 

.. .f}4rv' . n v-
. A. 1 ‘
. .

 

. .

a ., ,2...
b‘l.’.\‘:€‘l
‘1’}.‘23:

III-.4: .
.21 .. .
3:333)? t
. . .n x
hum». 13)..»
..
3.15.1?
|)‘D~.-Aibl (
n .5 . a?

 

...
Qua»? m . ...
.. 13.... n

.3:
E. v\
. . $1...:..u J...
.t ...-it.
this»
217;
319.

I .11.]? .. Ill . ,1 u 7.. ... a.
...ﬁ. ..... . erg: ; _ , 1:31.... Law...” .... :3..:_...,...;f.,;
. 23.5.3-1;wngﬁ. .. . yin; ﬁe???” ..w L. in»; exﬁgé 13E;

7" _ W‘JJJ'
..u... £333».

 

'
" 'T'Gi ‘5'

45'106“

u?

*0\
1‘)
ﬁx
‘3

This is to certify that the
dissertation entitled

Predicting Downtown and Small Business Success:

A Resource-Based View

presented by
Rodney C. Runyan

has been accepted towards fulﬁllment
of the requirements for the

Ph.D. degree in Human Environment: Design
and Management

ﬂw Mary/mo

Major Professor’s Signature

 

May 12, 2005

Date

MSU is an Aﬂ‘irmativa Action/Equal Opportunity Institution

-.- — --—.—.—-a-v--o-a—--'-n- —

 

LIBRARIES
MICHIGAN STATE UNIVERSITY
EAST LANSING, MICH 48824-1048

PLACE IN RETURN BOX to remove this checkout from your record.
TO AVOID FINES return on or before date due.
MAY BE RECALLED with earlier due date if requested.

 

DATE DUE

DATE DUE

DATE DUE

 

AUG 2 0 2005

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

2M5 c:/ClRC/DateDue.indd—p.15

 

PREDICTING DOWNTOWN AND SMALL BUSINESS SUCCESS:
A RESOURCE-BASED VIEW

By

Rodney C. Runyan

A DISSERTATION

Submitted to
Michigan State University
in partial fulﬁllment of the

requirements for the degree of

DOCTOR OF PHILOSOPHY

Department of Human Environment: Design and Management

2005

ABSTRACT

PREDICTING DOWNTOWN AND SMALL BUSINESS SUCCESS:
A RESOURCE-BASED VIEW

By

Rodney C. Runyan

This study was a unique examination of small downtown business districts, and
the businesses within each respective district. I employed resource-based theory to put
forward a model that identiﬁes a downtown’s resources, and connects those to success. I
posited that a downtown has two separate types of resources at its disposal: business
resources and structural resources. Business resources are manifest in downtown
businesses, while structural resources are created by the downtown (or community) itself.
When utilized, these resources have a positive effect on the downtown’s success.

Data were collected from 267 business owners in 11 small and medium-sized
downtowns in Michigan, using a hand delivered, self-report survey instrument. I utilized
structural equation modeling to test the hypothesized relationships between measurement
variables and latent constructs. Conﬁrmatory factor analysis was used to test the
reliability and validity of the measurement variables. The model proposed in this study
was a complex factor model, where some latent constructs were themselves indicated by
latent factors. Business and structural resources, and downtown success were latent
endogenous constructs. Business resources were indicated by the latent factors of
entrepreneurial orientation, small business orientation and social capital. Structural

resources were indicated by brand identity, business mix and community characteristics.

Downtown success was indicated by vacancies, relative ﬁrm performance and longevity
of businesses.

Entrepreneurial orientation was indicated by innovativeness, proactiveness and
risk taking. Small business orientation was indicated by business owner goals and
emotional attachment. Social capital was indicated by trust, reciprocity, shared vision,
homophily, network density and frequency of network interaction. Downtown image and
positioning were indicators of brand identity. Business mix was indicated by business
diversity and complementary businesses. Community characteristics were indicated by
sense of place.

Results from ﬁtting the structural model to the data showed small business
orientation and social capital were signiﬁcant indicators of the business resource
construct. Social capital, brand image, business mix and community characteristics were
all signiﬁcant indicators of the structural resources construct. Business resources were not
a signiﬁcant predictor of downtown success, though the parameter estimate points to a
moderate effect size. Structural resources were found to be a signiﬁcant and positive
predictor of downtown success.

Results support resource-based theory which maintains that ﬁrms with superior
resources will have a competitive advantage over competitors. A key to this competitive
advantage is the ability to maintain and differentiate resources from the competition’s.
Implications for downtowns imply that structural resources such as brand identity,
business mix and sense of place are resources which may provide a sustainable

competitive advantage.

Cepyn'ght by
RODNEY c. RUNYAN
2005

DEDICATION
To my wife Kimberly, my children Kyle and Brett. Your support, patience and

conﬁdence in my ability to undertake this journey were essential to my success.

To my parents Jack and Rosemary, for instilling in me at an early age, the value of

education and learning.

To my extended family including Pat and Judy Sweeney, Sharon, Susan and Kevin

Sweeney for your unwavering support of Kimberly and our family.

ACKNOWLEDGMENTS

My sincere gratitude is extended to those who have served as mentors throughout
my doctoral program, and especially while working on this dissertation. Dr. Patricia
Huddleston, Committee Chair, provided just the right amounts of nurturing and strong
advice in the right quantities and at the right times. Her guidance from the beginning of
my program until the completion of my dissertation was invaluable. I also offer my
appreciation to the members of my committee. Dr. Brenda Stemquist and Dr. Cornelia
Droge provided valuable guidance on theoretical issues, and feedback on research ideas
early on in my program. That guidance helped me to better focus my research direction
for this study. Dr. Edward Wolfe gave me a renewed appreciation for the various ways in
which we can view data, and their analyses. Both Dr. Huddleston and Dr. Droge were
very helpful in the conceptualization and construction of my survey instrument. I thank
Dr. Sally Helvenston, Department Chair, for her support also. Judy Osbun of HED
offered invaluable technical help on many occasions.

I would also like to thank several persons from Northwood University, where I
served as a full-time faculty member while completing the course work for my degree.
Dr. Margaret Colarelli and Dr. Eugene Beckham were very accommodating to my
scheduling needs, and supported my pursuit of the doctorate. Jill Ouellette was extremely
supportive as a colleague, and without her taking on some of my duties, I may not have
completed this degree in such a timely manner.

Finally, I again thank my family for all of the times that I was not able to be home
at night or on the weekends. Though this caused some logistical difﬁculties, you all

pitched in and helped us get through this!

vi

Table of Contents

List of Figures .................................................................... ix
List of Tables ...................................................................... x
Chapter 1

Introduction ........................................................................ 1

Significance of Study .................................................... 1

Problem Definition ...................................................... 6

Chapter 2

Review of Relevant Literature ................................................ 9

Theoretical Framework ................................................ 10

Resource-based Theory ....................................... 12

Business Resources ..................................................... 15

Entrepreneurial Orientation ................................. 18

Small Business Orientation .................................. 22

Social Capital ........................................................... 26

Social Network Theory ....................................... 30

Structural Resources ................................................... 37

Brand Identity .................................................. 37

Business Mix ................................................... 41

Community Characteristics .................................. 45

Downtown Success ...................................................... 51
Chapter 3

Methodology ...................................................................... 55

Focus Groups ............................................................ 55

Sample ..................................................................... 63

Instrument ............................................................... 64

Data Collection Procedures .......................................... 79

Sample Description .................................................... 81
Chapter 4

Results ............................................................................. 83

Factor Development ................................................. 85

Confirmatory Factor Analyses ..................................... 86

Hypothesis Testing .................................................... 94

Structural Model Fitting ............................................. 102

Summary ............................................................... 105

vii

Chapter 5

Discussion and Conclusion .................................................. 107
Business Resources ................................................... 108
Structural Resources ................................................. 1 13
Alternative Models .................................................... 120
Limitations ' ............................................................... 1 20
Recommendations .................................................... 122
Managerial Implications .......................................... 122
Future Research ...................................................... 125
Conclusion .......................................................... 128

Appendix A: Figures .......................................................... 130

Appendix B: Tables ......................................................... 143
References ....................................................................... 172

viii

List of Figures

Figure 1 — Conceptual Model ...........................................
Figure 2 — Measurement Model .......................................
Figure 3 — Structural Model ...........................................
Figure 4 — Chamber of Commerce Letter ..........................
Figure 5 — DDA Letter ..................................................

Figure 6 — Survey Instrument ........................................

ix

131

132

133

134

135

136

List of Tables

Table 1 — Latent Constructs and Observed Indicators .............. 144
Table 2 — Sample Characteristics ...................................... 152
Table 3 - Community Characteristics ................................ 153
Table 4 — Hypotheses ..................................................... 157

Table 5 — Parameter Estimates for First-Order CFAs - ENTREP.. 159

Table 6 - Parameter Estimates for First-Order CFAs — SBO ...... 160

Table 7 — Parameter Estimates for First-Order CFAs — SOCAP .. 161

Table 8 — Parameter Estimates for First-Order CFAs — SNTIES... 162

Table 9 — Parameter Estimates for First-Order CFAs - BRID 163

Table 10 — Parameter Estimates for First-Order CFAs — BUSMIX.164

Table 11 - Parameter Estimates for First-Order CFAs —
COMCHAR ..................................................... 165

Table 12 — Parameter Estimates for First-Order CFAs — DTSUC ..166

Table 13 — Parameter Estimates for CFAs — BUSRES ............ 167
Table 14 — Parameter Estimates for CFAs — STRURES ........... 168
Table 15 — Parameter Estimates for CFAs - DTSUC .............. 169
Table 16 - Parameter Estimates for Measurement Model ......... 170

Table 17 — Parameter Estimates for Structural Model ............... 171

Chapter 1
Introduction

Signiﬁcance of study

A city or town’s “downtown” area has historically served as its cultural center,
and its central business district. It is described by some as a community’s “heart,” and
reflective of the community as a whole (Michigan Cool Cities, 2003; Runyan 2004a).
Traditionally, a city’s oldest businesses (including retailers) originated in the downtown
area. For example, today’s department stores (Macy’s, May, Dillard’s, Harrod’s, etc.)
originated in city centers. Though these large, well-known stores are used for illustration
purposes, most downtowns had versions of these general merchandisers in their history
(Levy & Weitz, 2004). Many moved to the suburbs when room for expansion was limited
in the downtown area, while some simply went out of business. Due to the building
constraints inherent in the typical downtown (Filion, Hoernig, Bunting & Sands, 2004),
businesses requiring large amounts of space have typically moved from or located outside
of the downtown (Levy & Weitz, 2004). Therefore, the downtown business was and is
typically a small and independent ﬁrm.

Small retailers have been called the “glue” that bonds communities together
(Irwin, Tolbert & Lyson, 1997). Over the past two decades the decline of the small,
independent retailer has been well chronicled in the trade press, as well as in local,
regional and national newspapers. Speculation as to the reasons behind this decline range
from urban decay and business ﬂight to the suburban malls (Hazel, 2002), to the lack of
strategic planning (Conant and White, 1999), to the rise of Wal-Mart in small towns

across America (McCune, 1994). Changes in the demographic landscape of the US, as

well as consolidation in the retail industry, has led to retail activity increasingly being
concentrated in large-scale retail formats (Stone, 1995).

To illustrate, the effects of this decline in small independents can be seen in the
restaurant and bookseller segments. From 1993 to 2004, the number of independent
booksellers in the US. fell from 4,700 to 1,885, as measured by membership in the
American Booksellers Association (Trachtenberg, 2004). They have of course been hit in
succession by mall competition in the 1980’s (e.g., Waldenbooks), discounters and bi g-
box retailers in the 1990’s (e.g., Kmart, Wal-mart, Border’s), and the Internet in this
millennium (e. g., Amazon, Barnes & Noble). Similarly, the independent restaurant has
suffered from excessive competition from chains. Today, less than 15% of the full-
service restaurants in the US. are independents (Gorodesky & Mchon, 2003). From
the 1960’s, they were pressured by fast-food formats such as McDonald’s. But in recent
years, they have been deluged by “mega-chains” (Gorodesky & McCarron, 2003) that
can utilize niche-type strategies (e. g., Chi-chi’s, Olive Garden, etc.) while obtaining
economies of scale.

The movement of retailers and businesses to suburban locations may not cause
concern for those in larger, urban areas. But the health and vitality of downtowns in non-
urban areas is a different situation. Robertson (1999) describes the image projected by the
downtown area of a small city as being “intertwined” with the “identity” of the
community itself. Anyone who has driven through small towns can readily attest to the
negative feeling one gets from a downtown with multiple empty storefronts and soaped
windows. The State of Michigan has launched a statewide initiative called “Cool Cities,”

aimed at identifying and supporting the qualities that cities need to thrive in the future.

One of the key elements of this initiative is a city’s “downtown” area, as this has been
identiﬁed by Florida (2002) as an important part of attracting and keeping young, creative
people. According to Florida, young and creative people are important for communities
to grow and prosper.

Small towns are growing in population in many areas of the US. (McCarthy,
2004). Prior to 2003, the US. Census Bureau referred to most areas outside of urban
centers and their surrounding suburbs, as “rural.” That was probably a misnomer because
towns of up to 50,000 residents, or small groups of towns that totaled even greater
population totals, were labeled as rural. For example a town such as West Lafayette,
Indiana, with a population of 29,300 (based on the 2000 Census) would have previously
been labeled “rural.” This city has a large world-class university (Purdue University), and
the resultant diverse population base. It was not until 2003 that the Census ofﬁcially
recognized these non-urban, non-rural areas as “micropolitan” (McCarthy, 2004),
signaling a recognition of the signiﬁcant differences between these areas, urban areas and
rural America.

The importance of the downtown is not just to the “psyche” of the surrounding
community (Robertson, 1999). If we accept the downtown as a community’s center or
“heart,” then it is important that center be vibrant and economically vital. Downtowns in
small and medium-sized cities are typically “stored” with small businesses (Robertson,
1999; Billesbach & Walker, 2003; Guy & Duckett, 2003). Despite the attention paid to
large retailers and chains, the small business is still very important to the economic
vitality of cities, states and the country (Robbins, Pantuosco, Parker & Fuller, 2000).

Though small business faces threats from the likes of Wal-mart (McCune, 1994),

opportunities for entrepreneurs still exist. Gillete (1998) notes that in most communities
where Wal—mart is located, Wal-mart seldom tops 10% of area sales dollars. Small
businesses can satisfy consumer demand by ﬁlling the “gaps” where items or services are
not proﬁtable for large ﬁrms (J ulien, 1993; Buss, 1996). When entrepreneurs take
advantage of opportunities they create jobs, lowering the unemployment rate, decreasing
wage inﬂation and improving the overall productivity (Robbins, et al., 2000).

Small businesses are a source of continuous innovation and testing of new
methods, products etc. (Acs, 1999). This process plays a vital role in market renewal and
structure. There is little innovative about a Wal-mart moving into a town that already has
a Target in place (for example). Further, when large retailers do enter, anecdotal evidence
points to a decrease in the beneﬁts and increased costs to local communities (Milchen,
2000). This includes increases in infrastructure spending, losses of existing jobs, and
losses in general giving to the community. The loss of beneﬁts was supported in focus
group interviews (Runyan, 2004a), when one downtown coordinator suggested that the
annual charitable giving of a large discounter was less than half that which had been
given by the local grocer (who was being put out of business, presumably by the large
discounter).

Yet for all the importance attributed to downtowns, a paucity of empirical work
about downtowns appears in the literature. This is true in the marketing, retailing and
urban planning ﬁelds. Robertson (1999) notes that most of the literature on downtown
development neglects small cities, focusing rather on large urban areas. In the marketing
and retailing literature, a similar gap exists, with the bulk of research being conducted on

large retailers (Runyan, 2004b). The research involving non-urban, small town retailers

has tended to focus on either consumer behavior issues, or individual retailer behavior. In
other words, previous research has looked at all of the components separately
(downtowns and businesses) but not in a comprehensive manner.

The urban planning and public policy literature have reported research on the
downtown from a developmental point of view, and often use planners or other city
government ofﬁcials as informants (Robertson, 1999; F ilion, Hoernig, Bunting & Sands,
2004). Much of the city center research has been qualitative in nature, but takes a
prescriptive approach, with no goal of theory building or causal inference (e. g., Schiller,
1994; Tomalin & Pal, 1994; O’Callaghan & O’Riordan, 2003). The small retailing and
marketing literature have reported investigations of downtown retailers from a
performance perspective, using business owners as informants (Miller & Kean, 1997b;
Miller & Kim, 1997; Billesbach & Walker, 2003). This leaves a large gap in the
literature, as both planners and business owners may have different goals for the same
location, which produce different results for the downtown as a whole. For example,
Robertson (1999) found that city planners (in small towns) listed potential problems in
their downtown that included revitalization issues. Yet Billesbach and Walker (2003)
found that small, downtown retailers listed revitalization as one of the least important
factors in competing with major discount chains. To date, only a few scholars have
investigated the performance of downtowns as an entity, and the variables that lead to
successful performance (N iehm, 2002; Kean, Gaskill, Leisritz, Jasper, Bastow-Shoop,
Jolly & Stemquist, 1998). What contributes to the success of a downtown is an important

(and unanswered) question that calls for empirical research.

Problem Definition:

Do downtowns act like ﬁrms? The resource-based theory of the ﬁrm postulates
that ﬁrms seek to gain competitive advantage in order to earn proﬁts (Porter, 1980;
Wemerfelt, 1984; Barney, 1991; Peteraf, 1993). To do this they utilize resources, and to
the extent that those resources are superior to competitors,’ they will be more successful.
This is the resource-based view (RBV) of the ﬁrm, and it has become the prevailing
theoretical framework in the management strategy literature (Peng, 2001 ). However no
one (to date) has treated or identiﬁed downtowns as ﬁrms. As early as 1959, Penrose
deﬁned a ﬁrm as a collection of resources that can be human or physical. A downtown is
a collection of ﬁrms, the collective performance of which is reﬂected in the success level
of the downtown. As such, downtowns as entities may be looked at as ﬁrms themselves.
Using this rationale, I propose that the deﬁnition of the “ﬁrm” be expanded to include
downtown business districts as ﬁrms.

Downtowns are unplarmed business districts (Levy & Weitz, 2004), eclectic in
nature compared to most other business centers (e.g., malls, strip centers, industrial
parks). Businesses that comprise a typical downtown might include retail, service,
professional, medical, government, food, hotel, and entertainment. Perhaps due to this
eclectic mix, or to the inherent independence of small business owners, many owners in
downtowns do not see themselves as part of a group. Yet conceptually, members of the
local community consider a downtown a single entity. Therefore, downtowns that
knowingly (or unknowingly) act like a single business may be strengthening that
perception amongst the local community (or other potential consumers). In a large focus

group study involving 40 Michigan cities, respondents (local citizens) identiﬁed unique

strengths such as “walkable” downtowns, diverse shopping opportunities and hometown
pride that their community possessed, that help their town compete with other cities
(Michigan Cool Cities. . ., 2003). Focus group interviews with business owners have
revealed the same sense of the city or downtown as a single entity (Runyan, 2004a).

Much of the research involving small businesses (and in particular retailers) has
focused several themes: what makes for a successful business (DeKimpe & Morrison,
1991; Bates, 1995), how to compete with larger chains (Stone, 1995; Billesbach &
Walker, 2003), or entrepreneurial issues such as network ties (Frazier, 2000); gender
differences (Schmidt & Parker, 2003); goal orientation (Stewart, Carland, Carland,
Watson & Sweo, 2003), and risk (Stewart & Roth, 2001). I argue that for individual small
businesses in downtowns, none of the aforementioned strategies guarantee success. If a
downtown is collectively unsuccessful, then existing businesses will (ipsofacto) be
unsuccessful. Businesses may still be open, but will not be successful (i.e., just “hanging
on”). Additionally, it will be difﬁcult for new ventures to be successful. So for a
downtown to be successful, its businesses must be successful. But I posit that it is also
true that for businesses to be successful, the downtown itself must be successful. To date
no one has empirically investigated this issue ﬁom a resource-based view.

If downtowns really do act like ﬁrms, what are the resources that they possess,
and of those, which do they utilize? Do successful downtowns achieve a type of
“synergy” whereby the whole is greater than the sum of its parts (those parts being the
independent businesses)? Do the businesses within a downtown exist in a “symbiotic”
relationship with each other, whereby each has a small but signiﬁcant effect on the

others? One of the criticisms of RBV is that resources may be intangible, or poorly

understood by the organization (Rouse & Dallenbach, 1999). An a priori identiﬁcation of
constructs is made, based on focus group results, as well as extensive review of the
literature. Speciﬁcally I identify two main areas from which a downtown may draw
resources: its member businesses and structural sources. The business-speciﬁc constructs
identiﬁed are entrepreneurial characteristics, social capital, and social network ties. The
structural constructs are brand identity, business mix and community characteristics. A
key contribution of this study is that it is designed to identify the resources that
successful, non-urban downtowns possess. This will contribute to the body of literature in
the RBV, urban planning and small business domains. It will also serve to guide
downtown managers in identifying and nurturing proprietary resources, to help

downtowns gain the competitive advantage needed in today’s business environment.

Chapter 2
Review of Relevant Literature
Introduction

Downtowns have been characterized as a community’s “heart and soul”
(Robertson, 1999). Small retailers have been called the “glue” that bonds communities
together (Irwin, Tolbert & Lyson, 1997). These statements may sound like hyperbole to
some, but to downtown business owners these descriptions ﬁt well. A downtown is a
collection of ﬁrms, the collective performance of which is reﬂected in the success level
of the downtown. As such, downtowns as entities may be viewed as ﬁrms themselves.
The trade press has covered the demise, then recent rebirth of the downtown in large,
medium and small towns (Hazel, 2002). Thus, the existence of successful and
unsuccessful downtowns has been documented in that medium.

This research aims to investigate what makes a downtown successﬁrl, from a
resource-based view. The RBV is a theoretical framework that has been used to explain
the performance of ﬁrms. My study presents a new and unique conceptualization of
downtowns and the small businesses that contribute to a downtown’s economic viability.
Figure 1 illustrates the conceptual model that I use to describe this relationship
structurally. Hypotheses will be put forth for each of the posited relationships in the
model. The model is complex, with two second-order factors: business and structural
resources. These are indicated by several latent constructs. Thus hypotheses will be

harmed from this conceptual viewpoint.

Theoretical Framework

The resource-based view (RBV) of the ﬁrm has become one of the most widely
used theoretical frameworks in the management literature. The foci of RBV are
' competitive advantages generated by the ﬁrm, ﬁom its unique set of resources
(Wemerfelt, 1984; Barney, 1986; 1991; Peteraf, 1993). Barney (1991) identiﬁed four key
attributes that a resource must have, in order to yield a sustainable competitive advantage.
A resource must be: valuable, rare, imperfectly mobile, and non-substitutable. Peteraf
(1993) ﬁrrther reﬁned the ﬁamework by positing that resources must have the following
four attributes to help a ﬁrm sustain competitive advantages: be heterogeneous; have ex
ante and ex post limits in place to competition for that resource; and be imperfectly
mobile. RBV is a still-emerging theory, and as such is still in the conceptualization phase.
For example, though both Barney (1986; 1991; 2001) and Peteraf (1993; 1994) were
early champions of RBV, neither has moved forward in an attempt to operationalize their
own constructs of sustained competitive advantage. Studies that have been conducted
using RBV as a ﬁamework for both model and theory building (Delios & Beamish, 1999;
Peng, 2001; Dhanaraj & Beamish, 2003) have shown mixed results in terms of
operationalizing constructs. But RBV has been cited as an important theoretical base,
with marketing theorists such as Shelby Hunt (Hunt & Derozier, 2004) weighing in on its
behalf.

It is beyond the scope of this study to operationalize all the constructs proposed in
the RBV literature. Rather, RBV provides an overarching basis to identify the resources
that downtowns possess and utilize, as well as answer the question of whether

downtowns act like ﬁrms. As is the case in most of the RBV—based literature, RBV is

10

used to help identify resources that help ﬁrms gain and sustain competitive advantage.
But further theory is often needed to support conceptual constructs. For example, when
applying RBV to the ﬁnance sphere, ﬁnancial theories would be used to support
constructs of a ﬁrrn’s ﬁnancial resources. In the same manner, each of the constructs in
the proposed model is supported by its conceptual genesis.

I propose that business and structural resources are different in the following
ways: Business resources are those created and possessed by individual downtown
business owners. The business owners directly control the extent of each of these
resources. They may beneﬁt individually or collectively from those resources, and the
downtown also may beneﬁt from those resources. Managerial resources (the skills and
abilities of managers) are important contributors to a ﬁrm (Castanias & Helfat, 1991). In
the current study, business owners are conceptualized as the “managers” and the
downtown as the ﬁrm.

Structural resources are those that are more macro in nature than the business
resources previously discussed. They are resources that downtown business owners may
beneﬁt from or contribute to, but do not possess individually. These resources are also
ones that may simply exist (e.g., economic base, diverse community) or may be created
or directed (brand identity, business mix). However, direction of the resource in this case
would not come from business owners. Downtown business owners may support a
town’s brand identity, beneﬁt from and add to or support a diverse business mix and
community characteristics. But they don’t direct, or individually “create” these resources.

Literature from several domains provides the foundation for the resource-based

view of downtowns. Entrepreneurial orientation, social capital and social networks form

11

the business resource construct. Those that form the structural resource construct are:
brand identity, retail mix and community characteristics. I review research appearing in
the extant literature and present support for hypothesized relationships of the latent
indicators of both business and structural resources, as well as the inﬂuence of those
resources on downtown success.
Resource Based Theory

The resource-based view of the ﬁrm has been referred to as the most inﬂuential
framework for understanding strategic management (Barney, Wright & Ketchen 2001;
Peng 2001). The seminal article on RBV is considered to be Wemerfelt’s 1984 paper, and
was based on Porter’s (1980) competitive advantages work. Since that time Wemerfelt’s
work has been cited, along with Barney (1991), more than any other works in the
management literature (Barney, Wright & Ketchen, 2001). It is used by most RBV
authors to describe and operationalize constructs of competitive advantage. The key to
competitive advantage though, is for ﬁrms to be able to sustain the advantages gained
from superior resources. Sustained competitive advantage comes ﬁom a ﬁrm’s resources
and capabilities that include management skills, organizational processes and skills,
information and lmowledge (Barney 1991). In an early work that has since become
widely used in the management ﬁeld, Peteraf (1993) stated that there are four conditions
that underlie sustainable competitive advantages. These are: heterogeneous resources
within an industry; ex ante and ex post limits to competition; and imperfect resource
mobility. Each of these extend from the resource-based view that a ﬁrrn’s resources that
are distinctive or superior relative to its rivals, may become the basis for competitive

advantage if they are utilized in the appropriate environmental opportunities (Andrews,

12

1971; Thompson and Strickland, 1990). Peteraf’s (1993) four dimensions of competitive
advantage deserve further explanation:

Heterogeneitv- This concept implies that ﬁrms in an industry compete, but with varying
capabilities (resources). Firms with marginal capabilities will only break even, while
those with superior resources will earn proﬁts. Though downtowns don’t earn proﬁts per
se, the concept of just surviving (breaking even) versus thriving (earning proﬁts) is
applicable. All downtowns have some different types of resources. For example, a town
might be situated on a large body of water, which would serve as a resource (perhaps
attracting tourists). Another town down the road might be situated on the same body of
water, but due to geography, also have a harbor within its downtown area. In this case,
both towns share a similar resource, but the resource is heterogeneous and the one with
the harbor might enjoy the beneﬁt of not only tourists who arrive via car, but also those
who arrive via boat. One might easily see the analogy here being applied to a town
located on an interstate highway, versus one with a lesser volume of trafﬁc located far off
the same highway.

Ex ante limits to competition for resources- Prior to establishing a superior resource
position, there must be limited competition for that resource or position. If there are no
limits to competition for the resource, the cost of that resource will likely be “bid” up to
the point that future proﬁts will be depressed. Proﬁts come from ex ante uncertainty. For
example, if a retailer chooses a prime location of which no other retailers has knowledge,
there would be ex ante limits to competition for that resource. Lower competition derived
ﬁ'om knowledge heterogeneity (one knew what others did not), would keep the cost of

acquiring the location down. If the retailer chooses correctly, then proﬁts would be

13

realized. To explain further, in the above example of two towns on a body of water, both
have access to the same resource, but one is slightly different. If, recognizing that its
harbor might attract boaters, the downtown expanded towards the harbor (thus enticing
boaters to disembark and shop, eat, etc.), it would have a ﬁrst-mover advantage over the
other town without any harbor.

Ex post limits to competition for resources- Competitive advantage can only be sustained
if heterogeneity is preserved. Ex post limits are those that are in place to push down any
competitive attacks after entry into the marketplace. The two main strategies to this are:
imperfect imitability (making it difﬁcult to imitate the superior resource), and imperfect
substitutability (making it difﬁcult for an inferior resource to be substituted for a superior
one). Using our retailer location example, if no other locations as good as the original are
available in a trading area, then this would satisfy imperfect imitability. If the original
location were superior enough that a retailer of otherwise equal resources could not
compete from another location, then this would satisfy imperfect substitutability. In the
town comparison, it would be difﬁcult for the town without the harbor to imitate (i.e.,
build their own harbor). However, if the town sans-harbor were to build a large “pier”
into the body of water to substitute for a harbor (thus allowing boaters to dock and
disembark), this could be a substitute. To the extent that the harbor was superior to the
dock (as measured by boaters disembarking at one or the other), the resource would be
imperfectly imitable and provide ex post protections.

Imperfect mobilitv— Resources are perfectly immobile if they cannot be traded. Resources
that are imperfectly mobile are those that can be traded, but would be of less value to an

outside ﬁrm, than to the one that currently employs them. A ﬁrrn’s reputation might be an

14

example of perfect immobility, while a ﬁrrn’s tacit knowledge might be imperfectly
mobile. Locations can of course be traded. However, in the example of the retailer with
the prime location, we can also apply this dimension of RBV. Knowledge of the superior
location is now known to others (ex post of course), therefore increasing its value. For
another retailer to acquire that location, a premium would have to be paid, thus lowering
the rents realized in ﬁrture trading. The example of the town with a harbor is an example
of a perfectly immobile resource. One can’t simply trade a “harbor” to another town.

In this research study, I posit that a downtown will possess certain resources with
which it can gain competitive advantages. Those resources have been articulated as
originating from two separate (yet related) sources: business and structural resources. In
order for ﬁrms (downtowns here) to realize sustained competitive advantages, the four
previous conditions need to be met (Peteraf, 1993). These four conditions are not
conceptualized in the literature as constructs themselves, and have therefore not been
operationalized. However the resource-based view of the ﬁrm maintains implicitly that
successful ﬁrms (or downtowns) have resources that meet these four criteria.

Business Resources
Entregreneurial Orientation

Entrepreneurial characteristics are viewed as resources to both the entrepreneur as
well as the ﬁrm (Alvarez & Busenitz, 2001). In general, most researchers see
entrepreneurs as individuals who tend to be innovative risk takers (Schumpeter, 1934;
Baumol, 1993). Schurnpeter (1934) described innovation as the single function that most
characterizes an entrepreneur. In fact, much of the extant entrepreneurship literature

assumes that entrepreneurs are a mostly homogeneous group (Stewart, et al., 2003). Yet a

15

search for an operational deﬁnition yields a number of similar yet disparate versions.
These include one who is innovative and takes initiative (Schumpeter, 1934); one who
has a personal value orientation (Gasse, 1982); one who is innovative and growth-
oriented (Carland, Hoy, Bolton & Carland,1984): one who displays competitive
aggressiveness (Covin & Slevin, 1989); one who undertakes a “new entry” (Lumpkin &
Dess, 1996); or one who simply owns and actively manages a small business (Stewart &
Roth, 2001). Depending on the frame with which one examines entrepreneurship, any
deﬁnition may ﬁt. For the purposes of this study, an entrepreneur is an “individual who
assumes risk” in a venture, and “provides management for the ﬁrm” (Kilby, 1971).
Entrepreneurial orientation is deﬁned as innovativeness, pro-activeness and risk-taking
(Covin & Slevin, 1989). Covin and Slevin refer to the construct as entrepreneurial
orientation.

Entrepreneurs have traditionally been considered to exist only within small ﬁrms
(Coase, 1937), but this view has changed (Carland, Carland & Busbin, 1997). The fact
that employees within a large corporation can act in an entrepreneurial manner led to the
term “intrapreneur” (Pinchot, 1985). Yet, the traditional view has led to a large corpus of
research on the entrepreneurial orientation of small business owners. But there is likely a
range of entrepreneurial characteristics that all business owners possess (Shane &
Venkataraman, 2000). This was the view that Vesper (1980) took, when suggesting that
entrepreneurs existed along a sort of continuum.

The sort of continuum to which Vesper (1980) refers may have at one end, a sort
of “superpreneur” as reported by Niehm (2002). On the other end may be the small

business owner who is not prone to risk and/or innovation. Carland et al. (1984) make a

16

distinction between entrepreneurs and small business owners; they describe an
entrepreneur as one who “capitalizes on innovative combinations of resources for the
purposes of proﬁt and growth. . ..”. However, the small business owner is one who
“operates a business to further personal goals and to produce family income. . ..”.

For this study, two separate theoretical frames for the entrepreneurship construct
are employed, as I posit that entrepreneurship is a key dimension of business resources
for downtowns. The ﬁrst theoretical frame is that of entrepreneurial orientation. The
concept of entrepreneurial orientation was operationalized by Covin and Slevin (1989)
and consisted of 3 dimensions: innovativeness, risk taking and proactiveness. To a certain
extent, small business owners would need to possess some higher levels of these
dimensions (than the average person) in the face of competition in today’s marketplace.

