe, I993

Travelers were segmented based on site
preferences of visiting the U.S. Virgin
Islands National Park and two clusters
were found: consumptive/tour type
and conservationist type

 

Behavior

Court & Lupton, I997

Adopters, inactives and rejecters were
identiﬁed outside of New Mexico
residents

 

Behavior

May, Bastian, Taylor, & Whipple,
2001

Snowmobilers based on reasons for
snowmobiling; ﬁve clusters identiﬁed:
achievement/stimulation, escape
personal/social pressure, enjoy
nature/leaming, being with family and
friends and escape physical pressure

 

Psychographic and
behavior

Dolnicar & Leisch, 2003

Winter vacationers in Austria were
classiﬁed into ﬁve groups: fun and
snow; relaxation and health; moderate
culture tourist; pure culture tourist;
and fun, snow, snowboards, and discos

 

36

Segmentation bases started from geographic and demographic variables. However,
there was general agreement that demographic segmentation did not predict tourist
behavior well enough (Johns & Gyimothy, 2002; Prentice, Witt, & Hamer, 1998) and
may not be appropriate as a primary base for segmenting (Frochot & Morrison, 2000).
Therefore, researchers tried to move beyond demographic segmentation by using
additional segmentation bases, such as psychographic bases (Wells, 1975).

Besides those common bases in marketing research, tourism marketing
researchers also searched for additional factors such as communication channels (e. g.,
Hsieh & O’Leary, 1993), resort hospitality elements (e.g., Brey, Klenosky, Lehto, &
Morrison, 2008), and activities (e.g., Choi & Tsang, 1999; Hsieh, O'Leary, & Morrison,
1992; Jeffrey & Xie, 1995; Morrison, S., & O' Leary, 1994; Moscardo, Morrison, Pearce,
Lang, & O'Leary, 1996; Nicholson & Pearce, 2000) to segment the market efﬁciently.
These segmentation bases sometimes could not be deﬁned in exclusive categories. For
example, Sarigollti and Huang (2005) suggested that beneﬁts were deﬁned as visitors’
ratings of desired environments or activities that were covered in Well’s psychographic
bases: activities, interests, opinions, needs, values, and personality traits (Wells, 1975).
Some common and speciﬁc segmentation bases applied in tourism studies are shown in

Table 2-3.

37

Table 2-3: Popular Market Segmentation Bases Used in Tourism Research

 

Broad Type of Base Study

Motivations

Bieger & Laesser, 2002

Speciﬁc Examples of Characteristics Used in
Each Study

Swiss pleasure travelers were segmented into
four groups: compulsory travel, cultural
hedonism, family travel, and me(e/a)t
marketing clusters

 

Motivations

Andreu, et al., 2005

British visitors visiting Turkey were segmented
into ﬁve groups: fuzzy, recreational, active,
escape, and relax

 

Motivations

Cha, McCIeary, & Uysal,
1995

Japanese travelers selecting a destination were
classiﬁed into three clusters: sports seekers,
novelty seekers, and family/relaxation seekers

 

Motivations

Formica & Uysal, I998

Spoleto Festival visitors in Italy were clustered
into two groups: enthusiasts and moderates

 

Expenditure

Wilton & nonna Polovitz,
2006

Travelers who did not live in Montana but travel
to the State were classiﬁed based on
expenditure.

Expenditure by purpose of trip and mode of
transportation, and by top nine main
attractions to Montana

 

Expenditure

Mok & lverson, 2000

Light, medium and heavy spenders among
Taiwanese tourists to Guam

 

Expenditure

Jang, Josef, & Ham, 2002

Light, medium and heavy spenders among
Japanese outbound pleasure travelers

 

Beneﬁts

Jang, Monison, &
O'Leary, 2002

Japanese travelers to USA and Canada were
classiﬁed into three groups: novelty/nature,
escape/relaxation, family/outdoor activities
seekers

 

Beneﬁts

F rochot, 2005

Rural tourists to Scotland were classiﬁed into
four groups: actives, relaxers, gazers, and
rurals

 

Beneﬁts

Sarigolltl & Huang. 2005

Visitors to Latin America were grouped into four
segments: adventurer, multifarious, fun and
relaxation seeker, and urbane.

 

Decision-makin g
factors

Sung,2004

Adventure travelers were clustered based on
their traveler characteristics and consumer and
travel behavior in decision making.

Six groups were identiﬁed: general enthusiasts,
budget youngsters, soﬂ moderates, upper high
naturalists, family vacationers, active soloists

 

 

Involvement Park, et al., 2002 Casinos in Black Hawk Colorado
Low gambling, high centrality gambling, high
enjoyment gambling, and high self-expression
gambling involvement groups
Demographic, Kim, et al., 2003 West Australian senior tourists were segmented

motivations and
concerns to travel

into four groups: active learner, relaxed family
body, careful participant and elementary
vacationer

 

38

Activity-based Segmentation

The activity-based segmentation approach was ﬁrst applied in the early 19603 (de
Grazia, 1964) and has been used consistently since to understand tourists’ behavior
(Moscardo, et al., 1996). McKercher, Ho, du Cros, and So-Ming (2002) explained that
activity-based segmentation meant deﬁning tourists by their behavior or visitation
patterns. Jang, Cai, Morrison, and O’Leary (2005) explained that travel activity
participation can not only be characterized as a form of traveler behavior but also treated
as the outcome of traveler preferences, which can be considered a demand attribute.

Canter (1977) (as cited in Moscardo, et al., 1996) developed a concept of place
which was made up of three major components: the physical environment or setting, the
activities that people engaged in, and their conceptions or perceptions of the place.
Moscardo et a1. (1996) explained that activities can be used to connect motivations (push
factors) to destinations (pull factors), because motivations can be perceived as tourists’
expectations of activities, and destinations can be treated as places of the activities. So
activities connect the places to tourists’ expectations. The activity-based model treated
activities as one of the attributes of destinations, which could be perceived as travel
motives in the process of decision making when choosing a destination. Gunn and Var
(2002) mentioned that attractions are seen as the major resource to visitors and places for
tourist activities. These concepts appear to echo Canter’s place concept in supporting the
important role of activities in understanding destination choice. Therefore, activity-based
segmentation played an important role in market segmentation (Romsa, 1973) because
the sets of activities were important in themselves and their analysis would provide a

better understanding of demand for tourism planners (Choi & Tsang, 1999).

39

Lang, O’Leary, and Morrison (1993) summarized the advantages of activity-
based segmentation: (a) To better understand visitors regarding their travel choices and
patterns; (b) To help tourism planners focus on bundled activities instead of every
activity and more effectively focus their efforts to target speciﬁc segments (Tatham &
Domoff, 1971); (c) To develop marketing strategies in more manageable ways because
developing marketing strategies based on each activity is not seen necessarily as
manageable; (d) To discover a more stable planning criterion than individual activities;
and (e) To save tourism planners time and effort (Burton, 1971).

A variety of studies have developed measures of activity preferences in a variety
of tourists such as nature-based tourists (e. g., Mehmetoglu, 2007) and Japanese female
overseas travelers (e.g., Lang, et al., 1993). Some researchers have only focused on
discussing one activity preference and proﬁling a certain type of activity. For example,
Moscardo (2004) examined only shopping activity. Some researchers have used a set of
activities for employing the post hoc approach (e. g., Huang & Sarigollii, 2007; Kim &
Jogaratnam, 2003; McKercher, et al., 2002). The activity-based approach has been
applied in a wide range of research to better understand tourists in general; however, to
the author’s knowledge, in the study of rural tourists, it has not been seen in publication

(see Table 2-4).

40

 

 

 

 

 

 

 

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00. :50: 0-0 030:

Developing Proﬁles of Resulting Segments

After developing the segments, researchers then discover the commonalities and
dissirnilarities of the segments using appropriate statistical analyses. The proﬁling
segments procedure can be considered a classiﬁcation data analysis technique and is used
to provide a better understanding of each segment (Cardoso & Moutinho, 2003).

The function of proﬁling is to provide the necessity for effective market
segmentation (Wedel & Kamakura, 2000), so the segments’ characteristics can help
marketers design and promote a product more efficiently (Andereck & Caldwell, 1994).
Tourism managers are typically interested in the categorization of tourists by socio-
economic characteristics, as it helps them to target the potential market better (Bargeman,
et al., 1999). But it is also necessary to research additional characteristics such as
geographical, demographic, socio-economic, behavioral and life style to proﬁle segments
(Mazanec, 1992). Market segmentation strategies tend to fail if the segments are not
described using these characteristics.

The purpose of conducting segmentation is to discover the segments in which
respondents are likely to possess similar perceptions, personal traits, attitudes, behaviors,
and consumption patterns (Chen, 2003a). Thus, it is important to further test for
differences between/among the resulting segments to ensure that the resulting segments
are somewhat different. Descriptive analyses, such as percentages and means, are used to
portray segments’ demographic characteristics as well as other distinct traits (e. g., trip
characteristics) of interest. Tourism researchers have also utilized chi-square analysis,
analysis of variance (AN OVA), linear discriminant analysis, logistic regression, and logit

modeling to discover the different characteristics existing among the segments. Among

46

those methods, the most commonly used statistical analyses for discriminating are linear
discriminant analysis and logistic regression (Rao & Wang, 1995). Linear discriminant
analysis and logistic regression have also been employed to evaluate the classiﬁcation
rate of the resulting segments. Linear discriminant analysis is most frequently used for
proﬁling. However, the data don not oﬁen meet the assumptions of the normal
distributions among the descriptors. Logistic regression, therefore, is an alternative
approach when the normality assumption violates (Press & Wilson, 1978).

Based on segmentation analyses, researchers are able to provide the related
marketing and managerial implications using the results from descriptive and inferential
analyses. Many factors have been found to be helpful in explaining segments. Hsieh, et a1.
(1994) found that gender differentiated between package and non-package travelers.
Mehmetoglu (2006) concluded that there was a strong correlation between education and
income. Those with higher education are likely to have higher income. Morrison, et al.
(1994) found signiﬁcant differences between individuals’ travel arrangements and marital
status. Hsieh, O’Leary, Morrison, and Chang (1993) stated that size of the travel party
was also related to travel behavior and was worth examination. Some of the research,
such as geographic segmentation research (e.g., Moscardo, et al., 2000), psychographic
segmentation studies (e.g., Galloway, 2002; Homeman, Carter, Wei, & Ruys, 2002), and
behavior segmentation literature (e.g., David & Lawton, 2002; Park, et al., 2002) has
found signiﬁcant demographic differences.

Socio-demographic variables are the basic attributes used to proﬁle segments.
Other attributes identiﬁed in the tourism research include activity preferences (e. g., Keng

& Cheng, 1999; Madrigal & Kahle, 1994; Park, et al., 2002), travel patterns (e.g.,

47

Baloglu & Uysal, 1996), usage of travel information sources (e.g., Yannopoulos &
Rotenberg, 1999), tourist attractions (e. g., Davis, Chappelle, Sternquist, & Pysarchik,
1993), and motivation (e.g., MacKay, et al., 2002). Most researchers have examined at
least one set of these attributes in their segmentation research.

Pull and push factors are well known factors that inﬂuence people to travel and
determine choice of destination. This theory states that people are driven by internal and
external forces. Push factors refer to socio-psychological factors such as escape from
routine schedules and curiosity for adventure. Pull factors are the attractions at the
destination that draw tourists to visit. Pull factors can be seen as destination attributes
such as natural or cultural attractions, which can be categorized into tangible attributes
such as famous landmarks and intangible ones such as destination image (Crompton,
1979; Dann, 1977; Goossens, 2000; Klenosky, 2002; Uysal & Jurowski, 1994; Yuan &
McDonald, 1990).

The review of literature indicated that the theory of push and pull motivations is a
helpful concept to understand travel motivations (Klenosky, 2002). Studies have
emphasized the importance of knowing what motivated people to travel (push factors) as
well as the products that a destination could offer (pull factors), because understanding
both factors helps managers better understand and satisfy tourists’ needs (Cha, et al.,
1995) and enhance the appeal of the destinations. With consideration of both factors, a
more successful marketing plan for a destination could be designed (Beh & Bruyere,
2007; Jang & Cai, 2002; Jang & Wu, 2006). But most researchers have emphasized push
factors and have used them as a segmentation base (e.g., Andreu, et al., 2005; Bieger &

Laesser, 2002). Some of the studies have considered both pull and push factors as

48

segmentation bases (e.g., Baloglu & Uysal, 1996; Hallab, 2006; Kim, et al., 2006;
Sangpikul, 2008). But the use of both push and pull factors to proﬁle segments is not very
popular in tourism research.

Summary of the Literature Review

Rural destinations are places that furnish multi-faceted atmospheres and
environments in which tourists can enjoy a variety of activities. As leisure spending in
families has changed with the deep economic recession (Barro, 2009, March 4; Healy,
2009, January 29), rural tourism destinations often offer better value for some families.
In order to maintain quality leisure time, travel distance will likely decrease to make
pleasure trips more affordable. Therefore, rural tourism will still remain interesting,
especially for a destination perceived as a regional destination. Since the relationship
between rural settings and activity choices is strong, the development of a rural
destination should focus on identifying tourists’ needs and designing a marketing plan to
attract those who are interested in visiting such destinations.

Market segmentation has been demonstrated as a powerful tool for identifying
target markets. Both market segmentation approaches (a priori and post hoc) and their
pros and cons have been addressed. From the literature review, it is clear that activity-
based segmentation has become increasingly important and has multiple practical
implications for recreation providers or tourism organizations to more effectively
advertise their products. However, there is no reference in the literature to activity-based
segmentation being used in segmenting rural tourists. Attributes for proﬁling resulting

segments have also been demonstrated in the literature review.

49

Based on the literature review, this study explores rural tourists using activity-
based segmentation. Both a priori and post hoc approaches are applied to provide tourism
planners with more sophisticated suggestions for marketing rural destinations to meet
tourists’ needs.

This study includes socio-demographics, travel expenditures, and trip patterns,
characteristics that showed certain signiﬁcant differences among segments in different
research. Also, both push (motivation attributes) and pull (destination attractions) are
examined to proﬁle the resulting segments. At the end this study includes a strategic plan

based on the results, rather than only addressing the results of technical analysis.

50

CHAPTER 3 METHODOLODY

This chapter introduces the methods used for conducting the study. First, the
study area is introduced. Next, the sample and population are described. Then, the
research instruments and statistical methods employed are explained.

Study Area

The study area includes the following west-central Michigan counties: Manistee,
Mason, Oceana, Lake, and Newaygo (abbreviated as WCMI). The ﬁve counties are
located on or near the west coast of Michigan. According to the deﬁnition of urban and
rural populations from the US Census Bureau (1995), “places with less than 2,500 people
incorporated as cities, villages and towns, which describes most of the areas within this
region, are deﬁned as rural”. Manistee is 60% rural; Mason, 65%; Newaygo, 83%; and,
Lake and Oceana, 100% (see Table 3-1).

Table 3-2 shows that construction and health care are major industries in these
areas. The area produces many agricultural crops such as corn and vegetables even
though agriculture is not the dominant industry from an employment perspective (see
Table 3-1). Accommodations and food services related to the tourism industry provide

additional job opportunities, especially for women.

51

Manistee

Mason Lake

. Newaygo

Oceana

 

Figure 3-1: Study Area Map

52

Table 3-1: The Rural Settings in Each Study Area County

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Lake Manistee Mason Oceana Newaygo
€230? p°p“'a“°" '“ 12,069 25,226 28,986 28,473 50,019
Percent Rural 100 59 65 1 00 83
Land area 363,136 347,904 316,928 345,920 539,136
acres acres acres acres acres
Water area 4,608 471,893 477,8? 490,433 '2, 160
acres acres acres acres acres
125% 57.56% 60.13% 58.64% 231%
Average size of farms 135 acres 147 acres 167 acres 197 acres 150 acres
Percentage of farms
operated by a 94.22% 94.29% 90.59% 88.12% 93.02%
family or individual
A: Water area is primarily related to the county‘s Lake Michigan shoreline.
Sources: (Onboard Informatics, n.d.-a, n.d.-b, n.d.-c, n.d.-d, n.d.-e)
Table 3 -2.° Percentage Employment in Selected Industry Types by Gender
Lake Manistee Mason Oceana Newaygo
MA 1:3 M F M F M F M F
Accomm‘lm'on and 4 1 1 5 1 1 5 9 4 8 N/A 8
food servrces
Agriculture, forestry, C
ﬁshing and hunting N/A N/A N/A 4 N/A 9 N/A 5 N/A
Ans’ entertainmem’ N/A N/A 4 6 N/A N/A N/A N/A N/A N/A
and recreation
Chemical N/A N/A 6 N/A N/A N/A N/A N/A N/A N/A
Construction 16 N/A 14 N/A 14 N/A 14 N/A 15 N/A
Educational N/A 10 5 10 6 13 4 13 5 13
Health care N/A 1 1 N/A 21. N/A 19 N/A 16 5 14
Metal and metal 7 N/A lA N/A 8 N/A 11 N/A 6 N/A
products
Public administration 7 10 6 5 5 4 N/A 5 N/A 4
Tmipmamn 7 N/A N/A N/A N/A N/A 8 5 8 7
equipment

 

A: Male, B: Female, C: Not available

Sources: (Onboard Informatics, n.d.-a, n.d.-b, n.d.-c, n.d.-d, n.d.-e)

53

Table 3-3: The Attractions in Each County

 

Lake

 

Notable locations

Lakes, reservoirs,
and swamps

Old Grade Campground, Old Grade Trail Camp, Kellars Corners, Carrieville
Recreation Site, Gleasons Landing Recreation Area, Lake Olga Recreation Site,
Raymond Corners, Lincoln Bridge Canoe Access, Little Leverentz Lake Recreation
Site, Upper Branch Bridge Access, Elm Flats Canoe Access, Bowman Bridge
Campground, Branch, Bray Creek Recreation Site, Lukes Corners, Forks Public
Access.

Little Lake Number One, Little Lake Number Two, Little Lake Number Three,
Little Leverentz Lake. Ahmikwam Lake. Little Star Lake, Alice Lake, Little Syers
Lake.

 

Rivers and creeks

Baker Creek, Beaver Creek, Blood Creek. Bray Creek, Coe Creek. Cole Creek,
Cool Creek, Middle Branch Pere Marquette River. McCarthy Creek.

 

Park

Driftwood Recreation Area

 

Historical places

Lake County lnfonnational Designation, Brown Trout Informational Designation,
Shrine of the Pines, Idlewild Historic District, Marlborough Historic District.

 

Manistee

 

Notable locations

Low Bridge Canoe Access, Old High Bridge R R Crossing Site, Old Stronach
Public Access, Myers School, Hale Comers, Rainbow Bend Public Access, Glenoak
School, Inner Light, Manistee National Forest District Ranger Station, Manistee
Drop Forge Corporation, Manikiwa Recreation Site, Bear Creek Public Access,
Marsh, Red Bridge Public Access, Packaging Corporation of America, Kenny
School, Claybank School, Kilbom School, Udell Rollways Recreation Area, Big
Four Comers.

 

Lakes, reservoirs,
and swamps
Rivers and creeks

Adamson Lake, Lost Lake, Arcadia Lake. Makinen Pond, Manistee Lake. Ball
Pond, Bar Lake. Bear Lake.

Arquilla Creek. Bear Creek, Beaver Creek, Boswell Creek, Bowens Creek,
Chamberlain Creek. Chicken Creek, Chief Creek, Claybank Bayou.

 

Parks

Domer Lake Recreation Site, Mangoon Creek Park, Manistee River State Game
Area, Sand Lake Recreation Area, Orchard Beach State Park, Sundling Park.

 

Historical places

Museums

Notable locations

Lakes, reservoirs,

and swamps
Rivers and creeks

Great Fire of 1871 Informational Designation; Quimby, Harriet, Childhood Home;
Kaleva Informational Designation; Makinen, John 1., Bottle House; Babcock,
Simeon, House; Camp Tosebo Historic District; Church of the Holy Trinity
(Episcopal) and Rectory; Danish Lutheran Church; Douglas; William, House; First
Congregational Church; First Scandinavian Lutheran Church; Manistee Central
Business District; Manistee City Library; Manistee Fire Hall; Manistee Harbor,
South Breakwater; Manistee North Pier; Ramsdell Theatre; Sandenburgh-Rogers
Summer Resort Complex; Vincent, William 1., House; Harris, Roscoe E., Summer
Cottage; Portage Point Inn Complex; Manistee County Courthouse Fountain; Udell
Lookout Tower; Hotel Wellston.
Arcadia Area Historical Museum, Kaleva Art Gallery, Kaleva Bottle House Historic
Museum, Manistee Art Institute, Manistee County Historical Museum.

Mason
Ludington Hydroelectric Plant, Ludington Hills Golf Course, Round Lake School,
Lower Branch Bridge Access, Batcheller School, Lincoln Hills Golf Course, Reek
School, Big Sable Light, Howell School, Elm Flats School, Kings Comers, Kistlers
Comets, Stiles Comers, Star Port Marina, Ford Lake School, Bridges Resort,
Ltﬂgton Winter Sports Area.
Allen Lake, Alma Lake, Long Lake, Lost Lake, Augustine Lake, Bailey Lake,
McKimzie Lake, Meade Lake.
Big Sable River, Big South Branch Pere Marquette River, Burr Creek, Carr Creek,
Cooper Creek, Costello Creek, Ritters Creek, Lichte Creek, Frog Paradise Drain.

54

Table 3-3 (Cont ’d)

 

Parks

Mason
Ludington State Park, Buttersville Park, Victory Memorial Park.

 

Historical places

Big Sable Point Light Station; Ghost Town of Hamlin Informational Site;
Marquette’s Death Informational Site; Notipekago Commemorative Designation;
Reorganized Church of Jesus Christ of Latter Day Saints; Camp Arcadia; Armistice
Day Storm Informational Designation; First Mason County Courthouse; Fish
House; Goodenough, Daniel W., House; Latimer, Frank N. and Fanny Allen,
House; Lessard, Ray, House; Mason County Courthouse; S.S. Badger; S.S. Pere
Marquette 18 Informational Site; Scottville Informational Designation.

 

Museum

Historic White Pine Village.

 

Oceana

 

Notable locations

Lakes, reservoirs,
and swamps
Rivers and creeks

Bucks Comers, Halls Comers, Smith Comers, Sischo Canoe Landing, Camp
Keewana, Camp Manistee, Little Point Sable Lighthouse, Oceana Country Club,
Diamond Access Canoe Landing, Pines Point Recreation Site, Podunk Canoe
Access, Twin Bridges Comers, Town Comets.

Acker Lake. Little Pebawma Lake. Little Wildcat Lake. Long Lake, Lost Lake,
Lucky Lake. Marshville Pond, McLaren Lake.

Allen Drain, Au Sable Creek. Bear Creek. Bender Creek. Big Springs Creek.
Brayton Drain. Burke Creek. Cargill Creek, Cedar Creek.

 

Parks

Rentwater River State Game Area, Silver Lake State Park, Cedar Point Park,
Marshville Roadside Park.

 

Historical places

Benona Township Hall; Gay, Jared H., House; Little Sable Point Light Station;
Mears, Charles, Silver Lake Boardinghouse; Hart Historic Industrial District;
Veterans Day Storm Informational Designation.

 

Museums

Oceana County Historical Park, Claybanks Pottery.

 

Newaygo

 

Notable locations

Camp Echo, Henry Camp, Pine Avenue Public Access, Ottawa Boy Scott Camp,
Newaygo Recreation Area, Nichols Lake Recreation Site, White River Roughing
Area Campground, Drew Siding, Walkers Comers, Benton Lake Recreation Area,
Trumbull Comers, Highbank Lake Recreation Area, Jackson Corners, High
Rollway Public Access.

 

Lakes, reservoirs,
and swamps

Abeys Lake. Ackland Lake. Little Lake. Little Lake Placid. Little Martin Lake.
Little Mud Lake. Little Robinson Lake. Alley Lake.

 

Rivers and creeks

Allen Creek. Bear Creek. Beaver Creek. Bigelow Creek. Black Creek. Bowman
Drain, Brayden Creek. Butler Creek. Cedar Creek.

 

Parks

Hardy Dam County Park, Newaygo County Sports Park, Newaygo Experimental
Forest, Newaygo State Park, Mena Creek Waterfowl Area, Breezy Knoll Park,
Sandy Beach County Park, Loda Lake Recreation Site, Loda Lake Wildﬂower
Sanctuary.

 

Historical places

Big Prairie Grange No. 935 Hall; Hardy Hydroelectric Plant; Croton Congregational
Church; Croton Hydroelectric Plant; Oak Grove District No. 3 Schoolhouse; Ensley
Windmill Tower; First Christian Reformed Church; Gerber, Cornelius, Cottage;
City of Grant Depot and Water Tower; Weaver, Daniel, House; Penoyer’s Sawmill;
Saint Mark’s Episcopal Church; Woods, John F., Residence; Birch Grove School;
White Cloud Village Hall.

 

Museum

Newaygo County Historical Museum.

Source: (State Historic Preservation Ofﬁce, Michigan Historical Center, Michigan Department of State,
"Lake County, Michigan," n.d.; Manistee County, Michigan," n.d.; Mason County, Michigan,"
n.d.; Newaygo County, Michigan," n.d.; Oceana County, Michigan ", n.d.)

55

From a geographic perspective, Manistee, Mason and Oceana counties border
Lake Michigan, which attracts people mostly during the summer season. Lake and
Newaygo are inland counties with less area covered with water. Even so, natural
resources such as creeks and lakes create various outdoor activity opportunities such as
ﬁshing, hunting, boating, sightseeing, etc. According to the supply assessment/inventory
reports for these ﬁve counties prepared by the Michigan State University Travel, Tourism
and Recreation Resource Center, visitor services such as lodging and transportation are
well deveIOped for tourism (Herbowicz, Bristor, Holecek, & Yang, n.d.). A variety of
other historic, cultural and outdoor recreation resources exist (Table 3-3).

Although there are a variety of resources, the study area still faces some
limitations with regard to tourism. First, the scale of tourism development is smaller than
in neighboring Benzie County, where the Sleeping Bear Dunes National Lakeshore is
located. The tourism resources in the study area tend to be ignored because of their being
less striking than those of their neighbors. Competition in the tourism industry and threats
by other similar tourism resources seem to be unavoidable. However, the ﬁve counties
still can capitalize on ﬂows of tourists if the area can create an alternative impetus for
tourists to stop instead of passing through on their way to another destination.

Second, until recently, the tourism industry had been expanding very rapidly. But
as found elsewhere, tourism is also perceived as producing minimum-wage, seasonal jobs
rather than high-wage, year-round jobs. Mainly, the problems that challenge tourism
development in this area are not only the lack of perceived beneﬁts of rural tourism
development, but also the need for the creation of a unique destination image that

separates these counties from surrounding areas.

56

Third, tourism has been one of the WCMI’s most important industries, especially
in those counties bordered by Lake Michigan. However, economic development and local
tourism leaders have noticed that the whole region lacks a regional brand to compete with
other areas; the regional marketing organization does not have funding to promote a
tourism industry with inland area especially facing the under-promotion issue; and
tourists mostly visit the region during summer (Herbowicz, et al., n.d.).

In 2003, representatives from the WCMI region met with personnel from
Michigan State University (MSU) Extension and the MSU Travel, Tourism and
Recreation Resource Center (TTRRC) to discuss how to strengthen the region’s tourism
industry. The “Toruism Assistance Project” (TAP) was led by TTRRC as long-term
research and outreach to link the many representatives of the region’s tourism industry to
assist and create a strategic plan. The TAP planning committee included representatives
from local chambers of commerce, convention and visitors bureaus, and county economic
development ofﬁces as well as MSU faculty members and MSU county extension
personnel. This TAP serves as the source of primary data for the current study.

Population and Sample

The TAP was conducted in ﬁve phases: (a) A supply assessment in Fall 2003
consisting of an inventory of existing cultural, natural, and recreation resources; (b) A
demand assessment in Spring-Winter 2004 consisting of an overnight visitor survey, a
transient visitor survey, and a potential visitor survey; (c) A needs and preferences
assessment in Spring-Summer 2005 consisting of a resident attitude survey and a
business/supplier survey; (d) The formation of a regional destination marketing

organization in Fall 2005; and (e) The creation of a strategic marketing plan. The results

57

of the three surveys completed during the course of the TAP were used in this study (i.e.,
transient visitors, overnight tourists, and potential visitors).
Transient Visitors
The transient visitor survey, an on-site, self-administered survey, caught transient
visitors who visited the area but did not live within 50 miles. This surveying was
conducted at several locations in the study region along US-131, M-3 7, and US-3 1,
mostly gas stations where transient visitors were likely to stop on a random basis.
Respondents were sampled according to a pre-determined sampling schedule on both
weekdays and weekends. To identify respondents, individuals were approached and
asked if their primary residence was at least 50 miles away. If so, the person was asked to
complete the survey.
Overnight Tourists
The overnight visitor survey was mailed to a sample of visitors who stayed
overnight at commercial lodging (hotels, motels, campgrounds) in the study area. The
names and addresses of survey recipients were provided by commercial lodging owners
and managers. The goal was to survey about 500 recent visitors per county, or 2,500
visitors total. The data were intended to be collected from all lodging owners in the areas;
however, not all the lodging owners were willing to provide their lists of visitors. Also,
some lists only included visitors from one area. Such lists were discarded because the
names obviously were not selected randomly as was requested.
Potential Visitors
This survey was designed to reach potential visitors to the study area. Based on a

review of other secondary data sources (e. g., national data from the former U.S. Travel

58

Data Center), the geographic area that supplied the majority of visitors to the study areas
was determined. A list of potential respondents from this area was purchased from
Survey Sampling International of Connecticut. It consisted of residents from six
Designated Market Areas (DMAs): Chicago, Detroit, Flint, Grand Rapids, Lansing, and
South Bend (see Table 3-4). These DMAs were identiﬁed as the most likely potential
visitor areas for the WCMI region. Five hundred randomly selected households in each
DMA, a total of 3,000, were sent surveys by TTRRC. The expected target rate was 20%
or 100 completed questionnaires per DMA.

Table 3-4.’ County Covered and Number of Households in Six DMAs

 

. . Total .
Counties 1n Each Area Households Sample Srze
Chicago Cook, IL.; DeKalb, IL.; Du Page, IL.; Grundy, IL.;
Kane, IL.; Kankakee, IL.; Kendall, lL.; Lake, IL.; La
Salle, IL.; McHenry, IL.; Will, IL.; Jasper, IN.; Lake,
IN.; LA Porte, IN .; Newton, IN.; Porter, IN.

3,358,363 500

 

Detroit Lapeer, ML; Livingston, ML; Macomb, ML; Monroe,
ML; Oakland, ML; St. Clair, ML; Sanilac, ML; 193,8628 500
Washtenaw, ML; Wayne, MI.

 

Flint Arenac, ML; Bay, ML; Genesee, ML; Gladwin, ML;
Gratiot, ML; Huron, M1,; Iosco, ML; Isabella, ML;
Midland, ML; Ogemaw, ML; Saginaw, ML;
Shiawassee, MI; Tuscola, MI.

47,6205 500

 

Grand Rapids Allegan, ML; Barry, ML; Branch, ML; Calhoun, ML;
Ionia, M1,; Kalamazoo, MI; Kent, ML; Montcalm, ML;
Muskegon, ML; Newaygo, ML; Oceana, ML; Ottawa, 753,217 500
ML; St. Joseph, ML; Van Buren, MI

 

Lansing Clinton, ML; Eaton, ML; Hillsdale, ML; Ingham, ML;

Jackson, MI. 271,019 500

 

South Bend Elkhart, IN.; Fulton, IN.; Kosciusko, IN.; Lagrange,
IN.; Marshall, IN.; Pulaski, IN.; St Joseph, IN.; Starke, 327,499 500
IN.; Berrien, ML; Cass, MI.

 

Total 7,124,93 1 3,000

 

Source: Survey Sampling International of Connecticut

In light of response rates achieved in related prior studies presented in Table 3-5,
the targeted 20% response rate for the potential visitor survey was high, and, even if

achieved, nonresponse bias would be an issue. Hence, it is important for this study to

59

examine response bias by comparing early and late responses on the key constructs, as

suggested by Coviello, Winklhofer, and Hamilton (2006).

Table 3 -5 .' Low Response Rate Studies in Tourism Research

 

 

 

 

 

Study Response Rate
Balglu & Shoemaker, 2001 11.8%

Bauer, Law, Tse, & Weber, 2008 0.3%

Chen, 2001 13%

Coviello, et al., 2006 13%

 

Dopson, 2004
lllum, Ivanov, & Liang, in press

1 1%
05-06% through academics ,
004-0. 15% through auto travelers

 

 

 

 

Opperrnann, 2000 9.3%

McGehee, Wattanakamolchai, Perdue, & Calvert, 4.2%
2009

Sigla, Airey, Jones, & Lockwood, 2004 7%

Shoemaker, 2000 l 1.8%

 

Survey Instruments

The survey instruments comprised three questionnaires that were developed by

TTRRC. The three questionnaires contained some variations. Slightly different wording

reﬂected the nature of the three sample groups (transient, overnight, and potential ) (see

Tables 3-6, 3-7, 3-8, 3-9).

Activity preferences and some trip-related characteristics were measured using

nominal scales. Motivations, awareness of and visits to WCMI attractions, perceptions

regarding WCMI destination attributes, and some trip-related characteristics such as

intention to re-visit the WCMI and willingness to recommend the WCMI to others were

measured using Likert-type scales. The method of measurement associated with each

item dictated the analysis methods used later in the study.

60

Table 3-6: Variables Used for Analysis in the Surveys--Activity

 

 

Sample Group Activity
Transient Survey: -Antique shopping °Jet skiing
Which of the following activities are of -Bicycling °Lighthouse touring
interest to you or others in your family? oBoating -Live theatre
Overnight Survey: ~Camping -Movie (at a cinema)
While in the WCMI region, in which of the -Canoeing/kayaking/tubing °Museum
following activities did you and your °Casino gaming °Mushroom collecting
immediate travel party participate in on this oConcert -Nature center
visit? Cross-country skiing °Off-roading
"”9“.“ Survey: _ , , , Dining out (excluding fast -Photography
Which of the followrng actrvrtres do you and food)
your immediate travel party participate in 'Downhill °Sailing
most often while on pleasure trips? skiin g/sn owboardin g
°Farm market/u— -Scuba diving/snorkeling
pick/winery
festival/event °Shopping
-Fishing, charter -Sightseeing (general)
-Fishing, ﬂy -Snowmobiling
-Fishing, ice 'Sports tournament
~Fishing, other -Swimming (lake, pond,
river)
-Golﬁng Swimming (pool)
oHiking/walking -Theme/amusement park
°Historic site -Visiting a federal/state
park
Horseback riding -Visiting friends/relatives
-Hunting, deer °Wildlife viewing/bird
watching
°Hunting, small game -Wind surﬁng

~Hunting, turkey

 

61

Table 3- 7: Variables Used for Analysis in the Surveys-- T rip-related Characteristics

 

 

 

Sample Group Trip-related Characteristics
Overnight Survey: 'AAA
MOST RECENT OVERNIGHT VISIT WITHIN °Newspaper

the WCMI region -Billboards/outdoor advertising
Which of the following sources of information did 'Radio

you use in planning this visit?

