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3101710 9848

LIBRARY
mchigan State
University L

1 WW

2:;

 

 

 

This is to certify that the

dissertation entitled

Influence and Exchange Networks Among Tourism Oriented
Businesses in Four Michigan Communities

presented by

Marcia Cobb

has been accepted towards fulfillment

 

 

of the requirements for
Ph. D. degreeinPark & Recreation
Resources

%W%M /\

Major professor

Date §21/7/88

MS U is an Affirmative Action/Equal Opportunity Institution 0-12771

 

MSU

LIBRARIES

 

RETURNING MATERIALS:
Place in book drop to
remove this checkout from
your record. FINES will
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SEP 0 31995

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INFLUENCE AND EXCHANGE NETWORKS AMONG TOURISM ORIENTED

BUSINESSES IN FOUR MICHIGAN COMMUNITIES

by

Marcia Cobb

A DISSERTATION

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

DOCTOR OF PHILOSOPHY

Department of Parks and Recreation Resources

1988

Copyright by
MARCIA COBB

1987

ABSTRACT

INFLUENCE AND EXCHANGE NETWORKS AMONG TOURISM ORIENTED

BUSINESSES IN FOUR MICHIGAN COMMUNITIES

BY

Marcia Cobb

The purpose of this study is to examine communication
patterns between owners and/or managers of tourism oriented
businesses in four Michigan communities using network
analysis techniques. Very little tourism research has taken
a communication perspective, and practically no one has used
network analysis in examining tourism.

One important factor to tourism management and promotion
is understanding the way information flows within the
industry. The control over and access to information leads
certain businesses to have power and influence within the
community. The state of Michigan’s tourism promotion is
based on its tourism oriented communities. These communities
and the surrounding natural resources are Michigan’s tourism
product. Coordination and cooperation among the communities
and tourism oriented coordinating organizations, such as the
State Travel Bureau, are essential for continued success of
promoting Michigan as a tourism destination. Network

analysis helped to uncover the communication patterns and

strategies used to gain influence and control over the
community’s tourism development.

This study developed a model which described strategies
used by business owners/managers to gain influence or power
over the way communities react to tourism development. The
model was developed based on the hypothesis that centrality
in certain exchange networks would be important in
predicting influence. Results proved this to be true. Three
strategies proved to be significantly correlated to gaining
centrality in the exchange networks and to acquiring
influence. These strategies were 1) being involved in
professional and local communities organizations, 2) talking
to staff of community tourism coordinating organizations and
3) talking to staff of coordinating organizations outside

the community.

ACKNOWLEDGMENTS

First, I would like to acknowledge the Sea Grant College
Program for two years of financial support. I would also
like to thank my committee members Dr. Daniel Stynes, Dr.
Richard Vincent Farace, and Dr. Phillip Marcus for their
helpful and timely support. Dan Stynes, in particular,
provided much appreciated statistical and methodical advice.
It was wonderful having such an intelligent person so close
at hand.

A special thanks must go to my major professor, Dr.
Maureen H. McDonough, who through the long haul provided
financial, academic and emotional support. I greatly value
my association and friendship with her.

Thanks to my family for their continued concern. To
Doug, thank you for your constant and quiet confidence in my
abilities, as well as help in proofing and printing.
Appreciation goes to Thord, who though he could not
understand my feelings through this long and difficult task,
provided unwaivering financial support, love and
companionship, without which I may not have finished this
project. Finally, thanks to all my friends, who helped in

many ways.

ii

TABLE OF CONTENTS

LIST OFTABIJESOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOIOO Vi

LISTOF FIGURESOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOCOOOOOOOO Viii

1. INTRODUCTIONOOOOOOOOOOOOOOOOOOOOOOOOO00.0.0.0....0. 1

1.1 Thawimpprtance of Tourism............ ..... . 1
1. Z’Aauedel»of Exchange Networks in the

Tourlsm Industry. I O O O O O O O O O O O O O O O O O O O O O I O O O 5
1. 3 Statement of the Problem................... 8
1. 4MMOWOf the. WW::: 0‘ O O O O O O O O O O O O I O O O O O O O O 10
1. 5 Document summary. 0 O O O O 0 O O O 0 0 O 0 O O O O O O O O O O O O O 11
2. PROBLEM JUSTIFICATION............. ..... ....... ..... 13

Past Approaches to Studying Tourism........ 13
Approaches to Studying Tourism in
Communities................................ 16
A New Way of Studying Tourism.............. 19
Social Network Analysis of Tourism
Businesses Exchanges....................... 22
2.4.1 Why Study Communication Exchange
Networks in Tourism................. 22
2.4.2 Background to Network Analysis...... 24
2.4.3 Network Centrality.................. 31
2.4.4 Power and Influence................. 38
2.5 Summary.................................... 40

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3. THEORETICAL FRAMEWORKOOOOOOOOOOOOOOOOOOOOOOOOOOOOOO 41

1 Introduction............................... 41
.2 Power and Community Research............... 41
3 Exchange Theory............................ 45
4 Conceptualizing Power as Influence -
Burt’s Model............................... 47
.5 Boje and Whetten Model..................... 50
.6 Model of Influence and Network Centrality
in Tourism Business Communities............ 59
3.7 Propositions............................... 64
3.7.1 General Relationships............... 64
3.7.2 Strategy Propositions............... 66
3.7.3 Characteristics Propositions........ 68
3.8 Summary.................................... 70

iii

4.;ggznggst...........................................

4.1
4.2

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5. RESULTS

mm
00
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How the Communities were Selected..........
Data calleCtionOOOOOOOOOOOOOOOOOOOOOOOOO...

..4.2.1 How the Sampling Frame was

Developed...........................

4.2.2 How the Sample was Drawn............
4.2.3 Who was Interviewed and Why.........
4.2.4 Instrument Development..............
4.2.5 Sampling Procedure and Schedule.....
Definition of Terms........................
Variable Operationalization................
Data Analysis..............................

AND DISCUSSION OF DESCRIPTIVE FREQUENCIES..

Description of Study Sample................
5.1.1 How the Communities Compare to
Each 0ther..........................
5.1.2 Comparison of Businesses
Characteristics.....................
5.1.3 Comparison of Owner/Manager
Characteristics....................
What Kinds of Businesses are Influential..
Talking and Referral Activity Frequency
Analysis..................................
5.3.1 Community Comparisons of Talk and
Referral Activity..................
5.3.2 Respondent and Total Sample
Comparisons........................
5.3.3 Top Scorers in Talk and Referral
Activity...........................
Influence and Talk and Referral Activity-
Correlations..............................
Joint Programs Activity Lack of Response..
Communication with 0rganizations..........
Summary...................................

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6. RESULTS or THELMODEL Tssmxnggi....................

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Introduction..............................
General Relationships.....................

6.2.1 Measures of Centrality.............
Strategy Propositions.....................
Characteristics Propostions...............
Summary...................................

iv

71

72
75

75
76
78
78
8O
81
83
85

88

88

88

97

102
105

114

115

118

120

131
135
136
138

140

140
141
142
148
153
157

7. CONCLUSIONSOOOOOOOO00.0.0.0....00... ........ 0.0... 161

7.1 Summary of the Study...................... 161
7.2 Conclusions............................... 165
7.3 Implication for the Tourism Industry...... 167
7.4 Limitations of the Study.................. 169
7.5 Suggested Research Directions ............. 171

APPENDICES
Appendix A: Sample Interview Schedules........ 173
Appendix B: Sample Cover Letter.......... ..... 184

Appendix C: Results of Descriptive
Frequencies....................... 186

BIBLIOGRAPHY....................... .................. 224

10.

11.

12.

13.

14.

15.

16.

17.

18.

LIST OF TABLES

List of Coastal Communities Considered for Study... 74

Number and Percent of Sample of Different Types of
BuSineSSes.O..0.0.0000...OOOOOOOOOOOCOOOOOOOOOO0.0. 77

List of Variables and Questions that Operationalize

ThemOOOOOOOOOOOOCO...OCOOOOOIOOOOOOOOOOOOOO00...... 84

Median Relative Total Gross Revenues............... 92
Tourism Development Indicators.. ................... 93
Characteristics of Businesses...................... 98
Characteristics of Owners/Managers................ 103

Numbers of Influentials: Comparisons Among
communities.OOOIOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOO 107

Community L Busineeses Ordered by Influence
scoreSOOOOOOOOOIOOOOOOOOOOOOOOOOOOOOOOIOOOOOO0.0.. 109

Community C Busineeses Ordered by Influence
scoreSOOOOOOOOOOOOOOOOOOOOOOO ..... 0.0.0.0... ..... 111

Community P Busineeses Ordered by Influence
scores.O...00.00.00.00000000000000000000000000000 112

Community M Busineeses Ordered by Influence
scores.O....0....0...0.000000000000000000000000.. 113

Talk and Referral Network Activity............... 117

Talk and Referral Frequencies: Comparisons Among
communitieSOOOOOOOOOOOOIOOOOOOOOOOOOOOOOOIOOOOOOO 119

Community L Businesses Ordered by Total Talk
Scores and Weighted Talk Scores.................. 121

Community L Businesses Ordered by Total Refer
Scores and Weighted Refer Scores................. 122

Community C Businesses Ordered by Total Talk
Scores and Weighted Talk Scores.................. 123

Community C Businesses Ordered by Total Refer
Scores and Weighted Refer Scores................. 125

vi

19.

20.

21.

22.

23.

24.

25.

26.

27.

Community P Businesses Ordered by Total Talk
Scores and Weighted Talk Scores..................

Community P Businesses Ordered by Total Refer
Scores and Weighted Refer Scores.................

Community M Businesses Ordered by Total Talk
Scores and Weighted Talk Scores..................

Community M Businesses Ordered by Total Refer
Scores and Weighted Refer Scores.................

Correlations of Influence with Talk and
Referral Activity................................

Business Communication with Tourism Oriented
Coordinating Agencies............................

Correlation Between Influence and Measures of
CentralitYOO...OOOOOOOOOOOOOOOOOO0.0.0.0000...0..

Tests of Strategy Propositions...................

Tests of Characteristics Propostitions...........

vii

126

127

129

130

132

137

143

149

154

LIST OF FIGURES

1. Michigan’s Tourism System ................... ....... 2
2. Basic Parts of Tourism............................. 4
3. The Tourism Information System in Michigan......... 7
4. Examples of Network Centrality ........... ......... 30
5. Graph with Five Nodes and Four Links.............. 33
6. Ten Paths from Graph of Figure 5.. ................ 35
7. Aspects of Power.... ................ .............. 49
8. Boje and Whetten Model............................ 54
9. Path Analysis of a Revised Causal Model ........... 58
10. Predicted Relationships of Model................. 63

11. Revised Model Based on Results... ............... 158

viii

CHAPTERI

INTRODUCTION

1.1 The Importance of Tourism

Tourism is rapidly becoming one of the most important
industries in Michigan. This sector of the economy is
currently estimated to generate over 11 billion dollars
yearly. An effectively managed and promoted tourism
industry is seen as a major factor in solving Michigan's
economic diversification problems. The amount invested in
the "Yes Michigan" promotional campaign is some indication
of the emphasis that State government in Michigan places on
tourism. The Michigan Travel Bureau has a 1986 budget of
approximately eleven million dollars for tourism promotion
(Savich, 1986).

Tourism as an industry is diverse and complex. Many
have tried to define what is included in tourism (McIntosh
and Goeldner, 1984; Holecek, 1981). It is often remarked
that many businesses that may be tourism oriented are not
even aware of it. It is not a very organized system
regardless of the criteria one might select to compare it to
other industries. Holecek (1981) suggests a model to
describe the Michigan tourism system which has five major
components. These are: Tourists, Private Firms, Public

Agencies, Public Resources and Private Resources (Figure 1).

a~85——fl

Economy Social/Cultural

 

 

      
   
   

 

 

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Figure l. Michigan’s Tourism System
Source: (llolecclr, 1981)

"The system is bounded by an environment which impacts it
but which is beyond the direct control of the components
within the system". This environment includes the following
components: the Economy, Government, Technology,

Social/Cultural and Climate (Holecek, 1981). Each part of

the system is dependent on the other components. This study
focuses primarily on the private firms and their
interactions with each other and with the public agencies.

McIntosh and Goeldner (1984) take a more marketing
oriented approach in their model of tourism, which has the
industry comprised of four main parts: accommodations,
shopping, activities, and transportation resting on a base
of natural resources, with marketing and selling being
important in each sector (Figure 2). All the parts of this
model are included in this dissertation.

Research on tourism comes from a diversity of
disciplines. Studies may focus on the tourist from a
psychological point of view, examining their perceptions,
attitudes or motivations (Mayo & Jarvis, 1981). Some

researchers study tourism from a sociological perspective,

looking at social structure, for example (MacCannell, 1976;

Machlis and Burch, 1983). Others research the primary and

secondary economic impacts of tourism on the community

J‘n.
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3 (Spotts, 1984, Propst, 1985). Anthropologists, such as

u ”mgr-r"

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Smith (1987) anthi-Sunyer (1978) have examined the
social/cultural effects of tourism on communities. The

approach taken by this dissertation is somewhat different

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limousine
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Business 8. Trade
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Studies
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Indigenous Markets
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Natural
Resources

 

Figure 2. The basic parts of tourism.
Source: [McIntosh and Goeldner 1984]

than past approaches to tourism research because it takes a
social network perspective which focuses on relationships
between tourism businesses rather than on specific
attributes or characteristics of tourists or tourism
businesses. Network analysis is used to examine the social
interactions among tourism oriented businesses and between
the business and tourism coordinating agencies.

Examining social interaction or exchanges means focusing
our study on all purposeful transfers of information or
actual resources between people. Thus when one person talks
to another person, some information is passed, (even if it
is only "hello"), and this can be consider a social
exchange. Exchange patterns are referred to as a network
and network analysis techniques can be used to study the

patterns of exchanges in all parts of the tourism industry.

1.2 A Model of Exchange Networks in the Tourism Industry

In order to study the social networks of exchange, it is
useful to focus on three components of the tourism industry:
1) tourists, who for the most part recreate in traveling
groups, 2) businesses oriented towards tourism in
communities that perceive themselves as tourist
destinations, (e.g. lodging, entertainment and dining
facilities) and 3) organizations that coordinate tourism.

In Michigan these coordinating organizations include

agencies such as the State Travel Bureau and its subsidiary

regional tourist associations, the Department of
Transportation and its Travel Information Centers, Chambers
of Commerce, and county tourist bureaus or councils.

The social exchange networks of the tourism industry
consist of a pattern of interactions within, between, and
among each of the three parts. There are a variety of
different types of networks within each of the different
parts. Within the tourism subsystem, there are
interpersonal channels of exchange, and exchanges between
one traveling group and another. There are also
interactions between the traveling groups and the tourism
oriented businesses. Finally there are occasional exchanges
of information between the tourists and the coordinating
organizations. From the business perspective, there are
exchanges within a business, between different businesses in
the community and between businesses and coordinating
organizations. The same holds true for the coordinating
organizations (Figure 3).

This study focuses on exchanges among tourism oriented
businesses in four Michigan communities which see themselves
as tourist destinations. Also examined are the links
between the tourist businesses and public and private
coordinating organizations. The exchanges under examination

are indicated in heavier lines in Figure 3.

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1.3 Statement of the Problem

Why study the exchange pattern between tourism oriented
businesses? What can they tell us? What is affected by
these patterns? Why is it important?

Several studies have examined the exchange of
information within traveling groups and between traveling
groups and the tourism oriented businesses and coordinating
agencies (McDonough and Eckstein, 1983; McDonough, 1983).
Eckstein (1983) found that travellers commonly used tourism
oriented businesses (such as hotels and restaurants) as
sources of information about where to eat, where to shop and
where to recreate. As a result, depending on who they
asked, they got different answers. In other words, the
traveller received filtered or biased information. These
biases would help promote some businesses at the expense of
others. Getting referrals from other businesses is very
beneficial to a business in most cases. Thus, these
referrals can be thought of as a desired resource and the
type of resource which business owners or managers would
seek.

One business refers its customers to another business
and that business may or may not return the favor by
referring its customers to those businesses from whom they
receive referrals. In any case, most all tourism oriented
businesses are asked by their customers for referral

information. Each referral can be visualized as a link

between two businesses: the one referring the customer and
the one receiving the referral. Put together all the links
of business referral and you have a network. One assumes
that the businesses that receive many referrals view it as a
benefit and are desirous to receive more, since it is a
valued resource.

Being well connected (or in network terms, having high
centrality) in the referral network may bring a business
more than just customers. Businesses which have control
over this valued resource of referrals may use this resource
to gain power. Community power or influence is defined as
the ability of certain businesses to affect the outcome of
certain events in their communities in order to benefit
themselves. Businesses may not always use the valued
resource to garner influence, but they have the potential to
do so. The manner in which influentials (those individuals
who have influence) use their power to influence the manner
in which the community reacts towards tourism development
(i.e. whether they work to enhance tourism development or
not) is not the focus of this study. It is assumed that
influential tourism oriented business owners or managers
attempt and often succeed in improving the community's
tourism product. Towns which have succeeded in building and
improving their tourism product have testified that it was
the work of a few influential businesspersons that made the
difference in improving the towns tourism image (Town of

Gaylord, First Annual Governor's Tourism Conference, 1984).

10

Several studies (Boje & Whetten, 1981) have examined
referral networks in other types fo situations, such as
social service organizations, and found that being central
in the referral network was correlated to high attributed
(ascribed by others in the network) influence. Social
service organizations vied for influence using various
strategies to increase their centrality in the referral
network and increase their power.

However, influence among tourism oriented businesses is
likely garnered in other manners than by just gaining
centrality in the referral network. It is probable that
having information, knowledge and contacts would help
improve the influence that is attributed to you. It will be
hypothesized in the study reported here that working to
become central in an information or communication network
may increase a business's ability to garner influence and

may also help gain referrals.

1.4 Purpose of the Study

The general purpose of the study is to examine the
relationship between patterns of exchanges among owners
and/or managers of tourism oriented businesses and the

amount of influence attributed to each owner or manager.

11

1.5 Document Summary

The rest of this document will be organized in the
following manner. Chapter 2 examines the need for the
study, including how tourism has traditionally been studied
and how social network analysis is a new approach to
understanding a community's tourism oriented businesses and
organizations. Chapter 2 also introduces the reader to
network analysis, its history and some basic concepts and
issues. A review of centrality and how it relates to
influence concludes Chapter 2. Chapter 3 lays out the
theoretical framework on which this dissertation is based.
Included are a review of different approaches to power
research at the community level, an explanation of Burt's
model of power and influence, a presentation of Boje and
Whetten's model of interorganizational centrality and
influence and finally the model and hypotheses which is
tested in this study. Chapter 4 contains a discussion of
the methods or procedures used in conducting this study.
Chapter 5 presents the results and a discussion of the
descriptive aspects of the study. Chapter 6 presents the
results of testing the model developed in this dissertation.
Sections will cover results of tests of the general
relationship between centrality and attributed influence,
strategies used by businesses to acquire influence and

constraints placed on businesses by the environment.

12

Chapter 7 contains conclusions and implications of the

findings.

CHAPTER 2

PROBLEM JUSTIFICATION

This chapter will discuss the problem being addressed by
this research. It includes a discussion of past approaches
to studying tourism, approaches to studying tourism in com-
munities, why this research presents a different approach,

and applying social network analysis to tourism.

2 . 1 Past approaches to studying tourism

The history of tourism research has been rather brief
and fragmented. It has only been since the early 1930 ' s
that tourism began to be recognized as a suitable subject
for study (Jafari, 1982) . The attitudes and approaches
towards tourism and tourism research have changed over the
last five decades.

In a brief review of the history of tourism research,
Jafari (1982) notes that studies of tourism from the 1930's
to the 1950's focused primarily on the economic benefits of
tourism development (League of Nations, 1936; Ogilvie, 1933;
Lickorish and Kershaw, 1954 , 1958) . He contends that the
view of tourism as primarily beneficial extended through the
1960 '8. Tourism was perceived as "an invaluable trade or
business" throughout the world. International agencies

(IBRD 1966; ILO, 1966; IUOTO 1966, 1967, 1968) proclaimed

13

14

tourism's benefits. The United Nations (1963) declared
"tourism is an important factor contributing to the
strengthening of the economies of all countries, and
particularly of the developing countries...". Many
developing countries rushed to promote tourism as a major
industry in their economies. "For these countries, tourism
was painted as a relatively easy trade to develop with
manifold economic returns. In short, by merely studying the
economic contribution, the Sixties presented an artificial
abstraction of tourism, tearing it out of its own context
and evaluating it independent of its host system" (Jafari
1982).