However, I also believe that there are a signiﬁcant number of downtown business
owners who are not entrepreneurs, yet are successful and contribute to the success of
downtowns. Because downtowns and small business owners may be unique (compared to
other organizations or ﬁrms), a second framework is utilized to measure entrepreneurial
orientation. Carland, et al., (1984) established a typology of entrepreneurs by
distinguishing between entrepreneurs and small business owners. Small business owners
likely have different goals than entrepreneurs (Davidsson, 1989; Woo, Cooper &
Dunkelberg, 1991). For example, entrepreneurs tend to place a greater emphasis on ﬁrm
growth, while small business owners are more concerned with income goals (Carland, et
al., 1984). However small business owners also may act in entrepreneurial ways, by
making proactive decisions for example (Vesper, 1980; Carland, Carland & Busbin,

1997)

17

Entrepreneurial Orientation:

The concept of entrepreneurial orientation (EO) refers to the processes, practices
and decision activities leading to new entry or opportunity for an individual/ﬁrm (Covin
& Slevin, 1989). Some of the constructs of EO were suggested by earlier authors in the
strategy domain. For example Miller & Friesen (1978) identiﬁed risk taking and
innovation as effective management strategies. Fredrickson (1986) suggested
proactiveness, risk taking and assertiveness. In the entrepreneurship domain, the
construct of entrepreneurial orientation was operationalized by Miller (1983) and Covin
and Slevin (1989). Their construct consisted of 3 dimensions: innovativeness, risk taking,
proactiveness.

Innovativeness- Based on Schumpeter’s (1934) early work, the concept of

 

entrepreneurs as innovators has become accepted in the literature. Very few scholars
argue this point. lnnovativeness is an indicator of a ﬁrm’s tendency to engage in and
support new ideas, processes and creative methods. This type of activity may result in
new processes, services or technologies (Lumpkin & Dess, 1996). Though the bulk of the
extant innovation literature has focused on technology, innovation can occur in many
areas. This includes management processes, promotion, human resources, visual
merchandising, and other aspects of running a small business. These are all areas where a
ﬁrm or small business owner could employ innovative techniques to improve the
performance of their business. Innovation is an important aspect of EO as it reﬂects the
means by which ﬁrms might pursue new opportunities (Lumpkin & Dess, 1996).

Some have described innovation as “a radically different way of doing

something” (Niehm, 2002). This may be an extreme deﬁnition though, and have less

18

applicability to most entrepreneurs and especially small business owners. To be
innovative, one need not be radically different. Radically different seems to imply a
revolutionary change. It is more likely that the term evolutionary change is applicable in
this case. This is an important distinction, supported by Lumpkin and Dess (1996), as
well as Vesper (1980). Lumpkin and Dess (1996) suggest that innovativeness occurs on a
continuum. This can include the willingness to try a new product line or committing fully
to a new technology. An often-used method for assessing innovation is the number of
new products or services a ﬁrm introduces, or the frequency of product/service line
changes (Miller & Friesen, 1982: Covin & Slevin, 1989). This is innovative but not
radical, and may actually be a form of creativity. Creativity has been used by many as a
synonym for innovative behavior (Carland, et al., 1984; Stewart, et al., 2003).

H1 :1: Innovativeness is one of three significant and positive measurement
indicators of the entrepreneurial orientation construct.

Risk Taking- One of the earliest characteristics ascribed to entrepreneurs was that
of risk taking (Lumpkin & Dess, 1996). The very idea of working for “oneself” implies
the risk of not only lost capital, but the opportunity cost of having earned wages in the
employ of another ﬁrm. The term risk has various meanings, depending on the context of
the application. Three types of strategic risk were identiﬁed by Baird & Thomas (1985)
as: a) venturing into the unknown; b) investing a large portion of assets; and c) heavy
borrowing. The ﬁrst of these types applies to small business owners in the sense that it
implies a sense of uncertainty, as is discussed in the entrepreneurship literature in terms
of social, personal or psychological risk (Gasse, 1982). Small business owners who adopt

new ways of doing business or try a new product line are taking on risk to some degree.

19

Most studies of entrepreneurship have focused on the individual, rather than the
ﬁrm (Lumpkin & Dess, 1996). That fact is germane to this study, as the entrepreneurship
component is measured at the individual business level. EO has been used by Miller
(1983) and Covin and Slevin (1991) to investigate risk taking by individuals within ﬁrms.
This was operationalized as the manager being more likely to engage in bold rather than
cautious acts. Some studies have addressed the problem of the individual who has a
propensity to take risks, becoming risk averse within a ﬁrm (Miller, 1983; Lumpkin &
Dess, 1996). This would lead to a ﬁrm being overall more risk averse. However, the very
fact that individuals within a ﬁrm take risks should lead to a risk-taking ﬁrm. This same
logic should apply to downtowns, since the risk taking propensity of its business owners
should affect the overall risk taking perception of the downtown.

As is the case in most measures of behavior, there seems to be a range of risk
taking (Lumpkin & Dess, 1996). Business owners will probably range from risk averse to
risk prone. It is likely that the density of risk taking owners will have something to do
with the risk taking characteristics of a downtown. Downtowns with business owners
who are more risk prone, may lead to a downtown that is overall more risk prone. A
downtown that is more risk prone (like a ﬁrm) would be more likely to take chances
(incur debt, try new technology) in order to take advantage of marketplace opportunities.

H1 b: Risk taking is one of three significant and positive measurement indicators
of the entrepreneurial orientation construct.

Prtgtctiveness- Proactiveness is the act of anticipating problems or opportunities
prior to their occurrence, in order to be prepared for the problems and take advantage of
the opportunities. Miller (1983) suggests that entrepreneurial ﬁrms are ones that are

“ﬁrst” to develop proactive innovations. This seems self-evident, as an innovation is a

20

new way of doing something, and thus by deﬁnition proactive. So though it is related to
innovation, proactiveness is focused more on the pursuit of opportunities and initiating
activities (Covin & Slevin, 1989).

Proactive ﬁrms seek new operations that may or may not be related to their
present business, eliminate operations in declining stages of the life cycle, and bring in
new products ahead of the competition (Venkataraman, 1989). They are willing to grab
onto new market opportunities as leaders, even if they are not the ﬁrst (Lumpkin & Dess,
1996). Lumpkin and Dess also characterize the opposite of proactiveness as being
“passive” rather than “reactive.” This too is an important distinction, as a small business
owner with little foresight (i.e., not proactive), who nonetheless reacts to a market change
or opportunity is likely to be in better shape long-term than the one who is passive and
does nothing. Covin & Slevin (1989) describe one of the attributes of proactiveness as
being competitively aggressive. Competitive aggressiveness describes the manner in
which ﬁrms or business owners relate or respond to competitors. More speciﬁcally it
refers to a ﬁrrn’s inclination to directly challenge its competition, with intensity
(Lumpkin & Dess, 1996) or even unconventional tactics (Cooper, Willard & Woo, 1986).
Utilizing unconventional methods to compete with others in the marketplace may be
particularly important for downtown business owners (Cooper & Dunkelberg, 1986;
Stone, 1995).

These characteristics (proactiveness; competitive aggression) would seem to
apply to small, downtown business owners. For many reasons, but primarily economic,
these types of businesses have little available capital for innovation. Yet they may adopt

new processes that others have innovated, or bring new products to customers before

21

their competitors. The small business owner, exhibiting competitive aggression through
proactiveness, is more apt to be successﬁrl than competitors.

H] c: Proactiveness is one of three signiﬁcant and positive measurement
indicators of the entrepreneurial orientation construct.

When a small business owner acts in an entrepreneurial manner, that individual is
more likely to be successful (Covin & Slevin, 1989; Stone, 1995; Niehm, 2002). As with
a large ﬁrm made up of SBU’s, the more successful the individual SBU’s are, the more
successful the ﬁrm is overall. I posit that successful downtowns have a signiﬁcant
number of small businesses within that are themselves successful.

H1 (1: Entrepreneurial orientation constitutes one of four signiﬁcant and positive
factorial indicators of the latent factor called business resources.

Small Business Orientation- Because downtowns and small business owners may
be unique (compared to other organizations or ﬁrms), it is important to make the
distinction between “pure” entrepreneurs and small business owners. Carland, et al.,
(1984) established a typology of business owners by distinguishing between
entrepreneurs and small business owners. They suggested that researchers should
distinguish between entrepreneurial and small business “ventures,” because they have
different short and long-term goals. They recommend that there are also distinctions
between entrepreneurs and small business owners, and deﬁne each as follows.

A small business venture is described as one that is independently owned, not
dominant in its ﬁeld, and does not engage in any new or innovative practices or
marketing. Entrepreneurial ventures, on the other hand, are ones that engage in at least
one of ﬁve categories of innovation suggested by Schumpeter (1934): introduce new

goods, introduce new methods, open new markets, open new sources of supply and/or

22

business reorganization. A small business owner is one who establishes and manages a
business with the purpose of furthering personal goals. An entrepreneur is one who
establishes a business principally to pursue proﬁt and growth. The entrepreneur is one
who exhibits innovative behavior and employs strategic business practices.

Small business owners have different goals than entrepreneurs (Carland, et al.,
1984; Davidsson, 1989; Woo, et. al., 1991). Stewart, et al., (2003) found that these two
groups had different goals in terms of achievement motivation and risk taking, yet found
no difference in innovativeness. Stewart and Roth (2001) found that growth-oriented
business owners (entrepreneurs) had higher levels of risk propensity than income-
oriented (small) business owners. Vesper (1980) points out that many business owners
never intend for their venture to grow beyond a level that is controllable (by the owner),
supporting the Carland, et al. (1980) typology. But this thinking does not take into
consideration the very real possibility that an entrepreneur’s original intentions may
change over time. If entrepreneurs begin with goals of proﬁt and growth, and
subsequently decide to take a more controlled, low-risk, income-generating approach,
they would be classiﬁed as small business owners and no longer as entrepreneurs.

Small business owners are more risk-averse (Stewart & Roth, 2001) and seek to
keep their business at a controllable level (Vesper, 1980). These tendencies should lead to
slower growth, but may also translate into long-term stability for the ﬁrm. This is
distinctly different than the entrepreneur, who seeks proﬁt maximization and growth.
Stability may not be the result of high-risk, growth oriented strategies.

H2 a: Business owner goals are one of two significant and positive indicators of
the small business orientation construct.

23

Small business owners have different reasons than entrepreneurs for entering into
and continuing with the operation of their business (Carland, et al., 1984; Stewart, et al.,
2003). Small business owners’ reasons tend to be more personal than economic (Vesper,
1980; Stewart & Roth, 2001). The owner perceives the business as an extension of his
personality, and closely intertwined with family needs and desires (Carland, et al., 1984).
The business will take up the majority of the owner’s time and resources, and will be the
primary source of income (Carland, et al., 1984; Stewart & Roth, 2001). Income-oriented
small business owners will be substantially more risk-averse than growth-oriented
entrepreneurs (Stewart & Roth, 2001).

As small business owners consider their business an extension of their own
personality, they may channel their innovativeness by creatively matching their
personalities to their business. Kanter (1988) found creativity to be a key component to
competitiveness within established companies. Small business owners likely act in
creative ways, perhaps in response to competition or small amounts of available capital.
Litz & Stewart (2000) identiﬁed successful small retailers who responded to increased
competition by offering customers the option of hours that amounted to the owner being
“on call.” This is certainly a creative method of adjusting hours of operation.

The personal motives of small business owners, as well as the signiﬁcant amount
of time and resources put into a business, point to a certain level of emotional attachment
to the business that entrepreneurs may not exhibit. The attachment of the small business
owner to his/her business may manifest itself in such ways as risk aversion (Stewart &
Roth, 2001). But emotional attachment to a business may lead small business owners to

work harder and longer at making the business a success. Such success would contribute

24

not only to overall business resources, but also to the success of the downtown as a
whole.

H2 b: Emotional attachment is one of two significant and positive indicators of
the small business orientation construct.

Small retailers that succeed over the long term must adapt to change in a positive
way, and are proactive in the face of an ever-changing economy. Whether they are
classiﬁed as entrepreneurs or small business owners, the characteristics of innovativeness
and proactiveness should describe the characteristics of all successful small business
owners. A continuum of business owners, from high to low on the entrepreneurial
orientation scale, is likely to exist in any downtown. This is supported by Vesper (1980).
Because of the increasing pressures faced by downtown businesses, those who exhibit
little or no E0 would be at one end of the Vesper (1980) continuum of entrepreneurship.
And would be prone to ﬁnancial difﬁculties. Superpreneurs (N iehm, 2003) in small
towns would exhibit high levels on all the E0 dimensions. This type of business owner
would be the other extreme on the E0 continuum.

Niehm found that the presence of superpreneurs had a positive effect on
community success. Those business owners who measure highly on the E0 construct will
then be a positive force for any downtown. Yet, there can be negative results from too
many ﬁrms that are high on the E0 construct. When too many entrepreneurs practice
innovation and risk taking, failures are bound to increase (Carland, Carland & Busbin,
1997). Downtowns should have a mix of business owners that falls along the continuum.
As with any organization, the largest number of businesses will probably fall in the
middle, meaning they have some entrepreneurial tendencies, but are neither high on the

E0 continuum nor low. They are the small business owner who spends the majority of

25

his/her time and energy on their business. These businesses lend stability to the
downtown, by exhibiting steady, measured growth. Their business purposes are personal,
and farnily-oriented, and they are emotionally attached to their business. These
tendencies indicate a small business orientation, and also contribute to business
resources.

H2 c: Small business orientation constitutes one of four factorial indicators of the
latent factor called business resources.

Social Capital

Portes & Sensenbrenner (1993) conceptualized social capital as the expectations
for action within a group or organization, that affect economic goals of its members.
Social capital is an intangible resource, and a term originally used to describe relational
resources, occurring in cross-cutting personal ties (Tsai & Ghoshal, 1998). Social capital
is manifest from social structures comprised of relationships (Putnam, 1995). Close
relationships can create trust and obligations, and deﬁne expectations among trading
partners (Gulati, 1995). Coleman (1988) suggested that social capital exists in
organizations and communities alike. Like the economic version of capital, it is a
productive resource for businesses (Coleman, 1990; Burt, 1992). Its value is derived from

its focus on the positive outcomes of sociability (Portes, 1998).

While business owners can build up social capital amongst themselves, it is the
community aspect that is important. Social capital may help to create competitive
advantage for a ﬁrm, through the exchange of information among members (Nahpiet &
Ghoshal, 1997). It can serve as a resource for downtown business owners, if it helps to

increase the number of local consumers who patronize the downtown. Social capital

26

theory provides a means to help explain the interaction of local consumers and downtown
business owners. Putnam (1993) found that there is a positive relationship between the
amount of available social capital in an area, and the area’s economic well being. Social
capital theory may help to identify and explain a very perplexing resource for
downtowns: local consumer behavior. Miller & Kim (1999) found evidence that social
capital does explain some of the “inshopping” of local consumers in rural communities.
Niehm (2002) suggests that the structure of small communities offers opportunities for
the creation of social capital through network associations. The concept of social capital
is also the basis for Miller & Kim’s (1999) work on level of attachment to community,

and was found to be a positive inﬂuence on local consumers’ attachment.

The components of social capital that are salient to the current research are
reciprocity, trust and shared vision (Tsai & Goshal, 1998). Relationships between
individuals who have built trust, reciprocity and commitment through their networks have
a comparative advantage (Burt, 1997; Tsai & Goshal, 1998), leading to deeper and ﬁner-
grained information exchange. If a downtown business owner can develop these types of
relationships with local consumers, it may lead to better consumer feedback, and market
knowledge. Berry (1993) for example, found that consumers’ attitudes about a retailer’s
trustworthiness (a component of social capital) were important in forming patronage

relationships.

Reciprocity refers to a “networ ” in which each member has something to provide
to the other. When something is provided, there is an expectation of some sort of quid pro
quo. Reciprocity contributes to social capital through network members who amass

favors, which can be called upon as resources when needed (Portes & Sensenbrenner,

27

1993). Favors called “in” may lead to increased tacit knowledge between members.
Miller & Kean (1997a) refer to community reciprocity as an expected exchange between
local consumers and local retailers. They found that local consumers were more likely to
shop with local retailers when those retailers expressed a high level of support for the
community. Lumpkin, Hawes and Darden (1986) had similar ﬁndings, but also found that
consumer attitudes about relationships with local retailers were a more important
determinant of patronage than any other variable. Support for the relationship between
reciprocity’s effect on small business owners was found by Miller (2001). In her study of
consumers in two rural towns, consumer satisfaction with reciprocity levels was a
signiﬁcant predictor of inshopping behavior. Thus, reciprocity helps small business

owners to develop social capital with local consumers.

H3 3: Reciprocity is one of three significant and positive measurement indicators
of the social capital construct.

T_rus_t is deﬁned as an assured reliance on the truth of someone or something
(Merriem-Webster, 2005). It is a dependence on something in the future, or a
contingency. Trust is a component of loyalty that underlies any ﬁrrn’s ability to create or
maintain loyal customers. Trust and loyalty are often used interchangeably in the
literature (cf. Chaudhuir & Holbrook, 2001; Datta, 2003). When a consumer develops
trust in a brand or store, they may become loyal towards that store or brand, even if
changes occur in the future (Datta, 2003). This is the type of loyalty small town retailers
look to develop as an insulator against the future incursion of large retailers (Runyan &
Johnson, 2003). Large retailers spend millions of dollars developing their name, in an
effort to build loyalty from consumers by gaining their trust (Stone, 1995; Levy & Weitz,

2004). Understanding this, small retailers who have a well-developed level of social

28

capital in their community might be able to establish trust with local consumers, leading

to greater loyalty and thereby gaining competitive advantage.

H3 b: Trust is one of three significant and positive measurement indicators of the
social capital construct.

Shared vision is the collective goals of a group, organization or in this case, a
community (Tsai & Goshal, 1998). It is a part of social capital that pertains less to the
idea of economic transaction between business owner and customer (implied in the
constructs of reciprocity and trust), as it does to the collective interest or values of
business owners. In this sense, shared vision is a construct related to how members of the
downtown business group envision themselves as part of the downtown, and what
common goals are shared. How downtown business owners perceive themselves in terms
of group membership is discussed later in the social network section. Not all business
owners may perceive themselves to be a cohesive group. In spite of this, shared vision
can mitigate this perceived lack of cohesion, as studies have shown that a shared vision
can serve to hold together a loosely coupled system (Orton & Weick, 1990). Nahapiet &
Ghoshal (1997) referred to shared vision as one that facilitates a common understanding
of collective goals. Tsai & Ghoshal (1998) found that it was a statistically signiﬁcant and
positive indicator of social capital.

Shared vision is also an important area with regards to the local community.
Focus groups have shown that it is important for all stakeholders (especially downtown
stakeholders) to support and “buy in” to marketing programs and image creation

(Michigan Cool Cities. . ., 2003). To the extent that members of a community and

29

downtown business owners all view the importance of the downtown similarly, that
downtown should be successful.

H3 c: Shared vision is one of three significant and positive measurement
indicators of the social capital construct.

Small business owners who can build a trusting relationship with local consumers
will likely receive customer loyalty in return. If the loyalty from the consumer is manifest
in patronizing the small business, then reciprocity has occurred. These two components
of social capital should lead to success for the small business owner. The third
component of social capital is shared vision. When business owners in a downtown share
the same goals, it may translate into a more uniﬁed image to the consumer. If this occurs,
then the social capital manifested in the trust and reciprocity from consumers could have
a “halo” effect for other downtown businesses. The successful small business thus
contributes to the success of the downtown through the development and use of social
capital.

H3 d: Social capital constitutes one of four significant and positive factorial
indicators of the latent factor called business resources.

Social Network Theogy

The social networks framework has its foci in the relationships between people
(rather than their attributes), their interdependence and emergent effects (Granovetter,
1973; Borgatti,1999; 2003). One of the most inﬂuential works in social network theory
was produced by Granovetter (1973), where the concept of weak versus strong ties in
network relationships was introduced. Motivation for Granovetter’s theory included the
question of how large groups coordinate to make things happen (e. g., meeting an outside

threat). This theoretical base has been used in the study of rural businesses and towns

30

(Frazier, 2000), the study of entrepreneurs in small towns (Niehm, 2002), career mobility
(Granovetter, 1985; Burt, 1992), innovation diffusion (Kontopoulos, 1993; Swan &
Newell, 1995) and consumer buying behavior (Miller & Kean, 1997a).

My study breaks new ground in this area by assessing downtowns from the
framework of its resources, which are posited to include network ties. To maintain
parsimony, dimensions of network theory that relate speciﬁcally to downtown business
owners will be used. (To facilitate this, focus group results guide construct
conceptualization). Much of the previous social network research has involved ﬁrms or
business units (e.g., Ibarra, 1993; Burt, 1997; Tsai & Ghoshal, 1997; Shah, 1998), or
unrelated individuals (e.g., Granovetter, 1973; Marsden & Campbell, 1984; Frenzen &
Nakarnoto, 1993). As independent business people, downtown business owners likely
interact on informal bases with most of their neighboring business owners. Brush (1992)
found that small ﬁrm managers engaged in person-to-person networking for
environmental scanning. This supports other studies that have shown that small-ﬁrm
entrepreneurs prefer personal information sources as opposed to non-personal sources
(Birley, 1985; Peters & Brush, 1996).

The preference for personal information may be even more pronounced in those
with high levels of entrepreneurial orientation. Frazier (2000) found that “superpreneurs”
preferred these types of sources. However, small business owners likely act in
autonomous ways, as they are responsible for their own business. Though downtown
business owners may not see themselves as part of a group, they are apt to interact with
each other on a formal basis as well. Formal interactions, within the context of their

business roles, would be expected to occur with downtown business meetings. However,

31

formal interaction is not necessarily important for downtown business owners, as
research shows little relationship between small ﬁrm performance and formal networking
(Birley, 1985; Bates, 1994).

Density of networks refers to the number of ties that link network members,
compared to the total possible ties (Granovetter, 1973). It is an indication of the
interconnectedness of members in a network (Wellrnan, 1999). Dense networks may also
be needed to form the social capital dimension of shared vision (Rowley, 1997; Brass,
Butterﬁeld & Skaggs, 1998). Density is an indicator of cohesiveness, and helps to
establish trust among network members (Axelrod, 1984; Greve, 1995). Cooperation,
commitment and collaboration are enhanced through density of network ties (Axelrod,
1984; Cross, Borgatti & Parker, 2002).

Previous research has shown that in an environment where everyone knows
everyone else, dense (and “cliquish”) networks are a disadvantage (Granovetter, 1985;
Burt, 1992). This is because cliques form under conditions of strong ties, and one clique
may withhold information from another (Granovetter, 1985). However, such
environments with dense networks of weak ties should display few numbers of cliques.
Following the earlier discussion of interaction, it is feasible that there are few strong ties
between downtown businesses, except occasional cross-patronizing of businesses. Based
on this logic, I posit that most ties between downtown business owners will be weak in
nature. Granovetter (1973) showed that weak ties are usually more effective than strong
ties in terms of disseminating new ideas and coordinating changes. The extent to which
business owners establish informal network ties, may be an important factor in the

success of fellow business owners and downtowns in general. This contention is

32

supported by Frazier’s (2000) study of small business owners, where the network
dimensions with strong ties did not have a signiﬁcant effect on ﬁrm performance.

Ties within networks (between individuals) are affected by the density of those
ties, the frequency with which interaction occurs, and the perceived level of homophily
between members (Granovetter, 1973; Cross, Borgatti & Parker, 2002). These three
dimensions of network theory are most relevant to this study. Ties between network
members can be dense and frequent and be among homophilous persons, yet those same
ties can and remain informal, and weak in nature (Cross, et al., 2002). In other words,
small business owners within a downtown may tend to be similar (homophilous) and
interact with each other frequently, yet maintain an informal relationship which manifests
weak ties. Granovetter (1985, pg. 1361) refers to this as a “nodding” relationship between
neighbors, where little is exchanged but small talk.

How network norms and values are disseminated and adopted will be a ﬁrnction
of the underlying structure of the network (Borgatti & Foster, 2003). Network density can
help explain shared attitudes and culture through interaction. Davis (1991), found (in a
study of corporate takeovers), that density of networks was more important than strength
of ties in explaining company adoption of “poison pills.” The diffusion of an idea or
shared practice is modeled as a function of interpersonal transmission along some
“durable” communication channel (Borgatti & Foster, 2003). Weak ties between units of
a ﬁrm were found to be valuable sources for new knowledge (Hansen, 1999), though
those same weak ties were found to inhibit the transfer of complex knowledge.
Information that may need to be disseminated among downtown businesses is probably

not complex in nature. Further, small business owners are independent and would not be

33

prone to rely on other business owners for complex information. These business owners
are more apt to rely on heuristics or outside expert advice. The announcement of a new
business opening in a downtown would likely be disseminated between business owners,
but the workings of a new small-business tax would probably be discussed with the
owner’s accountant.

H4 a: Network density is one of three significant and positive measurement
indicators of the social network construct.

Frequency of interaction is an indicator of resource “ﬂows” between network
members. Frequent contact between members provides increased opportunities to
exchange information (Berg, Piner & Frank, 1986). This type of autonomy may lead to
“loose” as opposed to “strong” ties with other business owners. High levels of
communication have been positively associated with performance (Ostgaard & Birley,
1996). Coser (1974) refers to a large amount of weak ties as the “seedbed of individual
autonomy.” Successful downtowns may have small business owners who are members of
several local organizations (e.g., Chamber of Commerce, Optimist’s, Downtown
Business Association, etc.), thus increasing the frequency of opportunity to interact and
share knowledge, if they participate in meetings and gatherings. Small business owners
have a smaller margin of error in decision making than larger, more resource “rich”
competitors. Therefore, information from the immediate environment is critical, requiring
more frequent collection through network interaction (Brush, 1992). When members in a
social system interact in multiple roles over time, social capital is enhanced (Flora, 1998).

H4 b: Frequency of interaction is one of three significant and positive
measurement indicators of the social network construct.

34

Perceptual homophily is the extent to which persons perceive others as being like
themselves (Blau, 1961; Lazerfeld & Merton, 1964; Cross, Borgatti & Parker, 2002). The
notion that people tend to associate with others whom they perceive as similar is
supported in general in the trade media and scholarly journals alike (McPherson, Smith-
Lovin & Cook, 2001). Cross, et al., (2002) see homophily as increasing the likelihood of
communication in groups. This is supported by previous research on networks. Ibarra
(1992) found gender differences in communication, where those of the same sex were
more likely to communicate within groups. Race has also been shown to have such an
effect, as group members of the same race tend to communicate more often with each
other (Ibarra, 1993). Age and position tenure produced the same type of within-group
communication (Zenger & Lawrence, 1989). Frazier (2000) found that the levels of
perceived homophily among small retailers was a positive indicator of network ties, and
through network ties, a contributor to social capital.

H4 c: The level of perceived homophily in the downtown is one of three
significant and positive measurement indicators of the social network construct.

A common practice of individuals seeking information is to communicate with
those around them, particularly those who face similar decisions (Rogers, 1995). Small
ﬁrm owners prefer sources of information that are personal in nature, including face-to-
face meetings with sales representatives, industry experts or fellow small business owners
(Birley, 1985; Brush, 1992; Peters & Brush, 1996). Personal sources of information
would be in contrast to sources such as trade journals, or product catalogs. This
preference for personal sources of information helps to explain how new ideas spread

through and between community networks. When studying the relationship between

35

small business owners who exhibit varying levels of entrepreneurial orientation, this
preference for personal information sources should be relevant.

H4 (1: Social network ties constitute one of four significant and positive latent
factorial indicators of the latent factor called business resources.

Entrepreneurial orientation, small business orientation, social capital and network
ties have all been hypothesized indicators of business resources. The entrepreneurial
tendencies of proactiveness, innovation and risk-taking have been found to be predictors
of successﬁrl ﬁrms (Miller & Friesen, 1978; Covin & Slevin, 1989). Small business
owners are different from entrepreneurs (Carland, et al., 1984; Stewart, et al., 1999) and
bring different strengths to their business than entrepreneurs (Carland, et al., 1984), but
may provide stability to downtowns. Small business owners and entrepreneurs alike have
opportunities to acquire social capital from the community (Miller & Kean, 1997a; Miller
& Kim, 1999). This social capital has been shown to result in increased business from
local consumers (Miller, 2001). Owners may also acquire social capital from the shared
vision of fellow downtown business owners (Tsai & Ghoshal, 1998), as shared vision
leads to a common understanding of downtown goals. Since small business owners are
often full-time managers too, the opportunity to share information may be limited.
Therefore the existence of a dense, but weak network of ties to other small business
owners may facilitate the sharing of information about the downtown or community. This
is an asset to small business owners, and the downtown as a whole.

These theoretical constructs have been shown to be interconnected by previous
research. Burt (1997) found that social capital is most often produced when individuals
determine on their own, how best to perform a job. This seems to describe the typical

entrepreneur or small business owner, who usually does not have the luxury of a

36

company manual or mentor to guide them. Research by Flora (1998) revealed that social
capital and entrepreneurial infrastructure contribute independently and jointly to
community economic development. These are all resources possessed by small business
owners and entrepreneurs, and available to the downtown. Through those business
owners, small businesses form a resource for the downtown. To the extent that these
business resources are present in some positive combination, they should provide a
source of competitive advantage to the downtown, thus contributing to the overall
success of the downtown.

H5: Business resources are significantly and positively related to downtown
success.

Structural Resources

Brand Identity

For my study, brand identity is conceptualized as the image that a downtown’s
CBD possesses, that differentiates it from other community shopping areas, as well as
from competing downtowns. The term brand is one that is familiar to most and is
traditionally used in conjunction with products. The classic deﬁnition from the American
Marketing Association (1960) is that a brand is “a name, term, symbol or design, or a
combination of them, intended to identify the goods or services of one seller or a group of
sellers and to differentiate them from those of competitors.” Grandi and Grimaldi (2003)
describe brand as “a combination of attributes, communicated through a name, or a
symbol, that inﬂuences a thought-process in the mind of an audience and creates value.”
They further argue that entities that can be considered as a brand include corporations,

universities, countries or even a person. If we accept these deﬁnitions of brand, then it is

37

plausible that a downtown (3 group of sellers) can achieve, hold or aspire to become a
unique brand.

Brand identity research related to downtowns (or communities) is in its infancy in
terms of theory and empirical work. A thorough search of the urban studies, marketing
and branding literature revealed no work involving construct or measurement validation
for downtown or place brand identity. Requests from experts in the marketing ﬁeld
resulted in recommendations that led to consumer behavior scales, designed to measure
the recognition and loyalty for speciﬁc brands. There have been several recent studies
which have attempted to operationalize the “town as a brand” construct (e.g., Walmsley
& Jenkins, 1993; Walmsley & Young, 1998; Coshall, 2000; Hankinson, 2004a).

Most of the place-brand identity literature addresses tourism as its central focus,
and can be found in two academic areas: urban planning and tourism/vacation marketing
(Hankinson, 2004a). Creating a brand image has been deemed an important part of
tourism marketing (Hankinson, 2004b). Ward (1998) reviews the historical development
of place as “product.” The construct of place as a product is further developed by other
researchers (c.f. Ward & Gold, 1994; Ashworth & Voogt, 1994; van den Bergh & Braun,
1999). They identify the complexities of place, and therefore the difﬁculty with which
they are marketed. As an example, for tourist destinations Ashworth and Voogt (1990)
describe the destination “product” as a bundle of services and experiences. However, they
also note that destinations operate at different spatial levels. Therefore, dissimilar
constituents of consumers may view the “bundle” of services, products and experiences
differently. Downtowns are “managed” by a mixture of public and private stakeholders,

often making it difﬁcult to present a consistent positioning proposition (Hankinson,

38

2001). Since consumers may perceive services and products differently, and the
downtown itself is managed by different stakeholders, a positioning statement may not
only be inconsistent, it may be perceived differently by the targets of the statement (i.e.,
consumers).

D’Hauteserre (2001) argues that branding is necessary for tourist destinations,
due to the limited experience travelers may have with the destination. It is likely that the
majority of downtowns in the US. are not tourist destinations themselves, or even part of
a tourism community. But they still may seek to distinguish themselves from other towns.
Like product brands, destinations can generate a set of expectations or image to the
consumer or visitor (Metelka, 1981; Hankinson, 2004a). For tourist destinations, that
brand image is the basis for a prospective tourist’s choice (Pearce, 1982). This type of
choice calculus might also be employed by local consumers, when deciding whether to
shop (or dine, etc.) downtown. A downtown’s brand image is therefore critical to its
marketing success (Leisen, 2001).

Recently the concept of “branding” of towns has received widespread interest in
both the trade press and scholarly journals. Towns across the US. have embarked on an
effort to establish/create a local “brand” to stand out from other towns, through what is
called “municipal branding” (Kershaw, 2004). Regardless of what it is labeled, the
concept of branding is practiced by many towns and designed to create or highlight a
sense of “place.” But the concept of brand creation is different for each downtown. This
is because downtowns do not all begin at the same “level” (Hankinson, 2004). A
downtown that is within a community with a long history of economic decline would

likely experience a strong negative image (Zelinsky, 1994). A well-known example of

39

this would be Flint, Michigan, which has been in economic decline for decades and is
viewed negatively by people in that region of Michigan. The other end of the spectrum
would be a community with a rich cultural history or long economic stability.
Downtowns in these communities would have built a strong, positive image. These
images that have been developed over a long period of time are often referred to as
organic images (Gunn, 1997).

H6 a: Image of the downtown is one of two significant and positive measurement
indicators of the brand identity construct.

