Declining Retail Establishments: The Case of Century III Mall

Transcription

Indiana University of Pennsylvania
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Theses and Dissertations
8-2015
Declining Retail Establishments: The Case of
Century III Mall
Joseph J. Tokosh
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DECLINING RETAIL ESTABLISHMENTS: THE CASE OF CENTURY III MALL
A Thesis
Submitted to the School of Graduate Studies and Research
in Partial Fulfillment of the
Requirements for the Degree
Master of Science
Joseph J. Tokosh
August 2015
School of Graduate Studies and Research
Department of Geography and Regional Planning
We hereby approve the thesis of
Joseph J. Tokosh
Candidate for the degree of Master of Science
John E. Benhart, Jr. Ph.D., Advisor
Professor and Chair of Geography & Regional Planning
Donald D. Buckwalter, Ph.D.
Professor of Geography & Regional Planning
Joseph W. Bencloski, Ph.D.
Professor Emeritus of Geography & Regional Planning
ACCEPTED
Randy L. Martin, PhD
Dean
School of Graduate Studies and Research
ii
Title: Declining Retail Establishments: The Case of Century III Mall
Author: Joseph J. Tokosh
Thesis Chairman: Dr. John E. Benhart Jr.
Thesis Committee Members: Dr. Joseph W. Bencloski
Dr. Donald D. Buckwalter
This study will investigate the relationships between declining occupancy rates at
Century III Mall, located in West Mifflin, Pa, and the socioeconomic status of the communities
that service the mall. The service area of the mall will be determined through various trade area
analysis techniques. A statistical analysis of the neighborhoods that make up the malls service
area will indicate which socioeconomic elements are probable predictors of the mall’s decline.
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ACKNOWLEDGEMENTS
I would like to thank the Geography and Regional Planning faculty and staff for contributions
made to this thesis. In particular, Dr. John Benhart, Dr. Donald Buckwalter and Dr. Joseph
Bencloski for their help in formulating a thesis topic and working with me throughout the
process.
Also, I would like to thank the Shoe Department at Century III Mall. If I hadn’t been offered a
part time job 5 years ago, the spark for this type of research would have never taken place.
A special thanks to The Directory of Major Malls, for making mall data available and to the
Dead Malls website for support and cooperation during this research.
Further acknowledgements go to the Geography and Regional Planning class of 2015 Graduate
cohort, especially Tony Harris, who provided feedback, distractions, and support throughout this
process.
Lastly, my girlfriend Aleah, who has been supportive and inspiring during the completion of this
thesis.
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TABLE OF CONTENTS
Chapter
Page
INTRODUCTION .................................................................................................................1
I
REVIEW OF THE RELATED LITERATURE ............................................7
Deindustrialization and Economic Ramifications .........................................7
Neighborhood Decline and Poverty ...............................................................7
Trade Area Analysis ......................................................................................13
Shopping Behavior and Competition .............................................................17
II
METHODS ....................................................................................................22
Technique 1- Customer Spotting ...................................................................22
Technique 2 – Trend Surface Mapping .........................................................26
Technique 3 – Construction of Theissen Polygons........................................28
Final Service Area Evaluation .......................................................................29
Variable Identification ...................................................................................31
Difference of Means and Difference of Proportions Tests ............................35
Interpretation of Results .................................................................................37
III
RESEARCH DISCUSSION AND SUMMARY ...........................................43
Summary ........................................................................................................43
Economic Restructuring Revisited ................................................................46
Prospective Research .....................................................................................48
REFERENCES ......................................................................................................................51
APPENDICES .......................................................................................................................57
Appendix A - Spearman’s Correlation Results ..............................................57
Appendix B - Store Data for Century III Mall...............................................58
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LIST OF TABLES
Table
Page
1
Anchor Store History of Century III Mall .................................................................6
2
Survey Data Results ...................................................................................................22
3
Variables Used in Difference of Means and Difference of
Proportions Tests .......................................................................................................37
4
Difference of Means and Difference of Proportions Tests 1990
Results ........................................................................................................................38
5
Results ........................................................................................................................38
6
Results ........................................................................................................................38
7