Potential Survey:

Which of the following sources of information do
you use when planning your pleasure trips?

Chamber of commerce

°State travel ofﬁce/Travel Michigan
'Convention and visitors bureau
oTelevision

~Friends or relatives

°Travel guide(s)/brochure(s)
°Highway welcome center

°Word of mouth

~1ntemet/web site(s)

°Magazine

°Highway tourist information centers"
°Local visitor guides“

 

Transient Survey:

What type of lodging are you using on this trip?

Overnight Survey:

MOST RECENT OVERNIGHT VISIT WITHIN
the WCMI region

Which of the following types of lodging did you use
in the WCMI region on this trip?

-Friend’s or relative’s home

°Owned second or seasonal home
°Hotel, motel or resort

Campground or RV park

-Bed & Breakfast

'Rented cabin, cottage or condominium

 

Transient Survey:

About how far in advance did you begin to make
plans for it?

Overnight Survey:

MOST RECENT OVERNIGHT VISIT WITHIN
the WCMI region

About how far in advance did you begin to plan this
trip?

Potential Survey:

In general, about how far in advance do you begin
to plan your
weekend getaway

Transient Survey:

What is/was the primary destination of your current
trip?

Overnight Survey:

MOST RECENT OVERNIGHT VISIT WITHIN
the WCMI region

What was the primary destination of this trip?

Transient Survey:

How many nights do you plan to be away from
home on this trip?

Overnight Survey:

MOST RECENT OVERNIGHT VISIT WITHIN
the WCMI region

How many nights did you spend within the WCMI
region on this trip?

(# of days)

Number of days

City
County

Number of nights

62

Table 3-7 (Cont ’d)

 

Sample Group

Trip-related Characteristics

 

Transient Survey:

Did you visit the WCMI region any time before this
trip?

Potential Survey:

Havgyou ever visited the WCMI region?

Yes, No

 

Overnight Survey:

How likely are you to visit the WCMI region within
next three years?

Potential Survey:

How likely are you to visit the WCMI region within
the next three (3) years?

Overnight Survey:

How would you rate your overall experience in the
WCMI region on this visit?

Deﬁnitely will visit, Very likely, Somewhat
likely, Somewhat unlikely, Very unlikely, Will
not visit the area again

Much better than I expected, Somewhat better
than I expected-A bout what I expected,
Somewhat worse than I expected, Much
worse than I expected

 

Sample Group

Travel Expenditures

 

Overnight Survey:

In total, approximately how much did M
immediate travel party spend in the each of the
following categories within the WC MI region on
this visit?

~Activities (equipment rentals, lessons, etc.)
~Attractions (tickets, entrance fees, etc.)
°Gas/ﬁrel

°Groceries

°Lodging

'Meals at restaurants/fast food

~Shopping (clothes, souvenirs, etc.)

 

*The item is included in the potential visitor survey but not in the ovemight visitor survey.

63

Table 3-8: Variables Used for Analysis in the Surveys-~Pull and Push Factors

 

Sample Group

Travel Motivations-Push Factors

 

Potential Survey:

How important to you are each of the following
factors when selecting a pleasure trip
destination?

-Upscale facilities/services

°Travel time/distance

-Cost

°Family-friendly place and/or opportunities

Safety/security

-Variety of shopping opportunities

~1nteresting scenery

°Service quality

-Variety of attractions and/or activities

°Nightlife activities

'Accessibility for disabled persons

-Pet accommodations

Not at all important, Not so important, Somewhat
important, Important, Very important

 

Sample Group

WCMI Attractions-Pull Factors

 

Transient Survey, Overnight Survey:

Have you visited, or are you aware of, the
following facilities or attractions in this
region?

-Little River Casino

-Recreation on Manistee River

°Ludington Car Ferry

°Ludington State Park/beaches

-Recreation on Pere Marquette River

~1rons/Lake County snowmobile trails

~Recreation on Muskegon River

~Newaygo State Park

Driving/riding on sand dunes at Silver Lake

Mart-Montague (rail) Trail

Have Visited, Aware, but Not Visited, Not Aware of
This Place

 

Sample Group

WCMI Destination Attributes-Pull Factors

 

Overnight Survey:
How much do you agree with the following
statements about the WCMI region?

The WCMI region...

°Has good roads

'Is a great winter destination

-Has great outdoor recreation opportunities

'15 a safe place to visit

-Has high quality lodging

'15 an exciting place to visit

°Has many interesting historical sites

'15 close enough for a weekend getaway

-Is a good place to meet friendly people

'15 easily accessible

-Is a great family vacation destination

-Offers exceptional value for the money

'15 a great place to start a business

°Offers exciting nightlife and entertainment

01s a great spring destination

°Offers great dining opportunities

'15 a great summer destination

-Offers great shopping opportunities

013 a great fall destination

-Offers much scenic appeal

On scale ﬁ‘om I to 10, where 1 means “do not agree
at all ” and 10 means “agree conioletely ”

 

64

Table 3 -9: Variables Used for Analysis in the Surveys--Soci0-demographic

Characteristics

 

Sample Group
Overnight Survey:
Do you have any of the following persons living in
your household?

Transient Survey, Overnight Survey:
What is your employment status

Socio-demographic Characteristics
Pre-school child(ren)
Senior citizen(s)
School-age child(ren)
Handicapped person(s)
Employed full-time, Self-employed, Employed
part-time, Student,, Homemaker,
Unemployed, Retired

 

Potential Survey:

What is the highest level of education you have
attained?

Overnight Survey:

What is the highest level of education you’ve
completed?

Potential Survey:

The U.S. median annual household income before
taxes is about $43,000. Would you say that your
total household income before taxes in 2004
was...

Overnight Survey, Transient Survey:

The U.S. median household income before taxes is
about US$42,500. Would you say that your total
household income before taxes in 2003 was ....?

Elementary school, Some high school, High
school, Some college, College
graduate/professional, Post-graduate

Below US $42,500, Between US $42, 500 and US
$75,000, Above US $75,000

 

Transient Survey, Overnight Survey, Potential
Survey:
What is your gender?

Male, Female

 

Transient Survey, Overnight Survey, Potential Age
Survey:

What is your age?

Transient Survey, Overnight Survey, Potential Zip code

Survey:
What is the zip code of your primary residence?

Data Collection

Data were collected by mail questionnaire from potential and overnight visitors. A

self—administered survey of transient visitors was administered on site. For the mail

questionnaires, several methods were employed to increase response rates. These

methods can be categorized into two approaches: technique and timing. The technique

component emphasizes the design of the survey to encourage a higher return rate. This

65

component can include the design of the questionnaire (for example, length, format, and
color), personalized wording of the cover letter, survey sponsorship, rewards, and
inclusion of return envelopes (Kanuk & Berenson, 1975). In this project, the length of the
questionnaire was limited to two pages in the transient survey and six pages in the
potential and overnight visitor surveys. Both mail survey questionnaires were sent in a
package that included the actual survey, a cover letter describing the purpose of the study
as well as addressing conﬁdentiality issues, and a stamped return envelope. In addition,
the package included some discount coupons and a cash drawing form to be returned to
TTRRC for a chance to win an ovemight-stay package donated by local businesses.

The timing approach emphasizes sending surveys or reminders at different
sampling stages to encourage participation. This approach can be implemented at the
preliminary stage (i.e., pre-notice letter), concurrent stage (for example, sending an extra
copy of the survey shortly after the ﬁrst survey was sent), or the later stage as follow-up
(i.e., delivering a reminder or an extra copy of the survey to non-respondents when the
deadline is approaching). The data used in this study were collected through a mailed
self-administered questionnaire following the modiﬁed Dillman approach (1978), which
suggested the use of the follow-up method. Given budget constraints, only one follow-up
questionnaire was sent to the sampled household residents one month after the ﬁrst rrm of
the surveys was sent. The response rate increased marginally after the follow-up reminder
was sent.

Transient Visitor Survey Response Rates
The transient survey was conducted between mid-July and the beginning of

September 2004. A total of 1274 people were approached; 483 did not qualify since they

66

lived within 50 miles of the sampling site, another 103 refused to participate (see Table 3-
10). A total of 688 responded to the survey. However, 156 of these responses were
invalid.

Table 3-10: Transient Visitor Survey Response Counts

 

 

 

 

 

Lake Manistee Mason Newaygo Oceana Mail Total
Rejected 0 28 27 25 23 - 103
Not qualiﬁed 65 185 180 88 121 - 639
Valid 93 156 148 15 103 17 532
Total 158 369 355 128 247 17 1274

 

Overnight Visitor Survey Response Rates

A total of 1,642 questionnaires were mailed out to overnight visitors to the study
area. The ﬁnal return from the ﬁve county visitors was: 94 from Lake County, 50 from
Manistee County, 43 from Mason County, 42 from Newaygo and 38 from Oceana
County. The ﬁnal return rate was 18.2% (see Table 3-11). The response rate was higher
in Lake County where there are many cottages and cabins. Those who stayed in the
private cottages might have had more time and been more willing to answer surveys, or
felt a greater sense of attachment or interest in the destination.

Table 3-11: Overnight Visitor Survey Response Rates

 

 

 

 

 

Lake Manistee Mason Newaygo Oceana Total
Sent out 429 314 244 255 400 1642
Non-deliverable 26 20 I4 25 91 176
Returned 94 50 43 42 38 267
Effective response
rate (%) 23.3 17.0 18.7 18.3 12.3 18.2

 

Potential Visitor Survey Response Rates
The potential visitor survey was mailed to 3,000 people, 500 in each of the six

DMAs identiﬁed. After sending out surveys in two waves, a total of 312 questionnaires

67

were returned. Of the 2,688 nonresponses, 347 were due to incorrect addresses. Thus, the
overall response rate was 11.8%. Chicago had the lowest response rate (see Table 3-12),
probably because people there are less familiar with the study area and thus have less
interest in completing the survey.

Table 3 -1 2: Potential Visitor Survey Response Rates

 

 

 

 

. . . Grand . South
Chicago Detrort Flmt Rapids Lansrng Bend
Sent out 500 500 500 500 500 500
Returned 2 1 49 61 64 72 34
Effective response a
rate (%) N/A N/A N/A N/A N/A N/A

 

a: The effective response rate in each MDA is not available due to the undeliverable cases being
unidentiﬁable.

Nonresponse Study

The mail surveys conducted in this study followed various techniques proposed
by Dillman (1978) such as ﬁnancial incentives, material incentives (e. g., coupons),
follow-up reminders, inclusion of a cover letter, identiﬁcation of the source of survey
sponsorship, a statement regarding the anonymity of responses, and limited questionnaire
length in order to maximize the response rate. However, the study still experienced a low
response rate, an occurrence noted as a limitation in many tourism research studies.

Several adjustment procedures were employed to handle the suspected biases in
the survey. One commonly used procedure of handling this issue is to ﬁnd substitute
survey participants while the survey is still in process (Platek, Singh, & Tremblay, 1977).
Due to limited time and funding, neither participant substitution nor the conduct of
nonresponse research was carried out during the survey period.

Nonresponse bias is assessed according to the theory developed by Armstrong
and Overton (1977) and Fodness and Murray (1999), which considers late respondents as

nonrespondents and assesses whether their responses substantially differ from those who

68

returned their surveys earlier. This approach was used here to evaluate the overnight and
potential surveys. First wave and second wave respondents were compared in each item
to examine differences. If differences did not appear in most of the items, then the data
did not need further adjustment.

Weighting is often used to correct selection bias (Whitehead, Groothuis, Hoban,
& Clifford, 1994). Weights can be used in calculating estimates for various sampling
schemes and can also be employed to adjust for nonresponse (Lohr, 1999). When small
groups are discreetly oversampled because of their importance to the evaluation,
weighting is used to correct the problem of the sample being distributed disproportionally
with respect to the target population of interest (Bamberger, Rugh, & Mabry, 2006;
Pfefferrnann, 1993).

Imputation for Survey Sampling

Baloglu and Assante (1999) examined 1,073 articles published in ﬁve primary
hospitality management journals from 1990 to 1996 in order to understand the research
directions and boundaries of hospitality research. They found that the majority of the
articles used non-probability sampling (64.0% of the articles in Cornell Hotel and
Restaurant Administration Quarterly, 65.0% in International Journal of Hospitality
Management, 66.7% in Hospitality Research Journal, 84.8 % in F1 U Hospitality Review
and 87.7% of the articles in International Journal of Contemporary Hospitality
Management), that most of the researchers were unable to generalize their ﬁndings
beyond the studied population, and that almost no articles tested for nonresponse bias.

De Vaus (1986) stated that probability sampling techniques are often either

impractical or unnecessary. According to De Vaus, non-probability sampling techniques

69

are appropriate when sampling frames are unavailable or the population is not clustered
in a way that would make cluster sampling rational. Some researchers are not interested
in sampling proportionally and would rather explore people’s thoughts and ideas.

The potential visitor survey in this study deﬁned six Designated Marketing Areas
(DMAs) as the potential market based on their geographical location relative to the study
area. The sample was drawn randomly but disproportionately from those six DMAs with
500 survey addresses from each area. The TAP project simply tried to get a range of
people in the sample, with less concern for whether each DMA was represented in its
correct proportion. However, the sample was examined carefully for its
representativeness of the population prior to any conclusions being drawn in the study.

Weighting is a common tool to adjust the proportion of certain groups in the
sample in accordance with their proper proportion in the total population. Nevertheless,
all adjustment techniques require assumptions. In practice, the assumptions behind the
adjustment process are normally untestable. Sometimes, the adjustments might even
produce problems (Groves, 2006). Therefore, weighting are processed only if the data are
found not to represent the population. The weight are calculated by the percentage of
respondents in each area divided by the percentage of households in each area (Lohr,
1999), as utilized by Latkova (2008) in her study of factors predicting residents’ support
for tourism development in Emmet, Saginaw, and Tuscola counties in Michigan.

Data Analysis
Data analyses were based on the two study approaches (a priori and post hoc

approaches) and analysis methods were based on major frameworks in two steps:

70

identiﬁcation of bases for segmenting the market and development of proﬁles of resulting
segments.

A Priori Approach
Step I .'

A series of chi-square analyses were performed to identify differences in activity
preferences between visitors and non-visitors in the potential survey. For the transient
and overnight surveys, chi-square analyses were performed to compare differences in
activity preferences between those who deﬁnitely will revisit and those who will not
revisit the study area.

Step 2:

Logistic regression has been one of the most extensively used statistical methods
in social sciences research (Hamilton, 1992), and it is the appropriate technique for this
research to predict the probability of activities participation based on a variety of factors,
since the research questions yield dichotomous responses of either “yes” or “no.”

A series of logistic regression models were constructed after choosing
differentiable activity between the two groups from the previous step. Activities that were
found to be signiﬁcantly more popular among visitors (repeat travelers) than among non-
visitors (non-repeat travelers) from the ﬁrst step analyses were included in each model as
the dependent variables in order to examine their relationship with demographic
characteristics and other attributes. Activities found signiﬁcantly less popular in the ﬁrst
step were not considered when examining the relationship with other attributes in the

next step.

71

In the logistic regression models, criteria for variable selection involve several
steps as follows: (a) Start with a univariable analysis of each variable; (b) Any variable in
the univariable test with a p-value lower than 0.25 is a candidate for the multivariable
model along with all variables known for research importance; and (c) Verify model ﬁt:
(i) Examine the Wald statistic for each variable; (ii) Compare each estimated coefﬁcient
with the coefﬁcient from the model including only that variable. The ﬁnal model will
contain all of the important variables (p-value < 0.05) and exclude those that are
clinically and statistically unimportant (Hosmer & Lemeshow, 2000).

Also, in the logistic regression model, classical methods for variable selection
include forward selection, backward elimination, and stepwise regression (Chen & Dey,
2003). In this study, a backward stepwise technique was adopted in light of Peng, So, and
Stage’s (2002) and Connor’s (2002) suggestion that the stepwise logistic regression
technique is a powerful exploratory tool for the identiﬁcation of plausible models. In the
ﬁrst step, the variables were entered into the model together and were tested by removing
them one by one. Model building stops when no more variables meet the removal criteria
or when the current model is the same as a previous model (SPSS, 2006). The backward
method is a preferable model to the forward method because the suppressor effect occurs
when a predictor has a signiﬁcant effect but only when another variable is held constant
(Field, 2005).

Exploratory factor analysis was performed for certain attributes (such as travel
information searching sources) in order to examine whether underlying dimensions exist
among the items and to decrease the number of independent variables. The principal

component factor procedure with varimax rotation has been widely practiced in the

72

literature (Andriotis, Agiomirgianakis, & Mihiotis, 2008; Cha, et al., 1995; Kibicho, 2006;
Sangpikul, 2008)and was employed for this task.

Post Hoc Approach
Step I:

Cluster analysis groups objects (for example, respondents, products) so each
object shares some homogeneity within the cluster with respect to some predetermined
criterion and higher heterogeneity between clusters (Hair, et al., 1998). Cluster analysis
has often been used as a data-driven market segmentation approach in many disciplines,
including tourism studies (Andriotis, et al., 2008; Inbakaran & Jackson, 2005, 2006) and
market research (Dibb & Stern, 1995; Everitt, Landau, & Leese, 2001). However,
Aldenderfer and Blashﬁeld (1984) noted four cautions about cluster analysis: (a) Most
cluster analysis methods are relatively simple with important mathematical properties that
do not sustain complicated data structure; (b) Not all the data are equally useful to
building a classiﬁcation. It is necessary to consider which type of data can be useful. (c)
Different methods may produce different clusters from the same data set; and (d) Cluster
analysis is an exploratory process, not a model-based procedure; therefore, the strategy of
this method is structure-seeking and the process is more like structure-imposing.

One of the biggest disadvantages related to cluster analysis is the decision as to
the number of clusters (Thorndike, 1953), which is arbitrary and subject to change when
different datasets are analyzed. Also, given the nature of outcome variables being a
binary factor, latent class analysis (LCA) was employed using Mplus in this step. As
Bassi stated (2007), LCA can be regarded as a probabilistic variant of k-means clustering.

But unlike cluster analysis (CA), LCA is a model-based clustering technique. Because of

this, statistical indices such as Akaike’s Information Criterion (AIC), Bayesian
Information Criterion (BIC), and sample-size adjusted BIC were employed to help
evaluate model ﬁt and decide how many clusters might be retained.

Step 2:

The validity of each segment was tested statistically using the t-test, AN OVA or
chi-square tests in order to examine how different activity clusters might consist of
people with different social-demographic backgrounds or with different trip
characteristics.

Analysis of variance (ANOVA) and t-tests were used to test for signiﬁcant
differences between means in groups; t-tests were used to test two groups and ANOVA to
examine more than two groups. Chi-square was used to compare the counts of categorical

responses between two (or more) independent groups.

74

CHAPTER 4 RESULTS

This chapter, the results of data analysis, includes: (a) Examination of the
representativeness of the potential visitor sample; (b) Details regarding nonresponse; (c)
A description of the sample focusing on socio-demographics and other variables related
to the study questions and hypotheses; (d) A priori individual activity analyses using chi-
square to examine differences between groups with respect to activity preference in the
three samples (H1 .1.1), and logistic regression to predict activity participation with
respect to different sets of variables (H12 to H.1.9); and (e) Post hoc bundle activities
analyses using latent class analysis to identify the segments in three samples (H21) and
proﬁle the segments with respect to a series of factors (for example, age, income) (H22-
2.7).

Examination of the Representativeness of the Potential Visitor Sample

In the potential visitor survey, the sample was drawn randomly but not with equal
proportions from the six DMA areas. The data were examined for representativeness of
the population in three ways:

First, the activities in the study were related to rural tourism destinations. The
activities reported by the Travel Industry Association of America (TIA) were a useful
resource to compare with the data from the potential visitor survey. The activities
identiﬁed as popular among potential visitors in this study were consistent with those
activities listed in the TIA 2001 Rural Tourism Travel Poll (Table 4-1). These activities
are: dining, visiting historical sites, shopping, and festivals and events.

The Michigan Travel Market Survey, also called the Household Survey (HHS),

was a second resource for comparison. The HHS was conducted from January 1996 to

75

June 2003 and it collected over 37,000 completed surveys. These were daily phone
surveys of randomly selected households in Michigan, Illinois, Indiana, Ohio, Wisconsin,
and the province of Ontario (Canada). Table 4-2 is based on over 2,300 surveys of
respondents who had taken a trip to Michigan within the past 12 months; these data were
collected between January 2001 and June 2003. Based on the visitor proﬁle in the
primary destination region of the Michigan Travel Market Survey, the average household
size and number of days of trip planning showed similar results to the potential visitors in
the potential visitor survey.

Table 4-1: Activities that Rural Tourists Like to Participate in

 

 

Activity Rural Tourism Potential Visitors
Travel Poll (%) to the WCMI(%)

Dining 70 73.6

Shopping 5 8 55.4

Visit historical sites 41 56.4

Attend festival/fair 29 47.0

Camping 21 37.2

Visit winery/visit farm/orchard 15 31.3

Gamblinggaming 12 29.1

 

Source: TIA, 2001

Table 4-2: Comparison of Potential Visitor Sample and Michigan Travel Market Sample

 

 

Trip Characteristics Potential Michigan Travel
Vrsrtor Survey Market Survey
Average household size, Mean 2.6 2.9
Primary Destination in the Region/State, %
Michigan 70 57
Illinois 8 8
Indiana 6 10
Ohio 4 10
Other 12 15
Trip planning period. %
0-30 days 61 68
31-60 days 12 11
Over 60 days 27 21

 

Source: Bristor, Herbowicz, Holecek, and Yang (2005)

76

The third data resource used to examine the representativeness of the potential
visitor sample was the transient and overnight visitor surveys. The sample distributions
from both surveys showed that the visitors mainly came from Michigan. The potential
visitor survey returns were similar to the sample distributions of overnight and transient
visitors (Table 4-3).

Table 4-3: Comparison of Place ofResidence of Potential Visitors, Overnight Visitors,
and Transient Visitors

 

 

Residence Transient Visitor Overnight Visitor Potential
Survey (%) Survey (%) Visitor Survey (%)

MI 79.1 72.6 86.7

‘N 3.2 6.1 7.0

0H 1.1 5.7 _ A

‘L 5-I 4.9 6.3

W 0.9 1.5 _ A

Other State 10.6 9.2 A

 

A: Not in sample area

Based on these results, the potential visitor survey seemed not to capture the
Chicago market, as compared to other areas; however, the sampling reﬂected similar
proﬁles of overnight visitors, transient visitors, and households. Weighting based on the
population proportion in each area and ignoring where tourists come from might create
more bias. Also, the study did not intend to investigate the different travel and socio-
demographic characteristics in each area, and this is an exploratory study aiming for
general popular activity preferences in the potential market. Therefore, the study did not
consider any weighting in the data analyses.

Nonrespondent Bias Study

When comparing the ﬁrst and second waves of overnight visitor respondents, one

test for nonresponse bias, the activity and socio-demographic items, a total of 52

variables, did not show any signiﬁcant differences between the two waves of survey

77

groups (see Table 4-4). In the potential visitor survey, 4 out of 50 (8%) variables showed
signiﬁcant differences between the two waves of survey groups (see Table 4-5).
Therefore, even though the survey response rates were low in the study, nonresponse bias
was not found using statistical tests comparing the ﬁrst and second wave respondents.

Table 4-4: Comparison of First and Second Wave Respondents in the Overnight Visitor

 

 

Survey
Overnight Visitor Survey 1:211:37 211:6 Seiﬁgg7Wave Test Statistics
Activities in which you participated on
your most recent visit to the WCMI, %
Antique shopping 16.1 10.5 X2 =1-103, df=l
Bicycling 8.1 10.5 X2 =0-349, df=l
Boating 21.8 24.6 x2 =0.364, df=1
Camping 22.1 12.3 x2=2.546,df=1
Canoeing/kayaking/tubing 11.4 8.8 X2 =0.315, df=l
Casino gaming 21.8 14.0 )(2 =1.682, df=l
Concert 4.3 5.3 x2 =0.104, df=1
Cross-country skiing 2.4 1.8 X2 =0-078, df=l
Dining out (excluding fast food) 62.6 64.9 X2 =0-107, df=l
Downhill skiing/snowboarding 0.9 0.0 X2 =0.544, df=l
Farm market/u-pick/winery 16.1 15.8 X2 =0-004, df=l
Festival/event 12.8 14.0 X2 =0.061, df-‘l
Fishing, charter 5.7 5.3 X2 :0.015, df=l
Fishing, ﬂy 8.5 1.8 X2 =3.129, df=l
Fishing, ice 0.9 0.0 X2 =0.544, df=1
Fishing, other 20.9 22.8 X2 =0.102, df=l
Golﬁng 12.3 15.8 x2 =0-475, df=l
Hiking/walking 39.8 33.3 x2 =0.796, df=1
Historic site 17.1 19.3 x2 =0-155,df=1
Horseback riding 1.4 3.5 X2 =1.068, df=l
Hunting, deer 7.1 5.3 X2 =0.244, df=1
Hunting, small game 5.2 3.5 x2 =0.282, df=1
Hunting, turkey 0.9 1.8 12 =0.264, df———1
Jet skiing 3.8 7.0 x2 =l.092, df=l
Live theatre 0.0 1.8 x2 =3.716, df=1
Movie (at a cinema) 8.1 1.8 X2 22.845, df=l

 

78

Table 4-4 (Cont ’d)

 

 

. . . First Wave Second Wave . .
Ovem1ght Vrsrtor Survey 01:an (11:57) Test Statistrcs
2
Museum 6.3 7.0 x =0.044, df=1
2
Mushroom collecting 4.3 3.5 X =0-065, df=l
2
Nature center 7.1 7.0 X =0.001, df=l
2
Off-roading 9.0 8.8 x =0.003, df=1
2
Photography 20.9 26.3 X =0-730, df=l
2
Sailing 1.9 1.8 x =0.005,df=1
2
Scuba diving/snorkeling 0.0 1.8 X =3.716, df=l
2
Shopping 38.4 43.9 x =0.562, df=1
2
Sightseeing (general) 41.7 42.1 x =0.003, df=1
2
Snowmobiling 3.3 3.5 x =0-005, Clle
2
Sports tournament 2.4 0.0 X =1 .376, df=l
2
Swimming (lake, pond, river) 27.5 29.8 X =0.122, df=l
2
Swimming (pool) 27.5 29.8 x =0.122, df=1
2
Theme/amusement park 2.8 5.3 X =0-810, df=1
2
Visiting a federal/state park 24.6 24.6 X =0.000, dﬁl
2
Visiting friends/relatives 23.7 22.8 X =0.020, (1%]
2
Wildlife viewing/bird watching 20.9 19.3 X. =0-067, df=l
2
Wind surﬁng 0.5 0.0 x =0.271, df=1
Socio-demographic characteristics
2
Gender (female), % 37.9 34.0 x =0.284, df= '1
Age, Mean (SD) 50.9 (12.3) 51.1 (10.9) F=0.155, df=250
Living with children under 18 years 2
old, % 37.0 33.3 X =0-257, df=l
2
Income, % x =4.157, df=2
Below $42,500 24.4 13.2
Between $42,500 and $75,000 37.8 50.9
Above $75,000 37.8 35.8
2
Employment, % x =2.326, df=2
Employed 73.4 74. 1
Unemployed 3.9 0.0
Retired 22.7 25.9
2
Education, % x =2.243, df=2
High school, some high school 18.0 16.1
Some college, college
graduate/professional 59.5 69.6
Post-graduate 22.4 14.3

 

Tp<.05, **p<.01

79

Table 4—5: Comparison of First and Second Wave Respondents in the Potential Visitor

 

 

Survey
Potential Visitor Survey 1:315:53? seigggZXWe Test Statistics
Activities you and your immediate travel
party most oﬁen participated in while
on pleasure trips, %

Antique shopping 17.5 24.2 x2 =l.783, df=l
Bicycling 19.9 20.9 x2 =0.037, df=1
Boating 27.5 19.8 x2 =2.006, df=l
Camping 38.9 31.9 x2 =l.338, df=1
Canoeing/kayaking/tubing 20.9 16.5 X2 =0.772, df=l
Casino gaming 29.4 29.7 x2 =0.003, df=1
Concert 21.8 20.9 x2 =0.032, df=1
Cross-country skiing 6.2 12.1 x2 =3.053, df=1
Dining out (excluding fast food) 72.0 76.9 x2 =0.779, df=1
Downhill skiing/snowboarding 9.5 9.9 x2 =0 .012, df=l
Farm market/u-pick/winery 27.5 39.6 x2 =4.322*, df=l
Festival/event 45.5 49.5 x2=0-399, df=l
Fishing, charter 11.4 9.9 x2 =0.144, df=1
Fishing, ﬂy 3.8 7.7 x2 =2.050, df=l
Fishing, ice 6.6 6.6 X2 =0 .000, df=1
Fishing, other 23.7 20.9 x2 =0 .286, df=1
Golﬁng 26.1 25.3 x2 =0 .021, df=1
Hiking/walking 46.0 48.4 x2 =0 .145, df=1
Historic site 51.2 67.0 x2 =6.480*, df=1
Horseback riding 8.1 9.9 x2 =0 .272, df=1
Hunting, deer 15.6 14.3 x2 =0.090, df=1
Hunting, small game 5.2 7.7 X2 =0 .697, df=l
Hunting, turkey 3.6 2.0 x2 =0 .228, df=1
Jet skiing 3.8 3.3 X2 =0 .044, df=1
Lighthouse touring 16.9 10.6 X2 =3.856*, df=l
Live theatre 17.1 26.4 x2 =3.463, df=1
Movie (at a cinema) 23.7 27.5 X2 =0 .486, df=l
Museum 36.5 48.4 x2 =3.723, df=1
Mushroom collecting 5.7 8.8 X2 =0.99l, df=l
Nature center 32.2 29.7 x2 =0.193, df=1
Off-roading 5.7 17.6 x2 =10.695*, df=l
Photography 37.0 37.4 x2 =0.004, df=1

 

80

Table 4-5 (Cont ’d)

 

First Wave Second Wave

 

Potential Vrsrtor Survey ("2249) (n=53) Test Statrstrcs
2
Sailing 2.8 4,4 x =0.478, df=1
2
Scuba diving/snorkeling 4.3 8.8 X =2.451, df=l
2
Shopping 53.6 59.3 x =0-86l. df=1
2
Sightseeing (general) 72.0 76.9 x =0.779, df=1
2
Sports tournament 10.9 11.0 X =0-001, df=l
2
Snowmobiling 3.3 3.3 x =0.000, df=1
2
Swimming (lake, pond, river) 36.0 37.4 X =0-050, dle
2
Swimming (pool) 32.7 31.9 X =0.020, df=1
2
Theme/amusement park 23.7 26.4 1 =0.246. df=l
2
Visiting a federal/state park 59.7 62.2 X =0-l66, df=1
2
Visiting friends/relatives 49.8 45.1 x =0.564, df=1
2
Wildlife viewing/bird watching 21.8 31.9 X =3.452, df=1
2
Wind surﬁng 0.5 2.2 X 21921, df=1
Socio-demographic characteristics
Age, Mean (SD) 52.9(14.3) 54.1(13.0) F=2.567, df=288
2
Gender (female), % 43.3 44.9 x =0-07l. df=1
Living with children under 18 years 2
old, % 32.1 28.3 x =0.297, df=1
2
Income, % x =5.393, df=2
Below $43,000 30.0 13.3
Between $43,000 and $75,000 35.7 46.7
Above $75,000 34.3 40.0
2
Education, % x =0.170, df=2
High school, some high school 16.2 14.6
Some college, college
graduate/professional 63.2 62.5
Post-graduate 20.6 22.9

 

*p<.05, **p<.01

Description of the Sample
Transient Visitors
The transient visitor survey was designed to catch those who stopped by the
WCMI on their trip. In this case, the WCMI might not be their primary destination. The
investigation of differences within the transient visitor group focused on those whose

primary destination was the WCMI (group 1) and those whose primary destination was

81

not the WCMI for their current trip (group 2). Therefore, respondents were grouped into
these two groups. Less than half (41.6%) of the respondents were female while 58.4% of
the respondents were male (Table 4-6). Only 6.5% of the respondents answered that they
were unemployed with the majority of the respondents employed (77.5%) and the
remainder retired. A total of 46.3% of the respondents indicated their household income
ranged between $42,500 and $75,000. Around 30% of the respondents answered that
they lived with children under 18 years old. On average, respondents were 45 years old.
Differences between the two groups of interest within the transient visitor sample were
not statistically signiﬁcant in terms of demographic characteristics.

Table 4-6: Transient Visitors ’Socio-demographic Proﬁle

 

Primary Primary

 

Socio-demographic destination- destination- Total . .
Characteristics WCMI not WCMI (n = 532) Te“ Stanwcs
(n=351) (n=181)
Age, Mean (SD) 44.1(13.2) 45.5(14.2) 44.6(13.5) t= -1.147, df= 512
2
Gender (female) , % 42.9 38.9 41.6 X = 0.301, df= 1
Living with children under 18 2
years old , % 31.6 27.6 30.3 X = 0-905, df= l
2
Income, % x = 1.363, df = 2
Below $42,500 18.4 22.8 20.0
Between $42,500 and
$75,000 47.6 43.8 46.3
Above $75,000 34.0 33.3 33.8
2
Employment, % x = 3.142, df = 2
Employed 78.7 75.2 77.5
Unemployed 5.1 9.3 6.5
Retired 16.2 15.5 16.0

 

*p<.05, **p<.01

Table 4-7 presents the factors that are related to the current trip for transient
visitors. In general, respondents planned this trip 82 days beforehand and planned to
spend four nights on this trip. The most popular accommodation respondents chose was
campground (RV park) (26.9%). The difference between the two groups was statistically

82

signiﬁcant in terms of choosing a hotel, motel or resort, bed and breakfast, or second or
seasonal home. Those whose primary destination was the WCMI were more likely to
own a second or seasonal home and less likely to choose a hotel, motel or resort, or bed
and breakfast. There was no statistically signiﬁcant difference between the groups in
terms of staying with friends and relatives, in a rented cabin, cottage or condominium, or
at a campground or RV park.