To a large extent, tourism research in the 1970's was a
reaction to the strong pro-tourism writings of the previous
decades. Jafari (1982) refers to scores of tourism studies
of the 70's which view tourism in a more skeptical manner,
often questioning the potential of tourism (Boissevain 1977:

deKadt 1979; McKean 1977; MacCannell 1976; Turner and Ash

1975; Young 1973). For example, Smith's nosts and Guests:
the Anthropology of Tourism (1977) is a collection of

articles documenting a variety of impacts of tourism (many
of them negative) on different cultures throughout the
world. In the introduction, Smith states that "the effects
of tourism can be assessed along a continuum from a highly
positive relationship that benefits all, to a highly disrup-
tive, negative interaction fraught with conflict. " Two

apparent bases for conflict and stress are economic and

15

social. These will be discussed further in the next
section.

The decade of the Eighties seems to be "a more balanced
period." Jafari (1982) feels that publications and
conferences of this decade try to "understand tourism in its
broadest spectrum, by considering both the hidden and
obvious problems and prospects of tourism, and by placing
more emphasis on the utilization of social science theories
and techniques" (Brewer 1981; Cohen 1981, 1982: Gray 1982:
Pi-Sunyer 1981; Smith 1980).

A number of conclusions can be drawn concerning the
brief history of tourism research. First, research in this
area has been fragmented (Mathieson and Wall 1982), and
lacking an integrated framework (Jafari 1982). Second, the
focus has been primarily on international and crosscultural
tourism. Though authors such as Archer (1978), Gray (1981)
and Williams (1979) have discussed the role and significance
of domestic tourism, tourism is often conceived as an
international phenomena. Finally, although a number of
recent studies (discussed in the following section) have
examined tourism at the community level, tourism at the
national or regional level has received most of the
attention.

The research presented in this document takes a less
conventional approach and examines domestic tourism at the

community level. It takes a systems approach and tries to

16

integrate community social and communication networks with

networks within the state.

2.2 Approaches to studying tourism in communities

Though tourism is important at the national and state
levels in that it brings in necessary income, it is at the
community level that the impacts are most felt. Much of the
research on tourism to date has focused on state or national
aspects. This may be due to the funding sources for tourism
research which are usually not locally oriented. State
travel bureaus or travel commissions are interested in the
state as a whole. National organizations, such as the U. S.
Travel Data Center focus their attention on the entire
United States. This section will briefly review some of the
approaches taken in researching tourism at the community
level.

As McIntosh and Goeldner (1984) point out, "tourism
commonly is approached through a variety of methods .
However, there is little or no agreement on how the study of
tourism should be undertaken" . This holds true regardless
of the level at which you examine tourism, whether it be
foreign or domestic, national, state or community. In
addition to the different levels at which tourism can be
examined, from international to community, there are many
approaches or disciplines from whose perspective one can

research tourism. McIntosh and Goeldner cite Jafari who

17

suggests sixteen possible choices of disciplines with which
tourism could be examined. Several of these disciplines
will be briefly reviewed. Some examples of tourism studies
at the community level, adapting methods and theories from
economic, cultural, psychological and social disciplines
will be briefly mentioned.

Brian Archer (1978) has been a leader in researching the
primary and secondary economic impacts tourism has on a
community, using input-output models as his major tool.
Smith (1977) examines the impacts of tourism on culture in a
variety of communities around the world. Although most of
the authors in this volume point out the negative impact of
tourism on many communities, some note that tourism has had
a positive impact on the community in addition to economic
benefits which tourist money brings to a community. However
a number of negative impacts can arise as well. First, the
wide economic disparity between rich tourists and relatively
poor local residents can cause severe stress on the local
population. In addition, often "only a limited segment of
the population -either already established leaders or bilin-
gual, bicultural "marginal men"- can truly profit..."
(Smith, 1977). Disruption of the local economic system is
another type of negative economic impact faced by some
communities. For example, in Tonga many locals, drawn by
the lure of greater cash incomes, abandoned subsistence
agriculture thus reducing local food production (Urbanowicz,

1977). Finally, Smith notes that the rise of large-scale

18

tourism often necessitates the transfer of local control to
the central government. Governmental agencies have "the
power to compete internationally for the tourist trade by
offering concessions in the form of favorable taxes or
negotiated land values to induce major hotel chains to
construct facilities" (Smith 1977). Central governments may
spend large sums of money constructing roads and hotels for
the tourists, when from the local perspective, the money
might have been better spent on agricultural aid or endemic
health problems, since tourism will only benefit a few
individuals locally.

Philip Pearce (1982) discusses the social contact
between tourists and hosts with the aim of elucidating the
"general processes and forces which account for the percep-
tion and behavior of the people in contact." The questions
that he is concerned with include the following. Is it true
that travel builds international understanding through an
appreciation of the qualities of the individuals of each
nation? Are modern tourists generally getting only a
superficial view of the local people thus reinforcing their
prevailing stereotypes of each other? He concludes that
hosts' perceptions of tourists are generally negative when
the following factors are involved: "asymmetry of frequent,
transitory contact, the opportunity for exploitation, and
considerable cultural differences between the parties which

produces a host of small interaction difficulties." When

19

examining research on tourists' perceptions of the hosts,
Pearce finds the results varied and states "there are just
too few studies on too few hosts to permit generalization".
Although researchers from a number of different fields,
such as psychology, anthropology, and economics have
recently become interested in studying tourism, little or no
research has been done on the tourism industry from a
communications perspective. Communications science is the
perspective from which this research approaches the tourism

industry.

2.3 A New way of studying tourism

In the early 1980's, a new way of looking at one aspect
of the tourism industry brought new techniques and
approaches from communications and sociology to bear on the
study of tourism. Many local marketing studies for tourism
businesses and organizations ask questions about where
tourists get their information (Stynes and Mahoney, 1986;
MDOT, 1986; Norman, 1987) . The results are often used to
help businesses know where to advertise (i.e. which
magazines, radio stations, TV stations, newspapers, etc.) .
However, most tourists don't use many formal sources of
information. Yet formal sources are the most commonly
inquired about in present tourism research. Tourists
primarily use information from friends and relatives or they

are repeat visitors (McDonough and Eckstein, 1983: MDOT

20

1986, 1982). The majority of tourists visiting Michigan are
repeat visitors. These repeat visitors do not use any
sources of information in trip planning or vacation
decision-making, except their personal knowledge of the area
to be visited. For example, when Michigan Highway Welcome
Center visitors were asked "Where did you get information to
help you choose where to go on this trip?", 58% of out-of-
state visitors said they had been there before and 48%
listed friends and relatives as a source. These were the
two top sources of information. Out of the seventeen other
possible sources of information, the majority of sources
were used by less than 10% of the visitors (MDOT 1986).
Results such as these led researchers to begin to probe more
deeply into trying to understand these informal sources of
information found to be so important (McDonough, 1984).

A communications approach to examining the tourism
social system is a very different approach than past
research in tourism. As the above review of literature
makes obvious, much of the past research has centered on the
impacts of tourism, both positive and negative. These were
first attempts to study communication patterns in the
tourism industry and, though each study has built upon the
others, much of the work was exploratory by necessity.

Since 1982 a number of studies have been conducted
looking at networks within the Michigan tourism industry
(McDonough and Eckstein 1983, Eckstein 1983, Elmer 1987).

The purpose of these studies was to describe the information

21

exchange system in the industry and to look at communication
patterns that would influence how that system operated. The
information exchange system was identified as having three
components - travellers organized into travelling groups,
tourism businesses organized into communities and organiza-
tions that coordinate tourism activities (e.g. Chamber of
Commerce, Travel Bureau) (Figure 3). The links within and
between these components were studied. Results shed a great
deal of insight into how the system operated. This under-
standing allows businesses, communities and coordinating
organizations to better manage and control these information
systems.

The purpose of initial studies was descriptive. Studies
of links between travelling groups and tourism businesses
indicated that, for the most part, information for trip
planning and information while at the destination was not
gathered through formally established links (Eckstein 1983,
McDonough 1982, McDonough and Eckstein 1983). Rather,
information was gathered through informal links with family
and friends. A closer look at information exchange before
the trip revealed that the repeat visitor in the travelling
group was a key source of information for the group. Under-
standing this communication pattern has been very helpful
for promotional planning because it highlights the need for
hospitality and information training for people in tourism
communities. They are not only bridges between their com-

munity and travellers but are also responsible for determin-

22

ing whether a person will become a repeat visitor and
therefore, another important node in the communication
network relative to their community (McDonough and Cobb,
1985). This dissertation focuses on the results of studies

done as a follow-up to these initial studies.

2.4 Social network analysis of tourism business exchanges

The focus of this study is the network among the
tourism-oriented businesses (the exchanges with each other) ,
and their links with tourism-oriented organizations. As the
focus moved from tourists to tourist-oriented businesses,
the type of information being studied changed from the
information tourists used to make decisions about where to
go and what to do while recreating, to the information
businesses exchange in everyday practices and how some
businesses acquire influence to affect the events of the

community as they related to tourism development.

2 . 4 . ; Why study communication exchange networks 1 n
toutism? Why is it important to understand exchange

networks in the tourism industry? The exchange networks
which this study focuses on reflect the patterns of com-
munication within the tourism industry. Communication is a
critical tool for tourism planning, promotion and marketing.
It is estimated that human beings spend up to 70% of their

waking hours engaged in some form of communication

23

(Mortenson 1979). Information is a powerful resource and
those who have access to and/or control over the
dissemination of information have the ability to influence a
great deal of human activity (McDonough and Cobb 1986). The
influence of communication is a vital part of most aspects
of the tourism industry. Communication serves as a link
between the consumers and the experience which they are
consuming, as well as a link between the tourism oriented
businesses themselves. These are not simple links involving
a source sending a message to a receiver who then acts on
the information. Communication in the tourism industry
involves complex information systems which include many
actors with a wide range of motivations that influence both
how the messages are constructed and received. In addition,
the purposes of communication vary widely (McDonough and
Cobb 1986).

One important factor to tourism management and promotion
is understanding the way information flows within the
industry. This study focuses on the flow or exchange of
information among tourism business and with tourism
organizations. It uses network analysis to understand
several different types of exchange among the tourism
business community. The control over and access to
information leads certain businesses to have power within
the community. The State of Michigan's tourism promotion is
based on its tourism oriented communities. These

communities and the surrounding natural resources are

24

Michigan's tourism product. Coordination and cooperation
among the communities and tourism oriented organizations,
such as the Michigan Travel Bureau are essential for
continued success of promoting Michigan as a tourist
destination. Network analysis can help to uncover the
communication patterns and strategies used to gain influence
and control over a community's tourism development.

Before going into more detail about the approach of the
study, it is necessary to briefly review the background and
terminology of the tool being used to examine the problem,

network analysis.

2.5.2 Background to network analysis Network analysis

is a relatively new and rapidly changing set of methods used
to study relationships so that patterns of order (or struc-
ture) underlying empirical observations can be uncovered.
Unlike more traditional methodologies such as regression,
analysis of variance or log-linear models that have large
bodies of literature, there are very few basic comprehensive
treatments of network analysis (Knoke and Kuklinski 1982).
Network analysis techniques have been developed by a variety
of social science disciplines such as geography, sociology,
communications, and anthropology, often with little
reference to each other. Terminology is as diverse as the
applications to which network analysis has been put. One
term may be calculated in several different ways, meaning

different things to different people. However, all of

25

network analysis has a basis in mathematics. For example,
Harary's Graph Theory (Harary and Cartwright, 1965) is an
important component of many aspects of network analysis.
This section will briefly review the history and introduce
some of the basic terms and concepts of network analysis as
it has been developed and used in communications and
sociology.

Sociological applications of network analysis had their
beginnings in the social preference and sociometry studies
of the 1930's (Moreno 1934). Sociologists have been a major
force in the rapid growth of network analysis, using it to
study many issues involving social structure within com-
munities, organizations, the elite, corporations and other
social systems. Anthropologists have also examined the
importance of network analysis for both conceptual and
pragmatic problems (Barnes 1972). For example, Bott (1971)
used anthropological methods in a classic study of the
interpersonal networks of husbands and wives. Communication
scientists made a significant contribution in the early
stages of development to network analysis through laboratory
experiments on small group communication (Bavelas 1950,
Leavitt 1951). Research in organizational communication
regularly uses network analysis techniques to study such
issues as morale and satisfaction (Wigand 1974, Danowski and
Farace 1974, Farace et al. 1982).

Over the past two decades, the number of researchers

using network analysis has rapidly increased and with it

26

network analysis terminology has flourished. Three concepts
or terms which are important in understanding network
analysis are 1) nodes, 2) links and 3) networks. Nodes
(also called actors) are the entities, persons,
organizations or events which define the network. In
information exchange networks, nodes are the peOple that are
exchanging information. For example, in the tourism
industry, one might study the exchange of information among
charter boat captains to see how coordination or conflict
arises. In this case the nodes would be the captains
themselves.

Nodes have attributes, such as age, education,
attitudes, opinions and values. These attributes or
characteristics are generally the focus of more traditional
research designs. In network analysis, however, the focus
is on the characteristics of nodes that come about as a
result of their being part of the system or network. If a
node has many links to others in the network, then it has
different network characteristics than a node with few links
within the network or a node that only has links to nodes
outside its network. For example, one concern of the
charter boat industry is whether part time captains lack
necessary information. No data are available to
substantiate this concern. Understanding node
characteristics might help explain whether part time charter
boat captains are isolated from this information and would

suggest ways to alleviate the problem if it exists.

27

A critical issue for network researchers is specifying
the boundaries of the system under consideration. By this
we mean clearly defining which nodes are to be considered in
the system and which are not. This is important because
many network analysis techniques require a complete census
(not just a sample) of all nodes. For some systems, rules
for inclusion are easier to specify than others. For
example, if the system is a company, then being an employee
may be the qualifying factor. However, defining the
boundaries of the system depends a great deal on the
researcher's purpose and is not always a clear-cut decision.
Consider a large city, for example, where it is impossible
to interview everyone. A subset of the system must be
chosen or different methods must be used. Basically two
ways to specify boundaries are to, 1) set apriori boundaries
or, 2) have the nodes themselves specify the boundaries
using snowball sampling (Knoke and Kuklinski 1982). For
example, if you were interested in a tourism organization's
influence on legislation, an apriori boundary specification
might be based on a selected geographic area (e.g. tourism
oriented organizations in the Straits area) or
organizational size (e.g. the ten organizations with the
largest memberships). To let nodes specify the boundaries,
you might have legislators involved in tourism legislation
list influential organizations. The organizations would

then provide an additional list of contacts.

28

The second concept is that of links. Links are the
relationships between the nodes and they can be most any
type of relationship. Links may be roads between cities,
money transferred among businesses, gifts given among
friends or publications sent to subscribers. Links have
content. In this dissertation, information exchange is
emphasized. The link is the act of exchanging and the
content of the link may be a certain type of information,
such as business management ideas, what events are happening
in town or in boating and sport fishing regulations.
Besides content, links can have strength and direction.
Strength may be measured in various ways, such as frequency
of communication. For example, how often do tourists
exchange information about conditions at a certain resort?
Sometimes links are considered as either present or not
present and therefore have no variation in strength.
Direction of the link may be important in some situations,
such as in determining the influence or power of a node.
For example, the power of a manager to enact change may be
defined as increasing with the number of information links
directed towards that manager which s/he does not
reciprocate.

The third concept is that of the network. Networks are
the patterns formed from the combination of all the nodes
and links within the system. They have characteristics of
their own and may even be chosen as a unit of analysis.

There are a variety of network characteristics. For

29

example, networks may be dense, having many links, or they
may be sparse, having few links throughout the system.

Another example of a network characteristic is that of
network centrality (Freeman 1979). Networks may have one
central node/actor with links from many nodes directed to
it. This is a network with high network centrality. A
network with low centrality has several groups and no
central figure (Figure 4). Network centrality would be
important in the spread of information by an agency within a
community if the community is the unit of analysis. In a
community with high network centrality it would be crucial
to have the central node committed to agency information.
Because of the structure of the network, the information
could be disseminated efficiently. This may be the case in
a tourism-oriented community with a strong and active
leader. A person desiring to build a tourism attraction
might have only to win the support of the leader to garner
the support of the community. In a community with low
network centrality, where there is no central figure, it
would be harder to win community support and many more
people would have to be informed and influenced.

In any discussion of the concepts of network analysis it
is important to state that the descriptive results are only
one part of the utility of network analysis. Of even more
consequence is the potential for using identified patterns
of communication as an explanatory variable in understanding

the functioning of social systems (Knoke and Kuklinski

$52.56 .2255: E moi—=53 .e 2.3.:

2:253 c.3332 2:... 3:228 .225»: :3:

 

 

31

1982). A goal of this study is to test the use of the
network congeptiofmcentrality to predict which businesses
owners or managers are influential in affecting the outcomes

of tourism development in their communities.

2.3.3 Network cegtgglity The first research application

of network centrality was made in the late 1940's by a group
at the Massachusetts Institute of Technology doing
experimenting on small group communication (Leavitt, 1951;
Bavelas, 1950). These early reports concluded that central-
ity was related to group efficiency in problem—solving,
perceptions of leadership, and the personal satisfaction of
participants. Since then the concept of centrality has been
applied to other areas of study besides power. For example,
Czepiel (1974) found centrality was related to early
adoption of new technology and Pitt (1965) examined the
consequences of centrality in communication and transporta-
tion paths on the emergence of Moscow as a city from the
many hamlets in the area.

The relationship between centrality and power has also
been studied at the organizational level. Hinings et a1.
(1974) found a modest correlation between centrality in an
organization's workflow and the power attributed to the work
unit.

One of the problems that developed as more researchers
used the concept of centrality in studying different areas

was that no one agreed on what exactly centrality was and

32

how to measure it. This is still true today to some extent.
Freeman (1979), however, went a long way toward clearing up
the confusion by clarifying three main concepts of
centrality and the calculations for each. In reviewing the
literature, Freeman (1979) found three related measures of
centrality: 1) degrees or number of contacts, 2)
betweenness, and 3) closeness or proximity. Others have
used his framework and measurements (Brass, 1984).

The simplest and the most intuitively obvious is the
degrees measure. It is calculated by simply counting the
number of other points to which any given point is
connected. "With respect to communication, a point with
relatively high degree (centrality) is somehow 'in the thick
of things'... As the process of communication goes on in a
social network, a person who is in a position that permits
direct contact with many others should begin to see himself
and be seen by these others as a major channel of
information. In some sense he is a focal point of
communication, at least with respect to the others with whom
he is in contact, and he is likely to develop a sense of
being in the mainstream of information flow in the network"
(Freeman, 1979). Thus the degree measure of centrality is
considered a measure of potential communication activity.
To illustrate, in Figure 5 point A is connected to or
adjacent to four other points; therefore its degree is four.

The second measure of centrality discussed by Freeman

(1979) is betweenness. It is based on the frequency with

33

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5..... 2...— ..5. «2...: 2... 5..... is... .m 2.5:

 

 

34

which a point falls between pairs of other points on the
shortest paths connecting them. In Figure 5 point (node) A,
the center, shows the highest betweenness. Figure 6 shows
the ten possible paths (or as Freeman calls them geodesics)
for Figure 5. Four of them (1,2,3,4) have distances of one
(two points linked with one path; these connect A with each
of the other points. The other six all have distances of
two and all involve point A as the mid-point between the
others. Thus, A falls in the paths (in between two points)
for six out of the ten geodesics in this graph. The
betweenness for point A is six.

A point or node that "falls on the communication paths
between other points exhibits a potential for control of
their communication. It is this potential for control that
defines the centrality of these points" (Freeman, 1979).

Calculating betweenness becomes more complicated when it
involves more than a simple graph. Freeman developed a
measure which deals with the fact that in more complex
networks, there are many possible paths. He makes the
assumption that any one of the paths could be used to
control the transfer the information from one node to
another and that all paths of the same length have the same
potential for control. Freeman describes this as partial
betweenness and defines it in terms of the probability of
using one path versus another. The overall centrality of a
point is the sum of the partial betweenness values (Freeman,

1979).

35

 

 

 

 

 

 

 

 

 

 

 

 

I. E aA 6
E i“
90 L 1“
2. 7
i“ be
r“ all
3. .9 9 a.
a:
9“ 3c
4 9.
or c“ all
I:
5 a A o w A
E

Figure 6. The ten paths from graph of Figure 5
Source: (Freeman 1979)

36

The third type of centrality is based on the degree to
which a point is close to all other points in the graph. In
Figure 5, node A is at a distance of one from all other
nodes in the graph. The rest of the points are at a
distance of one from point A, but at a distance of two from
all other points (they have to pass through point A).
Therefore, point A is the point closest to all other points.