Fiol (2001) posits that an organization’s identity can be a source of competitive
advantage. This identity helps to deﬁne to some extent a shared and collective sense of
“who we are,” (Albert & Whetten, 1985). Brand identity or image has been posited as a
resource in the RBV literature (Barney, 1991; Peteraf, 1993; Runyan, 2004b). Brands are
often used as examples of the type of resource that may be imperfectly mobile
(Wemerfelt, 1984; Peteraf, 1993), as they may be traded; but only to the extent that they
bring equal value to the new owner are they mobile. Thus, the creation of brand identity
may be a key strategy in marketing a downtown to local consumers as well as tourists and
other visitors. Brand names themselves have been considered corporate assets for decades
(Levy & Weitz, 2004).

The message that conveys brand image to consumers is often referred to as
positioning (McDaniel & Gates, 2001). A positioning statement communicates to
consumers, how one ﬁrm’s offerings are differentiated from competitors’ offerings. It
also signals to the consumer, how the ﬁrm wishes to be seen or perceived. As an
example, the J .C. Penney website begins its positioning statement with “Who We Are”

(J .C. Penney Positioning... 2004). Positioning is often accompanied by corporate slogans

40

or symbols, designed to convey and reinforce the ﬁrm’s position in the marketplace
(McDaniel & Gates, 2001). A strong and consistent position statement is necessary to
stand out against competitors, and should help sharpen and strengthen a downtown’s
brand identity.

H6 b: Positioning is one of two significant and positive measurement indicators
of the brand identity construct.

Brand creation will be different for each downtown, and depend on the other
resources that are available (Hankinson, 2004). It is becoming increasingly important for
towns to create some sort of image, in order to distinguish themselves. Though research
in this area has focused on tourism destinations (D’Hauteserre ,2001; Hankinson, 2004b),
programs such as Michigan’s Cool Cities Initiative (2003) have brought renewed focus to
downtowns. Marketing and retailing textbooks advise that a clear and consistent
positioning statement is imperative for any business (McDaniel & Gates, 2001; Levy &
Weitz, 2004). Brand and image have long been considered resources (Barney, 1991;
Peteraf, 1993). The ability to convey that image to the consumer, through a consistent
positioning statement will lead to a strong brand identity, which becomes a resource for
the downtown itself.

H6 C: Brand identity constitutes one of three significant and positive factorial
indicators of the latent factor called structural resources.

Business Mix

The concept of business diversity within a downtown or community does not
appear ﬁequently in the literature. However, it sometimes appears as retail mix (Levy &
Weitz, 2004). I believe that the proper operationalization of this concept is optimal

business mix, and I believe this term will be ultimately more valid, than retail mix or

41

business diversity. Empirical research from the central place domain supports the
inclusion of optimal business mix. Though traditional central place theory (e. g.,
Christaller, 1935) did not account for the interrelationship between retailers, it does
assume retailers selling different types of goods. Improvements on the original model,
made by Huff (1963) and Reilly (1931), showed the agglomeration of diverse retailers in
towns or shopping centers increased the attractiveness of those areas for consumers.
Ghosh (1986) referred to this phenomenon as multi-purpose shopping. O’Kelly (1981)
found that over 60% of all shopping trips are multi-purpose.

The foci of those earlier studies were consumer trips (Reilly, 1931; Christaller,
1935; Huff, 1963), site location of stores (Houston & Stanton, 1984) or the spatial
organization of shops (Eaton & Lipsey, 1982; Mulligan, 1984; Ghosh, 1986). The latter
two research streams are unrelated to the current study, as downtowns are almost
exclusively unplanned shopping areas (Levy & Weitz, 2004). Therefore site selection and
the spatial orientation of stores is out of the control of business owners and others in the
downtown. In a planned center (e. g., mall, strip center), a developer or mall manager may
be able to move tenants to optimal locations. In a downtown, business owners may own
the building in which they are located, and therefore can’t be forced to move. Owners
who do not occupy the buildings they own, will need to lease to those who can pay the
rent. Recalling the previous discussion of retail mix, it sometimes may be outside of the
downtown stakeholders’ (owners, consumers, etc.) control to determine who locates in
the downtown area.

Retail agglomeration is bounded not only be the unplanned nature of downtowns

and multiple ownership, but also by spatial constraints. Multi-purpose shopping has led to

42

(or been fueled by) the increase in large retail formats (e.g., hypermarkets, big box,
category killers, etc.) as well as planned centers (e.g., malls, power centers, etc.).
Messinger and Narasimhan (1997) concluded that the increase in large store formats have
indeed been driven by increased multi-purpose shopping behavior. The space needed for
these types of formats is not readily available in small downtowns. For this reason, it may
be even more important for downtowns to actively seek a diverse mixture of businesses.
Arentze, Oppewal and Timmennans (2005) found that different store types all contribute
to destination attractiveness, even if no purchases are made from these stores. The very
fact that a diversity of businesses exist in a downtown will likely have a positive effect on
the downtown as a whole.

H7 a: The level of diversity in business formats is one of three significant and
positive indicators of the business mix construct.

Beyond the central place literature, there is a dearth of empirical research
involving the concept of retail or business mix. Brown (1994) noted that the bulk of the
location research is at the national, regional or urban levels of analysis. This leaves a gap
in the literature pertaining to location within unplanned shopping districts (e.g.,
downtowns). In an extensive review of the location literature by Brown (1994), principle
ﬁndings ﬁom studies identiﬁed two dimensions that are salient to my research. The ﬁrst
is the inﬂuence of magnet or “attractor” stores. These are referred to as anchors when
discussing malls, and of course are usually large department stores. But Brown insists
that the literature is clear on this construct- magnet stores are important customer—
generators. Burns (1992) studied the perceived effects of anchor stores on nearby stores
in a mall. He found that a “halo” effect can be generated to stores adjacent to anchor

stores in malls. Wu and Petroshius (1987) found similar results, but the more familiar a

43

person is with the adjacent (non-anchor) store, the lower the halo-effects were. Few
downtowns still have a large department store located in its district. However, other
businesses may act as magnet stores for downtowns.

H7 b: Magnet businesses are one of three significant and positive measurement
indicators of the business mix construct.

Complementary products and services are key strategies used by retailers to
increase sales-per-customer (Levy & Weitz, 2004). Complementary products are those
that may be presented or sold separately, but that often go together after the sale. A
classic example is peanut butter, jelly and bread. Each is sold and displayed in separate
sections of a grocery, but are often put together by the end user. By selling all three at
once to the consumer, a grocer can increase revenue (Levy & Weitz, 2004). In the same
manner, Brown (1994) notes that when customers move between shops in a center (or
downtown, mall, etc.), they move between shops of similar or complementary products.
This is consistent with classic gravity theory (Reilly, 1931), as is manifest in areas where
restaurants agglomerate, etc.

Both the need for business diversity and the ﬁnding that customers shop at similar
stores are supported in work by Maronick & Stiff (1985). They found that when choosing
to shop downtown, the perceived assortment of beneﬁts (quality and variety of stores)
was the most important factor. The majority of the shoppers in this study were classiﬁed
as “recreational” shoppers who are characterized as wanting store variety and a large
number of related services (Bellenger & Korgaonkar, 1980). These empirical ﬁndings
point to an optimal business mix for a downtown that is not a “one of each” type mix, but
rather several of each type. In other words, any concerns by small business owners that

new businesses in town will detract from their own revenues may be unfounded.

44

H7 c: The number of complementary businesses is one of three significant and
positive measurement indicators of the business mix construct.

Optimal business mix may be difﬁcult to quantify and to operationalize. Some
research points to a “trial and error” method for developing retail mixes (Danneels,
1996). This is also the case for malls and shopping centers establishing a suitable store
mix (W enthe, Fredenberger & DeThomas, 1988). By identifying the business mixes of
successful downtowns, this study attempts to extend the research on location and retail
mix. Downtowns and small business owners have had decades to experiment with
optimal mixes. An optimal business mix will be a valuable resource for a downtown.

H7 (1: Business mix constitutes one of three significant and positive factorial
indicators of the latent factor called structural resources.

Community Characteristics

Robertson (1999) as well as Filion, et al. (2004) make the observation that there
is little in the extant literature that is focused on small town development, as its focus is
generally on larger metropolitan downtowns. But Robertson (1999) also notes that many
small downtowns have seen a shift from a retail base to more of a service base. In many
cases, this has not been a planned shiﬁ, but one necessitated by the many small retailers
who have been pushed out of business by malls and major discounters. In a study of
small retailers, Billesbach and Walker (2003) found that a majority were not only located
in downtowns or similar areas, but were also within 1 mile of a major discount chain. In
recent years, many small retailers that were mainstays of downtowns (e. g., pharmacies,
hardware, clothing) have disappeared due to the entrance of large retailers in their

markets.

45

Since the early 1980’s, signiﬁcant changes have occurred in communities
nationwide. Restructuring of many of our economic sectors (e.g., manufacturing,
agriculture, services) has led to economic stress for many metropolitan and rural areas
(Barkley, 1993; Leistritz & Harnm, 1994; Kean, et al., 1998). This stress has included
major declines in relative income and migration to urban areas (Cook, 1990). Counties
and commrmities in the US. that are adjacent to metropolitan areas have had fewer
problems than rural areas (Leistritz & Harnm, 1994). This is also true for those
communities whose economies are tourism-based (Frederick, 1993), though they too
have been affected.

All of these changes have created what many would characterize as a hostile
environment. Hostile environments are ones distinguished by precarious industry
settings, harsh business climates, and the relative lack of exploitable opportunities (Covin
& Slevin, 1989). Hostile environments are unpredictable in nature (Mintzberg, 1979), and
in such environments, successful ﬁrms will be those who are proactive in gaining and
maintaining competitive advantage (Covin & Slevin, 1989). Khandwalla (1977)
developed the construct of environmental hostilities in a study of small Canadian ﬁrms.
Results of that study support Covin & Slevin’s (1989) contention that proactive and fast-
moving small ﬁrms are more successful in hostile environments.

However a key ﬁnding in both the Khandwalla (1977) and Covin and Slevin
(1989) studies concerns ﬁrms that are less proactive and entrepreneurially oriented.
Those ﬁrms performed better in benign environments than in hostile ones. The inference
in both studies is that entrepreneurial ﬁrms will perform well in either environment.

Small businesses in general may be negatively affected by hostile environments. Kean et.

46

al., (1998) found that as hostility increased, small retailers relied less on focused strategy.
They also found that increased hostility led to decreased retailer performance.

H8 an Environmental hostility is one of three significant measurement indicators

of the construct of community characteristics. It is negatively related to

community characteristics.

Previous researchers have used measures of community characteristics that
include economic base, market size and the change in market size and purchasing power
(Leistritz & Harnm, 1994; Kean, et al., 1998). If downtown business owners are proactive
and innovative, market size may be less relevant. The reduction in market size may also
not be problematic, if the change is small and not too dramatic. Entrepreneurs will ﬁnd
gaps in the market left by large retailers (Williams, 1999). But a community’s economic
base will likely play a very big role in the success of the downtown (Kean, et al., 1998).
Kean, et al., (1998) used economic base as a measure of community characteristics. They
utilized an a priori classiﬁcation of communities as agricultural, tourism or
manufacturing bases of economy.

H8 b: The community economic base is one of three significant measurement

indicators of the community characteristics construct. It is positively related to

community characteristics.

The economic grth levels in a community are often seen as being dependent on
attracting large manufacturing companies to locate in an area. Efforts to attract
corporations or whole industries to a community have become commonplace in the past
10-15 years (Florida, 2002). The goal of attracting corporations or industries is
manifested in the building of industrial or technology “parks,” often utilizing public

monies for construction and marketing of these “parks.” The message: if we build it they

will come. According to Florida (2002), this is a fallacy in today’s information economy.

47

Today and in the future, the growth of cities and communities may depend on embracing
and attracting the “creative class” (Florida, 2002; Michigan Cool Cities ...2003).
Florida’s hypothesis is that long-term growth in communities will come from
creating places where those who are creative want to live and work. He developed an
index called the creative index, which is a composite of four other indices: innovation
index, high tech index, gay index and the creative class index. The innovation index is
based on the number of patents granted to persons or ﬁrms in a region. The high-tech
index is a measure of high tech ﬁrms’ output in a region, while the gay index is a
measure of a region’s number of gay couples. Finally, the creative class index is a
combination of the percentage of super-creative and creative workers in a region.
Members of the super-creative class include the following occupations: computer, math,
architecture, engineering, education, training, library, arts, design, entertainment, sports,
media, physical and social science. Creative professions include: management, business
and ﬁnancial operations, legal, healthcare, high-end sales and sales management. What
most of these professions have in common is that a college education is required.
Florida (2002) uses census data to support his theory of the creative class, though
he has not operationalized any of those constructs. Yet, the dimensions of the constructs
that he articulates apply to my study. Speciﬁcally, the creative classes that he describes
seek attributes from the communities in which they live that include: diversity, tolerance,
safety and a sense of place. The Michigan Cool Cities Initiative (2003) identiﬁed the top
three qualities that people want in a small or medium community as: walkable
community, business development and historic preservation. The intersection of these

two studies can be found in “safety/walkable communities” and “sense of place/historic

48

preservation.” Small and medium-sized communities may not be as concerned with
making their population diverse or in the dimension of tolerance. A case can be made that
safe, walkable communities and historic preservation are dimensions of a sense of place.
Finally, a downtown’s non-shopping attractions can include a range of non-retail urban
attractions, like work places and leisure facilities (Arentze, et al., 2005). This allows
combining shopping with other activities such as banking, dentist/doctor visits, etc.,
creating other types of multi-purpose trips. When a downtown is a pleasant place to visit
(for reasons other than shopping), local consumers develop a level of familiarity with the
downtown district. Arentze, et al., (2005) posit that this may lead to more visits over
time.

H8 c: Sense of place is one of three significant measurement indicators of the

community characteristics construct. It is positively related to community

characteristics.

A downtown is part of a larger community. Downtown business owners will
derive much of their revenues from consumers who live within the community (Huff,
1963). In my study, I have conceptualized the characteristics of the community as being
reﬂected in its economic base, the overall external environment, the sense of place it
creates and the diversity of the people who live there. The economic base of a community
is one that has probably been created over a relatively long period of time. It is therefore
difﬁcult to change quickly. Communities with a diverse economic base will be more apt
to be successful over the long run. A community that experiences extended periods of
economic stress will provide little in the way of support and resources for a downtown.

The environment external to the community has a direct effect on that community. As an

example, a signiﬁcant drop in automobile sales would negatively affect a community

49

heavily dependent on jobs tied to the automotive industry. A community that is a pleasant
one in which to live, one with a “hometown” feel, is an asset to the downtown. This
would be in contrast to the “bedroom” communities often referred to in the mainstream
press, where people stay in between commuting to a large city to work and seek
entertainment. The diversity of its citizens will play a large role in the personality of a
community. Florida (2002) has shown that a diverse population, with a large percentage
of creative and highly educated workers, is the most attractive of places for employers
and other creative people.

All of these characteristics of a community are posited to be resources for the
downtown. Even environmental hostility may be viewed as a resource, as it may spur
innovative responses (Covin & Slevin, 1989). To the extent that a community has been
proactive in diversifying its economic base, its population and has created a strong sense
of place, it may insulate itself from the negative aspects of a hostile environment.

H8 (1: Community characteristics is one of three significant and positive factorial
indicators of the latent construct called structural resources.

The interest in place branding has appeared recently in the mainstream press
(Kershaw, 2004), and is posited to be an important part of the marketing of tourist
destinations (Hankinson, 2004b). A downtown’s brand identity is critical to its marketing
success (Leisen, 2001). Many downtowns across the US. are placing a greater emphasis
on attracting tourists, due in part to the large amounts of revenue they inject into the local
community (Kershaw, 2004). To attract local consumers, as well as to entice tourists to
spend more money, downtowns must provide an optimal mix of businesses at which
these consumers may shop. Central place theory holds that shoppers will be attracted by a

large and diverse number of shops (Christaller, 1935; Huff, 1963), and that centers that

50

have magnet stores are also sought by customers (Brown, 1994). However, the shops
must be the right blend of products and services, with the correct mix of complementary
businesses (Maronick & Stiff, 1985; Brown, 1994).

The community in which a downtown exists is also crucial to its success. Small
businesses are particularly susceptible to hostile environments (Khandawalla, 1977;
Covin & Slevin, 1989). The surrounding community can help mitigate some of the
negative effects of such environments through providing a sense of place (Robertson,
1999; Florida, 20043). Some aspects that make up a sense of place include safe
downtowns, and a “hometown” feel. These characteristics may entice visitors to the
town, or local consumers to inshop more often.

Together, brand identity, business mix and community characteristics are all
posited to reﬂect the structural resources for a downtown. These resources are those that
exist due to geography, or the collective efforts of the downtown (business mix). They
are not possessed or created by any single small business owner, or downtown employee.
Their presence in some level will be a predictor, through the construct of structural
resources, of downtown success.

H9: Structural resources are significantly and positively related to downtown
success.

Downtown Success
Downtowns do not produce ﬁnancial statements in the manner of public and
private ﬁrms. Therefore the ability to “measure” downtown performance is difﬁcult at
best. Because downtowns do not own the small businesses within their boundaries, they
do not receive revenues from them. Many CBDs assess fees or dues (Tomalin & Pal,

1994), but these are for operation and community activities that beneﬁt all businesses in

51

the CBD (e.g., marketing, street cleaning, parking, infrastructure, etc.). Thus downtown
business owners are not only invested in downtown success via their own success, but
also in the ﬁmding they provide to the CBD for communal purposes. Small business
owners are likely to be aware not only of local competition (e.g., mall, discounters), but
also nearby downtowns. They have then, a unique perspective on the performance of
their CBD, relative to other competitors.

H10 a: The relative performance of the downtown is one of four significant

measurement indicators of the downtown success construct. It is positively related

to downtown success.

The performance of ﬁrms is indicated by many different measures, but the most
common are ﬁnancial in nature (Paige & Littrell, 2002). In the extant literature, the unit
of analysis in small business research is the small business itself (Runyan, 2004b). With
very few exceptions (Kean, et al., 1998), downtowns are not utilized as a unit of analysis.
Yet the preceding literature has formed a basis for viewing the downtown as a ﬁrm with
resources, and as such, downtown performance is a measurable construct. A ﬁrm’s
performance may be measured by the performance of its strategic business units (Alon,
2001), including number of units and annual growth of those units. As this study has
deﬁned the downtown as a ﬁrm with resources (one of which is its small businesses),

then the success of those small businesses should be an indicator of downtown success.

H10 b: Firm performance is one of four significant measurement indicators of the
downtown success construct. It is positively related to downtown success.

Success should also be a ﬁrnction of small business longevity (Wenthe,
Fredenberger, & DeThomas, 1988). Stores that are in business longer, will likely be those

that have adapted to changes in the marketplace. They are more likely to be ﬁnancially

52

stable (Bates, 1995) than new ventures. Therefore, downtowns with a large percentage of
long—lived businesses should have long-term stability and be more successful overall.

H10 c: Longevity is one of four significant measurement indicators of the
downtown success construct. It is positively related to downtown success.

A measure of downtown success that is closely tied to ﬁrm longevity is vacancies.
Vacancies are storefronts that are not currently occupied by an active business. Vacancies
may be a sign of poor economic conditions, reﬂecting the volatility of the small business
sector. They may be a retail life-cycle issue (Fiorito & Greenwood, 1986) or an
ineffective economic development authority (Morris, 2002). Conversely, vacancies may
be a positive result of an active entrepreneurial community that fosters and encourages
innovation and risk taking (Morris, 2002). The causes of business failure (which lead to
vacant storefronts) have been the subject of many studies (Dekimpe & Morrison, 1991;
Bates, 1995; O’Callaghan & O’Riordan, 2003). Yet the effects of the resulting vacancies
upon the remaining small businesses has not been studied, and does not appear in the
extant literature.

Downtown business districts are generally unplanned, but ﬁnite in size (Levy &
Weitz, 2004). That is, unlike a mall or shopping center, it is difﬁcult for a downtown to
“expand,” so vacant storefronts will be caused by a small business owner moving or
going out of business. The CBD is usually deﬁned by street boundaries, and buildings are
already in place (Levy & Weitz, 2004). Logically the more successful ﬁrms are, the
longer they will be in business and fewer vacant storefronts will exist.

H10 (1: Vacancies is one of four significant measurement indicators of the
downtown success construct. It is negatively related to downtown success.

53

Summary

A downtown and the businesses that occupy its buildings, are involved in a
symbiotic relationship. Each has resources that they share at some level, in order to
compete and survive. These resources are posited to be business-speciﬁc or structural.
The extent to which each business owner and CBD (individually and collectively)
recognize, utilize and nurture those resources will determine the success of both the

business and the downtown.

54

Chapter 3
Methodology
Focus Group Research

The theoretical framework used for the current study is unique, and therefore
required some level of a priori qualitative research. When building theory or testing
existing theory in a different ﬁeld, there is often a need for qualitative research
(Varadarajan, 1996; Summers, 2001). Qualitative research can often help the researcher
to clarify the existence of constructs, conceptualize existing constructs or uncover
previously unknown constructs (Summers, 2001).

Focus group interviews were conducted with small business owners and directors
of the Downtown Development Authority (DDA) or similar group, in four Michigan
towns. These towns, and their characteristics, are listed in Table 3, along with all towns
in the current study. The towns had a population of between 4,700 and 14,000. The
population of communities in this study includes that of the city or town, as well as the
township within which the downtown exists. Population ﬁgures and characteristics for
both the city and the township were obtained from the US. Census Bureau’s “F actﬁnder”
website (U .S. Census, 2004). The general proﬁle of these cities supported including them
in this focus group study.

The procedure used to enlist the participation of downtown businesses for the
focus group research included a three-step process. I conducted a search of the World
Chamber of Commerce Website for the email address of each downtown’s Chamber of

Commerce. Previous experience has shown that there is no “central” website for

downtown groups or organizations. The World Chamber Website has proven in the past

55

to be very comprehensive (Runyan & Johnson, 2003), and a large percentage of even
small downtowns have a Chamber of Commerce. The Chamber director was sent an
email (see Appendix A) requesting the name and email address of the Downtown
Development Authority (DDA) director, or a person in a similar position. Of the
approximately 90 emails sent to Chambers of Commerce, nearly 65 were returned with
names and addresses. I then sent emails to those 65 DDA directors. Of those 65, 45
replied to the email request to participate in focus groups. Of those 45, 32 expressed
interest in further discussing the opportunity. Based on heuristics, I chose ﬁve
downtowns that seemed to have some similarities, but also had diverse locations and
economic bases. After establishing scheduled dates with ﬁve downtowns, I emailed the
remaining downtowns and thanked them for their interest. The ﬁfth DDA director
eventually postponed, then cancelled the focus group date. By that time, I had completed
the ﬁrst four groups, and had gathered sufﬁcient data to consider the project complete.
The traditional classiﬁcations of agricultural versus manufacturing based
economies may not be as relevant as they once were. It is well documented that few
communities in the US. still rely on agriculture, with less than one-percent of all
residents engaged in an agriculture-related ﬁeld (US. Census, 2004). Additionally, the
manufacturing sectors of industry have also been under pressure. For these reasons, I
sought to include towns that had more diverse economic bases. Additionally, one of the
largest industries in Michigan is tourism. I classiﬁed the focus group towns as either
tourist-dependent or non toruist-dependent, based on the opinion of the respective DDA
Director, and my own knowledge of the towns and the state. The towns included in the

focus group study had economic bases that were divergent from both the US. averages

56

and to each other on the following criteria: manufacturing, retail and food, agriculture
and self—employment. Thus the sample of cities used was diverse enough to expect a
varied range of answers on economic-related issues. The interviews were conducted at a
business or ofﬁce, located in each respective downtown.

The review of literature, and the conceptual model (Figure 1) served as the basis
for developing a discussion guide for the interviews. The construct of “brand identity”
was not conceptualized prior to the focus groups. Interviews were conducted with groups
of between 8 and 12 participants, as recommended for optimal feedback and group
interaction (McDaniel & Gates, 2001). All interviews were audiotaped, and then
transcribed for further analysis. I kept ﬁeld notes from each meeting. This served to ﬁll in
gaps where answers from participants were garbled, or too faint to understand. Every
effort was made to utilize the same questions and discussion frame in both groups.

Following the focus group session with the fourth CBD, convergence was found
on most of the key constructs in the conceptual model. The decision was made to stop
following the fourth group, and not continue with more CBDs, due to this convergence of
feedback. Additionally, the construct of “brand identity” (which had not appeared in the
original discussion guide) was articulated to some degree by members in each of the
groups. From these interviews, general constructs were conﬁrmed, and others identiﬁed
that seemed to describe the perceptions small business owners had towards their own
business, their fellow business owners, local and regional competition and their own
downtown business district. These interviews also were very important in determining
scales to use, as well as in guiding the creation of scales (where none existed) to measure

a construct.

57

Findings

Entrepreneurial orientation: Most focus group members were in agreement about
what deﬁnes an entrepreneur. Terms such as risk taker, creative, innovative and
motivated were used to describe entrepreneurs.

Small business orientation: Though members agreed on what terms describe an
entrepreneur, many did not describe themselves as entrepreneurs. Several noted that they
started their business because it ﬁt their family schedule (e.g., children, school, spouse’s
job, etc.). Others felt that their downtown needed a certain type of business, and took it
upon themselves to do the job. Several members felt that business owners brought
different strengths to the downtown. Every group acknowledged the importance of
owner-operated businesses in the downtown, as well as the importance of owners who
lived within the community. One group noted that they would rather have an owner-
operator of a national franchise chain in the downtown, than an independent but absentee
owner. These group members opined that businesses with local owners lent more stability
to the downtown.

Social capital: The existence of social capital received mixed support from focus
groups. In terms of local consumers, some felt that local consumers expected downtown
business owners to support the community, but this did not necessarily translate to
improved business. Some said that if they stopped supporting the community (e.g.,
donations, etc.), local consumers would stop patronizing their business. Others disagreed.
But what was generally agreed upon by tourist-dependent communities was: that the level
of commitment made by the downtown business owners was not reciprocated by local

consumers; and local consumers are not as loyal as “second-homers.” Second-homers are

58

those people who make a community their second home. An example of this is the
Chicago resident who owns a second home in Wisconsin or Michigan, and spend all or
part of the summer living there instead of in Chicago. These consumers tend to want the
downtown to maintain its “home town” feel and ambience. Thus, they tend to support the
downtown businesses to a greater degree than local consumers who live there year-round.

Focus groups generally agreed that for the most part, local consumers trusted
downtown business owners to be honest and fair in their business dealings. The groups
that were split on this topic were the tourist-dependent towns. Several business owners
from these groups reported that some local consumers had expressed feelings of being
overcharged by downtown merchants.

Social network ties: When asked if they were part of a group, the majority of
focus group members from all four towns answered in the negative. Some expressed
wishes of acting more like a group, but most agreed that downtown business owners were

9’ 6‘

not a group. Members used words like “independent, my decision” and “autonomous”
when asked why they did not make more group strategic decisions. These owners also
reported having few really strong ties with other downtown business owners. They note
that their ties and interaction tended to occur with cross-patronizing of businesses rather
than during formal or informal meetings. An exception to this was one of the tourist-
dependent towns, whose focus group members had very strong ties to each other.

Brand identity: The term “brand” did not surface often in the focus group
discussions. However, the concept of identity did. The downtown was described by a few

members (in different groups) as their community’s “heart,” and reﬂective of the

community as a whole. These comments elicited near unanimous agreement from the

59

other members of each group. It was agreed that the image of the downtown was an
important issue to all stakeholders, including those consumers who did not often shop
downtown. In other words, if a downtown had a negative image or identity, the entire
community might be seen in the same light.

Similarly, the term “positioning” did not emerge from any of the discussions. The
term “message” was articulated several times though, and the general discussion was then
directed towards exploring this topic further. There was mixed feedback about how each
town conveyed its image to consumers (local and visitors). What was important to most
though, was that their town tried to convey the message. One notion that emerged in
particular was the theme of consistency. That is, the idea that all stakeholders in the
downtown should be conveying the same image and the same message to consumers.
This seems to be a problem in some towns, where the local government is perceived as
not being in harmony with the needs and or desires of downtown business owners. Yet
these business owners also expressed their desire to remain independent when it was
suggested that they (owners) act more like a cohesive group, to address problems with
local government (for example).

Business mix: Of approximately 40 participants in the four separate downtowns,
38 agreed that a diverse business mix was a key to downtown success. It was also
articulated as diverse “retail mix” by some participants, but it was clear from discussions
that the meaning was the same. From focus group discussions, it was clear that
downtowns needed (and wanted) a signiﬁcantly large percentage of its storeﬁ'onts to be
retail in nature. No distinction was made between product or service retailers, as the

opinion was that retail stores bring consumers downtown to shop. Many respondents

60

opined that a doctor’s ofﬁce brought people downtown only for their appointment and
did not facilitate shopping. However, participants thought that the presence of businesses
such as banks, barbers, travel agents, etc. were just as important in differentiating a
downtown (and bringing customers there). Yet the point was persistently made in
subsequent focus groups that too many non-retail stores seemed to stiﬂe “foot trafﬁc.

In a downtown, business owners often own the building in which they are located,
and therefore can’t be forced to move. If the building/business owner does not maintain
the building properly, this has a negative effect on neighboring businesses. An example
noted in one focus group was a particular jewelry store, whose owner owned both the
existing building as well as the adjacent one. The problem in this case was two-fold: the
existing store carried very old and unappealing inventory, while the adjacent building had
been vacant for several years. As independent business owners, there was little that
neighboring businesses could do to ameliorate the situation. This exempliﬁes both an
optimal business mix issue (the jewelry store) as well as a turnover issue. Though the
vacant building had not turned over for years, it was still vacant and detracted from the
overall ambience or image of the downtown.

The type of business that locates downtown may often affect business mix. As
noted earlier, professional businesses such as doctors and lawyers are not perceived as
trafﬁc builders for other downtown businesses. Yet group members lamented that
building owners (even ones who were fellow small business owners) often rented to these
types of professionals. Building owners are forced to lease their buildings to those who
can pay the rent. Small business owners understand that unless they are willing to buy the

building themselves, they have little say in what type of business moves in next door.

61

Community characteristics: Contrary to some reports (Michigan Cool Cities. . .,
2003), communities do not seem to be as concerned with making their downtowns “hip
and cool.” One recent newspaper poll found that walkable streets, sense of community,
gathering places were more important than arts, nightlife, etc. (Hornbeck, 2004). Focus
group members revealed a similar concern with walkable downtowns to encourage
browsing, as well as a sense of community. Little was said regarding population
diversity, tolerance or other social issues that are the foci of the initiatives such as
Michigan’s Cool Cities. When the topic of historic preservation was brought up, the
reaction of focus group members was that it (historic preservation) was often a waste of
their money. The only reason many would be in favor of preservation was if it was
intended for store front preservation, as this helped to maintain a consistent image to the
consumer. From focus group feedback, a case can be made that safety, walkable
communities and historic preservation are all dimensions of a sense of place.

Downtown success: When asked if their downtown was successful, there were
mixed responses ﬁom each group. The difﬁculty seemed to be in the deﬁnition of
success. Members whose businesses were struggling often said that the downtown was
not successful, while those whose businesses were going well said the opposite. When
asked if the number of vacancies might be a measure, there was general agreement ﬁ'om
two of the four groups. The other two reported very few vacancies. One group member
noted that a neighboring town had many vacant storefronts, and its DDA director often
complained of how poorly the existing businesses were performing. Small business
owners in most towns are likely aware of not only local competition (e. g., mall,

discounters), but also nearby downtowns. If they do not know business owners in

62

neighboring towns, they likely hear through informal networks including customers from
neighboring towns. These downtown business owners have then, a unique perspective on
the performance of their CBD, relative to other competitors.

The feedback from these focus groups was a key source of information in setting
an initial direction for the literature review, measurement and scale construction, and
sample population parameter. The contributions of the focus group data can be found in
many of the measures that reﬂect the structural resources of downtowns.

Sam 1e

This study utilized a judgment sample, whereby 11 towns in Michigan were
selected for inclusion. A judgment sample is a non-probability sample (also referred to as
purposive), the elements of which are handpicked to serve the purpose of the study
(Churchill & Iacobucci, 2002). For this study, I wanted only small-to-medium sized, non-
urban rural communities. The USDA (1996) deﬁnes these communities as having
populations of 5,000 to 30,000, and being located more than 30 miles from a
metropolitan statistical area (MSA). Niehm (2002) included both these types of
communities in her study, as well as those that are classiﬁed as non-urban small (30,000-
60,000 and 20 miles or more from a MSA). With the growth of chains, malls and large
discounters, it is likely that most communities of that larger size will have a great variety
of shopping and entertainment choices (Gorodesky & McCarron, 2003; Levy & Weitz,
2004). Focus group feedback points to greater choices for shopping and entertaimnent as
reducing the importance of the downtown to the community as a whole. In the review of
the literature, I have established the importance of the downtown to small communities

(Robertson, 1999). The success of the downtown and its small businesses should be more

63

important to smaller communities, than larger communities. Larger communities will
have a greater number of shopping, dining and retail options than a small community.

The towns included in this sample ﬁt the non-urban rural criteria used by the US.
Census Bureau (2004). I selected three downtowns that are part of a tourist-dependent
community, and three that are part of a non tourist-dependent community. These were
classiﬁed a priori based on feedback from the DDA directors in each downtown.
Business owners that are listed as part of the CBD are included in the sampling frame,
and surveyed to provide their perceptions of the resources that their respective downtown
possesses. Though previous studies in this area have excluded chain or franchise stores
(Frazier, 2000), I included any business that does business within a downtown district.
Instrument

A self-report questionnaire is used to measure the constructs in the model. The
instrument is developed from existing scales, the extant literature and from focus group
responses. Structural equation analysis will be employed to test the previously articulated
hypotheses. Structural equation modeling (SEM) uses speciﬁc nomenclature to describe
the variables (endogenous, exogenous, dependent, independent, mediating and
moderating) within the model. Variables that are not directly measurable are referred to
as latent variables or constructs (Kline, 1998). These latent variables are indicated by
using observed or manifest variables, which are directly measurable (Bollen, 1989).