Difference of Means and Difference of Proportions Test
Descriptive Analysis ..................................................................................................39
8
National Mean Housing Values .................................................................................41
9
Spearman’s Correlation Results.................................................................................58
10
Store Data for Century III Mall .................................................................................59
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LIST OF FIGURES
Figure
Page
1
Commons area of the mall in 1993 ............................................................................1
2
Richard Simmons in Century III Mall in 1994 ..........................................................2
3
Pep rally in Century III Mall in 1996.........................................................................2
4
Parking garage of Century III Mall in 2012...............................................................3
5
Middle area of Century III Mall in 2010 ...................................................................3
6
Slag dump in Century III ...........................................................................................4
7
Occupancy rate of Century III Mall 2005-2015 ........................................................5
8
Theissen polygon construction process .....................................................................15
9
Competition analysis as defined by Horton (1986) ...................................................17
10
Distribution of mall shoppers as whole numbers .......................................................23
11
Distribution of mall shoppers as a percentage ...........................................................23
12
Primary service area of Century III Mall ...................................................................25
13
Secondary service area of Century III Mall ...............................................................25
14
Centroid map used in trend surface analysis..............................................................27
15
Customers per households map .................................................................................27
16
Theissen polygon construction ..................................................................................28
17
Final service area of Century III Mall........................................................................30
18
Significant regions for difference of means and difference of
proportions results......................................................................................................36
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INTRODUCTION
The 1970’s brought with it a demand for a new type of retail model, a multi-stop,
multipurpose facility that shoppers can patronize in a single trip (O’Kelly 1981). Century III
Mall fit this model perfectly. This gargantuan 100 million dollar shopping complex contained
170 charter tenets at its opening, in 1979. A vast number of retail shops, mixed with eateries and
services made Century III an attractive destination for shoppers living in Pittsburgh and its
suburbs because of its prime location in West Mifflin, PA, less than 30 minutes from the city
center. During the 1990s Century III’s hallways were bustling with activity. A large number of
retail stores attracted a steady customer flow, especially during holiday seasons when the mall
was swarming with patrons. Furthermore, events such as pep rallies, concerts and talent shows
were a constant presence during the 1990s (Figures 1, 2 and 3).
Figure 1. Commons area of the mall in 1993 (https://www.flickr.com/photos/c3nostalgia/3925371788/).
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Figure 2. Richard Simmons in Century III Mall in 1994 (https://www.flickr.com/groups/c3nostalgia/pool/c3nostalgia).
Figure 3. Pep rally in Century III Mall in 1996 (https://www.flickr.com/photos/c3nostalgia/4148204255/in/pool-c3nostalgia).
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Though this mall triumphed in the 1980s and 1990s, its current circumstance is a
different story. Whole sections of the parking garage are blocked off from general public (Figure
4). It is not unusual to see more employees in the stores than customers (Figure 5). Many stores
have closed or have moved over the last decade. What is causing this mall’s steep decline? This
study hypothesizes that the neighborhoods that Century III serves (i.e., its service area) are going
through a socioeconomic decline, which is in turn triggering the mall’s decline.
Figure 4. Parking garage of Century III Mall in 2012 (www.tubecityonline.com ).
Figure 5. Middle area of Century III Mall in 2010 (http://image.frompo.com).
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Century III Mall is a three floor enclosed shopping mall located in West Mifflin, PA, a
southern suburb of Pittsburgh. When Century III opened in 1979, it was the largest enclosed
shopping mall in the greater Pittsburgh area, and the third largest in the world, with 1.2 million
square feet of retail space. Upon opening in 1979 the mall was owned and operated by the
Edward J. DeBartolo Corporation. After 1996, ownership of Century III Mall changed hands
with first Simon Property Group (1996-2013) and then Moon Beam Capital Investments LLC
(2013-present). Collaboration between the original owners and the Pittsburgh based U.S. Steel
Corporation in the 1970’s led to the mall’s development. The mall was constructed on top of a
slag (steel waste product) dump in a former industrial area of the U.S. Steel Company. Rail cars
transported this waste material from the mills of Pittsburgh to the West Mifflin site, until
eventually the slag pile grew to become an artificial mountain (Figure 6). The surface eventually
became large enough to support a shopping mall as well as other satellite stores. The Century III
Mall site was one of the first large scale brownfield developments in the United States.