This third view of point centrality is also related to
control of communication but in a somewhat different way.
Here, a point is viewed as central to the extent that it can
avoid the control potential of others. Bavelas (1950) noted
that a non-central position "must relay messages through ...
others". A central position is one that is not dependent on
intermediaries for messages and is therefore associated with
independence (Leavitt, 1951). Freeman (1979) developed his
measure for closeness based on Sabidussi (1966) who defined
the most central point in a network as the one with the
minimum cost of time for communicating with all other
points. "With respect to time and cost efficiency, then, a
point is central to the degree that the distances associated
with all its (paths) are minimum. Short distances mean
fewer message transmissions, shorter times and lower costs"
(Freeman, 1979). This measure is only meaningful for a
connected graph. A connected graph is one in which there
are no isolated nodes or groups of nodes, each node is
connected to the rest of the network in some manner. In an

unconnected graph, every point is at an infinite distance to

37

at least one point so the measure cannot be calculated
(Freeman, 1979).

Brass (1984) uses two of Freeman's three definitions of
centrality to examine "the relationships between structural
positions and influence at the individual level of analy-
sis". The betweenness and closeness measures of centrality
"roughly correspond to the two conditions for acquiring
power. Decreasing one's dependence on others corresponds to
increasing independent access (the closeness measure), while
increasing others' dependence increases control (the
betweenness measure)". Brass labels closeness as access and
betweenness as control. "The distinction between the two
centrality measures is exemplified when one considers a
group of individuals who each have direct links to all
others in the group. All individuals would receive the
highest possible access score (because of direct links to
all others), but all would also receive the lowest possible
control score (because no individual mediates any of these
connections)" (Brass, 1984).

Brass is one of the few who have used more than one of
Freeman's centrality measures. This study also uses
multiple measures of centrality and seeks to compare each
measure's ability to predict attributed influence. Because
of the exploratory nature of applying centrality measures to
predict influence among tourism-oriented businesses within
communities, and because each of the three measures

describes different aspects of a person's ability to acquire

38

influence, all three measures are examined in this
dissertation. They will be titled according to Freeman's
labels: degrees, betweenness and closeness. How these
measures are used in this research is described in more

detail in Chapter 3 - Theoretical Framework.

2.4.4 nge; and Influenge Power and influence within

the community is related to the ability certain people and
organizations have to manipulate their environment.
Tourism-oriented businesses want to be able to affect the
outcome of events related to tourism development in order to
enhance it and hopefully thereby to gain more business
themselves. This then is the foundation for this research:
that businesses within communities seek to gain influence by
using strategies to gain control over valued resources.

Tourism is greatly affected by the community as a whole.
Tourists perceive a community or area as part of a system of
things to do and see, places to eat, place to stay. They
don't choose a destination based on one factor alone. All
parts on the recreation experience come into play.
Different tourists have different preferences as well as
impressions of an area (Fridgen, 1983), but the community's
general attraction for tourists comes from a favorable
combination of all or at least most of the businesses in a
community.

The manner in which a community develops is largely

dependent on a few influential people (Laumann 1966:

39

Laumann, Marsden and Galaskiewicz 1977). Much has been
written about these people, usually referred to as
"community elites" in the sociological and communications
literature (Galaskiewicz 1979: Laumann and Pappi 1976:
Hunter 1953; Keller 1963: Perucci and Pilisuk 1970; Liebert
and Imersheim, 1977). Many of these researchers have
examined the means, methods or manner in which elites gain
power and influence. These are the people who will steer
the development of the town towards tourism or prevent the
growth of tourism-oriented enterprises. Tourism workshops,
such as one put on by the City of Gaylord at the 1984 First
Annual Governor's Tourism Conference have also stressed the
importance of community leaders in making tourism happen in
a community. Who is influential in a town, that is who has
the power to effect the manner in which tourism develops or
doesn't develop, is an important issue for both individual
communities and for the state as a whole.

Power theories posit that power is derived from control
over strategic interdependencies (Emerson, 1962: Cook, 1977:
Yuchtman and Seashore, 1967: Benson, 1975; Aldrich, 1976:
Aldrich and Pfeffer, 1976: Pfeffer and Salancik, 1978). In
exchange networks power is acquired by gaining control over
various types of valued resources. These resources include
information, monetary supplies from outside the network and
alliances (Boje and Whetten, 1981). "A basic postulate of
power theories is that network centrality enhances power

because the ability to control valued resources increases as

40

a function of proximity to the core of a system of transac-

tions" (Boje and Whetten, 1981).

2.5 Summary

This chapter reviewed the literature in the field of
tourism to give a perspective to the different approach to
tourism research this dissertation is taking. The problem
was laid out in general and the history and terminology of
network analysis was briefly reviewed. The chapter
concluded with the reasons for applying network analysis to
the problem, what type of exchanges were part of the study,
why attributed influence was important to tourism

communities and an explanation of centrality.

CHAPTER 3

THEORETICAL FRAMEWORK

3.1 Introduction

This chapter lays out the theoretical framework guiding
this research. It begins by reviewing the literature in the
areas of power and community research. Then two models on
which much of this dissertation is based are presented:
Burt's model of power as influence and Boje and Whetten's
model of network centrality and attributed influence.
Finally my model of exchange networks and influence in
tourism oriented communities is presented along with the

propositions which are to be tested in this study.

3.2 Power and community research

Liebert and Imershein (1977) begin the book ngez, Pata-
gigms, end ggmmunity Beseerch with a paper in which they
review the past traditions of community power research and
categorize them into three alternative paradigms. Until the
mid-1960's much of community research was dominated by a
classic debate pitting elitists against pluralists on the
question of power structure. On one side was Hunter with
his easily replicable methodology for finding reputed local

power elites, and on the other side was Dahl's more involved

41

42

technique for finding pluralist sets of decision par-
ticipants in each of a variety of issues. As it became more
obvious that findings of the elitists and the pluralists
were much the same, the argument subsided.

Three alternative research paradigms developed. Some
researchers took the more macroscopic view, focusing on the
power of organization and system's capacity to produce
public programs. Others took a more microscopic approach
looking at the individually experienced impact and creation
of power. A third group merged the elitist-pluralist
approaches into a more elaborate interpretation of
interpersonal community influence relations via the social
exchange of resources among diverse elites (Liebert and
Imershein, 1977).

The earliest paradigm to evolve was that of the struc-
turalists, emphasizing the macrolevels of organization and
system. They were concerned with the impact of the manner
in which power was arranged in a local community. The
community or the system was the unit of analysis and the
dependent variables were typically public policy outcomes.
Important to this tradition are the "assumption of the
central importance of structure among formal organizations,
interest groups, and interlocking hierarchies, yielding a
notion of power as a wholly systemic rather than personal or
interpersonal property. Social change is envisioned as
largely an unguided process determined by the distribution

of population, organizations and technology. The ways in

43

which this kind of power are said to be organized involve
such matters as the diversity of organized units, the degree
of interface or interlock among them, dependence on or
access to extra-community power centers, concentration or
centralization in the local managerial or decision making
sectors, and the relative size of various local interest
groups of power centers" (Liebert and Imershein, 1977).

The data commonly used by researchers of this paradigm
is often crude and imperfect, but it's availability from
census, government and corporate records has caused an
explosion of research on comparative urban public policy.
Critics (Alford, 1975) of this approach generally note that
the variables sometimes do not mean what researchers claim
they mean. This research misses the qualitative aspects of
power, or how various actors, interests and benefits in
power relations develop.

The second paradigm developed as a result of this
concern with the qualitative and more individualized aspects
of power. These researchers are interested in ways in which
individuals "exercise countervailing power against the
rationalized technocracy of bureaucratic dominance" (Liebert
and Imershein, 1977). This approach sees social change as a
fairly indeterminate process of individuals actively
creating new options and rejecting old ones. There is an
emphasis on the impacts of policy decisions on the
community. They criticize the approach frequently taken by

the structuralists of studying only the elite decision

44

making component of the community and ignoring the rest.

The need for qualitative analysis is stressed, as Warren
(1974) notes, some of the most relevant data came from
qualitative and biographical interviews which allow for
unanticipated evaluative responses. The third paradigm
focuses on power as it is expressed through social exchange.
The method of this approach is to try to determine those
individuals in a community who possess certain major
resources for power (reputation, position, wealth) and to
explore the patterns of relationships between these
individuals. By tracing the pattern of exchanges, one finds
the dynamics of power utilization. Resources are defined
broadly and include not only material, quantitative
resources, such as wealth, but qualitative non-material
resources such as information. The basic observations are
behavior or reported behavior of persons or organizations
relating to each other, which builds into a network of
linkages. Liebert and Imershein feel the most distinctive
aspect of the exchange approach is the "central concern with
the elementary building blocks of more or less directly
measured dyadic relations, the elaboration of these
relations within matrices, and the identification of power
and influence within and between sectors of the full
system". Community social change is seen as a deliberately
pursued process of redistributing the resources or altering

the structural contingencies which affect their utilization,

45

with "community decision making" being a manifestation of
the change (Liebert and Imershein, 1977).

The research described by this dissertation falls into
the social exchange paradigm. It views communication and
information as resources exchanged between people for
purposeful reasons, just as money is exchanged for reasons
of gaining desired results or products. The result of
interest in this research is the garnering of influence and
power within the community. Communication resources
exchanges are correlated with influence and patterns of
communication exchange predict attributed influence. The
following section briefly reviews exchange theory as it is

applied to this research.

3.3 Exchange Theory

It is important to remember that the subject of the
study is communication in tourism. Communication is viewed
as an exchange of information between two or more parties.
There are many different theoretical approaches taken by
communication scientists to understand different aspects of
communication. However, one theory which is particularly
applicable to communication among tourism businesses and
organizations is exchange theory. When applied to
communications, exchange theory is based on the idea that

communication and information are valued resources exchanged

46

by persons in a purposeful manner with the intent of gaining
something, just as money is exchanged.

Since the subject of the research is communication
between community tourism oriented businesses and organiza-
tions, crucial questions arise about what types of
communications exchanges are made between businesses and
organizations and between each business and why are they
made. Exchange theory would say the exchanges are made in
order to gain something. One of the important things many
businesses and organization want to gain is the ability to
manipulate their environment to the extent possible.

Prestige and influence within the community are related
to the ability certain people and organizations have to
manipulate their environment. Tourism oriented businesses
want to be able to affect the outcome of events related to
tourism development in order to enhance it and hopefully
thereby to gain more business themselves. This then is the
foundation for this research: that businesses within com-
munities seek to gain influence by using strategies to gain
control over valued resources.

What are the valued resources that businesses seek to
gain control of? Farace et a1. (1982) reviewed Levine and
White's (1961) pioneering work which first linked social
exchange theory with interorganizational relations. Levine
and White grouped interorganizational exchanges into two
general categories: 1) the exchange of actual elements

(money, clients, services) and 2) the exchanges of informa-

47

tion about "the availability of these organizational
elements and on rights and obligations regarding them".
Coordination is the important issue in most
interorganizational analyses (Yep 1974). Researchers
usually seek to identify the exchange links necessary for
coordination. Three major aspects of coordination are
information exchange, resources exchange and sentiment
exchange (Farace et al. 1982). For example, Mitchell
(1969) lists three main networks important to
interorganizational relations: communication, resource and
social. Galaskiewicz (1979) has three similar exchange
networks: information, money and support.

The exchange links chosen for examination in this
dissertation research are: communication, referral of
clients, and joint programs. These exchange types related
generally to the types reviewed above. They were chosen
based on a model by Boje And Whetten, which is explained in

some detail later in this Chapter.

3.3 Conceptualizing power as influence - Burt's model

Traditional approaches to defining and measuring power
have generally set power equal to manifestations of power or
network of influence relations. That is, a person is
consider powerful if actors in the system say s/he is
influential in affecting the outcome of events. The method

is popular because it is easy to measure using relational

48

data, but it doesn't answer the question of why one actor
influences another or how.

Burt (1977) lays out a model of power from an exchange
perspective, which Boje and Whetten (1981) incorporate into
their model of centrality and influence in an interorganiza-
tional network. Burt argues that there are three aspects of
power: 1) control over the resources as bases of power, 2)
processes of converting the bases of power into the manifes—
tations of power, 3) the networks of influence relationships
as a manifestation of power. Figure 7 presents his model of
these three aspects of power in causal order.

One important aspect to being powerful is the ability to
control resources. Burt states a basic assumption of his
research is that "actors are assumed to be purposive in that
they will use their control of valuable resources in order
to improve their individual well-being". Determining what
resources need to be controlled for an actor to gain power
and which actors have large proportions of these resources
has been a popular way for researchers to examine power
within systems. This is because of the obvious importance
of the distribution of resources among actors and because of
the relative ease of developing measures of the distribution
of many types of resources. The major drawback to this
equation is that it fails to specify whether of not an actor
will use the resources to determine the outcomes of events
which affect the other actors in the system.

The traditional method discussed above of examining

49

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.25....

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2.. «a «2.22... «......e.«..._....... 2.. ......
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50

manifestations of power asks what actors are viewed by what
other actors as being powerful. The manifestation of the
distribution of power is the network of attributed influence
and deference relations among actors. The influence of one
actor over another is the belief by one that the other has
greater relative power. Burt criticizes this approach
because it ignores the process of converting control over
resources into the distribution of influence among actors in
the system.

The third aspect of power links the two previous aspects
together by focusing on how the bases of power are converted
into manifestations of power via the processes of power.

The control over valuable resources is expanded to consider
both possession of actual resources and constraint, defined
as the channels used to constrain others from gaining
resources. The processes of converting bases of power into
manifestations of power are the manipulations of economic
and social exchange relations, "such that each actor jointly
occupies a network position with those actors who possess
resources of greatest interest to him - given his initial

pattern of exchange relations" (Burt 1977).
3.4 Boje and Whetten model

Boje and Whetten (1981) use Burt's typology of power and
include all three aspects of power in their causal model,

though they do not use his methods in determining each

51

aspect. They examine "the effects of organizational stra-
tegies and contextual constraints on location in interor-
ganizational transaction networks and the effects of strate-
gies, constraints and network position on attributions of
influence". They propose a model with 11 propositions
specific to social service organizations. Working with
client referral networks in 17 communities, they found that
centrality in 1) the referral network, 2) communication
exchanges and 3) joint program activities were all
positively associated with attributions of influence. The
model of attributed influence in tourism oriented
communities proposed in this dissertation is based on Boje
and Whetten's model which will be discussed in detail.

The exchange perspective, on which Boje and Whetten base
their model, states that there are two important dimensions
of interorganizational networks. First, there are the
horizontal differentiations of exchanges or transactions
into a more or less stable subsystem and second, there are
vertical differentiations into the dominant elite core and
the less powerful and more dependent periphery. Research by
Aldrich (1972), Benson (1975), Cook (1977) and Cook,
Emerson, Gillmore, and Yamagishi (1983) have sought to more
clearly conceptualize the emergence of power differentials
and other patterns of relations in vertical dominance.
However, as Boje and Whetten note, most interorganizational

network studies have focused on horizontal patterns.

52

A basic postulate of power theory is that network
centrality enhances power in exchange networks because the
ability to control valued resources increases as one moves
closer to the core of the system of transactions. The
relationship between network centrality and attributed
influence has been tested in small groups, (Bavelas, 1948,
1950: Leavitt, 1951: Davis, 1969: Shaw 1971) intraorganiza-
tional environments (Hinings et al., 1974: Brass, 1984) and
interorganizational settings (Laumann and Pappi, 1976;
Galaskiewicz, 1979) and found generally to hold.

The purpose of Boje and Whetten's study was to develop
and test a causal model that would reflect the unique
characteristics of interorganizational networks.
Interorganizational networks are those between
organizations, groups or businesses. Intraorganizational
networks are those within an organization or business.
There are two main differences between interorganizational
networks and group or intraorganizational networks. First,
interorganizational networks are emergent in nature, they
emerge of their own accord and are not determined as a
result of the formal structure of an organization. Formal
organizational structure lays out the formal pattern of
communications (e.g. the president talks to the vice-
presidents and they in turn talk to the managers, etc.).
Some group network analysis studies use formal experiments
and therefore impose structure for purposive experimental

manipulation.

53

The second difference is visibility. At the inter-
organizational level it is more difficult to perceive the
underlying relationship between network centrality and
attributed influence. In the other settings, the network
position is represented by the physical arrangements or
shown graphically on organizational charts.

Boje and Whetten (1981) found that an organization's
desire to increase it's prestige among other organizations
greatly affected each organization's programs and policies.
Their model examined the strategies used in "jostling for
position in the pecking order". It was also known that
these social service organizations were significantly
constrained by funding and policy decisions originating from
outside the network. Figure 8 displays the general aspects
of the model, which show how constraints and strategies
affect centrality which in turn affects influence.

Boje and Whetten (1981) state that all three aspects of
Burt's typology are included in the model.

"Organizations formulate strategic plans to
improve their resource transaction position.
However, this is limited by contextual constraints.
Over time, strategies and constraints combine to
determine the organization's position in a local
resource exchange network. Out of the structure
of dyadic exchanges emerges a network configura-
tion wherein a position of centrality enables an
organization to control the flow of resources
between more peripheral actors. Network position
becomes translated into attributed influence as
others attribute to central actors greater poten-
tial for establishing coalitions to enhance

their political influence".

Eleven propositions were chosen as tests for the

model: one general one on attributed influence as a result

54

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55

of centrality, four propositions on organizational
strategies affecting referral centrality, and six on
constraints upon referral centrality. The propositions and

results are as follows:

Attributed Influence resulting from Network Centrality
Eroposition ;. Relatively central organizations will

have greater influence across three types of network rela-
tionships: (1) referral exchanges; (2) joint program stra-
tegies; and (3) formal and informal strategic communica-
tions. This proposition was well supported, with a mean
correlation of .60. All correlations were significant at

the .001 level.

Organizational Srrategies Affecting Referral Centrality
Eropositign . A joint program strategy will increase

the flow of clients through an organization and enhance its
attributed influence. Strong support was found for this.

Eroposition . Strategic formal and informal communica—
tion linkages will increase both an organization's knowledge
of critical contingencies affecting attributed influence and
its client intake and output options. Strong support was
found for this.

o os' on . The placement of administrators from

other organizations on an advisory council or board of

directors will enhance influence with organizations

56

referring clients or providing services. No support was
found for this proposition.

Ergpggirigg_§. Providing services for clients who are
less difficult to treat will facilitate the maintenance of a
large number of interorganizational relationships, which
will increase attributed influence. Only moderate support
was found for this.

ons ’ ts u on er 't

Eroposition . Locally administered organizations will
be better adapted to local constraints and will therefore
have greater attributed influence than state of federal
organizations. The reverse was found to be true. Non-local
organizations were attributed more influence.

Eroposition 7. Larger organizations, in terms of number
of staff members and number of services offered, will be
less constrained in obtaining and making client referrals
and will have more attributed influence. This proposition
received only partial support.

Ergposition . Organizations more geographically proxi-
mate to other community organizations will receive more
referrals and will consequently have more attributed in-
fluence. This proposition also received only partial
support.

Eropositign 2. Organizations having ideologies dis-

similar to the ideologies of other network organizations

57

will be less central and will have less attributed
influence. No support was found for this.

Eroposition 10. Organizations having a greater number
of imposed relationships with organizations will be more
likely to be central but will have less attributed
influence. Only partial support was found for this.

Ergpggirigg_;_. Organizations having a large number of
interpersonal ties with other organizations will be more
able to take a central position in the referral network and
will have more attributed influence. A distinction was made
between social ties and work ties. It was predicted that
work ties would have a stronger effect on network centrality
because it would produce more relevant information. Work
ties was the only measure found to be significant.

To test the relative contributions of the strategy
and constraint variables on the referral process and to see
how these combined with referral centrality to account for
attributed influence a path analysis was done. First a
factor analysis was done to reduce the number of variables
to a manageable size. This resulted in three constraint
factors and one strategy factor. The results of the path
analysis are shown in Figure 9.

Boje and Whetten (1981) discuss five comments on these
results. First, dropping several variables and grouping the
others had only a modest effect, dropping the R2 to .56 from
.60. Second, as predicted, the direct effects of referral

network centrality and strategies on attributed influence

58

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59

were significant. Third, the direct effects of the three
constraints on attributed influence did not prove to be
significant. Fourth, there was a substantial indirect
effect of strategies on attributed influence (.15). Fifth,
the two constraint indices (organizational size and informal
boundary spanner ties) had significant effects on
strategies, as predicted. Sixth, only one of the constraint
indices (low administrative autonomy) had significant
effects on referral network centrality.

Based on this idea of strategies and constraints affect-
ing network centrality and attributed influence, we will now
look at how this must be adjusted to be applied to tourism
oriented businesses. The next section will present the

model, propositions and specific hypotheses.