Instrument development On the following pages, each construct and its scales are
discussed, including reliabilities where reported. Existing scales were chosen based on
theory, and each measure’s ﬁt with the current study. Scales that I have developed for

this study are pre-tested to determine their psychometric properties, as well as to

64

determine how well they measure the constructs that they are intended to indicate
(validity).

Following suggestions from Dillrnan (2000) and Churchill & Iacobucci (2002),
the survey was laid out in such a way as to optimize response rate and completion of the
entire survey. To obtain optimal results, the ﬁrst part of the survey contained interesting
questions that sought to show the respondent that I was interested in issues important to
them. The middle part of the survey contained measures that were more difﬁcult to
answer, while sensitive and demographic variables were measured at the end of the
survey. Pre-testing of the instrument before beginning the full study is also recommended
(Dillrnan, 2000; Churchill & Iacobucci, 2002).

Pre-testing was conducted using 29 small business owners to complete the entire
questionnaire. Though this is less than the recommended minimum number for a pre-test,
the fact that all of the 29 respondents ﬁt the proﬁle of those who would be surveyed in
the full study led me to accept this smaller number as sufﬁcient. Undergraduate students
were offered the opportunity to earn extra credit points, by taking one questionnaire each
back to their hometown (the size of the town was required to match the proﬁle of the
sample). Each student was given speciﬁc instructions to ﬁnd a small business owner in
their downtown, and ask them to ﬁll out the questionnaire. Each student was given a
separate sheet of paper on which they were required to have the business owner write
their name, address and phone number for follow-up after survey completion. This served
two purposes: it insured that the students did not complete the survey themselves, and it
allowed me to solicit feedback from the business owners regarding the survey and its

measures.

65

The results of the pre-test were generally positive. One respondent felt that the bi-
polar statements were confusing, and one other circled the bi-polar statements themselves
rather than the corresponding scale number. Since these scales had been used in several
previous studies, obtaining moderate to high reliabilities, I decided to leave them in their
original form. The ﬁrst four items in the pre-test instrument were forced-ranking scales
that measured aspects of sense of place and community characteristics. Respondents were
asked to rank the top three attributes out of 9—12 choices. More than half of the
respondents incorrectly marked these items. Some ranked all three as “one,” while others
simply made check marks. The concept of sense of place, and community characteristics
were also measured using other scales. Respondents were able to better understand these,
and marked them according to instructions. For this reason, the ﬁrst four items were
eliminated from the ﬁnal instrument. No other scales or measures were changed.

Scale reliabilities were all acceptable with the exception of business diversity and
vacancies. Each of these produced Cronbach’s alphas between .455 and .500. These
scales were developed with feedback and assistance from two experienced researchers.
Since no other scales exist to measure these two concepts, I decided to retain the scales as
they were. This judgment was based on the exploratory nature of this study, expert input
on scale development, as well as the small number of respondents. It was hoped that a
larger sample would help improve these alphas. Scale alphas that were obtained ﬁom the
ﬁnal instrument are shown with their respective scales in Table 1.

Entrepreneurial orientation (ENTREPL
Entrepreneurial orientation is a latent factor, indicated in this study by three ﬁrst-

order factors called innovativeness (INNOV), proactiveness (PROACT) and risk taking

66

(RISK). These three ﬁrst-order factors are indicated by measured variables from three
scales. For these constructs I utilize scales from Covin and Slevin’s (1989) strategic
posture scale. The Covin and Slevin scale contains nine items that focus on innovation,
proactiveness and risk-taking, and make up what they conceptualized as an
entrepreneurial orientation (E0). The innovation scale and two of the risk-taking
measures were adapted ﬁ'om existing instruments (Miller & F riesen, 1982). The
proactiveness scale as well as one of the risk taking measures were created by Covin and
Slevin (1989). Though the items in the scale focus on different dimensions of
entrepreneurial orientation, it is important to assess the construct validity of the entire
scale. Covin and Slevin (1989) factor analyzed the nine items and found them to be
empirically related, constituting a distinct unidimensional entrepreneurial orientation.
Composite reliability for the E0 scale was .87 for the Covin and Slevin (1989) study.
The measures of the E0 construct appear in Table 1. Each item is measured on a
seven-point semantic differential scale, utilizing separate bi-polar statements.
Respondents are asked to characterize their own strategic posture in terms of the nine
items, with each item being a different set of bi-polar statements (i.e., there was no
repeated anchor as in a Likert scale). The mean ratings on the items are used as the small
business owner’s entrepreneurial orientation score. The higher the score, the more

entrepreneurial they are considered to be.

Small Business Orientation (SBO):

Small business orientation is a latent factor, indicated in this study by two ﬁrst-

order factors called purpose and goals (PURP) with ﬁve measures, and emotional

67

attachment (EMOT), with four measures. No scales exist that are intended to measure
attributes of small business owners (in contrast to entrepreneurs). Much of the work that
has been done to understand the differences between entrepreneurs and small business
owners (cf. Carland, et al., 1984; Stewart, et al., 2003) has utilized proprietary personality
scales such as the Jackson Personality Inventory (J P1) (Jackson, 1977). I propose a scale
that measures the attributes of small business owners, as articulated by Carland, et al.,
(1984). These items are listed in Table 1, and are measured on a seven-point Likert scale
anchored from “strongly disagree” to “strongly agree.” Respondents are asked to note
their level of agreement with nine separate statements about their purposes in establishing
the small business, goals for the business and their emotional attachment to the business.
Social Capital (SOCAP):

Social capital is a latent factor, indicated in this study by three ﬁrst-order factors
called trust (TRU), reciprocity (REC) and shared vision (VIS). Scales that measure the
constructs of trust and reciprocity are adapted from previously reviewed studies. To
measure trust, I adapt scales originally operationalized by Tsai & Ghoshal (1998) and
also utilized by Frazier (2000). The scale is a ﬁve-item scale, measured on a seven-point
Likert scale anchored from “strongly disagree” to “strongly agree.” The items are
designed to measure how trustworthy the small business owner perceives he/she is in the
minds of local consumers. The reliability for the scale was .96 in the Tsai & Ghoshal
(1998) study and .75 in the Frazier (2000) study.

Reciprocity is measured using a scale consisting of ﬁve items, and appears in. The
ﬁrst three items in this scale have been used previously by Miller & Kean (1997a),

achieving a reliability of .85, and Frazier (2000) who reported a reliability of .87. Each

68

item is measured on a seven-point Likert scale anchored from “strongly disagree” to
“strongly agree.” The items are intended to measure the extent to which small business
owners feel that local consumers patronize their business, due to built up social capital
(i.e., returning favors, quid pro quo, etc.). I add two items to the scale intended to
measure whether the small business owner feels that their support of the community is
directly reciprocated by customers patronizing their business.

Shared vision is a construct of social capital that I apply to the interaction of small
business owners within a CBD. This part of social capital provides a level of support for
small business owners, assuring them that others in their downtown are working for the
same type of goals. I use the three-item shared vision scale from Tsai and Ghoshal
(1998), as it ﬁts the context of this study well. I made one change in the wording of the
items to adapt to this setting. Speciﬁcally, I substituted the word “downtown” for the
word “community” in the measurements. These items are measured on a seven-point
Likert scale, anchored from “strongly disagree” to “strongly agree.” The reliability

reported by Tsai and Ghoshal (1998) was .71.

Social Network Ties (SN TIES) :

Social networks is a latent factor, indicated in this study by three ﬁrst-order
factors called density (DENS), homophily (HOM) and frequency of interaction (FREQ).
Social networks enable small business owners to share values and norms with fellow
business owners. The extent to which this is successful will be a function of the density

of the network ties (Borgatti & Foster, 2003), the frequency of interaction (Brush, 1992),

69

and the level of perceived homophily (Frazier, 2000) between members of the network. I
use the Frazier (2000) scales to measure density and homophily. These scales were
designed to measure networks of small retailers, and ﬁt the framework of this study well.
These scales were also operationalized by Niehm (2002) in a study of retail
entrepreneurs. For the frequency of interaction scale, I use a scale based on the literature
as well as focus group research.

Density is the number of ties that network members maintain (Granovetter, 1973).
The construct is measured on a three-item, summated rating scale. Each item is intended
to identify the degree to which network members interact with each other. Items were
measured on a ﬁve-point scale, anchored from “not true at all” to “very true” in the
original study. For my study I adapt the scale by increasing it to a seven-point summated
scale with the same anchors. The reliability for the scale was reported as .84 by Frazier
(2000), and as .89 in the study by Niehm (2002).

Perceived homophily refers to the extent to which people perceive others as being
like them (Cross, Borgatti & Parker, 2002). The scale items used here to measure
homophily were operationalized by Frazier (2000) and later by Niehm (2002). They are
intended to measure the degree to which respondents feel that other network members
share their outlook on life, values and business philosophy. The scale contains four items,
each measured on a seven-point Likert scale anchored from “strongly disagree” to
“strongly agree.” The original scale was measured on a ﬁve-point Likert scale, and in
studies using similar samples to my study, had reliabilities of .76 (Frazier, 2000) and .87

(Niehm, 2002)

70

Frequency of interaction is an indicator of how often network members have
contact with each other (Berg, Piner & Frank, 1986). I utilize self-designed measures for
this scale to better suit the context of this study. Frazier (2000) attempted to measure this
construct using a three-item scale. The resulting reliability coefﬁcient of .46 for the scale
forced her to use a single-item measure, and eventually remove the measure ﬁ'om the
theoretical model in the study. I believe that the wording of the instructions in the
instrument that Frazier used may have contributed to the measurement difﬁculties.

Frequency of interaction is assessed using a three-item scale, intended to establish
the number of times per week that a small business owner interacts with any of his fellow
downtown business owners. Each item is measured on a different scale. To measure the
number of fellow business owners with whom each respondent talks each week, an open-
ended ratio measure is used. The respondent can answer from zero to as high as the total
number of fellow business owners in the downtown. To measure the number of times per
week (on average) the respondent talks with these fellow business owners, a seven-point
ratio scale corresponding to the number of days in the week is used. To measure the
number of times the respondent attends formal downtown business group meetings, an
ordinal scale is used, with choices of never, once, twice, six or twelve times per year.
Brand Identity (BRID):

Brand identity is a latent factor, indicated in this study by two ﬁrst-order factors
called image (IMAG) and positioning (POS). As established in the literature review,
brand identity as it pertains to places has seen little empirical work (Hankinson, 2001;
2004a; 2004b). Scales measuring brand identity for place (and certainly for downtowns)

are non-existent. Guided by the extant literature and focus group feedback, I have

71

established scales to measure the perceptions of small business owners, of the
downtown’s brand image using a ﬁve-item scale and positioning statement, using a four-
item scale. Each item is measured on a seven-point Likert scale, anchored from “strongly
disagree” to “strongly agree.”

Image refers to the small business owner’s perception of whether an image for the
downtown exists, if it is positive in nature and if it is consistently understood by fellow
business owners and consumers. A downtown’s positioning statement is tied to its image,
but is different in that it deals with a stated symbol, name or word that provides
information to consumers about what the downtown stands for. This is measured by
asking respondents if the downtown has a symbol that is recognized by consumers, if that
symbol is distinct ﬁ'om competitors’ and is endorsed by fellow small business owners.
Business Mix (B USMIX):

Business mix is a latent factor, indicated in this study by two ﬁrst-order factors
called diversity (DIV), magnet businesses (MAG) and one observed variable called
complementary businesses (COMP). An optimal business mix refers to the diversity of
businesses within the downtown, which is the most attractive to potential consumers. The
theoretical framework for the business mix is drawn mostly from the central place
literature, which has shown that agglomerations of diverse shopping outlets are most
attractive to consumers (Huff, 1963; Brown, 1994). The importance of business diversity
is also supported by small business owners within CBDs, as discovered in focus group
research. The greater part of the empirical research on central place is based on
mathematical analyses of secondary data (e. g., population, density, number of stores).

Scale development is lacking in this area, requiring creation of scales to measure this

72

construct. Business mix is a latent construct in the model, not readily measurable. The
literature and focus group feedback points to three salient indicators of business mix:
diversity, magnets and complementary businesses (Brown, 1994).

Diversity of business offerings refers to the number of different formats, product
and service offerings that exist in a downtown. I have developed a four-item scale that is
proposed to assess the perception of the diversity of shops in the downtown, the
willingness of existing businesses to accept new formats, and the perception of how
optimal the current mix is that exists in the downtown. Each item is measured on a seven-
point Likert scale, anchored ﬁom “strongly disagree” to “strongly agree.” A ﬁfth measure
is employed to test the changes in business types over time. Wenthe, et al., (1988) found
that changes in format and product offerings was a natural way for shopping districts to
“ﬁne-tune” their retail mix. Respondents were asked to note the length of time they had
occupied their current location. If the time were less than two years, they were asked to
list the type of store or product which was sold in that spot immediately preceding them.

Magnet businesses are those which attract consumers to the downtown area on
their own. These are referred to often as destination stores (Brown, 1994; Levy & Weitz,
2004), and consumers will travel downtown just to shop at these stores. These types of
stores will attract customers who will then often patronize other businesses downtown
(Brown, 1994). The magnet business scale is designed to measure the presence of magnet
stores in a downtown, perceived importance of magnet stores in a downtown and the
perceived drawing strength of those stores. Each of these three items is measured on a

seven-point Likert scale, anchored from “strongly disagree” to “strongly agree.”

73

Complementary businesses are those that sell similar products, or products that
may be sold in tandem. An example might be a sporting goods store and a uniform shop,
or an art gallery and a camera shop. Research shows that a large percentage of customers
move between complementary shops when in a downtown or shopping center (Brown,
1994). Other research supports this notion, as shoppers often seek a large number of
related services when on a shopping trip (Bellenger & Korgaonkar, 1980).

Two measures are used to assess the level of complementary businesses, only one
of which is subjective. The subjective item measures respondents’ perceptions of the
number of downtown businesses that complement the respondent’s business. The second
measure is based on the DDAs listing of businesses within the CBD. As a matter of
business procedure, most DDAs maintain a list of member businesses, along with a brief
description of the line of business in which each engages. This will allow an independent,
post hoc analysis of each town’s mix of complementary businesses. A downtown with a
large amount of cross-complementary businesses should be most attractive to consumers
seeking to maximize a shopping trip (Bellenger & Korgaonkar, 1980; Brown, 1994).
CommuniLy characteristics ( C OMCHAR):

Community characteristics is a latent factor, indicated in this study by two ﬁrst-
order factors called environmental hostility (HOST) and sense of place (SENSE), as well
as two observed indicators. The two observed indicators are economic base and creative
class. Community characteristics have been operationalized in previous studies (Kean,
Niemeyer & Miller, 1996; Kean, et al., 1998). The Kean, et al., (1998) study used
economic base as a characteristic measure. The study also used business environment as a

predictor of small ﬁrm performance. I posit that the business environment is part of

74

community characteristics, and include it in this study. Recent work by Florida (2002)
has shown that business climate may be more severely affected by community
characteristics than has been previously thought. He has shown that there is a direct
correlation between characteristics such as education levels, number of creative jobs and
a sense of place, and the vitality of a community or region.

Environmental hostility may cause difﬁculties for small businesses, as the
environment within which they operate is very important to their success (Khandawalla,
1977; Covin & Slevin, 1989). If the business environment is a hostile one, it will likely
be more difﬁcult to achieve success (Covin & Slevin, 1989). The Khandawalla (1977)
environmental hostility scale is used for this study, as one of the indicators of community
characteristics. The scale is a three—item scale, measured in a similar manner to the E0
scales (Covin & Slevin, 1989). It asks respondents to characterize the external
environment within which their ﬁrm operates, based on a seven-point semantic
differential scale with bi-polar anchors.

The economic base of the community will likely also have an impact on the
success of businesses and the downtown. The US. Census allows for multiple ad hoc
classiﬁcations of economic bases. For this study, I make an a priori classiﬁcation of
downtown’s surrounding economic base. Support for the ordinality of this scale can be
found in countless media over the past two decades. The US. economy has changed to
the point where few communities are based on agriculture, and manufacturing
communities have suffered greatly too. Though the terrorist attacks in New York City

(September 11, 2001) caused tourism to dip in the US. for some time, tourism is still

75

likely to be a stronger economic base for most communities than manufacturing or
agriculture.

Further, my focus group research points to communities with second-home
economic bases as having both strong tourism as well as wealthy part-time residents.
Therefore an economic base dominated by agriculture would be posited to be the
weakest, while one dominated by second-homers would be the strongest. The census data
do allow for the classiﬁcation of communities based on “second-homers.” The number of
housing units in a city is reported within the state of Michigan website (Michigan. gov).
Along with this is reported the number of vacant housing units, as well as the number of
housing units that are for “seasonal, recreational or occasional use.” This of course does
not include those second-home owners who rent apartments, but it would include
condominiums used for seasonal rental.

A sense of place refers to how local residents feel about the community in which
they reside. Focus group studies have shown that being able to safely walk around a
downtown is an important attribute, and this is supported in other studies (Florida, 2002;
Michigan Cool Cities, 2003). Important too are cultural attributes such as historic
preservation and cultural diversity (Florida, 2002). The construct of sense of place is
measured using four items. Respondents are asked to rate their downtown on the
attributes of safety, walkability, historic preservation and cultural diversity. These items
are measured using a seven-point summated rating scale anchored from “very” to “not at
all” (e. g., very safe to not at all safe).

Creative class is a fourth indicator of community characteristics that is measured

using attributes suggested by Florida (2002). From US. Census data, the number of

76

residents with college degrees, as well as those who ﬁll jobs classiﬁed as being part of
the creative class, will be calculated. Communities with a large percentage of residents
who hold college degrees, tend to be more vibrant and attractive to others with college
degrees (Florida, 2002). The greater the base of college-educated residents, the more
attractive a community is to potential employers. The second attribute that is attractive to
potential employers is the number of creative-type people in an area. Florida (2002)
classiﬁes two levels of creative class workers: super-creative core, and creative class. The
presence of these groups is posited by Florida to enhance the attractiveness of a
community. Therefore, the greater the percentage of workers who hold jobs classiﬁed in
one of these two categories, the more attractive the community to other creative people,
and following that, employers. A community that is high on the creative class measure
and has a large percentage of residents with college degrees, should be a more vital
community and have a more successful downtown.

Downtown Success (D TS U C) :

Downtown success is a latent factor, and the dependent variable in this study. It is
indicated by three ﬁrst-order factors called relative downtown performance
(RELTOWN), relative business performance (RELBIZ) and vacancies (VAC). It is also
indicated by one observed variable called longevity (LONG). The success of a downtown
is the end result of recognizing, utilizing and nurturing the resources at its disposal. This
success depends on the success of both the small businesses within its boundaries and the
development of resources within the community and the downtown.

To assess the relative performance of the downtown, I adapt the relative

performance measure used by Frazier (2000) and Niehm (2002) to measure relative ﬁrm

77

performance. This is a three-item scale, asking the respondent to describe the
performance of their ﬁrm compared to last year, compared to major competitors and
compared other similar ﬁrms in the industry. The items are measured on a ﬁve-point
summated rating scale, anchored from “poor” to “excellent.” For my study, I adapt the
scale to a seven-point, semantic differential scale, using the same anchors. I also have
replaced “your store,” with “your downtown” and “major competitors” and “other stores”
with “major competing downtowns” and “other downtowns” respectively. Downtown
business owners should be in the unique position to assess the performance of their
downtown compared to other downtowns, as they have a unique perspective. As
members of a downtown, they know whether or not their own downtown is successful.
Focus group members reported going to neighboring downtowns to shop the competition,
thus being able to expertly compare the two. The reliability for the original study was .84
(Frazier, 2000).

To measure the relative performance of downtown businesses I use the same scale
as originally devised by Frazier (2000), making the same adaptation from a ﬁve-point to
a seven-point scale. Small businesses are known for their reluctance to divulge ﬁnancial
information. These assessments have been shown to be consistent with actual
performance measures such as revenue and proﬁt growth (Venkatraman & Ramanujam,
1986). The reliability for the original study was .84.

Longevity is conceived to be a valid measure of downtown success, as it indicates
stability. The sense of place articulated as an important attribute (Robertson, 1999;
Florida, 2002) of downtowns, should be enhanced when businesses are successﬁrl and

able to maintain a successful location. A single measure of longevity is utilized, asking

78

respondents to note how many years their business has been located downtown. The
question speciﬁcally is designed to measure how long they have been downtown, and not
in that “one” location. This distinction is made to avoid assigning a lower longevity score
to a business that has been at its current location downtown for 1 year (e. g.), but for 20
previous years was across the street in the same downtown.

Vacancy is posited as an indicator of downtown success. The causes of store
mortality and duration have been the subject of previous studies (Houston & Stanton,
1984; Wenthe, et al. 1988). Empirical research has not been conducted on the effects of
vacancies on shopping center image, downtown businesses or adjacent businesses. I use
two separate measures to assess vacancies: the ﬁrst is a simple ratio level variable that
compares the number of vacant storefronts with the total number of downtown
storefronts. A higher ratio would indicate a high rate of turnover or a slow rate of re-
leasing. The second measure is a self-designed, three-item scale intended to gauge the
perceived effects of vacancies on existing businesses. The items are measured using a
seven-point Likert scale anchored from “strongly disagree” to “strongly agree.”
Procedure

I employed a data—gathering procedure used in two previous studies and similar to
the focus group research gathered for this study. I identiﬁed all downtowns in Michigan
that ﬁt the prescribed proﬁle of between 5,000 and 30,000 in population, from the US.
Census data. The same steps were followed, including emailing Chambers of Commerce
to obtain those DDA director’s emails that I did not already have (from the focus group

studies). For this study, I did not select any certain number or type of downtowns or

79

communities. To ensure as large a sample as possible, I accepted any downtown that
requested to be included.

Dillman (2000) offers many suggestions to help increase response rates in mail
surveys. Since this study was not a mail survey, I adapted several of his suggestions to
this study. The ﬁrst was the process of pre-notiﬁcation. The second was offering an
incentive. Following agreement of the DDA director to participate in the study, the
director becomes a “champion” of the research project. This entails announcing the study
to downtown business owners (pre-notiﬁcation), supporting the research as important to
the downtown (incentive), and requesting their participation. Tire director also sets a date
for distribution and a date for picking up the completed survey. In previous studies using
this method, I have obtained response levels of between 38% and 72% from ﬁve
downtowns (Runyan & Johnson, 2003). In that study, only one downtown had an
extremely low response rate (8%).

A total of 14 downtowns requested/agreed to participate in the study. Of those,
only 11 actually followed through and participated. Those towns are listed in Table 3
(Appendix A). The DDA director was instructed to denote the stores that were in its
traditional CBD. One of four procedures was used for distribution and collection of
surveys. The DDA director dropped off surveys, and picked them up; the DDA director
dropped off surveys and I picked them up; I dropped off surveys and the DDA director
picked them up; I dropped off surveys and picked them up. The method used for each
town is denoted in Table 3, to allow for assessment of response rate differences. In each
town, business owners who had not completed their survey by the assigned day were

given the option of completing while pickup continued (allowing from 1-2 hours

80

additional time), or dropping off their survey later to the DDA director’s ofﬁce. All
surveys were disseminated and collected over a three-week period. One week following
the last collection, I received 20 total additional surveys from 7 different downtowns.
Since these were returned so closely to the collection date, and were a small percentage
of the total collected, no tests for response differences were considered necessary.

A total of 1108 surveys were disseminated in the 11 towns. A total of 272 were
returned, for an initial response rate of 24.5%. Of those 272, ﬁve were deemed unusable
for different reasons, including being completely blank. This provided a ﬁnal response
rate of 24. 1 %. This is a favorable response rate, considering what is normally achieved in
studies of small businesses (e. g., Conant & White, 1999 — 13.1%; Frazier, 2000 — 12.1%).
Copies of the questionnaire, emails to the Chamber of Commerce and the DDA director
are located in Appendix A.

Sample Description

 

The sample consisted of 267 owners or managers of small businesses within the
CBD of downtowns in 11 Michigan communities. The populations of these communities
ranged from 2972 to 25496. There was a fairly even split in terms of gender, with 52%
male and 44.9% female (less than 100% due to missing data). As a comparison, the
Frazier (2000) study was based on a sample with 71% female small business owners,
while the Miller (2001) study’s sample was 65.3% female. The gender breakdown in my
study is much more representative of the population of small business owners in the US.
According to the US. Census (2004), males make up 65% of the persons in the US. who
are self-employed (either incorporated or unincorporated). Respondents tended to be

highly educated, with over 52% holding a bachelor’s degree or higher. Approximately

81

43% of the sample (61% of those who responded to this question) were under 50 years of
age.

Longevity of business operations was quite high for the sample, with 78% of the
businesses being in existence for 7 or more years. F ifty—seven percent of the respondents
reported their business as having existed for 16 or more years. The percentage of those
businesses who had been located downtown were similar in terms of duration, with 69%
reporting that their business had been downtown for more than 7 years. F orty-ei ght
percent had been downtown for 16 or more years. In general, ﬁrms ﬁt the proﬁle of a
small business as measured by number of full and part-time employees. F orty-four
percent of the respondents reported having 2 or fewer full-time employees, including
themselves. Over 65% of the respondents reported employing ﬁve or fewer part-time
employees, including themselves. A detailed account of the sample characteristics can be

found in Table 2.

82

Chapter 4
RESULTS
In this chapter, I will discuss the process used to test the hypotheses posited

earlier. This includes explanation of data input, screening and initial analysis, as well as
conﬁrmatory factor analyses used to develop the ﬁnal structural model. Much of the
hypothesis testing is carried out within the process of ﬁtting the CF As. Due to the large
number of hypotheses, I will brieﬂy discuss each hypothesis test immediately following
the respective CFAs. I used SPSS 11.5 to create the database for further analysis. After
screening the data for input errors, I also looked at the raw data to assess any potential
problems with non-normality. Some suggestions are offered for levels of skewness and
kurtosis that are problematic (Hu, et al., 1992; Kline, 1998). These authors suggest 3.0 as
a cutoff for skewness, and 10.0 for kurtosis. Above these levels, there may be problems
further on when attempting to ﬁt measurement and structural models. There were a few
variables that had some potential problems in the current study. The most serious
concerns were with the trust (TRU) scale. These data exhibited extremely skewed (over
3.0 in absolute magnitude) and kurtotic (over 10 in absolute magnitude) distributions.
Since this scale had been used successfully in a previous SEM study (Frazier, 2000), I

decided to use the scales without modiﬁcation for initial CFAs.

83

The other variables which exhibited evidence of non-normality were much less
problematic. These involved outliers that constituted a small number of cases per
variable. Thus simply removing the outliers eliminated the skewness and kurtosis, while
not contributing substantively to missing values problems. Those variables were V33
(frequency of interaction measure) and V74 (complementary business measure).

Structural equation modeling with LISREL 8.7 was used to test the hypothesized
relationships as well as ﬁt in the measurement and structural models. A two-step process
was used, where conﬁrmatory factor analyses (CFA) were conducted on the measurement
model (Anderson & Gerbing, 1988) before testing the structural model. Estimates of
structural parameters were obtained using the maximum likelihood (ML) in most cases.
When another method of estimation was used due to normality problems, the method
used is noted. ML estimation has been shown to be fairly robust to violations of
normality, provided sample sizes are not too small (Bollen, 1989; MacCallum, 1995;
Kline, 1998). Lisrel tends to be very sensitive to missing data values. For this reason,
imputation is often required when even a seemingly small (e.g., 10%) portion of cases
have missing values. I also used SPSS 11.5 to replace missing data values, prior to
importing the data to Lisrel. The method used was replacing missing values with adjacent
data points (within two points of the missing value).

Model ﬁt was assessed using several methods. Following model speciﬁcation and
ﬁtting the data, I assessed model ﬁt by reviewing the x2 statistic. This measures the
difference between the speciﬁed model’s covariance structure, and the observed
covariance structure (Bollen, 1989). What is desired is a non-statistically signiﬁcant x2

statistic that suggests that the speciﬁed model is not signiﬁcantly different from the null

84

model. If ﬁt was not acceptable, I then reviewed the standardized residual matrices. This
is done to assess large residuals which contribute most to poor ﬁt. LISREL also provides
modiﬁcation indices based on Lagrangian Multiplier (LM) tests. LM tests identify
parameters not speciﬁed, which if speciﬁed may contribute to better model ﬁt. When
reviewing residuals and modiﬁcation indices, I ﬁrst assessed the theoretical implications
of any model re-speciﬁcation. Modiﬁcations contrary to theory were not made, unless the
change could be logically justiﬁed.

The majority of the data analyzed were multi-variate normal in nature, with the
exception of the few previously addressed. Because x2 does not perform well under
conditions of large sample sizes and non-normality, I also utilized several other statistics
to assess model ﬁt. These included root mean square error of approximation (RMSEA),
non-norrned ﬁt index (NNFI) and adjusted goodness of ﬁt (AGFI). All three of these
indices adjust for model complexity (Bollen, 1989; Kline, 1998). I will use the following
cutoff criteria in assessing model ﬁt: RMSEA < .08; AGFI > .90; NNFI > .90; p>.05.
These criteria are generally acknowledged as acceptable model ﬁt (Bagozzi & Yi, 1988;
Bollen, 1989; Kline, 1998). Where the )8 statistic is less than the p>.05 threshold, 1 will
consider the other ﬁt indices in deciding whether to accept the model as speciﬁed.

Factor Devemment

 

Using the two-step process, the measurement model is estimated using CF A. The
exogenous indicators in the model include entrepreneurial orientation, small business
orientation, social capital and social network ties, which are posited to indicate the
endogenous construct of business resources, and brand identity, business mix and

community characteristics posited to indicate the endogenous construct of structural

85

resources. Each of these factors are indicated by ﬁrst-order factors (listed below by
CF A), which in turn are indicated by manifest variables discussed in the literature review.

The conceptual model contains a large number of manifest (observed) variables,
thus there is danger of producing an underidentiﬁed model due too many parameter
estimates required. A solution to this potential problem is to average manifest indicators
into a smaller number of indicators for latent constructs (Yuan, Bentler & Kano, 1997). I
ﬁrst conducted CFAs on each ﬁrst-order factor. The purpose of this step was to conﬁrm
the loadings of manifest variables upon the theoretical latent construct, and provide
direction for averaging the indicators for further model ﬁtting. I will report the ﬁrst-order
CFAs using scales of manifest variables and explain how those variables were averaged
to form composite measurement indicators for the latent factors in the model. Following
that, I will report the ﬁtting of the full measurement model for the latent constructs. The
ﬁrst set of CFAs below are for the business resource construct (as indicated by ENTREP,
SBO, SOCAP and SNTIES) and the structural resources construct (as indicated by BRID,
BUSMIX and COMCHAR).
F irst-Order Conﬁrmatorv Factor Analysis- Business Resources

Entrepreneurial orientation (ENTREP) was posited to be indicated by three latent
indicators called innovativeness (INNOV), proactiveness (PROAC) and risk taking
(RISK). The initial model was speciﬁed to include all variables in the original scales (see
Table 1). Fit indices showed unacceptable ﬁt for this initial model. The CFA revealed
two variables that cross-loaded onto more than one construct (v21 loaded on INNOV and
RISK; v22 loaded on both RISK and INNOV). The goal of this step of the analysis was

to create composite scores for further model ﬁtting. Additionally, Anderson and Gerbing

86

(1988) note that unidimensional measures allow for better testing of discriminant and
convergent validity with latent constructs. For these reasons, v21 and v22 were dropped
from the model. Additionally, the LM tests indicated that the errors of v28 & v29, as well
as v24 and v27 should be allowed to covary. The wording of the measures for v28 & v29
was similar as was that for v24 & v27, and it made theoretical sense to allow the errors to
covary.

Results of the CF A for the ﬁrst-order factor after re-speciﬁcation, were
(x2 =14.12, df=9, n=256, p=0.118, RMSEA=.047, AGFI=.95). The manifest variables
exhibited discriminant validity, as none loaded on more than one variable. All free
parameters were statistically signiﬁcant at the .05 level, indicating the measures had
convergent validity (see Table 5).

Small Business Orientation (SBO) was indicated by two latent constructs called
purpose and goals (PURP) and emotional attachment (EMOT). The initial model was
speciﬁed to include all variables in the original scales (see Table l). The ﬁt indices were
well outside of acceptable limits for this initial model. Results showed that two variables
(v3 and v10) cross-loaded on both constructs. Additionally the LISREL output showed
very high residuals between several measures and v3 as well as v4. Since the wording of
v3 and v6 were similar, and that of v4 was similar to that of v2, it seemed that the
measures were redundant and could be removed from the model without losing either
reliability or validity. An argument could not be made for allowing v10 to load on the
PURP construct. LM tests suggested allowing the errors of v7 and v8 to covary, along
with the errors of v8 and v9. Since these were items within the same scale, their

correlation was not unexpected, and it made sense to allow their errors covary.

87

Results of the CFA for the ﬁrst-order factor, after re-speciﬁcation were (x2
=17.77, df=11, n=264, p=0.087, RMSEA=.048, AGFI=.95). The manifest variables
exhibited discriminant validity, as none loaded on more than one variable. However,
though the model exhibited very good ﬁt, there were problems with the PURP measures.
None of the indicators were statistically signiﬁcant at the .05 level, and two of the three
(v2 and v5) were in the opposite direction of what was posited (see Table 6). The
measurement indicators of EMOT all loaded signiﬁcantly and in the posited direction,
exhibiting convergent validity. Because most authors discourage using single-item
indicators for latent constructs (cf. Kline, 1998), I was reluctant to discard the ﬁrst-order
construct of PURP. I decided to keep it in the model, with the intention of removing it if
it caused ﬁt problems with the ﬁrll measurement model.