Table 4- 7: Transient Visitors ’ Trip-related Characteristics

 

Primary Primary
. . . destination- destination- Total _ . .
Tr1porelated Characteristics WCMI not W CMI (n = 532) Test Statrstrcs
(n=351) (n=181)

Days in advance to plan this trip,

Mean (SD) 78.0(135.9) 88.6(180.4) 81.8(153.3) t = -0.712, df= 457
Nights planned to be away from
home, Mean (SD) 4.2( 10.0) 4.700) 4.4(9.1) t= -0.600, df = 504
Types of lodging used in the
WCMI, %
2
Friend's or relative's home 20.8 25.4 22.4 X = 1.466, df= l
2
Hotel, motel or resort 8.3 28.7 15.2 x = 38.757”, df= 1
2
Bed & breakfast 0.3 2.2 0.9 X = 4753*, df= l
Rented cabin, cottage or 2
condominium 5.4 5.0 5.3 X = 0.047, df = l
Owned second or seasonal 2
home 17.9 3.3 13.0 X = 22.656“, df= l
2
Campground or RV park 27.4 26.0 26.9 X = 0-116, df= 1

 

*p<.05, Mp<.01

The most visited attractions among transient visitors were the Ludington State
Park/beaches (visited by 59.4% of the sample), Silver Lake Sand Dunes (48.8%), and
Manistee River (44.0%). The attractions of which respondents were least aware included
the Hart-Montague (rail) Trail (35.2% not aware), Irons/Lake County snowmobile trails
(34.4%) and Newaygo State Park (31.2%). The two groups showed a signiﬁcant
difference in terms of the Ludington State Park, a signiﬁcant WCMI attraction (Table 4-
8).

83

Table 4-8: Transient Visitors ’Knowledge of Attractions

 

 

Primary Primary
WCMI Attractions dewgalblrlm- iﬁt$g§3 3:22;] Test Statistics
(%) (%)
Little River Casino (n = 309) (n = 156) (n = 465) x2 = 2.256, df= 2
Have Visited 36.6 40.4 37.8
Aware, but Not Visited 46.3 39.1 43.9
Not Aware of This Place 17.2 20.5 18.3
Manistee River (n = 309) (n = 148) (n = 457) x2 = 4.672, df: 2
Have Visited 47.2 37.2 44.0
Aware, but Not Visited 34.0 43.2 37.0
Not Aware of This Place 18.8 19.6 19.0
Ludington Car Ferry (n = 296) (n = 152) (n = 448) X2 = 2.532, df= 2
Have Visited 32.8 31.6 32.4
Aware, but Not Visited 56.1 52.0 54.7
Not Aware of This Place 1 1.1 16.4 12.9
Ludington State Park/beaches (n = 307) (n = 151) (n = 458) X2 —_— 7577*, df= 2
Have Visited 62.5 53.0 59.4
Aware, but Not Visited 29.0 30.5 29.5
Not Aware ofThis Place 8.5 16.6 1 1.1
Pere Marquette River (11 = 295) (n = 147) (n = 442) x2 z 2662 df= 2
Have Visited 38.3 32.7 36.4
Aware, but Not Visited 40.3 39.5 40.0
Not Aware of This Place 21.4 27.9 23.5 2
Irons/Lake oun sn wm bil
trails C W 0 0 e (n=283) (n=l41) (n=424) x =0.334,dr=2
Have Visited 22.3 19.9 21.5
Aware, but Not Visited 43.8 44.7 44.1
Not Aware of This Place 33.9 35.5 34.4
Muskegon River (n = 298) (n = 146) (n = 444) x2 = 2.370, df: 2
Have Visited 31.2 35.6 32.7
Aware, but Not Visited 44.0 36.3 41.4
Not Aware of This Place 24.8 28.1 25.9
Newaygo State Park (n = 293) (n = 140) (n = 433) x2 = 1.049, df: 2
Have Visited 18.8 22.1 19.9
Aware, but Not Visited 50.5 45.7 49.0
Not Aware ofThis Place 30.7 32.1 31.2
Sand Dunes at Silver Lake (11 = 302) (n = 145) (n = 447) x2 = 4040, df= 2
Have Visited 50.0 46.2 48.8
Aware, but Not Visited 40.1 37.2 39.1
Not Aware ofThis Place 9.9 16.6 12.1
Hart-Montague (rail) Trail (n = 292) (n = 143) (n = 435) X2 = 0013, df= 2
Have Visited 22.6 23.1 22.8
Aware, but Not Visited 42.1 42.0 42.1
Not Aware ofThis Place 35.3 35.0 35.2

 

$<.05, **p<.Ol

84

Overnight Visitors

Overnight visitors were sampled from those who had stayed overnight in the
WCMI. The investigation of differences targeted length of stay. Therefore, respondents
were classiﬁed into two groups: those who spent two nights or less in their most recent
trip to the WCMI (group 1) and those who spent at least three nights at the destination
(group 2). In the potential visitor questionnaire, a weekend getaway was deﬁned as a trip
of two nights or less, and vacation was deﬁned as at least a three-night trip. The cut-off
point for separating those two groups among overnight visitors was set at two nights to
explore the differences between getaway and vacation visitors. A proﬁle of overnight
survey respondents as well as analysis of group demographic differences is shown in
Table 4-9. The mean age of the respondents was 51 years. Most of the respondents were
male (63.5% male and 36.5% female), and possessed at least some college education
(82.2%). Even though the majority of the respondents earned incomes of more than
$43,000, there were more respondents who earned incomes above $75,000 in group 2
than in group 1. Those who spent more nights in the WCMI tended to live without
children under 18 years old (65.6%), while 40.9% of those who spent fewer nights lived
with children under 18 years old. However, among these socio-demographic
characteristics, statistically signiﬁcant difference between the two groups was only found

in terms of respondents’ age.

85

Table 4-9: Overnight Visitors ’Socio-demographic Proﬁle

 

<=2 nights > 2 nights

 

Sgiﬁiggﬁjfmc visitors visitors 01:02?!” Test Statistics
(11 = 132) (n = 122)
Age, Mean (SD) 50.3(11.7) 51.9(12.3) 50.9(12.0) t= -1.076*, df= 246
2
Gender (female), % 39.2 33.6 36.5 X = 0-359, df= 1
Living with children under 18 2
years old, % 40.9 34.4 37.8 X = 1.133, df= 1
2
Income, % x = 2.359, df= 2
Below $43,000 22.5 20.2 21.4
Between $43,000 and
$75,000 43.3 36.0 39.7
Above $75,000 34.2 43.8 38.9
2
Employment, % x = 1.120, df = 2
Employed 76.6 70.7 73.7
Unemployed 3.1 3.4 3.3
Retired 20.3 25.9 23.0
2
Education, % x = 5.760, df = 2
High school, some high
school 13 .2 22.9 17.8
Some college, college
graduate/professional 68.2 54.2 61 .6
Post-gduate 18.6 22.9 20.6

 

*p<.05, **p<.01

In general, overnight travelers relied most on information from friends or relatives
to plan their most recent trip to the WCMI (37.8%) (Table 4-10). The Intemet/web sites
were also important sources of information for travelers (36.2%). Most respondents
indicated that they stayed in a hotel, motel or resort (57.1%), and planned the trip about
86 days in advance. Based on a ﬁve point Likert scale, respondents were very likely to
revisit the WCMI within the next three years (mean = 1.4) and the overall experience was

just about what they expected (mean = 2.5).

86

Table 4-10: Overnight Visitors ’ Trip-related Characteristics

 

<=2 nights > 2 nights Total
Trip-related Characteristics visitors visitors Test Statistics

 

(n = 132) (n = 122) (n = 254)
Days in advance to plan this trip,

Mean (SD) 53.3(78.0) 123.5(126.9) 86.3(109.2) t= -5.063*, df = 236
Travel party size, Mean (SD) 3.0(19) 4.1(2.9) 3.5(25) t = -3.780*, df= 251
Ovberall experience in the WCMI

. Mean (SD) 2.6(0.8) 2.4(09) 2.5(09) t = 1.775, df= 240
Likely to visit the \gCMI within

next three years . Mean (SD) 1.4(0.8) 1.4(0.9) 1.4(0.9) t = -0243, df= 248
Sources of information used in

planning this visit, %

2
AAA 17.4 11.5 14.6 X = 1-303,df= l
Billboards/outdoor 2
advertising 4.5 1.6 3.1 X = 1.755, df= l
2
Chamber of commerce 10.6 19.7 15.0 X = 4096*, df= 1
Convention and visitors 2
bureau 8.3 13.9 11.0 x = 2.028, df= l
2
Friends or relatives 36.4 39.3 37.8 X = 0.240, df= l
2
Highway welcome centers 3.8 4.1 3.9 X = 0.016, df= l
2
Intemet/web site(s) 37.1 35.2 36.2 X = 0-097, df= 1
2
Magazine 8.3 5.7 7.1 X = 0.649, df= l
2
Newspaper 0.8 2.5 1.6 X = 1.184, df= 1
2
Radio 0.8 0.8 0.8 x = 0.003, df= 1
State travel oﬁice/Travel 2
Michigan 5.3 11.5 8.3 X = 3.185, df= 1
2
Television 1.5 0.8 1.2 X = 0.263, df = l
2
Travel guide(s)/brochure(s) 12.9 23.8 18.1 X = 5072*, df= l
2
Word of mouth 22.7 27.9 25.2 X = 0-839, df= 1
Types of lodging used in the
WCMI, %
2
Friend's or relative's home 10.6 8.2 9.4 X = 0.430, df= l
2
Hotel, motel or resort 66.7 46.7 57.1 X = 10.296", df= l
2
Bed & breakfast 6.8 4.9 5.9 X = 0.412, df= l
Rented cabin, cottage or 2
condominium 3.0 28.7 15.4 X = 32.116", df= l
Owned second or seasonal 2
home 11.4 13.1 12.2 X =0.181,df=l
2
Campground or RV park 12.1 13.9 12.1 X = 0-184, df= l

 

*p<.05, **p<.01

A: 1: Much better than I expected, 2: Somewhat better than I expected, 3: About what I expected, 4:
Somewhat worse than I expected, 5:Much worse than I expected

B: 1: Deﬁnitely will visit, 2: Very likely, 3: Somewhat likely, 4: Somewhat unlikely, 5: Very unlikely, 6:
Will not visit the area again

87

Important differences emerged in the two groups’ advance planning and party size.
Group 1 respondents (two nights or less) were more likely to plan the trip less than two
months in advance and the travel party size was three on average, while group 2
respondents (at least three nights) tended to plan the trip at least four months ahead and
their party size was about four people. Group 2 respondents used sources such as the
chamber of commerce and travel guide brochures to ﬁnd travel information signiﬁcantly
more often than group 1 respondents. In terms of accommodation, group 1 respondents
were more likely to stay in a hotel, motel or resort while group 2 respondents were more
likely to stay in a rented cabin, cottage or condominium.

Regarding knowledge of WCMI attractions, the Ludington State Park/beaches
(74.5%), Silver Lake Sand Dunes (46.1%) and Pere Marquette River (44.2%) were
ranked as the top three attractions visited (Table 4-11). Respondents were aware of but
had not visited the Ludington Car Ferry (54.8%), Manistee River (42.2%) and Newaygo
State Park (40.6%). The Irons/Lake County snowmobile trails, Hart-Montague (rail) Trail,
and Newaygo State Park were the least well-known attractions. When comparing the
awareness of these attractions between groups 1 and 2, no statistically signiﬁcant

differences were found.

88

Table 4-11: Overnight Visitors ’Knowledge of Attractions

 

 

<=2 nights > 2 nights Total
WCMI Attractions visitors visitors (%) Test Statistics
(%) (%)
Little River Casino (n = 120) (n = 109) (n = 229) 22 = 0 .044, df= 2
Have Visited 35.8 35.8 35.8
Aware, but Not Visited 38.4 39.4 38.9
Not Aware of This Place 25.8 24.8 25.3
Manistee River (n = 117) (n = 106) (n = 223) 22 = 4,672, df= 2
Have Visited 41.0 34.9 38.1
Aware, but Not Visited 39.3 45.3 42.2
Not Aware ofThis Place 19.7 19.8 19.7
Ludington Car Ferry (n=119) (n= 111) (n=230) x2 =0.371,df:2
Have Visited 40.3 36.9 38.7
Aware, but Not Visited 53.8 55.9 54.8
Not Aware of This Place 5.9 7.2 6.5
Ludington State Park/beaches (n = 119) (n = 109) (n = 223) 22 = 1.376, df= 2
Have Visited 73.9 75.2 74.5
Aware, but Not Visited 20.2 22.0 21.1
Not Aware of This Place 5.9 2.8 4.4
Pete Marquette River (n = 114) (n = 110) (n = 224) 22 = 0.983, df: 2
Have Visited 47.4 41.0 44.2
Aware, but Not Visited 29.8 34.5 32.1
Not Aware of This Place 22.8 24.5 23.7 2
Ir w il
$711521... countysno Inch 6 (n=111) (n= 100) (n=211) X 20'334’dfzz
Have Visited 18.9 14.0 16.6
Aware, but Not Visited 38.7 36.0 37.4
Not Aware of This Place 42.3 50.0 46.0
Muskegon River (n = 112) (n = 104) (n = 216) 22 = 0.214, df: 2
Have Visited 33.0 30.8 31.9
Aware, but Not Visited 38.4 41.3 39.9
Not Aware of This Place 28.6 27.9 28.2
Newaygo State Park (n = 112) (n = 104) (n = 214) 22 = 4.581, df: 2
Have Visited 23.2 32.4 27.6
Aware, but Not Visited 47.3 33.3 40.6
Not Aware ofThis Place 29.5 34.3 31.8
Sand Dunes at Silver Lake (n = 117) (n = 104) (n = 221) 22 : 2.802, df: 2
Have Visited 50.5 41.3 46.1
Aware, but Not Visited 33.3 44.3 38.5
Not Aware of This Place 16.2 14.4 15.4
Hart-Montague (rail) Trail (n = 107) (n = 99) (n = 206) x2 ._. 3.729, df: 2
Have Visited 14.0 23.2 18.4
Aware, but Not Visited 41.1 31.3 36.4
44.9 45.5 45.2

 

*p<.05, **p<.01

89

An independent samples t-test was conducted for WCMI destination attributes to
determine whether signiﬁcant differences existed between group 1 (two nights or less)
and group 2 (at least three nights). Based on a ten point Likert scale (where ten indicates
complete agreement), the top rated attributes for respondents were “Offers much scenic
appeal” (9.1), “Has great outdoor recreation opportunities” (9.1), “Is a great summer
destination” (9.0), “Is a safe place to visit” (8.8), “Is a great family vacation destination”
(8.7), and “Is a great fall destination” (8.7). Table 4-12 shows that signiﬁcant differences
were found in perceptions of the attributes “Is a great family vacation destination,” “Is a
good place to meet friendly people,” and “Is an exciting place to visit” with group 2
respondents indicating signiﬁcantly higher levels of agreement with these attributes than
group 1 respondents. Also group 1 respondents agreed more that the WCMI “Is close

enough for a weekend getaway.”

90

Table 4-12: Overnight Visitors ’Perceptions of Destination Attributes

 

 

<=2 nights > 2 nights
A visitors visitors Total . .
WCMI Destination Attributes (n = 132) (n = 122) (n = 254) TCSt Statrstrcs
Mean (SD) Mean (SD) Mean (SD)

Has good roads 7.7(2.1) 7.8(2. 1) 7.7(2.1) t = -0.177, df= 229
Has great outdoor recreation

opportunities 9.1(1.5) 9.2(1.4) 9.1(l.4) t= -0.790, df= 225
Has high quality lodging 7.3(2.2) 7.6(1.8) 7.5(2.0) t= -0.967, df = 228
Has interesting historical sites 7.3(2.2) 7.6(1.8) 7.4(2.0) t = -0.974, df = 208
Is a good place to meet

friendly people 7.7(2.0) 8.4(1.7) 8.0(1.9) = -2.598*, df= 223
Is a great family vacation

destination 8.3( l .9) 9.0(1.3) 8.7(1.7) t = -2.987**, df= 228
Is a great place to start a

business 4.9(2.4) 5.2(2.2) 5.1(2.3) t = -0.831, df= 177
Is a great spring destination 7.0(2.4) 7.2(2.3) 7.1(2.3) t = -0.774, df = 212
Is a great summer destination 8.8(1.7) 9.2(1.3) 9.0(l.5) = -l .554, df= 228
Is a great fall destination 8.6(1.8) 8.8(1.5) 8.7(1.7) t= -0.793, df= 225
Is a great winter destination 7.2(2.6) 7.3(2.6) 7.2(2.6) = -0.173, df = 200
Is a safe place to visit 8.6(1.6) 8.9(1.3) 8.8(1.5) t= -1.509, df= 230
Is an exciting place to visit 7.0(2.3) 7.7(1.9) 7.3(2.2) t = -2.541*, df= 218
Is close enough for a weekend

getaway 8.2(2.5) 7.0(3.4) 7.7(3.0) t = 3.039,M df = 226
Is easily accessible 8.3(2.2) 8.1(2.3) 8.2(2.2) t = 0.671, df = 223
Offers exceptional value for

the money 7.4(1.9) 7.5(1.9) 7.5(1.9) t = -0.340, df= 216
Offers exciting nightlife and

entertainment 5.1(2.4) 5.3(2.3) 5.2(2.4) t = -0.533, df= 192
Offers great dining

opportunities 6.4(2.3) 6.7(2.2) 6.5(2.3) t = -0.755, df= 213
Offers great shopping

opportunities 6.2(2.2) 6.6(2.0) 6.4(2.1) = -1.240, df= 206
Offers much scenic appeal 9.1(1.5) 9.2(1 .3) 9.1(l.4) t = -0.984, df= 227

 

*p<.05, **p<.01

A: On a scale from 1 to 10, where 1 means “do not agree at all” and 10 means “agree completely”

An independent samples t-test was conducted for travel expenditures to determine
whether signiﬁcant differences existed between group 1 (two nights or less) and group 2
(at least three nights) (Table 4-13). Results Show that spending on gas, groceries, lodging
and meals is signiﬁcantly different between the two groups. However, only grocery
spending in group 2 signiﬁcantly exceeded group 1; in the other three cases, group 1

spending exceeded that of group 2.

91

Table 4-13.‘ Overnight Visitors ’ Travel Expenditures

 

<=2 nights > 2 nights

A visitors visitors Total
Expenditure = = (n = 254) Test Statistics
(11 132) (n 122) Me (SD)

Mean (SD) Mean (SD)

“I'm‘eslequ‘l’memrentals: 3.8(11.9) 2.3(5.6) 3.0(94) t=l.255,df=218

lessons, etc.),

Amammslmketsremanc" 4.8(14.8) 1.9(6.1) 3.4(11.5) t==1.890,df=218

fees, etc.)
Gas/fuel 11.1(11.5) 6.2(7.4) 8.7(10.0) t= 3.839", df= 218
Groceries 3.3(5.5) 5.1(4.3) 4.1(5.0) = -2.728**, df= 218
Lodging 31.1(34.3) 20.0(20.8) 25.5(29.0) t= 2.864", df = 219

Meals at restaurants/fast food 19.2(18.3) 8.4(10.4) 14.0(15.9) t = 5.457", df= 218

Shoilzénfldmhes’50mm“: 10.7(38.6) 5.0(7.6) 7.9(28.3) t=1.525,df=218

 

*p<.05, **p<.01
A: Per person per day expenditures

Potential Visitors

Potential visitors were sampled from six potential market areas. In this case, the
investigation of differences was focused on visitation to the WCMI. Therefore,
respondents were clustered into two groups: those who have never visited the WCMI
(non-visitors, group 1) and those who have visited the WCMI (visitors, group 2). Fifty-
six percent of the respondents were male (Table 4-14). The mean age was 53 years. There
was a signiﬁcant difference between visitors and non-visitors in terms of age, with the
average age of visitors being higher than that of non-visitors. Thirty-two percent of the
respondents lived with children under 18 years old. Some college education (63.0%) was
the dominant educational level of the respondents. Many respondents (37.6%) had an
annual income ranging from $43,000 to $75,000. The percentages of visitors with post-
graduate level education or an income above $43,000 were slightly higher than the
percentages of non-visitors: however, there were no signiﬁcant differences among these

factors.

92

Table 4-14: Potential Visitors ’Socio-demographic Profile

 

Never

 

Socio-demographic . . Have Visited Total . .
Characteristics “:1th (n = 233) (n = 296) Test Statrstrcs
(n - 63)
Age, Mean (SD) 48.9(15.3) 54.0(13.6) 53.1(14.1) t= -2.507*, df= 284
2
Gender (female), % 51.6 41.6 43.7 X = 2-009, df= 1
Living with children under 18 2
years old, % 36.5 30.5 31.5 X = 0-334, df= 1
Income, % x2 = 2.198, df = 2
Below $43,000 33.9 24.5 26.6
Between $43,000 and
$75,000 35.6 38.2 37.6
Above $75,000 30.5 37.3 35.8
Education, % 12 = 2.938, df = 2
High school, some high
school 18.3 15.0 15.8
Some college, college
graduate/professional 68.3 61.5 63 .0
Post-graduate 13.3 23 .5 21 .2

 

*p<.05, **p<.0l

In Table 4-15, chi-square and t-test procedures reveal the similarities and
differences in trip characteristics between visitors and non-visitors. It appears that for
planning pleasure trips, use of travel sources such as billboards/outdoor advertising,
magazine, and newspaper signiﬁcantly differed between visitors and non-visitors while
use of other sources such as AAA, chamber of commerce and so forth was similar
between the two groups. Visitors more frequently used billboards/outdoor advertising,
magazine and newspaper as sources to plan a pleasure trip than non-visitors. The advance

planning time for pleasure trips did not differ signiﬁcantly between the two groups.

93

Table 4-15: Potential Visitors ’ Trip-related Characteristics

 

Never . .
Trip—related Characteristics Visited Have Vrsrted Total Test Statistics

(11 2 63) (n = 233) (n = 296)
Days in advance to plan your
pleasure trip, Mean (SD) 22.0(23.0) 25.1(36.8) 24.9(34.3) t = —0.675, df = 261
Sources of information used in
planning pleasure trip, %

2
AAA 27.0 33.5 32.l X = 0.959, df= l
Billboards/outdoor 2
advertising 3.2 12.9 10.8 x = 4.840". df = I
2
Chamber of commerce 14.3 20.6 19.3 X = 1.272. df= 1
Convention and visitors 2
bureau 25.4 20.2 21.3 x = 0.808, df = I
2
Friends or relatives 73.0 74.2 74.0 X = 0.039, df = 1
Highway tourist information 2
centers 27.0 28.3 28.0 X = 0-044. df= l
2
Highway welcome centers 22.2 30.9 29.1 X = 1.312, df= l
2
Intemet/web site(s) 60.3 67.4 65.9 x = 1.101, df = I
2
Local visitor guides 28.6 34.3 33.1 X = 0.744, df= l
2
Magazine 25.4 42.1 38.5 X = 5.315“, df= l
2
Newspaper 19.0 36.9 33.1 X = 7.144", df= l
2
Radio 7.9 10.7 10.1 X = 0.425, df= l
2
State travel ofﬁce 9.5 12.0 11.5 X = 0.303, df= 1
2
Television 19.0 21.9 21.3 X = 0.239. df= l
2
Travel guide(s)/brochure(s) 54.0 52.8 53.0 X = 0-023, df= l
2
Word of mouth 66.7 67.0 66.9 X = 0.002, df= l

 

*p<.05, **p<.01

AS to travel motivations, of the twelve attributes, safety/security, interesting
scenery, service quality, cost, a variety of attractions and/or activities, and family-friendly
place and/or opportunities were rated as important (mean > =3.5). The results imply that
respondents were less concerned about pet accommodations, accessibility for disabled
persons, and nightlife activities. When comparing visitors and non-visitors, signiﬁcant

differences were found in cost, interesting scenery, and nightlife activities. Visitors rated

94

cost and nightlife activities as less important and considered interesting scenery more

important than non-visitors (Table 4-16).

Table 4-16: Potential Visitors ’ Travel Motivations

 

 

$1:ng Have Visited Total _ .
Motivation (n : 63) (n = 233) (n = 296) Test Statrstrcs
Mean (SD) Mean (SD) Mean (SD)

Upscale facilities/services 3.2(1.2) 3.0(1. 1) 3.0(1.1) t= 1.418, df = 288
Travel time/distance 3.4(1.0) 3.2(1.0) 3.3(1.0) t = 1.461, df = 293
Cost 4.1(0.8) 3.8(0.9) 3.9(0.9) t = 2128*, df= 286
Family-friendly place and/or

opportunities 3.6(1.2) 3.5(1.2) 3.5(1.2) t= 0.631, df= 289
Safety/security 4.1(0.9) 4.3(0.9) 4.2(0.9) t= -1.224, df = 291
Variety of shopping

opportunities 3.1(1.1) 2.9(1.1) 2.9(l.1) t= 1.307, df=291
Interesting scenery 3.9(0.9) 4.2(0.7) 4.1(0.8) t= -2.061*, df= 292
Service quality 4.0(0.9) 4.0(0.7) 4.0(0.7) t= -0.563, df = 289
Variety of attractions and/or

activities 3.8(1.0) 3.6(0.9) 3.7(0.9) t= 1.049, df = 292
Nightlife activities 2.7(1.2) 2.3(1.1) 2.4(1.1) t = 2.402*, df = 289
Accessibility for disabled

persons 2.2(1.4) 2.0(1.1) 2.0(1.2) t = 0.916, df = 291
Pet accommodations 1.9(1.1) 1.9(1 .3) 1.9(1 .3) t = -0.326, df = 287

 

*p<.05, **p<.01

A: Ranking the importance of each factor when selecting a pleasure trip destination, I: Not at all important,
2: Not so important, 3: Somewhat important, 4: Important, 5: Very important.

Choice of Market Segmentation Bases

Testing of the Study Hypotheses

A Priori Approach--Individual Activity

HI. I: There is a significant difference between the two groups in each sample with

respect to their participation in each activity.

In order to identify the popular activities among the assigned groups in each

survey sample, chi-square tests were employed to investigate differences across groups of

visitors. The results are discussed in the following sections.

95

Transient Visitors
Table 4-17 represents the proﬁling of each group by the activities that were

interesting to respondents or others in their family on this current trip. The most popular
activity among all transient visitors was camping (60.7%), followed by boating (52.6%),
canoeing/kayaking/tubing (47.2%), sightseeing (44.5%), dining out (43.4%), swimming
(lake, pond, river) (41.7%), ﬁshing (41.5%), and bicycling (41.4%). Signiﬁcant
differences between the two groups were found for boating, festival/event, ﬂy ﬁshing,
deer hunting, off-roading and sports tournament. Group 1 respondents (whose primary
destination was the WCMI) showed consistently higher interest than group 2 respondents
in these activities. The ten most popular activities with a participation rate above 35%
were compared between the two groups. Statically signiﬁcant differences were found for

boating and festival/event.

96

Table 4-17: Types of Activities that Interested Transient Visitors on Their Current Trip

 

Primary Primary
destination destination— Total

 

Activity -WCMI not WCMI (n = 532) Chi-square value
(n=351) (n=181) (%)
(%) (%)

Antique shopping 21.4 14.4 19.0 3.808, df = 1
Bicycling 40.7 42.5 41.4 0.160, df = 1
Boating 56.7 44.8 52.6 6.833", df = 1
Camping 63.2 55.8 60.7 2.776, df = 1
Canoeing/kayaking/tubing 50.1 41.4 47.2 3.632, df = 1
Casino gaming 29.3 22.1 26.9 3.189, df = 1
Concert 25.4 22.1 24.2 0.690, df = 1
Cross-country skiing 15.7 18.2 16.5 0.568, df = 1
Dining out (excluding fast food) 42.7 44.8 43.4 0.124, df = 1
Downhill skiing/snowboarding 18.2 13.3 16.5 2.140, df = 1
Farm market/u-pick/winery 25.9 24.3 25.4 0.165, df = 1
Festival/event 42.7 33.1 39.5 4.593*, df = 1
Fishing, charter 23.1 23.8 23.3 0.031, df = 1
Fishing, ﬂy 23.1 13.3 19.7 7.265”, df = 1
Fishing, ice 19.4 14.9 17.9 1.617, df= 1
Fishing, other 43.9 37.0 41.5 2.313, df= l
Golﬁng 32.5 30.9 32.0 0.130, df= 1
Hiking/walking 41.0 37.6 39.8 0.595, df= 1
Historic site 28.5 23.8 26.9 1.361, df= l
Horseback riding 18.5 14.9 17.3 1.083, df = 1
Hunting, deer 35.9 26.5 32.7 4.772*, df= 1
Hunting, small game 24.5 17.1 22.0 3.785, df = 1
Hunting, turkey 16.0 10.5 14.1 2.937, df = 1
Jet skiing 16.2 15.5 16.0 0.053, df = 1
Lighthouse touring 32.5 28.7 31.2 0.782, df = 1
Live theatre 15.4 14.9 15.2 0.020, df = 1
Movie (at a cinema) 27.1 24.3 26.1 0.470, df = 1
Museum 21.4 21.5 21.4 0.002, df = 1
Mushroom collecting 16.2 15.5 16.0 0.053, df = 1
Nature center 19.1 21.5 19.9 0.452, df = 1
Off-roading 24.2 14.4 20.9 7.020", df = 1
Photography 23.1 22.1 22.7 0.065, df = 1
Sailing 12.5 9.9 1 1.7 0.779, df = 1
Scuba diving/snorkeling 8.8 9.9 9.2 0.177, df = 1
Shopping 38.2 33.7 36.7 1.030, df = 1
Sightseeing (general) 45.6 42.5 44.5 0.448, df = 1
Snowmobiling 24.8 18.8 22.7 2.448, df = 1
Sports tournament 12.8 4.4 10.0 9.395”, df = 1
Swimming (lake, pond, river) 44.7 35.9 41.7 3.819, df = 1
Swimming (pool) 25.6 23.8 25.0 0.226, df = 1
Theme/amusement park 23.4 18.8 21.8 1.467, df = 1

 

97

Table 4-1 7 (Cont ’d)

 

Primary
destination destination-

Primary

Total

 

 

Activity -WCMI not WCMI (n = 532) Chi-square value
(n=351) (n=181) (%)
(%) (%)
Visiting a federal/state park 34.8 33.7 34.4 0.059, df = 1
Visiting friends/relatives 37.9 38.1 38.0 0.003, df = 1
Wildlife viewing/bird watching 29.1 24.3 27.4 1.353, df = 1
Wind surﬁng 2.6 3.9 3.0 0.695, df = l
*p<.05, **p<.01
Overnight Visitors

Among the activities that overnight visitors or others in their family participated
in while visiting the WCMI, dining out (excluding fast food) (65.4%), sightseeing (42.9%)

and shopping (40.6%) were the most popular activities. When compared across groups,

signiﬁcant differences were found for antique shopping, boating,

canoeing/kayaking/tubing, concert, farm market/u-pick/winery, ﬁshing, hiking/walking,
shopping, and swimming (lake, pond, river); these activities were more favored by group
2 respondents who spent at least three nights on the most recent overnight visit to the
WCMI than group 1 respondents who spent two nights or less on the most recent

overnight visit to the WCMI (Table 4-18). Only shopping and hiking/walking exhibited

participation rates above 30%.

98

Table 4—18: Types of Activities Overnight Visitors and Family Participated in during their
Trips to West-central Michigan

 

<=2 nights > 2 nights

 

. . visitors visitors Total .

Actrvrty (n = 132) (n = 122) (n =0254) Chl-square value
(%) (%) V”)

Antique shopping 10.6 21.3 15.7 5.476*, df = 1
Bicycling 6.1 11.5 8.7 2.350, df = 1
Boating 12.9 32.0 22.0 13.442", df = 1
Camping 16.7 23.0 19.7 1.584, df = l
Canoeing/kayaking/tubing 4.5 18.9 11.4 12.832", df = 1
Casino gaming 21.2 19.7 20.5 0.092, df = 1
Concert 1.5 A 8.2 A 4.7 6.288*, df = 1
Cross-country skiing 2-3 2.5 2.4 0.010, df = 1
Dining out (excluding fast food) 65.9 A 64.8 A 65.4 0.037, df = 1
Downhill skiing/snowboarding 0-8 0-3 0.8 0.003, df = 1
Farm market/u-pick/winery 1 1.4 22.1 16.5 5.326‘“, df = 1
Festival/event 12.1 13.9 13.0 0.184, df = 1
Fishing, charter 5.3 5.7 5.5 0.023, df = 1
Fishing, ﬂy 5.3 A 9.8 A 7.5 1.882, df = 1
Fishing, ice 0-0 1-6 0.8 2.181, df = 1
Fishing, other 1 1.4 32.8 21.7 17.152", df = 1
Golﬁng 11.4 16.4 13.8 1.350, df = 1
Hiking/walking 29.5 50.8 39.8 11.980“, df = 1
Historic site 15.9 19.7 A 17.7 0.616, df = 1
Horseback riding 0.0 A 4.1 2.0 5.518*, df = 1
Hunting, deer 6.1 7.4 6.7 0.176, df = 1
Hunting, small game 3.8 A 5.7A 4.7 0.536, df = 1
Hunting, turkey 0.8 1-6 1.2 0.422, df = 1
Jet skiing 3.8 4.9 4.3 0.195, df = l
Lighthouse touring 15.9 A 19.7 A 17.7 0.616, df = 1
Live theatre 0-8 0.0 0.4 0.928, df = 1
Movie (at a cinema) 5.3 9.0 7.1 1.328, df= 1
Museum 5.3 7.4 6.3 0.462, df = 1
Mushroom collecting 4.5 4.1 4.3 0.031, df = 1
Nature center 5.3 9.8 7.5 1.882, df = l
Off-roading 9.1 9.8 9.4 0.041, df= 1
Photography 23.5 A 22.1 A 22.8 0.066, df = 1
Sailing 2-3 l-6 2.0 0.132, df= 1
Scuba diving/snorkeling 0-0 0-3 0.4 1.086, df = 1
Shopping 32.6 49.2 40.6 7.251", df = l

 

99

Table 4-18 (Cont ’d)

 

<=2 nights > 2 nights

 

visitors visitors Total
Actrvrty (n = 132) (n = 122) (n 8254) Chl-square value
(%) (%) °

Sightseeing (general) 39.4 A 46.7 A 42.9 1.390, df = 1
Snowmobiling 3.0A 4.1 A 3.5 0.212, df = 1
Sports tournament 2-3 1.6 2.0 0.132, df = 1
Swimming (lake, pond, river) 14.4 44.3 28.7 27.618", df = 1
Swimming (pool) 9.1 16.4 A 12.6 3.071, df = l

A
Theme/amusement park 3-0 4-1 3.5 0.212, df = 1
Visiting a federal/state park 23.5 27.9 25.6 0.640, df = 1
Visiting friends/relatives 22.7 25.4 24.0 0.250, df = 1
Wildlife viewing/bird watching 19.7 23.8 21.7 0.620, df = 1

A A
Wind surﬁng 0-3 0-0 0.4 0.928, df = l

 

*p<.05, **p<.01
A:The cell has expected count less than 5.