3.5 HOdel of Influence and Network Centrality in Tourism

Business Communities

In order to adapt the Boje and Whetten model to tourism
oriented business exchanges within a community, we must
examine the differences in each of the parts of the model.
To begin with, the resources which are valued may differ.
Though referrals from other businesses are important and
valued resources, the visibility of the referrals, meaning
which businesses gets lots of referrals, is not as obvious
as it probably is among social services organizations. For

example, Boje and Whetten (1981) note that in their study

60

"CETA was one of two most central actors in 13 of the
networks. CETA was repeatedly described as 'the richest kid
on the block'. 'They used their money and clout to muscle
into old established organizational c1iques.' It appeared
that people were attributing power to CETA on the basis of
its actions associated with its movement into the center of
the manpower network." In the interviews for this study,
owner/managers knew who was powerful, but this power was not
usually conscientiously thought of as a result of control
over the resource of referrals.

In addition, who gets the referral depends to some
extent on what the product of the business is. Thus, most
businesses are likely to refer more to certain types of
businesses. For example, stores are more likely to have the
opportunity to refer their customers to other stores, and
motels are often asked to refer to restaurants.

A resource valued among businesses is information about
a wide variety of business oriented subjects. Thus being
central in an information network would be likely to predict
attributed influence. Who is knowledgeable and who is not
is probably more obvious to business owners/managers than
who is receiving referrals. Thus, the model will suggest
that there are two networks in which centrality will predict
influence.

Secondly, the strategies used by the businesses to gain
centrality in the information and referral networks and gain

attributed influence directly will be different from social

61

service organizations. The strategies examined in this
research are laid out in the specific propositions. There
are, of course, more possible strategies, but by necessity
the study must be limited to make it manageable. The stra-
tegies include, 1) levels of direct involvement in community
and professional organizations, 2) communication with staff
of community organizations, 3) communication with staff of
out—ofecommunity organizations, 4) joint programs and
cooperation among businesses and, 5) whether the owner/mana-
ger tracks their customers. Communication with other
business owners/managers is also a strategy used to gain
referrals, and the communication and referral centrality are
possibly correlated. The basis on which each of these
strategies was chosen is described under each of the ap-
propriate propositions in the next section.

Thirdly, the environmental constraint in a tourism
business community environment will be somewhat different
from social service organizations. There are less formal
outside constraints, especially in terms of funding, since
these businesses don't often get government support (except
recreation entities, like State Parks, etc.). Boje and
Whetten note that "attributions of influence may ... be
based on inherent organizational characteristics." They
designate them as contextual constraints in their model
(Boje and Whetten 1981). Though they may be viewed as
constraints, this model will consider them simply as

characteristics of the businesses or the owner/managers, and

62

label them as such. The characteristics examined are also
laid out in the propositions along with the background and
justification. These include characteristics of the
businesses, such as geographic location or size of the
business, and characteristics of the owner/manager,
including age, educational background, and location of
residence.

The model of influence and network centrality in a
tourism business community proposes that influence is
attributed more to those business owner/managers who are
central in both the communication (talk) and the referral
networks than to those who are peripheral or less central
(Figure 10). In addition, the model suggests that there are
strategies that are used by business owner/managers to gain
centrality in either or both the talk and the referral
networks. These strategies may also be used to gain the
owner/managers influence directly, rather than through
gaining network centrality. Furthermore, certain charac-
teristics of the owner/managers or the businesses serve to
constrain gaining network centrality or influence attribu-
tions. Each of the strategies and characteristics and its
proposed correlation are listed in Figure 10. and further

discussed in the following propositions.

63

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3.6 Propositions

3.6.1 General relationships

£rgpggi§ign_;: Attributed influence will be correlated
with the degree or amount of talk such that the more people
in the network say that they talk to a business, the more
that business will be considered influential.

Ergpgsitign_a: Attributed influence will be correlated
with the degree or amount of referrals, such that the more
people in the network refer their customers to a business,
the more that business will be considered influential.

o 03' 'o : Attributed influence will be correlated
with all three types of centrality (Betweenness, Closeness,
and Degrees) in the talk network, such that the more
central, the more influential they will be considered.

Boje and Whetten (1981) examined two communication
mediums: interpersonal exchanges between organizational
representatives, and formal transmittal of written
correspondence, brochures, and program announcements. They
found a strong correlation between both types of
communication and influence and referral centrality.
Informal communication was more strongly correlated with
attributed influence and formal communication had stronger
correlation with referral centrality.

Rogers and Haas (1979) found that selectivity in the

release of information about activities and preferences was

65

an important strategy in interorganizational networks. Some
even argue that information is the medium through which
influence and control are transmitted (Pondy 1977). Some
examples of this include supplying other organizations with
strategic information about agency activities and initiating
personal communication with decision makers in other or-
ganizations in order to frame decision premises, define
alternatives, influence actual decisions, or block implemen-
tation of decision results (Bachrach and Baratz, 1970).

05' ' n : Attributed influence will be correlated
with all three types of centrality (Betweenness, Closeness,
and Degrees) in the referral network, such that the more
central, the more influential they will be considered.

Receiving customer referrals from other businesses is an
important means of increasing one's clientele. Referring
ones own customers to other businesses in the community is
also beneficial in several ways. It keeps the customer in
town, which is good for the community. Hopefully the
customers get what they want and are satisfied with their
experience within the community. Lastly, the favor of
referring customers will likely be returned by the other
owner/managers. Boje and Whetten (1981) state that client
referral is a most important type of exchange for social
service organizations. "Regardless of an agency's
motivation for exchanging clients, these transactions

represent the most important purpose for developing an

66

interorganizational network among these agencies (Boje and
Whetten 1981).

As actors gain more referrals, they gain control over
this resource and they become central in the referral
exchange networks. This will, in turn, lead other actors to

attribute greater influence to them (Boje and Whetten 1981).

r o 08' 'ons

Ergpgsitign_§: High centrality in the talk network will
be correlated to high centrality in the referral network.
Boje and Whetten (1981) note that "strategies to achieve
centrality in communication... networks can be viewed as
strategies to enhance position in the referral network."
Ergposition §: Involvement in community or professional
organizations will be correlated with influence and high
centrality in both the talk and the referral networks.
Perrucci and Pilisuk (1970) found that persons influen-
tial in the community held positions in a significantly
larger number of community based organizations than persons
not judged to be influential. By occupying numerous posi-
tions, these people were strategically located at the
intersections of communication and resource networks, and as
a result were viewed as potential coalition builders. It is
proposed that involvement in community or professional
organizations by business owner/managers in tourism oriented

communities would allow those most involved to gain central

67

positions within the talk and referral networks by giving
them the opportunity to regularly talk with other
owner/managers and particularly other community
influentials.

Ergpg§i§i2n_1: High degree or amount (descriptive
frequencies) of talking to organizations both inside and
outside the community will be related to high centrality in
the talk and referral network and high amount of attributed
influence.

The importance of communication and gaining centrality
in communication networks has already been discussed in
detail. Communication with tourism oriented coordinating
organizations is no less important. Brass (1984) found that
informal communication with top-level executives and
supervisor was instrumental to acquiring influence. These
leaders in the business world can be seen as very similar to
the leaders of organizations, such as the Travel Bureau,
which coordinate and promote tourism in Michigan.
Communication with these organizations' staff is likely to
increase ones knowledge and contacts, thus increasing
control over valued resources.

Ergpggitign_§: High amount of joint programs with other
businesses will be correlated to high centrality in both the
talk and referral network and attributed influence. Boje
and Whetten (1981) found strong support for joint programs
increasing the flow of clients through an organization and

enhancing its attributed influence.

68

Ezgpggi§19g_2: Those owner/managers who use methods to
gather knowledge about their customers will be higher in
talk centrality than those who do not use methods to track
their customers.

Owners/managers who track their customers will be viewed

as more knowledgeable and will be sought out for advice.

3,§,3 gharacteristics gropositiogs

Eroposition l0: The older the business the greater the
influence, and higher the talk and referral centrality.

Older businesses are less constrained by lack of aware-
ness on the part of other business owners/mangers.

Ergpgsitign_;1: The larger the business (in terms of
number of employees) the greater the influence, and higher
the talk and referral centrality.

Larger businesses will be viewed by other owner/managers
as having a larger interest in the community and thus more
influence. The larger the size the more likely it will
offer products that are desired and thus will be referred to
more often. Boje and Whetten (1981) found some support for
larger organizations having more attributed influence.
Having a large staff tends to increase an organization's or
business's visibility in the community (Boje and Whetten
1981). A large organization can maintain more
interorganizational ties, can process more clients

(Galaskiewicz 1979), and can offer a wider range of services

69

(Roger and Haas 1979). Being able to offer numerous
services enables organizations to establish linkages with a
variety of interorganizational networks and client pools
(Boje and Whetten 1981).

o ' 'on : The older the owner/manager, the higher
the talk centrality.

Older owners/managers are viewed as having more ex-
perience and will sought out for advice.

Eroposition lg: The more education, training and ex-
perience the owner/manager has, the higher the talk central-
ity.

Owners/managers with greater education are viewed as
more intelligent and will sought out for advice. This could
be viewed as a strategy as well, in that those who seek to
increase their education and training will increase their
probability of having higher talk centrality.

£rgpgsitigg_11: If the owner/manager lives near the
city and is a year-round resident of the city, they will
have higher talk centrality.

Owners/managers who live close by and stay year-round
(as opposed to living some where else for the off-season)
will be viewed as being more familiar with the community,
more knowledgeable and aware of what is happening in the
community and therefore will be sought out for advice. This
could be viewed as a strategy as well, in that those who

choose to live close to the community will be viewed as more

7O

familiar and concerned with the community and will have
higher talk centrality.

Eropgsition 15: Those owner/managers whose main source
of income is their business will be more central in the talk
and referral networks.

If a tourism oriented business is not the sole source of
income for a owner/manager, it is more likely to be treated
like a hobby and the best business management practices are
likely to not be applied. This will make other owner/manag-
ers less likely to refer their customers to them and less

likely to seek them out for advice.

3.7 Summary

This chapter explained the theoretical framework on
which this dissertation is based. It began by describing
research in power and the community, and three paradigms
used to organize different types of research. Exchange
theory as applied in this study then was discussed. Burt's
model of conceptualizing power and influence was discussed
as it is a base for Boje and Whetten's model, which was then
explained in detail. Finally, the model which this

dissertation tests is described and propositions given.

METHODS

The purpose of this study is to examine the relationship
between communication exchange network variables and
attributed influence in the tourism industry in Michigan.
This research followed two years of data collection on use
of information by tourists and tourism-oriented businesses
(McDonough and Eckstein, 1983; Eckstein, 1983). However,
this dissertation project collected information of a
slightly different nature due to its focus on attributed
influence and how network variables are related. The
methods used here more closely follow the methods developed
and used for interorganizational and organizational
communication and social network research.

The methods used in collecting data are crucial to the
validity of the study. There are always a variety of ways
to gather data, each way having advantages and
disadvantages. Data collection procedures for network
analysis studies are probably less standardized than for
other types of studies because there has not been a long
history of development and testing of different methods. A
number of specific considerations need to be made due to the
nature of network analysis concepts. This chapter will

describe these considerations and the procedures selected.

71

72

This includes a discussion of how the communities were
selected, data collection procedures, definition of terms,

variable operationalization and data analysis strategies.

4.1 How the communities were selected

Four communities were selected to represent the
population of tourism oriented businesses in Michigan.

These were chosen based on an evaluation of the following
factors: 1) size of the community, 2) location within the
state of Michigan, 3) elimination of towns previously part
of this series of studies (in order to broaden the number of
communities where information network data was collected),
4) estimation of the cooperation expected in the community,
and S) the degree to which the community perceived
themselves as being tourism oriented.

Size of the community was segmented into classes of
large (larger than 10,000) and small (10,000 or less).
Location in the state considered several subfactors. First,
only coastal communities were considered because the project
is funded by Sea Grant, whose emphasis is on the water
resources of the Great Lakes. Second, a fairly even
distribution of the communities throughout the state was
desired representing both east and west coasts and north and
south halves of Michigan's lower peninsula. Third, travel

distance from East Lansing was a consideration. Therefore,

73

Upper Peninsula communities were judged to be out of range
for financial reasons.

The two other factors were determined by "expert
judgement" obtained by means of an informal telephone
interview of the four Sea Grant Extension agents in the
lower peninsula. These agents are responsible for working
with coastal communities in their respective regions to help
improve tourism development. They rated (high, medium, low)
the communities in their districts based on their
impressions of how the communities viewed themselves in
terms of being tourism oriented. For example, Mackinaw City
was posed as a community that would probably rate themselves
as highly tourist oriented. Rogers City was a city
suggested as one that might be in the low category. The
communities which the Sea Grant Agent mentioned and rated
are listed in Table 1.

One community was purposively selected from each
category of high and medium perception of tourism
orientation and large and small size. The four communities
chosen were Port Huron, Cheboygan, Leland, and Muskegon.

There are advantages and limitations to choosing the
communities based on the above criteria. One advantage is
the variety in both size and location of the communities
which is achieved. A second advantage is that bias which
might result from sampling in only one location or in one
size town is eliminated. However, a limitation is that

because the communities are different, they cannot be as

Table 1:

74

List of Coastal Oamunities Considered for Study

0

 

Jam (over 10.00)

 

 

Mackinac Island Holland
Mackinaw City Grand Haven
Saugatuck St. Ignace
High gland Traverse City
Swth Haven W
Northport
Sutton Bay Alpena
Glen Arbor Benton Harbor-St. Joseph
Medium Pentwater w
Petoskey Sault St. Marie
New Buffalo Detroit Area
Inddingtm
Frankfort
Charlevoix
Tawas
Whitehall-Montague
Osooda
Caseville area
m
low Rogers City Monroe
E. Jordan

 

 

 

* Note - Circle indicates selection for study

75

easily compared to each other. For some questions it is
desirable to compare cities that are similar in size and
relatively close in location. Such cities may even perceive
themselves in competition with each other for the same
customers. Studying how social exchange links varied in
similar communities would be helpful in answering questions

about why one city is more tourism oriented than another.

4.2 Data collection

5.2.1 How the sampling frame was developed - The

sampling frame was developed using the best available
listing of tourism oriented businesses in each community.

In Cheboygan, the initial list was drawn from the telephone
book and checked against the Cheboygan Chamber of Commerce's
list of businesses in the community. In Leland, a recently
published walking map of Leland businesses was used to check
the list developed from the telephone book. In Port Huron,
the phone book list was checked using the membership
listings from the Chamber of Commerce and Downtown Port
Huron, Inc. (the downtown development organization). In
Muskegon, the lists developed by the Visitors and Convention
Bureau were used exclusively. Except in Muskegon, where the
lists used for the sampling frame were already compiled and
printed, the lists were compiled on the basis of which
businesses appeared to be most oriented towards tourism.

This was a judgement call to some extent, using first hand

76

knowledge of the communities through pre-testing and contact
with community leaders. Defining which businesses are
tourism oriented and which are not is not an easy and
clearcut task, especially with the data available before
interviewing. Stynes (1986) has developed a method for
ranking Michigan counties in terms of tourism dependency.
However this method would not work for the needs of this
study. Choosing the businesses by standard industrial codes
was also tried and proved infeasible. See the section on
definition of terms for further discussion.

In all cases, a five mile circle was drawn on a map with
the center of the circle being the center of town (as
defined by town leaders). Businesses outside that circle

were not included in the sampling frame.

Apzpz. How the sample was gpawn - A random sample was
drawn stratified by type of business. Table 2 lists the
types used to stratify. This was done to assure a large
enough sample for statistical testing in each type of
business. Table 2 shows the number of each type of business
in the four communities. Some types of businesses by their
nature (such as stores or shops) are much more common than
other types of businesses (for example recreation
facilities). The sample was drawn proportionate to the
number of each type of business. Therefore, there was a
wide variation in the number of businesses sampled in each

of the different types.

77

WardWofSanpleofDiffexurt'lypesofmsm

Table 2:

.

87.10“.J¢D..O.=~4..

24.14.8334.5
1.. 1. 3

6291730 7
3 4 7122

217 100. 0

 

1

8

OmLmity M
N

4..24.6Qu$4..31

6.4." ohmduo.
.....1 v.13

100.0

68

 

3

OmnutyP
1‘1

6.180.53885

1m4.16.34..4.‘w

1.03 41334
1 n 2

100.0

62

 

%

N

55.36.880.88
30.54..1.O.-I.18

263411415
1 2

100.0

57

 

%

OmnunityL ‘Ccmnmityc

 

WL

nw7.o.7.0.7.0.37

0.6.0.6.0.“0.3m

05020n015

 

100.0

30

Totals

(N) -Na':-frar::haised

(F) "

78

4.2.3 Who was interviewed 5 why - In order to collect

data about the exchanges within the tourism oriented
business community, someone had to be selected to represent
that business. It could be an employee, a manager, or the
owner and one would get different answers to the questions,
depending on who was asked. In order to get as consistent
answers as possible, we attempted to interview the owner or
manager most actively involved in running the business at
the community level. In most cases, choosing the
appropriate person was no problem, since the vast majority
of tourism oriented businesses are very small operations and

usually owner-operated.

5,2.5 Instrumgnt devglopmenp - A variety of survey

instruments were considered, such as mail questionnaires,
telephone interviews, self administered questionnaires in
group meetings and personal interviews. From a review of
the network methodology literature, talking to experts in
network analysis data collection and from pretesting
specific questions, personal interviews were concluded to be
the best method. Self administered questionnaires were
pretested, but found to be too confusing and cumbersome for
the type of data needed. A separate interview was
developed for each community since the nature of the
questions were specific to the businesses and organizations
in the four sample cities. A copy of the Muskegon interview

schedule is included in Appendix A as a sample.

79

The questionnaire or interview schedule was divided into
seven parts. The first two sheets collected basic
information about the business (e.g. name, address, etc.),
the respondent (sex, race) and information used in setting
up the interview, as well as reactions on how the interview
went. The second part inquired about characteristics of the
business. The third part was about characteristics of the
respondent. Part four (labeled C) was on the respondents
involvement in the community.

Part five (D) was a census of all applicable businesses,
which was used to tally the respondents answers regarding
the exchanges of informal communication, customer referrals,
and joint programs. The respondent was given an
alphabetized list of businesses for each type of business in
the tourism industry, one type at a time. They were asked
who they talked to, referred their customers to and work
together with. To determine a frequency of exchange, the
respondents were also given a small index card indicating
the frequency scale (1=about once or twice a year: 2=about 3
to 6 times a year; 3=about once or twice a month; 4=once a
week or more). This card was often used by the respondent
as a guide to run down the lists of businesses. The
final two sections (E and F) were part of a Sea Grant
research project, but not used in analyses presented in this
dissertation. The results of these sections are reported in

McDonough and Cobb (1985).

80

5,2,; Samplipg ppopedure and schedplg - The interviews

were developed and pretested in the spring and summer of
1984. Interviewing began in August, 1984 for Cheboygan and
Leland. These interviews were all collected by the Project
Coordinator. Port Huron businesses were interviewed in
October and November using an additional three interviewers.
Data for Muskegon was collected in December, 1984, using the
same interviewers.

Cover letters were sent to the sample selected
approximately one week before interviewing began (see
Appendix B for a sample letter). Each respondent was then
contacted in person by one of the interviewers. Interviews
were done immediately, if the respondent consented,
otherwise any arrangements were made to return and complete
the interview. These procedures, though time consuming,
worked well in keeping the refusal rate low. Only one
person refused to be interviewed in Cheboygan. In Leland,
there were no refusals. In Port Huron, the refusal rate was
two and in Muskegon no one refused to be interviewed once
contacted in person. There were, however, a variety of
reasons why every business that was sent a letter was not
interviewed. These included 1) the owner or manger being
unavailable for an interview for various reasons such as
illness, 2) the business being closed or 3) a very recent
change of management.

Many of the techniques used in network analysis are

based on the assumption that everyone in the system has been

81

interviewed and thus given the opportunity to give input on
his or her links with others. In a community, even a very
small one, it is very difficult to define the boundary of
the system. In organizations, where network analysis
techniques are more commonly used, it is easier to define
the boundary. Either a person is a member or s/he is not.
Either they are employed by the company or not. However,
because we are interested in tourism businesses in a
community, one first has to decide what is a tourism
oriented business (this is discussed in Chapter 2) and then
how many businesses to include within a community. For
financial and data handling reasons, not every tourism
oriented business could be interviewed. A sample of
approximately 60 businesses for each community (except for
the smallest community) was drawn from the sampling frame to
represent the population. They were drawn proportionate to

the number of businesses in each type (Table 2).

4.3 Definition of terms

It is important to understand how the concepts and
terminology of network analysis is applied to this study.