Social @pital (SOCAP) was posited to be indicated by three latent indicators
called trust (TRU), reciprocity (RECIP) and shared vision (V IS). The initial model was
speciﬁed to include all variables in the original scales (see Table 1). Fit indices showed
unacceptable ﬁt for this initial model. No variables cross-loaded in the CF A, but there
were two variables that were causing large standardized residuals (v14 and v15). These
variables were very highly correlated with both v12 and v13, and thus were removed
from the model. The model was then re-speciﬁed and a second CF A conducted.

Results of the CF A for the ﬁrst-order factor, after re-speciﬁcation were ()8
=31.04, df=24, n=267, p=0.153, RMSEA=.033, AGFI=.95). The manifest variables

exhibited discriminant validity, as none loaded on more than one variable (Table 7). The

88

measures showed convergent validity also, as all estimated parameters were statistically
signiﬁcant at the .05 level.

Social Network Ties (SNTIES) was indicated by three latent indicators called
density (DENS), homophily (HOM), and frequency of interaction (FREQ). The initial
model was speciﬁed to include all variables in the original scales (see Table 1). The
initial attempt at ﬁtting this CFA resulted in unacceptable ﬁt measures. One measurement
variable loaded on two constructs (v42 on HOM and FREQ). No substantive argument
could be made for allowing this parameter to be freely estimated, as v42 is a measure of
homophily and not of interaction frequency. There were no large standardized residuals,
but the LM tests suggested allowing the errors of the following variables to covary: v34
and v35; v39 and V41; V42 and v43; v42 and v45. Allowing these errors to covary made
sense theoretically, and since there were no large residuals, an argument could be made to
keep all variables in the model. The model was re-speciﬁed with the LM modiﬁcations
included.

Results of the CF A for the ﬁrst-order factor, after re-speciﬁcation were (x2
=36.66, df=28, n=267, p=0.126, RMSEA=.034, AGFI=.95). The manifest variables
exhibited discriminant validity, as none loaded on more than one variable. The measures
showed convergent validity also, as all estimated parameters were statistically signiﬁcant
at the .05 level (Table 8).

First-Order Conﬁrrnatorv Factor An_alvsis- Structural Resources

Brand identity (BRID) was indicated by two latent indicators called image

(IMAGE) and positioning (POS). The initial model was speciﬁed to include all variables

in the original scales (see Table 1). The initial attempt at ﬁtting this CFA produced

89

several problem parameters and unacceptable ﬁt measures. While there were no cross-
loading variables, several variables had very substantial standardized residuals.
Speciﬁcally, v49 and v77 displayed high residuals with several variables from both
scales. Variable 49 was a reverse-worded version of variable 51, and had a high negative
correlation in univariate analyses. This meant that v49 and v51 were likely redundant
measures of the same concept. Additionally, v77 was very similar to v78 in its wording
and meaning, and both were highly correlated. For these reasons, both variables (v49 &
v77) were dropped ﬁom further analyses.

LM tests suggested that 7 different errors covary. Rather than risk an over-
speciﬁed model, I decided to run the model again without v49 and v77, but without any
ﬁnther modiﬁcations. This second CFA produced much better ﬁt, but still below
suggested cutoff criteria. This time the LM tests suggested only one modiﬁcation, which
was a path allowing the error terms of v52 and v58 to covary. Though the wording of the
measures was similar, they were measuring two different ideas and thus a compelling
reason for removing one or the other could not be made. But allowing their errors to
covary was acceptable from a theoretical point, as they were part of the same scale.

Results of the CFA for the ﬁrst-order factor, after the second re-speciﬁcation were
(x2 =23.54, df=12, n=259, p=0.023, RMSEA=.061, AGFI=.94). Though the model did
not produce a non-statistically signiﬁcant x2 statistic, the other ﬁt indices were within the
acceptable-to-good ﬁt ranges. Though many authors suggest a RMSEA cutoff of .05 for
good ﬁt, and .08 for acceptable, Hu and Bentler (1995) have suggested that .06 is a better

cutoff for good ﬁt. The manifest variables exhibited discriminant validity, as none loaded

90

on more than one variable (Table 9). Convergent validity was also achieved, as all
estimated parameters were statistically signiﬁcant at the .05 level.

Business mix (BUSMIX) was posited to be indicated by two latent indicators
called diversity (DIV) and magnet businesses (MAG), as well as one observed variable
called complementary businesses (COMP). COMP was not included in this CFA, but
rather will be included in the second CFA along with the composite variables derived
from this CFA. The initial model was speciﬁed to include all variables in the original
scales (see Table 1).

The ﬁrst CFA produced a very good ﬁtting model, with no items cross-loading.
Results of the CFA for the ﬁrst-order factor were (x2 =17.03, df=13, n=259, p=0.198,
RMSEA=.035, AGFI=.96). There were two variables that exhibited standardized
residuals that were moderately large, but with such a good ﬁtting model it did not make
sense to modify it further. All of the variables loaded cleanly on one factor, which
provides discriminant validity for the construct. All parameters were statistically
signiﬁcant at the .05 level providing support for the convergent validity of the measures
(Table 10).

Community characteristics (COMCHAR) was posited to be indicated by two
latent indicators called environmental hostility (HOST) and sense of place (SENSE), as
well as two observed variables called economic base and creative class. Both of the
observed variables proved to have severe measurement problems, as many respondents
did not answer the questions designed to measure these variables, or answered them

incorrectly. Therefore post hoc tests will be performed to test whether there are

91

differences between downtowns based on economic base and creative class membership
as indicated by Census data.

The initial model was speciﬁed to include all variables in the original scales (see
Table 1). The ﬁrst CF A produced less-than-acceptable ﬁt, though there was no cross-
loading of variables or large standardized residuals. The LM tests recommended that the
errors be allowed to covary amongst all of the SENSE indicators. The one that made
sense from a theoretical standpoint was to let v53 and v54 covary, as each measured
similar ideas though distinct enough to keep both measures in the model. I decided to
attempt to make only this one modiﬁcation ﬁrst, rather than any of the other suggested
changes.

Following re-speciﬁcation, the model was ﬁt again and this time produced very
good ﬁt. Results of the CFA for the ﬁrst-order factor were (x2 =17.07, df=12, n=259,
p=0. 147, RMSEA=.040, AGFI=.96). All of the variables loaded on only one factor each,
which veriﬁes the discriminant validity of the indicators. Convergent validity was also
achieved, as each of the parameters in the model were statistically signiﬁcant at the .05
level (Table l 1).

First-Order Conﬁrmatorv Factor Analysis- Downtown Success

Downtown success (DTSUC) is a latent construct conceptualized as being
indicated by three latent variables called relative downtown performance (RELTOWN),
relative business performance (RELBIZ) and vacancies (VAC), and one observed
indicator called longevity (LONG). LONG was not included in this CFA, but will be
included along with the composite variables derived from this CFA, for ﬁtting the second

CFA. The initial model was speciﬁed to include all variables in the original scales (see

92

Table 1). The model achieved very poor ﬁt for all indices, and there were multiple
problems with large standardized residuals. The two variables that seemed to be the most
problematic were v63 and v65 from the VAC scale. Both were causing large residuals
with multiple measurement variables from other factors. Additionally, both were cross-
loading on RELBIZ.

I ﬁrst re-speciﬁed the model by dropping only v65, as it seemed that v63 was
likely measuring a similar concept. There were no longer any cross-loading variables and
ﬁt improved markedly, but was still below acceptable cutoff levels. There were still
standardized residuals involving v63. I re-speciﬁed the model without VAC and its
measurement variables to see if this would provide not only a more parsimonious model
but also better ﬁt. The results were very poor, consequently I felt that leaving the VAC
manifest variables v63 and v64 would make for a stronger test of the full measurement
model later. LM tests suggested modiﬁcations including allowing several of the variable
errors to covary. Since it was hypothesized that vacancies would affect downtown
business performance, it made sense that one of the vacancy variables might be correlated
with the RELTOWN or RELBIZ measures. It was also suggested that v80 and v83 be
allowed to covary. There was a substantive argument for this, as v80 measures overall
downtown performance and v83 measures overall business performance. Theoretically, if
a downtown is successful, more of its small businesses will also be successful (and vice-
versa).

Using the LM suggestions as a guide, I re-speciﬁed the model and allowed V63
and v80 to covary with v83. This provided a model with much better overall ﬁt. Results

of the CFA for the ﬁrst-order factor were (x2 =25.32, df=15, n=259, p=0.046,

93

RMSEA=.051, AGFI=.94). All of the variables loaded on one factor each, which veriﬁes
the discriminant validity of the indicators. Convergent validity was also achieved, as each
of the parameters in the model were statistically signiﬁcant at the .05 level (Table 12).
Conﬁrmatory Factor Analyses and Hypothesis Testing

The structural model tested in this study was complex in nature. There were a
large ntunber of formal hypotheses posited between ﬁrst and second order constructs, as
well as between second order constructs and endogenous factors. Each hypothesis is
listed brieﬂy in Table 4, and includes the overall result of the hypothesis testing (i.e.,
whether it was supported or not supported). I will discuss each of the hypotheses brieﬂy
in this section and note whether the hypothesis was supported, and brieﬂy whether the
results conﬁrm or contradict the previous research. Parameter estimates and t-values can
be found in the appendices, in each respective table as noted.

Using the CF As for each of the ﬁrst-order factors, composite variables were
constructed for the next step in the model-ﬁtting process. For this step, I wanted to test
the ﬁt of the both the endogenous latent constructs: business resources and structural
resources. From a theoretical viewpoint, these two constructs represent distinct concepts,
and should be indicated by discrete measures. To model this, I took an extra step in the
process and conducted separate CFAs for business and structural resources.

Business resources (BUSRES) were indicated by the latent indicators of
entrepreneurial orientation (ENTREP), small business orientation (SBO), social capital
(SOCAP) and social network ties (SNTIES). The model was speciﬁed with each of the
composite ﬁrst-order factors serving as manifest indicators of the latent constructs. The

initial speciﬁcation resulted in unacceptable ﬁt. Review of the standardized residuals

94

uncovered the following difﬁculties: the purpose and goals (PURP) indicator of SBO was
causing severe ﬁt problems; trust (TRU), reciprocity (RECIP), shared vision (VIS),
homophily (HOM) and density (DENS) were all cross-loading onto SOCAP and
SNTIES, as well as exhibiting large residuals amongst each other.

In evaluating the social capital and social network ties measures, it became
apparent that there were two separate types of social capital being measured. I
reconceptualized these two constructs as social capital (SOCAP), and combined four of
the measures into one. The ﬁrst new measure I called local capital (LOCAP), which
represented measures of trust (TRU) and reciprocity (RECIP) “from” local consumers to
the downtown business owner. The second measure I called business capital (BIZCAP),
and it represented measures of shared vision (V IS) and homophily (HOM) amongst the
downtown business owners. I also re-conceptualized social capital as one latent construct,
dropping SNTIES in favor of only SOCAP. This meant that SOCAP was now indicated
by four variables: LOCAP, BIZCAP, FREQ and DENS.

Because most researchers discourage using single-indicators for latent variables, I
was reluctant to remove PURP from the model. The model was re-speciﬁed as noted
above and ﬁtted to the data, but with PURP included. The ﬁt was quite improved, though
the RMSEA still was above .08. The modiﬁcation indices suggested that allowing
LOCAP to load on SBO would greatly improve model ﬁt. It seemed an effective
argument, to consider SBO to be a function of emotional inputs, including the local

capital (LOCAP) measures of trust and reciprocity.

95

The re-speciﬁed model demonstrated very good ﬁt. Results of the CF A for the
latent factor of BUSRES were (x2 =38.06, df=23, n=267, p=0.025, RMSEA=.050,
AGFI=.94). Dropping PURP from this model would have slightly improved the overall
ﬁt (x2 =23.58, df=16, n=267, p=0.099, RMSEA=.042, AGFI=.95). However the loss of 7
degrees of freedom in exchange for approximately 14 chi-square units did not seem
worth eliminating an important indicator from model. Therefore, I retained the ﬁrst CFA
which included PURP. The model lacked discriminant validity somewhat, as LOCAP
loaded on both SBO and SOCAP. However, all other indicators loaded cleanly on one
construct (Table 13). The model did show convergent validity as all of the parameters
were statistically signiﬁcant at the .05 level.

Hypotheses Ia-c: Innovativeness (INNOV), risk taking (RISK) and proactiveness
(PROAC) were all hypothesized to be statistically signiﬁcant and positive measurement
indicators of entrepreneurial orientation (ENTREP). Each of these hypotheses were
supported. The CFA revealed that each parameter estimate was statistically signiﬁcant at
the p<.05 level, and in the positive direction. This supports the previous research that has
shown that innovativeness, risk taking and proactiveness measure entrepreneurial
orientation (Covin & Slevin, 1989). This orientation is positive in nature, and helps
predict success (Lumpkin & Dess, 1996).

Hypotheses 2a,b: Business owner purposes and goals (PURP), and emotional
attachment (EMOT) to their business were each posited to be measurement indicators of
the small business orientation construct (SBO). While H2a was not supported, the
relationship between PURP and SBO was positive in nature, though not as strong enough

to be statistically signiﬁcant. The magnitude of this effect was not very large though, as

96

the standardized coefﬁcient of the parameter was .232. Though larger than the .10
threshold considered to be a small effect, it is below the level considered to be a medium
effect (Kline, 1998). However PURP seems to be an important part of the SBO construct,
as the data ﬁt the model better with PURP as part of the equation. H2b was supported, as
the parameter estimate was statistically signiﬁcant at the p<.05 level. This study is a ﬁrst
attempt to operationalize scales used to measure these constructs. The literature points to
differences in entrepreneurs and small business owners (Carland, et al., 1984; Stewart, et
al., 2003). Two key areas in which they differ are goals and purpose. The small business
owner has goals that are more personally driven (e. g., family, personality, etc.). Based on
my results, the goals that appear to be more important to the small business owner are
those that involve the emotional attachment/investment in the business, than the
“business” purpose of owning the ﬁrm.

Hypothesis 3a-c; 4a-c: Reciprocity (RECIP), trust (TRUST) and shared vision
(VIS) were hypothesized to be statistically signiﬁcant measurement indicators of the
social capital (SOCAP) construct. Network density (DENS), frequency of interaction
(FREQ) and perceived homophily (HOM) were hypothesized to be statistically
signiﬁcant measurement indicators of the social network ties (SNTIES) construct. The
two latent constructs of SOCAP and SNTIES, and their respective measurement
indicators, were causing multi-collinearity problems with model ﬁtting. Previous research
has found that network ties contribute to social capital (Rowley, 1997; Frazier, 2000). For
these reasons, SNTIES and SOCAP were re-conceptualized as SOCAP. Additionally,

RECIP and TRUST were measures involving local consumers,’ while VIS and HOM

97

were measures involving downtown business owners. RECIP and TRUST became
LOCAP (local capital) and VIS and HOM became BIZCAP (business capital).

Therefore hypotheses 3a-c and 4a-c should be restated as follows: LOCAP,
BIZCAP, FREQ and DENS are statistically signiﬁcant measurement indicators of the
SOCAP construct. Support was found for all four of these hypothesized relationships,
with parameter estimates that were statistically signiﬁcant at the p<.05 level. These
ﬁndings are in line with the large body of research that has found that these indicators are
all important in the formation of social capital (c.f., Frazier, 2000; Cross, et al., 2002).

Structural resources (STRURES) were indicated by the composite latent
indicators of brand image (BRID), business mix (BUSMIX) and community
characteristics (COMCHAR). One additional manifest variable was included as an
indicator of COMCHAR in this CFA; complementary businesses (COMP). As a single-
item measure, it had not been included in the ﬁrst-order CFA. The model was speciﬁed
with each of the composite ﬁrst-order factors serving as manifest indicators of the latent
constructs. Using the results of the ﬁrst-order CFAs, the model was initially speciﬁed
using all manifest indicators. This initial model displayed very poor levels of ﬁt.
Examination of the standardized residuals highlighted one serious problem area, which
was magnet businesses (MAG). Though the variable itself is an important part of the
theoretical framework, the ﬁt problems it was causing required its removal from the
model.

The model was re-speciﬁed without MAG, and the ﬁt improved dramatically.
Results of the CFA for the latent factor of STRURES were (x2 =12.33, df=6, n=267,

p=0.055, RMSEA=.063, AGFI=.95). The measures showed discriminant validity as each

98

loaded on separate constructs (Table 14). One indicator (HOST) was not statistically
signiﬁcant (at the .05 level), thus weakening the convergent validity of the scale.
However the ﬁt of the model was better with HOST than without, and I did not want to
continue to the measurement model ﬁtting stage using COMCHAR as a single-item
construct. For those reasons, I left HOST in the model.

Hypotheses 6a,b: Image (IMAGE) and positioning (POS) were hypothesized to
be statistically signiﬁcant measurement indicators of a downtown’s brand identity
(BRID). Both of these hypotheses were supported, with parameter estimates that were
statistically signiﬁcant at the p<.05 level. This supports emerging research in the area of
community brand image (Hankinson, 2004b), as well as the concept of image as a
component of brand (Grandi & Grimaldi, 2003).

Hypotheses 7a-c: Diversity of businesses (DIV), magnet businesses (MAG) and
complementary businesses (COMP) were posited to be statistically signiﬁcant
measurement indicators of the business mix (BUSMIX) construct. Hypotheses 7a and 7c
were both supported, as parameter estimates were statistically signiﬁcant at the p<.05
level. Hypothesis 7b was not supported. Hypothesis 7a and b ﬁnd support in the
theoretical literature on retail agglomeration (Huff, 1963; Ghosh, 1986; Arentze, et al.,
2005)

Hypotheses 8a,c: Environmental hostility (HOST) and sense of place (SENSE)
were hypothesized to be statistically signiﬁcant measurement indicators of community
characteristics (COMCHAR). Support for hypothesis So was found, as parameter
estimates were statistically signiﬁcant at the p<.05 level. This follows the theories of

Florida (2002) that posit a community is deﬁned by its sense of place. Hypothesis 8a

99

regarding environmental hostility was not supported. This is contrary to previous research
that has found that environmental hostility is an actual measure of what is “happening” in
a community from an economic perspective. However most of the ﬁrms in this study
were small, and had been located downtown for many years. It is possible that they are
insulated to some extent from environmental factors, and are even unaware of the level of
economic hostility in their community.

Downtown success (DTSUC) was indicated by the composite latent indicators of
vacancies (VAC), relative downtown performance (RELTOWN), relative business
performance (RELBIZ), and the observed variable longevity of businesses (LONG). The
model was speciﬁed using all four indicators of DTSUC. The ﬁt was nearly acceptable,
though LM tests suggested that allowing the error terms of VAC and RELBIZ to covary,
would greatly increase ﬁt. Following this change, the model was re-ﬁt to the data, and
very good ﬁt was achieved (x2 =2.33, df=2, n=267, p=0.312, RMSEA=.025, AGFI=.98).
The indicators showed partial convergent validity, as RELTOWN and RELBIZ were
statistically signiﬁcant at a p<.05 level, though VAC and LONG were not statistically
signiﬁcant. The lack of convergent validity was a concern, but neither of the variables
showed problematic standardized residuals. Additionally, removing either from the
model caused the ﬁt of the model to decrease considerably. For these reasons, I retained
all four indicators to be used in the structural model ﬁtting stage (Table 15).

Hypotheses IOa-d: Relative downtown performance (RELTOWN), relative
business performance (RELBIZ), vacancies (VAC) and business longevity (LONG) were
all hypothesized to be statistically signiﬁcant measurement indicators of downtown

success. Both measures of relative success were found to be statistically signiﬁcant

100

indicators of downtown success, with parameter estimates at the p<.05 level. Thus
hypotheses 10a and b were supported. Previous research has shown that these types of
measures serve as accurate measures of success (Frazier, 2000). Both VAC and LONG
were attempts at creating new measures of success for downtowns. However neither were
found to be statistically signiﬁcant, thus both hypotheses 10c and d were not supported.
Measurement model:

From these ﬁnal two CFAs, the following measurement indicators were retained
for each endogenous construct:

Business resources (BUSRES): ENTREP, SBO, SOCAP

ENTREP is indicated by innovativeness (INNOV), proactiveness (PROAC) and

risk taking (RISK).

SBO is indicated by emotional attachment (EMOT), purpose and goals (PURP)

and local capital (LOCAP)

SOCAP is indicated by local capital (LOCAP), business capital (BIZCAP),
frequency of interaction (FREQ) and density of ties (DENS)

Structural resources (STRURES): BRID, BUSMIX, COMCHAR

BRID is indicated by image (IMAGE) and positioning (POS)

BUSMIX is indicated by complementary businesses (COMP) and business

diversity (DIV)

COMCHAR is indicated by environmental hostility (HOST) and sense of place

(SENSE)

Using the manifest indicators derived ﬁ'om the preceding CF As, a full
measurement model can now be ﬁtted to the data. I speciﬁed the model based on the
CFAs, and achieved moderate ﬁt with an RMSEA of .063. However the AGFI (.88) and
NNFI (.86) were both below the acceptable levels suggested. There were large
standardized residuals between BIZCAP and several other indicators of social capital.

Since SOCAP was indicated by four measurement variables, it seemed acceptable to re-

ﬁt the model to see if BIZCAP was perhaps a redundant measure of social capital.

101

Modiﬁcation indices also suggested allowing the errors of EMOT and LOCAP to covary.
I re-speciﬁed the model with these two changes, and the resulting ﬁt was very good.
Results of the measurement model were (x2 =74.20, df=60, n=267, p=0.103,
RMSEA=.030, AGFI=.92). Based on all but one indicator loading cleanly on separate
constructs, the model showed signs of discriminant validity (Table 16). As earlier,
LOCAP loaded on both SBO and SOCAP. The model also exhibited convergent validity,
as all measures were statistically signiﬁcant at the .05 level, except HOST.
Structural Model

The ﬁnal phase of the analysis is estimating the structural model. It is this step at
which the key theoretical constructs, which were hypothesized earlier, are tested. The
ﬁnal structural model provides the basis to test whether the crucial variables actually
reﬂect the latent factors as hypothesized, and whether those factors actually predict the
success of downtowns. This is conducted using the co-variances of the factors produced
by the ﬁnal measurement model (Bollen, 1989). The variables that measured ENTREP,
SBO, SOCAP, BRID, BUSMIX and COMCHAR, were used to produce composite
indicator variables for the two endogenous constructs of business resources (BUSRES)
and structural resources (STRURES). When the structural model was initially ﬁt to the
data, the model would not converge. By examining feedback from the Lisrel output, there
were two initial problems identiﬁed: the ﬁrst included negative error variances, and the
second was a negative theta epsilon matrix. To remedy this, I ﬁxed the negative error
variances (ﬁ'om VAC and BUSMIX) and also ﬁxed the error co-variances of RELBIZ
and RELTOWN. This allowed the model to converge, though the ﬁt was far below

acceptable levels. The LM tests suggested allowing the errors of ENTREP and SBO to

102

covary. I made this change, as well as releasing the constraint on the RELBIZ and
RELTOWN that was previously imposed. The model converged, but the ﬁt was still not
good. LM tests suggested several modiﬁcations, including allowing SOCAP to load on
BUSRES as well as STRURES, and allowing the errors of SBO and SOCAP, and
SOCAP and BRID to covary.

I re-speciﬁed the model with these changes, and the ﬁt improved noticeably. But
the parameters from the independent constructs to the dependent construct were both
statistically insigniﬁcant. After reviewing the standardized residuals and modiﬁcation
indices, a problem was revealed with the RELTOWN indicator of downtown success.
The correlation between this and RELBIZ was very high, and seemed to be the largest
contributor to poor model ﬁt. It seemed plausible that RELTOWN and RELBIZ were
redundant in this model, so there was a case to be made for removing it. I re-speciﬁed the
model without RELTOWN, and the model achieved acceptable levels of ﬁt (x2 =47.02,
df=24, n=267, p=0.003, RMSEA=.060, AGFI=.94). Additionally, the previously
statistically insigniﬁcant path from STRURES to DTSUC became signiﬁcant (Table 17).

Hypotheses I d; 2c; 3d; 4d; 5: In hypothesis 1d, entrepreneurial orientation
(ENTREP) was posited to be a statistically signiﬁcant indicator of the business resources
construct (BUSRES). This was not supported in the ﬁnal structural model (Figure 3).
Hypothesis 2c stated that small business orientation (SBO) was a statistically signiﬁcant
indicator of BUSRES. This was supported with the parameter estimate statistically
signiﬁcant at the p<.05 level. Social capital (SOCAP) and social network ties (SNTIES)

were hypothesized in 3d and 4d (respectively) to be statistically signiﬁcant indicators of

103

BUSRES. SOCAP and SNTIES were combined into one indicator labeled SOCAP and
thus both hypotheses were also
combined. This new hypothesis was supported, as SOCAP was a statistically signiﬁcant
indicator of BUSRES at the p<.05 level. Finally, BUSRES was hypothesized in H5 to be
a statistically signiﬁcant predictor of DTSUC. This was not supported however.

Hypotheses 6c; 7d; 8d; 9: Brand identity (BRID) is hypothesized (6c) to be a
statistically signiﬁcant indicator of structural resources (STRURES). There was support
for H60, as the standardized parameter estimate was .565. This parameter did not produce
a t-value, as the path was ﬁxed at 1.00 to provide the metric for the latent construct of
BUSRES. It is advised that the researcher use a variable that is expected to be a
statistically signiﬁcant indicator as the ﬁxed variable when ﬁtting the structural model
(Bollen, 1989; Kline, 1998). The parameter estimate for BRID was .565, which was
larger that the .400 for business mix (BUSMDC), which had a statistically signiﬁcant
parameter estimate at the p<.05 level. In hypothesis 7d, BUSMIX was posited to be a
statistically signiﬁcant indicator of STRURES. This was supported, as the parameter
estimate was statistically signiﬁcant at the p<.05 level. Community characteristics
(COMCHAR) were hypothesized in 8d to be a statistically signiﬁcant indicator of
STRURES. This hypothesis was supported at the p<.05 level. In hypothesis 9, STRURES
is posited to be a statistically signiﬁcant predictor of DTSUC. This is supported as the
parameter estimate is statistically signiﬁcant at the p<.05 level.

Hypothesis 8b: Economic base was hypothesized to be a statistically signiﬁcant
indicator of the commrurity characteristics construct. I was unable to test this hypothesis

as written. The measure of economic base as constructed in the survey was unusable for

104

any meaningful analysis due to missing answers, as well as incorrect marking of surveys.
I re-conceptualized the hypothesis to state that there was a statistically signiﬁcant
difference in the structural model ﬁt of towns with economic bases that had high levels of
second-home owners or tourists. To test this, I used a cross-validation method in Lisrel.

I separated the data set into two groups. One group contained cases from the three
downtowns whose economic bases are dominated by second-home owners and/or
tourists. This group totaled 88 respondents. The second group consisted of the remaining
8 downtowns whose economic bases were a mixture of manufacturing, retail and other.
This group totaled 179. The alternative hypothesis for this method is that at least two
parameters from the structural model are not identical across the two groups. As opposed
to usual model ﬁtting, I was looking for a statistically signiﬁcant chi-square, with a
p<.05. I ﬁt both groups to the data, and the results were (x2 =512.l4, df=69, n=267,
p=0.00, RMSEA=.220, AGFI=1.00). The poor chi—square and the small p value allows
for rejecting the null hypothesis that the samples ﬁt the data equally. In other words, there
is a statistically signiﬁcant difference between downtowns due to economic bases, thus
supporting the re-formulated hypothesis.

Summary of Results

The results of the overall analysis were positive in nature. Most of the established
scales were found to indicate their hypothesized ﬁrst-order factors. Scales that were
constructed speciﬁcally for this study also performed well. Several scales did not perform
as well as hoped, but show promise with future reﬁnement. Though there was some
correlation of error terms between SBO and ENTREP, those factors appear to be

perceived by small business owners in downtowns as different concepts. Though SOCAP

105

and SNTIES were eventually combined due to multi-collinearity, this did not conﬂict on
a theoretical level with previous research. Social network ties has been found to be a part
of social capital (Frazier, 2000). BRID, BUSMIX and COMCHAR were all new
constructs, the scales for which were developed speciﬁcally for this study. All three
factors were statistically signiﬁcant indicators of STRURES. SOCAP loaded on both
BUSRES and STRURES in the structural model. Possible reasons for this are discussed
in Chapter 5. While two of the four proposed indicators of DTSUC were not statistically
signiﬁcant (VAC, LONG), they were important enough to model ﬁt that they were
retained for the structural model.

The measurement model (Phi matrix) exhibited good ﬁt, and allowed me to
establish that business resources and structural resources are two distinct conceptual
constructs. LOCAP did load on the SBO construct, but it was the only cross loading
measurement variable. From the cross loading of SOCAP in the structural model, it
appears that there are some underlying aspects to social capital that affect many different
types of business resources.

The overall ﬁt of the hypothesized structural model was good (see Figure
3). Structural resources were found to be a statistically signiﬁcant predictor of downtown
success. However business resources were not a statistically signiﬁcant predictor of
downtown success. What is intriguing about these ﬁndings is that the standardized
parameter estimate for BUSRES is over .500, exhibiting a moderate effect size. The t-
value for the parameter is greater than the t-value for the statistically signiﬁcant

parameter of STRURES. I will discuss possible causes for this in the conclusion section.

106

CHAPTER 5
DISCUSSION AND CONCLUSION

The goal of this study was to elucidate the existence of resources possessed by
downtowns, and how successful downtowns utilize those resources to gain competitive
advantage. To accomplish this, I conceptualized downtowns as ﬁrms and utilized
resource-based theory as a framework for proposing that: downtowns possess resources
that may or may not be unique. To the extent that they are unique and can be effectively
employed and maintained, the downtown will be successﬁrl (Porter, 1980; Wemerfelt,
1984; Barney, 1986).

Resource-based theory (also referred to as RBV: resource-based view) is often
used as a framework for explaining ﬁrm performance and holds that ﬁrms gain
competitive advantage from unique resources that they possess (Barney, 1986; Peteraf,
1993). Peteraf (1993) proposed that RBV can help a ﬁrm to understand how to sustain its
competitive advantages. She posited that resources must be heterogeneous, have ex ante
limits to competition, ex post limits to competition and be imperfectly mobile. Through
focus groups and literature search, I have attempted to identify the resources that
downtowns may possess. Though this study does not measure the extent to which any
resource meets Peteraf’s four conditions, it was with those conditions in mind that the
resources were identiﬁed.

To illustrate how the resources considered in this study meet Peteraf’s (1993) four
conditions for sustainability, the following four measures are noted. Downtowns may
have a unique mix of businesses, compared to other downtowns. This gives them

heterogeneity in comparison. To the extent that mix is optimal, they may be more

107

successful. Entrepreneurs have been identiﬁed as being both innovative and proactive
(Schumpeter, 1934). Entrepreneurs within a downtown who develop a new business
concept provide ex ante limits to competition for customers, by providing ﬁrst-mover
advantages. Small business owners who have an emotional attachment to their business
may remain in the downtown, eschewing a move to another area or town. This provides
ex post limits to competition for the downtown’s resource (the small business). Finally, a
downtown whose business owners have built up social capital within the community,
provide a resource with imperfect mobility. To move a business to another community
would require sacriﬁcing any social capital built up within the existing community.

Though every measure in this study does not meet all four of the conditions
proposed by Peteraf (1993), each was designed with the underlying theory in mind. I
posited that downtowns would possess two distinct types of resources: those derived from
business owners (business resources) and those derived from the downtown itself as well
as the community (structural resources). The model put forward in this study was
reﬂective of RBV, as it was complex in nature. I proposed a structural model with a large
number of latent constructs indicating business and structural resources (respectively) and
downtown success.
Business Resources

In this study, business owners within the downtown were hypothesized to possess
certain resources. These resources contribute to the business’s success, and then directly
as well as indirectly to the success of the downtown. I posited that business resources
were represented by four latent constructs: entrepreneurial orientation, small business

orientation, social capital and social network ties.

108

Conﬁrmatory factor analysis revealed that entrepreneurial orientation was not a
statistically signiﬁcant indicator of business resources. This seemed to be surprising, as
studies like Niehm’s (2002) found that the presence of superpreneurs in a community
added to success. Entrepreneurs bring new ideas, concepts and ways of doing business
(Miller, 1983; Covin & Slevin, 1989) and should bring those to a downtown as well.
However, it is possible that there are not a large enough number of entrepreneurs in small
downtowns to have an effect on the success of the downtown. The mean score of the
sample, for the ENTREP factor was 4.49, with a variance of .99. This meant that the
average respondent was fairly close to the middle on the 7-point scale, indicating a less-
than-high tendency towards entrepreneurship.

There may be some link here between the size of a town and the types of
entrepreneurs that locate within those towns. Schumpeter (1934) attached the term
“innovative” to entrepreneurship, and innovativeness is an important component of
entrepreneurial orientation Covin and Slevin’s (1989). Creativity and innovativeness are
similar characteristics of entrepreneurs (Lumpkin & Dess, 1996). Florida (2002) posits
that creative people tend to seek towns or cities that offer them a certain “quality of life.”
This includes cultural and ethnic diversity, found in larger cities. Perhaps the most
creative entrepreneurs locate in larger cities, as a lifestyle choice, as Florida suggests.

Over the past 20 years, many different types of retail and business locations have
emerged. Retailers can locate in strip centers, malls and fashion centers. Technologically
oriented entrepreneurs can locate in high-tech incubation centers, surrounded by other
like-minded entrepreneurs. Small downtowns have ﬁnite areas in which to expand,

causing rents to increase to the point where entrepreneurs see more potential return for

109

their investment elsewhere. Larger metropolitan areas most likely offer more
opportunities for entrepreneurs to try their ideas.