Potential Visitors

Among the activities that potential visitors or others in their family participated in
most often while on pleasure trips, dining out (excluding fast food) (73.6%), sightseeing
(73.6%), and visiting friends/relatives (60.7%) were the most popular (see Table 4-18).
Statistically signiﬁcant differences between the two groups were found for antique
shopping, ﬁshing, golﬁng, deer hunting, lighthouse touring, shopping and visiting a
federal/state park. Antique shopping, ﬁshing, golﬁng, deer hunting, turkey hunting,
lighthouse touring, and visiting a federal/state park showed a higher frequency among the
visitor group while shopping appeared to have a higher participation rate among the non-

visitor group (see Table 4-19).

100

Table 4-19: Types of Activities Potential Visitors Participated in .Most Often during
Pleasure Trips

 

Never Have

Visited Visited Tow”

 

Activity (n = 63) (n = 233) (n =0 296) Chi-square value
1%) (%) W
Antique shopping 9.5 22.3 19.6 5.15”“, df = 1
Bicycling 14.3 22.3 20.6 1.96, df = 1
Boating 20.6 27.0 25.7 1.07, df = 1
Camping 28.6 39.5 37.2 2.53, df = 1
Canoeing/kayaking/tubing 15.9 21.0 19.9 0.83, df = 1
Casino gaming 30.2 28.8 29.1 0.05, df = 1
Concert 28.6 20.2 22.0 2.04, df = 1
Cross-country skiing 4.8 9.0 8.1 1.20, df = 1
Dining out (excluding fast food) 77.8 72.5 73.6 0.70, df = 1
Downhill skiing/snowboarding 7.9 10.3 9.8 0.31, df = 1
Farm market/u-pick/winery 23.8 33.0 31.3 1.98, df = 1
Festival/event 54.0 45.1 47.0 1.58, df = 1
Fishing, charter 6.3 A 12.4 11.1 1.86, df = 1
Fishing, ﬂy 3.2 A 5.6 5.1 0.60, df = 1
Fishing, ice 3.2 7.7 6.8 1.63, df = 1
Fishing, other 9.5 27.0 23.3 8.51“, df = 1
Golﬁng 14.3 29.2 26.0 5.72*, df = 1
Hiking/walking 38.1 49.8 47.3 2.72, df = 1
Historic site 58.7 55.8 56.4 0.17, df = 1
Horseback riding 14.3 7.3 8.8 3.02, df = 1
Hunting, deer 3.2 A 18.9 15.5 9.32", df = 1
Hunting, small game 1-6 A 7.3 6.1 2.83, df = 1
Hunting, turkey 0.0 A 7.3 5.7 4.88*, df = 1
Jet skiing 7-9 2.6 3.7 3.98, df = 1
Lighthouse touring 15.9 30.9 27.7 5.59*, df = 1
Live theatre 19.0 20.2 19.9 0.04, df = 1
Movie (at a cinema) 28.6 24.0 25.0 0.54, df = 1
Museum 46.0 39.1 40.5 1.00, df = 1
Mushroom collecting 3.2 7.3 6.4 1.40, df = 1
Nature center 28.6 33.0 32.1 0.46, df = 1
Off-roading 7.9 9.4 9.1. 0.14, df = 1
Photography 30.2 38.6 36.8 1.53, df = 1
Sailing 3.2:: 3.4 3.4 0.01, df = 1
Scuba diving/snorkeling 6-3 5.6 5.7 0.05, df = 1
Shopping 66.7 52.4 55.4 4.11*, df = 1
Sightseeing (general) 69.8 74.7 73.6 0.60, df = 1
Snowmobiling 1.6 3.9 3.4 0.79, df = 1
Sports tournament 12.7 10.7 11.1 0.19, df = 1

 

101

Table 4-19 (Cont ’d)

 

 

. . “FY“ 11".” Total .
Actrvrty Visited Vrsrted (n = 29 6) Chi-square value
(11 = 63) (n = 233)

Swimming (lake, pond, river) 36.5 36.9 36.8 0.00, df = 1
Swimming (pool) 38.1 31.3 32.8 1.03, df = l
Theme/amusement park 33.3 22.3 24.7 3.24, df = 1
Visiting a federal/state park 33.3 53.2 49.0 7.85“, df = 1
Visiting friends/relatives 66.7 59.1 60.7 1.20, df = 1
Wildlife viewing/bird watching 19.0 A 27.0 A 25.3 1.67, df = 1

Wind surﬁng 1-6 0-9 1.0 0.26, df = 1

 

*p<.05, **p<.01
A:The cell has expected count less than 5.

Development of Profiles of Resulting Segments

After ﬁnding out the signiﬁcant differences between the deﬁned groups and
among each sample with respect to activity preference, those activities were examined for
their relationships with other variables. However, most of the activities that showed
statistically Signiﬁcant differences between groups experienced low participation rates
(below 30%). This study only proﬁled the most popular and signiﬁcant activities for
marketing suggestions. As a result, boating and festival/event for transient visitors,
Shopping and hiking for overnight visitors, and visiting a federal/state park for potential
visitors were chosen to further develop the proﬁle. The descriptive statistics reports are
listed in Appendix A.

The aim of this inquiry was to ascertain the factors (socio-demographic
characteristics, trip-related characteristics and so forth) that increase the likelihood that a
visitor will participate in each activity. The analysis began with a careful univariate
analysis of each variable. Any variable with a univariate test p-value lower than 0.25 was
a candidate for the multivariable model. Backward stepwise elimination, which starts

with a comprehensive model that includes all the testable variables, was applied in this

102

model selection process. In selecting the ﬁnal model, variables are removed one by one
until the equation included only those signiﬁcant variables that ensure that the ﬁnal
model still sufﬁciently ﬁts the data (Hosmer & Lemeshow, 2000; Menard, 2002).

A series of binomial logistic regression analyses was performed. To better
understand the visitors, variables such as (a) Consumption differences between those
whose primary destination was the WCMI and those whose primary destination was not
the WCMI among transient visitors, (b) Consumption differences between those who had
stayed three or more nights and those who had stayed two nights or less among overnight
visitors, and (c) Consumption differences between those who have visited the WCMI and
those who have not visited the WCMI among potential visitors were kept in the logistic
regression model as the control variable in each data set.

Using prediction of participation in boating among transient visitors as an
example, with socio-demographic characteristics, the activity served as the dependent
variable in the logistic regression model to examine its relationship with other factors
such as socio-demographic characteristics, along with visitors whose primary destination
was the WCMI. In logistic regression analysis the predicted y-value is treated as
probabilities, P(Y = 1) = 1t and P(Y = 0) = 1 — 1:, whereas a regression coefﬁcient ([3) is
interpreted as the effect of an independent variable (x) on the probability of Y = 1 (in this
case the probability that a visitor participated in boating). A probability function for a
visitor participating in the activity could be modeled linearly as follows:

ln(7r/(1—1r)) =Bo+Bl - X1+Bz'X2+...+Bk' xk

Socio-demographic characteristics included age, gender, employment, income,

and living with children under 18 years old. However, only age and living with children

103

under 18 years old had a p—value lower than 0.25 when running the univariate analysis of
each variable to predict participation in boating activity. Backward stepwise logistic
regression analysis then started with all the independent variables (age, primary
destination, and living with children under 18 years old) in the model, and at each step
the variable that had the highest p-value was eliminated. In the ﬁrst step, three variables
were examined. At the last stage, age (p < 0 .05), and primary destination (p < O .05)
remained signiﬁcant. The results are discussed below.
H I .2: Socio-demographic characteristics can be used to predict activity participation
while controlling the variable.

Transient Visitors-Boating

The ﬁnal model is represented as follows:

Ln(1t/l-7t) = 0.902 - 0.432(Destination) - 0.015(Age)

The Ln (n/ 1—7r) term in the formula represents the log of the odds of participation
in boating where Ln means the natural logarithm and the value is the probability of
participating in boating. For example, the coefﬁcient of age (-0.015) shows the change in
the log of the odds for a one-year increase in a respondent’s age controlling for other
independent variables in the model (see Table 4-20). Similarly, the coefﬁcient of
destination (—0.432) shows the change in the log of the odds for not choosing the WCMI
as the primary destination. Since the coefﬁcients for age and destination are negative, the
probability of participating in boating decreases as a respondent’s age increases. Also, the
results suggest that visitors whose primary destination was the WCMI were more likely

to participate in boating than those whose primary destination was not the WCMI.

104

In logistic regression analysis, R-squared measures have been developed by a

number of researchers, and Cox and Snell's R2 is one of them (Menard, 2002). However,

rt rs not actual percent of vanance explained as R 1n a linear regressron model. Thrs

statistic is not perceived as a goodness of ﬁt test index, but rather as an index of
measuring the strength of relationship between dependent variables and independent

variables (Garson, 2009a; UCLA: Academic Technology Services Statistical Consulting

Group).The Hosmer and Lemeshow omnibus test was not signiﬁcant, x2 (8) = 4.078, p =

0.85. That is, it failed to reject the null hypothesis, implying that the model's estimates ﬁt
the data at an acceptable level. The coefﬁcients were able to successfully classify 55% of
the respondents based on their likelihood to engage in boating.

Table 4-20: Logistic Regression Model Results--Boating by Socio-demographic
Characteristics for Transient Visitors

 

 

 

 

 

 

 

 

Dependent Variable Boating

Variables in the Equation B S.E. Wald df Sig. lExp(B)

DesReg( 1) -0.432 0.187 5.321 1 0.021 0.649

Age -0.015 0.007 4.804 1 0.028 0.986

Constant 0.902 0.3 1 5 8.209 1 0.004 2.465

Variables Not in the Equation Score df Sig.

HvChi1d(l) 2.1 10 1 0.146

M d l -2 Log COX and Snell R Hosmer and

Sui“; likelihood Square Lemeshow Chi-square df Sig.
a’y 700.545 0.021 Test 4.078 8 0.350

 

Transient Visitors--Festival/event
The estimated coefﬁcient for gender is 0.84 as shown in Table 4-21. The exp (B)
is 2.316, indicating that female visitors were 2.316 times more likely to participate in a

festival/event than males while controlling the factor of primary destination.

1.05

Table 4-21: Logistic Regression Model Results--Festival/event by Socio-demographic
Characteristics for Transient Visitors

 

 

 

 

 

 

 

 

Dependent Variable Festival/event
Variables in the Equation B S.E. Wald df Sig. Exp(B)
DesReg(l) -0.432 0.205 4.439 1 0.035 0.649
Gender 0.840 0.193 18.984 1 0.000 2.316
Constant -1.395 0.300 21.563 1 0.000 0.248
Variables Not in the Equation Score df Sig.
Emplmnt 0.310 2 0.856
Emplmnt( l) 0.1 18 1 0.731
Emplmnt(2) 0. 154 1 0.694
HvChi1d( 1) 2.120 1 0.145
-2 Log COX and Snell R Hosmer and . .
g‘u‘l’ndg likelihood Square Lemeshow Ch"sq“a’e df S‘g'
a” 618.371 0.050 Test 4.850 2 0.088

 

Overnight VisitorS--Hiking/walking
As Shown in Table 4-22, income does not seem to be a Signiﬁcant factor. Gender
was signiﬁcant at the 0.029 level with the positive coefﬁcient indicating that females
were more likely to participate in hiking/walking while on a pleasure trip than males.

Table 4-22: Logistic Regression Model Results--Hiking/walking by Socio-demographic
Characteristics for Overnight Visitors

 

 

 

 

 

 

 

 

Dependent Variable Hiking/walking
Variables in the Equation B S.E. Wald df Sig. Exp(B)
NghtCat(l) 0.981 0.288 1 1.572 1 0.001 2.667
Gender( 1) 0.656 0.300 4.787 1 0.029 1.926
Income 5.828 2 0.054
Income(1) 0178 0.386 0.214 1 0.644 0.837
Income(2) 0.578 0.378 2.334 1 0.127 1.783
Constant -1.342 0.372 12.990 1 0.000 0.261
Variables Not in the Equation Score df Sig.
Edctn 2.018 2 0.365
Edctn(l) 0.092 1 0.762
Edctn(2) 1.433 1 0.231
lgtlodel “1:11;: 50 d COX 2512:2328" R Egggzggad Chi-square df Sig.
“mma’y 283.838 0.092 Test 6.404 8 0.602

 

106

Overnight Visitors-—Shopping
While examining the relationship of socio-demographic characteristics and
participation in shopping, only gender Showed a difference at a Signiﬁcant level. In
general, females were more likely to participate in shopping (see Table 4-23).

Table 4-23: Logistic Regression Model Results--Shopping by Socio-demographic
Characteristics for Overnight Visitors

 

 

 

 

 

 

 

 

Dependent Variable Shopping
Variables in the Equation B S.E. Wald df Sig. Exp(B)
NghtCat(l) 0.701 0.271 6.692 1 0.010 2.016
Gender( 1) 0.697 0.280 6.182 1 0.013 2.007
Constant -0.977 0.226 18.595 1 0.000 0.377
Variables Not in the Equation Score df Sig.
Age 2.325 1 0.127
Edctn 1.283 2 0.527
Edctn( 1) 1.259 1 0.262
Edctn(2) 0.687 1 0.407
gtlodel Him?“ C“ gag?" R $331";ng cmsoooio df Sig.
“mmary 313.501 0.049 Test 1.721 2 0.423

 

Potential Visitors--Visiting a Federal/state Park
As Shown in Table 4-24, education (Edctn) was the only socio-demographic
factor that was associated with visiting a federal/state park. Among the three categories of
education level, the estimated coefﬁcient for education at the post graduate level was
0.847. As shown in Table 4-24, the exp (B) is 2.3 34, indicating that visitors with a post
graduate degree were 2.3 34 times more likely to visit a federal/state park than those who

have a high school degree.

107

Table 4-24.‘ Logistic Regression Model Results-- Visiting a F ederal/state Park by Socio-
demographic Characteristics for Potential Visitors

 

 

 

 

 

 

Dependent Variable Visiting a Federal/state Park

Variables in the Equation B S.E. Wald df Sig. 13ng
VisWCM(1) 0.761 0.309 6.048 1 0.014 2.140
Edctn 4.739 2 0.094

Edctn( 1) 0.279 0.352 0.631 1 0.427 1.322
Edctn(2) 0.847 0.418 4.1 14 1 0.043 2.334
Constant -1.005 0.398 6.372 1 0.012 0.366

-2 Log Cox and Snell R Hosmer and . .
$335161 likelihood Square Lemeshow Chl-square df Slg'
a” 366.019 0.044 Test 1.570 4 0.814

 

H. 1.3: Travel motivations can be used to predict activity participation while controlling
the variable.
Potential VisitorS--Visiting a F ederal/state Park

For twelve motivation attributes, respondents were asked “How important to you
is each of the following factors when selecting a pleasure trip destination?” Cost (MCost)
and the variety of attractions and/or activities (MAttr) had signiﬁcant estimated
coefﬁcients, and this result suggests that visitors who considered cost and attractions
more were about 1.5 times more likely to visit a federal/state park. In contrast, the
negative coefﬁcient of nightlife activities (MNlife) indicated that visitors who were
concerned more about nightlife activities were less likely to visit a federal/state park.
Interesting scenery (MScen) had a marginally signiﬁcant level (0.057); this suggests that
the concern of interesting scenery did not strongly predict visiting a federal/state park

(see Table 4-25).

108

Table 4-25: Logistic Regression Model Results-- Visiting a F ederal/State Park by Travel
Motivations for Potential Visitors

 

Dependent Variable Visiting a F ederal/State Park

 

 

 

 

 

 

 

Variables in the Equation B S.E. Wald df Sig. Exp(B)
ViSWCM(l) 1.035 0.329 9.890 1 0.002 2.814
MCost 0.409 0.154 7.052 1 0.008 1.505
MScen 0.324 0.170 3.630 1 0.057 1.383
MAttr 0.426 0.162 6.893 1 0.009 1.531
MNlife -0.295 0.133 4.950 1 0.026 0.745
Constant -4.628 1.101 17.658 1 0.000 0.010
Variables Not in the Equation Score df Sig.
MTime 0.087 1 0.768
MShop 1 .444 1 0.230
-2 Lo ox and Snell R . .
gﬂmy likelihfod C Square $232313? “"5““ df S'g'
355.153 0.111 Test 2.755 8 0.949

 

H. 1. 4: Trip characteristics can be used to predict activity participation while controlling
the variable.
Transient Visitors--Boating

The variables related to trip characteristics included “How many nights do you
plan to be away from home on this trip?” (Nights), “About how far in advance did you
begin to make plans for it?” (PlanDay), “Did you visit the west-central Michigan region
any time before this trip?” (Pastvst), and travel party Size (PartySz). While conducting
univariate analysis in the ﬁrst step, only PlanDay and PartySz were qualiﬁed (P<0.25) to
be entered in the logistic regression model. The results showed that only party size is

related to participation in boating. However, it did not strongly predict participation in

boating (see Appendix B, Table B-l).

Transient VisitorS--Festival/event

The candidate independent variables for running the logistic regression model

included PlanDay and PartySz. However, the analysis did not produce any signiﬁcant

109

 

results. Trip-related characteristics did not successfully predict festival/event
participation while controlling the primary destination variable (Appendix B, Table B-2).
Overnight VisitorS--Hiking/walking
While conducting univariate analysis in the ﬁrst step, only PlanDay and Expmce
were qualiﬁed to enter the logistic regression model. Experience (Expmce) has a
marginally overall Signiﬁcance level (0.075) only if the threshold of statistical Signiﬁcant
is relaxed to 0.10 (Appendix B, Table B-3).
Overnight Visitors--Shopping
The variables related to trip characteristics in the overnight visitor survey
included PlanDay, PartySz, “About how many times have you stayed overnight in the
WCMI during the past three (3) years” (N oVisit), “How would you rate your overall
experience in the WCMI region on this visit?” (Expmce) and “How likely are you to visit
the WCMI region within next three years?” (Visit). Of the trip-related characteristics,
days planning in advance and likeliness to visit the WCMI within the next three years
explained signiﬁcant participation in Shopping activity while controlling the stay-
overnight variable (1 for staying two nights or less in the WCMI, and 2 for staying at
least three nights in the WCMI). Visitors who tended to visit the WCMI were more likely
to participate in shopping, while those who planned the current trip more in advance were

more likely to participate in Shopping (Table 4-26).

110

Table 4-26: Logistic Regression Model Results--Shopping by Trip-related Characteristics
for Overnight Visitors

 

 

 

 

 

 

 

 

Dependent Variable Shopping
Variables in the Equation B S.E. Wald df Sig. Exp(B)
NghtCat(l) 0.477 0.299 2.548 1 0.1 10 1.611
PlanDay 0.004 0.002 7.3 85 1 0.007 1.004
Visit 0439 0.188 5.459 1 0.019 0.645
Constant -0.313 0.320 0.955 1 0.328 0.731
Variables Not in the Equation Score df Sig.
PartySz 0. 1 06 1 0.744
Expmce 1 . 106 1 0.293
811mg}; likzellihfod COX Salli?" R lllgfnrgzligllvd Chi'squm df Sig‘
283.258 0.086 Test 9.122 8 0.332

 

Potential VisitorS--Visiting a Federal/state Park
Potential visitors were only asked two questions related to trip characteristics:
“How likely are you to visit the WCMI within the next three (3) years?” and “In general,
about how far in advance do you begin to plan a weekend getaway?” However, no trip-
related characteristics were found to be associated with participation in visiting a
federal/state park while controlling the factor of visitation to the WCMI (Appendix B,
Table B-4).
H. 1.5: Travel spending can be used to predict activity participation while controlling the
variable.
Overnight Visitors--Hiking/walking
AS shown in Table 4-27, expenditure has been recalculated per person per day. It
would be expected that the odds ratios and the negative sign of expenditure on gas (EGas)
indicated that visitors who spent less on gas were more likely to participate in

hiking/walking. Expenditure on groceries (EGrcry) was signiﬁcant (at 0.000, with a

111

positive estimated coefﬁcient), indicating that those who spent more money on groceries

were more predisposed to go hiking.

Table 4—2 7: Logistic Regression Model Results--Hiking/walking by Travel Expenditures

for Overnight Visitors

 

 

 

 

 

 

 

 

Dependent Variable Hiking/walking
Variables in the Equation B S.E. Wald df Sig. Exp(B)
NghtCat(1) 0.352 0.325 1.172 1 0.279 1.422
EActivity 0.028 0.016 3.222 1 0.073 1.029
EGaS -0.098 0.026 14.009 1 0.000 0.907
EGrcry 0.223 0.045 24.484 1 0.000 1.250
Constant -0.879 0.292 9.077 1 0.003 0.415
Variables Not in the Equation Score df Sig.
ELdgng 0.135 1 0.713
EMeals 0.023 1 0.880
-2 Lo Cox n 11 R . .
Slim; likelihfod 2812;111:138 [3.3231231 Ch"sq“a’e df S'g'
247.357 0.199 Test 5.192 8 0.737

 

Overnight Visitors-Shopping

It would be expected that the positive estimated coefﬁcient of expenditure on

Shopping (Eshppng) indicated that visitors who spent more on shopping were more likely

to participate in shopping (see Table 4-28).

Table 4-28: Logistic Regression Model Results--Shopping by Travel Expenditures for

Overnight Visitors

 

 

 

 

 

 

Dependent Variable Shopping
Variables in the Equation B S.E. Wald df Sig. Exp(B)
NghtCat(1) 0.992 0.318 9.727 1 0.002 2.697
Eshppng 0.1 19 0.022 28.538 1 0.000 1.126
Constant -1.555 0.274 32.153 1 0.000 0.211
3.13.221, iiiiliifoo “"3113?” 1.222323." Chi-square df Sig-
246.157 0.21 1 Test 19.805 5 0.001

 

112

H. 1. 6: The type of travel information sources utilized can be used to predict activity

participation while controlling the variable.

Overnight Visitors--Hiking/walking
When examining the relationship between types of travel information sources and
participation in hiking/walking for overnight visitors, only the use of convention and
visitors bureaus was Si gniﬁcant (at 0.037 with a positive estimated coefﬁcient), indicating
that overnight visitors who used convention and visitors bureaus as travel information
sources were 2.404 times more likely to participate in hiking/walking activity (Table 4-
29).

Table 4-29: Logistic Regression Model Results--Hiking/walking by Travel Sources Used
by Overnight Visitors

 

 

 

 

 

 

 

 

Dependent Variable Hiking/walking
Variables in the Equation B S.E. Wald df Sig. Exp(B)
NghtCat(1) 0.868 0.266 10.673 1 0.001 2.381
1CVB(1) 0.877 0.420 4.361 1 0.037 2.404
Constant -0.952 0.197 23.361 1 0.000 0.386
Variables Not in the Equation Score df Sig.
lChmbr( 1) 0.992 1 0.319
ITrvlffc( 1 ) 0.546 1 0.460
lGuide( 1) 1.032 1 0.310
S r1103; likzelliﬁfod COX 82:11:26" R T232312? “"5““ df S'g'
324.862 0.063 Test 0.133 1 0.715

 

Overnight Visitors-~Shopping
The variables related to the type of information sources used for travel planning
included AAA (IAAA), newspapers (Inwsppr), billboards/outdoor advertising (IBillbrd),
radio (IRadio), chamber of commerce (IChmbr), state travel ofﬁce/Travel Michigan
(ITrvlffc), convention and visitors bureau (ICVB), television (ITV), friends or relatives
(IFrids), travel guides/brochures (lGuide), highway welcome center (IchmCtr), word of

113

mouth (IWord), Intemet/web Site (IIntrnt), and magazine (IMgzne). The estimated
coefﬁcient for friends or relatives was 0.623 (see Table 4-30). The exp (B) was 1.865,
indicating that visitors who used ﬁiends or relatives for travel information were 1.865
times more likely to participate in Shopping.

Table 4-30: Logistic Regression Model Results-Shopping by Travel Sources Used by
Overnight Visitors

 

 

 

 

 

 

 

 

Dependent Variable Shopping
Variables in the Equation B S.E. Wald df Sig. Exp(B)
NghtCat( 1) 0.691 0.262 6.944 1 0.008 1.996
1Frids( 1) 0.623 0.268 5.427 1 0.020 1.865
Constant -0.969 0.217 19.869 1 0.000 0.379
Variables Not in the Equation Score df Sig.
lChmbr( 1) 0.600 1 0.439
1CVB(1) 1.914 1 0.167
ITrvlffc( 1) 1 .623 1 0.203
1Guide(l) 1.158 1 0.282
- Lo Cox and Snell R . .
sillilﬁly Iikilihfod Square 3:231:15 “"5“” df S'g‘
330.257 0.049 Test 1.271 2 0.530

 

Potential Visitors--Visiting a Federal/state Park
Among sources of information used when planning a pleasure trip, Internet

(IIntrnt), local visitor guides (lGuide), and radio (lRadio) were associated with visiting a
federal/state park. Those who preferred to use these sources tended to visit a federal/state
park more than those who did not prefer to use this information. Ibroch was Signiﬁcant at
the 0.057 level indicating that visitors who chose travel guides/brochures were slightly
more likely to visit a federal/state park. The AAA source of information had a signiﬁcant
level (0.091) only if the threshold of statistical Signiﬁcance is relaxed to 0.1 as Shown in

Table 4-31. Therefore, the AAA source might not be considered a Signiﬁcant source.

114

Table 4-31: Logistic Regression Model Results-- Visiting a F ederal/state Park by Travel
Sources Used by Potential Visitors

 

 

 

 

 

 

 

 

Dependent Variable Visiting a Federal/state Park

Variables in the Equation B S.E. Wald df Sig. Exp(B)
ViSWCM( 1) 0.768 0.312 6.037 1 0.014 2.154
[AAA( 1) 0.450 0.266 2.851 1 0.091 1.568
11ntrnt( 1) 0.639 0.270 5.627 1 0.018 1.895
lGuide(l) 0.696 0.269 6.722 1 0.010 2.006
1Radio(1) 1.1 14 0.444 6.282 1 0.012 3.046
lbroch( 1) 0.486 0.256 3.610 1 0.057 1.625
Constant -1.814 0.371 23.857 1 0.000 0.163
Variables Not in the Equation Score df Sig.
lBillbrd(1) 2.763 1 0.096
1CVB(1) 0.008 1 0.929
llnfc( 1) 2.651 1 0.104
IchmCtr(l) 2.913 1 0.088
IMgzne( 1) 0.469 1 0.493
1nwsppr( 1) 0.108 1 0.742
ITrvlffc( 1) 2.692 1 0.101
lWord( 1) 0.487 1 0.485

-2 Lo Cox and Snell R . .
Sﬁﬁy likelihfod Square T221333? Chl'squm df S‘g'
371.254 0.123 Test 5.402 8 0.714

 

H. 1.7: The choice of lodging type can be used to predict activity participation while
controlling the variable.
Transient Visitors-~Boating
Candidate independent variables for the logistic regression model included
staying in bed and breakfasts, cabins, and second homes. However, the analysis did not
Show any signiﬁcant results. Lodging type did not predict boating while controlling the

primary destination variable (Appendix B, Table B-5).

115

Transient VisitorS--Festival/event
Analysis did not Show any signiﬁcant results. Thus, choice of lodging type did not

predict festival/event participation while controlling the primary destination variable (see

Appendix B, Table B-6).

Overnight Visitors--Hiking/walking

Based on the survey regarding overnight visitors’ most current trip to the WCMI,

the choice of second home had a very Signiﬁcant level (p = 0.002), indicating that visitors

who chose to stay in a second home tended to participate in hiking (Table 4-32). For

those who participated in hiking/walking, the probability of staying in a cabin was 2.462

times higher than those that did not to stay in the cabin.

Table 4-3 2: Logistic Regression Model Results--Hiking/walking by Accommodation Type

for Overnight Visitors

 

 

 

 

 

 

 

 

Dependent Variable Hiking/walking
Variables in the Equation B S.E. Wald df Sig. Exp(B)
NghtCat(1) 0.675 0.288 5.497 1 0.019 1.964
LCabin(1) 0.901 0.395 5.190 1 0.023 2.462
LSnde( 1) 1.264 0.414 9.318 1 0.002 3.539
Lcamp(1) 0.690 0.397 3.017 1 0.082 1.993
Constant -1 . 157 0.217 28.454 1 0.000 0.314
Variables Not in the Equation Score df Sig.
LHotel(1) 0.151 1 0.697
83:33:; likillllfod COX 81:11:26“ R 22312313131 Chi'sqwe df Slg'
315.805 0.096 Test 0.530 4 0.971

 

Overnight Visitors--Shopping

The type of lodging chosen for the most current trip to the WCMI did not predict

participation in Shopping while controlling the variable of nights stayed in the WCMI

(Appendix B, Table 4-7).

116

H. 1.8: W CMI attraction visits can be used to predict activity preferences while
controlling the variable.
Transient Visitors--Boating
AS shown in Table 4-33, DLudFrr(2) was signiﬁcant at the 0.004 level with a
negative estimated coefﬁcient, indicating that transient visitors who have visited the
Ludington Car Ferry were more likely to participate in boating than those who were not
aware of this place. Other WCMI attractions did not predict boating for transient visitors.
Transient VisitorS--Festival/event
As shown in Table 4-34, the estimated coefﬁcient for primary destination was -
0.422. The exp (B) 0.656 indicated that visitors whose primary destination was not the
WCMI were 0.656 times less likely to participate in a festival/event than those whose
destination was the WCMI. DCasino(l) was Signiﬁcant at 0.022 level with a negative
estimated coefﬁcient, indicating that those who were aware of but have not visited the
Little River Casino tended to participate in a festival/event less than those who have
visited the casino. Knowledge of the Silver Lake Sand Dunes (DSnan) was also
associated with participation in a festival/event. The negative coefﬁcients of these
variables suggest that, in general, visitors who have visited the Silver Lake Sand Dunes
were more likely to participate in a festival/event than those who were aware but have not

visited and those who were not aware of this place.

117

Table 4-33: Logistic Regression Model Results--Boating by Attractions for Transient

 

 

 

 

 

 

 

 

Visitors

Dependent Variable Boating

Variables in the Equation B S.E. Wald df Sig. Exp(B)
DesReg(l) -0.518 0.219 5.581 1 0.018 0.596
DLudFrr 8.360 2 0.015
DLuan'(1) -0.41 1 0.239 2.947 1 0.086 0.663
DLudFrr(2) -0.990 0.346 8.168 1 0.004 0.3 72
Constant 0.823 0.210 15.375 1 0.000 2.276
Variables Not in the Equation Score df Sig.
DCasino 0.959 2 0.619
DCasino(1) 0.336 1 0.562
DCasino(2) 0.921 1 0.337
DMnsthr 0.669 2 0.716
DMnsthr( 1) 0.296 1 0.586
DMnsthr(2) 0.592 1 0.442
DLudSth 2.315 2 0.314
DLudSth( 1) 2.148 1 0.143
DLudSth(2) 0.001 1 0.973
Dquthr 0.604 2 0.739
Dquthr(1) 0.001 1 0.971
Dquthr(2) 0.467 1 0.494
DMsker 0.507 2 0.776
DMsker(l) 0.063 1 0.802
DMsker(2) 0.196 1 0.658
DSnan 1 .090 2 0.580
DSnan(1) 0.954 1 0.329
DSnan(2) 0.002 1 0.962
DHartTrl 1.351 2 0.509
DHartTrl( 1) 1.020 1 0.312
DHartTrl(2) 0.044 1 0.834

-2 Lo Cox and Snell R r . -
$323311}, likelihfod Square T232331 Ch"sq"a’e df S'g'
5 1 6.75 5(a) 0.038 Test 0.486 3 0.922

 

118

Table 4-34: Logistic Regression Model Results--Festival/event by Attractions for
Transient Visitors

 

 

 

 

 

 

 

 

Dependent Variable Festival/event
Variables in the Equation B S.E. Wald df Sig. Exp(B)
DesReg( 1) -0.422 0.219 3 .726 1 0.054 0.656
DCasino 7.316 2 0.026
DCasino(1) -0.520 0.227 5.221 1 0.022 0.595
DCasino(2) 0.104 0.304 0.117 1 0.732 1.110
DSnan 12.037 2 0.002
DSnan( 1) -0.422 0.216 3.837 1 0.050 0.656
DSnan(2) -1.207 0.369 10.734 1 0.001 0.299
Constant 0.484 0.202 5.745 1 0.017 1.623
Variables Not in the Equation Score df Sig.
DLudSth 3.259 2 0.196
DLudSth( 1 ) 3 .224 1 0.073
DLudSth(2) 0.089 1 0.766
-2 Lo Cox and Snell R . .
83:33:15] likelihfod Square 1:232:12?! Chi-square df Slg'
555.747 0.057 Test 5.803 7 0.563

 

Overnight VisitorS--Hiking/walking

With regard to awareness of attractions, the Little River Casino, Ludington State
Park, and Irons/Lake County snowmobile trails were associated with hiking/walking.
Those who were aware of but did not visit the Little River Casino were 2.431 times more
likely to participate in hiking than those who have visited the Little River Casino (see
Table 4-35). Overall, the Ludington State Park (DLudSth ) did not seem to be a
Signiﬁcant variable as there was no difference among those who have visited that place,
those who were aware but did not visit, and those who were not aware. Those who were
aware but did not visit the Irons/Lake County snowmobile trails were 4.427 times more
likely to participate in hiking/walking than those who have visited. Also those who were
not aware of this place were 2.339 times more likely to participate in hiking/walking than

those who have visited.