39555 - The nodes of this research are tourism oriented
businesses in four selected communities in Michigan. What
makes a business tourism oriented is not a particularly easy
question to answer. One clothing shop in town might be a

prime tourist spot depending on it's location and what it

82

sells. This is true even for motels which are perhaps the
most commonly thought of tourist oriented business. Some
motels serve only daily business and others are more like
apartments rented out by the month. It is a somewhat
arbitrary decision as to which business to include and which
to exclude in this sub-sector of a community's economy.
This study tended to define tourist orientation rather
broadly and included most stores (even auto parts stores),
all restaurants, motels, campgrounds, gas/service stations
and recreation, such as state parks, museums, marinas and
charter boat captains.

Another issue is whether to consider these businesses as
organizations or individual people. Tourism oriented
businesses are very diverse in their nature. Some are large
complex operations with hundreds of staff members, such as
Disneyland in California or the Grand Hotel on Mackinac
Island. However, especially in Michigan, the vast majority
of tourism oriented businesses are run by a very few people,
usually the owner, or a manager. Preliminary research found
that business owners/managers generally knew other
owner/managers and identified the business usually with one
person (or occasionally with a family). Thus in this
research, although the business' name was used in soliciting
respondents' answers, it was the business owner or manager
who became the focus and thus served as the node.

Links - The links used in this study were three types of

exchanges between businesses: talk, referrals and joint

83

programs. Exchange theory holds that there are many
different types of exchanges between people and/or
organizations. Since this research was interested in the
tourism oriented businesses as businesses rather than
individual persons, we asked about business oriented
exchanges and used the organizational literature as the
basis for choosing the types of links to examine. Clearly
though, business and personal exchanges are hard to
separate, especially in smaller communities where people

know each other well.

4.4 variable operationalization

The variables used in the study are listed in Table 3
along with the question/s used to operationalize them.
Attributed influence was measured by asking each respondent
to list all owners and managers that they thought "had a
definite influence, either positively or negatively, over
the way the community responds to an opportunity or problem
involving tourism". See part D of the interview schedule
for the interviewer's instructions on collecting network
data.

Each network variable was analyzed for all three types
of centrality mentioned by Freeman (section 2.4.3): Degrees,
Betweenness and Closeness. Therefore, each network variable
could be considered three separate variables, each

correlated with influence.

84

Table 3: List of Variables and Questions That Operationalized

Them

VARIABLE QUESTION #

 

MW:
Attributed influence
Network‘variables

information links
referral links

1W
Characteristics variables

characteristics of the business
type of business
location
percent customers which are tourists
size of staff and its seasonal variation
size in terms of number of products or services
age of business
number of different owners
seasonality of the business
seasonal variations in profit (relative)

owner/manager's characteristics
age
gender
education level
training and experience
importance of business for income
familiarity with community

Strategy variables

involvement in the community

information links with coordinating organization
centrality in joint programs network

knowledge of customers

D4

D1
DZ

16
AB,A9
A11

A1,A2,A3
A4,A5
A12

A7

83,84,85
88
81,82

ClA-J
C2,CB
D3
A10

 

Question numbers refer to the questionnaire found in Appendix A.

85

4.5 Data analysis

Data analysis was conducted in three parts. First,
descriptive frequencies were calculated on all the
variables. Frequencies for the network variables were
calculated by tallying the number of times each respondent
said they talked, referred or worked together with each of
the other businesses on the list in each of the four
categories (see Section 4.2.4 for discussion of the four
point scale). The frequencies in each of the four
categories were then totaled. Also the four categories of
amount of exchange were weighted and then totaled to develop
a scale which included not just how many people talked (or
referred, or worked together) to each other, but how often
they talk.

The advantage of this analysis was that any business
that was mentioned by the respondents, was included (as
opposed to the network measure which could only include
respondents). These analyses gave some perspective to how
the random sample of respondents compared to other
businesses in the sampling frame. Attributed influence was
also calculated in a similar manner and each of the exchange
network frequency totals (talk, refer, work together) was
correlated to influence. These were compared to other
network analysis calculations. LOTUS 1-2-3 was used to
prepare the tables in the form of a spreadsheet and perform

some of the calculations and statistics.

86

Frequencies for the non-network variables were also
calculated and used to describe the communities, the
businesses and the owner/managers. Statistical Package for
the Social Sciences (SPSS) (Nie, et al., 1975) was used for
all descriptive statistics.

The second part of the analysis involved calculating the
centrality scores using a microcomputer program developed by
Linton Freeman called CENTER. This program could only
handle dichotomized data (0,1). Some experimentation was
done on the Cheboygan data with different ways of
dichotomizing the data. It was concluded that the most
meaningful dichotomization was to make any mention of
exchange, no matter what the frequency (amount: 1,2,3 or 4),
count as 1 and a zero count as zero.

The CENTER program also could symmetricize the data
matrix by union or intersection or could do the calculations
on a non-symmetrical matrix. Centrality scores were
calculated for each exchange network on all three options
for symmetrization: union, intersection and none. The
program calculations are based on graph theory and use
matrix algebra on square matrices of respondents only.

This study will discuss the advantages and disadvantages
of each type of symmetrizing. Union symmetrizing
establishes a link between two nodes whenever either node
mentions an exchange with the other, the exchange does not
have to be reciprocal. Thus there are more links in a

network established by union symmetry that in any other.

87

One cannot tell the direction of the link with this type of
symmetry. Intersection symmetry establishes a link between
two nodes only when there are reciprocated exchanges.
Networks established with intersection symmetry have the
fewest links of any of the three types of network
symmetricizations. There is direction indicated with this
type of symmetry, in that it is always reciprocal. Each
node must mention the other node to establish a link. The
final type of symmetry was to not symmetrize at all. In
this situation, the calculations were done on the matrix as
it was originally entered. This resulted in calculations of
both in-degrees (the number of links reported to each node)
and out-degrees (the number of links which each node reports
themselves). This is a useful calculation for the analysis
of influence.

The third phase used the SPSS-PC package (SPSS for the
Personal Computer) to perform the correlation analyses to
determine the extent to which the independent variables were
related to each of the dependent variables (influence, talk

centrality and referral centrality).

CHAPTER 5

RESULTS AND DISCUSSION OF DESCRIPTIVE FREQUENCIES

This chapter will discuss the descriptive results of
the study. Although one of the main purposes of this
dissertation is to describe and test a model of community
influence, much is needed to set the stage for understanding
the situation in which this model was tested. What are
these communities and businesses like? What are the kinds
or types of businesses that are influential? This chapter
will describe and compare the four communities, the
characteristics of the businesses and of the owners and
managers. It will also discuss which types of businesses
are influential , which have a high amount of communication
and which have high referral activity. The correlations of
talk and referral activity with influence are reported. Two
final points discussed are joint program activity and

communication with organizations .

5. 1 Description of study sample

ow un t 'es co ar e - The
four communities studied fall along a continuum in size from
the smallest, Leland (township population: 1, 446 year round

residents) to Cheboygan, (pop. 5,106) larger, but still a

88

89

small town, to Port Huron (pop. 33,981) a small city, to the
largest community, Muskegon (pop. 40,823). I will refer to
the communities from now on by their first initial in order
to turn the focus away from the specific communities to the
more general characteristics.

Community L is by far the most tourism oriented of the
four towns studied. Its economic base depends on summer
tourism. It has large seasonal variations in the number of
residents and amount of economic activity. Businesses must
make the majority of their profit in about three or four
summer and fall months. The majority of businesses close
after color tour time in mid-October and don't open until
June. Some businesses close by early September. Only
certain businesses, such as the food store and service
station, stay open year round.

Community C was a more tourism dependent town when
Route 27 through town was the main route to Mackinac Island.
This was before Interstate 75 bypassed the town and before
the Mackinac Bridge was built. Now the community is largely
dependent on a few mid-sized manufacturing companies, but is
trying to attract more tourism-oriented businesses and
tourists.

Community P, located where the St. Clair River opens
into Lake Huron, has a strong attraction for boating and is
the starting point for one of Michigan's largest sailboat

races. Part of Marysville, a smaller city to the south of

90

Port Huron, was included in the sample because it was in the
five mile radius.

Community M, the largest of the communities sampled,
has a fairly diverse heavy manufacturing base. Originally
the town had a strong lumber baron influence. Now it
attracts tourists because its location near many of Lake
Michigan's most popular sand beaches, as well as a number of
cultural and entertainment attractions. Part of North
Muskegon was included within the five mile radius.

Besides the qualitative sense of the tourism
orientation of each community, the interviews collected data
on a number of variables selected to gain some insight into
the tourism orientation in a more quantitative sense. No
one variable is sufficient to determine the amount or degree
of tourism orientation of a town. The results of the data
collected in the interviews for several variables will be
discussed as indicators of tourism orientation of the
communities.

Before discussing the results of this study it must be
noted that others have looked at various data sets to
determine the tourism orientation or tourism-dependency of
Michigan counties (Stynes 1986; Stynes & Pigozzi 1983). In
a recent study which ranked Michigan's 83 counties by
seasonality in tourism-related employment and by lodging use
tax data the counties in which these four communities are
located were ranked as follows: Leland (Leelanau County)

fourth in tourism-related employment and seventh in lodging

91

use tax, Cheboygan (Cheboygan County) third and tenth, Port
Huron (St. Clair County) 52nd and 54th, and Muskegon
(Muskegon County) 45th and 47th.

One indication of a tourism based economy in a
community is a wide variation in business' total gross
revenues between the tourist and non-tourist seasons. The
interview asked each owner/manager to estimate whether their
total gross revenue was high (3), medium (2), or low (1) for
each month. The variations in median relative total gross
income for each month for the four communities (Table 4)
shows Community L has the most dramatic variations, ranging
from the lowest possible score of 1 in March to the highest
possible score of 3 in July and August. All the communities
are highest in July, indicating fairly strong tourism
influence. Community P stays the highest throughout the
fall and is highest for the Christmas season, indicating the
respondents sampled included more strong retail businesses.
Community P had the least variation. In general, however,
the smaller communities varied more indicating that tourism
was a more significant part of their economies.

Data were gathered in the interview on several other
tourism development indicators (Table 5). First, the
variation in the number of full-time and part-time employees
between busiest and slowest months is described. Here again
the widest variations are in community L, the smallest
community. If the variation is calculated by adding the

means of the number of full-time and part-time employees

92

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93

Table 5: Tourism Development Indicators

 

COMMUNITY
L 59 P _u

HEAE_HHHBEB_QI_£HELQXEE§
full-time, busiest month 0.6 8.1 0.5 6.2
part-time, busiest month 3.0 7.4 7.1 5.1
full-time, slowest month 3.7 7.4 8.7 4.6
part-time, slowest month 1.9 7.7 3.8 3.8

variation 8.0 0.4 5.1 2.9
percent closed 73 19 5 12
mean number of months open 6.0 6.2 7.0 4.5
percent of owners/managers
who are year round residents 76 93 91 94

0

percent tourism 30 57 67 63
percent not tourism 70 43 33 37
In July, mean percent
non-locals or part-time residents 87 47 45 40
In March, mean percent
non-locals or part-time residents 35 13 27 27
HEAH.IH!QL!EHEHI.§QQBE * 2.5 4.5 2.8 2.4

 

* Involvement in community or professional organizations

94

together and subtracting the results of the slowest months
from that of the busiest, one sees that community L varies
by 8.0 employees on the average. Variation in this variable
is also an indication of tourism development. Community C
varies the least in this variable. Community M has the
second lowest variation.

The next variable is percentage of businesses within
the community that close for more than a few days. Again,
the results indicate that community L, which is the highest,
is the most tourism based community. Community P is the
lowest and probably the least tourism oriented. The mean
number of months in which these part-time businesses are
open varies between a low of 4.5 in community M to a high of
7.0 months open in Community P, where the sample of
respondents least reflect a summer tourism orientation.

The percentage of business owners that are year round
residents of the county can be seen as a commitment of the
business owners to the community and also indicates the
seasonality of the business community. Community L has the
lowest percentage of year round residents among its business
owners and managers. Community M, the largest community,
has the highest percentage.

Whether the business is a sole source of income is
another variable considered related to tourism development
in a community. A similar pattern exists with this

'variable. Community L has the highest percentage of

95

businesses which are not the sole source of income.
Community P has the lowest percentage in this category.

The percentage of customers who are non-local (e.g.
tourists, business people) or part-time residents (e.g.
those with summer cottages in the area) is examined both in
July and March. These are based on the owner/managers'
perceptions of their clientele. In July, Community L has by
far the most non-local or part-time residents and Community
M has the fewest. In March, Community L still has the most
non-locals. This is probably because of the small sample of
businesses that are open in March and of the small number of
customers they get at this time. A few tourists can make
the percentages high. Community C has the fewest non-locals
in March (mean of 13%). Communities P and M have about the
same (mean of 27%) number of non-locals in March. These are
probably mostly out-of-town shoppers or businesses visitors.

Finally, an involvement score is calculated which
measures the average extent to which businesses are involved
in professional and tourism oriented organizations both
locally and non-locally. Community C is the highest meaning
more involvement in the organizations and community M is the
lowest, indicating the least involvement. Community L has
the second lowest score, which is probably a result of a
lack of organizations to be involved in due to its small
size. Some of these tourism indicator variables, such as

involvement and number of employees, will be used in the

96

model and will be discussed more in depth at a later point
in the dissertation.

In summary, Community L is the most tourism dependent
town, with the greatest variation in number of employees,
almost three-quarters of the businesses closed for several
months, the fewest owner/managers which are year round
residents or whose business is their main source of income,
and the highest percentage of non-local customers in both
July and March. On the whole, Community C is the next most
tourism dependent community, though not all the indicators
would rank it thus. It has the next to the highest
percentage of businesses closed for more than a few days,
not the main source of income and non-local July customers.
It also has the highest mean score of involvement by
community members in community and professional
organizations, which would tend to indicate that there is
active business support for Community C organizations, which
may be working towards improving their tourism product.

The two larger communities rank about the same
according to these tourism indicators. Community P appears
somewhat more tourism oriented by being higher on indicators
such as seasonal variation in employee numbers, fewer year
round residents, more July non-local customers, and higher
mean involvement. However, Community M is more tourism
dependent based on the indicators of closed for more than a
few days and having fewer businesses that serve as a main

source of income.

97

It is helpful for two reasons to keep in mind the
tourism orientation or dependency of each of these
communities when examining the results of the relationships
between influence and exchange networks. First, the
patterns of tourism orientation of the community may be
similar to the patterns of correlation between influence and
exchange network centrality. Second, several of these
variables are used in the model as strategies or
characteristics to try to explain network centrality and

influence patterns in these four communities.

WWW - This
section will summarizes other characteristics of businesses
in the sample (Table 6). The first variable is the location
of the business: 1) downtown, which is a draw for tourists
in these four communities, 2) on a tourist route, but not
downtown (these are often on the main roads into town or
include malls), and 3) not in tourist areas. Community M
has the smallest percentage of respondents being located
downtown and the largest percentage on tourist routes and
not in tourist areas. This is because Community M is a
larger city with many areas not associated with downtown,
especially on the waterfront, that are a draw for tourists.
Because it is larger it also has more areas which are not in
tourist areas but are services which could be utilized by
tourists. Community P has the majority of the sample on a

tourist route, but not downtown (53%), although 32% of the

98

Table 6: Characteristics of Businesses

 

COMMUNITIES
L C P M

LQQAIIQH

percent downtown (1) 32 48 32 12

percent on tourist route

(not downtown) (2) 54 40 53 60

percent not in tourist area (3) 14 12 15 29

percent rated 5 (very attractive) 27 13 23 8

percent rated 4 46 37 39 42

percent rated 3 19 33 26 28

percent rated 2 8 13 10 13

percent rated 1 (not attractive) 0 2 2 8

mean ratings 3.9 3.4 3.7 3.3
NnHBER_QI_XEAB§_AI_EB£§EHI_LQQAIIQH

mean number of years 27.5 30.5 25.5 19.0

median number of years 12.5 14.3 19.8 11.0

percent that had moved 30 25 24 4

percent that had not moved 70 75 76 96

median number of years NR 7 5 5
IIHE_BnSIHE55.9EHED_BX_EBE§EHI_QEEEB

mean number of years 17.0 18.8 12.4 8.6

median number of years 6.5 12.3 6.5 5.1
IIME_HESIEEEfi.9flHED_BX_EBE!IQQ§_QEHEB

mean number of years 12 14 16 12

median number of owners 1.2 1.2 1.4 1.2

 

99

businesses interviewed were downtown. Community C has the
largest percentage of the interviewed businesses downtown
(48%) and 40% of the businesses on tourist routes into the
city. Community L has a downtown area and a highly
developed tourist area, located close by which was labeled
on a tourist route.

The description of the business on a five point scale
from "very attractive" to "not attractive" was a subjective
judgement on the part of the interviewer with guidance from
the Project Director. The mean was highest in the smallest
community (L) and lowest for the largest community (M),
which means that in general, Community L and Community P
were considered more attractive communities than Communities
C and M, in terms of the appearances of the tourism oriented
businesses.

The age of the business was measured in a number of
different ways. The number of years at the present location
is the first measure of age of a business. The businesses
sampled in community M are the youngest of the four
communities with a mean of 19 years and a median of 11.
Community L had the next youngest businesses with a median
of 12.5. However, a few very old businesses pushed the mean
up to 27.5. Community C also had a number of very old
businesses which made the mean 30.5 (the highest mean), but
the median was 14.3. The highest median, 19.8, was for
Community P, meaning the businesses in general were older

for this community. The majority of businesses in all the

100

communities had not been at a different location. Community
' L, the smallest town had the largest percentage of
businesses at a different location (which may be more a
reflection of the small sample size). Community M had the
smallest percentage of businesses at different locations,
which may be related to having the youngest businesses and
therefore less time to have moved around much. The
variable, number of years at a different location, was
gathered to add to the variable, number of years at present
location to get a total age of the business. However, many
respondents did not know the number of years at different
locations and the response rate for this question was low.

Another way to look at the age of businesses is to look
at ownership patterns. The number of years owned by present
owner varied from a mean of 8.6 years in Community M to 19
years in Community C. The higher means in the two smaller
communities again reflected a few businesses being owned by
the same owner for a very long time. The median was
generally around 6 years in all communities except Community
C where it was 12 years.

The mean of the "number of years owned by previous
owner" variable varied from 12 years in Community L to 16
years in Community P. Data on the number of different
owners in the past ten years was gathered to examine the
stability of the ownership patterns. There was very little

variation with most businesses owned by only one owner in

101

the last ten years, indicating high stability in all
communities among those businesses sampled.

These data were collected to see if there were any
striking patterns or differences in characteristics between
these four communities and if these characteristics
predicted the influence and network centrality patterns. 0f
major interest in this section were patterns of stability in
ownership and location of the businesses. The most striking
result is the relative newness of Community M businesses.
They had the lowest mean and median of years at present
location and owned by present and past owners. The vast
majority of Community M businesses had not been at a
different location. This may represent a lack of stability
in the tourism oriented business community where it is
difficult to stay in business for long. However, this
newness may also be seen as an asset, because the newer
businesses may reflect more modern business management
techniques and modern appearances.

Both Community L and C have a few very old businesses
in town which have been operated in the same location and by
the same family for over 100 years in some cases. This
causes the means to be much higher than the medians.

Looking at the medians, Community L ranks next in relative
newness of business locations and ownership with Communities
C and P having the most long term, stable or staid

businesses.

102

These patterns in business characteristics may help
explain influence and centrality. Many of the variables are
used in the model to predict referral and information
centrality and influence, discussed in Chapter 6. Other
variables used in the model include the characteristics of
the owner/managers, which are discussed in the following

section.

cup. ., . .J,- ”a...- ,1 . - 7_ g -
Data were collected concerning several characteristics of
owners and/or managers to help predict influence and network
centrality patterns (Table 7). Most of the respondents were
both the owner and the manager of the business except in
Community M were the majority of those interviewed were
strictly managers (51%). In most communities about 40% were
female and 60% were male. The overwhelming majority of the
respondents were Caucasian. The median age was around 42
years old, though slightly younger in Community L and
slightly older in Community C. The respondents generally
had a high school education, except in Community L, where
the median was 15 years of school.