Small business orientation was found to be a statistically signiﬁcant predictor of
business resources. Previous research in this area has not sought to investigate the effects
of a small business orientation (Carland, et. al., 1984; Stewart, et. al., 1999; 2003). Rather
those studies have established that there is such a thing as a SBO. Previous studies that
have examined the effects of entrepreneurial orientation on business did not include the
concept of SBO in their framework (Cooper & Dunkelberg, 1986; Stone, 1995; Niehm,
2002). Downtowns (especially small ones) are likely to have a large number of business
owners who possess tendencies more closely associated with a small business orientation
(Carland, et. al., 1984) than with entrepreneurs.

Overall, social capital was a very important factor in this model, as it was an
indicator of both business and structural resources. However this “cross-loading” also
caused some difﬁculties with construct multi-collinearity. I believe that after reviewing
the results of this study however, the problem was that I tried to propose a framework
where social capital existed in two separate domains: between small business owners and
local consumers, and among small business owners within a downtown. The literature
clearly demonstrates that social capital manifests from groups, interaction and
relationships (Coleman, 1988; Portes & Sensenbrenner, 1993; Putnam, 1995; Tsai &
Ghoshal, 1998). It is probable that the social capital that exists between business owners
and local consumers is intertwined or at least related, to the relationships amongst
business owners. This may be due to the size of the communities in the sample, or the

nature of downtown businesses in general.

110

The smaller the community, the more likely it will be that downtown business
owners will interact socially with a signiﬁcant number of local consumers and with
fellow business owners. Business owners and their families may interact with many
others in the community at school, religious and other social functions. Within each of
the communities involved in this study, only one public high school exists. This makes it
more likely that business owners with children will interact socially with their customers
(at least during the time when the children are school-aged) at school-related functions.
Downtowns are seen by both local residents and business owners as a center point of a
community. This brings local residents to the downtown for more than just shopping, as
the downtown is often used for local parades, festivals and social gatherings. This
certainly is not the case with a mall or a strip center.

Social capital was a statistically signiﬁcant predictor of both business resources
and structural resources. As a predictor of business resources, social capital helps to
explain why business owners locate downtown and why they remain downtown. Miller
and Kim (1999) found evidence that consumer inshopping as well as community
attachment is affected by the presence of social capital. This type of “loyalty” to local
business owners may serve to establish an owner’s afﬁnity for the location, that
transcends monetary considerations. This would lend support to the contention of
Carland, et. al., (1984), that small business owners are motivated less by proﬁt growth
than by personal goals attached to their business.

Business resources were not found to be a statistically signiﬁcant predictor of
downtown success, based on the speciﬁcation of the structural model. The reasons for

this are difﬁcult to explain in light of previous research. Prior research has shown

11]

entrepreneurial orientation to be a predictor of business success (Covin & Slevin, 1989;
Niehm, 2002). Other studies have shown social capital contributes to ﬁrm success
(Frazier, 2002) as well as to community economic well-being (Putnam, 1993). Though
small business orientation has not been studied in the context of its outcomes to a ﬁrm,
ﬁom a resource-based view it should contribute to downtown success. As I
conceptualized SBO in this study, it is a set of positive characteristics. A small business
owner’s emotional attachment to his or her business may have something to do with
longevity in a downtown. A small business owner who is emotionally attached to a
business, may work harder to make that business a success, thus contributing to the
success of the downtown as a whole. Customers who patronize a small business whose
owner is enthusiastic and enjoys his or her business, may be more likely to return to that
business and thus to the downtown.

The answer to the question of why business resources were not a statistically
signiﬁcant predictor of downtown success may have more to do with measurement issues
than theoretical issues. Both business resources and structural resources exhibited high
standard parameter estimates of their paths to downtown success. Yet only structural
resources were statistically signiﬁcant at the p<.05 level. Often times this type of
occurrence is caused by collinearityamong the manifest indicators (Kline, 1998). Review
of the SBO and ENTREP data revealed that there are some aspects of each being
measured within the other respective scales. Additionally, focus group feedback
identiﬁed some entrepreneurial characteristics that were exhibited by many self-described

small business oriented persons.

112

Based on the preceding argument, it seems premature to conclude that business
resources (indicated by ENTREP, SBO and SOCAP) is not a predictor of downtown
success. Both entrepreneurial orientation and social capital have been shown to contribute
to ﬁrm success. A small business orientation scale such as the one used in this study, has
not been previously operationalized, nor has SBO been included in an investigation of
ﬁrm success. The scales themselves need some reﬁnement to eliminate redundant
measures, and reduce the collinearity between constructs. But there is some promise in
the conceptualization of business resources as being a unique set of resources for
downtowns.

Structural Resources

The structural resources construct was the second set of resources hypothesized to
predict downtown success. This construct was indicated by three latent factors: brand
identity, business mix and community characteristics. The literature on these three factors
is sparse in nature, and very little appears that involves the use of any of these constructs
as predictors of downtown success. Florida (2002) uses some community characteristics
as predictors of overall community success. However his work, like much of the work in
this area, has its focus on large metropolitan areas. Florida utilized secondary data to
support his theories. He combined several existing indices to form a new index of the
“creative class.” Though he makes an effective argument for the causal relationship
between the existence of a creative “core” and a community’s success, the relationship

could be spurious.

113

Conﬁrmatory factor analysis indicated that brand identity was a statistically
signiﬁcant indicator of structural resources. I conceptualized brand identity as the image
that a downtown creates (or possesses) that differentiates it from other downtowns or
shopping areas. The operationalization of brand identity or brand image as a measurable
construct is in the early stages of work (Coshall, 2000; Hankinson, 2004a). However, the
construct of “brand” has been the subject of much research in the past (cf. Grandi &
Grimaldi, 2003). Brand identity is considered to be a resource (Barney, 1991), and as
such can be utilized to gain competitive advantage by downtowns that have a positive
brand identity.

Tourism may be a moderating factor in this case. Focus group results pointed to a
difference in the existence of a downtown “image” between tourism-dependent
downtowns and others. Tourism-dependent downtowns spoke of the “image” that they
must maintain with potential visitors. The measures in the scale were designed to
determine if the downtown had any image, and if local business owners and city
government consistently supported it. Focus group feedback indicated that not all
downtown business owners in tourist towns, were tourist dependent. Yet, the fact that
tourism is the base for many fellow business owners could inﬂuence the results. Many
authors consider brand image to be more important for tourist-dependent towns than
others (Pearce, 1982; D’Hauteserre, 2001). If this is the case, then these downtowns are
beneﬁting from their image. Tourism may serve as a sort of catalyst that helps spur
business owners to support and maintain the downtown’s brand identity, thus helping to

present a consistent positioning statement to consumers.

114

Business mix was determined to be a statistically signiﬁcant indicator of
structural resources. The measurement indicators that made up this composite variable
included complementary businesses and diversity of ﬁrms. Both of these measures
emerge from the central place literature (Reilly, 1931; Huff, 1963; Ghosh, 1986). The
concept of retail agglomeration is based on the theory that consumers shop where there
are large numbers of diverse shops, which enables multi-purpose shopping (O’Kelly,
1981; Ghosh, 1986; Arentze, et al., 2005). Shoppers also tend to shop between similar
businesses (Bellenger & Korgaonkar, 1980; Maronick & Stiff, 1985), lending support for
the importance of complementary businesses.

Downtowns place a high level of importance on having a diverse mix of
businesses, as was articulated in my focus group interviews. Recent work by Arentze, et.
al., (2005) support this contention, as they found that different store types all contribute
to the attractiveness of a destination, even if consumers do not actually purchase from
those stores. In other words, the fact that there is variety of shopping choices at a location
is an important incentive for consumers to come downtown. The diversity of businesses
scale was constructed for this study, and is still in need of some reﬁnement. The alpha
was above the customary .500 level for acceptance, but I am not sure of the validity of
one of the measures. Variable 61 (Table 1) may have been more a measure of acceptance
of new businesses (i.e., belief in competition perhaps), than a measure of business mix
diversity. Yet the scale overall, shows promise for future research in this important, yet

under-researched area.

115

Caution should be used when interpreting the importance of complementary
businesses in this study. The measure was based on a single item, which some
respondents noted was difﬁcult to answer. Originally, I intended to use an objective
measure of complementary businesses to improve the validity of this construct. However
only 4 of the 11 downtowns provided me with an independent listing of business type.
This precluded using that measure in this study.

Community characteristics as indicated by environmental hostility, economic base
and sense of place, were a statistically signiﬁcant indicator of structural resources. While
environmental hostility, as an indicator of COMCHAR, was not statistically signiﬁcant, it
was retained in the initial model ﬁtting for theoretical reasons only. For the structural
model, COMCHAR encompassed the sense of place measures. This is an important
distinction, considering that one should be cautious when using single-item indicators for
latent variables (Bollen, 1989; Kline, 1998). However, sense of place is a construct that
appears often, in the urban planning literature (Robertson, 1999; F ilion, et. al., 2004), and
seems to be an important part of a downtown’s resources. Sense of place was a concept
suggested in many different ways during my focus group work. Sense of place is central
to Florida’s (2002) theory of the creative class. Downtowns that create and maintain a
strong sense of place that is safe and walkable, will be increasing their structural resource
base.

Structural resources overall, were a statistically signiﬁcant predictor of downtown
success. Through brand identity, business mix and community characteristics, a
downtown uses its structural resources to gain a competitive advantage. This competitive

advantage translates to overall downtown success. The importance of brand identity to

116

ﬁrms has been well established, and is now part of basic marketing strategy (McDaniel &
Gates, 2001; Levy & Weitz, 2004). Based on the ﬁndings in this study, brand is also an
important resource for downtowns. Brands in general are posited to be imperfectly
mobile, as they are difﬁcult to trade as well as copy (Wemerfelt, 1984). Brands as a
resource may be more immobile than other resources a town possesses. An example is
found among the downtowns in this study. One town is known for its “Bavarian” image,
and the downtown reﬂects this in its business mix and architecture. During focus group
interviews with business owners from this downtown, a “copycat” downtown was
discussed. The town is approximately 70 miles to the south of the original, and in a more
densely populated part of the state. Though I had heard of the town, I was unaware of its
“copycat” branding attempts. Anecdotal reports are that the downtown does not enjoy the
same level of success as the original.

Business mix is an important component of a downtown’s success. This resource
is developed through various means. Some towns report that they actively engage in
seeking out small business owners that may meet a need in their town. Others report
taking a more laissezfaire approach to development, preferring to allow the market to
predict and meet needs. Research has shown that the agglomeration of businesses has a
positive effect on customer trafﬁc (Reilly, 1931; Huff, 1963). This is because the
majority of shoppers seek to maximize shopping time through multi-purpose trips
(O’Kelly, 1981; Ghosh, 1986). Recent research has shown that multi-purpose shopping is
a driving force behind the grth of large store formats (Messinger & Narasimhan,
1997). To compete successfully with these larger stores, as well as malls and power

centers, downtowns must achieve optimal mixes of businesses. This includes the concept

117

of diversity as well as complementary businesses. The presence of magnet businesses
does not appear to inﬂuence downtown success.

Community characteristics such as population demographics, economic bases and
vitality are those over which downtowns may have little control. What a downtown can
control is the sense of place that the downtown conveys to the consumers. This may be
initiated at the DDA director’s level, but the downtown business owners as a group must
be supportive to maintain consistency. Recent research has revealed a desire for safe and
walkable downtowns (Robertson, 1999; Florida, 2002; Michigan Cool Cities. . ., 2003). A
downtown that is “inviting” to local consumers as well as visitors, draws the type of
“foot” trafﬁc that all retailers and small business owners desire. A term that is often used
in the urban planning literature when discussing downtown revitalization is “historic
preservation.” This term seldom came up in my focus group interviews. However historic
preservation may be embodied in some of the measures of brand identity. For example,
the positioning construct (P08) is indicated by measures of a town’s symbols or logo’s.
The previous example of the “Bavarian” downtown ﬁom this study can be used to further
explicate. The preservation of “Bavarian-style” architecture in that downtown helps to
maintain the consistent image of a “Bavarian” village. This is a symbol recognized by
local consumers and tourists alike.

The importance of historic preservation to both consumers and downtown
business owners may be consistency. A symbol, slogan or logo that conveys a message
about a downtown, helps to build and maintain its brand image. From this viewpoint,
historic preservation projects as well as brand building campaigns have the same types of

consistency goals.

118

Social capital was found to be an indicator of both business and structural
resources. I hypothesized that social capital would be an indicator of business resources,
but not structural resources. SOCAP was a composite variable consisting of trust,
reciprocity, frequency of interaction and density of network ties. Structural resources
were posited to be made up of brand identity, business mix and community
characteristics. Social capital may have a part in the creation of a brand identity, and the
characteristics of the community. Building a brand image involves communication of
ideas and images. Large companies do this in many different ways, using many different
media. Small companies often do not have the ﬁnancial ability to use the forms of
communication used by large companies (e.g., billboards, radio, television, catalogs,
etc.). Small businesses often rely on personal contact and word of mouth, to get out their
marketing message. By communicating their brand message to local consumers,
downtown business owners may also be building up social capital. This may present a
sort of “feedback” loop, where brand building leads to the creation of social capital,
which leads to opportunities to reinforce brand image through further communication
with local consumers.

The components of sense of place include safe and walkable downtowns, as well
as historic preservation and cultural diversity. Downtown business owners, through their
interaction with local consumers, are able to convey the positive aspects of the
downtown. By engaging in brand building, business owners build social capital and

engender trust in local consumers. By maintaining a consistent positioning message, the

downtown is a central place that provides a reliable focal point for the community. This

119

furthers the feeling of trust, and perhaps inspires reciprocity between consumers and
business owners.
Alternative models

As with any structural equation modeling effort, there are myriad alternative
models that could ﬁt the data in my study. From the theoretical framework that I put forth
in chapter 2, and following the methodology for testing structural models suggested by
Anderson and Gerbing (1988) and Yuan, et. al., (1997), I speciﬁed the CFAs and the
structural model from theory. In structural equation modeling researchers are continually
faced with the dilemma of sacriﬁcing model ﬁt for theoretical relevance. I tried to
balance these two goals in this study. It seemed equally important to achieve a good ﬁt of
the data to the theoretical model, as it was to retain the originally speciﬁed model. I
engaged in item trimming based on review of standardized residuals produced in the
Lisrel output. I re-speciﬁed model paths and co-variances based on LM tests, suggested
through modiﬁcation indices in the Lisrel output. Much of this study was exploratory in
nature; the structural resource side of my model especially so, as few scales existed to
measure the latent constructs I proposed.
Limitations of this Study

As with any research, the results of this study should be interpreted cautiously
based on several factors. First, the sample was not random but rather was a judgment
sample. This served the purpose of increasing response rates, as well as insuring
inclusion of various types of downtowns. Secondly, there were several new scales used
for this study. Some of them exhibited very high levels of reliability, while others were

disappointing. These measures need further reﬁnement before we can be conﬁdent in

120

their ability to measure the concepts for which they are intended. Thirdly, there were
some difﬁculties in using some of established scales. The trust measures exhibited large
amounts of both skewness and kurtosis.

The bi-polar statements used for several of the entrepreneurial orientation items
seemed to be difﬁcult for some respondents to comprehend. This was not a problem in
the pre—test, and was not mentioned as a limitation in the literature (Khandawalla, 1977;
Covin & Slevin, 1989). It appeared that some respondents were expecting a Likert scale,
with 1 meaning “strongly disagree” and 7 meaning “strongly agree.” Several respondents
circled the number 7, but then circled the entire statement at the opposite end of the scale.
In other words, they seemed to be saying that they “strongly agreed” with the statement at
the other end of the scale. It may be more effective to place these bi-polar scales at the
beginning of the survey in the future. However it seems rather problematic to do so, as
these semantic differential scales are rather difﬁcult to answer regardless. Difﬁcult
measures should normally be placed in the middle part of the survey, so that the
respondent has some level of commitment to the survey (Dillman, 2000; Churchill &
Iacobucci, 2002).

Finally, these data were gathered from business owners in the downtown areas of
small-to-medium sized communities. The general hypothesis of my study is that business
owners in downtowns act differently than those located in other areas of a community.
Additionally I believed that downtowns as ﬁrms act differently than other business
locations (e.g., malls, strip centers, etc.). Findings from this study should not be
generalized to other types of ﬁrms or small business owners. Large downtowns may have

different mixes of business and residential properties, and still be successful. Small

121

downtowns tend not to focus on residential concerns, as opposed to larger urban
downtowns. Large urban downtowns are concerned with attracting people to the
downtown to live, as well as to shop (Florida, 2002). The thesis is that people who live
downtown will shop downtown.

The results of the CFAs as well as the measurement model indicate that measures
designed for this study require further improvement. Scales developed for emotional
attachment, brand image, positioning, business diversity and sense of place all performed
well in this study. Scales used to measure business owner goals, complementary business,
magnet stores and vacancies did not perform well. It is not clear if magnet stores are
important to downtowns. The other scales noted here seem to address important
constructs, and should therefore be further reﬁned. The scales adapted for this study also
produced mixed results. Serious difﬁculties were encountered with the trust measures
(Tsai & Ghoshal, 1989) and the environmental hostility scales (Khandawalla, 1977).
Though both scales have been used in previous studies (Miller & Kim, 1997; Frazier,
2000), they are in need of further reﬁnement when studying downtown businesses. The
skewness encountered with the trust scales is predictable, as most people believe
themselves to be trustworthy.

Recommendations
ManagerrULlIrnplications

Downtowns and the businesses within their boundaries are important components
of a successful community. The connection between the downtown and the larger
community has been established by previous researchers and a key component of this

connection is social capital (Miller & Kim, 1999; Miller, 2001). The ﬁndings from this

122

study lend support for that, in that social capital was found to be part of the structural
resource construct that positively affects downtown success. The importance of creating,
strengthening and maintaining social capital between downtown businesses and the
community, as well as among downtown business owners should be heeded by DDA
directors as well as city governments. Though the business resources were not shown to
have a statistically signiﬁcant impact on downtown success, those resources did have
some level of positive effect. Rather than trying to attract entrepreneurs, perhaps making
a purposeful attempt to recruit business owners who have a small business orientation
may have a more positive long-term effect on the downtown. I am not sure how a DDA
might identify these types of individuals, or even if it is feasible.

Brand identity is an important part of any ﬁrm’s overall strategic focus. Although
in its early stages, research involving town-branding shows that it should be a focal point
of downtown’s overall strategy. Having an image that is both recognizable and consistent
may be an effective tool in attracting and keeping customers (local and tourist). From an
organizational perspective, the consistency component may be the most difﬁcult for
downtowns to control. One could see how the power of Wal-mart’s “always the low
price” slogan would be diluted if individual store managers decided they wanted to stock
and sell designer clothing, rather than low-cost private label products. It would likewise
be problematic for the “Bavarian-themed” downtown in this study, if a local merchant
decided to build a Mexican restaurant with southwest-inﬂuenced architecture right on
Main St. Yet a successful downtown should consist of successful small businesses, and

such small business owners would avoid such a “transgression” if it posed a threat to the

image of the downtown.

123

Business mix was the second component of structural resources that led to
downtown success. From focus group interviews, I found that some DDAs actively seek
to affect the mixture of businesses downtown. Because I did not identity which
downtowns in this study engage in that practice, I can’t determine if this had an inﬂuence
on success. However I did ﬁnd that business mix is an important part of downtown
success. Downtowns with business owners who reported an optimal mix within their
downtown, were more successful. Consumers prefer destinations that allow for multi-
purpose shopping. When shopping in downtowns this may extend to multi-purpose
“errands,” where consumers can bank, lunch and shop for a greeting card all within one
block. From these ﬁndings it seems clear that DDA directors should take a more active
role in encouraging diversity of formats in their downtowns. This may be problematic for
those directors without explicit authority to take such a role, as downtown business
owners are independent business owners who don’t like any portion of their business
controlled by others.

Community characteristics are the most nebulous of the resources hypothesized in
this study. The resource that had the most impact on structural resources was sense of
place. Business owners have little control over the aspect of sense of place such as safety,
walkability and overall ambience. Business owners control only their store and storefront.
But collectively, they may exercise considerable control over these variables. DDA
directors, business owners and local government should recognize the importance that a
sense of place holds for downtowns, in the consumers’ minds. Programs that include
historic preservation then, are important to the extent that they increase the variables that

make up a sense of place: safety, walkability, overall location ambience.

124

Future Research

 

As this study broke new ground in several research areas, the need for further
reﬁnement of theory, construct operationalization and scale development was to be
expected. Some of these reﬁnements and changes were covered in the discussion section,
but there are a few other speciﬁc items that have yet to be mentioned.

Entrepreneurial orientation was not as important to business resources as a small
business orientation. 1 suggested in the previous section, that it might be a good strategy
for a DDA to identify and recruit those with a SBO, rather than try to attract
entrepreneurs. Though Carland, et. al. (1984) ﬁrst posited 20 years ago that there was a
difference between entrepreneurs and small business owners, little has been done to fully
operationalize the construct of a small business orientation. Based on my results, this
distinction does exist, and SBO is more important to downtown success than
entrepreneurship. Future research in this area should include further reﬁnement of the
SBO scale. A second instrument, that may be even more useful to DDA directors, would
be an instrument to identify potential small business owners.

The creative class construct did not work well for this study. Florida (2002) used
objective measures from the census. It may be necessary to rethink this construct as it
pertains to small and medium-sized downtowns. The indices used by Florida (Gay,
Bohemian and Creative) may not be relevant to, and are unavailable for small towns.
These indices may not be relevant, as they reﬂect the type of people that tend to locate in
large metropolitan areas. This is the main thesis of Florida’s (2002) work; the creative
class, which includes those who are gay or “bohemian.” Though as a society we are much

more accepting of ethnic and cultural diversity, small towns are probably not the types of

125

places to which gays and “bohemians” gravitate. However to test whether the creative
class has an impact on downtowns, scales will need to be developed that measure both
the presence of these groups, as well as their perceived effects on small business.

Economic base was not be able to be measured at anything other than a
community basis, which means further attempts at measuring this at an individual
respondent level may need to be abandoned. I had hoped to measure the communities
economic base as perceived by the downtown business owners. Since focus groups had
indicated that different businesses within a downtown relied on different segments of the
customer base, it seemed that the importance of different bases might be revealed.
However too many respondents either did not answer the question, or were so far from
the correct proportion as to render their answer unusable.

Since the business mix construct is important to downtown success, a measure of
this needs to be developed for future study. Though there was a measure of business-type,
it was difﬁcult to determine the effects of certain types of businesses on downtown
success. One attempt was made by asking about magnet businesses. Future research may
inform this area if measures are devised to determine the perceived effects that certain
types of businesses have on downtowns. As an extension of this, the importance of
complementary businesses was established in this study. But further work is needed to
develop a reliable scale.

The downtowns in this study were part of moderately small to medium-sized
communities. I would not generalize the results to very small towns, towns in rural areas,
or to downtowns in urban areas. It would be interesting to extend this research to these

areas. My sense is that the ﬁndings would be similar for rural and very small towns,

126

while the ﬁndings would be different in urban areas. I would posit that the social capital
component of both business and structural resources would either diminish in importance,
or disappear completely. Urban areas are spread out over larger distances, with shoppers
and workers both commuting from far-ﬂung suburbs. The chances to interact socially
with customers would be few. A sense of place may exist in those urban downtowns, but
I think it would be less proprietary (from the residents’ view) than for small downtowns.
Finally, this research used the RBV framework to investigate downtowns. Little
empirical work has been done using RBV. To date, no one has attempted to
operationalize Peteraf’s (1993) four conditions for maintaining a competitive advantage
through RBV. Using this study as an initial step, it may be possible to test the extent to
which some of the constructs in my study meet those conditions. For example, levels of
SBO and ENTREP within a downtown may be cases of heterogeneous resources. Sense
of place and brand image may provide ex ante limits to competition, if the downtown has
a ﬁrst-mover advantage. If a downtown has empowered its DDA with some authority for
decision making, ex post limits may be realized by enforcing a consistent brand image
throughout the downtown, or helping a key business to stay downtown. The historic or
cultural preservation of a downtown, as well as social capital in general are imperfectly
mobile. To “move” these resources to another location would certainly reduce their value.
These constructs can all be measured and compared with other downtowns, to test the
extent to which they meet Peteraf’s (1993) conditions for resource-based competitive

advantage.

127

Conclusion

Focus group feedback indicated a certain frustration by DDA directors and
business owners, in their ability to inﬂuence the performance of the downtown. From this
study it is clear that there are several resources at the disposal of downtowns on which
they can capitalize (e. g., brand identity, social capital, business mix). Those resources can
be controlled to some extent, and they have been shown in this study to have a
statistically signiﬁcant effect on downtown performance.

Results of this study are encouraging, in that I have been able to investigate new
research areas within an established theoretical perspective. This research extends the
literature in several spheres of study. Speciﬁcally, it serves as an attempt to
operationalize the RBV framework, which is lacking in the extant literature. In addition,
new ground is broken by linking small business research and the study of downtowns.
Previous research has established the link between entrepreneurial orientation and ﬁrm
success, but my study is the ﬁrst to examine the link between small business orientation
and ﬁrm success. Prior research on downtowns has focused on agglomeration (diversity
of formats) and historic preservation (sense of place), or on the small businesses within
those towns. No studies have looked at the links between brand image and downtown

SUCCESS.

128

APPENDICES

129

APPENDIX A

Figures

130

 

 

 

 

LhLNﬁglriiw‘“\ Figure l (..‘LWIlLZlfl'Htlitl b'lodcl

l l l :r 1 \xx /. , __\

  

Cliliﬂﬁ‘ii ..... if“ “H, ..__.\) 1mm P \

Cm ...,

AW” lllul
PI RP 1 li3a+ ("l/ﬂ Silt) >1

  

as"???
11 1 L1 l

 

-.... N
./ \.\\

 

 
   

 
  

   

   

 

..-...“ ....__.i...... ....... £\
[ myrm l‘” 112m " N x
m ...... BUSRES )
...//<
[:33er ____ ﬂat—— “3,“ ”WM-“~— [1341+ /
[WW I But AP >/
114.: r ”5 ’ ....-.”
\ RISZIJI‘QW’N
[ FREQ F3“ . /
...- 1H.“ 7, «x ~ 1110.3 e .
L.» RELBIZ

H l Obi

 
 

17181.5("

 

‘ ........ are“ A-” 1141» A S‘N'li'll-ES

“4 /)\ ”IL
» . \,.‘

\\
6’
m}, /\g\ ...... /
H r Odi—
lW NA“? ““““ Mai“: 11611 T§§ﬂ
‘ ‘Vﬁm .._.,'.:._~__....-.;i..._..

\>
C m. If)
«AA flab-r “A“... ,,,R 1-19- i»

    

It?

  

 

 

 

 

 

Fwnrﬁfjk I 160+
"W“ it in i H--. ,3...” .......
l: ...-JiEiEL- if“ “71“ «AC/W BUSMIX «e.— 137:1: Seymjmgg ’
“/k
.i- e
r ( ramp 3% ”7‘“ \MAW/
HE... 118(13-
”HOST Lg
' 4 1183— \Z
" :- ...... (L‘C)‘vlC.‘-- . \
use .. 5...“, r trail)
{ SLEZEESE TAM/I" H m‘. i

131

 

 

 

073‘)
\v

 

045 w— SNXUV
(1741

\
iax’i‘kiip )

035 3

 

()4?

0.91

 

 
   
 

SCXfl-‘x P

 

0.33

0.76

0.97 “- COMP 0.“

0.99 . HOST . 0.10

 

(M35

 

132

“gimp 2 .\‘.u-.2.\Lw‘1': W MWL“

13:71.20. mam. . _<7 13:0: ma RMSI'i;—\:URU. .—\(‘H?l:.‘)3

   

 

 

 

”411.3% {ZN'I'REP _ Figure} Simcmm Mndci

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

>Hu’33a» SBO 4———— 1.38

0.14 0.10 ’ 033 VAC 4* 0.96

>055 A» SOCAP \ (“9”
70303 054 A» RELBIZ 4——-—- 0:71

\3 0.40 W» BRID 0’3? . / . .

0.68 0.1...
0'77 \A LONG «Am 0.98

0.86 ms» 81753.41}: 4»— (3.3.8 @
0.04

 

0.5.9 A» COMCHAR A/

 

 

 

$247,012, DF:2A, 112267, 33:0.003, RMSEA2060. AGFI:.J4

133

 

 

Figure 4 — Letter of request for DDA Director’s contact information
Dear Chamber Director,

Would you please provide me with the name and contact information for

Your Downtown Development Authority Director? If your downtown does not have a
DDA, would you please send me the name and contact number of the Downtown
Business Group or similar organization in your town? Please feel free to respond to my
email address with the information.

Sincere regards,
Rod Runyan
Doctoral Candidate

Michigan State University
East Lansing, MI

134

Figure 5 — Letter of invitation to participate in research study, sent to DDA Director
Dear DDA Director:

Would your central business district (CBD) be interested in an opportunity to learn what
its key resources are, and how to use them to develop sustainable competitive
advantages? We are launching a research project aimed at doing just that. Before going
further, we want to assure you that there will be no monetary costs to your CBD or its
members. This research is being conducted as part of a doctoral dissertation study.

We will be selecting six CBDs to participate in this research, which will involve asking
your downtown business owners to complete a survey. This is the second stage in a study
begun last spring. The ﬁrst stage involved focus groups with small business owners.
From the data we collected in the ﬁrst stage, a survey instrument was developed to
discover the resources that help make CBDs successful, and how those resources might
be nurtured to provide competitive advantages. We will provide participating CBDs with
the results of the study, as well as suggestions for identifying, creating and developing the
resources that help CBDs to be successful.

If you are interested in being one of the participants in this research project, please reply
to this email with your contact information, or that of the person whom we should contact
in your community. This project will commence within the next month, so we will be
selecting the participating CBDs during that time ﬁame.

Sincere regards,

Rod C. Runyan Patricia Huddleston
Doctoral Candidate Professor

Michigan State University Michigan State University
East Lansing, MI 48224 East Lansing, MI 48224

runyanro@msu.edu
989-837-4287

135

Figure 6 — Survey Instrument

136

 

'ﬂiisquestionnaire bpartofastudy beingeonducted by MlchlganState University. It is partofa mid-community studyof
downtown businesses to identify options that might help improve business. Please aaewer to the best of your ability. if you
have question or concerns regarding your rights as a study participant, or are dbsatisfled at any time with any aspect of thh
study, you may contact - anonymously, if you wish - Peter Vasiienko, Ph.D., Chair of the University Committee on Research
involving Human Subjects (UCHRIS) by phone: (517) 355-2180, fax: (517)432-4503, e-nutil addrm: mm or
regular mil: MOHsHﬂLEamMMl 48824. Alianswerswiilbekeptconfidentiai. Ifyouhaveanyqnestlonsabout
thhreeearch project contactPatrlcia Huddleston, Professor: 112 Human Ecology Bldg. East Lansing, MI 48824 (517)353-
9907; huddledeedu

1.1 For the following statements, we would like to know you personal feelings about your business. Please circle the number that
best represents your level of agreement or disagreement with what the statement implies. A 1 mean that you strongly dhgree
withthestatemeut. while a7 meansthat youstrongiy agree withthestatement.

1.1

I established this business this because it better ﬁt

my personal life man working for someone else

i have no plans to signiﬁcantly expand this
business in size or sales revenue.

My goals for this business are more personally
oriented than ﬁnancially oriented

Thisbusinessismyprimarysotuceofincome

My goal for this business includes expanding to
multiple (2 or more) locations

[consider this business to be an extension of
my personality ..

My goals for this business are interwoven
(interconnected) with my family's weds

i love my business

1 am emotionally attached to my business

Strongly

Disagree
1 2
l 2
l 2
i 2
l 2
l 2
l 2
l 2
i 2

Neither agree
nor disagree

3 4

3 4

3 4

3 4

3 4

3 4

3 4

3 4

3 4

5
5

6

6

6

Stronsly
7

7

7

In this section. we want to know how you think local consumers feel about you as a business owner. Please circle the number that

best describes your agreement or disagreement with the statement.

Considering lune local consumers perceive use as a business owner“ .

'I‘ltesepeuplewouldtmstmewithpersonal
information about themselves

lamconsideredtobedependablebythesepeople

These people would say that I am trustworthy

Mpeoplewouldsaylamsinwre

i can be trusted by these people to not take advantage of them

137

Stmnsly
Disagree
1 2
1 2
l 2
l 2
l 2

Neither agree
nor disagree
3 4
3 4
3 4
3 4
3 4

Strongly
Agree

2.1

 

Considering local consam’pdronizing any business

Strongly Neither agree Strongly
Disagree nor disagree Agree
Thesepeoplearegeuerailyfairindealingswithme l 2 3 4 5 6 7
lfididnotsupportthecomrnunity,thesepeople l 2 3 4 5 6 7
would stop patronizing my business
Tiuepeople wouldbewillingtodomeafavorifasked l 2 3 4 5 6 7
Wedofavorsforeschotherfromtimetotime l 2 3 4 5 6 7

These people patronize my business because 1 support the community 1 2 3 4 5 6 7

When responding to the following statements. we would like to know your perceptions. as the owner or manager ofthe business.
For each statement, please circle the number that best represents how you feel about the two opposing viewpoints.