Figure 6. Slag dump in Century III (http://www.atlasservices.com/projects.html).
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Figure 7. Occupancy rate of Century III Mall 2005-2015 (Directory of Major Malls).
For its first 20 years the mall thrived. At its opening Century III had over 200 retail
stores, eateries and services. In 2015, Century III has declined to the point that it only has 90
establishments and a 38% occupancy rate. A possible explanation of Century III’s decline can be
attributed to a large scale retail development in Homestead, PA in 1999 that has since expanded.
South Hills Village Mall less than five miles to the west of Century III, underwent a major
renovation as well. Other factors such as the worsening economy and retailer bankruptcies could
have also led to the mall’s decline. In 2003 the mall was 20% vacant, and by 2006 it was 30%
vacant. Currently the mall is nearly 60% vacant (Figure 7). Numerous specialty stores and well
known retail chains such as Sears, KB Toys, Steve and Barry’s, Macy’s Furniture, Old Navy,
Ritz Camera, Charlotte Ruess, Aeropostale, American Eagle, Body Central, Vitamin World,
Express, Dollar Tree, The Disney Store, Radio Shack, Payless and Lady Footlocker have closed
or moved out. The food court is down to just four eateries after the departure of Subway,
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Manchu Wok, Orange Julius, Flamers, and Mrs. Fields. Some sections of the mall have been
completely blocked off from the general public. Nearly 200,000 square feet of retail space has
been blocked off since 2004. Three anchor stores remain at Century III: Macy’s, JCPenney and
Dick’s Sporting goods (Table 1).
Table 1. Anchor Store History of Century III Mall
Anchor History: [4]
Gimbels – Eventually Separated into TJ Maxx and Marshalls
Montgomery Ward – later became Horne's
Horne's – later became Lazarus
Lazarus – later became Kaufmann's Furniture
Kaufmann's Furniture – later became Macy's Furniture Gallery
Kaufmann's – now Macy's
TJ Maxx/TJ Maxx 'n More – later became Steve & Barry's
Marshalls – later became Wickes Furniture
Wickes Furniture – now Dick's Sporting Goods
Macy's Furniture Gallery – closed January 2009
Steve & Barry's – closed January 2009
Sears – closed December 2014
Some researchers cite socioeconomic decline in the surrounding areas as having an
adverse impact on the health of malls (Parlette and Cowen 2011; Anderson 2011; Berlant 2011).
Parlette and Cowen (2011), in particular, discuss how the waning core retail function of shopping
malls can be attributed to population decline, an increase in the elderly population and lack of
retail engagement. Competition (Burns and Warren 1995), crime (Krivo and Peterson 2000) and
income (Quercia and Galster 2000; Crane 1991) have all been identified as key indicators of
socioeconomic deterioration which can be related to decline in retail performance.
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CHAPTER I
REVIEW OF THE RELATED LITERATURE
Deindustrialization, and the socioeconomic ramifications that follow, have a profound
impact on the lives of the workers and citizens who live in areas where it is occurring (Iversen
and Cusack 2000; Perry 1987). Socioeconomic impacts of deindustrialization have been found to
affect retail establishments, such as Century III Mall, because of the economic impact this
process imposes on its customer base. Deindustrialization has been found to be related to
neighborhood decline. Increases in poverty and unemployment have been termed as being a
result of deindustrialization (Strait 2001; Cevik 2003).