119

Table 4-3 5 .' Logistic Regression Model Results--Hiking/walking by Attractions for

Overnight Visitors

 

 

 

 

 

 

 

 

Dependent Variable Hiking/walking
Variables in the Equation B S.E. Wald df Sig. Exp(B)
NghtCat(1) 0.926 0.331 7.830 1 0.005 2.524
DCasino 6.312 2 0.043
DCasino(1) 0.888 0.398 4.985 1 0.026 2.431
DCasino(2) 0.122 0.463 0.070 1 0.792 1.130
DLudSth 2.958 2 0.228
DLudSth(1) -0.7 l 4 0.415 2.958 1 0.085 0.490
DLudSth(2) -20.536 13968.518 0.000 1 0.999 0.000
DImsSnw 8.390 2 0.015
D1msSnw( 1) 1.488 0.545 7.439 1 0.006 4.427
DImsSnw(2) 0.850 0.555 2.344 1 0.126 2.339
Constant -2.045 0.565 13.1 18 1 0.000 0.129
Variables Not in the Equation Score df Sig.
DMnsthr 0.043 2 0.979
DMnsthr( 1) 0.016 1 0.90
DMnsthr(2) 0.008 1 0.927
DLudFrr 0.169 2 0.919
DLudFrr( 1) 0.001 1 0.971
DLudFrr(2) 0.165 1 0.684
Dquthr 3 .097 2 0.213
Dquthr( 1) 3 .094 1 0.079
Dquthr(2) 0.622 1 0.430
DMsker 0.972 2 0.615
DMsker( 1) 0.954 1 0.329
DMsker(2) 0.369 1 0.543
DNwSth 2.384 2 0.304
DNwSth( 1) 0.063 1 0.801
DNwSth(2) 2.038 1 0.153
DSnan 0.143 2 0.931
DSnan( 1) 0.096 1 0.756
DSnan(2) 0.088 1 0.767
Model 1.2 Log Cox and Snell R Hosmer and Chi-squ are df Sig.
Summary lkellhood Square Lemeshow
217.636 0.182 Test 6.979 8 0.539

‘_

 

Overnight Visitors-Shopping
The Pere Marquette River (Dquthr) was the only attraction associated with

Participation in Shopping activity while controlling the nights stayed in the WCMI. Those

120

who were not aware of the Pere Marquette River tended to participate in Shopping (see

 

 

 

 

 

 

 

 

Table 4-36).
Table 4-3 6: Logistic Regression Model Results--Shopping by Attractions for Overnight
Visitors

Dependent Variable Shopping
Variables in the Equation B S.E. Wald df Sig. Exp(B)
NghtCat(1) 0.721 0.31 1 5.372 1 0.020 2.057
Dquthr 8.573 2 0.014
Dquthr(1) 0.534 0.363 2.159 1 0.142 1.705
Dquthr(2) 1.153 0.394 8.553 1 0.003 3.167
Constant - l .243 0.298 17.449 1 0.000 0.289
Variables Not in the Equation Score df Sig.
DCasino 3 .927 2 0.140
DCasino(1) 3.349 1 0.067
DCasino(2) 2.417 1 0.120
DLudFrr 2.230 2 0.328
DLuan(1) 0.817 1 0.366
DLudFrr(2) 1 .992 1 0.158
DImsSnw 2.712 2 0.258
D1msSnw( 1) 0.009 1 0.925
DImsSnw(2) 1 .332 1 0.249
DMsker 1.838 2 0.399
DMsker(1) 0.929 1 0.335
DMsker(2) 1.764 1 0.184
DNwSth 2.378 2 0.305
DNwSth(1) 2.264 1 0.132
DNwSth(2) 0.3 13 1 0.576
DSnan 1.710 2 0.425
DSnan( 1) 0.181 1 0.670
DSnan(2) 1.010 1 0.315
DHartTrl 2.484 2 0.289
DHartTrl( 1) 1.387 1 0.239

_DHartTrl(2) 0.000 1 0.984

-2 Lo ox d nell R . .
8:18:12; likelihfod C goose Egrsnlgzhgvd “"5““ df S'g'
237.950 0.080 Test 0.606 4 0.962

 

121

H. 1. 9: Perception of WCMI destination attributes can be used to predict activity
preferences while controlling the variable.
Overnight Visitors--Hiking/walking
When exploring the relationship between WCMI attractions and participation in
hiking/walking, no signiﬁcant differences were found (Appendix B, Table B-8.
Overnight Visitors-Shopping
AS shown in Table 4-37, the positive Sign of the WCMI offering great Shopping
opportunities (MIShpng) indicated that those who thought the WCMI offered great
shopping opportunities were 1.182 times more likely to participate in Shopping during
their current trip to the WCMI.

Table 4-3 7: Logistic Regression Model Results--Shopping by Destination Attributes for
Overnight Visitors

 

 

 

 

 

 

 

 

Dependent Variable Shopping
Variables in the Equation B S.E. Wald df Sig. Exp(B)
NghtCat(1) 0.784 0.310 6.383 1 0.012 2.191
MIShpng 0.167 0.079 4.489 1 0.034 1 . 182
Constant -1.790 0.557 10.322 1 0.001 0.167
Variables Not in the Equation Score df Sig.
MIley 0.456 1 0.500
MlFall 1.554 1 0.213
MIWinter 0.132 1 0.716
MlSafe 0.072 1 0.788
MlExctng 0.284 1 0.594
MlScnc 0.803 1 0.370
-2 Lo Cox and Snell R . .
8mg; likelihfod Square 3333312333 “"5““ df S‘g'
236.399 0.064 Test 4.506 8 0.809

 

From the a priori approach, many activities were found to be Signiﬁcantly
popular in each sample based on the criteria set in each sample. The proﬁling factors

Were found useful in explaining the resulting segments (i.e., activity participation in

122

boating, festival/event, Shopping, hiking/walking, and visiting a federal/state park). But

these factors for predicting activity participation are Slightly different for each activity.
Post Hoc Approach--Bundle of Activities

The Choices of Market Segmentation Bases

Classes of respondents participating in the forty-ﬁve activities were identiﬁed
using latent class analysis (LCA). The assumption of LCA is that the relationship among
dichotomous variables is explained by the latent variable (Perron, Ilgen, Hasche, &
Howard, 2008). LCA was performed using Mplus (Version 5.2; Muthen and Muthen)
software to explore the classes of activity participation.

LCA was applied to explore classes, that is, groups in which respondents had
similar participation in activities among the 45 different activities which were
dichotomized in yes and no categories. Conditional probabilities and class probabilities are
two major model parameters in the LAC model with categorical outcomes. The conditional
probabilities represent the probabilities assigned to each class resulting from how a
participant responds in that class. The class probabilities are described as the percentage of
respondents in each class. A posterior probability is a number calculated to estimate class
probability for each respondent. Each respondent’s a posteriori probability in each class
was calculated, and then each respondent was assigned to the class with the highest a
posteriori probability (Clark & Muthe'n, 2009; McCutcheon, 1987).

A two-class model was examined ﬁrst, and the other classes were added one at a
time to the model until no further improvements in model ﬁt were observed. Nylund,
ASparouhov, and Muthen (2007) discussed how researchers applied mixture criteria to
decide the number of classes in mixture modeling. Statistical information criteria (IC)

such as Akaike’s Information Criterion (AIC) (Akaike, 1987) and Bayesian Information

123

Criterion (BIC) (Schwartz, 1978) are the best known indicators of model ﬁt for LCA.
Among ICS, BIC has been recommended in many articles and textbooks for selecting
classes (Collins, F idler, Wugalter, & Long, 1993; Hagenaars & McCutcheon, 2002; Yang,
2006; Zhang, 2004). Sample-Size-adjusted BIC suggested by Sclove (1987) is one of the
most accurate ICS for LCA (Yang, 2006). Lower values of ICS are usually considered
better. Likewise, entropy, a summary measure of the classiﬁcation, is another indicator
(Kreuter, Yan, & Tourangeau, 2008). Entropy values range from 0 to 1, with values that
are close to 1 indicating clear classiﬁcations. Based on these methods suggested by
previous research, in this study, LCA models with varying numbers of latent classes were

estimated using AIC, BIC, sample-size-adjusted BIC, entropy, and by visual inspection

of class proﬁles (F laherty, 2002; Perron, et al., 2008).

Following selection of the best ﬁtting model, comparisons among latent classes
on demographic, travel trip-related, travel motivations, WCMI attractions, and WCMI
destination attribution variables were conducted using chi-square for categorical variables

and analysis of variance (ANOVA) with Tukey's studentized range (honestly signiﬁcant

difference) test for continuous variables to proﬁle the classes.

H21: There are classes that can be identified using an activity-based approach.

Transient Visitors
LCA was conducted on the forty-ﬁve indicator variables related to respondents’

activity participation during their current trip. Two-, three-, four-, and ﬁve-class models
Were tested to examine model ﬁt. The four-class model was found to have the highest
entropy. AIC, BIC and sample-size adjusted BIC decrease with an increasing number of

Classes (see Table 4—38). The least change in any of the indices occurs between the four-

and ﬁve-class models. Thus, the four-class model was selected as the best ﬁtting model

based on the substantive considerations. Based on the selected four—class model,

respondents were classiﬁed into four classes as presented in Table 4-39.

Table 4-38: Comparison of Model F it Indicators for the Transient Visitor Sample

 

 

 

 

 

C=2 c=3 C=4 C=5

Akaike(A1C) 23481.418 22838.512 22320.449 22047.733
Bayesian (BIC) 23 870.592 23424.412 23103.075 23027.085
Sample-Size Adjusted BIC 23581.731 22989.533 22522.178 22300.169
Entropy 0.913 0.907 0.916 0.911

 

T able 4-39: Probability of Activity Participation across Latent Classes among Transient

 

 

Visitors
lily . L92 “iii? hill
Outdoor nglxcgl‘r/tiﬁor General General
Activity Participants Activity Activity
Part—relpants (n= 1 60) Particlpants Pam—crpants

(n—97) (n—96) (n— 1 79)
Antique shopping 0.10 0.08 0.38 0.23
Bicycling 0.22 0.25 0.70 0.51
Boating 0.70 0.25 0.86 0.50
Camping 0.78 0.36 0.86 0.60
Canoeing/kayaking/tubing 0.52 0.26 0.86 0.43
Casino gaming 0.31 0.16 0.42 0.26
Concert 0.13 0.05 0.59 0.29
Cross-country Skiing 0.13 0.04 0.35 0.20
Dining out (excluding fast food) 0.29 0.06 0.86 0.62
Downhill skiing/snowboarding 0.14 0.02 0.45 0.16
Farm market/u-pick/winery 0.09 0.04 0.65 0.32
Festival/event 0.20 0.10 0.84 0.53
Fishing, charter 0.46 0.11 0.37 0.15
Fishing, ﬂy 0.49 0.08 0.33 0.07
Fishing, ice 0.54 0.02 0.34 0.05
Fishing, other 0.79 0.15 0.69 0.31
Golﬁng 0.33 0.14 0.48 0.39
Hiking/walking 0.30 0.13 0.76 0.50
Historic site 0.13 0.01 0.62 0.39
Horseback riding 0.13 0.01 0.43 0.21
Hunting, deer 0.86 0.08 0.56 0.15
Hunting, small game 0.76 0.01 0.37 0.04
\Hunting, turkey 0.47 0.01 0.26 0.02

 

125

Table 4-39 (Cont ’d)

 

 

LC 1 LC 2 LC 3 LC 4
Heavy Light Outdoor Heavy nght
Outdoor Ac tivi General General
Activity Pa ltici athts Activity Activity
Participants (n=1 6%) Participants Participants

(n=97) (n=96) (n=179)
Jet skiing 0.15 0.04 0.41 0.14
Lighthouse touring 0.13 0.06 0.59 0.49
Live theatre 0.01 0.03 0.48 0.17
Movie (at a cinema) 0.06 0.02 0.74 0.32
Museum 0.06 0.03 0.54 0.29
Mushroom collecting 0.28 0.01 0.40 0.1 1
Nature center 0.1 1 0.03 0.47 0.25
Off-roading 0.37 0.08 0.44 0.12
Photography 0.07 0.04 0.55 0.31
Sailing 0.08 0.03 0.24 0.15
Scuba diving’snorkeling 0.05 0.02 0.26 0.09
Shopping 0.13 0.06 0.81 0.53
Sightseeing (general) 0.26 0.10 0.86 0.64
Snowmobiling 0.33 0.09 0.51 0.14
Sports tournament 0.12 0.00 0.34 0.05
Swimming (lake, pond, river) 0.41 0.05 0.90 0.49
Swimming (pool) 0.13 0.03 0.69 0.28
Theme/amusement park 0.07 0.01 0.68 0.24
Visiting a federal/state park 0.26 0.03 0.76 0.45
Visiting friends/relatives 0.28 0.08 0.83 0.46
Wildlife viewing/bird watching 0.24 0.01 0.63 0.34
Jind surﬁng 0.02 0.00 0.13 0.01

 

Latent class 1(LC 1, 18% of the sample), heavy outdoor activity participants,
included those who particularly participated in hunting and ﬁshing related activities.
Among those activities, the most popular activities with a participation probability higher
than 0.5 included hunting (deer) (0.855), ﬁshing (other) (0.791), camping (0.784),
hunting (small game) (0.764), boating (0.704), ﬁshing (ice) (0.538), and
Canoeing/kayaking/tubing (0.522). LC 2 (30.1% of the sample), light outdoor activity

participants, included those whose activity participation did not exceed 0.2 for any

126

activity except camping, canoeing/kayaking/tubing, boating, and bicycling. LC 3 (18.0%),

heavy general activity participants, included those whose probability of participating in
all the various activities was higher than 0.3 except for hunting (turkey), scuba
diving/snorkeling, sailing, and wind surﬁng. LC 4 (33.6% of the sample), light general
activity participants, was the largest class in transient visitors. The most popular activities
in this class with participation probability higher than 0.5 included sightseeing (general),
dining out (excluding fast food), camping, Shopping, festival/event, bicycling,
hiking/walking and boating. This class exhibits similar patterns to LC 3 but the

participation in each activity is lower than LC 3 (see Figure 4-1).

Overnight Visitors

The model ﬁt index represented in Table 4-40 had different suggestions on class
selection. BIC indicates that two classes is a better choice. But the indices of AIC and
sample-size adjusted BIC decrease as the number of classes increases indicating that the
four-class model performs better. However, the least change occurs in between the three-

and four-class models also indicating that the choice of three-class is not much different
from four-class. Entropy also seems to point to three classes. Also, after testing the
proﬁling data, the two-class model did not differentiate characteristics between the two
Classes. Therefore, the three-class model was chosen for overnight visitors (Table 4-41

and Figure 4-2).
Table 4-40: Comparison of Model F it Indicators for the Overnight Visitor Sample

 

 

 

 

 

\ C=2 c=3 C=4
$aikgAIC) 7333.793 7219.283 7177.104
\B_ayesian (BlC) 7660.573 7711.249 7834.255
Liimple-Size Adjusted BIC 73 72.047 7276.874 7254.031
@tmpy 0.860 0.898 0.851

 

127

 

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Figure 4-1: Probability of A ctivity Participation by Latent Classes among Transient Visitors

Table 4—41 : Probability of Activity Participation across Latent Classes among Overnight

 

 

Visitors
LC 1 LC 2 LC 3
No Preference Heavy General Light General
Activity Activity Activity
Participants Participants Participants

(n=146) (n=17) (n=105)
Antique shopping 0.08 0.40 0.20
Bicycling 0.04 0.17 0.14
Boating 0.08 0.59 0.33
Camping 0.15 0.53 0.21
Canoeing/kayaking/tubing 0.03 0.41 0.16
Casino gaming 0.19 0.58 0.16
Concert 0.02 0.29 0.04
Cross-country Skiing 0.01 0.17 0.01
Dining out (excluding fast food) 0.44 0.77 0.86
Downhill skiing/snowboarding 0.00 0.12 0.00
Farm market/u-pick/winery 0.03 0.65 0.25
Festival/event 0.05 0.69 0.15
Fishing, charter 0.02 0.41 0.04
Fishing, ﬂy 0.10 0.24 0.01
Fishing, ice 0.01 0.06 0.00
Fishing, other 0.15 0.54 0.24
Golﬁng 0.07 0.30 0.19
Hiking/walking 0.21 0.82 0.54
Historic site 0.06 0.52 0.27
Horseback riding 0.00 0.06 0.04
Hunting, deer 0.07 0.46 0.00
Hunting, small game 0.03 0.46 0.01
Hunting, turkey 0.01 0.12 0.00
Jet skiing 0.01 0.12 0.08
Lighthouse touring 0.01 0.25 0.36
Live theatre 0.01 0.00 0.00
Movie (at a cinema) 0.00 0.34 0.11
Museum 0.02 0.40 0.06
Mushroom collecting 0.03 0.35 0.01
Nature center 0.00 0.34 0.12
Off-roading 0.07 0.35 0.08
Photography 0.09 0.53 0.35
Sailing 0.00 0.06 0.04
Scuba diving/snorkeling 0.00 0.00 0.01
Shopping 0. l 2 0.76 0.69

\Sightseeing (general) 0.22 0.82 0.61

 

129

Table 4-41 (Cont ’d)

 

 

LC 1 LC 2 LC 3

No Preference Heavy General Light General
Activity Activity Activity

Participants Participants Participants
(n=146) (n=17) (n=105)
Snowmobiling 0.04 0.23 0.00
Sports tournament 0.02 0.12 0.00
Swimming (lake, pond, river) 0.04 0.54 0.55
Swimming (pool) 0.01 0.28 0.23
Theme/amusement park 0.01 0.12 0.05
Visiting a federal/State park 0.07 0.64 0.41
Visiting friends/relatives 0.19 0.58 0.24
Wildlife viewing/bird watching 0.1 1 0.70 0.26
Wind surﬁng 0.01 0.00 0.00

 

Latent class 1 (LC 1, 54.5%), no preference activity participants, was
characterized by light to medium activity participation by those who only responded to
dining, sightseeing, and hiking/walking with a probability of greater than 0.2 (Table 4-
43). Latent class 2 (LC 2, 6.3%), heavy general-activity participants, was characterized
by higher participation in a variety of activities. Activities such as sightseeing (general),
hiking/walking, dining out (excluding fast food), shopping, wildlife viewing/bird
watching, festival/event, farm market/u-pick/winery, visiting a federal/state park, boating,
casino gaming, visiting friends/relatives, ﬁshing (other), swimming (lake, pond, river),

Camping, photography, and historic Site exhibited probabilities of participation higher
than 0.5 in this class. Latent class 3 (LC 3, 39.2%), light general activity participants, was
characterized by medium activity participation because the probability of activity
participation mostly fell between the probability of the other two classes. In latent class 3,
the probabilities of participation in ﬁshing (ﬂy), casino gaming, hunting (small game),

mushroom collecting, hunting (deer), snowmobiling, sports tournament, hunting (turkey),

130

downhill Skiing/snowboarding, ﬁshing (ice), live theatre, and wind surﬁng were the

lowest among the three classes.

Potential Visitors

As found in the analyses of transient and overnight visitors, comparison of the

indicators in the LCA did not provide consistent evidence for selecting the best model.

AIC and sample-size adjusted BIC decreased as the number of classes increased, while

entropy increased as the number of classes increased. However, based on the index of

BIC as well as judging by the classiﬁcation proﬁle, the three-class model was a superior

relative ﬁt for potential visitors (Table 4-42).

YZIbIe 4-42: Comparison of Model F it Indicators for the Potential Visitor Sample

 

 

 

 

 

C=2 C=3 C=4 C=5
Akaike (AIC) 12317.859 11999.159 11861.544 11763.58]
Bayesian (BIC) 12655.508 12507.488 12540.552 12613.269
Sample-Size Adjusted BIC 12366.906 12072.999 11960.176 11887.006
Entropy 0.834 0.868 0.877 0.912

 

131

 

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132

Figure 4—2: Probability of Activity Participation by Latent Classes among Overnight Visitors

LC 1, general tourists (about 29.1% of the respondents), included those who
generally participated in different kinds of activities on their trips. Sightseeing (general)
( 91.8%), dining out (excluding fast food) (90.4%), historic site (87.8%), visiting a
federal/state park (81.7%), museum (81.2%), Shopping (78%), hiking/walking (74.5%),
festival/event (74.3%), visiting friends/relatives (74.3%), nature center (71.5%),

swimming (lake, pond, river) (54.3%), photography (52.7%), farm market/u-pick/winery
( 52.1%), camping (49.4%), lighthouse touring (47.9%), swimming (pool) (44.9), live
theater (42.8), movie (at a cinema) (42.3%) and bicycling (41.6%) were frequently
participated in within this class (Table 4-43 and Figure 4-3). Latent Class 2 (LC 2, 22.2%
of the respondents), outdoor tourists, was characterized by their stronger participation in
outdoor activities. Activity participation probability of LC 2 far exceeded members of
other classes in camping, wildlife viewing/bird watching, boating,
canoeing/kayaking/tubing, ﬁshing (other), ﬁshing (charter), hunting (deer), off-roading,
ﬁshing (ﬂy), ﬁshing (ice), hunting (small game), mushroom collecting, snowmobiling,
and hunting (turkey). Latent class 3 (LC 3, 48.7% of the respondents), cultural tourists,
was characterized by light-to-medium activity participation with the lowest probability in
all activities except Sightseeing (general), dining out (excluding fast food), historic site,

shopping, festival/event, and visiting ﬁiends/relatives.

133

Table 4-43: Probability of A ctivity Participation across Latent Classes among Potential

 

 

Visitors
.7 LC 1 LC 2 LC 3
General Tourists Outdoor Tourists Cultural Tourists
(n=88) (n=67) (n=147)
Antique shopping 0.36 0.03 0.17
Bicycling 0.42 0.24 0.05
Boating 0.33 0.50 0.09
Camping 0.49 0.79 0.09
Canoeing/kayaking/tubing 0.29 0.33 0.07
Casino gaming 0.33 0.27 0.29
Concert 0.40 0.07 0.17
Cross-country skiing 0.16 0.10 0.02
Dining out (excluding fast food) 0.90 0.49 0.75
Downhill skiing/snowboarding 0.17 0.15 0.03
Farm market/u-pick/winery 0.52 0.22 0.23
Festival/event 0.74 0.29 0.38
Fishing, charter 0.10 0.24 0.05
Fishing, ﬂy 0.05 0.15 0.00
Fishing, ice 0.03 0.25 0.00
Fishing, other 0.24 0.53 0.08
Golﬁng 0.29 0.29 0.22
H iking/walking 0.75 0.57 0.25
Historic site 0.88 0.37 0.45
Horseback riding 0.14 0.08 0.06
Hunting, deer 0.09 0.48 0.03
Hunting, small game 0.03 0.22 0.00
Hunting, turkey 0.00 0.25 0.00
Jet skiing 0.06 0.04 0.02
Lighthouse touring 0.48 0.20 0.18
Live theatre 0.43 0.02 0.14
Movie (at a cinema) 0.42 0.14 0.19
Museum 0.81 0.15 0.27
Mushroom collecting 0.01 0.20 0.04
Nature center 0.72 0.21 0.12
Off-reading 0.08 0.25 0.03
PhotOgraphy 0.53 0.41 0.26
Sailing 0.08 0.03 0.01
Scuba diving/snorkeling 0.10 0.07 0.02
Sh0pping 0.78 0.26 0.55
Sightseeing (general) 0.92 0.51 0.73
Snowmobiling 0.01 0.13 0.00
Wmament 0.13 0.12 0.09

 

134

721ble 4-43 (Cont 'd)

 

'

 

LC 1 LC 2 LC 3
General Tourists Outdoor Tourists Cultural Tourists
(n=88) (n=67) (n=147)

Swimming (lake, pond, river) 0.54 0.50 0.19
Swimming (pool) 0.45 0.24 0.29
Theme/amusement park 0.39 0. l 3 0.21
Visiting a federal/state park 0.82 0.55 0.24
Visiting friends/relatives 0.74 0.51 0.56
Wildlife viewing/bird watching 0.33 0.40 0.12
Wind surﬁng 0.01 0.03 0.00

 

I

135

 

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Figure 4-3: Probability of A ctivity Participation by Latent Classes among Potential Visitors

Development of Profiles of Resulting Segments

Chi-square analyses and ANOVAS were carried out to proﬁle each class’s socio-
demographic, trip-related, motivations, WCMI attraction, and WCMI destination
attributes. In an ANOVA test, if the Levene statistic is Signiﬁcant at the 0.05 level, the
researcher rejects the null hypothesis that the classes have equal variances, which means
tlie assumption of homogeneity of variances to ANOVA is violated and Welch's variance
would be recommended (Garson, 2009b; Howell, 2002). The post hoc test, Tukey’s
honestly Signiﬁcantly different (HDS), follows to determine the driving forces that cause
differences among the classes. Following the suggestion of Jaccard, Becker, and Wood
( 1 984), while the homogeneity of variances assumption is violated, the Games-Howell
results, a modiﬁed HSD test, are reported.
H22: There are signiﬁcant differences between classes with respect to respondents’

socio-demographic characteristics.
Transient Visitors

Within transient visitors, age and living with children under 18 years old showed
Si gniﬁcant differences among the classes. Based on Tukey’s HSD test, LC 3 (heavy
general activity participants) was signiﬁcantly younger than LC 2 (light outdoor activity
Participants) and LC 4 (light general activity participants). LC 3, the majority of females,
Was more likely to live with children under 18 years old while LC 2 was less likely to do
30- The other socio-demographic characteristics did not Show signiﬁcant differences

among the classes of transient visitors (Table 4-44).

137

Table 4-44: Socio-demographic Characteristics across Latent Classes among Transient

 

 

Visitors
LC 1 LC 2 LC 4
Heavy Light LC 3 Light
Outdoor Outdoor Heavy General General . .
Activity Activity Activity Activity Te“ Stalls”
Socio-demographic Participants Participants Participants Participants
Characteristics (n=97) (n= 1 60) (n=96) (n= 1 79)
"' A B
Age, Mean (SD) 43.6(13.9) 45.8039) 41003.0) 46003.1) F = 3.371 *
2
Gender (female) , % 19.8 37.8 54.2 49.7 X = 30319",
df = 3
Living with children
under 18 years 2
old ,% 28.9 21.9 38.5 34.1 x = 9776“, df= 3
2
Income, % x = 6.487, df = 6
Below $42,500 22.0 24.4 14.9 18.6
Between $42,500
and $75,000 51.6 44.5 47.9 43.7
Above $75,000 26.4 31.1 37.2 37.7 2
Employment, % X = 6.232, df= 6
Employed 79.3 79.5 79.1 74.3
Unemployed 3.3 4.9 9.9 7.6
Retired 17.4 15.6 11.0 18.1

 

*p<.05, **p<.01
A-B: Means in the same row followed by the same superscript were Signiﬁcantly different at p<.05.
W: Welch statistics was reported while robust tests of equality of means.

Overnight Visitors
Respondents in LC 2 (heavy general activity participants) tended to be younger
(mean age=50), less likely to live with children under 18 years old (29.4%) and 43.8 % of
them had an annual income higher than $75,000. However, of those socio-demographic
Characteristics, only living with children under 18 showed signiﬁcant differences among

the classes (Table 4-45).

138

Table 4-45: Socio-demographic Characteristics across Latent Classes among Overnight

 

 

Visitors
’ LC 1 LC 2 LC 3
No Heavy Light
Preference General General Test Statistics
Activity Activity Activity
Socio-demographic Participants Participants Participants
Characteristics (n=146) (n=17) (n=105)
Age, Mean (SD) 51.1(12.6) 49.7 (11.7) 51.0 (1 1.4) P; 0.107
Gender (female), % 32.1 29.4 44.8 x = 4-513, df= 2
Living with children under 18 2
years old, % 30.1 29.4 45.7 x = 6779‘: df= 2
Income, % X2 = 1-3379 df= 4
Below $43,000 19.8 18.8 25.3
Between $43,000 and
$75,000 42.7 37.5 38.4
Above $75,000 37.4 43.8 36.4
Employment, % x2 = 1.834, df = 4
Employed 73.0 66.7 75.2
Unemployed 2.8 0.0 4.0
Retired 24.1 33 .3 20.8
Education, % X2 = 4.417, df= 4
High school, some high
school 17.5 17.6 17.8
Some college, college
graduate/professional 65.7 47.1 58.4
Post-graduate 16.8 3 5 .3 23 .8

 

*p<.05, **p<.01
Potential Visitors
Table 4-46 Shows that only gender was signiﬁcantly different among the three
Classes. LC 1 (general tourists) seemed to have more females (53.4%), while LC 2

(outdoor tourists) was dominated by males (69.7%).

139

Table 4-46: Socio-demographic Characteristics across Latent Classes among Potential

 

 

Visitors
LC 1 LC 2 LC 3
Socio-demographic General Outdoor Cultural . .
Characteristics Tourists Tourists Tourists Test Statistics
(n=88) (n=67) (n=147)
W
Age, Mean (SD) 51.3(12.2) 51.4(12.3) 55.0(15.8) F2: 2331
Gender (female) , % 53.4 31.3 43.7 X = 7527*, df= 2
Living with children under 18 2
years old , % 35.2 40.3 25.2 X = 5-704, df= 2
2
Income, % X = 3.885, df= 4
Below $43,000 24.4 25.8 29.6
Between $43,000 and
$75,000 32.1 38.7 40.0
Above $75,000 43.6 35.5 30.4
2
Education, % X = 3-037, df= 4
High school, some high
school 8.6 14.8 20.9
Some college, college
graduate/professional 63 .0 67.2 61 .2
Post-graduate 28.4 18.0 17.9

 

T°=p<.05, **p<.01
W: Welch statistics was reported while robust tests of equality of means.

H2. 3: There are significant diﬂerences between classes with respect to respondents’ trip-
related characteristics.
Transient Visitors
In Table 4-47 the questions on trip-related characteristics indicated that only
“days in advance to plan this trip” was signiﬁcantly different. The post hoc test showed
that Signiﬁcant differences were found between LC 1 (heavy outdoor activity participants)
and LC 2 (light outdoor activity participants), as well as LC 1 and LC 4 (light general
activity participants). LC 1 tended to plan their trip 42 days in advance while LC 3

(heavy general activity participants) tended to plan the trip about 113 days ahead.

140

Table 4-4 7: T rip-Related Characteristics across Latent Classes among Transient Visitors

 

LC 1 LC 2 LC 3 LC 4

Heavy Light Heavy Light
Trip-related Outdoor Outdoor General General Test Statistics
Characteristics Activity Activity Activity Activity

Participants Participants Participants Participants
(n=97) (n= 160) (n=96) (n=179)

 

Days in advance to plan 41.5 77.4 113.1 88.1
"“5 “‘1” Med" (SD) (61.2) AB (112.7)A (2607) (127.3) B F = 7.093M W
Nights planned to be away 3.2 3.9 4.6 5.2 F = 1.213
from home, Mean (SD) (3.2) (4.8) (9.8) (13.0)
Types of lodging used in
the WCMI, °/o
Friend's or relative's 2
home 15.5 26.3 24.0 21.8 X = 4.226, df= 3
Hotel, motel or resort 15.5 16.3 10.4 16.8 X2 = 2.181, df= 3
Bed & breakfast 1.0 0.0 0.0 2.2 X2 = 5.661, df = 3
Rented cabin, cottage 2
or condominium 3.1 5.6 5.2 6.1 X = 1.238, df= 3
Owned second or 2
seasonal home 12.4 1 1.3 17.7 12.3 X = 2.433, df= 3
Campground or RV 2
park 27.8 27.5 27.1 25.7 x = 0.205, df= 3

 

*p<.05, **p<.01
A-B: Means in the same row followed by the same superscript were signiﬁcantly different at p< .05.
W: Welch Statistics was reported while robust tests of equality of means.

Overnight Visitors
Among trip-related attributes, LC 1 preferred to plan the trip about two months

ahead while LC 2 preferred to plan the trip about ﬁve months ahead, and LC 3 tended to
plan the trip four months in advance (Table 4-48). The statistic on days in advance to
plan this trip showed a Signiﬁcant difference between LC 1 and LC 2 and between LC 1
and LC 3. In terms of overall experiences in the WCMI, LC 3 had better experiences than
LC 1. LC 3 also spent more days in the WCM than LC 1. The Internet was the most
popular travel source for LC 3, while LC 2 Signiﬁcantly depended on billboards/outdoor
advertising, chambers of commerce, convention and visitors bureaus, state travel ofﬁces,
highway welcome centers, television and radio more than the other two classes. Among

lodging types, a signiﬁcant difference was only shown in the lodging type of rented cabin

141

or cottage. The heavy general activity participants preferred to rent cabins (23.5%) more
than the LC 1 (7.5%) while visiting the WCMI.
Potential Visitors

Chi-square analyses and ANOVAS were carried out to proﬁle each class’s trip-
related characteristics (see Table 4-49). With respect to sources of information used in
planning the pleasure trip, Signiﬁcant differences among classes were found in sources
such as billboards/outdoor advertising, highway welcome centers, Internet/web sites,
local visitor guides, magazines, radio, state travel ofﬁces, travel guides/brochures and
word of mouth. In general, the Internet, friends or relatives, and word of mouth were the
most important sources among the three classes. LC 1, general tourists, preferred to use
the Internet (78.4%) and word of mouth (75.0%), both of which were signiﬁcantly higher
than other classes. LC 2, outdoor tourists, used radio (16.4%) signiﬁcantly more than the
other classes while their use of the Internet was the lowest among the three classes.

The results of Tukey’s HSD post hoc tests Showed that the outdoor tourists were
Signiﬁcantly more likely to visit the WCMI in the next three years than the cultural
tourists, but were not signiﬁcantly different from the general tourists. LC 2 also was the

largest class with 94% having visited the WCMI.