(
In general, as the owner or manager ofthis business, I:

Pavorastrongernphasisonthe l 2 3 4 5 6 7 AstrongemphasisonRhD
marketingoftriedandtrueproducts technologicalieadenhipandorservices
innovation
Strongly favor low-risk projects 1 2 3 4 5 6 7 Strongly favor high-risk projects
(withnormalsndcertainratesofremrn) (withchancesofveryhighreuuns)
Believethatowingtothenatureofthe l 2 3 4 5 6 7 Beiievethatowingtothenattu'e
environment, it is best to explorer it of the environment. bold. wide-
viatimid, incremental behavior rangingactsarenecessaryto
achieve my ﬁrm's objectives

In tenns ofm lines ofprodacts our-services over thepastﬂveyeamyoarﬂnt:

Has introduced no new lines of l 2 3 4 5 6 7 Very many new lines of
products or services . products or services
Changesinproductorservice l 2 3 4 5 6 7 Changesinproductoreervice
lines havebeenmostly minorinnature lineshavebeeuquitedramatic
In dealing with competitors, my business:
Typically responds to actions which 1 2 3 4 5 6 7 Typically initiates actions which
competitors initiate conpetitors then respond to
Isseldomtheﬁrstbusinesstointroduce l 2 3 4 5 6 7 Isveryoftenthefirstto
new products/services, administrative introduce new products/services,
techniques. operating technologies. etc. administrative techniques,
operating technologies. etc.
Typically seeks to avoid competitive l 2 3 4 5 6 7 Typically adopts a very
clashes, preferring a “iive-and-let-live” competitive madame-competitors” posture
posture

When confronted with decision-making situations involving uncertainty, 1:
Typically adopt a cautious. “wait-and- l 2 3 4 5 6 7 Typically adopt a bold.

see” posture in order minimize the prob- agyessive posture in order
ability of making costly decisions to maximize probability of
exploiting potential opportunities

138

How would you characterize the external environment within which your business operates?

Very safe, little threat to the survival 1 2 3 4 5 6 7 Very risky. a false step can mean
and well-being of my business ‘ my business’ undoing

Rich in investment and marketing 1 2 3 4 5 6 7 Very stressful. exacting. hostile;
opportunities very hard to keep aﬂoat

An environment that my business can 1 2 3 4 5 6 7 A dominating environment in
control and manipulate to its own advan- which my business' initiatives
tags. such as a dominant ﬁrm has in an count for very little against the
industry with little coupetition and few tremendous competitive, political,
hindrances or technological forces

Mlnthissecﬁomwewanttoknowaboutyouandyourfellowbusinessownersandmanagers.

( 0
Considering your fellow downtown business owners and managers, how many of them do you talk with at least once per week?

 

Ofthmethuyounlkwithatleutonceperweehpleaseconsiderjustmeoneswithwhomyoutaikthemostfrequently.
How many days inaweekwouldyouusuallyspeakwiththesepeople?

lday__2days__ 3days__4days_ 5days__ 6days__ 7days_
How often do you attend (formal or informal) downtown business meetings?
everymonth_everyothermonth_ twiceayesr__ onceayear_never__

Pleasecirclethenumberthatbestrepresentsyourlevelofagteementordisayeemeutwithwhatthestatememimplies.Almeans
mayoumuglydhpuwimmemmmentwhika7meamMyoueuonglyagreewiman

Considering yourfeﬂow downtown business owners and managers... ‘
Strongly Neither agree Strongly

Disagree nor disagree
These people share the same ambitions and i 2 3 4 5 6 7
visions for our downtown’s future
The people like to work toward achieving downtown goals 1 2 3 4 5 6 7
These people are enthusiastic about projects 1 2 3 4 5 6 7

that beneﬁt the whole downtown

Stiilkeepinmindyomfeliowdowntownbusinessownersandmanage-sandcirclethenumberthenumbethatrepresentshow
truecrunuueyoufeeleachstatementis.Alnieansmatthesutememiseompletdyuau-uewhilea7meamthesmementis
verytrue. .

Considering your/came downtown business owners and seamen, is it true that...
Completely Neither true Completely

Untrue nor untrue True
They know each other by name 1 2 3 4 5 6 7
They talk to each other regularly about business/downtown issues 1 2 3 4 5 6 7
They see each other regularly in business/downtown situations 1 2 3 4 5 6 7
lamsimilartothesepeopleintermsof myoutlookonlife l 2 3 4 5 6 7
iamsimilartothesepeopleintermsof my likesanddislikes l 2 3 4 5 6 7

139

Completely

Untrue
i am similar to these people in terms of my business philosophy 1 2
Iamsimilartothesepeopleintermsof myvaluesandbeliefs l 2
These people would be willing to do me a favor if asked 1 2
Wedofavorsforeachotherfromtimetotime l 2
These people patronize my business because I support the community 1 2

3
3

Neither true

noruntrue
4 5
4 5
4 5
4 5
4 S

2.3 in this section we want to know your perceptions of aspects of your downtown. Please circle the

numberthat bestrepresents your level of agreementordisagreement with the statement. A l meansthat
youstrouglydkagreewiththestatemenuwhilea7meansthatyoustrongiyagreewiththestatement.

Inthensindsofloealconsunters, touristsorriritors...

Strongly
Disagree

Our downtown has a negative image
Ow downtown has an established image
Our downtown has a positive image

Downtown business owners and local government
present a consistent image of the downtown

Consumerswouldratethedowntownaresassafe
ConstunerswouldratethedowntownareaasWalkab ”

Consumers would rate the downtown as excellent in its
historic preservation

Comumers would rate the downtown as excellent in
its culttn'aily diverse opportunities
As a downtown business ownerorrnanager, Ibelieve that...

Downtown business owners have a consistent view of
the downtown’s image

Therearenotenoughdiffereutbusinessesdowntown
Themixofbusinessesinomdowntowuisoptimalforatuactingconsmners
Downtown business owners welcomenewbusineesesthatopenhere
Ourdowntownhasaverydiversemixofbusinesses

Less vacant storefronts would mean an increase in
consumers downtown. leading to increased business

The number of vacancies has a negative effect on
consumers’ perceptions of the downtown

Thenumberofvacancies is solowthatithasno
effect on downtown business

140

l

l

1

Neither agree
nor disagree

3 4

3 4

3 4

3 4

3 4

3 4

3 4

3 4

3 4

3 4

3 4

3 4

3 4

3 4

3 4

3 4

“MM“

“My.“

@6005

G

GOOQO

Completely
True

7

7

Smash

 

 

Magnet businesses an than that attract lags numbers of consumers to your downtown.

Magnet businesses are very important to our . 1 2 3 4 5 6 7
downtown's overall success

The magnet business or businesses in our downtown 1 2 3 4 5 6 7
have more drawing power than any other single business

[Is magnet business closed it would have a l 2 3 4 S 6 7
signiﬁcant. negative impact on the whole downtown

Does your downtown have magnet businesses? If yes, please write thenameornames of each in thespace provided. If youneed
morcspacefeelfreetoaddasheettothissurvey.

 

 

 

 

 

 

2.4

AWWhmMmMMWMmbﬂuﬂsmwquﬂﬁamma
starilararthatconrplsnuntyonn.

Howmanyotherdowntownhuiwssesseﬂprodwcmpmvidesuvicesthatuecomplememeyowbminess?

 

Farthsfoaowinnglsau considerasynrbolasalanlnrari(Macﬂnachﬂg¢),slogan(Watsr, WWW
arlogoﬂ'hthttsn), etc.

Strongly Neitha agree Stroneg
Disagree nor disagree Ayes
Our downtown has a symbol or symbols
readily recognized by consumers l 2 3 4 S 6 ‘7
Our symbol or symbols are distinct from 1 2 3 4 S 6 7
other downtowns that are our competitors
Our symbol or symbols are endorsed and l 2 3 4 5 6 ‘7
supported by downtown business owners
Downtown business owners and local 1 2 3 4 5 6 7

government endorse and support the same symbol(s)

Promthebusinesstypeshstedbelow,pleasechecktheonethatmostaccmatelynﬂectyowbusiness.

Type of business

__Fmancial Services __Retail: Jewelry. accessories
_Medical Services __Jletsil: Clothes. shoes
__J’ersonal Services __Retsil: Cards. gifts

_Real Estate _Retail: Housewares. Hardware
___Restaurant __Retail: Computers. electronics
_,Bar _Oallery: Art, museum. etc.

__Saleisepair Services _Other (please list)

141

2.4 In order to compare your downtown to other successful downtowns. the following information about performance is very
important. Please circle the number that best represents your estimate of the performance of both the downtown as a whole and
yourownbusiness. Al means youratetheitemaspooranda7means yourateitasexeellent.

Poor Neither poor Excellent

nor excellent
How would you describe the overall performance 1 2 3 4 5 6 7
of the downtown as a whole last year
Howwouldyoudescribetheoverall performance 1 2 3 4 5 6 7
of the downtown as a whole. relative to your
downtown’s major local competitors (e.g., mall. etc)
How would you describe the overall performance of the downtown 1 2 3 4 S 6 7
as a whole. relative to other downtowns in your region
How would you describe the overall performance 1 2 3 4 5 6 7
of your business last year
Howwouldyoudescribetheoverallperformanceof . l 2 3 4 5 6 7
your business relative to your major competitors
Howwouldyoudescribetheoverallperfornunceof l 2 3 4 5 6 7
yawbusinessrelativetootherbusinesses likeyours
in the industry

Ifyouhavebeeninyourlocationlessthantwoyeumpleaselistthetypeofstoreorproductwhichwassoldinyourlocation
immediately priortoopeningyour business.

 

Pleasenotethepercentageofeachcategorybelow.thatyoufeelmakesupyourcommrmity'seconomicbase.Pleasemahethe
amountsaddupto 100%.

Agricultural __ Manufacturing Tourism _ Secondhomers __ Other _

 

3.0
Is this a family owned business? _Yes _No How long have you owned this business? _Years
How long has this business existed? __Years How long have you been located downtown?__Years

Does your store have more than one location? Yes—No If yes. how many total units?

 

Whatistheestimstedrevenueofthestoreorbranchinthedowntownlocation?$

Inthepastﬁscalyear.whatlevelofnetprofitdidyorn'businessachiequheckonlyoneplease)
0_ l%_ 2%_ 3%_4%_5%_6%_7%_8%_9%_10%ormore
How many full-time people do you employ (including yourself)? _,part-time (including yourselt)?___
Whatisyourgender?_Male_Pemale Whatyearwereyouborn?_

What is the highest level of education you completed?
_High School graduate
Some college
_College graduate
_MS/MNMBA
__}’b.D.
Other

What ﬁeld. if any. did you work in prior to this business?

 

142

APPENDIX B

Tables

143

 

Table l. Latent Constructs and Observed Indicators: Content and Scale Reliabilities

 

Latent Factor

Entrepreneurial Orientation (ENTREP)

 

First-Order Factor

Innovativeness (INNOV) 1 to 7 Bi-polar statements

 

V21

Favor a strong emphasis
on the marketing of tried and OR
true products or services

A strong emphasis on
R&D, technological
leadership and innovation

 

 

 

V24 Has introduced no new lines Very many new lines
of products or services OR of products or services

V25 Changes in product or Changes in product or
service lines have been OR service lines have been
mostlxof a minor nature quite dramatic

Alpha .606

 

First-Order Factor

Proactiveness (PROAC) 1 to 7 Bi-polar statements

 

V26

Typically responds to actions

Typically initiates actions

 

 

 

which competitors initiate OR which competitors then
respond to

V27 Is seldom the ﬁrst business Is very often the ﬁrst to
to introduce new products/ introduce new products/
services, administrative 0R services, administrative
techniques, operating techniques, operating
technologies, etc. technologies, etc.

V28 Typically seeks to avoid Typically adopts a very
competitive clashes, OR competitive, “undo-the-
preferring a “live—and- competitors” posture
let-live” posture

Alpha .576

 

F irst-Order Factor

Risk Taking (RISK) 1 to 7 Bi—polar statements

 

 

 

 

 

V22 Strongly favor low-risk Strongly favor high-risk
projects (with normal and OR projects (with chances of
certain rates of return) Vﬂhigh return)

V23 Believe that owing to the Believe that owing to the
nature of the environment, nature of the environment,
it is best to explore it OR bold, wide-ranging acts
gradually via timid, are necessary to achieve
incremental behavior my ﬁrm’s objectives

V29 Typically adopt a cautious, Typically adopt a bold,
“wait-and-see” posture in aggressive posture in
order to minimize the OR in order to maximize the
probability of making probability of exploiting
costly decisions potential opportunities

Alpha .608

 

 

144

 

Table 1. continued. Latent Constructs and Observed Indicators

 

Latent Factor

Small Business Orientation (SBO)

 

 

 

 

 

 

 

 

 

 

 

 

 

 

First-Order Purpose and Goals (PURP) 1= Strongly disagree; 7 = strongly agree

Factor

V2 I established this business because it better ﬁt my personal life than
working for someone else.

V3 I have no plans to signiﬁcantly expand this business in size or sales
revenue

V4 My goals for this business are more personally oriented than ﬁnancially
oriented

V5 This business is my primary source of income

V6REV My goal for this business includes expanding to multiple (2 or more)
locations

Alpha .455

F irst-Order Emotional Attachment (EMOT) 1= Strongly disagree; 7 = strongly
agree

Factor

V7 I consider this business to be an extension of my personality

V8 My goals for this business are interwoven (interconnected) with my
family’s needs

V9 I love my business

V10 I am emotionally attached to my business

Alpha .706

 

 

145

 

Table 1. continued. Latent Constructs and Observed Indicators

 

Latent Factor

Social Capital (SOCAP)

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

F irst-Order Trust (TRU) 1= Strongly disagree; 7 = strongly agree

Factor

V11 These people would trust me with personal information about
themselves

V12 1 am considered to be dependable by these people

V13 These people would say that I am trustworthy

V14 These people would say I am sincere

V15 I can be trusted by these people to not take advantage of them

Alpha .947

F irst-Order Reciprocity (RECIP) 1= Strongly disagree; 7 = strongly agree

Factor

V16 These people are generally fair in dealings with me

V17 These pegale would be willing to do me a favor if asked

V18 We do favors for each other from time to time

V19 These people patronize my business because I support the community

V29 If I did not support the community, these people would stop
patronizing my business

Alpha .791

F irst-Order Shared Vision (VIS) 1= Strongly disagree; 7 = strongly agree

Factor

V36 These people share the same ambitions and visions for our downtown’s
future

V37 These people like to work toward achieving downtown goals

V38 These people are enthusiastic about projects that beneﬁt the whole
downtown

Alpha .871

 

 

146

 

 

Table 1. continued. Latent Constructs and Observed Indicators

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Latent Social Network Ties (SNTIES)

Factor

First- Density (DENS) 1: not true; 7= very true

Order

Factor

V39 They know each other by name

V40 They talk to each other regularly about business/downtown issues

V41 They see each other regularly in business/downtown situations

Alpha .867

F irst- Homophily (HOM) 1= not true; 7: very true

Order

Factor

V42 I am similar to these people in terms of my outlook on life

V43 I am similar to these people in terms of my likes and dislikes

V44 I am similar to these people in terms of my business philosophy

V45 I am similar to these people in terms of my values and beliefs

Alpha .862

F irst- Frequency of Interaction (FREQ) Ratio scales

Order

Factor

V33 Considering your fellow business owners and managers, how many of them
do you talk with at least once per week?

V34 Of those that you talk with at least once per week, please consider just the
ones with whom you talk the most frequently. How many days in a week
would you usually speak with these people? 1-2-3-4-5-6-7?

V35 How often do you attend downtown (formal or informal) business meetings:

 

every month; every other month; twice a year; once a year; never?

 

147

 

 

Table 1. continued. Latent Constructs and Observed Indicators

 

Latent Factor

Brand Identity (BRID)

 

 

 

 

 

 

 

 

 

 

 

 

 

 

F irst-Order Image (IMAGE) 1=Strongly disagree; 7=Strongly agree

Factor

V49REV Our downtown has a ngative image

V50 Our downtown has an established image

V51 Our downtown has a positive image

V52 Downtown business owners and local government present a consistent
image of the downtown

V58 Downtown business owners have a consistent view of the downtown’s
image

Alpha .847

First-Order Positioning (POS) 1=Strongly disagree; 7=Strongly agree

Factor

V75 Our downtown has a symbol or symbols readily recognized by
consumers

V76 Our symbol or symbols are distinct from other downtowns that are our
competitors

V77 Our symbol or symbols are endorsed and supported by downtown
business owners

V78 Downtown business owners and local government endorse and support
the same symbol(s)

Alpha .941

 

 

148

 

Table 1. continued. Latent Constructs and Observed Indicators

 

Latent Factor

Business Mix (BUSMIX)

 

 

 

 

 

 

 

 

 

 

 

 

 

 

First-Order Diversity (DIV) 1= Strongly disagree; 7=Strongly agree

Factor

V59REV There are not enough different businesses downtown

V60 The mix of businesses in our downtown is optimal for attracting
consumers

V61 Downtown business owners welcome new businesses that open here

V62 Our downtown has a very diverse mix of businesses

Alpha .563

F irst-Order Magnet Businesses (MAG) 1= Strongly disagree; 7=Strongly agree

Factor

V66 Magnet businesses are very important to our downtown’s overall success

V67 The magnet business or businesses in our downtown have more drawing
power than any other single business

V68 If a magnet business closed it would have a signiﬁcant, negative impact
on the whole downtown

Alpha .808

F irst-Order Complementary Businesses (COMP) Ratio scale of perceived number of
complementary businesses reported by each respondent.

Factor

V74 How many other downtown businesses sell products or services that are

 

complementary to your business?

 

149

 

Table 1. continued. Latent Constructs and Observed Indicators

 

Latent Factor

Community Characteristics (COMCHAR)

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

First-Order Environmental Hostility (HOST) 1 to 7 Bi-polar Statements

Factor

V3OREV Very safe, little threat to Very risky, a false step
the survival and well-being OR can mean my
of my business business’ undoing

V31REV Rich in investment and Very stressful, exacting,
marketing opportunities OR hostile; very hard to

keep afloat

V32 REV An environment that my A dominating
business can control and environment in which my
manipulate to its own business’ initiatives count
advantage, such as a OR for very little against the
dominant ﬁrm has in an tremendous competitive,
industry with little political, or technological
competition and few forces
hindrances

Alpha .749

First-Order Sense of Place (SENSE) 1=Strongly agree: 7=Strongly disagree

Factor

V53 Consumers would rate the downtown area as safe

V54 Consumers would rate the downtown area as “walkable”

V55 Consumers would rate the downtown as excellent in its historic
preservation

V56 Consumers would rate the downtown as excellent in its culturally
diverse opportunities

Alpha .752

V106 Economic Base of Community - Agricultural=l; Manufacturing=2;
Tourism=3; Second-homer’s=4

V107 Creative Class Jobs - Ratio scale of percentage of super-creative and
creative occupations in community

V108 Creative Class Education — Ratio scale of percentage of community that

 

holds a college degree or higher

 

150

 

Table 1. continued. Latent Constructs and Observed Indicators

 

Latent Factor

Downtown Success (DTSUC)

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

First-Order Relative Downtown Performance (RELTOWN) 1=Poor; 7=Excellent

Factor

V80 How would you describe the overall performance of the downtown as a
whole last year?

V81 How would you describe the overall performance of the downtown as a
whole, relative to your downtown’s major competitors (e.g., mall, etc.)

V82 How would you describe the overall performance of the downtown as a
whole, relative to other downtowns in your region

Alpha .907

First-Order Relative Business Performance (RELBIZ) 1=Poor; 7=Excellent

Factor

V83 How would you describe the overall performance of your business last
year

V84 How would you describe the overall performance of your business
relative to your major competitors

V85 How would you describe the overall performance of your business
relative to other businesses like yours in the industry

Alpha .873

First-Order Vacancies (VAC) 1=Strongly disagree; 7=Strongly agree

Factor

V63 Less vacant storefronts would mean an increase in the consumers
downtown, leading to increased business

V64 The number of vacancies has a negative effect on consumers’ perceptions
of the downtown

V65REV The number of vacancies is so low that it has no effect on downtown
business

Alpha .585

V95 Longevity — Ratio scale of the number of years the business has existed

 

downtown

 

151

 

Table 2. Sample Characteristics

 

 

 

 

 

 

 

 

 

 

Sample Characteristic Frequency Percentage*
Gender
Male 139 52.0
Female 120 44.9
Age
40 or less years 47 17.6
41-50 years 69 25.8
51 years and over 91 34.1
Education
High school graduate 35 13.1
Some college 73 27.3
College graduate 112 41.9
Post-graduate degree 28 10.5
Family Business
Yes 180 67.4
No 75 28.1
Years business has existed
6 or less 49 18.3
7-15 55 20.6
16-30 75 28.1
31 or more 78 29.2
Years in downtown
6 or less 74 27.7
7-15 56 21.0
16-30 66 24.7
31 or more 52 23.2
Years of current owner
6 or less 78 29.2
7-15 64 24.0
16-30 66 24.7
31 or more 14 5.2
F ull-time employees
None 27 10.1
1-2 90 33.7
3-5 54 20.2
6 or more 28 10.5
Part-time employees
None 24 9.0
1-2 86 32.2
3-5 65 24.3
6 or more 55 20.6

 

 

* Less than 100% due to missing
data

 

 

 

152

 

Table 3. Community Characteristics

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Characteristic Alma2 Brighton4 Cadillac4 Charlevoix”
Total Population 9275 670 1 10000 2994
Sex:
Female 5102 3572 (53.3%) 4774 1587 (53.0%)
(55%) (47.7%)
Male 4173 3129 (46.7%) 5226 1407 (47.0%)
(45%) (52.3%)
Race:
White 8695 6474 (96.6%) 9655 2842 (94.9%)
(93.7%) (96.6%)
Black 49 23 21 8
(0.5%) (0.3%) (0.2%) (0.3%L
Other 53 l 204 324 144
(5.8%) (3.1%) (3.2%) (4.8%)
Housing Occupancy:
Total housing units 3476 3241 4466 2096
Occupied housing units 3220 3103 (95.7%) 4118 1375 (65.6%)
(92.6%) (92.2%)
Vacant housing units 256 138 348 721
(7.4%) (4.3%) (7.8%) (34.4%)
For seasonal, recreational, or 20 13 78 644
occasional use (0.6%L (0.4%) (1.7%) (30.7%)
Educational Attainment:
High School Graduate 1862 1243 (27.1%) 2270 659
(includes equivalency) (34.6%) (35.2%) (31.8%)
Bachelor’s Degree 640 877 757 323
(11.9%) (19.1%) (11.7%) (15.6%)
Graduate or Professional 449 544 374 148
Degree (8.3%) (1 1.9%) (5.8%) (7.1%)
Industry:
Agriculture, forestry, ﬁshing 29 0 42 0
& hunting, and mining (0.7%) (0.9%)
Manufacturing 733 71 l 1 183 200
(18.2%) (19.2%) (26.4%) (15.8%)
Retail Trade 495 537 654 221
(12.3%) (14.5%) (14.6%) (17.5%)
Educational, health, and 1284 729 876 262
social services (3 l .9%) (19.7%) (19.5%) (20.7%)
Arts, entertainment, 435 342 485 132
recreation, accommodation, (10.8%) (9.2%) (10.8%) (10.4%)
and food services
Class of Worker:
Self-employed workers in 187 176 282 122
own not incorporated (4.6%) (4.7%) (6.3%) (9.6%)

business

 

 

 

 

 

153

 

Table 3. Continued - Community Characteristics

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Characteristic F rankenmuth"
Escanabal Fowlerville2 5 Lake Orion4
Total Population 13 140 2972 483 8 2715
Sex:
Female 7000 1553 (52.3%) 2680 1366 (50.3%)
(53.3%) (55.4%)
Male 6140 1419 (47.7%) 2158 1349 (49.7%)
(46.7%) (44.6%)
Race:
White 12570 (95.7%) 2862 (96.3%) 4780 2655 (97.8%)
(98.8%)
Black 14 5 13 7
(0.1%) (0.2%) (0.3%) (0.3%)
Other 556 105 45 53
(4.2%) (3.5%) (0.9%) (l .9%)
Housing Occupancy:
Total housing units 6258 1211 2240 1320
Occupied housing units 5800 1158 (95.5%) 2123 1198 (90.8%)
(92.7%) (94.8%)
Vacant housing units 458 55 117 122
(7.3%) (4.5%) (5.2%) (9.2%)
For seasonal, recreational, or 36 5 19 45
occasional use (0.6%) (0.4%) (0.8%) (3.4%)
Educational Attainment:
High School Graduate 3001 757 1100 343
(includes equivalency) (33.6%) (41.9%) (30.2%) (18.0%)
Bachelor’s Degree 1212 150 794 355
(13.6%) (8.3%) (21.8%) (18.6%)
Graduate or Professional 526 49 364 202
Degree (5.9%) (2.7%) (10%) (10.6%)
Industry:
Agriculture, forestry, ﬁshing 60 15 19 0
& hunting, and mining (0.1%) (1.1%) (0.9%)
Manufacturing 845 365 293 355
(14.6%) (26.7%) (13.6%) (21.9%)
Retail Trade 810 192 277 151
(14.0%) (14.0%) (12.9%) (9.3%)
Educational, health, and 1227 185 372 296
social services (21.2%) (13.5%) (17.3%) (18.2%)
Arts, entertainment, 821 103 208 166
recreation, accommodation, (14.2%) (7.5%) (9.7%) (10.2%)
and food services
Class of Worker:
Self-employed workers in 359 1 15 101 80
own not incorporated (6.2%) (8.4%) (4.7%) (4.9%)

business

 

 

 

 

 

154

 

Characteristic

recreational, or

ﬁshing & hunting, and

Arts,
recreation, accommod,

in own not incorporated

business

(0.4%)

3154
(24.0%)

398
(3.0%)

474
(14.2%)

11

774

(0.3%)

454
(14.4%)

(9.7%)

155

716
(1.4%) (24%)

(0.5%) (0.4%)

7
356

(17.3%) (11.2%)

258

(3.9%) (6.7%)

236

14
(0.3%)

(1.3%)

(6.6%)

(4.4%)

 

Key for Table 3

1 = DDA ofﬁce dropped off surveys and picked up
2 = DDA ofﬁce dropped off surveys and I picked up
3 = I dropped off surveys and DDA ofﬁce picked up
4 = I dropped off surveys and picked them up

5 = Focus group towns

156

Table 4. H

otheses — Business Resources Construct

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Hypothesis Variable/F actor Relationships Results
H1 a Innovativeness indicates entrepreneurial orientation supported
H1 b Risk taking indicates entrepreneurial orientation supported
H1 c Proactiveness indicates entrepreneurial orientation supported
H1 (1 Entrepreneurial orientation indicates business resources not
supported
H2 a Business owner goals indicates small business orientation not
supported
H2 b Emotional attachment indicates small business orientation supported
H2 c Small business orientation indicates business resources supported
H3 a Reciprocity indicates social capital supported
H3 b Trust indicates social capital supported
H3 c Shared vision indicates social capital supported
H3 (1 Social capital indicates business resources supported
H4 a Network density indicates social network ties supported
H4 b Frequency of interaction indicates social network ties supported
H4 c Perceived homophily indicates social network ties supported
H4 (1 Social network ties indicates business resources not
tested
H5 Business resources predict downtown success not
supported

 

 

157

 

Table 4. continued. Hypotheses — Structural Resources Construct and Downtown Success

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Hypothesis Variable/F actor Relationships Results
H 6 a Image indicates brand identity supported
H6 b Positioning indicates brand identity supported
H6 c Brand identity indicates structural resources supported
H7 a Diversity of businesses indicates business mix supported
H7 b Magnet businesses indicate business mix not
supported
H7 0 Complementary businesses indicate business mix supported
H7 (1 Business mix indicates structural resources supported
H8 a Environmental hostility indicates community characteristics not
supported
H8 b Economic base indicates community characteristics not
“PE“
H8 0 Sense of place indicates community characteristics supported
H8 (1 Community characteristics indicate structural resources supported
H9 Structural resources predict downtown success supported
H10 a Relative downtown performance indicates downtown success supported
H10 b Relative business performance indicates downtown success supported
H10 0 Vacancies indicate downtown success not
supported
H10 (1 Business longevity indicates downtown success not
supported

 

158

 

Table 5. Parameter Estimates for F irst-Order CFA - ENTREP

 

 

 

 

 

 

 

 

 

 

 

 

Path Label Parameter Standardized
Estimate t-value Estimate

V24, INNOV 1.12 1002* .656

V25, INNOV 1.45 1368* .933

V26, PROAC .84 9.26”“ .616

V27, PROAC 1.07 10.81* .721

V28, PROAC .60 5.53* .387

V23, RISK .75 5.74* .566

V29, RISK .44 4.37* .332

INNOV, PROAC .74 12.49”“

INNOV, RISK .80 6.49*

PROAC, RISK .80 6.27*

*p<.05

 

 

 

 

 

x2 =14.12, df=9, n=256,p=0.118, RMSEA=.047, AGFI=.95

 

159

 

Table 6. Parameter Estimates for First-Order CFA - SBO

 

 

 

 

 

 

 

 

 

 

Path Label Parameter Standardized
Estimate t-value Estimate
V2, PURP -.81 -3.05 .538
V5, PURP -.44 -2.73 .207
V6, PURP .01 .096 .000
V7, EMOT 1.13 8.97* .806
V8, EMOT .48 4.54* .276
V9, EMOT .71 8.34* .616
V10, EMOT 1.46 9.58* 1.05
PURP, EMOT -.65 -3.12
*p<.05

 

 

 

 

 

x2 =17.77, df=11, n=264, p=0.087, RMSEA=.048, AGFI=.95

 

160

 

Table 7. Parameter Estimates for First-Order CFA - SOCAP

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Path Label Parameter Standardized
Estimate t-value Estimate
V11, TRUST .69 12.58”“ .685
V12, TRUST .90 1827* .900
V13, TRUST .97 20.69’“ .969
V17, RECIP .36 5.59* .360
V18, RECIP .78 1292* .774
V19, RECIP .85 14.18* .848
V36, VIS .68 12.18’“ .685
V37, VIS .94 1851* .943
V38, VIS .85 16.08’“ .854
TRUST, RECIP .59 11.83*
TRUST, VIS -.03 -.43
RECIP, VIS .14 2.04
*p<.05

 

 

 

 

 

 

x2 =31.04, df=24, n=267, p=0.153, RMSEA=.033, AGFI=.95

 

161

 

Table 8. Parameter Estimates for First-Order CFA - SNTIES

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Path Label Parameter Standardized
Estimate t-value Estimate
V33, FREQ 3.38 2.65* .316
V34, FREQ .45 1.93* .314
V35, FREQ .74 2.57* .469
V39, DENS 1.08 12.63* .787
V40, DENS 1.10 14.44* .837
V41, DENS 1.15 15.65* .916
V42, HOM 1.16 13.97* .837
V43, HOM .89 11.43* .685
V44, HOM .98 13.01'“ .755
V45, HOM 1.03 13.83* .806
FREQ, DENS .30 2.55
FREQ, HOM .28 2.33
DENS, HOM .54 10.40*
*p<.05

 

 

 

 

 

x2 =36.66, df=28, n=261, p=0.126, RMSEA=.034, AGFI=.95

 

162

 

Table 9. Parameter Estimates for F irst-Order CFA - BRID

 

 

 

 

 

 

 

 

 

 

Path Label Parameter Standardized
Estimate t-value Estimate
V50, IMAGE .99 10.89* .663
V51, IMAGE 1.41 14.05* .825
V52, IMAGE 1.18 12.72* .761
V58, IMAGE .74 7.63* .510
V75, POS 1.80 18.17* .905
V76, POS 1.76 19.19* .933
V78, POS 1.40 15.20* .800
IMAGE, P08 .47 8.17*
*p<.05

 

 

 

 

 

x2 =23.54, df=12, n=259, p=0.023, RMSEA=.061, AGFI=.94

 

163

 

Table 10. Parameter Estimates for First-Order CFA - BUSMIX

 

 

 

 

 

 

 

 

 

 

Path Label Parameter Standardized
Estimate t-value Estimate
V59, DIV .59 5.03* .374
V60, DIV .70 4.78* .346
V61, DIV .56 494* .360
V62, DIV 1.64 8.03* .969
V66, MAG 1.32 13.32'“ .806
V67, MAG 1.26 13.22* .800
V68, MAG 1.14 10.83* .663
DIV, MAG .13 1.78
*p<.05

 

 

 

 

 

12 =17.03, df=l3, n=259,p=0.198, RMSEA=.O35, AGFI=.96

 

164

 

Table 11. Parameter Estimates for First-Order CFA - COMCHAR

 

 

 

 

 

 

 

 

 

 

Path Label Parameter Standardized
Estimate t-value Estimate
V30, HOST 1.05 10.46’“ .663
V31, HOST 1.03 12.07’“ .768
V32, HOST .94 11.55* .735
V53, SENSE .46 566* .374
V54, SENSE .72 7.27* .500
V55, SENSE 1.65 11.65* .985
V56, SENSE .95 8.32’“ .872
HOST, SENSE .08 1.15
*p<.05

 

 

 

 

 

xz =17.07, dﬁlZ, n=264, p=0.147, RMSEA=.O40, AGFI=.96

 

165

 

Table 12. Parameter Estimates for First-Order CF A - DTSUC

 

 

 

 

 

 

 

 

 

 

 

 

 

Path Label Parameter Standardized
Estimate t-value Estimate
V63, VAC .54 2.71* .400
V64, VAC 1.37 2.91* .916
V80, RELTOWN 1.29 17.22* .866
V81, RELTOWN 1.32 17.48* .883
V82, RELTOWN 1.32 16.12* .837
V83, RELBIZ .98 13.67* .728
V84, RELBIZ 1.10 17.86‘“ .905
V85, RELBIZ 1.06 17.24’“ .883
VAC, RELTOWN -.16 -1.85
VAC, RELBIZ .06 .85
RELTOWN, RELBIZ .48 8.95’“
*p<.05

 

 

 

 

 

x2 =25.32, df=15, n=263, p=0.046, RMSEA=.05], AGFI=.94

 

166

 

Table 13. Parameter Estimates for CF A - BUSRES

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Path Label Parameter Standardized
Estimate t-value Estimate
INNOV, ENTREP 1.00 .728
PROAC, ENTREP .76 7.91* .748
RISK, ENTREP .59 7.66* .608
PURP, SBO 1.00 .232
EMOT, SBO 2.54 2.58* .660
LOCAP, SBO 1.90 2.61* .663
FREQ, SOCAP .47 3.24* .268
DENS, SOCAP 1.00 .900
LOCAP, SOCAP .13 2.03* .663
BIZCAP, SOCAP .58 4.32* .583
ENTREP, SBO .07 1.99
ENTREP, SOCAP .21 2.33
SBO, SOCAP .04 1.37
*p<.05

 

 

 

 

 

x2 =38.06, df=23, n=267, p=0.025, RMSEA=.050, AGFI=.94

 

167

 

Table 14. Parameter Estimates for CFA - STRURES

 

 

 

 

 

 

 

 

 

 

 

Path Label Parameter Standardized
Estimate t-value Estimate

IMAGE, BRID 1.02 12.12* .848

P08, BRID .80 7.45* .479

COMP, BUSMIX .41 2.24* .164

DIV, BUSMIX .91 3.93* .818

HOST, COMCHAR .12 1.48 .104

SENSE, COMCHAR .77 2.77* .616

BRID, BUSMIX .69 3.82*

BRID, COMCHAR 1.21 2.82

BUSMIX, COMCHAR .99 2.32

*p<.05

 

 

 

 

 

x2 =12.33, df=6, n=267, p=0.055, RMSEA=.063, AGFI=.95

 

168

 

Table 15. Parameter Estimates for First-Order CFA - DTSUC

 

 

 

 

 

 

Path Label Parameter Standardized

Estimate t-value Estimate
VAC, DTSUC -.1 l -l .54 .094
RELTOWN, DTSUC 1.36 23.07* 1.00
RELBIZ, DTSUC .50 7.84* .458
LONG, DTSUC .97 .62 .038
*p<.05

 

 

 

 

 

x2 =2.33, df=2, n=267, p=0.31 l, RMSEA=.025, AGFI=.98

 

169

 

Table 16. Parameter Estimates for Measurement Model

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Path Label Parameter Standardized
Estimate t-value Estimate

INNOV, ENTREP 1.05 10.81* .742

PROAC, ENTREP .76 10.65* .728

RISK, ENTREP .58 8.61 * .574

PURP, SBO .50 3.56* .436

EMOT, SBO .30 2.57* .293

LOCAP, SBO .33 3.03* .479

LOCAP, SOCAP .14 1.91 * .479

FREQ, SOCAP .53 3.57* .298

DENS, SOCAP .89 6.09* .787

IMAGE, BRID .99 12.35* .825

POS, BRID .82 7.78'“ .490

COMP, BUSMIX .42 2.36* .170

DIV, BUSMIX .88 4.28* .794

HOST, COMCHAR .11 1.42 .099

SENSE, COMCHAR .82 2.83’“ .656

ENTREP, SBO .17 1.31

ENTREP, SOCAP .24 2.59‘“

ENTREP, BRID .00 -.01

ENTREP, BUSMIX .10 1.08

ENTREP, COMCHAR .16 1.32

SBO, SOCAP .31 1.87

SBO, BRID -.01 -.04

SBO, BUSMIX .35 2.21*

SBO, COMCHAR .04 .24

SOCAP, BRID .54 5.09*

SOCAP, BUSMIX .42 3.03*

SOCAP, COMCHAR .56 2.45’“

BRID, BUSMIX .72 4.17*

BRID, COMCHAR 1.17 2.88*

BUSMIX, COMCHAR .96 2.42*

 

*p<.05

 

 

 

 

 

x2 =74.20, df=60, n=267,p=0.103, RMSEA=.030, AGFI=.92

 

170

 

Table 17. Parameter Estimates for Structural Model

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Path Label Parameter Standardized
Estimate t-value Estimate

ENTREP, BUSRES 1.00 1.07
SBO, BUSRES 2.99 5.27’“ 1.28
SOCAP, BUSRES 4.83 2.84* .160
SOCAP, STRURES .76 4.45* .350
BRID, STRURES 1.00 .770
BUSMIX, STRURES .84 5.38* .380
COMCHAR, STRURES .83 8.86’“ .648
BUSRES, DTSUC 3.10 1.60 .220
STRURES, DTSUC 1.23 2.12* .680
DTSUC, VAC .17 .190
DTSUC, RELBIZ .44 2.09* .540
DTSUC, LONG 3.72 1.31 .130
*p<.05

 

 

 

 

 

x2 =47.02, DF=24, n=267, p=0.003, RMSEA=.O60, AGFI=.94

 

171

 

REFERENCES

Acs, 2.]. (1999), Are Small Firms Important: Their Role and Impact, Boston,
MA: Kluwer Academic Publishers.