The average number of years running their business was
highest in Community C (10.5) and lowest in Community M
(6.1). This makes sense since the businesses were youngest
and owned for the least amount of time in Community M. Most
respondents didn't have experience running a similar

business so the median was less than one year for all

103

 

Table 7: Characteristics of Owners/Managers
GIMJNITIES
L C j M

W

percent owner/managers 50 67 56 44

percent owner only 14 2 10 5

percent manager only 36 31 34 51

0

percent female 45 39 41 35

percent male 55 61 59 65
W

percent caucasian 100 98 90 97

percent other 0 2 10 3
W

mean 39.53 45.39 40.42 41.32

node 30 34 28 50

median 35.50 44.25 42.17 42.00
W

Mean 14.53 13.23 12.97 13.27

Mode 16 12 12 12

Median 15.00 12.86 12.41 12.48
W

Mean 8.56 10.51 8.18 6.12

Mode 4 1 l 1

Median 4.40 6.25 3.58 3.29
W

Mean 3.72 5.6 4.77 6.14

Mode 0 0 0 0

Median 0.39 0.38 0.30 0.50
W

percent with training 56 44 68 36

percent without training 44 56 32 64
W

percent within 5 miles of city 72 78 73 78

percent further than 5 miles 28 22 27 22
W

Mean 18.95 21.98 23.78 24.61

Median 15.00 17.17 20.50 22.50

 

104

communities. The majority of respondents had some training
in Communities L and P, but in Communities C and M the
majority of respondents did not have training.

In all communities, about 75% of the respondents lived
within five miles of the city. The average number of years
lived in the county varied from 19 years in Community L to
25 years in Community M.

As with the business characteristics, the purpose of
these analyses is to discover if there are any patterns or
significant differences between these four communities that
might help explain the influence and network centrality
patterns. The most interesting result of this section is
the characteristics of Community L owner/managers. They are
generally younger, more highly educated and trained, and
less likely to live within five miles of the town or even in
the county. This result fits with previous results
describing the tourism orientation and seasonal nature of
Community L. From conversations with respondents it was
clear that these younger, more educated business
owner/managers got into tourism oriented businesses in this
town for the fun of it and because they liked this
particular community. They may feel it is an interesting
way to spend their summers and supplement their income. No
other significant patterns stand out among the other
communities.

Many of these characteristics are considered in the

model to predict centrality and influence in tourism

105

communities and their effects in this model will be
discussed in Chapter 6. The following sections discuss
descriptive frequencies used to analyze influence and the
three different types of networks: talk, refer, and work

together.

5.2 that kinds of businesses are influential

Since respondents could cite businesses other than
those that were interviewed, the number of businesses on
which data about influence and exchange networks were
collected was significantly larger than the number of
respondents. The network analysis procedures required that
only the respondents be included in the sample, thus all
centrality scores are for the respondents only. It is
important to compare the respondents to this larger "total
sample" to gain insight into just who the respondents are
and how well they represent the larger sample. This larger
sample is also only a sample of the total population of
tourism oriented businesses in these four communities, which
in turn seek to represent tourism businesses throughout
Michigan and the entire United States. This section
examines the percentage of influentials in the respondent
sample as compared to the total sample. I also discuss
which types of businesses are influential in each community.

A number of comparisons concerning the number of

respondents and other businesses which were cited as

106

influential in each of the four communities are enlightening
(Table 8). Community C stands out as the community in which
many more business owner/mangers were cited as having
influence over the way in which the community reacted to
tourism development. Over 75% of the businesses had one or
more respondents mention them as being influential and 58%
had two or more mentions. There are several probable
reasons for this. First, this is a small and stable enough
community that most everyone knows each other. Second, the
community has a number of organizations and committees, both
tourism-oriented and service oriented, so that a lot of
different people are involved in one or another
organization, which is reflected in the involvement score
means (Table 5). Respondents tend to cite those people
involved in the organizations they know about as being
influential. The smallest community was more seasonal and
had a number of business owners from outside the community
that located a second store in Community L because of it
being a well-known tourism attraction.

Other than Community C, the trend was the larger the
community, the smaller the proportion of owner/managers
cited as influential in determining the manner in which the
community responds to tourism development, either positively
or negatively (ranging from 47% in the smallest community to
21% in the largest community). One reason for this may be
that in larger communities, the visibility of influentials

is less: people don't know each other as much.

Table 8: Numbers of Influentials: Omparisons among Ocmmnities

 

 

 

(IIMJNITIES
L C P M
NUMBER OF W IN SAMPIE 55 157 327 413
- one or more mentions
number 26 118 121 87
percent 47.3 75.2 37.0 21.1
- two or nore mentions
mmber 18 91 67 25
percent 32.7 58.0 20.5 6.1
30 57 63 68
a»: tenuous WITH...
- one or more mentions
number 21 51 30 15
percent 70.0 89.5 47.6 22.1
- two or more mentions
nunber 17 41 19 8
percent 56.7 71.9 30.2 11.8
- one or more mentions
percent 80.8 43.2 24.8 17.2
- two or more mentions
percent 94.4 45.1 28.4 32.0
OF P 0
W 9 7 4 5
Oneormrementiorsmeansthatthemsinessowner wasselected

byrespondentsoneormoretimfranalistofalltmrisnoriented
msinessesinthecamtmityasapersonwithadefiniteinfluerceover
thewayinwhidithecaummityvmldreacttoatourismdevelopnent
issue.

108

For the respondents the same pattern holds true: the
smaller the community, the larger the percentage of
influentials. In Community L over 80% of those influentials
receiving one or more mentions are respondents and 94% of
those receiving two or more mentions are respondents. This
compares to Community M where only 17% of those receiving
one or more mentions are respondents and 32% of those
receiving two or more mentions were interviewed. This
should be kept in mind when considering the model for
predicting influence. In Community M only 15 of the 68
respondents (22%) received one or more mentions as being
influential. In Community P, 48% of the respondents were
listed as being influential by at least one person. In
Community C, 90% of the respondents received at least one
mention and in Community L, 70% of the respondents were
considered influential. Of the ten business owner/mangers
cited by the most respondents as having the most influence,
nine of the ten were respondents in Community L, seven were
respondents in Community C, four were in Community P and
five were in Community M.

In Community L, the top ten most influential business
owner/ managers included five stores, two restaurants (both
owned by the same family), the only service station, and two
recreation facilities. The top two influentials are the two
oldest families in town, and both own key tourist oriented
real estate (Table 9). The next four influentials are

families with a long history of ownership in Community L.

109

Table9: OamnmityLBusiJnssesOrderedbyInfluerceScores

W
21 Store 80. 0 yes
5 Restaurant 70. 0 no
15 Oanbination 33 . 3 yes
19 Service Station 30. 0 yes
29 Store 23 . 3 yes
34 Store 23 . 3 yes
41 Recreation/Store 23 . 3 yes
6 Restaurant 20. 0 yes
26 Store 20.0 yes

31 Store 20.0 yes

 

110

Complete lists of each community's businesses with talk and
referral data sorted in order of most influential can be
found in Appendix C.

In Community C, the top ten influential businesses
include six stores, two restaurants, one lodging
establishment and one automobile dealer (Table 10). A
second auto dealer should be included since their influence
is the same as the tenth influential on the list. Auto
dealers were included as tourism businesses because they are
involved in repairing cars, just as the service stations
are. All four of the auto dealers came out as influential,
partially because they are considered wealthy business
persons. One of the restaurant owners had friends in high
places in State Government and had been established in town
for a long time, but wasn't particularly involved in local
organizations. The other restaurant owner/manager was very
involved in local tourism organizations, as were most of the
influential store owner/mangers and the lodging
establishment owner/managers.

In Community P, six out of the ten most influential
businesses were restaurants. At the top of the list was the
owner of two large motels in town. In addition, there were
three stores in the top ten (Table 11).

In Muskegon only the top ten names have more than two
citations as influential (Table 12). The top two
influentials are managers of large franchised lodging

establishment, which incidentally, are located next door to

111

Table 10: Cbnnumdty'c Businesses Ordered.by'Influence Scores

WM
43 Restaurant 61.4 yes
111 Store 54.4 yes
209 Store 54.4 yes
15 lodging 52.6 yes
229 Store 43.9 yes
24 Store 38.6 yes
45 "Restaurant 35.1 yes
267 Dealer 35.1 no
94 Store 33.3 no
260 Store 33.3 no

 

112

Table 11: Omnunity P Businesses Orderedby Influence Scores

ID Code iness Soc
6 lodging 28.6 yes
107 Restaurant 25.4 no
198 Store 17.5 no
250 Store 17.5 yes
35 Restaurant 15.9 no
192 Store 15.9 no
40 Restaurant 12.7 no
41 Restaurant 12.7 yes
109 Restaurant 12.7 yes
89 Restaurant 11. 1 yes

 

113

Table 12: Cammnity M Businesses Ordered by Influence Scores

ID Code 3:93 of Easiness Influence Score Went?
7 Lodging 14 . 7 yes
5 Lodging 10. 3 yes
103 Recreation 8 . 8 no
36 Recreation 5. 9 yes
104 Recreation 5. 9 no
9 Lodging 4 . 4 yes
35 Recreation 4 . 4 yes
38 Recreation 4 . 4 no
134 Restaurant 4.4 no
217 Recreation 4 . 4 no

 

114

each other. The next three top influentials on the list are
managers of recreation establishments. Recreation business
managers are influential in Community M. Six of the top ten
influential businesses are recreation. There is also one
restaurant, which is located near the top two lodging
businesses and one additional lodging/resort business
located on the outskirts of Muskegon on Lake Michigan, whose

owner/manager is active in local and regional organizations.
5.3 Talking and Referral Activity Frequency analyses

In this section, an analysis similar to the previous
section is extended to data collected about who talks to
whom and which businesses the respondents refer their
customers to. Also the question of how the sample of
respondents compares to the total list of businesses on
which each respondent could give information is addressed.
This chapter does not use traditional network analyses (this
is cover in Chapter 6) , but examines the activities of
talking and referring by analyzing the amount (frequency or
number of times) each business is cited by the respondents
as being talked to or referred to.

An additional part of the analysis includes the number
of times per year the respondents talk to or refer to other
businesses, or the strength of the link. Business A may
talk to business B only once per year and business C many

times per year; this measure of number of times of exchange

115

times per year: this measure of number of times of exchange
per year will be referred to as the strength of the
exchange. Yet business B may be talked to by 80% of the
businesses in town and business C may be talked to by only
business A and one other; this number of citations by the
respondents is referred to as the amount of the exchange.
This section examines the amount and strength of exchanges
for both the talk and referral exchange. The scores listed
for each talk and refer exchange represent the percentage of
respondents who stated that they talked or referred to each
of the businesses in each of the four possible strength

categories.

§,3.i Community Comparisons of Talk and Referrai
Activity To begin to understand the activity scores

(referred to as "total talk" and "total refer"), it is
helpful to look at these scores for the community as a
whole. Table 13 gives the results of the means for both the
talk and refer networks for all four communities. The
unweighted totals are the scores that include only the
number of links, but not the strength (as measured by
frequency) of the links. The column labeled weighted
1,2,3,4 weights the number of links that account for the
frequency. If the businesses said they talked to or
referred their customers to other businesses about once or
twice in the last year, their link was weighted 1. If they

said about three to six times a year, it was weighted 2,

116

about once a month was weighted 3, and once a week or more
was weighted 4. Thus the number and strength of the links
were accounted for.

The smaller communities, L and C, had much higher mean
number of links than the larger communities, P and M, (Table
13). The smaller communities also had proportionately
larger weighted scores (i.e. the frequency of the links was
much higher). However, the number of nodes varies widely
from N=55 in community L to N=413 in community M. Larger
communities have larger, less dense networks and the smaller
communities have much denser, but smaller networks.

The same general pattern holds true for the referral
network, with the smaller communities having denser
networks. The main difference is that the weighted refer
scores are lower than the weighted talk scores in the
smaller communities, meaning that the frequency of referring
is not as high as the frequency of talking in the smaller
communities. However, in community M the weighted referral
score is higher than the weighted talk score, meaning that,
in terms of frequency, businesses refer more often than they
talk. Therefore, there is less disparity between the
smaller and larger communities for weighted referral scores.
In general, this means that although people talk to each
other more often in smaller communities than in larger
communities, the frequency of referral is more similar;
businesses refer their customers to each other in similar

amounts, regardless of the community size.

117

Table 13: Talk.and.Referral Network Activity

 

 

 

Talk Newark W.
Mean Mean

Unweighted ‘Weighted theightewd ‘Weighted

Community Nbdes Totals 1.2.3.4 Tbtalg 1.2.3.4,
L 55 44.2 134.8 30.5 98.8
C 157 30.5 76.8 15.9 40.9
P 327 6.5 15.7 5.5 13.7
M 413 2.3 5.4 4.2 11.2

 

 

 

 

118

5.3.2 Respondent and Total Sample Comparisons - The

next question is, "How does the respondent sample compare to
the total sample and how does that vary between
communities". For the talk activity almost all businesses
(90%) get one citation (Table 14). This is due primarily to
the measures used, which included all respondents, but only
the other businesses if they received one talk, refer or
influence citation. The same pattern held true for almost
all businesses receiving at least one referral citation.
Community P is the lowest in both these categories (89.3%
and 83.5%, respectively). When examining those that
received two or more mentions, the percentages drop off
dramatically in the two larger communities. The most
dramatic drop is for "talk" citations in Community M, where
only 37.3% received two or more mentions.

The respondents with two or more mentions follows the
pattern of the total sample for both talk and referral
activity. Almost 100% of the respondents in the two smaller
communities received two or more mentions for talk and
refer. Community P has a higher percentage of respondents
with two or more referrals than the total business sample.
The two largest communities have only about 30% of the
respondents in the top 25% of those businesses which receive
the most talk and referral activity. It must be kept in
mind, however, that in the larger communities the top 25% is

a much larger number (N=104 compared to N=16) than in the

119

Table 14: Talk and Referral Frequencies: Omparisons among Omnunities

 

 

W

L C P M
NUMBER OF M IN SAMPLE 55 157 327 413

- me or more mentions in

gig network

number 55 155 292 391
percent 100.0 98.7 89.3 94.7

- two or more mentions in

talk retwork

umber 55 150 218 154
percent 100.0 95.5 66.7 37.3

- one or more mentions in

gig: network

nunber 54 144 273 401
percent 98.2 91.7 83.5 97.1

- two or more mentions in

refer network
mmber 53 130 195 268
percent 96.4 82.8 59.6 64.9
NUMBER OF M IN SAMPLE 30 57 63 68
- two or more mentions in
tail; network
nunber 30 57 49 33
percent 100.0 100.0 79.0 48.2
- in top 10 most M to 8 8 4 5
-intq>25%mostgiiggto 14 25 22 32
sample size 16 39 82 104
percent 87.5 64.1 26.8 30.8
- two or more mentions in
refer network

umber 30 54 44 43
percent 100.0 94.7 71.0 63.2
- in top 10 most ig_f§_r3e_d to 9 7 6 5
- in tcp 25% most m to 15 20 23 30
sanple size 16 39 82 104
percent 93.8 51.3 28.0 28.8

120

smaller communities and the respondent sample does not

represent as high a percentage of the total sample.

5.3.3. 19p Sgopers in Iaik and Refergal Activity -

Next the top scorers in both the talk and refer activities
(unweighted and weighted) are discussed for each community,
starting with the smallest. Community L's (Table 15) top
two businesses in term of being talked to are the service
station and the store owner/manager who owns many of the
tourism attraction buildings and rents them to other
businesses. Two of the businesses within the top 15 most
talked to have influence scores of zero. If the weighted
scores are examined, some shifts are made. Now store #21
become the top. The combination recreation/store #15 moves
into second place and the service station #19 moves to
third. Restaurant #5 moves to fourth.

Though the data are the same as the previous table,
Table 16 is sorted by "Total Refer" to make it easier to see
who shifts into the top positions. Here restaurant #5 is on
top, it receives the most referrals of customers.

Restaurant #6, owned by the same family, is second. Two
lodging establishments are next, followed by restaurant #7.

For Community C, Table 17 lists the top 33 businesses
sorted by total talk. With the exception of the two service
stations the top 10 business owner/managers are or were
active in local politics or tourism organizations. The top

four businesses being talked to are the same as the top four

121

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influentials. Weighting the scores for strength switches
the top two places and brings restaurant #45 and store #229
into third and fourth place.

The table sorted by total referral scores (Table 18)
shows the addition of several recreation businesses whose
owners or managers are not considered influential.
Recreation or "things to do and see" are the type of
referrals most often made for tourist customers, thus these
recreation businesses move to the top of the list.

Community P's (Table 19) ten most talked to business
owner/mangers includes seven out of the ten top
influentials. Three stores which were not in the top ten
most influential moved into the eighth, ninth and tenth
positins in terms of most talked to. A number of these
businesses are quite involved in local tourism
organizations. When looking at the effect of weighting on
the order, one notices some shuffling in place among the top
seven. However, the biggest jump is store #227 which moves
from fourteenth place to sixth as a result of having a
number of respondents mention that they talk to this
business owner/manager quite frequently.

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referral, recreation #127 was the most often referred to
with over 44% of the businesses referring customers to this
business about three to six times per year (Table 20). An
influential restaurant establishment (#107) was second most

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128

owner/manager was fifth and was joined by three other
lodging establishments. Only one store (#239) was in the
top ten most referred to and that was a large franchised
grocery store. Several restaurants rounded out the top ten.

Weighting moved store #238 up in the ranks, as well as
switching a few businesses around.

Community M scores sorted by total talk (Table 21)
shows that the same two businesses considered influential
are the most often talked to. Also in the top ten are two
other lodging establishments, two restaurants, two
recreation businesses, one service station (the owner owns
five stations in town) and a florist, store #143. Weighting
moves the service station into third place from eighth. It
also moves the recreation establishment #264 up and switches
lodging #12 with lodging #11.

As in Community C the top ten in referral mentions for
Community M brings more recreation activity businesses to
the top (Table 22). Six out of the top ten are recreation,
three are lodging and one is a restaurant. Weighting
doesn't effect the order of the top ten in referral very
much.

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denser networks. The larger the community, the larger, but
more sparse the network. In larger communities it is less
likely that any one actor will receive many mentions or
exchanges from other actors. It is simply easier to talk

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131

choices. In general, those most influential are those most
talked to and to a slightly lesser extent are those most
referred to. The type of business is important in
determining the most referred to, but not most influential
or talked to. Restaurants, lodging and recreation are the
types of businesses to which customers are most often
referred. Store owner/managers may be often talked to and
considered influential, but their businesses receive fewer
customer referrals. This is important to keep in mind when
examining the model in the next chapter. Centrality in the
referral network may be less able to predict attributed

influence because the effect of the type of business.

5.4 Influence and Talk and Referral Activity Correlations

The squared correlations (R squared results reported)
of attributed influence (dependent variable) with both
weighted and unweighted talk and referral scores in each
community are calculated , 1) for the respondents only, and
2) for the total business sample (Table 23) . This will
allow for easier comparison of these results with the
network centrality results of Chapter 6 where only the
respondents sample is correlated with attributed influence.

In general , there is a fairly high correlation between
attributed influence and each of the activity scores, with
the exception of Community L' s respondents ' referral scores.

The highest correlation is in Community C' s respondent

132

Table 23: Correlations of Influence with Talk and Referral Activity.

(R squared values)

 

 

 

Talk ivi ferral ivi
Weighted Weighted
Unweighted Scores Unweighted Scores
Scorgs 1.2.3.4 m 1.2.3.4
my.
All Elsinesses . 324 . 348 . 409 . 421
Respordents Only . 354 . 404 . 173 . 164
CIIMUN'ITY C
All Elsinasses . 549 . 600 . 631 . 471
Respondents any . 631 . 704 . 500 . 479
W
All Easinesses . 611 . 581 . 415 . 375
W mly . 576 . 563 . 369 . 301
W
All 3151113505 . 262 . 292 . 462 . 458
W Only . 490 . 509 . 609 . 604

 

133

weighted talk activity score (.704). This means that the
respondents who receive numerous mentions of others talking
to them (high amount) very often (high strength) are also
the people who are considered to be influential in the ways
the community reacts to tourism development. Those who
don't receive many or strong talk links are not considered
influential.

In nine of the 16 possible comparisons between
respondents and the whole sample, the respondents
correlation scores are higher; in seven of the 16 the
respondents' correlation scores are lower. This is a
reflection that the respondent sample is similar to the
whole sample in the patterns of correlation between
influence and talk and referral exchanges. This implies
that results from the respondent sample can safely to
generalized to the larger total sample and presumably to all
tourism oriented businesses in the community. If these
communities are typical of tourism oriented communities in
Michigan, then the results may be generalized to all these
communities.

For all but the largest community, the influence scores
are more highly correlated with talk than with referral
scores. This may be taken as a sign that it is those
businesses that one talks to that are considered
influential, rather than those businesses that one refers
ones customers to. In the largest community, Community M,

the opposite is true; the referral scores correlate more

134

highly with influence than the talk scores. This may be
because in the larger community it is harder to talk to
other business owner/mangers than in a smaller community,
where one is more likely to casually meet people in a non-
business setting.