142

Table 4-48: Trip-related Characteristics across Latent Classes among Overnight Visitors

 

 

LC 1 LC 2 LC 3
No Heavy Light
Trip-related Characteristics Preference General General Test Statistics
Activity Activity Activity
(n=146) (n=17) (n=105)
Days in advance to plan this trip, 59.3 144.7 112.5

Mean (SD)
Travel party size, Mean (SD)

AB A B w
(86.8) (161.3) (117.5) F= 8.219"
A A
3.2 (2.2) 3.2 (1.3) 4.0 (3.0) F= 3367*

Overall experience in the WCMl , A A W
Mean (SD) 2.7 (0.8) 2.4 (0.9) 2.3 (0.9) F = 5.087*
Likely to visit the WCMI within W
next three years, Mean (SD) 1.5 (0.9) 1.2 (0.5) 1.5 (0.8) F = 2042
Nights spent within the WCMI, A A
Mean (SD) 2.5 (1.8) 6.3 (8.6) 4.3 (5.1) F= 7267*

Sources of information used in
planning this visit, %

2
AAA 10.3 23.5 17.1 X = 3.864, df= 2
2
Billboards/outdoor advertising 2.7 17.6 19 X = 11,550", df= 2
2
Chamber of commerce 8.9 35.3 18.1 X = 10.890", df= 2
2
Convention and visitors bureau 6.8 35.3 12.4 X = 13203“, df= 2
2
Friends or relatives 33.6 58.8 36.2 X = 4-203, df= 2
2
Highway welcome centers 1.4 1 1.8 5.7 X = 6470*, df= 2
. 2
Intemet/web Slte(s) 29.5 17.6 43.8 x = 7.825“, df = 2
2
Magazine 4.1 17.6 8.6 X = 5.402, df= 2
2
Newspaper 0.7 5.9 1.9 X = 2-997, df= 2
2
Radio 0.0 5.9 1.0 X = 7212*, df= 2
State travel office/Travel 2
Michigan 2.1 41.2 12.4 X = 32380", df= 2
2
Television 0.7 11.8 1.0 X = 13057", df= 2
2
Travel guide(s)/brochure(s) 1 1.0 18.0 27.0 X = 10-420", df= 2
2
Word of mouth 23.3 23.5 24.8 X = 0.074, df = 2
Types of lodging used in the WCMI,
%
2
Friend's or relative's home 10.3 17.6 5.7 X = 3-239, df= 2
2
Hotel, motel or resort 51.4 64.7 56.2 x = 1.393, df = 2
2
Bed & breakfast 4.1 5.9 7.6 x = 1.426, df = 2
Rented cabin, cottage or 2
condominium 7.5 23.5 22.9 X = 12.709", df = 2
2
Owned second or seasonal home 13.7 17.6 8,6 x = 2.089, df = 2
2
Campground or RV park 8.9 17.6 19.0 X = 5.681, df = 2

 

*p<.05, **p<.01
A-B: Means in the same row followed by the same superscript were signiﬁcantly different at p<.05.
W: Welch statistics was reported while robust tests of equality of means.

143

Table 4-49: Trip-related Characteristics across Latent Classes among Potential Visitors

 

 

LC 1 LC 2 LC 3
. . . General Outdoor Cultural . .
Trip-related Characteristics Tourists Tourists Tourists Test Statistics
(n=88) (n=67) (n=147)
Days in advance to plan your
pleasure trip, Mean (SD) 22.2(22.9) 32.9(55.5) 22.8(25.8) F = 0.992
Likelihood to visit the WCMI in A A
the next 3 years, Mean (SD) 2.3(l.2) 1.9(1.l) 2.6(1.3) F = 8.776“
2
Have ever visited the WCMl, % 78.2 94.0 71.8 x = 13.412", df= 2
Sources of information used in
planning pleasure trip, %
2
AAA 40.9 25.4 29.9 X = 4.839, df= 2
Billboards/outdoor 2
advertising 18.2 13.4 5 .4 X = 9.734", df= 2
2
Chamber of commerce 21.6 19.4 17.0 X = 0.771, df = 2
Convention and visitors 2
bureau 27.3 22.4 16.3 x = 4.117, df= 2
. 2
Friends or relatives 77.3 74.6 70.7 X = 1.259, df = 2
Highway tourist information 2
centers 37.5 23.9 23.8 x = 5.802, df= 2
2
Highway welcome centers 40.9 26.9 22.4 X = 9305", df= 2
. 2
Intemet/web 5116(5) 78.4 53.7 62.6 x = 11.099", df = 2
. . . 2
Local Visitor gu1des 58.0 31.3 19.0 X = 37.744“, df= 2
. 2
Magazrne 51.1 38.8 29.9 X = 10.516“, df= 2
2
Newspaper 39.8 29.9 30.6 X = 2-499, df= 2
. 2
Radio 13.6 16.4 6.1 x = 6361*. df= 2
2
State travel ofﬁce 21.6 6.0 7.5 X = 13.376", df= 2
2
Television 20.5 23.9 21.1 x = 0.296, df = 2
2
Travel guide(s)/brochure(s) 67.0 47.8 46.3 X = 10-366", df= 2
2
Word ofmouth 75.0 74.6 57.8 X = 9316", df= 2

 

*p<.05, **p<.01
A-B: Means in the same row followed by the same superscript were Signiﬁcantly different at p< .05.
W: Welch statistics was reported while robust tests of equality of means.

H2. 4: There are signiﬁcant differences between classes with respect to respondents’
travel expenditures.
Overnight Visitors
In terms of travel expenditures, signiﬁcant differences among classes were found

only on grocery Spending. The LC 2 (heavy general activity participants) spent

144

signiﬁcantly more on groceries than LC 1 (no preference activity participants) and LC 3
(light general activity participants) (Table 4—50).

Table 4-50: Travel Expenditures across Latent Classes among Overnight Visitors

 

LCl

 

No LC 2 LC 3
Preference Heal/ytgieneral ngiitCiivineral
WCMI Destination Attributes ’ Activity P . . ty . . ty Test Statistics
Participants art1c1pants Part1c1pants
(":1 4 6) (n=17) (n=105)
Mean (SD) Mean (SD) Mean (SD)
Activities (equipment rentals, w
lessons, etc.) 1.6 (6.0) 3.0( 6.4) 5.0 (12.6) F = 2-834
Attractions (tickets, entrance
fees, etc.) 3.4 (14.6) 4.6 (7.4) 3.1 (6.2) F = 0.104
Gas/fuel 9.2 (11.0)A 11.2 (10.0)B 7.7 (8.6) F= 0.98
. A
Grocenes 3.5 (5.0) 8.4 (4.9) 4.3 (4.8) F = 6.529"
Lodging 24. 3 (27.1) 20.2 (14.7) 27.9 (33.0) F = 0.644

Meals at restaurants/fast food 13. 6 (16.9) 13.2 (11.1) 14.5 (15.4) F = 0.098
Shopping (clothes, souvenirs,
etc.) 6.8 (37.1) 10.5 (12.7) 9.0(11.9) F = 0.225
*p<.05, "p<.01
A-B: Means in the same row followed by the same superscript were signiﬁcantly different at p<.05.
W: Welch statistics was reported while robust tests of equality of means.

 

H25: There are significant differences betWeen classes with respect to respondents’
travel motivations.
Potential Visitors
With regard to travel motivations, signiﬁcant differences were found in upscale
facilities/services, a variety of shopping opportunities, interesting scenery, service quality,
and a variety of attractions and/or activities. From Tukey’s HSD test, speciﬁcally, LC 2,
outdoor tourists, were least motivated by upscale facilities, service quality, shopping
opportunities and a variety of attractions and/or activities. LC 1 was motivated by
interesting scenery (mean=4.3) which was signiﬁcantly different from LC 3 and, service
quality (mean=4.2) and a variety of shopping opportunities (mean=3.1) which were

statistically different from LC 2. It makes sense that the LC 1 is motivated to travel by a

145

variety of attractions and/or activities, more so than the other classes as LC 1 are general
tourists who enjoyed various kinds of activities (see Table 4-51).

Table 4-51: Travel Motivations across Latent Classes among Potential Visitors

 

 

LC 1 LC 2 LC 3
General Outdoor Cultural
Motivations Tourists Tourists Tourists Test Statistics
(n=88) (n=67) (n=147)
Mean (SD) Mean (SD) Mean (SD)
A AB B
Upscale facilities/services 320-0) 260-1) 32(1-1) F = 6.334 *"‘,
Travel time/distance 3.4(0.9) 3.1(0.9) 3.3(1.0) F = 1.122
Cost 4.1(0.8) 3.7(0.9) 3.8(0.9) F = 2.963
Family-friendly place and/or
opportunities 3.5(1.3) 3.7(1.2) 3.5(1.1) F= 0.532
Safety/security 4.2(0.8) 4.2(1.0) 4.3(0.8) F = 0.366
Variety of shopping A A
opportunities 3- 1(1-0) A 2150.1) 3.0(1.1) F = 4.072*
interesting scenery 4.3(0-7) A 4.2(0.7) A 4.0(0.8) A F = 4376*
Service quality 4.2(0.7) 3.9(0.8) 4.1(0.8) F = 3.381 *
Variety of attractions and/or AB A B W
activities 3.9(0.8) 3.5(1.0) 3.6(1.0) F = 6.723"
Nightlife activities 2.6(1.2) 2.2(1.0) 2.4(l.1) F = 2,911
Accessibility for disabled
persons 2.0(l.3) 1.8(1.0) 2.1(1.3) F = 1.374
Pet accommodations 1.9(1.3) 2.2(1.4) 1.8(1.1) F = 1.959 W

 

*p<.05, **p<.01
A-B: Means in the same row followed by the same superscript were signiﬁcantly different at p<.05.
W: Welch statistics was reported while robust tests of equality of means.

H2. 6: There are significant differences between classes with respect to respondents’
knowledge of WC MI attractions.
Transient Visitors

Awareness of WCMI attractions differed among these three classes represented in
Table 4-52. LC 1 was identiﬁed as heavy outdoor activity participants. Respondents had
visited water resource attractions such as the Manistee River, Ludington State
Park/beaches, Pere Marquette River, and Muskegon River. However, LC 2 was
characterized as lighter outdoor activity tourists; the majority of the LC 2 respondents fell

into the category of “aware but not visited” in water resource attractions such as the

146

Manistee River, Ludington State Park/beaches, Pere Marquette River, and Muskegon
River.

Table 4-52: Knowledge of Attractions across Latent Classes among Transient Visitors

 

LC 1 LC 2 LC 3 LC 4
Heavy Light Heavy Light
WCMI Attractions Outdoor Outdoor General General Test Statistics
Activity Activity Activity Activity
(n=97) (n=] 60) (n=96) (n=179)

 

Little River Casino X2 = 32.439“: df= 6
Have Visited 47.7 37 .4 47.9 27.4
Aware, but Not Visited 45.5 33.0 39.4 53.0
Not Aware ofThis Place 6.8 29.6 12.8 19.6
Manistee River X2 = 32.346“, df= 6
Have Visited 65.1 36.3 52.1 33.5
Aware, but Not Visited 29.1 38.1 29.8 44.5
Not Aware ofThis Place 5.8 25.7 18.1 22.0
Ludington Car Ferry X2 = 17.389", df= 6
Have Visited 43.9 25.2 39.4 27.3
Aware, but Not Visited 48.8 56.1 54.3 57.0
Not Aware of This Place 7.3 18.7 6.4 15.8
Ludington State Park/beaches X2 = 30.518“, df= 6
Have Visited 66.7 38.2 66.3 65.7
Aware, but Not Visited 28.7 44.5 25.0 22.5
Not Aware of This Place 4.6 17.3 8.7 1.1.8
Pere Marquette River X2 43404“, df= 6
Have Visited 56.0 25.7 43.5 29.2
Aware, but Not Visited 38.1 43.8 37.0 40.4
Not Aware ofThis Place 6.0 30.5 19.6 30.4 2
I’Z’liﬁiikcfb‘ii‘é’ii’iyns X = ‘2-063’df=6
Have Visited 29.3 22.8 22.2 16.5
Aware, but Not Visited 48.0 43.6 47.8 40.5
Not Aware of This Place 22.7 33.7 30.0 43.0
Muskegon River X2 = 16.73 54:, df= 6
Have Visited 46.3 23.4 37.0 29.4
Aware, but Not Visited 40.2 47.7 38.0 39.9
Not Aware of This Place 13.4 29.0 25.0 30.7
Newaygo State Park X2 = 9.594, df= 6
Have Visited 27.6 18.4 18.5 17.9
Aware, but Not Visited 55.3 47.6 48.9 46.9
Not Aware of This Place 17.1 34.0 32.6 35.2
Sand Dunes at Silver Lake X2 = 18.728“, df= 6
Have Visited 60.7 36.5 58.7 44.9
Aware, but Not Visited 34.5 45.2 31.5 41.9
Not Aware ofThis Place 4.8 18.3 9.8 13.2

 

147

Table 4—52 (Cont ’d)

 

LC 1 LC 2 LC 3 LC 4
Heavy Light Heavy Light
WCMI Attractions Outdoor Outdoor General General Test Statistics
Activity Activity Activity Activity
(n=97) (n=160) (n=96) (n=179)

 

Hart-Montague (rail) Trail X2 = 7.630, df= 6
Have Visited 25.6 18.8 26.9 21.5
Aware, but Not Visited 48.7 46.5 33.3 41.1
Not Aware ofThis Place 25.6 34.7 39.8 37.4

 

*p<.05, **p<.01

Overnight Visitors

The three classes showed signiﬁcant associations with awareness of WCMI

attractions such as the Manistee River, Ludington State Park/beaches, Pere Marquette
River, Irons/Lake County snowmobile trails, Newaygo State Park and Hart-Montague
(rail) Trail. LC 2, heavy general activity participants, had visited most of the attractions.
Eighty-two percent of LC 3, light general activity participants, had visited the Ludington
State Park/beaches, but only 8% had been to the Irons/Lake County snowmobile trails
(Table 4-53).

Table 4-53: Knowledge of A ttractions across Latent Classes among Overnight Visitors

 

LCl LC2

 

LC 3
NO Heavy Light General
WCMI Attractions Preference General . . Test Statistics
. . . . Act1v1ty
Act1v1ty Act1v1ty (n=105)
(n=146) (n=17)
Little River Casino 112 = 9.356, df= 4
Have Visited 39.3 56.3 28.1
Aware, but Not Visited 35.2 43.8 43.8
Not Aware of This Place 25.4 0.0 28.1
. . 2
Manistee River X = 13.807“, df= 4
Have Visited 35.9 84.6 34.4
Aware, but Not Visited 41.9 7.7 47.9
Not Aware of This Place 22.2 7.7 17.7
Ludington Car Ferry x2 = 5.504, df= 4
Have Visited 32.5 42.9 43.6
Aware, but Not Visited 58.3 50.0 53.5
Not Aware of This Place 9.2 7.1 3.0

 

148

Table 4-53 (Cont ’d)

 

LC 1 LC2

 

No Heavy Lighlfggneml
WCMI Attractions Preference General . . Test Statistics
Activity Activity Ac-tiv1ty
(n=146) (n=17) (“"05)
Ludington State Park/beaches 12 = 10.837*, df= 4
Have Visited 66.1 80.0 82.8
Aware, but Not Visited 26.3 20.0 16.2
Not Aware ofThis Place 7.6 0.0 1.0
Pere Marquette River X2 = 10.367*, df= 4
Have Visited 49.2 71.4 34.0
Aware, but Not Visited 29.2 7.1 39.4
Not Aware ofThis Place 21.7 21.4 26.6
Irqrn:i/lls.ake County snowmobile X2 = 14.737“, df= 4
Have Visited 19.6 42.9 8.0
Aware, but Not Visited 35.7 42.9 39.8
Not Aware of This Place 44.6 14.3 52.3
Muskegon River X2 = 7.968, df= 4
Have Visited 30.2 64.3 28.9
Aware, but Not Visited 41.4 28.6 40.0
Not Aware ofThis Place 28.4 7.1 31.1
Newaygo State Park X2 = 16.749“, df= 4
Have Visited 18.8 61.5 33.0
Aware, but Not Visited 50.0 30.8 31.9
Not Aware of This Place 31.3 7.7 35.2
Sand Dunes at Silver Lake X2 = 5.596, df= 4
Have Visited 42.7 41.7 51.6
Aware, but Not Visited 38.5 58.3 35.8
Not Aware of This Place 18.8 0.0 12.6
Hart-Montague (rail) Trail X2 = 14295:”, df= 4
Have Visited 1 1.1 41.7 24.7
Aware, but Not Visited 39.8 50.0 30.3
Not Aware of This Place 49.1 8.3 44.9

 

*p<.05, **p<.01
H2. 7: There are significant differences between classes with respect to respondents’
perceptions of WC MI destination attributes.
Overnight Visitors
Among the destination attributes, the three classes were statistically different with

respect to the WCMI being a great family vacation destination, being a great winter

149

destination, being a safe place to visit, offering great shopping opportunities, and offering
much scenic appeal. Compared to the other two classes, LC 2 more strongly agreed with
all of these attributes except the WCMI being a great family vacation destination. LC 3
more strongly agreed with the WCMI being a great family vacation destination than LC 1.
(see Table 4-54).

From post hoc results, a number of classes were found in each sample. The
activity-based segmentation was helpful in segmenting the classes. While proﬁling the
classes, these factors used in the a priori approach also explained the resulting classes.
The marketing suggestions based on the classes generated in each sample are discussed in

the next chapter.

150

Table 4-5 4: Perceptions of Destination Attributes across Latent Classes among Overnight

 

 

Visitors
LC 1 LC 2 LC 3
No Preference Heavy General Light General
WC Ml Destination Attributes ‘ Activity Activity Activity Test Statistics
Participants Participants Participants
(n=146) (n=17) (n=105)
W

Has good roads 7.9 (2.0) 7.2 (3.1) 7.6 (2.1) F = 0744
Has great outdoor recreation

opportunities 9.1 (1.6) 9.5 (0.9) 9.1 (1.2) F: 0.63
Has high quality lodging 7.3 (2.2) 7.8 (1.6) 7.6 (1.9) F = 0.979
Has interesting historical sites 7.2 (2.1) 7.5 (1.6) 7.7 (2.0) F = 1.722
ls a good place to meet

friendly people 8.1 (1 .9) 7.9 (1.7) 8.0(1.9) F = 0.026
Is a great family vacation w

destination 8.4 (1.9) 9.1 (1.2) 8.9 (1.3) F = 3.7 14*
Is a great place to start a w

business 4.8 (2.2) 6.2 (3.2) 5.2 (2.3) F = 1-327
15 a great spring destination 7.2 (2.2) 7.8 (2.5) 6.8 (2.5) F = 1.865
Is a great summer destination 8.9 (1.6) 8.9 (1.6) 9.2 (1.4) F = 1.239
Is a great fall destination 8.8 (1.6) 9.3 (1.1) 8.6 (1.9) F = 1.277

9.2 (1.1) A
A B W
Is a great winter destination 7-2 (2-7) 7-0 (2-5) F = 15-737"
A AB B W

[s a safe place to visit 8.7 (1.7) 9.4 (0.7) 8.8 (1.3) F = 4973*
ls an exciting place to visit 7.0 (2.3) 8.1 (1 .5) 7.6 (2.1) F = 2.821
ls close enough for a

weekend getaway 7.9 (2.9) 8.4 (3.1) 7.3 (3.2) F = 1.700
ls easily accessible 8.2 (2.2) 7.6 (3.0) 8.3 (2.1) F = 0.682
Offers exceptional value for

the money 7.5 (1 .9) 7.9 (2.2) 7.4 (1.8) F = 0.462
Offers exciting nightlife and

entertainment 5.1 (2.4) 5.9 (2.7) 5.2 (2.2) F = 0.666
Offers great dining

opportunities 6.4 (2.3) 7.1 (2.6) 6.7 (2.1) F = 1.037
Offers great shopping

opportunities 6.1 (2.2) A 7.3 (2.2) A 6.6 (1.9) F = 3.261 *W
Offers much scenic appeal 9-0 (1-5) 9-6 (05) 9.34.3) F = 536*

 

*p<.05, **p<.01
A-B: Means in the same row followed by the same superscript were signiﬁcantly different at p<.05.
W: Welch statistics was reported while robust tests of equality of means.

151

 

 

 

CHAPTER 5 DISCUSSION AND CONCLUSIONS

The intent of this study was to assess the effectiveness of activity-based
segmentation of the rural tourism market in the west-central Michigan region (WCMI). A
theoretical framework, activity-based market segmentation, consisting of both a priori
and post hoc approaches, was used to guide the study. Three samples were included:
transient, overnight, and potential visitors. Data were collected using mail questionnaires
for the overnight and potential visitor surveys and via in-person intercepts of transient
visitors.

Statistical analysis included: (a) Descriptive statistics focusing on tourists’ socio-
demographic proﬁles and key variables used in the conceptual model (i.e., activity
preference, trip-related characteristics, travel expenditures, travel motivations, WCMI
attractions, WCMI destination attributes); (b) Chi-square testing of popular activities
followed by logistic regression to examine the relationships between popular activities
and other variables; and (c) Latent class analysis distributing the samples into classes
from their participation in the 45 activities, then chi-square and one-way ANOVA testing
of the relationship between the classes by each factor. A post hoc test examined expected
differences among the classes.

Summary of Results and Discussion

The literature has generally suggested that activity participation is related to
socio-demographic characteristics (e. g., age), trip-related characteristics (e. g., trip
duration), and psychographic dimensions (e.g., travel motivations). However, few studies
in rural tourism have speciﬁcally examined the relationship between tourists’ activity

participation and their travel behavior. This study contributes to ﬁlling this gap by

152

examining tourists’ participation in activities on their trips in relation to some other
potentially inﬂuential factors that could be used to predict their activity participation
patterns. The key results of the study are discussed in three sections. The discussion from
both the a priori and post hoc approaches is addressed. Summary results with conclusions
and suggestions for applications for marketing strategies are presented. Then the

segmentation performance of the two approaches is compared and evaluated.

A Priori Approach

The ﬁrst part of the study focused on ﬁnding the groups based on participation in
a single activity; therefore, the proﬁles are developed on the basis of each activity chosen
from each sample.
Discussion of the Groups Resulting ﬁom the A Priori Approach

Among transient visitors, those whose primary destination on this current trip
was the WCMI were more likely to stay in privately owned vacation homes and less
likely to stay in hotels, motels, or resorts or bed and breakfasts. The Ludington State
Park/beaches was a very popular attraction among those whose primary destination was
the WCMI. The top two popular and distinctive activities transient visitors participated in
(boating and attending a festival/event) were chosen to develop the proﬁles. Younger
visitors tended to participate in boating more often. Visitors participating in boating also
tended to visit the Ludington Car Ferry. Female visitors tended to participate in
festivals/events. Visitors participating in a festival/event were more likely to have visited

the Little River Casino and the sand dunes at Silver Lake.

153

Among overnight visitors, some differences were found between those who spent
two nights or less on their most recent overnight visit to the WCMI and those who spent
at least three nights on their most recent overnight visit to the WCMI. Those who spent at
least three nights were more likely to plan their trips four months ahead, travel with a
larger party, and use chambers of commerce and travel guides/brochures than those who
spent two nights or less. Those who spent more nights also tended to stay in a rented
cabin, cottage or condominium but not in a hotel, motel or resort. Those who spent at
least three nights agreed signiﬁcantly more with the WCMI being a good place to meet
friendly people, a great family vacation destination, and an exciting place to visit. The
most popular and distinctive activities among overnight visitors regarding trip length
included hiking/walking and shopping. The activities with participation frequency higher
than 45% and also having signiﬁcantly more participation by those who spent at least
three nights in the WCMI (shopping and hiking/walking) were chosen for proﬁling.

Among hiking/walking participants, female visitors were more likely to
participate in hiking/walking. Visitors participating in hiking/walking tended to spend
more money on groceries but less on gas. They used travel information from convention
and visitor bureaus and were more likely to stay in cabins or second homes. Shoppers
tended to include more female visitors, preferred to plan the trip in advance, and were
motivated to shop and to spend more money. This group tended to rely on travel
information from friends or relatives. The shopping group was more likely to visit the
WCMI within the next three years. But the result seemed to conﬂict with potential
visitors’ choice of activity while on their pleasure trips. From potential visitors’ activity

participation, those who have visited the WCMI participated in shopping signiﬁcantly

154

less than these who have not visited the WMCI. Therefore, for promotion strategies
related to shopping activities, advertisements of shopping events would be better put in
media ahead of time, so shoppers can have relevant information early since they seemed
to plan their trips earlier. Shopping is a very popular activity (Carmichael & Smith, 2004);
it is worth creating more shopping opportunities or advertising them more to enhance
tourists’ shopping experiences.

Activities such as shopping and hiking/walking attract visitors to stay longer.
Promotions of these activity opportunities are suggested to come along with lodging
choices. For example, maps of hiking/walking trail routes placed in convention and
visitor bureaus could feature local businesses such as grocery stores and advertise cabins
and second home properties in this area.

Among potential visitors, activity participation differences between those who
have never visited the WCMI and those who have visited the WCMI were evaluated.
Those who have visited the WCMI tended to be older and preferred to use travel sources
such as billboards/outdoor advertising, magazines, and newspapers. The group was less
concerned with cost and nightlife activity but showed a higher concern for interesting
scenery. The most popular activity for those who have visited the WCMI compared with
those who have never visited the WCMI was visiting a federal/state park. The group who
participated in visiting a federal/state park tended to possess a post graduate degree and
was concerned more about cost and attractions and less about nightlife opportunities. The
travel sources used by this group included Internet, local visitor guides, and radio.
However, the Newaygo state park is not as well known among this group as the

Ludington state park; hence, promotion of the Newaygo state park could create more

155

 

interest in events related to its attractions and remind visitors of the low cost of enjoying
these attractions.
Identijjzing Bases for Market Segmentation in the A Priori Approach

The results demonstrate that activity is an effective segmentation base applied in
the a priori approach while segmenting rural tourists. Transient visitors whose primary
destination on this current trip was the WCMI signiﬁcantly participated in boating,
festival/event, ﬁshing (ﬂy), hunting (deer), off-roading, and sports tournaments.
Overnight visitors who spent at least three nights on the most recent overnight visit to the
WCMI enjoyed more antique shopping, boating, canoeing/kayaking/tubing, concerts,
farm market/u-pick/winery, ﬁshing (other), hiking/walking, shopping, and swimming
(lake, pond, river). Potential visitors who had visited the WCMI enjoyed more antique
shopping, ﬁshing (other), golﬁng, hunting (deer), lighthouse touring, and visiting a
federal/state park during their pleasure trips compared to those who had never visited the
WCMI. The transient survey was conducted over a two-month period; therefore, the
activity participation in the current trip was limited to summer and fall activities. That
sports tournaments were found to be signiﬁcant within the transient survey might be due
to some special events such as local golf tournaments that were held in that period of time.
But generally speaking, boating, ﬁshing, and deer hunting were conﬁrmed as the most

popular activities across the three different samples.

Developing Proﬁles of Resulting Segments in the A Priori Approach
Socio-demographic variables such as gender and education were confirmed as
valuable in proﬁling segments. This result is consistent with the ﬁndings in previous

studies (citied in Brown, 2001; Loker & Perdue, 1992). Among these variables, gender

156

 

was the most distinguishable variable. It was signiﬁcantly related to participation in
festivals/events, hiking/walking, and shopping. Jansen-Verbeke (1987) and Turner and
Reisinger (2001) suggested that there is a strong positive relationship between shopping
activity and the female gender. In this study, results conﬁrmed that the shopping market
was dominated by females. Also, festival/event and hiking/walking were found strongly
favored by females. This study area contains many easy hiking trails, and that may be the
reason why walking/hiking attracted females more than males in the study area. Boating
was more popular among younger visitors.

Motivations have been commonly discussed as a segmentation base in tourism
research (e. g., Bieger & Laesser, 2002; Cha, et al., 1995), and motivation variables were
useful in the segmentations developed in this study. In general, potential visitors who
visited a federal/state park were motivated to travel by some motivation factors. This
result is consistent with the previous study of MacKay, Kathleen, and Vogt (2002), which
suggested that motivation variables are helpful in segmentation studies. Thus,
promotional strategies for state parks could focus on the attractions in the state parks and
the reasonable cost that motivate tourists to visit them.

Trip-related variables such as party size and length of stay (e.g., Moscardo, 2004;
Spencer & Holecek, 2007) are among of the most accepted variables by researchers to
differentiate tourists. Differences in travel patterns were found between transient,
overnight, and potential visitors. However, though these variables were discussed in each
particular activity, most of the trip-related variables did not demonstrate any variation
between activity participation and non—activity participation, except for shopping. Thus,

trip-related variables in this study were not helpful in differentiating tourists by activity

157

participation. Yoon, Spencer, Holecek, and Kim (2000) found that festival/event tourists
in Michigan were more likely to start their trips during the summer time, plan their trips
and choose their destinations earlier, take longer trips, and spend more money on their
trips when compared to other tourists. In contrast, the ﬁndings of this study did not reveal
any relationship between festival/event and trip-related variables. This might be due to
the fact that the relationship was compared in overnight visitors, who already have
similar travel patterns such as length of stay.

Knowledge of expenditures can help to understand tourists’ consumption patterns.
Using spending patterns, tourism planners can create more opportunities for tourists and
produce economic development plans for locals as well. But, one important issue in
estimating expenditures, recall bias, often occurs in research (Rylander, Propst, &
McMurtry, 1995). This study asked respondents to recall the most recent trip to the
WCMI. This method would also result in recall bias. Investigation of expenditures of
visitors who spent two nights or less in the WCMI and those who stayed at least three
nights showed signiﬁcant differences for spending on gas, groceries, lodging, and meals
at restaurants. Proﬁling the activity participation in hiking/walking and shopping showed
that those who enjoyed hiking/walking would spend less on gas and but more on
groceries and those who liked shopping would spend more on shopping.

Generally speaking, ﬁiends/relatives, and the Internet were the most popular
information resources. According to the Domestic Travel Market Report by the Travel
Industry Association (TIA, wwwtiaorg) (as cited in Miller & Washington, 2009a), about

59% of shopping travelers obtained shopping information from friends, family, or co-

158

workers, and 25% searched from hotels. This study conﬁrmed the same result as the
report.

The results in choices of lodging type were only found to be signiﬁcant for
hiking/walking. The results echoed the patterns of consumption found for expenditures.
Given the signiﬁcant relationship between hiking/walking and staying in a cabin or
second home, the spending on groceries for hiking/walking participants was a rational
outcome.

Understanding WCMI attractions and activity participation can directly assist T
tourism planners to recognize the strengths and weaknesses of promoting the study area.
For example, among boating participants, 26.3% were aware of the Ludington State Park,
41.8% were aware of the Pere Marquette River, 40.6 % were aware of the Muskegon
River, and 50.4% were aware of the Newaygo State Park but have not visited these areas.
Further, 8.5% were not aware of the Ludington Sate Park, 19.3% were not aware of the
Pere Marquette River, 23.7% did not know of the Muskegon River, and 28.5% have not
heard about the Newaygo State Park. These destinations are known for abundant water
resources for boating activities. Surprisingly, there were still many respondents who did
not know or were aware but have not visited these attractions. In terms of hiking/walking,
37.6% of the participants were aware of the Hart-Montague (rail) Trail but have not
visited it, and 41.2% did not know about this trail. It seems that advertising for these
attractions has not yet been developed to efﬁciently attract visitors.

The particular activities that the study region can target include boating, festival
and events, hiking/walking, shopping, and visiting a federal/state park. Logistic

regression models identiﬁed some important socio-demographic variables such as age,

159

gender, and education, travel related attributes such as days in advance to plan this trip,
travel party size, and likelihood to visit the WCMI within next three years. WCMI
destination attributes such as the WCMI being a good place to meet ﬁiendly people and a
great family vacation destination explained activity participation. Also inﬂuencing the
dependent variables were travel sources such as AAA, Internet, local visitor guides, radio,
billboards/outdoor advertising, magazines, newspapers, chambers of commerce, travel
guides/brochures, ﬁiends or relatives, and knowledge of WCMI attractions such as the
Ludington State Park/beaches, Ludington Car Ferry, Little River Casino and sand dunes
at Silver Lake, Irons/Lake County snowmobile, and Marquette River. Travel motivations
such as shopping, cost, nightlife, and interesting scenery and travel expenditures for
shopping and groceries also predicted the dependent variable in different levels (see

Table 5-1).

160

Table 5 -1 .' Summary of Results Based on the A Priori Approach

 

Transient Visitors

Overnight Visitors

Potential Visitors

 

 

 

 

 

 

 

 

 

H1.1: Activity Boating, Antique shopping, Antique shopping,
type festival/event, boating, ﬁshing (other),
ﬁshing (ﬂy), canoeing/kayaking/tubing, golﬁng,
hunting (deer), concert, hunting (deer),
off-roading, farm market/u—pick/winery, lighthouse touring,
sports tournament, ﬁshing (other), visiting a federal/state
hiking/walking, park,
shopping, shopping (reverse)
swimming (lake, pond,
river)
Hl.2: Socio- Boating: age Hiking/walking: gender, Visiting a
demographic F estival/event: Shopping: gender F ederal/state Park:
characteristics gender education
H1 .3: Travel - - Visiting a
motivations Federal/state Park:
cost, attractions,
nightlife
H1 .4: Trip Boating: none Hiking/walking: none Visiting a
characteristics F estival/event: Shopping: plan days in F ederal/state Park:
none advance, visit none
H1.5: Travel - Hiking/walking: gas, -
spending grocery
Shopping: shopping
H1.6: Travel - Hiking/walking: convention Visiting a
sources and visitors bureaus F ederal/state Park:
Shopping: friends or Internet, guide, radio
relatives
H1 .7: Choice Boating: none Hiking/walking: cabin, -
of lodging Festival/event: second home
Jype none Shopping: none
H1.8: WCMI Boating: Ludington Hiking/walking: Little -
attractions Car Ferry River Casino, Irons/Lake
Festival/event: County snowmobile trails
Little River Shopping: Pere Marquette
Casino, sand River
dunes at Silver
Lake
H1.9: - Hiking/walking: none -
Destination Shopping: shopping
Attributes

 

161

Post Hoc Approach

The second part of the study focused on ﬁnding bundles of activities that can be
used to segment visitors; these segments (classes) can then be used to develop
motivational and travel behavior proﬁles of distinct segments of rural tourists (Table 5-2).
Discussion of the Classes Resulting from the Post Hoc Approach

Transient visitors

Transient visitors were classiﬁed into four classes. LC 1, named heavy outdoor
activity participants, contained more males who did not plan their trips far ahead. The
visitors in LC 1 have visited WCMI attractions such as the Manistee River, Ludington
State Park/beaches, Pere Marquette River, Irons/Lake County snowmobile trails,
Muskegon River, and sand dunes at Silver Lake.