Albert, S. and Whetten, DA. (1985), Organizational Identity: Research in

Organizational Behavior 7, Greenwich, CT: JAI Press.

 

Alon, I. (2001), The Use of Franchising by U.S.-based Retailers, Journal of Small
Business Management, 39 (2), 111-122.

Alvarez, SA. and Busenitz, L.W. (2001), “The entrepreneurship of resource-
based theory,” Journal of Management 27, 755-775.

 

American Marketing Association (1960), Marketing Deﬁnitions: A Glosgary of
Marketing Terms, American Marketing Association, Chicago: IL.

Andrews, KR. (1971), The concept of Corporate Strategy, Homewood, IL: Irwin.

Anderson, J.C. & Gerbing, D.W. (1988), Some methods for respecifying
measurement models to obtain uni-dimensional construct measurement, Journal of
Marketig Research 19, 453-460.

Arentze, T.A., Oppewal, H and Timmermans, H.J.P. (2005), A multi-purpose
shopping trip model to assess retail agglomeration effects, Journal of Ma_11<eting Researgh
£0), 109-129.

Ashworth, G]. and Voogt, H. ( 1994), Marketing and place promotion, in Gold,
J .R. and Ward, S. (eds), The Use of Publicity and Marketing) Sell Towns and Regions,
Chichester: UK: John Wiley.

Axelrod, RM. (1984), The Evolution of Cooperation, New York, NY: Basic
Books.

Bagozzi, R. and Yi, Y. (1988), On the evaluation of structural equation models,
Journal of the Agﬂemv of Marketing Science 16. 74-97.

Baird, I. and Thomas, H. (1985), Toward a contingency model of strategic risk
taking, Academy of Management Review 10, 320-343.

Barkley, D.L. (1993), Economic Adaptation: Alterniives for Nonmetropolitan
Areas Boulder, CO: Westview Press.

 

Barney, J .B. (1986), Strategic factor markets: Expectations, luck and business
strategy,” Management Science 42, 1231-1241.

172

Barney, J .B. (1991), “Firm resources and sustained competitive advantage,”
J oumal of Management 17, 99-120.

 

Barney, J .B., M. Wright & D.J. Ketchen (2001), The resource-based view of the
ﬁrm: Ten years after 1991 , J oumal of Management 27(6), 625-641.

Bates, T. (1994). Competing with the Giants Sporting Goods Business 27 (2), 78-
81.

Baumol, W. (1993), Formal entrepreneurship theory in economics: Existence and
bounds, Journal of Business Venturing 8(3), 197-211.

 

Bellenger, D.N. and Korgaonkar, P.K. ( 1980), Proﬁling the recreational shopper,”
Journal of Retailing,_v56(Fa11), 77-92.

Berg, J., Piner, K. and Frank, S. (1986), in Resource Theory: Explorations and
Applications, Foa, Converse, Torblom & Foa (eds), San Diego, CA: Academic Press.

Berry, LL. (1993), Playing fair in retailing, Arthur Anderson Retailing Issues
Letter 5(2).

Billesbach, T.J. and Walker, J. (2003), Stalking goliath: What successful small
businesses are doing against major discount chains, Journal of Business Venturing 15(2),
1-16.

Birley, S. (1985), The Role of Networks in the Entrepreneurial Process, Journal
of Business Venturing 1 (1), 107-118.

Blau, 2.8. (1961). Structural Constraints on Friendship in Old Age, American
Sociolojncal Review 26, 429-440.

Bollen, K.A. (1989). Structural equationa with latent variables. New York: John
Wiley & Sons.

Borgatti, S. (1999). What is social network analysis? [On-line].
Available: www.analytictech.com/networks/.

Borgatti, S. & Foster, P. (2003), The network paradigm in organizational
research: A review and typology,” Journal of Management 29(6), 991-1013.

Brass, D.J., Butterﬁeld, K.D. and Skaggs, B.C.(1998), Relationships and unethical

behavior: A social network perspective, The Academy of Management Review 23(1), 14-
31.

Brown, S. (1994), Retail location at the micro-scale: Inventory and prospect, lh_e
Service Industries Journal 14(4), 542-576.

173

Brush, C. (1992), Marketplace information scanning activities of new
manufacturing ventures, Jourgl of Snlaﬂ Business Management 30(4), 41-50.

Burns, DJ. (1992), Image transference and retail site selection, International
Journal of Retail and Distribution Management 20(5), 38-43.

Burt, RS. (1992), Structural Holes, Cambridge, MA: Harvard University Press.

Burt, R.S. ( 1997), The contingent value of social capital, Administrative Science
Quarterly 42, 339-365.

Buss, D. D. (1996), The little guys strike back, Nation’s Business 84(July), 18.

Carland, J .W., Hoy, F., Boulton, W.R. and Carland, J .C. (1984), Differentiating
entrepreneurs from small business owners: A Conceptualization, Academy of
Management Review 9(2), 354-359.

Carland, J .W., Carland, J .C. and Busbin, J .W. (1997), Nurturing entrepreneur-
ship: Reconsideration for competitive strategy, Advances in Competitiveness Research
5(1) 85-103.

Castanias, RP. and Helfat, CE. (2001), The managerial rents model: Theory and
empirical analysis, Journal of Ma_n_agement 27, 661-678.

Chaudhuri, A. and Holbrook, MB. (2001), The chain of effects from brand trust
and brand affect to brand performance: The role of brand loyalty, J ougal of Maglgeting
15(2), 81-94.

Christaller, W. (1935), Die Zentralen Orte in Sudlentchland, Jena, E. Germany: G.
Fischer.

Churchill, G.A. & Iacobucci, D. (2002), Marketing Regarch: Methodological
Foundations, Mason, OH: South-Westem Publishing.

Coleman, J .S. (1988), Foundations of 800311 Theory, Cambridge, MA: The
Belknap Press of Harvard University Press.

Coleman, J .S. (1990), Foundations of Social Theory. Cambridge, MA: Harvard
University Press.

Cook, AK. (1990), Retirement migration as a community development option,
Journal of the Community Development Society 21(1), 83-101.

Cooper, A.C., G.E. Willard & C.W. Woo (1986), Strategies of high-performing

new and small ﬁrms: A reexamination of the niche concept, Journal of Business
Venturing 1, 247-260.

174

Cooper, A.C. & Dunkleberg, W. (1986), Entrepreneurship and paths to business
ownership, Strategic Management Journal 7, 53-68.

Conant, J .S. and White, C. (1999), Marketing program planning, process beneﬁts
and store performance: An initial study among small retail ﬁrms, J ouraal of Retailing 75
(4), 525-541.

Coser, L. (1974), Greedy mgimtions, New York, NY: Free Press.

Coshall, J. (2000), Measurement of tourists’ images: The repertory grid approach,
Journal of Travel Research 39(1), 85-89.

Covin, J .G. and Slevin, D. P. (1989), Strategic management of small ﬁrms in
hostile and benign environments, Strategg: Management Journal 10(1), 57-75.

Cross, R., Borgatti, SP. and Parker, A. (2002), Making invisible work visible:
Using social network analysis to support strategic collaboration, California Management
Review 44(2), 25-46.

Daneels, E. (1996), Market segmentation: Normative model versus business
reality, European Journal of Marketing 30(6), 36-51.

Datta, PR. (2003), The determinants of brand loyalty, Journal of American
Academy of Business 3(1/2), 138-145.

Davidsson, P. (1989), Entrepreneurship-and aﬁer? A study of growth willingness
in small ﬁrms, J oumal of Business Venturing 4(3), 211-226.

Davis, GP. (1991), Agents without principles: The spread of the poison pill
through the intercorporate network, Administrative Science Ougterly 36, 586-613.

Dekimpe, MG. and Morrison, D.G. (1991), A modeling framework for analyzing
retail store durations, Journal of Retailing 67(1), 68-91.

Delios, A. and Beamish, P.W. (1999), Geographic scope, product diversiﬁcation,

and the corporate performance of Japanese ﬁrms, Strategic Management Journal 20 (8),
711-727.

Dhanaraj, C. and Beamish, P.W. (2003), A resource-based approach to the study
of export performance, Journal of Small Business Management 41(3), 295-311.

D’Hauteserre, A.M. (2001), Destination branding in a hostile environment,
Journal of Travel Research 39, 300-307.

Dillman, D. (2000), Mail and Telephone Surveys: The Tailored Desagi Method
(Second Edition), New York, NY: John Wiley and Sons.

175

Eaton, BC. and Lipsey, R.G. (1982), An economic theory of central places, m
Economic Journal 92(365), 56-73.

Fiorito, SS. and Greenwood, K.M. (1986), Growth stages of small apparel retail
ﬁrms, Journal of Small Business Management 24(3), 44-53.

F ilion, P., Hoernig, H., Bunting, T. and Sands, G. (2004), The successful few:
Healthy downtowns of small metropolitan regions,” Journal of the American Planning
Association 70(3), 328-343.

Fiol, GM. (2001), Revisiting an identity-based view of sustainable competitive
advantage,” Journal of Management 27, 691-699.

Flora, J.L. (1998), Social capital and commrmities of place, Rural Sociology
Q3(4), 481-506.

Florida, R. (2002), The Rise of the Creative Cla_§§,_New York, NY: Basic Books.

Frazier, B. (2000), The Inﬂuence of Network Characteristics on Information
Access, Marketing Competence, and Perceptions of Performance in Small, Rural

Businesses Unpublished doctoral dissertation, Michigan State University, East Lansing,
Michigan.

 

Frederick, M. (1993), Rural tourism and economic development, Economic
Development Quarterly 7(1), 215-224.

Fredrickson, J .W., (1986), The strategic decision process and organizational
structure, The Academy of Management Review 11(2), 280-298.

Frenzen, J. and Nakamoto, K. (1993), Structure, cooperation and the ﬂow of
market information, Journal of Consumer Research 20, 360-374.

Gasse, Y. (1982), Elaborations on the psychology of the entrepreneur, In C.A.
Kent, D.L. Sexton, and KR Vesper (Eds), Encyclopedia of Entrepreneurship,
Englewood Cliffs, NJ: Prentice Hall, 209-223.

Ghosh, A. (1986), The value of a mall and other insights from a revised central
place model, Journal of Retailing 62(1), 79-97.

Gillette, B. (1998), Smaller retailers facing plenty of marketplace challenges:
Competing with Wal-mart is only one of them, Mississippi Business J ourng 20, 12-13.

Gorodesky, R. and McCarron, E. (2003), Independent restaurant survival in a

mega-chain world, Restaurant Report, [online], www.restaurantreport.com/
features/ﬁ_megashain.html.

176

Grandi, A. and Grimaldi, R. (2003), Exploring the networking characteristics of
new venture founding teams, Small Business Economics 21(4), 329-340.

 

Granovetter, M. (1973), The strength of weak ties, American Journal of Sociology
$13), 481-510.

Granovetter, M. (1985), Economic action and social structures: The problem of
embeddedness, American Journal of Sociology 91(3), 481-510.

Greve, A. (1995). Networks and entrepreneurship: An analysis of social relations,
occupational background, and use of contacts during the establishment process
Scandinavian Management Journal 11(1), 1-24.

Gulati, R. (1995), Does familiarity breed trust? The implications of repeated ties
for contractual choice in alliances, A_cademy of Management Journal 38(1), 85-112.

Gunn, CA. (1997), Vacationscape: Developing tourist Areas, 2nd ed., Washington
DC: Taylor & Francis.

Guy, C. and Duckett, M. (2003), Small retailers in an inner city community: A
case study of Adamsdown, Cardiff, International Journal of Retail and Distribution
Management 31(8), 401-410.

 

Hankinson, G. (2001), Location branding: A study of 12 English cities, Journal
of Brand Management 9(2), 127-142.

Hankinson, G. (2004a), Relational network brands: Towards a conceptual model
of place brands, Journal of Vacation Marketing 10(2), 109-121.

Hankinson, G. (2004b), The brand images of tourism destinations: A study of the
saliency of organic images, Journal of Product & Brand Mmgement 13(1), 6-14.

Hansen, M. (1999), The search-transfer problem: The role of weak ties in sharing
knowledge across organization subunits, Administrative Science Quarterly 44(1), 82-111.

Hazel, D. (2002), Upward bound: Investment pouring into long-neglected Dallas
downtown, Shopping Centers Today (November), 27-28.

Hombeck, M. (2004), Comfort beats hip in “cool cities” poll, Detroit News April,
24, C-1.

Houston, RS. and Stanton, J. (1984), Evaluating Retail Trade Areas for
Convenience Stores, Journal of Retailing 60(1), 124-136.

Hu, L. T. and Bentler, PM. (1999), Cutoff criteria for ﬁt indexes in covariance
structure analysis: Conventional criteria versus new alternatives, Structural Equation

Modeling 6(1), 1-55.

177

Huff, D.L. (1963), A probabilistic analysis of shopping center trade areas, Land
Economics 39, 81-90.

Hunt, S. D. and Derozier, C. (2004), The normative imperatives of business and
marketing strategy: Grounding strategy in resource-advantage theory, Journal of
Business & Industrial Marketing19(1), 5-22.

Ibarra, H. (1992), Homophily and differential returns: Sex differences in network
structure access in an advertising ﬁrm, Administrative Science Ouarterly37(3), 422-447.

Ibarra, H. (1993), “Network centrality, power and innovation involvement:
Determinants of technical and administrative roles,” Academy of Ma_n_agement Journal,
v36(3), 471-501.

Irwin, M., Tolbert, C., and Lyson, T., (1997), How to build strong home towns.
American Demographics 19(2), 42-49.

Jackson. D.N. (197 7), Reliability of the Jackson Personality Inventory,”
Psychological Reports 40(2), 613-614.

J .C. Penney (2004), Positioning statement, http:// www.jcpenney.net /conp)any
/position/, retrieved ﬁ’om the World Wide Web, December 3, 2004.

 

J ulien, P. (1993), Small business as a research subject: Some reflections on
knowledge of small business and its affect on economic theory, Small Business
Economics 5(2), 157-166.

Kanter, RM. (1988), Supporting innovation and venture development in
established companies, Journal of Business Venturing 1, 47-60.

Kean, R.C., Niemeyer, S. and Miller, NJ. (1996), Competitive strategies in the
craft product retailing industry, Journal of Small Business Management 34(1), 13-23.

Kean, R.C., Gaskill, L.A., Leisritz, L., Jasper, C., Bastow-Shoop, H., Jolly, L. and
Stemquist, B. (1998), Effects of community characteristics, business environment and

competitive strategies on rural retail business performance, Journal of Smll Business
Management 36(2), 45-57.

Kershaw, S. (2004) Frog city USA: Municipal branding concept goes to the frogs
in Oregon town, Midland Daily News, (New York Times News Service), June 20, A1.

Khandwalla, RN. (1977), Some top management styles, their context and
performance, Organization and Administrative Sciences 74 43-50.

Kilby, P. (1977), Entrepreneurship and Economic Development, New York, NY:
Free Press.

178

Kline, R. B. (1998), Principles and Practices of Structural Equation Modeling.
New York, NY: Guilford Press.

Kontopoulos, K.M. (1993), The Logics of Social Structure, Cambridge, UK:
Cambridge University Press.

Lazerfeld, P. and Merton, R. (1964), Friendship as a social process, in M. Berger
(ed), Freedom and Control in Modern Society, New York, NY: Octagon.

Leisen, B. (2001), Image segmentation: The case of a tourism destination, Journal
of Services Marketing_§(l), 49-66.

Leistritz, FL. and Harnm, RR. (1994), Rural Economic Development, Westport:
CT: Greenwood Publishing Group.

 

Levy, M. and Weitz, B.W. (2004), Retailing Management, (5"1 ed.) New York,
NY: McGraw-Hill Irwin.

Litz, RA. and Stenart, A.C. (2000), The late show: The effects of aﬁemoon
accessibility on the performance of small retailers, Journal of Small Business
Managament 38(1), 1-26.

 

Long, W.L. (1983), The meaning of entrepreneurship, American Journal of Small
Business 8(2), 47-57.

Lumpkin, G.T, Hawes, J .M. and Darden, W.R. (1986), Shopping patterns of the
rural consmner: Exploring relationships between shopping orientation and outshopping,
Journal of Business Research 14, 63-81.

 

Lumpkin, GT and G. Dess (1996), Clarifying the entrepreneurial orientation
construct and linking it to performance, The Academy of Management Review 21(1), 35-
172.

MacCulltun, RC. (1995), Model speciﬁcation: Procedures, strategies, and related
issues, In R.H. Doyle (ed.) Structural Equation Modeling, Thousand Oaks, CA: Sage,
16-36.

Maronick, T.J. and Stiff, RM. (1985), The impact of a specialty retail center on
downtown shopping behavior, Academy of Marketing Science Journal 13(3), 292-306.

Marsden, P. and Campbell, K. (1984), Measuring Tie Strength, Social Forces 63
(2), 482-501.

McCarthy, M.J. (2004), Granbury, Texas isn’t a rural town: It’s a ‘micropolis,’
Wall Street Journal, June 3, A1.

179

McCune, J. (1994), In the shadow of Wal-Mart, Management Review 83( 12), 10-
12.

McDaniel, C. and Gates, R. (2001), Marketing Research Essentials (3rd Ed),
Cincinnati, OH: South-Westem Publishing.

McPherson, M., Smith-Lovin, L. and Cook, J. (2001), Birds of a feather:
Homophily in social networks, AnnuaIZReview of Sociology 27, 415-444.

Merriam-Webster (2005), Merriam-Webster Online, retrieved January 14, 2005
from http://www.m-w.com/cgi-bin/dictionary?book=Dictionary&va=trust.

Messinger, PR. and Narasimhan, C. (1997), A model of retail formats based on
consumers’ economizing on shopping time, Marketiag Science 16, (1), 1-23.

Metelka, C.J. (1981), The Dictionary of Tourism, Wheaton, IL: Merton House.

Michigan Cool Cities Initial Report (2003), Ofﬁce of the Governor, Lansing,
Michigan, December, 1-24.

Milchen, J. (2000), The beneﬁts of doing business locally, Reclaim
Democracy.org,_[online], January, retrieved on December 12, 2004 from
http://www.reclaimdemocracy.org/independent_business/local_business_beneﬁts.html.

Miller, D., and P. Friesen (1978), Archetypes of strategy formulation,
Management Science 24, 921-933.

Miller, D., (1983), The correlates of entrepreneurship in three types of ﬁrms,
Management Science 29, 770-791.

Miller, N. (2001), Contributions of social capital theory in predicting rural
community inshopping behavior, The Journal of Socio-Economics 30, 475-493.

Miller, N. and Kean, R.C. (1997a), Reciprocal exchange in rural communities:
Consumers inducement to inshop, Psychology & Marketing 14(7) 637-661.

Miller, N. and Kean, R.C. (1997b), Factors contributing to inshopping behavior
in rural trade areas: Implications for local retailers, Journal of Small Business
Management 35(2), 80-94.

Miller, N. and Kim, S. (1999), The importance of older customers to small
business survival: Evidence from rural Iowa, Joumalaof Small Business Management

3_7(4), 1-15.

Mintzberg, H. (1998), Strategy Safari: A Guided Tour Through the Wilds of
Strategic Management, New York, NY: Free Press.

180

Morris, M.H. (2002), If we build it, they will grow, Journal of Developmental
Entrepreneurship 7(4), v-vi.

 

Mulligan, G. (1984), Central place populations: Some implications of consumer
shopping behavior, Annals of the Association of American Geographers 74, 44-56.

Nahapiet, J ., and Ghoshal, M. S. (1998), Social capital, intellectual capital and the
organizational advantage, Academy of Management Review 23(2), 242-266.

 

Niehm, LS. (2002), Retail superpreneurs and their inﬂuence on small
communities, unpublished Doctoral Dissertation, Michigan State University, East
Lansing, MI.

O’Callaghan, E. and O’Riordan D. (2003), Retailing at the periphery: An analysis
of Dublin’s tertiary city center shopping streets (1972-2002), International J oumw
Retail and Distribution Management 31(8), 389-400.

O’Kelly, M. (1981), A Model of demand for retail facilities incorporating multi-
stop, multi-purpose trips, Geographical Analysis 13, 134-148.

Orton, J .D. and Weick, KB. (1990), Loosely coupled systems: A
reconceptualization, gardemy of Management Review 15, 203-223.

Ostgaard, T. and Birley, S. (1996), New venture growth and personal networks,
Journal of Business Research 36, 37-50.

Paige, RC. and Littrell, MA. (2002), Crat retailers’ criteria for success and
associated business strategies, Journal of Small Business Management 40(4), 314-331.

Pearce, PL. (1982), Perceived changes in holiday destinations, Annals of
Tourism Research 9, 145-164.

Peng, M. (2001), The resource-based view and international business, Journal of
Management 27, 803-829.

Penrose, ET. (1959), The Theory of the Growth of the Firm, New York, NY:
Wiley.

 

Peteraf, M. A. (1993), The cornerstones of competitive advantage: A resource-
based view, Strategic Management Journal 14, 179-191.

Peteraf, M. A. (1994), Commentary: The two schools of thought in resource-

based theory: Deﬁnitions and implications for research, Advances in Strategi_c
Management 10(a), 153-158.

181

Peters, M. and Brush, C. (1996), Market information scanning activities and
growth in new ventures: A comparison of service and manufacturing businesses, Journal
of Business Research 36, 81-89.

 

Pinchot, G. (1987), Innovation through intrapreneuring,” Research Management
3Q(2), 14-20.

Porter, M. (1980), Competitive strategy, New York, NY: Free Press.
Portes, A. and Sensenbrenner, J. (1993), Embeddedness and immigration: Notes
on the social determinants of economic action, American Journal of Sociology 98(6),

1320-1350.

Portes, A. (1998), Social capital: Its origins and applications in modern
sociology, Annual Review of Sociology 24, 1-24.

Putnam, RD. (1993), Making democrag work: Civic tradition_s in modern Italy,
Princeton, NJ: Princeton University Press.

Putnam, RD. (1995), Bowling alone: America’s declining social capital, Journal
of Democracy a 65-78.

 

Reilly, W. (1931), The Law of Retaia Gravitation, New York, NY: Knickerbocker
Press.

Robertson, K.A. (1999), Can small-city downtowns remain viable, Journal of the
American Planning Association 65(3), 270-283.

 

Robbins, D.K., Pantuosco, L.J., Parker, DR and Fuller, B.K. (2001), An
empirical assessment of the contribution of small business employment to US. state
economic performance, @111 Business Economics 15(4), 293-302.

Rogers, E. (1995), Diffusion of Innovations 14‘“ Ed.), New York, NY: Free Press.

Rouse, M.J. and Daellenbach, US. (1999), Rethinking research methods for the
resource-based perspective: Isolating the sources of sustainable competitive advantage,
Stratagic Management Journal 20, 487—494.

 

Rowley, T.J. (1997), Moving beyond dyadic ties: A network theory of stakeholder
inﬂuences, The Aczﬂemv of Mﬂigement Review 22(4), 887-910.

Runyan, RC. and Johnson, S. (2003), Downtown business and technology
adoption: The effects of efﬁcacy, owner characteristics and motivation, presented at the
10th Annual Conference on Recent Research in Retailing & Service Sciences, Portland,
OR (August).

182

Runyan, R.C. (2004a), Focus Group interviews with four Michigan downtown
small business owners’ groups, April and May.

Runyan, RC. and Droge, C. (2004b), A categorization of small retailer research
streams: What does it portend for future research?, working paper at Michigan State
University.

Schiller, R. (1994), Vitality and viability: Challenge to the town center,
International Journal of Retail and Distribution Management 22(6), 46-50.

Schmidt, RA. and Parker, C. (2003), Diversity in independent retailing: Barriers
and beneﬁts- the impact of gender, International Journal of Retail and Distribution
Management 31(8), 428-439.

Schmnpeter, J. (1934), The theory of economic development, Public Opinion
Quarterly 58, 358-480.

Shah, P. (1998), Who are employees’ social referents? Using a network
perspective to determine referent others, Academy of Management Journal 41(3), 249-
268.

 

Shane, S. and Venkataraman, S. (2000), The promise of entrepreneurship as a
ﬁeld of research, Academy of Management Review 25(1), 217-226.

Stewart, W.H., Carland, J .C., Carland, J .W., Watson, W.E. and Sweo R. (2003),
Entrepreneurial dispositions and goal orientations: A comparative exploration of United
States and Russian Entrepreneurs, Journal of Small Business Management 41(1), 27-46.

Stewart, W.H., Watson, W.E., Carland, J .C. and Carland, J .W. (1999), A
proclivity for entrepreneurship: A comparative study of entrepreneurs, small business
owners, and corporate managers, Journal of Business Venturing 14(20), 189-214.

Stewart, W.H., and Roth P.(2001), Risk propensity differences between
entrepreneurs and managers: A meta-analytic review, Journal of Applied Psychology
8_6_(1), 145-153.

Stone, Kenneth E. (1995) Competing with the retail giants: How to survive in the
new retail environment, New York, NY: John Wiley & Sons.

Summers, J .O. (2001), Guidelines for conducting research and publishing in
marketing: From conceptualization through the review process, Journal of the Acadenay
of Marketing Science 29(4), 405-415.

Swan, J .A. and Newell, S. (1995), The role of professional associations in
technology diffusion, Organization Studies 16(5), 847-875.

183

Thompson, AA. and A.J. Strickland (1990), Strategic Management: Concepts
and Cases, Homewood, IL: Irwin.

Tomalin, C. and Pal, J. (1994), Local authority responses to retail change: The
case for town center management, International Journal of Retail and Distribution

Management 221 6), 51-56.

Trachtenberg, J .A. (2004), To compete with book chains, some think big, T_h_e
Wall Street Journal 244(38), August 24, A1& A8.

 

Tsai, W. and Ghoshal, S. (1998), Social capital and value creation: The role of
intraﬁrm networks, Academy of Management J oum_al 41(4), 464-476.

US. Census Factﬁnder (2004) Retrieved on December 3, 2004 from
http://factﬁnder.census.gov/

Varadaraj an, PR. (1996), From the editor: Reﬂections on research and
publishing, Journal of Marketingﬂ(0ct), 3-6.

Van den Berg, L. and Braun, E. (1999), Urban competitiveness, marketing and the
need for organizing capacity, Uram Studies 36(5-6), 987-999.

Venkatraman, N. and Ramanujam, V. (1986), Measurement of business
performance in strategy research: A comparison of approaches, Apademy of Management
Review 1 1 (Oct), 801 -8 14.

Venkatraman, N. (1989a), Strategic orientation of business enterprises: The
construct, dimensionality, and measurement, Management Science 35, 942-962.

Vesper, K.H., (1980), New venture strategies, Englewood Cliffs, N.J.: Prentice-
Hall.

Walmsley, DJ. and Jenkins, J .M. (1993), Appraising images of tourism areas: An
application of personal constructs, Austglian Geographer 24(1), 1-13.

Walmsley, DJ. and Young, M. (1998), Evaluative images and tourism: The use of
personal constructs to describe the structure of destination images, Journal of Travel
Research 36, 65-69.

 

Ward, S.V. (1998), Selling places: The marketing of towns and citiea1850-2000,
London, UK: Routledge.

Ward, S.V. and Gold, J .R. (1994), Introduction, in J .R. Gold and S. Ward (eds),

Place Promotion: The Use of Publicity and Maaketing to Sell Towns and Regions,
Chichester, UK: John Wiley.

184

Wellman, B. (1988), Structural analysis: From method and metaphor to theory
and substance, in B. Wellman and SD. Berkowitz (eds), Social Networks: A Network
Approach, New York, NY: Cambridge University Press, 19-61.

Wenthe, J .F., Fredenberger, WC, and DeThomas, AR. (1988), Some empirical
evidence on store-tenant mortality rates in large regional shopping centers, Southwest
Journal of Business and Economics 6(1), 26-42.

 

Wemerfelt, B. (1984), A resource-based view of the ﬁrm, Strategic Management
Journal 5, pp171-180.

Williams, G. (1999), 2001: An Entrepreneurial Odyssey, Entrepreneur, 106-109.

 

Woo, C.Y., Cooper, A.C. and Dunkelberg, WC. (1991), The development and
interpretation of entrepreneurial typologies, Journal of Business Venturing_6(2), 93-115.

 

Wu, ET and Petroshius, SM. (1987), The halo effect in store image
management, Academy of Marketing Science Journal 15(3), 44-52.

Yuan, L.M., Bentler, RM. and Kano, Y. (1997), On averaging variables in a
conﬁrmatory factor analysis model, Behaviormetrilra 24(1), 71-83.

Zelinsky, W. (1994), Conventionland USA: The geography of a latter-day
phenomenon, Annals of the American Association of Geographers 84(1), 68-86.

 

Zenger, T. and Lawrence, B. (1989), Organizational demography: The differential
effects of age and tenure distributions on technical communication, Academy of
Maaagement Journal 32(2), 353-376.

185

 

1|"IIIIIEIIIIIIII1111'