Deindustrialization and Economic Ramifications
America’s basic industrial sector has been waning since the 1960s. The share of
manufacturing and industrial employment in the United States has declined continuously from
the 1960s into the 21st century (Kollmeyer 2009). This phenomenon is known as
deindustrialization (Hill and Negrey 1987; Rawthorn and Ramaswamy 1997). A process of
social and economic change follows deindustrialization. Early research in this area by Fuchs
(1968) and Baumol (1967) suggest that deindustrialization is not necessarily undesirable, but it is
a natural consequence of the dynamic industry-fueled economy we once lived in.
There are researchers, backed by the evidence of modern day city socioeconomic status,
who argue that deindustrialization causes economic uncertainty (Iverson and Cusack 2000).
Specifically, Pittsburgh saw a revolutionary social (Rosenberg 1999) and economic (Perry 1987)
change following its deindustrialization process. The identity of many communities along the
Ohio, Monongahela, and Allegheny Rivers were tied to the steel industry, as well as other
associated industries. There was a drastic physical change in the appearance of these
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communities as the manufacturing facilities were closed down and eventually demolished
(Rosenberg 1999).
In addition to the social change, economic change has occurred as well. Kortiz (1991),
studied deindustrialization in two heartland cities: Pittsburgh, Pennsylvania and Buffalo, New
York. Kortiz, analyzes the two cities separately claiming that this type of research must account
for different sequencing in the destructuring and restructuring processes that follow
deindustrialization. Buffalo experienced destructuring without any attempt at restructuring,
because of its ability to wait for elites to wield whatever power they have on such restructuring
tasks. Pittsburgh, on the other hand, experienced destructuring and restructuring almost
simultaneously. Pittsburgh’s situation is evident of the resiliency in capitalists’ institutions and at
the same time, the overall difficulty of the task (Kortiz 1991). Deindustrialization has occurred at
many locations in the United States. Hill and Negrey (1987), examined the question of whether
the United States as a whole was experiencing deindustrialization. They found that the Great
Lakes region, defined by the eight states that border the lakes, was experiencing
deindustrialization, moreso than any other region in their study. This region, of which Pittsburgh
is a part, experienced manufacturing decreases, a shrinking share of the nation’s industrial
employment, and lack of compensation for job loss in manufacturing since 1979.
Pittsburgh’s economic revival has been the topic of much literature since the beginning of
the 1990s (Deitrick 1999; Sbragia 1990; Clark 1989; Beauregard, Lawless and Deitrick 1992).
Industrial regions faced many challenges restructuring their economies in the 1990s. Deitrick
(1999), analyzed economic restructuring in Pittsburgh’s economy during the post-industrial
period of the 1980s and 1990s. Success in the revitalization process during the mid to late 1980s
was due to the incorporation of new nonprofit and community organizations into the city’s
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planning efforts. The success of this model faded by the 1990s, because of a lack of longevity,
and lack of support for the tax increases required (Deitrick 1999). Beauregard et.al (1992),
conducted a comparative study of the economic revitalization processes occurring in two postindustrial cities: Pittsburgh, PA and Sheffield, England. The purpose of their study was to
develop lessons for policy makers currently undertaking tasks such as regional or economic
development, in similar cities. Similar to Deitrick’s 1999 study, this study identifies weaknesses
in Pittsburgh’s post-industrial revitalization process by identifying weaknesses in publiccommunity organizations the city included in its planning processes. This study found that
identifying national, or even global trends in capital and following suit is a better strategy for
cities to attempt revitalization, as opposed to Pittsburgh’s swift, quick handed revitalization
efforts (Beauregard, Lawless, and Deitrick, 1992).
The consequences of economic restructuring in older industrial communities have been
associated with interrelated phenomena that includes: a weak labor force, extreme poverty, high
school dropout rates and welfare dependency (Haller 2005). Other ramifications on
neighborhoods as a result of economic restructuring include boosts in unemployment (Figura
2003), and increases in gang activity and elevated crime (Hagedorn 1991). In areas, like
Pittsburgh, where industrial organization was highly centralized, unions secured living wages for
large numbers of workers with low educational attainment. With deindustrialization comes the
disappearance of industry jobs which causes the circulation of capital within these older working
class communities to diminish. A decrease in capital, in turn, impacts the local businesses,
neighborhoods, public services and property values (Haller 2005).