Activities such as hunting, ﬁshing, camping, boating, and
canoeing/kayaking/tubing are strongly recommended for promoting to this class. It
seemed that this class knows the attractions in the study area well. The strategy for
targeting this class could be advertising any events related to outdoor activity in the
media one or two months in advance.

LC 2, named light outdoor activity participants, enjoyed camping,
canoeing/kayaking/tubing, boating, and bicycling (participation probability ranged from
0.2 to 0.4). But the probabilities of participation in these activities were less than for LC 1.
Light outdoor activity participants’ average age is 46 years old, and the majority are
males. They tended to plan their trips at least two months in advance. Most of them did

not live with children. The majority of this class had not visited the Manistee River,

162

Ludington Car Ferry, Ludington State Park, Pere Marquette River, Muskegon River, and
Silver Lake.

For targeting this class, activities such as camping, canoeing/kayaking/tubing,
boating, and bicycling are the most recommended. Promotion is suggested to provide
information about the various activities, locations, and activity-related events about two-
three months before the activity season starts. It seemed that many attractions are not well
known by this class. In terms of the strategy of marketing for this class, promotions of
attractions are suggested to cover more details about each activity’s location and related
events.

LC 3, named heavy general activity participants, contained the same proportion
(18%) as LC 1. The class was the youngest and had more females. The LC 3 has the
highest percentage of respondents with children under 18 years old among the four
classes. They also preferred to plan the trip further in advance. The Little River Casino,
Manistee River, Ludington State Park/beaches, Pere Marquette River, and sand dunes at
Silver Lake were popular attractions for this class.

Since heavy general activity participants were interested in various activities,
promotions of swimming (lake, pond), boating, sightseeing, dinning out, camping,
canoeing/kayaking/tubing, festival/event, visiting ﬁiends/relatives, hiking/walking,
movie, bicycling, swimming (pool), ﬁshing, theme/amusement park, farm market/u-
pick/winery, wildlife viewing/bird watching, and historic sites are recommended to match
their needs. Advertisements featuring more family friendly opportunities related to the

activities could be posted in the media three or four months ahead.

163

LC 4, named light general activity participants, was the largest and the oldest
class. This class was fairly even in gender. They planned their trips almost three months
ahead. They enjoyed staying in hotels, motels or resorts slightly more than the other
classes. This class included the largest proportion of respondents who were aware of but
have not visited the Little River Casino and the Ludington Car Ferry.

Activities such as sightseeing (general), dining out (excluding fast food), camping,
shopping, festival/event, bicycling, hiking/walking, boating, swimming (lake, pond,
river), lighthouse touring, visiting friends/relatives, visiting a federal/state park,
canoeing/kayaking/tubing are recommended for this class. Light general activity
participants tended to plan their trips about three months ahead. Promotions featuring
accommodation choices and providing coupons three or four months ahead would be
effective in drawing this class’s attention.

Overnight visitors

Overnight visitors were classiﬁed into three classes. LC 1, no preference activity
participants, planned their trip 40 days in advance and travelled in smaller groups.
Friends or relatives and word of mouth are the common information sources among
overnight visitors. This class stayed least often in a rented cabin, cottage or condominium.
This class had the lowest proportion of respondents who have visited the Ludington State
Park, the Newaygo State Park, and the Hart- Montague (rail) Trail. In terms of destination
attributes, “no preference activity” participants showed the lowest agreement on
attributes such as the WCMI “offers much scenic appeal,” “offers great shopping

opportunities,” “is a safe place to visit,” and “is a great family vacation destination.” In

164

general their overall experience in the WCMI was less satisfactory than for the other two
classes.

Dining out, sightseeing, and hiking were the most popular activities in this class;
the probability of participation in other activities was very low. The suggested marketing
plan for this class is to create more dining promotions or small town exhibits and
advertise those activities one to two months in advance. 3:

LC 2, consisting of heavy general activity participants, was the smallest class of
overnight visitors. The majority did not live with children. This class planned their trips
on average ﬁve months in advance and stayed longer in the WCMI. It used travel sources
such as billboards/outdoor advertising, chambers of commerce, convention and visitors
bureaus, highway welcome centers, radio, state travel office/Travel Michigan, and
television more often than other classes. Although hotels, motels or resorts were common
lodging choices among overnight visitors, LC 2 was more likely to stay in rented cabins,
cottages and condominiums. Compared to the other two classes, LC 2 included the
largest percentage of visitors who have visited WCMI attractions except Ludington State
Park/beaches. This class also agreed very strongly with the WCMI being a great family
vacation destination, a great winter destination, a safe place to visit, offering great
shopping opportunities, and offering much scenic appeal. The travel expenditure on
grocery shopping was higher than for the other classes.

Heavy general activity participants strongly engaged in sightseeing,
hiking/walking, dining out, shopping, wildlife viewing/bird watching, festival/event, farm
market/u-pick/winery, visiting a federal/state park, boating, visiting friends/relatives,

casino gaming, ﬁshing, swimming (lake, pond, and river), camping, photography, and

165

historic site. Hunting, ﬁshing, and water related activities should be promoted to this
class. Billboards/outdoor advertising, chambers of commerce, convention and visitors
bureaus, highway welcome centers, state travel ofﬁce/Travel Michigan, and television are
potentially useful sources to advertise these activities. Since this class tended to plan their
trip far in advance, activity-related information or any special offers should be posted
about four to six months before activity seasons. Promotion of these activities should also
include those distinctive WCMI destination attributes that attract “heavy general activity”
types of tourists.

LC 3, light general activity participants, included the majority of the respondents
who lived with children under 18 years old. This class typically planned their trips three
to four months ahead and stayed four nights on average. This class most often travelled in
bigger parties and used the Internet/web sites and travel guides/brochures more often for
gathering travel information than other classes. Besides hotels, these participants, like LC
2, enjoyed staying in a rented cabin, cottage or condominium. This class favored the
Ludington State Park/beaches more than the other classes. The majority of LC 3 were
aware of but have not visited the Pere Marquette River and Manistee River, and have the
highest proportion that were not aware of the Newaygo State Park compared with other
classes. This class agreed least with the WCMI being a great winter destination. Its
overall experience in the WCMI was rated the best among the three classes.

Major activities that could be promoted for LC 3 include dining out, shopping,
sightseeing, swimming (lake, pond, and river), hiking/walking, and visiting a federal/state
park. Since this class enjoyed summer more, tourism planners could design more events

during summer but at least three or four months in advance and post the information

166

 

along with accommodation promotions a couple of months in advance. Larger rooms and
rented cabins would attract more attention because this class tended to have larger travel
parties. Family friendly environment is another marketing theme. Attraction locations and
information about activities could follow the theme to create a family friendly
environment to better ﬁt this class’s needs.

Potential visitors

Potential visitors were classiﬁed into three classes. LC 1 was best described as
“general tourists” and included more females. The use of Internet, word of mouth, travel
guide(s)/brochure(s), magazines, and local visitor guides was signiﬁcantly more popular
than in the other classes. The class was motivated most strongly by interesting scenery,
service quality, a variety of shopping opportunities, upscale facilities/services, and a
variety of attractions and/or activities when travelling.

For targeting LC 1, activities such as sightseeing (general), dining out, historic
sites, visiting a federal/state park, museums, shopping, hiking/walking, festival/event,
visiting friends/relatives, nature center, swimming (lake, pond, river), photography, and
farm market/u-pick/winery are recommended. For accommodation choices, the
promotion could emphasize higher quality and service, interesting scenery, and variety of
attractions and /or activities to meet demands of this class.

LC 2, named outdoor tourists, had the largest proportion of males, had over 90%
who have visited the WCMI, and was more likely to visit the WCMI in the next three
years. They used the Internet relatively less but relied more on the radio for information
than the other classes. This class cared the least about upscale facilities/services, variety

of attractions and/or activities, and variety of shopping opportunities.

167

 

For targeting this class, camping, hiking/walking, visiting a federal/state park,
ﬁshing, sightseeing, visiting friends/relatives, swimming (lake, pond, and river),and
boating are the main activities suggested for promotions.

LC 3, named cultural tourists, was the largest class among potential visitors.
Concern for upscale facilities/services was signiﬁcantly higher than for outdoor tourists
(LC 2). Concern for interesting scenery was signiﬁcantly lower than for the other two
classes. This class contained the smallest proportion of respondents who have visited the
WCMI, and the likelihood of visiting the WCMI in the next three years was signiﬁcantly
lower than for the other classes. Again, travel information mainly came from friends or
relatives, Internet, and word of mouth. The use of radio, magazine, local visitor guides,
highway welcome center, billboards/outdoor advertising, travel guides(s)/brochure(s),
and word of mouth was signiﬁcantly less than for the other classes.

Promotions of the most popular activities such as dining out, sightseeing, visiting
friends/relatives, shopping, historic site, and festival/event are strongly recommended to
meet cultural tourists’ needs. Activities and related events could be posted one month
before the activity season starts. Marketing for the area could also emphasize the upscale
facilities/services.

Identiﬁ/ing Bases for Market Segmentation in the Post Hoc Approach

The study conﬁrmed that activity type provides a practical basis for segmenting
tourists. Activity-based segmentation helps to identify classes that participate in different
activities while traveling. These results appeared to echo other research studies
manifesting activity-based segmentation as an appropriate approach to segment tourists

(Beritelli & Boksberger, 2005; Lang, et al.. 1993).

168

From the transient visitor results, the study area was dominated by light general
activity participants and light outdoor activity participants. For overnight visitors, the
majority of visitors were no preference activity participants, and light general activity
participants. Visitors enjoyed participating in a variety of activities, but did not
particularly focus on certain types of activities.

Among potential visitors, the three classes (general, outdoor, cultural tourists)
identiﬁed seemed very different from the classiﬁcations of overnight (no preference
activity, light general activity, heavy general activity participants) and transient visitors
(light general activity, light outdoor activity, heavy outdoor activity, heavy general
activity participants) (Table 5-2). The samples from the overnight and transient surveys
may include similar types of tourists and it is difﬁcult to segment these tourists by
different activity participation. Because overnight visitors were actual rural tourists who
have visited the WCMI and transient visitors were identiﬁed as tourists who have stayed
in or passed through the WCMI, these two samples were found to have similar classes.

While discussing rural tourists in the WCMI, it is interesting to note that the
largest clusters (i.e., no preference activity participants, and light general activity
participants) comprised individuals whose probability of participating in activities was
low. But it is understandable since the WCMI is a rural setting offering a variety of
activities and marketing has not speciﬁcally focused on certain activities. Additionally,
the classes from the rural tourists in the study were different from other populations

shown in Table 2-4. The rural tourists showed different needs than other types of tourists.

169

Table 5 -2.' Summary of Results Based on the Post Hoc Approach--A ctivity Participation

 

Transient Visitors

Overnight Visitors

Potential Visitors

 

Activity

Heavy outdoor-activity
participants (LC 1):
(18.2%)

Hunting, ﬁshing, camping,
boating, and
canoeing/kayaking/tubing

Light outdoor activity
participants (LC 2)
(30.1)

Camping,
canoeing/kayaking/tubin g
, boating, and bicycle

Heavy general activity
participants (LC 3):
(18.1 %)

Swimming (lake, pond),
boating, sightseeing,
dinning out, camping,
canoeing/kayaking/tubing
, festival/event, visiting
friends/relatives,
hiking/walking, movie,
bicycling, swimming
(pool), ﬁshing,
theme/amusement park,
farm market/u-
pick/winery, wildlife
viewing/bird watching
and historic sites

Light general activity
participants (LC 4):
(33.6%)

Sightseeing (general),
dining out (excluding fast
food), camping,
shopping, festival/event,
bicycling,
hiking/walking, boating,
swimming (lake, pond,
river), lighthouse touring,
visiting friends/relatives,
visiting a federal/state
park,
canoeing’kayaking/tubing

No preference activity
participants (LC 1):
(54.5%)

Dining out, sightseeing
and hiking

Heavy general-activity
participants (LC 2):
(6.3%)

Sightseeing,
hiking/walking, dining
out, shopping, wildlife
viewing/bird watching,
festival/event, farm
market/u-pick/winery,
visiting a federal/state
park, boating, visiting
friends/relatives, casino
gaming, ﬁshing,
swimming (lake, pond,
and river), camping,
photography and historic
site

Light general activity
participants (LC 3):
(39.2%)

Dining out, shopping,
sightseeing, swimming
(lake, pond, and river),
hiking/walking, and
visiting a federal/state
park.

General tourists (LC 1):
(29.1 %)

Sightseeing (general),
dining out, historic site,
visiting a federal/state
park, museum, shopping,
hiking/walking,
festival/event, visiting
friends/relatives, nature
center, swimming (lake,
pond, river),
photography, and farm
market/u-pick/winery

Outdoor tourists (LC 2):
(22.2%)

Camping, hiking/walking,
visiting a federal/state
park, ﬁshing, sightseeing,
visiting friends/relatives,
swimming (lake, pond,
and river) and boating

Cultural tourists (LC 3):
(48.7%)

Dining out, sightseeing,
visiting ﬁ'iends/relatives,
shopping, historic site
and festival/event

 

170

Developing Proﬁles of Resulting Segments in the Post Hoc Approach

The results of this study were also consistent with the study of Choi and Tsang
(1999), Lang (1993), Moscardo (2004) and Sarigollii and Huang (2005) showing that
socio-demographic variables and trip-related attributes are helpful information for
creating market strategies for rural tourism markets. Motivation variables were conﬁrmed
as an important factor inﬂuencing tourists’ travel decisions, which was consistent with
the studies of Carr (2006), and McKercher, et al (2002).

This study also included the proﬁling factors for WCMI destination attributes and
WCMI attractions. These results could help to promote destination attractions more
efﬁciently to meet visitors’ needs.

Comparison between A Priori and Post Hoc Approaches

The performance of a priori and post hoc approaches can be compared by two
dimensions, identifying bases for market segmentation and proﬁles of resulting segments.
For identifying bases for market segmentation, both approaches were demonstrated to be
powerful bases in segmenting tourists based on activity participation. In the a priori
approach, activity participation in boating, festival/event, shopping, hiking/walking,
visiting a federal/state park, etc. was found to be signiﬁcantly higher in the deﬁned
groups regarding the criteria set in each sample. In the post hoc approach, samples were
segmented into three to four classes. Both approaches segmented samples well based on
activity participation. Regarding the performance of proﬁles of resulting segments, the
prediction of resulting segments from the a priori approach is not as powerful as from the
post hoc approach. More variables were found to explain the classes (from the post hoc

approach) better than the groups (from the a priori approach). Therefore, marketing

171

strategies based on the post hoc approach would be more practical for tourism planners.
But the a priori approach is easier to use, and when the tourism planner has a particular
target in mind, it is the direct approach to explore the characteristics of that group.

The combination of the two approaches is recommended. The promotion of only
one or two activities may not help in promoting rural tourism destinations since the
results have shown that rural tourists favor a wide variety of activities. But the
combination of a priori and post hoc approaches can complement each other in
developing effective promotion strategies. The general target from the post hoc approach
and certain targets from the a priori approach can both be applied not only to position the
study area to become a more popular rural destination based on general activities but also
to emphasize speciﬁc outdoor activities to highlight the characteristics of the study region.

The results from both a priori and post hoc approaches addressed the different
characteristics for activities chosen as important targets as well as for classes that were
classiﬁed from 45 activity categories. The study area possesses a variety of resources,
enabling its promotion to target different group visitors. Giving many choices to ﬁt
visitors’ diverse needs and targeting more than one group also includes the beneﬁt that
tourists can spread out in the study area instead of crowding in certain locations for
certain activities. The latter could affect tourist satisfaction and cause negative
environmental impact. However, tourism planners could introduce the variety of
resources well enough to draw visitors’ attention. Sometimes, the variety of resources
seemed to be perceived by tourists as having no distinguishing characteristics; the

outcome from the a priori approach, therefore, offered a complementary plan for the area

which would highlight distinct attritions. Tourism planners could choose and combine
some results and develop special marketing plans based on these proﬁles.

Webb and Quintana (2009) suggested that non-traditional marketing plans in rural
tourism should take advantage of the best information source expressway, the Internet.
The researchers introduced several search engines such as Google Adwords, Google
Maps, NAVTEQ.com, Twitter, and Skype as powerful tools to promote local business.

As evident from this study, the Internet has become very popular as one of the
important means for searching for travel information. Michigan’s Great Outdoors is an
informal partnership of tourism leaders in Manistee, Mason, Oceana, Lake, and Newaygo
counties and is set up for tourists to ﬁnd their interests in the study area. The results of
this study could be used for designing the web site. In order to save visitors time and
effort in searching for information on the web site, the activities could be bundled and
designed based on the results of this study as different packages so tourists could ﬁnd
their interests directly.

Implications

The results of this study have important implications for both future research
studies and rural tourism marketing. By providing an analysis of rural tourists by activity
categories, this study should contribute to future marketing research as a stepping stone
for others to link activities to rural destinations and also promote them to rural tourists.
The implications of the study can be discussed from a theoretical and a practical

perspective.

173

 

Theoretical

Activity-based market segmentation was applied for the ﬁrst time to better
understand market segments in rural tourism. The a priori approach that considered the
speciﬁc activity and the proﬁle related to that speciﬁc activity and the post hoc approach
that provided general ideas were both applied in the study. The two approaches were
convincingly effective. This study showed that activity is a practical base to segment
rural tourists in the WCMI either by the a priori or the post hoc approach.

Also, latent class analysis was applied in the post hoc approach. The analysis
provides model ﬁt indices that can help researchers to select appropriate classes (groups),
but researchers still need to decide the number of classes based on their professional
judgment. Overall, the classes identiﬁed in the study were determined to be useful. The
classes could be proﬁled by a series of variables. Some signiﬁcant characteristics were
found to differentiate the classes.

The study included more sets of attributes than previously analyzed in tourism
market segmentation. For example, attributes included socio-demographic, travel
motivations, satisfaction, trip characteristics, spending, WCMI attractions, and WCMI
destination attributes. These characteristics considered not only demand-side attributes
but also supply-side attributes. The study could be utilized to develop marketing
strategies as part of tourism planning.

Practical Significance

The study provided a comprehensive tourist proﬁle through market segmentation

based on combining the results from the two approaches. The building of precise tourist

proﬁles through market segmentation enables tourism planners to design more effective

174

promotional strategies as well as provide the necessary supply of tourist services. The
results of this study have important implications for management. In the potential visitor
survey, the results of the post hoc approach showed that tourists can be classiﬁed into
three classes. The “cultural tourists” dominated and “general tourists” who favored a
variety of activities followed as the second largest class. The WCMI region can target
“general tourists” based on LCA results from overnight and transient visitors. Overnight
visitors had the largest class among “no preference activity participants” and transient
visitors had the largest class among “light general activity participants.” Those three
classes can be treated similarly due to their general activity participation.

The WCMI can also meet needs for outdoor-type tourists. Based on results from
the a priori approach, the speciﬁc activities that can be promoted in the study area
include state parks, boating, festival and events, shopping, and hiking/walking; shopping
and hiking/walking were considered to attract tourists to stay longer.

Further, the speciﬁc market segment characteristics and motivations can be taken
into account. With both pull (attractions and destination attributes) and push (travel
motivations) factors included while proﬁling the segments, the results help tourism
planners to understand the visitors in more detail. The marketing strategies can thus be
designed more effectively to reach the target markets.

The study included three samples, on-site visitors, overnight visitors, and
potential visitors. The study explored visitors’ activity participation through
multidimensional prospects. Tourism planners could create more ﬂexible and efficient

marketing plans based on the comprehensive ﬁndings.

175

Limitation of Findings

There are several limitations to the study. First, the mail survey response rates
were low. The study was limited to considering the interaction among independent
variables. Second, transient visitor surveys were scheduled for both weekdays and
weekends, but only conducted in the summer time, which may not capture enough winter
travelers. This may have led to bias in the selection of summer activities at the expense of
activities that tourists would enjoy in winter. Third, in the potential visitor survey,
activity preferences were asked in general. Therefore, the market segmentation results
from the potential visitor survey can be applied generally to other rural destinations
similar to the study region in Michigan. However, the overnight and transient surveys
asked about activity preferences in the study area; thus, the results from those data may
not be applied to other destinations due to the uniqueness of the destination resources in
the study region. Fourth, the study only collected general travel patterns. Variables were
not tested consistently across all three surveys. For example, some important attributes
such as travel motivations were not discussed in the potential and transient visitor surveys,
and travel information was not identiﬁed in the transient visitor survey.

Future Research

As the ﬁrst known study of activity-based market segmentation of rural tourists in
the United States, the study was somewhat exploratory in nature. Further studies of rural
tourists — both in Michigan and across the US — are recommended so that the framework
used in this study can be tested and improved.

In this current study, the activity participation questions were asked in a ‘yes’ or

‘no’ format. The intensity of activity participation was not considered. Future research is

176

encouraged to analyze the intensity of activity participation and its inﬂuence on travel
experiences. In addition, the current research asked tourists about their participation in a
list of 45 activities. Future research is suggested to ask about additional activities in
which they are interested. For example, some health-oriented activities such as spas,
aromatherapy, and organic food and farm events should be evaluated to identify tourists’
other interests and develop new products to meet their needs.

Tourism research emphasizes the importance of travel motivations in shaping
tourists’ choices of travel destination (Baloglu & McCleary, 1999; Moco, 2007). Travel
motivations have been discussed broadly in research, yet the current study only
considered twelve major factors. Further research is encouraged to discover more detailed
motivation factors that are more speciﬁcally related to rural tourism products in order to
better understand the relationship between activity participation and travel motivations.

Use of the a priori approach in this study allowed discussion of the relationship
between activity and factors such as socio-demographic and trip-related factors. The
logistic regression models included a speciﬁed set of variables; however, they did not
consider the interaction among factors. Further research is encouraged to examine
interactions between these factors.

In this study, a total of 20 WCMI attributes were discussed, providing a rather
broad view of the study area. For regional rural planning purposes, it is suggested that
more attributes related to study area resources such as water, natural, and historical
resources be considered. These attributes could provide more speciﬁc information to
identify area strengths from tourists’ point of view, allowing tourism planners to evaluate

the results and create tourism plans to balance the supply that the destination can

177

realistically offer with the demand expressed by tourists. Also, the number of destination
attractions considered could be extended to as many as tourism planners are interested in
promoting. From the latent class analysis results among transient visitors, light outdoor
activity participants were aware of but did not visit the Manistee River, Ludington State
Park and beaches, Pere Marquette River, and Muskegon River. The results provide
tourism planners with an awareness of the need to promote these areas more. Therefore,
the destinations that are potentially worthy of promotion are recommended for future
study. Thus, the tourism planners could be conscious of weaknesses in their
advertisements and ﬁnd ways to strengthen them.

Based on market segmentation research, psychographic factors such as lifestyle
and personality characteristics and behavioral factors such as beneﬁts and loyalty are
recognized as important market segmentation bases. These factors can help tourism
planners understand tourists’ needs. For example, by understanding reading and
television preferences, tourism planners could advertise activities in selected magazines
or television programs. For future study, it is recommended that these factors be used to
strengthen the efﬁciency of activity-based segmentation study in rural tourism.

There are many limitations when advertising in the mass media (Lau, Lee, Lam,
& Ho, 2001). Due to the expense of mass media and the disadvantage of unknown targets
and certain display time slots, it seems that the Internet is a more effective approach to
targeting the right customers. The current study conﬁrmed that the Internet is a popular
travel information source. Therefore, future research should broadly capture other factors,
such as tourists’ destination choice patterns, travel motivations, and travel loyalties, to

help provide a more comprehensive strategy to promote and develop the study area. In

178

order to tailor a better plan for the area, speciﬁc factors should be examined instead of
generalizing conclusions from other studies.

There are always impacts associated with tourism development; these impacts can
be positive, but they can also be negative when the tourism industry grows too fast. The
central image of the study area is built upon the variety of relaxing, peaceful, countryside
leisure activities it has to offer. Therefore, plans for tourism development should be
reexamined regularly, recognizing that impacts change over time. Also, consideration of
residents’ activity preferences and their attitudes toward tourism development can help to

reduce the negative impacts of tourism development.

179

APPENDIX A: Descriptive Statistics for Activity Participation in the

 

 

Study
Table A-1 : Transient Visitors ’Socio-demographic Profile—-Participation in Boating and
F estivaI/event
. . . . Boating Festival/event
Socro-demographic Characteristics (n = 280) (n = 210)
43.28 44-9
Age, Mean (Std. Dev) (13.578) (13.606)
Gender (female) , % 41.5 53'6
Living with children under 18 years old , % 33.9 34.8
Income, % (n = 267) (n =198)
Below $42,500 19.1 19.7
Between $42,500 and $75,000 47.6 49.5
Above $75,000 33.3 30.8
Employment, % (n = 267 (n =198)
Employed 77.9 77.8
Unemployed 7.1 6.6
Retired 15 .0 15.7

 

Table A-2: Transient Visitors ’ Trip-related C haracteristics--Participation in Boating and

 

 

F estival/event
Trip-related Characteristics (331333) Fe(slt]i:a12/f(\;)ent
Days in advance to plan this trip, Mean (SD) 89.34 (181.664) 96.28(204.975)
Nights planned to be away from home, Mean (SD) 4.19 (6.789) 4.07(7.518)
Primary destination is the WCMI % 71.1 71.4
Types of lodging used in the WC Ml, %
Friend's or relative’s home 22.5 22.4
Hotel, motel or resort 13.6 14.8
Bed & breakfast 1.4 1.4
Rented cabin, cottage or condominium 6.4 5.7
Owned second or seasonal home 14.6 15.2
Campground or RV park 26.8 24.8

 

180

Table A-3: Transient Visitors ’Knowledge of A ttractions--Participation in Boating and

 

 

F estival/event
WCMI Attractions Brzagng Festizagevent
Little River Casino (n = 258) (n = 202)
Have Visited 39.1 42.1
Aware, but Not Visited 47.3 39.1
Not Aware of This Place 13.6 18.8
Manistee River (n = 255) (n = 200)
Have Visited 46.7 43.0
Aware, but Not Visited 38.0 38.5
Not Aware ofThis Place 15.3 18.5
Ludington Car Ferry (n = 253) (n = 199)
Have Visited 37.9 35.7
Aware, but Not Visited 53.8 52.2
Not Aware of This Place 8.3 12.1
Ludington State Park/beaches (n = 259) (n = 199)
Have Visited 65.2 65.9
Aware, but Not Visited 26.3 24.6
Not Aware ofThis Place 8.5 9.5
Pere Marquette River (n = 249) (n = 193)
Have Visited 39.0 35.2
Aware, but Not Visited 41.7 37.9
Not Aware of This Place 19.3 26.9
Irons/Lake County snowmobile trails (n = 237) (n = 189)
Have Visited 22.4 22.8
Aware, but Not Visited 45.1 40.7
Not Aware ofThis Place 32.5 36.5
Muskegon River (11 = 249) (n =l96)
Have Visited 35.7 33.7
Aware, but Not Visited 40.6 40.3
Not Aware of This Place 23.7 26.0
Newaygo State Park (11 = 242) (n =195)
Have Visited 21.1 19.0
Aware, but Not Visited 50.4 49.7
Not Aware ofThis Place 28.5 31.3
Sand Dunes at Silver Lake (11 = 252) (n = 199)
Have Visited 52.0 56.8
Aware, but Not Visited 38.1 36.2
Not Aware of This Place 9.9 7.0
Hart-Montague (rail) Trail (11 = 245) (n = 198)
Have Visited 25.7 25.8
Aware, but Not Visited 42.1 39.4
Not Aware ofThis Place 32.2 34.8

 

181

Table A-4: Overnight Visitors ’Socio-demographic Proﬁle--Participation in

Hiking/walking and Shopping

 

 

Socio—demographic Characteristics H1121:i/v1v3;l;mg 33%;?
Age, Mean (SD) 50.6(11.0) 52.6(11.2)
Gender (female), % 43.7 46.7
Living with children under 18 years old, % 39.8 35.8
Income, % (n = 95) (n = 98)
Below $43,000 20.0 21.4
Between $43,000 and $75,000 32.6 37.8
Above $75,000 47.4 40.8
Employment, % (n = 97) (n = 104)
Employed 76.3 70.2
Unemployed 4.1 3 .8
Retired 19.6 26.0
Education, % (n = 99) (n = 104)
High school, some high school 15.2 20.2
Some college, college graduate/professional 57,6 54.8
Post-graduate 27.3 25.0

 

182

Table A-5: Overnight Visitors ’ Trip-related Characteristics on their Most Recent Trip to
W CMI—-Participation in Hiking/walking and Shopping

 

 

Trip-related Characteristics “11::ng $333“ 33%;?
Days in advance to plan this trip, Mean (SD) 100.8(120.1) l 12.9(126.6)
Travel party size, Mean (SD) 3.7(2.527) 3.9(2.792)
Overall experience in the WCMI, Mean (SD) 2.4(0.8) 2.4(0.8)
Likseg) to visit the WCMI within next three years, Mean 1.43(0.815) 1.3(0.6)
Spending at least 3 nights within the WCMI, % 61.4 58.3
Sources of information used in planning this visit, %
AAA 13.6 14.2
Billboards/outdoor advertising 4.9 3.8
Chamber of commerce 20.4 17.9
Convention and visitors bureau 17.5 15.1
Friends or relatives 35.9 46.2
highway welcome centers 4.9 4.7
lntemet/web site(s) 37.9 31.1
Magazine 7.8 7.5
Newspaper l .0 3 .8
Radio 0.0 1.9
State travel office/Travel Michigan 12.6 13.2
Television 3 .9 1.9
Travel guide(s)/brochure(s) 24.3 22.6
Word of mouth 23.3 24.5
Types of lodging used in the WCMI, %
Friend's or relative's home 6.8 8.5
Hotel, motel or resort 44.7 57.5
Bed & breakfast 6.8 8.5
Rented cabin, cottage or condominium 22.3 16.0
Owned second or seasonal home 19.4 13.2
Campground or RV park 16.5 16.0

 

183

Table A-6: Overnight Visitors ’Knowledge of A ttractions--Participation in Hiking/walking

 

 

and Shopping
Hiking/walking Shopping
WCMI Attractions (n = 103) (n = 106)
(%) (%)
Little River Casino (n = 95) (n = 97)
Have Visited 28.4 38.1
Aware, but Not Visited 52.7 42.3
Not Aware of This Place 18.9 19.6
Manistee River (n = 91) (n = 95)
Have Visited 40.7 36.8
Aware, but Not Visited 43.9 44.3
Not Aware ofThis Place 15.4 18.9
Ludington Car Ferry (n = 95) (n = 100)
Have Visited 40.0 39.0
Aware, but Not Visited 57.9 57.0
Not Aware of This Place 2.1 4.0
Ludington State Park/beaches (n = 96) (n = 97)
Have Visited 82.3 76.3
Aware, but Not Visited 17.7 20.6
Not Aware of This Place 0.0 3.1
Pere Marquette River (n = 93) (n = 92)
Have Visited 51.6 34.8
Aware, but Not Visited 28.0 33.7
Not Aware of This Place 20.4 31.5
Irons/Lake County snowmobile trails (n = 86) (n = 88)
Have Visited 8.1 12.5
Aware, but Not Visited 24.4 35.2
Not Aware ofThis Place 67.5 52.3
Muskegon River (n = 89) (n = 88)
Have Visited 36.0 27.2
Aware, but Not Visited 40.4 36.4
Not Aware of This Place 23.6 36.4
Newaygo State Park (11 = 87) (n = 88)
Have Visited 32.2 31.8
Aware, but Not Visited 44.8 29.5
Not Aware of This Place 23.0 38.6
Sand Dunes at Silver Lake (n = 88) (n = 94)
Have Visited 45.5 53.2
Aware, but Not Visited 45.5 34.0
Not Aware of This Place 9.0 12.8
Hart-Montague (rail) Trail (n = 85) (n = 85)
Have Visited 21.2 25.9
Aware, but Not Visited 37.6 27.1
Not Aware ofThis Place 41.2 47.0

 

184

 

 

Table A-7: Overnight Visitors ’Perceptions of Destination Attributes--Participation in

Hiking/walking and Shopping

 

 

WCMI Destination Attributes Hllzglgz/Ygggmg (81:1:pp52g
Has good roads 7.6(2.2) 7.7(2.1)
Has great outdoor recreation opportunities 9.3(1 . 1) 9.1(1.3)
Has high quality lodging 7.6(1.8) 7.4(2.0)
Has interesting historical sites 7.5(2.0) 7.5(1.9)
Is a good place to meet friendly people 8.1(2.0) 8.1(1.9)
Is a great family vacation destination 8.9(1.6) 9.0(1.3)
Is a great place to start a business 5.3(2.4) 5.2(2.4)
Is a great spring destination 7.4(2.3) 7.2(2.5)
Is a great summer destination 9.2(1.5) 9.1(1.4)
Is a great fall destination 8.9(1.6) 8.9(1.4)
Is a great winter destination 7.7(2.2) 7.5(2.5)
Is a safe place to visit 8.8( 1.5) 8.9(1.3)
Is an exciting place to visit 7.5(2.2) 7.5(2.0)
ls close enough for a weekend getaway 7.5(3.2) 7.7(3.2)
ls easily accessible 8.4(2.1) 8.3(2.3)
Offers exceptional value for the money 7.5(1.8) 7.6(1.8)
Offers exciting nightlife and entertainment 5.2(2.3) 5.3(2.4)
Offers great dining opportunities 6.6(2.2) 6.7(2.l)
Offers great shopping opportunities 6.6(2.2) 6.8(2.0)
Offers much scenic appeal 9.3(1.2) 9.4(1.1)

 

Table A-8: Overnight Visitors ’ Travel Expenditures«Participation in Hiking/walking and

 

 

Shopping
. Hikin walkin Sho in
Expenditures ("if 103) g (n 3&6?
Activities (equipment rentals, lessons, etc.) 3.8(] 1.9) 2.8(7.3)
Attractions (tickets, entrance fees, etc.) 4.8(14.8) 2.4(4.6)
Gas/fuel 11.1(11.5) 8.0(8.5)
Groceries 3.3(5.5) 4.6(5.0)
Lodging 31.1(34.3) 23.0(18.7)
Meals at restaurants/fast food 19.2(18.3) 12.8(13.3)
Shopping (clothes, souvenirs, etc.) 10.7(38.6) 15.3(42.7)

 

185

 