In all but Community P, the effect of weighting on the
talk scores increases the correlation with influence.
However, for the refer scores in seven out of the eight
cases, weighting decreases the correlations between
influence and refer scores. This means that for the talk
network, the strength (amount of talk per year) is important
to being considered influential; the more often you are
talked to the more likely you are to be considered
influential and the less often, the less influential. For
referral, however, the strength of the link is not
important. It doesn't matter if one refers their customers
many times per year or not, it only matters if many
different owner/managers refer their customers in order to
be considered influential.

In connection with weighted scores, another attempt to
determine if strength of the link was important to
predicting influence was made by normalizing the strength
scores and correlating them with influence. These were
normalized by dividing each score by the total score for
that business, so that the normalized scores for each
business added up to 100%. The correlations with influence

for each column of normalized score found that there was no

135

correlation at all between any of the normalized scores.
This means that no one particular strength score accounted
for influence. Thus, for example, it wasn't that only those
businesses which received mentions at a strength of four
were considered influential or only those who receive
mentions at low strengths were attributed influence.

One final point to remember in terms of correlations is
that it is useful to note that the talk and refer scores are
intercorrelated. The R squared values are as follows:
Community L, .5140; Community C, .6436; Community P, .4361:
Community M, .3441. This intercorrelation will become more

evident in the model testing discussed in the next chapter.

5.5 Joint Programs Activity Lack of Response

It was proposed that businesses would work together to
get a competitive edge and more effectively increase their
market share. Though this is beginning to happen more and
more, especially with the larger and more sophisticated
chain stores and restaurants, the number of businesses
engaged in this kind of activity were too few to warrant
including joint program activity as a strategy in this
study. Therefore, data from joint program networks is

excluded from any further analyses.

136

5.6 Communication with Organizations

Communication with organizations, both inside and
outside the community, in addition to involvement in
organizations, is a strategy predicted to correlate with
influence. Total talk scores were calculated for each
coordinating organization based on the frequency and
strength of ‘the mentions by each respondent, in the same
manner as total talk scores were calculated for each
business. Table 24 reports the means of these scores for
each community. Approximately 23 coordinating organizations
were inquired about for each community. Weighted scores
were also examined, the weighting system is the same as
described above. The first column shows each community' s
mean score for all organizations. The next column shows the
mean scores for only the 12 organizations within the
community and the column on the far right shows the mean
scores for the 11 organizations outside the community.

Since the number of organizations doesn't vary
significantly between the communities, the size of the
community is less of an explanatory variable. However, in
general the smaller communities have higher means, both
unweighted and weighted which implies that more businesses
in these smaller communities talk to organizations more
often than the businesses in the larger communities. This
makes sense within the community since it is easier to know

or be involved in organizations in smaller communities than

 

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138

in larger communities. It is interesting to note that
Community L actually has a lower unweighted score than the
larger Community C. This is probably due to the fact that
the smaller communities have fewer tourism coordinating

organizations to talk to or get involved with.

5.7 Summary

This chapter began by describing each community and
summarizing the descriptive variables for each. This
included variables which described the tourism orientation
of the community, characteristics of the businesses and then
of the owner/managers. Results of descriptive frequencies
for the exchange network variables were discussed. The
businesses in each community which were ranked most
influential, most talked to and referred to were described.
It was found that in general those in the top ten
influentials were often in the top ten most talked to, but
businesses that were not even considered influential could
be in the top ten most often referred to. Restaurants,
recreation and lodging were the type of businesses most
often referred to. Influence was found to be correlated
with both talk and referral activity. The strength of the
link (weighted scores) was more correlated with influence
than the unweighted scores for talk activity but not for
referral activity. The chapter concluded with a brief

discussion of the joint programs variable and a description

139

of businesses communication patterns with organizations.
The next chapter takes these variables and combines them
with several variables calculated to test the model of

centrality and attributed influence in tourism oriented

communities.

CHAPTER 6

RESULTS OF TESTING THE MODEL

6.1 Introduction

This chapter will examine the results of testing the
model and propositions about the development of attributed
influence within the tourism oriented business community.
First the general propositions concerning attributed
influence and talk and referral network variables will be
discussed. Second, results of the strategy propositions
will be presented, followed by the characteristics
propositions results. Finally the model will be presented
in a revised form, taking into consideration the proposition
results.

Figure 10 combines all the predicted relationships to be
tested into one figure. The general relationships test for
the correlation between influence and the two network
variables talk and referral. In the succeeding analyses,
these three variables are considered dependent variables
with the strategy and characteristics variables as
independent variables. The strategy propositions examine
the relationships between the various methods or strategies
for increasing one's centrality in the talk and referral

networks and one's influence. The characteristics

140

141

propositions test the effects of several variables to

predict influence, and talk and referral centrality.

6.2 General relationships

Four propositions were made concerning centrality and
attributed influence. Two, involving the descriptive
frequencies, were discussed in Chapter 5 and will only be
reviewed here. The other two propositions involved network
centrality measures for talking and referrals.

Eropositigg 1: Attributed influence will be positively
correlated with the degree or amount of talk (descriptive
frequencies), i.e. the more people in the network say that
they talk to a business, the more the business will be
considered influential.

Ergpgsitign_z: Attributed influence will be positively
correlated with the degree or amount of referrals, i.e. the
more that people in the network refer their customers to a
business, the more that business will be considered
influential.

The correlation results (Table 23) were supportive of
proposition 1 and 2. The more business owner/mangers say
they talk to a certain business, the more they attribute
influence to that business owner/manger. Also the more
others refer their customer to a business, the more they
attribute influence to that owner/manger. Section 5.4

discusses these results in more detail.

142

6.3.1 Measures of Centrality —
Proposition 3: Attributed influence will be positively

correlated with all three types of centrality (Betweenness,

Closeness, and Degrees) in the talk network, i.e. the more

 

central an individual is, the more influential they will be
considered.

Ergposirion 4: Attributed influence will be positively
correlated with all three types of centrality (Betweenness,
Closeness, and Degrees) in the referral network, i.e. the
more central an individual business is, the more influential
they will be considered.

Ten possible measures for centrality in both the talk
and the referral networks for each of the four communities
were examined. There are three types of symmetrization
(union, intersection, not symmetrized) as discussed in the
methods chapter. There are also three types of centrality:
degree, betweenness, and closeness (degree has both in-
degree and out-degree measures when not symmetrized). Each
of these measures a different aspect of centrality and
provides unique information. Table 25 reports the
correlations between influence and these ten centrality
measures for both the talk and referral network.

First, the results of the different methods of
symmetrizing will be discussed. Union symmetry of the talk
network produces the least correlation with influence. One
cannot tell the direction of the link with this type of

symmetry. This is a disadvantage in predicting influence,

143
Table 25: Cbrrelation Between Influence and Measures of Centrality.
(R.squarednvalues)

Talk.Network Refer Network
CEntrality Symmgrrizaticn Symmgtrizaticn
ME§§EEE§ Union Largrgegricn, Hone union Intersection ane
mum
Betweeness 0.12 0.47 0.36 0.02 0.01 0.06
Closeness 0.22 na na 0.05 na na
In-Degree 0.22 0.33 0.37 0.05 0.04 0.17
Cut-Degree na na 0.15 na na 0.01
M12
Betweeness 0.32 0.40 0.48 0.43 0.31 0.42
Closeness 0.43 0.43 na 0.48 na na
In-Degree 0.43 0.61 0.61 0.48 0.40 0.50
Out-Degree na na 0.36 na na 0.17
mag:
Betweeness 0.19 0.30 0.28 0.21 0.22 0.12
Closeness 0.16 na na 0.17 na na
InrDegree 0.32 0.43 0.59 0.24 0.22 0.37
Cutenegree na na 0.03 na na 0.01
Communiry I.
Betweeness 0.22 0.34 0.30 0.54 0.22 0.36
Closeness na na na na na na
InrDegree 0.26 0.36 0.49 0.42 0.32 0.61
Cut-Degree na na 0.06 na na 0.03

 

The notation "na" means that it.was not.possible to calculate values.

144

because those considered influential are those who receive
mentions, but not necessarily those who reciprocate these
exchanges, as will be discussed later. However, for the
referral network, union symmetry produces a better
correlation with influence than in the talk network.
Apparently, direction of the link is not as important in the
referral process. Union symmetry has the second best
correlation with influence in the referral network.

For the talk network, intersection symmetry consistently
produces centrality measures which predict attributed
influence better than union symmetrized measures (Table 25).
For the two smallest communities, intersection centrality
measures were the best predictors of influence. This is
probably because influentials generally talk more to other
influentials, and not as much to non-influentials. All non-
influential nodes who gave influentials a mention were not
counted when the influentials did not reciprocate the
mention.

This is not the case with referrals where measures
created with intersection were generally the least
correlated with influence. Obviously reciprocity is not as
important in the referral process in terms of correlating it
with influence. These results support the results of
Chapter 5, which found that referral exchanges were more
dependent on the type of business and not highly correlated

with influence. The referral network measures generated

145

with network analysis techniques are also generally less
correlated with attributed influence.

The centrality measures most highly correlated with
influence are those where no symmetrization occurred. This
high correlation occurs only for the in-degree measure of
centrality for both the talk and referral networks.
Closeness, betweenness and out-degree measures of centrality
calculated with no symmetrizing were not highly correlated
with influence (Table 25).

The results of the different types of centrality will

now be discussed (all types of symmetry are included in this
discussion). The degrees (in-degree and out-degree are the
same when union and intersection symmetry are used, only not
symmetrizing produces different in-degree and out-degree
measures) measure of centrality was the most highly
correlated with influence in all but the smallest community
(where betweenness was highest). In Community L degrees was
the second highest for talk and highest for referral. As
discussed in Chapter 2, degree is calculated by counting the
number of other points connected to any given point.
Freeman (1979) describes degrees as a measure of potential
communication or referral activity. Consistently it is this
measure of centrality that is the best predictor of
influence in these four tourism oriented communities, except
for the talk network in Community L (Table 25).

A business owner/manager whose position allows him/her

to have communication contacts with many others is likely to

146

be viewed as "in the thick of things" and be considered as
possessing valued resources of information and therefore be
influential. Having numerous contacts with others also
allows others to know about one's numerous contacts. This
high visibility of the resources one possesses is helpful in
having others view one as influential.

Betweenness was the next best centrality measure at
predicting influence (Table 25). Betweenness measures the
potential for control of resources, such as
talk/communication or referrals (Freeman 1979). In a
community setting, it is difficult for business
managers/owners to control the resources of communication
and referrals. Certain types of information, such as
developers' plans may be controlled by some individuals,
especially by those involved in local government or business
development organizations. However, because this study
didn't break down the type of information exchanged, asking
only about who talked to who in a general sense, the results
of who had control over communication wasn't as important a
factor in predicting attributed influence as degree (which
measures activity).

The closeness measure of centrality was least effective,
in part because it could not even be calculated for many of
the networks. Closeness measures the distance from all
other points and therefore all points must be connected
through some link or path. That means there can be no

isolates within the entire network system or the

147

calculations cannot be made. Isolates are points not
connected in some way to the rest of the matrix. Closeness
measures the potential for independent access to others, or
stated another way, the closest point is the one least
dependent on others for the resource exchange (whether it be
talk or referral).

When studying a community or a segment of the community
(in this case tourism oriented businesses) it is not
surprising that all the points or nodes are not connected.

A community is not a highly structured organization. Not
everyone is likely to talk to or refer to everyone else.
Thus the calculation of closeness in a situation such as
this is always going to be difficult. Independent access
for communication or referral exchange is not an important
aspect in predicting influence because of the nature of
situation. Communities are open systems, and most business
owner/manager are able to talk to or refer to each other
with minimal restrictions caused by roles.

Since in-degree centrality with no symmetrization is the
best measure of centrality in both the talk and the refer
network for predicting attributed influence (Table 25), this
will be the measure used throughout the rest of the
dissertation and will be referred to simply as talk and

referral network centrality.

148

6.3 Strategy Propositions

The model proposes that owner/managers consciously
engage in strategies to improve their influence over the
manner in which the community reacts to tourism development
decisions. To become influential one strives to become
central in the communication and referral networks among
business owner/managers in the community. This section
discusses the results (Table 26) of testing the five
propositions concerning strategies for improving one's
centrality and/or one's influence.

£rgpgsi§ign_§: High centrality in the talk network will
be correlated to high centrality in the referral network.

Correlations of talk centrality with referral centrality
were found to be significantly different from zero (alpha <
.001). R2 values ranged from .34 to .60. Those businesses
which receive many referrals of customers also have
communication directed at them to a larger extent (Table
26).

Businesses are always eager to increase the number of
customer referrals to their business. One way to help
increase referrals is to get to know other business
owner/managers and have them understand and support your
business. This is best done with face to face
communication. Thus, the more businesses one communicates
with, the more referrals one is likely to receive. The

correlations indicate that being central in the talk network

149

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150

is a good strategy for being central in the referral
network.

Ergposirign 6: Involvement in community or professional
organizations will be correlated with influence and high
centrality in both the talk and the referral networks.

In all but two cases, involvement was significantly
correlated with influence talk and referral network
centrality (alpha < .05). However, the R2 values were
generally below .30. Involvement in organizations was more
related to talk centrality and influence than to referral
network centrality, except in Community M. Communities C
and M had the highest correlations, ranging from a low of
.20 for Community M's involvement correlated with talk
centrality to a high of .46 for Community C's involvement
correlated with influence. Community M also had involvement
significantly correlated with referral network centrality.

These results indicate that those involved in
organizations, especially community oriented organizations,
and especially in Community C and M, were those central in
the talk network and were considered influential. Though it
can not be said that getting involved necessarily caused one
to be more influential, information from interviews indicate
that business owner/managers did choose to get involved in
organizations (especially tourism oriented organizations,
such as Chambers of Commerce) to increase their

communication with other business owner/mangers and improve

151

their ability to influence the manner in which the community
reacted to tourism development.

Proposirign 7: High degree or amount (descriptive
frequencies) of talking to organizations both inside and
outside the community will be related to high centrality in
the talk and referral networks and a high amount of
attributed influence.

Results of the correlations (Table 26) for this
proposition show strong correlations (alpha < .01) with all
three dependent variables for both talking with staff of
organizations in the community and outside the community.
Though the F test is significant at .10 or better in all
cases, the correlation is not consistently strong for all
the relationships. For talking to organizations' staff
within the community, the most consistent correlations are
with influence and the talk centrality dependent variables.

This means that those owner/mangers who say they talk to
staff of organizations within the community (such as
Chambers of Commerce, City Council, County Planning Board,
etc.) are the owner/mangers who receive the most
communication from the respondents. In all but the smallest
community, the owner/managers who talk to community
organizations are also those to whom influence is
attributed. In the smallest and largest community talking
to community organizations' staff is correlated with
receiving referrals. Therefore, there is some support for

the proposition that as a strategy talking to community

152

organizations improves ones centrality in the talk and
referral networks and increases one influence in community
tourism oriented decision-making.

Results for talking to organizations outside the
community are not consistently strong for all dependent
variables. Only referral centrality is consistently
correlated with talking to staff of extra-community
organizations, though the correlations are generally low
with adjusted R squares ranging from .11 to .20. In all but
the largest community, talking to extra-community
organizations is also correlated with attributed influence.
In other words, those owner/managers who receive more
attributions of influence are those who say they talk to
more organizations outside the community (such as the State
Travel Bureau, Regional Tourist Associations, the Travel
Information Centers). Talking to organizations outside the
community also seems to be a good strategy (though not
consistently good) for increasing one's influence and
network centrality.

£r229§1r19n_§: The amount of joint programs with other
businesses will be correlated to centrality in both the talk
and referral network.

This proposition was dropped due to inadequate
databases, resulting from lack of use of this strategy in
the businesses sampled in the four communities.

£r229si§ign_2: Those owner/managers who use methods to

gather knowledge about their customers will be higher in

153

talk centrality than those who do not use methods to track
their customers.

Results of the correlations showed no support for this
proposition at all. Apparently those business
owner/managers who track their customers are no more likely
to be central in the talk network than those who do not

track their customers.
6.4 Characteristics Propositions

Results of the individual correlations testing the
degree to which the characteristic variables are related to
the dependent variables of influence, talk and referral
network centrality show no consistent support for any of the
characteristics propositions, except for in the smallest
community (Table 27). Only in Community L did propositions
10 and 11 find some significant support with adjusted R2
values of about .10. For the rest of the propositions in
the other communities, although there were some results that
were significant at the .10 and .05 alpha levels, most
adjusted R2 values were less than .10 and thus indicate
little correlation.

What this suggests is that the characteristic
propositions (as measured in this study) were not able to
explain a significant amount of the variance in any of the
three dependent variables (influence, talk and referral

centrality). This could be caused by several things.

154

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First, those factors hypothesized to be characteristics
related to developing network centrality and influence are
not the important factors and perhaps other factors not
hypothesized are the important variables. Since this was an
exploratory study and research in this area is scant, there
was little background for developing hypotheses involving
the characteristics related to developing network centrality
and influence in tourism oriented business communities.

The second possible cause of finding no correlations for
the constraint variables is inadequate measurement of the
variable. Perhaps the interview questions were not clearly
understood by the respondents (though no indications of this
were observed by any of the interviewers). Thus it might be
that measurement and not the variable itself, was a reason
for the lack of correlation. Finally, it could be that
there are no particular characteristics of businesses or
owner/mangers that are related to developing influence or
network centrality.

The constraint propositions will be stated below for
reference with the results (Table 27), but will not be
discussed further.

Ezopositigg 19: The age of the business will be related
to influence, talk and referral centrality, such that the
older the business the greater the influence, and the higher
the talk and referral centrality.

Ergpgsitign_11: The size (in terms of number of

employees in both the busiest and slowest month) of the

157

business will be related to influence, talk and referral
centrality, such that the larger the business the greater
the influence, and higher the talk and referral centrality.

Ezopositign 1 : The age of the owner/manager will be
related to talk centrality, such that the older they are,
the higher the talk centrality.

Egoposition ; : The more education, training and
experience the owner/manager has, the higher the talk
centrality.

£rgpgsitign_;§: If the owner/manager lives near the city
and is a year round resident of the city, they will have
higher talk centrality.

Ezopgsition l5: Those owner/managers whose main source
of income is their business will be more central in the talk

and referral networks.

6.5 Summary

With all the propositions tested, one can now examine
the revised model based on the results (Figure 11). It is a
model of strategies correlated to network centrality and
influence. The primary means of gaining influence,
according to the model, is through becoming central in the
talk and referral networks. Being central in one type of
network is correlated to being central in the other. Thus

an important strategy in gaining influence might be to work

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159

to gain centrality in one or the other network in order to
improve centrality in the other and ultimately gain more
influence. Three other strategies proved useful for gaining
net-work centrality and for directly gaining influence.

The first strategy was involvement in community and
professional organizations (such as the Chamber of Commerce,
Downtown development organizations or Rotary). Being highly
involved, such as being an officer in one organization or
involved in many, improved centrality in the talk network
the most. However, involvement was also correlated to
referral network centrality and directly to influence. This
was the only strategy which improved referral network
centrality, except talk network centrality (Figure 11).

The second strategy was talking to staff of community
organizations. This strategy was not as strongly correlated
with talk network centrality and influence as involvement,
but nevertheless could prove a useful tool for gaining
influence over the direction in which the community react
towards tourism. Talking to staff of community
organizations did not gain centrality in the referral
network however (Figure 11).

The third and final strategy of this model is talking to
staff of tourism coordinating organizations outside the
community, such as the Travel Bureau, the State Chamber of
Commerce or Michigan Department of Transportation. This
strategy was strongly correlated to talk network centrality

and to influence directly, though not as strongly. Talking

160

to outside organizations helps gain control over information
and causes other businesses to want to talk to you, thus
moving you to a more central location in the talk network
(Figure 11).

This chapter discussed the results of individual
correlations used to test the ability of the variables laid
out in 15 propositions to predict the variation in the
dependent variables. Support was found for all but one of
the strategy propositions. However, no consistent support
was found for any of the constraint variables. The next
chapter will discuss the conclusion and implications of

these results.

CHAPTER 7

CONCLUSIONS

The general purpose of this study was to examine the
relationship between patterns of exchange among
owner/mangers of tourism oriented businesses and the amount
of influence attributed to them. The two networks of
exchange focused on were general communication (or talk) and
the referral of customers between businesses. A model was
developed which predicted influence with network variables
as well as strategy and constraint variables. Thus the
purpose of the study was accomplished and chapters 5 and 6
discuss the results. This chapter summarizes the study,
presents some conclusions and implications, states the
limitations of this research and concludes with suggestions

for future research directions.
7.1 Summary of the study

Tourism is a relatively new field for research. Few
studies have focused on social or communication exchanges or
used network analysis to examine the tourism industry. This
study focused on the networks of exchange among tourism
oriented businesses in four communities in Michigan which

consider themselves tourist destinations. The four

161

162

communities varied in size, location, and amount of tourism
development. They provided a continuum from small and
highly tourism dependent to large and more diverse in their
economic bases.