Table A-9: Potential Visitors ’Socio-demographic Proﬁle -- Visiting a F ederal/state Park

 

 

Visiting a
Socio-demographic Characteristics Federal/state Park
(11 = 146)
Age, Mean (SD) 52.3(13.1)
Gender (female), % 45.9
Living with children under 18 years old. % 342
Income, % (n= 131)
Below $43,000 26.7
Between $43,000 and $75,000 34.4
Above $75,000 38.9
Education, % (n= 134)
High school, some high school 13.4
Some college, college graduate/professional 59.7
Post-graduate 26.9

 

Table A-10: Potential Visitors ’ Trip-related C haracteristics-- Visiting a F ederal/state Park

 

 

Visiting a
Trip—related Characteristics F ederal/state Park
(n = 146)
Days in advance to plan your pleasure trip, Mean (SD) 25.6(40.2)
Have visited WCMI, % 85.5
Sources of information used in planning pleasure trip, %
AAA 37.7
Billboards/outdoor advertising 16.4
Chamber of commerce 20.5
Convention and visitors bureau 24.0
Friends or relatives 75.3
Highway tourist information centers 34.9
Highway welcome centers 35.6
lntemet/web site(s) 74.0
Local visitor guides 43.2
Magazine 45 .9
Newspaper 37.7
Radio 15.1
State travel office 16.4
Television 22.6
Travel guide(s)/brochure(s) 61 .6
Word of mouth 70.5

 

186

Table A-II .' Potential Visitors ’ Travel Motivations-- Visiting a F ederal/state Park

 

 

 

Visiting a
Motivation Federal/state Park

(n = 146)
Upscale facilities/services 3.0(1.1)
Travel time/distance 3.3(0.9)
Cost 4.0(0.8)
Family-friendly place and/or opportunities 3.6(1.2)
Safety/security 4.2(0.9)
Variety of shopping opportunities 2.9(1.0)
Interesting scenery 4.2(0.7)
Service quality 4.1(0.7)
Variety of attractions and/or activities 3.8(0.9)
Nightlife activities 2.3(1. 1)
Accessibility for disabled persons 2.0(1.2)
Pet accommodations 1.9(1.3)

 

187

APPENDIX B: Logistic Regression Model Results--without Signiﬁcant
Findings

Table B—1 .' Logistic Regression Model Results--Boating by Trip-related Characteristics
for Transient Visitors

 

 

 

 

 

 

 

 

 

Dependent Variable Boating
Variables in the Equation B S.E. Wald df Sig. Exp(B)
DesReg( 1) -0.462 0.197 5.472 1 0.019 0.630
PartySz 0.1 10 0.061 3.274 1 0.070 1.117
Constant 0.012 0.212 0.003 1 0.956 1.012
Variables Not in the Equation Score df Sig.
PlanDay 1.003 1 0.315
grinder 111.2. 1‘;ng d CO" 2215.?" R 3332:;ng Chi-square df Sig.
"mmary 621.219 0.021 Test 4.973 7 0.663

 

Table B-2.‘ Logistic Regression Model Results--Festival/event by Trip-related
Characteristics for Transient Visitors

 

 

 

 

 

 

 

 

Dependent Variable Festival/event
Variables in the Equation B S.E. Wald df Sig. Exp(B)
DesReg(l) -0.337 0.201 2.796 1 0.095 0.714
Constant -0.276 0.1 18 5.445 1 0.020 0.759
Variables Not in the Equation Score df Sig.
PartySz 0.194 1 0.660
PlanDay 2.874 1 0.090
Model 1:2 Log COX and Snell R Hosmer and Chi-square df Sig.
Summary rkelihood Square Lemeshow

613.266 0.006 Test 0.000 0

 

188

Table B-3: Logistic Regression Model Results--Hiking/walking by T rip-related

Characteristics for Overnight Visitors

 

 

 

 

 

 

 

 

Dependent Variable Hiking/walking
Variables in the Equation B S.E. Wald df Sig. Exp(B)
NghtCat( 1) 0.810 0.288 7.925 1 0.005 2.248
Expmce -0.298 0.167 3.175 1 0.075 0.742
Constant -0.051 0.470 0.012 1 0.914 0.950
Variables Not in the Equation Score df Sig.
NoVisit 2.876 1 0.090
PlanDay 0.81 l 1 0.368
333:3; iikzeiiiifod COX $111326” R [32:62:33 Ch"sq“a'e df S‘g'
275.988 0.056 Test 1.063 5 0.957

 

Table B-4: Logistic Regression Model Results-- Visiting a F ederal/state Park by Trip-
related Characteristics for Potential Visitors

 

 

 

 

 

 

 

 

Dependent Variable Visiting a Federal/state Park
Variables in the Equation B 8.5. Wald df Sig. Exp(B)
VisWCM(1) 0.869 0.32 7.368 1 0.007 2.384
Constant -0.721 0.287 6.287 1 0.012 0.486
Variables Not in the Equation Score df Sig.
ReVisit 0.111 1 0.739
PlanDay 0.050 1 0.824
Model ~2. Log COX and Snell R Hosmer and . .
Summary likelihood Square Lemeshow Chl-SCﬂlal’e df Sig.
349.854 0.030 Test 0.000 0

 

189

Table B-5: Logistic Regression Model Results--Boating by Travel Sources for Transient

 

 

 

 

 

 

 

 

Visitors

Dependent Variable Boating

Variables in the Equation B S.E. Wald df Sig. Exp(B)
DesReg( 1) -0.480 0.184 6.791 1 0.009 0.619
Constant 0.269 0.108 6.256 1 0.012 1.309
Variables Not in the Equation Score df Sig.
LBB(1) 2.227 1 0.136
LCabin( 1) 1.569 1 0.210
LSnde( 1) 0.477 1 0.490

$4.333, likzelliﬁfod COX 82:12:?” R 1:62:33an Chi'squm df S’g'
729.198 0.013 Test 0.000 0

 

 

Table B-6.‘ Logistic Regression Model Results--Festival/event by Accommodation Type
for Transient Visitors

 

 

 

 

 

 

 

 

Dependent Variable F estival/event !
Variables in the Equation B S.E. Wald df Sig. Exp(B)
DesReg( 1) -0.409 0.191 4.569 1 0.033 0.664
Constant -0.293 0.108 7.358 1 0.007 0.746
Variables Not in the Equation Score df Sig.
LSnde( 1) 0.684 1 0.408

Model .-2 Log Cox and Snell R Hosmer and Chi-square df Sig.
Summary likelihood Square Lemeshow

709. 106 0.009 Test 0.000 0

 

Table B- 7: Logistic Regression Model Results--Shopping by Accommodation Type for
Overnight Visitors

 

 

 

 

 

 

Dependent Variable Shopping
Variables in the Equation B S.E. Wald df Sig. Exp(B)
NghtCat( 1) 0.721 0.262 7.592 1 0.006 2.057
LBB(1) 0.928 0.553 2.817 1 0.093 2.530
Constant -0.797 0.192 17.238 1 0.000 0.451
Model “-2 Log Cox and Snell R Hosmer and Chi-square df Sig.
S m likelihood Square Lemeshow
"m 3” 332.821 0.039 Test 0.003 1 0.956

 

190

 

Table B—8: Logistic Regression Model Results--Hiking/walking by Destination Attributes
for Overnight Visitors

 

 

 

 

 

 

 

 

Dependent Variable Hiking/walking
Variables in the Equation B S.E. Wald df Sig. Exp(B)
NghtCat(1) 0.973 0.333 8.546 1 0.003 2.645
Constant -1.027 0.238 18.624 1 0.000 0.358
Variables Not in the Equation Score df Sig.
MlOutdr 1.291 1 0.256
Mlley 1.270 1 0.260
MlBsnss 0.21 1 1 0.646
MlSpring 0.612 1 0.434
MISummer 1.588 1 0.208
MlFall 1.347 1 0.246
MIWinter 2.266 1 0.132
MlExctng 0.403 1 0.525
MlScnc 2.061 1 0.151
-2 L x n . .
5mg; likelilihgod CO 82:23:“ R Illegtsiingzl'lzgvd Ch"’qua’e df S‘g'
207.533 0.052 Test 0.000 O

 

191

 

APPENDIX C: Transient Visitor Survey

(IRB# 04-547)

The Travel, Tourism, & Recreation Resource Center at
Michigan State University is conducting a survey to learn more
about people passing through the west—central Michigan region
(see attached map for deﬁned area). We would appreciate your
help in answering a few questions.

 

 

Site:

 

Date:

Survey #:

Int. #

 

 

What is the ZIP code of your primary residence?

 

What is! was the primary destination of your current trip? (city)
(state)

 

How many nights do you plan to be away from home on this trip? (If “0”,
please go to question 5.)

What type of lodging are you using on this trip? (Please check all that apply.)

1:] Friend’s or relative’s home D Owned second or seasonal home
E1 Hotel, motel, or resort E1 Campground or RV park
Cl Bed & Breakfast Cl Other (please specify)

C1 Rented cabin, cottage, or condominium

B_eginning with yourself, what is the gender and age of each member in your
immediate travel gag! on this trip?

Gender Age Gender Age Gender Age
Yourself-) C1 M 121 F [:1 M D F [:1 M El F
El M D F D M C] F El M CI F
[:1 M [II F 121 M [:1 F El M C] F

What is your primary purpose for visiting the west-central Michigan region on this trip?
(Please check one.)

Cl Passing through D Visiting friends or relatives
E! Business 121 Other (please specify)
D Recreation / pleasure

 

About how far in advance did you begin to make plans for it?
Year(s) Month(s) Day(s)
Did you visit the west-central Michigan region any time before this trip?

D Yes -) When did you visit? (month, year)
Cl No -) (Please go to question 10)

What was the primary purpose of your last visit to this region? (Please check one.)

D Passing through 121 Visiting friends or relatives
0 Business 0 Other (please specify)
[:1 Recreation / pleasure

 

192

 

10. What do you like most about this region as a tourist destinatlon?

 

 

11. What do you like least about this region as a tourist destination?

 

 

12. What would attract you to visit this area in...

Spring:

 

Summer:
Fall:
Winter:

 

 

 

13. Have you visited, or are you aware of, any of the following facilities or attractions in
this region?

(Please mark only one box per entry.)

 

14. On a scale of 1 to 10, please rate the following locations based on their desirability as
a tourist destination:

  
     

1 = not desirable
10 = extreme/y
desirable

 
    

1 = not desirable

10= extremely desirable Rating

 

193

15. Which of the following activities are of interest to you or others in your family? (Please

check all that apply.)

CI Antiquing
Cl Bicycling

C1 Boating

C1 Camping
D Canoeing/
kayaking / tubing

D Casino gaming

0 Concert

Cl Cross-country
skiing

C1 Dining out
(excluding fast food)
[:1 Downhill skiing /
snowboarding

0 Farm market / u-
pick I winery

C1 Festival / event

CI Fishing, charter
El Fishing, ﬂy

CI Fishing, ice
C1 Fishing, other
El Golﬁng

Cl Hiking lwalking
[:1 Historic site
Cl Horseback riding

C1 Hunting, deer

Cl Hunting, small
game

0 Hunting, turkey

El Jet skiing

Cl Lighthouse
touring

E1 Live theatre

D Movie (at a
cinema)

[:1 Museum
0 Mushroom
collecting

D Nature center

16. What is your employment status? (Please check one.)

El Employed full-time

E1 Employed part-time

C1 Homemaker

D Retired

El Self-employed
Cl Unemployed

D Snowmobiling

Cl Sports
tournament

C1 Swimming (lake,
pond, river)

D Swimming (pool)
CI Theme /
amusement park

Cl Visiting a federal /
state park

 

 

. Cl Visiting friends /
D Off-reading relatives
El Wildlife viewing /

D Ph°t°graphy bird watching
E1 Sailing CI Wind surﬁng
Cl Scuba diving / E1 Other (please
snorkeling specify):
El Shopping
El Sightseeing
(general)

CI Student

CI Other (please specify)

 

17. The U.S. median household income before taxes is about US$42.500. Would you say

that your total household income before taxes in 2003 was...

E] Below $42,500

121 Between $42,500 and

$75,000

[:1 Above $75,000

:

[—hank you for your participation in the study!

 

*This study was reviewed and approved by MSU IRB, approval number IRB# 04-547.

194

 

 

APPENDIX D: Overnight Visitor Survey

(IRB# 04-547)

Sault Ste.
Marie

West-central \
Michigan ‘
region

Arcadia

. Copemlsh a.
Bear Lake
0

Onekama M3!“

. Norwalk
Manistee

.Wellston 1

M“ 3 13
Mo“ Wolf Lake
Scottville Walhel a .

. ‘ Idlewlld cm“
Ludington rich

0 O
Cluster B . O . . Musk 12

 

C
Pentwater .Sm' . Lllley Ho

0 Brahman
Hesperia

p .Whlte
Cloud

Detroit

 

Newaygo

0 Fremont

0 Newaygo

0 Grant

 

 

 

Travel, Tourism and Recreation Resource Center
Dept. of Community, Agriculture, Recreation and Resource Studies

Michlgan State University
172 Natural Resources Bldg.
East Lansing, M148824-1222

195

MICHIGAN STATE
11 N 1 v E R s 1 T Y
Fall 2004

 

Dear Sir / Madam:

Your household has been randomly selected to participate in a tourism study (named
"Tourism Unlimited TAP") conducted by Michigan State University on behalf of the
communities in the west-central Michigan (W-C MI) region. The purpose of this survey is to
gather information about any recent trips you might have made to that area and your
opinions of west-central Michigan as a vacation destination. Your answers will provide the
local communities with the information they need to better serve area visitors.

Your responses are vital to the success of this study, but your participation in it is completely
voluntary and you are free to discontinue your participation in the survey at any time. You give
your consent to voluntarily participate in this study by completing this approximately 15-minute
survey and returning it to us. When ﬁlling out the survey, you are free to skip any items to which
you do not want to reply. Your privacy will be protected to the maximum extent allowable by law,
and your responses will be reported in combination with those of other respondents and your
name will not be associated with the ﬁndings in any way.

Please return your completed survey in the provided, postage-paid, envelope by Nov. 15, 2004.

As a "thank you" for your participation, we have enclosed a coupon package donated by west-
central Michigan tourism businesses. The coupons are yours to keep whether you decide to
participate in this survey or not.

If you return a filled-out drawing card by the due date, you will also be included in the drawing for
one of the following grand prizes donated by west-central Michigan tourism businesses:

- One of two gift certiﬁcates of two mid-week nights free at Northern Escape Lodge in Branch, MI.

-One gift certiﬁcate of one night stay in a hot-tub room, Sun-~Thu at The Shack (Bed & Breakfast) in
White Cloud, M1; and one gift certiﬁcate for two 9-hole rounds of golf.

- One gift certiﬁcate of one free night lodging at the Sierra Sands Hotel in Mears, MI; excluding
weekends.

- One of two certiﬁcates for two-night mid-week free in a furnished cabin at The Blueberry Patch
Motel in Baldwin, Ml.

Thank you very much for your assistance.
(... __ / /./7
Kimble/T” "<._
Dr. Donald F. Holecek

Professor and Director of Travel, Tourism and Recreation Resource Center

Please feel free to contact me (dholecek@msu.edu. 517-353-0793) if you have any questions
about the survey. If you have questions or concerns regarding your rights as a study participant,
or are dissatisﬁed at any time with any aspect of this study, you may contact — anonymously, if
you wish —Peter Vasilenko, Ph.D., Chair of the University Committee on Research Involving
Human Subjects (UCRIHS) by phone: (517) 355—2180, fax: (517) 432-4503, e-mail:
ucrihs@msu.edu, or regular mail: 202 Olds Hall, East Lansing, MI 48824.

 

”T his study was reviewed and approved by MSU IRB, approval number IRB# 04-547.

196

 

1. What Is the zip code of your primary residence?

 

2. Have you ever stayed overnight in the west-central Michigan (W-C Ml) region? (Please
refer to the map on the front page.)

Cl Yes
E1 No (Please go to question 28.)

3. In which months have you stayed overnight in the west-central Michigan (W-C MI)
region? (Please check all that apply.)

121 January CI April Cl July 121 October
Cl February I] May D August El November
CI March E1 June 0 September 13 December

4. About how many times have you stayed overnight in the west-central Michigan (W-C MI)
region during the past three (3) years? #

Part 1.

Now, we would like to ask you a few questions about your MOST RECENT OVERNIGHT
VISIT WITHIN the west-central Michigan (W-C MI) region. (If your ovemight visit in this region
was only a pan‘ of a much longer trip to a different destination, when responding to these
questions, please refer to your experiences only in this region. Please refer to the map on the
front page of the questionnaire.)

5. In which month and year was your most recent overnight visit within the W-C MI region?
Month: . Year:

6. Was this your ﬁrst visit within the W-C MI region?
CI Yes Cl No

7. About how far in advance did you begin to plan this trip?

 

# Year(s), # Month(s), # Day(s)

8. Which of the following sources of information did you use in planning this visit? (Please
check all that apply.)

C1 AAA CI Newspaper

CI Billboards / outdoor advertising C1 Radio

CI Chamber of commerce CI State travel ofﬁce I Travel Michigan
CI Convention and visitors bureau CI Television

CI Friends or relatives E1 Travel guide(s) / brochure(s)

E] Highway welcome center Cl Word of mouth

Cl Internet / web site(s) D Other (please specify)

121 Magazine 13 None

9. What was the grimag gurgose of this visit? (Please check one.)

197

 

 

El Business El Stop over on the way to another
desﬂnaﬂon
CI Recreation D Other (please specify)

 

Cl Visiting friends / relatives

10. What was the primary destination of this trip?
City: , State or Country:

 

11. How many nights did you spend within the W-C MI region on this trip? nights

12. In I near which townls) in the W-C Ml region did you stay overnight on this trip?

 

 

13. Which of the following types of lodging did you use in the W-C Ml region on this trip?
(Please check all that apply.)

Cl Friend's or relative's home El Owned second or seasonal home
El Hotel, motel or resort El Campground or RV park
CI Bed & Breakfast D Other (please specify)

 

CI Rented cabin, cottage or
condominium

14. Who was included in your immediate travel 2am on this visit? (Please check all that

apply.)
[3 Immediate family (parents with children; siblings; spouse / signiﬁcant other)

Cl Extended family (grandparents with grandchildren; uncles with nieces / nephews;
cousins)
Mixed family (any combination of immediate and extended family)

Business colleague(s)
F riend(s)
Nobody (went alone)

[30000

Other (please specify)

 

15. Beginning with yourself, what was the gender and age of each member in your

immediate travel 2am on this visit?

Gender Age Gender Age Gender Age
Yourself-) CIM CIF _ EIM DF _ DM DF _
CI M CI F __ Ci M D F __ CI M E] F __
CI M D F _ D M CI F _ El M III F _
El M El F El M El F El M C] F

16. While in the W-C Ml region, In which of the following activities did you and your
immediate travel party participate on this visit? (Please check all that apply.)

CI Antiquing El Golﬁng CI Sailing

198

 

 

 

 

CI Bicycling El Hiking/walking Ci Scuba diving/
snorkeling
El Boating Cl Historic site Cl Shopping
CI Camping CI Horseback riding Cl Sightseeing (general)
0 Canoeing/kayaking/ CI Hunting, deer CI Snowmobiling
tubing
El Casino gaming El Hunting, small CI Sports tournament
game
El Concert D Hunting, turkey El Swimming (lake,
pond, river)
CI Cross—country skiing CI Jet skiing Cl Swimming (pool)
D Dining out (excluding D Lighthouse touring CI Theme/amusement
fast food) park
El Downhill skiing/ B Live theatre El Visitingafederall
snowboarding state park
CI Farm market/u-pick/ Cl Movie (atacinema) CI Visiting friendsl
winery relatives
El Festival/event El Museum Cl Wildlife viewing/bird
watching
D Fishing, charter CI Mushroom El V\ﬁnd surﬁng
collecting
D Fishing, ﬂy Cl Nature center CI Other (please
specify):
0 Fishing, ice 0 Off-roading
El Fishing, other Cl Photography

 

17. Is there anything in the W-C Ml region that you think needs improvement?

 

 

 

18. What activities or opportunities are missing in the W-C MI region that m would like to

participate in?

 

 

 

19. In total, approximately how much did your immediate travel paLty spend in the each of
the following categories within the W-C Ml region on this visit?

Gas / fuel
Groceries

Lodging

Attractions (tickets, entrance fees, etc.)

Activities (equipment rentals, lessons, etc.)

 

Meals at restaurants / fast food
Shopping (clothes, souvenirs, etc.)

Other (please specify)

 

a £3 69 69 {-9 69 69 '69 £9

TOTAL spending in the W-C Ml L29. ion

199

20. How would you rate your overall experience in the W-C Ml region on this visit?

 

CI Much better than I expected } Why?
CI Somewhat better than I expected
El About what I expected
El Somewhat worse than I expected
} Why?

 

El Much worse than I expected

Part 2.
The following questions are about your general perceptions of the WEST-CENTRAL
MICHIGAN REGION and any experiences you might have had there any time in the past.

21. Have you visited, or are you aware of, the following facilities or attractions in this
region? (Please give responses about all facilities or attractions listed below.)

 

Have Visited ‘ Aware, But " Not Aware
This Place Not Visited Of This Place‘

 

           
   
 
 

  
 

Recreation on Manistee River CI , El 2 CI

  

Recreation on Muskegon River ‘ Cl El 1 Cl

 

  
 
  
  
 
 

Driving / riding on sand dunes at Silver Lake

 

22. What do you like most about the W-C Ml region as a tourist destination?

 

 

23. What do you like least about the W-C Ml region as a tourist destination?

 

24. How would you describe W-C Ml region to your family or friends?

 

25. Would you recommend the W-C Ml region as a tourist destination to your family or
friends?
D Yes [II No

200

26. On scale from 1 to 10, where 1 means “do not agree at all” and 10 means “agree
completely", how much do you agree with the following statements about the W-C MI
region?

The west-central Michigan region...

.. has good roads .. is a great winter destination
has great outdoor recreation . if A f”. . ' A if
'.‘_ -. Opportunities . ,. . 5'".— 'S if”? p?” t9 “5“... ._

.. has high quality lodging .. is an exciting place to visit

has many interesting historical

-- sites . , 7 . . — '
is a good place to meet friendly

— people — '
is’a great famﬂy vacation ' '

~7— _ destination, , -_--_-
is a great place to start a

— business —

.. is close enough for a weekend getaway
.. is easily accessible 7

offers exceptional value for the money ;
offers exciting nightlife and. entertainment
is a great spring destination offers'great dining opportunities 7 l
.. is a great summer destination ...offers great shopping opportunities

.. is a great fall destination

offers much scenic appeal
27. Would you consider living in the W-C MI region ...? (Please check one.)

Cl in a primary residence
El in second / seasonal home
El would not want to live there

28. What would attract you to visit the W-C Ml region in the following seasons?

Spﬁng:

 

Summer:

 

Fall:

 

Winter:

 

29. How likely are you to visit the W-C Ml region within next three years?

Cl Deﬁnitely will visit Cl Somewhat unlikely
El Very likely Cl Very unlikely
CI Somewhat likely El Will not visit the area again

30. Which three factors most inﬂuence your decision to visit a tourist destination?
(Please enter "1" next to the item you deem most important, and "2" and "3", next to the items
you deem second and third most important.)

Climate / weather

201

 

Comfort

Convenience / distance

Cost I . .
Family-friendly place and l or opportunities
Safety / security .
Service quality

Variety of attractions and / or activities

Other (please specify)

 

Part 3.
To conclude our survey, we would like to ask you a few questions that will help us
organize the responses.

31. Do you have any of the following persons living in your household?
(Please check all that apply.)

D Pre-schoolchild(ren) D Senior citizen(s)
D School-age child(ren) D Handicapped person(s)

32. What is your employment status? (Please check one.)

D Employed full-time D Self-employed
D Employed part-time D Student
D Homemaker D Unemployed
D Retired D Other (please specify)
33. What is the highest level of education you've completed? (Please check one.)
D Elementary school D College graduate / professional
D Some high school D Post-graduate
D High school D Other (please specify)

D Some college

34. The U.S. median household income before taxes is about US$42,500. Would you say
that your total household income before taxes in 2003 was ....?

D Below US $42,500
D Between US $42,500 and US $75,000
D Above US $75,000

Thank you for your participation in the study.

202

 

 

APPENDIX E: Potential Visitor Survey

(IRB# X05—244)

Sault Ste.

Marie

West-central
Michigan
region

Arcadia
. Copemlsh
Bear Lake
e

Onekama

Manistee

. Norwalk
Manistee

. Wellston

lab

Wolf Lake
e

Mason
Scottville Wlihﬂl

Ludington '

Idlewlld CM“

. ' Branch

cluster Musk

 

e
Pentwater

. Crystal
Valley

eLllley Ho

e Brohman
Hesperla

p .Whlte
Cloud

mane

N
e Fremont

 

e Newaygo
e Grant

 

 

 

Detroit

Travel, Tourism and Recreation Resource Center

Dept. of Community, Agriculture, Recreation and Resource Studies
Michigan State University

172 Natural Resources Bldg.

East Lansing, MI 488244222

203

 

 

Fall 2005
Dear Sir/ Madam:

Your household has been randomly selected to participate in a tourism study conducted by
Michigan State University on behalf of the communities in the west-central Michigan (W-C
MI) region. The purpose of this survey is to gather your opinions of west-central Michigan
as a travel destination and your general travel preferences. Your answers will provide W-C
MI communities with the information they need to better serve area visitors.

Your responses are vital to the success of this study, but your participation in it is completely
voluntary. You are free to discontinue your participation in the survey at any time. You give your
consent to voluntarily participate in this study by completing this approximately 10-minute

survey and returning it to us. When ﬁlling out the survey, you are free to skip any items to which
you do not want to reply. Your privacy will be protected to the maximum extent allowable by law,
and your responses will only be reported in combination with those of other respondents and your
name will not be associated with the ﬁndings in any way.

Please return your completed survey in the provided.postage-paid, envelope by December 16,
2005.

As a "thank you" for your participation, we have enclosed a coupon package donated by
west-central Michigan tourism businesses. The coupons are yours to keep whether you
decide to participate in this survey or not.

If you return a filled-out drawing card by the due date, you will also be included in a
drawing for seven Land’s End gift certiﬁcates: one for $100, two for $50 each and four for
$25 each.

Thank you very much for your assistance.

('7 "“ /’/’// ' i;
A Mfr/.7 WA; «4;-

Dr. Donald F. Holecek
Professor and Director of Travel, Tourism and Recreation Resource Center

Please feel free to contact me (dholccek@msu.edu, 517-353-0793) if you have any questions
about the survey. If you have questions or concerns regarding your rights as a study participant,
or are dissatisﬁed at any time with any aspect of this study, you may contact — anonymously, if
you wish — Peter Vasilenko, Ph.D., Chair of the University Committee on Research Involving
Human Subjects (UCRIHS) by phone: (517) 355-2180, fax: (517)432-4503, e-mail:
ucrihs@msu.edu, or regular mail: 202 Olds Hall, East Lansing, MI 48824.

 

 

‘T his study was reviewed and approved by MSU IRB, approval number IRB# X05-244.

204

 

 

 

 

We deﬁne a "pleasure trip" as any overnight or day trip to a place at least
50 miles from your home that was made for your enjoyment, including
weekend getaways, vacations, shopping trips, trips to a second home, and

trips to visit friends or relatives.

1. Have you taken a pleasure trip to any destination during the past three (3) years?

D Yes
D No

2. How important to you are each of the following factors when selecting a pleasure trip
destination? (Please check one box for each category.)

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Not at all Not so Somewhat Very
important important important Important important
Upscale facilities / services D D D D D
Travel time / distance D D D D D
Cost D D D D D
Family-friendly place and / D D D D D
or opportunities
Safety / security D D D D D
Variety of shopping D D D D D
opportunities
Interesting scenery D D D D D
Service quality D D D D D
Variety of attractions and / D D D D D
or actrvrtres
Nightlife activities D D D D D
Access:bility for disabled D D D D D
persons
Pet accommodations D D D D D

 

 

 

 

 

3. Which of the following sources of information do you use when planning your pleasure

trip? (Please check all that apply.)

DAAA

Billboards / outdoor advertising

D
D Chambers of commerce
D Convention and visitors bureaus
D

Friends or relatives

DECIDE]

205

Magazines
Newspapers
Radio

State travel ofﬁces

Television

 

 

Highway tourist information centers D

D
D Highway welcome centers
D

Internet / web sites

D
Cl
Cl

Travel guides / brochures

Word of mouth
Other
(specify)
None

 

 

D Local visitor guides

4. Which of the following activities do you and your immediate travel party participate in
most often while on pleasure trips? (Please check all that apply.)

D Antiquing

D Bicycling

D Boating

D Camping

D Canoeing / kayaking /
tubing

D Casino gaming

D Concert

D Cross-country skiing
D Dining out (excluding fast
food)

D Downhill skiing /
snowboarding

D Farm market / u-pick/

winery
D Festival / event

D Fishing, charter
D Fishing, ﬂy

D Fishing, ice

D Fishing, other

D Golﬁng

D Hiking l walking

D Historic site

D Horseback riding

D Hunting, deer

D Hunting, small game
D Hunting, turkey

D Jet skiing

D Lighthouse touring
D Live theatre

D Movie (at a cinema)
D Museum

D Mushroom collecting
D Nature center

D Off-roading

D Photography

D Sailing

D Scuba diving l snorkeling
D Shopping

D Sightseeing (general)

D Snowmobiling

D Sports tournament

D Swimming (lake, pond,
river)
D Swimming (pool)

D Theme / amusement
park

D Visiting a federal / state
park

D Visiting friends I relatives

D Wildlife viewing / bird
watching
D Wind surﬁng

D Other (please specify):

a)

 

b)

 

5. In general, about how far in advance do you begin to plan your

weekend getaway

6. Who usually accompanies you on pleasure trips?

(# of days) vacation (4-day or longer trips)

(# of days)

D Immediate family (parents with children; siblings; spouse / signiﬁcant other)
Extended family (grandparents with grandchildren; uncles with nieces / nephews;

cousins)

D
D Mixed family (any combination of immediate and extended family)
D

Business colleague(s)

206

 

D Friend(s)
D Nobody (usually go alone)
D Other (please specify)

 

7. Where do you go most often on weekend pleasure trips? (Please list the state and the
nearest city of the three destinations you visit most often on weekend getaway trips.)

 

Weekend destination City State or country

 

 

 

 

 

 

 

 

 

8. Where do you go most often on vacation (4-day or longer) trips? (Please list the state
and the nearest city of the three destinations you visit most often on vacation trips.)

 

Vacation destination City State or country

 

 

 

 

 

 

 

 

 

9. About how many nights in total did you spend in each of the following types of lodging
while on pleasure trips during each of the past 12 months? (Please note that the list of
months starts with November 2004.)

D My family and l have [191 taken any pleasure trips during the past 12 months.

 

Your own or a friend or relative’s seasonal
home or
your friend or relative's primary home
(# of nights)

 

Hotel, motel, inn, Campground,
bed & breakfast cabin, RV park
(# of nights) (# of nights)

 

Nov 2004

Dec 2004

Jan 2005

 

 

 

 

Feb 2005

 

Mar 2005

 

Apr 2005

 

May zoos

 

Jun 2005

 

 

 

 

 

 

207

 

 

 

 

 

Hotel, motel, inn, Campground, Your own or a ”1:22": orslative’s w
Month be; $333“ 0?: '31" SEER your friend 02 #redafltr'iigehsts g1) 'mam home
Jul 2005
Aug 2005
Sep 2005
Oct 2005

 

 

 

 

 

 

10. Have you ever visited the west-central Michigan (W-C MI) region? (Please refer to the
map on the front cover.)

 

D Yes => Please skip to question 12.

D No
11. What was a reason (or reasons) that you have never visited this region? (After you
respond to question 12, please skip to question 16.)

 

=> Please skip to question 16.

12. In which months have you stayed overnight in the W-C MI region in the past? (Please
check all that apply.)

D January D April D July D October
D February D May D August D November
D March D June D September D December

D I have never stayed overnight in the W-C Michigan region.

13. What do you like most about this region as a tourist destination?
1)
2)

 

 

14. What do you like least about this region as a tourist destination?

1)
2)

 

 

15. Assuming that there is always room for improvement, what would be your three
recommendations for Improvements In the W-C Michigan region as a tourist destination?

1)
2)

 

 

208

 

 

16. The west-central Michigan region offers many tourist attractions including: Lake
Michigan shoreline; sand dunes; rivers and inland lakes; camping, hiking, biking and
snowmobile trails; hunting, ﬁshing and other year-round outdoor recreation opportunities;
as well as farm markets, quaint shops, and a relaxing and family-friendly atmosphere.

Based on your knowledge of the region from the past visits andlor on the description
above, what would attract you to visit the W-C Michigan ggion in each of the following

222m

Spnng:

 

Summer:
Fall:
Winter:

 

 

 

17. How likely are you to visit the W-C Ml region within the next three (3) years? (Please
check only one.)

D Deﬁnitely will visit => Please skip to question 19. D Somewhat unlikely
D Very likely => Please skip to question 19. D Very unlikely
D Somewhat likely => Please skip to question 19. D Will not visit the area again

18. Why do you think you are not likely to visit this area in the near future?

1)
2)

 

 

19. Do you own any of the following? (Please check all that apply.)

D Camper/RV D Second (seasonal) home
D Horse D Snowmobile
D Off-road vehicle D Watercraft

20. What is the zip code of your primary residence?

 

21. Beginning with yourself, please brieﬂy describe each member of your household.

# Gender Age # Gender Age
Yourself -> 1 D Male D Female 5 D Male D Female
2 D Male D Female 6 D Male D Female

209

3 D Male D Female 7 D Male D Female
4 D Male D Female 8 D Male D Female

22. What is the highest level of education you have attained? (Please check only one.)

DUE!

D

 

Elementary school D College graduate l professional
Some high school D Post—graduate

High school D Other (please specify)

Some college

23. The U.S. median annual household income before taxes is about $43,000. Would you
say that your total household income before taxes in 2004 was... ..

D Below $43,000
D Between US $43,000 and $75,000
D Above $75,000

Thank you for your participation in this study.

Please place your completed survey and a drawing card into the postage-paid envelope and
mail it to: Tourism Resource Center; Dept. of CARRS; Michigan State University; 172 Natural
Resources Building; East Lansing, MI 48824-1222

210

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