Since the purpose of this research was to examine
factors which accounted for attributions of influence or
power among business owner/managers, models of attributed
influence for other community settings were used as a basis
for developing this model of influence and exchange
networks. Centrality in appropriate networks was
hypothesized as an important factor in explaining which
business owner/managers were influential. Two types of
exchange networks were examined (1) the communication or
talk network, and (2) the referral of customers between
businesses network.

A model was developed based on the hypothesis that
centrality in the talk and referral networks would be
important in predicting influence. Also hypothesized were
five strategies to gain network centrality and influence.
Briefly stated these were:

I) talk centrality predicts referral centrality,

2) involvement in organizations predicts influence, talk

and referral centrality,

3) communication with organizations both inside and

outside the community predicts influence, talk and

referral centrality,

163

4) joint programs with other businesses predicts talk
and referral network centrality and

5) those that track their customers will be more central
in the talk network than those who do not track

customers.

Six characteristics propositions were hypothesized.
These included:

1) age of business would be correlated with influence,
talk and referral centrality,

2) size of the business would be correlated with
influence, talk and referral centrality,

3) age of the owner/manager would be correlated with
talk centrality,

4) education, training, and experience would be
correlated with talk centrality,

5) owner/manager living close to the city and being a
year round resident would predict talk centrality and

6) having the business be a sole source of income would

predict talk and referral centrality.

Chapter 5 examined several characteristics of the
communities which indicate an orientation towards tourism
and found the smallest town, Community L, to be the most
tourism dependent, Community C to be the next most dependent
and the two larger communities less dependent.

Characteristics of the businesses and the owner/managers

164

were analyzed to note any striking patterns which would help
explain the results of the model as well as providing a
descriptive background for the results of testing the
model's propositions. The most interesting result of the
analysis of business characteristics was the relative
newness of Community M's businesses. The largest community
resembled the smallest community and the two mid-sized
communities resembled each other as far as these
characteristics were concerned. The characteristics of the
owner/managers supported the description of Community L as
highly tourism dependent. Owner/managers on Community L
were the most strikingly different from owner/managers of
the other three communities in that they were generally
younger, more educated and trained, and less tied to the
community year round.

These characteristics serve as some explanation for the
differences among the communities in testing the results of
the model. Community L was the one community in which the
results of the model testing differed from the other
communities' results. Referral network centrality in
Community L was not as highly correlated with influence as
in the other communities and it was not the in-degree
centrality network that was most correlated with influence
as in the other communities, it was the betweenness measure
of centrality that was most correlated.

The lack of correlation in between referral and

influence scores was probably a reflection of the respondent

165

sample not capturing the relevant actors. The sample of all
businesses mentioned by respondents show a much higher
correlation between referral and influence scores (Table
23). This would lead us to believe that if a larger sample
of respondents was taken the results might show correlation
results similar to the other communities.

The differences in characteristics between communities
may serve as a basis for explaining other differences in
results. The results of the model testing reported in
Chapter 6 found that talk and referral activity and
centrality were strongly correlated with attributed
influence. All the strategy propositions were supported,
except the one about tracking customers. However, none of
the constraint proposals received consistent support from

the correlation analyses.

7.2 Conclusions

Several conclusions can be drawn from the results of
this study. First, those owner/managers who receive
attributions of influence are those who people say they talk
to. In other words, they have high centrality in the in-
degrees talking network. These influentials do not
necessarily talk to a lot of other owner/managers. It is
only that others indicate that they talk to influentials
which gains them influence. It is possible that

influentials don't remember talking to everyone, yet the

166

less influential individual remembers the communication
because talking to influential persons is more significant
to them.

Secondly, influential owner/managers' businesses receive
more customer referrals than non-influential businesses.
They have high referral network centrality. Once again it
is not the number of referrals that these businesses give
out, but those that they receive that predicts their
influence. Those owner/managers who receive referrals and
communication have control over valued resources. This
control of resources gives them power and their power is
reflected in the fact that others attribute influence to
them.

Third, individuals who are highly central in the talk
network are highly central in the referral network. Those
who are low in centrality in one network are generally low
in the other network. Thus having control over one type of
valued resource, communication, is related to control of
another resource, referrals of customers. This implies that
if one works to increase centrality in one type of network,
such as the talk network, this may improve centrality in the
referral network as well.

Fourth, being involved in community and professional
organizations is a good strategy to increase one's
centrality in the talk and referral network and to increase

influence.

167

Finally, talking to staff of tourism related
organizations both inside and outside the community is
generally a useful strategy for improving influence and, in
some cases, for increasing ones' centrality in both the talk

and referral network.

7.3. Implications for the Tourism Industry

A number of implications can be drawn for the tourism
industry from the results of this research. First, it is
important for all those who desire to improve the Michigan
tourism industry to keep in mind the pervasive influence of
communication patterns throughout the industry.
Communication not only serves as a link between tourists and
their experience, but it is a vital linking mechanism among
the tourism oriented businesses which are a major part of
the tourism resource consumed by tourists. Communication
needs to be thought of in its broadest sense, not just as
advertising or as other forms of formal communication.

Second, communication patterns within tourism oriented
communities are critical to the establishment and
maintenance of power, influence and leadership within
tourism oriented communities. It is the influential members
of a community which will determine the community's response
to tourism development. The establishment and maintenance
of a quality state tourism product for the State of Michigan

depends on the direction in which tourism develops within

-
L - .

168

these communities and whether high standards are maintained.
Therefore, who is influential in these communities and how
they become influential and stay that way should be of
concern not only to state coordinating organizations, but
all those interested in a healthy tourism economy.

Third, once these relationships between influence and
communication networks and strategies are known, they can be
manipulated and managed. The strategy of involvement in
community and professional organizations, which helps
increase centrality in the talk network and influence in the
community can be used in a number of ways to produce
different effects. An obvious use would be by someone who
desired to be more influential. Getting involved and taking
responsibility for leadership in one or more organizations
will likely increase your influence within the community,
depending on the organizations you choose to be involved in.

Another use of the involvement strategy might be by
community politicians or leaders who want to change or
manipulate the influence patterns within the community.

They could create new organizations or opportunities to get
people or businesses involved. This may bring new people
influence or increase the influence of the already
influential.

A fourth implication of this research involves the
strategy of talking to staff of organizations outside the
community. Coordinating organizations should be aware and

ready to manage the fact that those with whom they

169

communicate gain influence within the community as a result
of this communication link. If a state coordinating
organization wants to get something accomplished within a
community, it may need only look to those to whom they
regularly communicate, for these people will likely be the
influentials in the town. If state organizations don't have
a contact within a community, they may choose a business
owner/manager with whom to communicate and the information
that is communicated may greatly increase the influence of
that person by giving s/he the ability to become more
central in the communication network. Organizations within
the community may also use a strategy of communicating to

individuals who then gain network centrality and influence.

7.4 Limitations of the study

The exploratory nature of this area of research
(applying network analysis to tourism research) suggested
many possible approaches. However, in order to focus on a
subject narrow enough to accomplish within time and budget
constraints, limitations need to be placed on any study.
First, definition and application of the definition of which
businesses were tourism oriented and which were not was
difficult and imprecise. The study was limited by this.

Not all businesses could be included and the types of

businesses included in the sample for smaller towns could

170

not always be included in the sample of the larger
communities.

A second methodological limitation was how to define the
network boundary in each community in terms of the networks
among businesses. The legal or geographic boundary did not
necessarily coincide with the boundary of businesses network
links. Not having clear definitions of who should belong in
the sampling frame made data collection of the network
variables difficult. A related problem was the large size
of the communities. This made it impossible to interview
everyone (as is often done in network analysis procedures).

Thirdly, since the communities were selected to
represent a range of different types and sizes of
communities, this didn't allow for comparing the communities
very well. Also the communities could not be combined into
one sample for testing.

A fourth limitation was the lack of research on tourism
oriented businesses from a social network point of view.
This meant the theoretical basis for this study had to be
based on studies of other types of organizations, such as
social service organizations.

Another limitation involves the correlation analysis
approach taken by this study. This research took the
initial steps in testing the relationships between
influence, network centrality and several strategies.
However the model needs to have intervening variables

controlled for while trying to explain the relationships. A

171

multivariate regression or path analysis would need to be
done to accomplish this control.

A final limitation of this study concerns the nature of
cross-sectional studies. The data was collected at one
point in time and though the survey asked about relevant
information for the past two years, it is limited to the
time period in which it was collected. People change and
situations change and who is influential now and why will
most certainly change over time. Understanding dynamics

would be helpful in specifying a more complete model.

7.5 Suggested research directions

Several ideas for future research developed in the
process of the research for this study. First, it would be
useful to conduct a study such as this one in several
communities which were selected based on similarity rather
than differences. If variations in characteristics such as
size, level of tourism development, and location could be
minimized, the communities could be more easily compared.

Different predictive variables need to be considered,
especially with regards to the characteristics variables.
It is likely that variables not considered in this study are
the constraining factors for increasing ones' influence.

Other dependent variables could be examined. Network

.

analysis has been used to study such issues as conflict,

cooperation and satisfaction in other settings. Some of

172

these analyses may prove useful to understanding aspects of
the tourism industry other than the development of power and
influence among tourismoriented businesses.

The strategies used to gain influence and network
centrality could be explored in far greater detail. For
example, involvement in which types of organizations brings
the most influence or most effective network position?

Also, is level of involvement more important for predicting
influence? Does being an officer or leader in organizations
bring more influence than merely participating in many

organizations as a member?

APPENDIX A

Sample Interview Schedules

173

174

SOCIAL NETWORKS AMONG MICHIGAN TOURIST BUSINESSES

 

 

1. Recnum
2. Name of business
3. Address .

 

 

. type of business

 

Phone

 

\lOlU‘I-D

Respondent's name
Position

 

 

Call Record

called = C A _ r
stooped by = S I '

 

“-5
IN
loo

[45

 

Time (am or pm)

 

Date

 

Day of week

 

Results

 

caller's initials

 

 

 

 

 

 

 

Interview set for: Date:

 

Time:
(Place:

 

 

Directions to place:

 

 

175

10. Date

 

11. Sex of respondent

 

12. Race of respondent

 

13. Length of interview Hours Minutes

 

 

14. Time at beginning of interview

 

15. Time at end of interview

 

16. Location of business
a. downtown
b. on tourist route, but not downtown
c. not in tourist area

MAPPED Yes No

17. Description of businesses

not attractive very attractive
1 2 3 4 5

18. Short description of business:

 

 

 

19. Description of room where interview held:

20. Other people in room where interview was held:

21. Description of respondent's attitude:

1376

_A_. CHARACTERISTICS 2: THE BUSINESS

Al. How long has this business been located at this present location?

4years

 

A2. Has this business been located at a different location?

Yes No

A2a. Where?

 

AZb. How many years was it there?

 

A3. How long has this business been owned by the present owner?
(if stayed within one family, count as one owner).

Agyears

A4. Has this business had a previous owner?

Yes

No

Aka. How long was the business owned by that person?

years

A5. How many different owners has this business had during the
(If not 10 years old, just

past 10 years?
count the total number of owners during its life time).

 

A6. I am interested in the number of different types of products or services
that this business offers or sells throughout the year.

the period from January 1983 and June 1984, which of the

following products or services apply to this business?
Check all that apply.

lodging
dining
camping
shopping
bar

 

bait and tackle
boat launch facilities
swimming

Thinking about

recreational equipment sales or rental

gas/service station

 

recreational facilities (such as bowling, golf or movies)
other (please specify)

A7. Does your business usually close for more than a few days
during any part of the year?

A7a. What are the dates?
dates closed

dates open

Yes No

 

 

 

 

A8. I am interested in seasonal differences in your total gross
revenue relative to each other, not in absolute amount.

example of the information I am interested in.
whether your total gross revenue is high, medium or low for each month.

CARD B is an

Please tell

 

 

 

 

 

 

 

§ I Jaml Feb. I Maul Apnl May Jun. [Jul.| Aug. LSept. Oct. Nov. I Dec.

:5 3 J
d any: I I I I I I I I J
3 >HedIUMI I I I I I .I L I
3.2m J I L f I I L l J,

 

177

A9. On the average during the month of July, approximately what percent
of your customers are: '

permanent local residents (from within 30 miles) 1
non-locals or part-time residents 1

 

A9a. Of the non-locals, what percent would you estimate are:

business travelers (main purpose of trip is business) I
tourists (main purpose of trip is vacation or recreation) X
part-time residents (live here less than 9 months/year) I

 

A10. On the average during the month of March, approximately what percent
of your customers are:

permanent local residents (from within 30 miles) I
non-locals or part-time residents 1

AlOa. Of the non-locals, what percent would you estimate are:
business travelers (main purpose of trip is business)

I
tourists (main purpose of trip is vacation or recreation) 1
part-time residents (live here less than 9 months/year) I

 

All. Do you have any way of knowing where your customers come from?
(for example, checking credit card receipts, checks or license plate
numbers, regularly talking to customers)? Yes No

Alla. which method(s) have you used?

 

 

Allb. How many times per year do use each method?
times per year

A12. How many people (both full- and part-time) does your business employ
(include yourself and any family member that work for the business):

A12a. during the busiest month? full-time
part-time
Ale. during the slowest month? full-time
part-time

178

B. CHARACTERISTICS 9: THE RESPONDENT

_.

 

 

Bl. Is your home in or within 5 miles of the City of Muskegon?

 

Yes No If no, how far is it?
82. Are you a year-round resident of Muskegon County? Yes No
823. How long have you lived in Muskegon County? years
B3. How many years have you run this business? years

BA. How many years experience did you have running a business
of this type before you started to run this business? years

BS. Did you have any training before beginning to run this business?

 

 

Yes No
853. How many years? years
BSb. Where?
B6. What is your age? years

B7. How many years of education have you completed?

High school College
1 2 3 A 5 6 7 8 9 10 11 12 13 14 15 16 17+
88. Is this business you sole source of income? Yes No

B7a. What type of other income do you have, e.g.
retirement, other family income?

 

179

c. RESPONDENT'§ INVOLVEMENT _I_N_ COMMUNITY

 

 

Cl. Which of the following organizations at present a member of?

CARD

X - if MEMBER at present
0 - if held an OFFICE or on COMMITTEE in the organization in the past 2 years

A.

QFQHIO'II

Service organizations (e.g. Rotary, Lions Club)
Which one(s)?

 

 

 

. Religious groups
. Chamber of Commerce

Committees names:

 

 

 

Local business association(s) list names.

National professional organizations or groups
Which one(s)?

 

 

 

 

Visitor and/or Convention bureau

Political organization or clubs

Fraternal lodges or veterans' organizations
City Council

City Planning commision

School Board

 

. Others

 

C2.

C3.

C>C>C>C>C>C>C>C>C>C>C>C>

C>C>C>C>C>C>C>C1C>C>

180

About how often do you talk to STAFF of the following organizations
about your business? (Use the scale on CARD F, tell me the number that
best represents the amount of communication you have with each of the
organizations that I list.

0
l
2
3
6

l

l

l

l

l

l

1

l

l

l

l

l

NNNNNNI‘QNNNNN

UEVQF

about once or twice a year
about 3 to 6 times a year
about once a month

once a week or more

wuwuuwuwwwuw

L‘fibbbbbbbbbb

Muskegon Area Chamber of Commerce
City Council

City Manager's office

City Planning board

County planning commission

County Board of Commissioners

Mayor office

Convention & Visitors Bureau of Muskegon Co.
Industrial DeveIOpment Commission
Local AAA office

Local C00perative Extension Service
Sea Grant Advisory Agent

About how often do you talk to STAFF of the following organizations
or peOple about your business? (Using the same scale on the CARD tell
me the number that best represents the amount of communication you
have with each organization).

”Hp—lp—Op-lp-‘p—lp-d'dy‘

NNNNNNNNNN

wwwwwuuwuu

b§bfi§b§§bfi

State Travel Bureau

West Michigan Tourist Association

Travel Information Centers

Michigan Department of Natural Resources
State AAA office

White Lake Area Chamber of Commerce

State Convention 6 visitors bureau

State office of your professional association
Your United States Senator or Representative
Your Michigan Representative or Senator

181

Part 9 -Metwork Information

D1. Looking over this list. can you please tell me which of these
businesses have you talked (informally) to the owners or managers of
over the past two years? Using the scale on this card can you
tell me about how often.

SCALE
1 - about once or twice a year 3 - about once or twice a month
2 - about 9 to 6 times a year 4 I once a week or more
if never - Skip the line

Dis. Is the person you talk to the owner. the manager or an
owner/operator?

 

mark 0 - if you are the OWNER only
mark M - if you are the MANAGER only

(I am interested in their contacts with only owners 2; ganagers

of these businesses, NOT ANY OTHER OF THE EMPLOYEES.

Leave the line blank if they have not had contact with the OWNER OR MANAGER
within the past 2 years.)

(If there is more than one owner and/or manager (such as husband and wife

or father and son) of a business, choose the owner or manager of the busines
that that the respondent has had the MOST contact with over the last 2 years
and ask all the questions with respect to that one person).

Dib. Is the owner/manager is related to you by blood or marriage?

(If so.put a check in the column labelled KIN. e.g. father.
mother-in-law. brother. cousin, aunt. grandfather. etc.)

D2. Which of the businesses on this list have you referred your
customers to in the last two years and about often using the
same scale?

D3. Which of the businesses on the list have you worked with in
planning an event or promotional activity, such as a package
deal between several businesses (other than through a community
organization)? About how often? (use same scale)

D4. Looking back over this list of businesses. which of the
owners or managers do think have a definate influence, either
positively or negatively. over the way the community responds to
an opportunity or problem involving tourism?

(Put check in "Influence" column if mentioned).

1£32

about once or twice a year
about 3 to 6 times a year
about once or twice a month
once a week or more

chin-INF
IIII

tal I: , customer work
informally referral together

Names of businesses listed here

 

1133

B. INTRA—ORGANIZAIO’I‘NAL INFORMATIONTLINKS

El. To what extent do you think that the people working for you
refer your customers to the same places that you do ?

almost exactly

very much only not not at
the same places the somewhat very all
for the same
estion same the UDCh the
qu same the same same
5 4 3 2 1
E2. Do you have store meetings with your employees? yes no

82a. How often?

32b. What is covered at these meetings?

E3. Have your emploees ever gone to any kind of seminars or training
programs ?
yes no

E3a. If so, how many?

E3b. What are the seminars about ?

E4. Do your employees ever go to Chamber of Commerce of Business Assoc.
meetings?
yes no

34a. If yes, how often?

ES. Do you talk to your employees about what happens or what you learn
at Chamber or Business Association meetings?

yes no

ESa. Can you give me an example of the kind of thing you pass on to your
employees?

APPENDIX B

Sample Cover Letter

184

1135

MICHIGAN STATE UNIVERSITY

 

DEPARTNI-IN‘I‘ 0" PARK AND RECREATION III-SOURCES EAST LANSING ' MICHIGAN ' 481124
NATURAL RESOURCE BUILDING

October 25, 1984

Dear business owner or manager,

We are all concerned about the future of Michigan's economic development
and we are all aware of the importance of the tourist industry in the prosperity
of the state. Businesses such as yours, and the daily decisions you make,
contribute to the welfare of us all.

To help provide better information to the small businesses engaged in
tourism, we are conducting a study to learn about some of the central factors
that affect decision making. Very little is known about this subject at present,
and only with such information can we offer the help that is often needed
by people such as yourself, others in the industry, and the state and local
officials who are dedicated to the growth for the Muchigan tourism industry.

Port Huron is one of the four communities where we will be collecting
information this summer. Your business was selected, along with many others
in the Port Huron area, because it could play a central role in tourism in
your community. Within the next week, one of us will be contacting you to
gather your views on business conditions in your community. The interview
will take just a few minutes of your time.

All of the information you provide will be held strictly confidential
and no one other than the two chief researchers will ever see the original
data. 'Mo person or business will ever be identified in any way in any written
report. You will receive a summary of our findings and we hope you will find
them interesting and useful in your own decision making.

‘ Only persons such as yourself, active in the business community, can
give us the information we need to help the continued development of the

Michigan economy. Wb thank you in advance for your help and look forward
to talking to you in the near future.

Sincerely yours,

Maureen B. Marci Cobb
Assistant Professor - Project Director

"SI I is . ”fir-naive Active/Esme! Weed" Institution

APPENDIX C

Results of Descriptive Frequencies

186

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09.08 09.0300: .5 ouucom - “£30..

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189

 

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