Redeveloping former industrial sites (i.e., brownfields) is necessary, but it may not be a
sufficient precursor to economic restoration and job creation. Brownfield redevelopment is
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complicated due to site location in weak land markets and around distressed neighborhoods.
Therefore, successful redevelopment and job creation requires substantial government
investment (Howland 2007). Pittsburgh’s South Side saw a change relatively quickly in the
aftermath of deindustrialization. A transitional time period, resulting from the loss of the steel
industry, brought commercial development to this neighborhood (Benhart and Benhart 2000).
This type of transitional shift provides a period of economic stability until the wage inequality
becomes too great. Century III Mall in West Mifflin, and Nine Mile Run in Pittsburgh are other
examples of brownfield redevelopments (De Sousa 2008). It has been suggested that these types
of economic recovery efforts bring with them little fiscal enhancement. Higher paying jobs of the
steel industry are replaced by lower paying jobs and increases in part time employment in the
lower wage trade and service sector (Beeson and Tannery 2004). Beeson et al. (2001) report an
increase in wage inequality in communities where the steel industry was once dominant. Interindustry employment shifts are regularly cited as a likely source of unemployment, poverty and
wage inequality (Katz and Murphy 1992; Bound and Freeman 1992; Bluestone and Harrison
1988).
Changes in the job market and wage earnings in the Pittsburgh region have had a direct
impact on the status of its neighborhoods and suburbs as they may provide insights into the
socioeconomic circumstances in the neighborhoods that make up Century III Mall’s market area,
and their impacts on the mall’s performance.
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Neighborhood Decline and Poverty
Socioeconomic variables such as race, income, poverty rate, education, gender, housing
situation and population have been identified by many authors as important factors in
neighborhood decline. (Crane 1991; Quercia and Galster 2000; Shulz, Zenk, Israel, Mentz and
Galea 2008; Silverman Yin and Patterson 2012; Owens 2012; Ding and Knaap 2012) Crane
(1991), found that race and living inside or outside of the city were descriptive variables of
school dropout rates and teen child bearing. As the percentage of black and Hispanic people
increase in a neighborhood, the likelihood of a youth developing a social problem increases.
Furthermore, Crane found that there is a correlation between living in the city and the rise in
probability that one of these social issues will occur.
Other scholars have studied the social and physical environments of neighborhoods in
Detroit, Michigan (Shulz, Zenk, Israel, Mentz and Galea 2008). The studies found a correlation
between percent African American and high levels of social and physical stress. They also found
that percent poverty and percent Latino were positively associated with physical stress while
neighborhood stability was positively associated with social stress. At the individual level it was
found that whites had higher perceived levels of social and physical stress compared to other
races of the same block group when controlling for age, gender and occupation variables. In
addition to socioeconomic exploration, the benefits of studying stress levels of neighborhoods
include the identification of the limits on liberal urban policies (Imbroscio 2011), environmental
contributions of pregnancy disparities (Miranda, Maxson and Edwards 2009), and qualitative
environmental health research (Scammel 2010).
Other research in neighborhood change include identifying where neighborhood
socioeconomic elements may be declining. Quercia and Galster (2000), cite several theories of
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neighborhood change that imply the existence of origins of socioeconomic decline and identify
six mechanisms for generating these origins. These mechanisms are the values attitudes and
behavior of individuals; the variation in crime and delinquency rates; forms of decision making;
a tolerance of neighborhood residence based on the actions of the neighbors; neo-classical
economics and contagion models, which also deals with neighborhood tolerance. The
establishment of these inceptions, has led to other research on the empirical exploration of
neighborhoods (Glaster, Quercia), crime (Krivo and Peterson 2000) and property values (Ding
and Knaap 2002).
Silverman, Yin and Patterson (2012) studied the residential vacancy patterns of Buffalo,
New York. Multiple regression models were used to identify significant relationships between
vacancy patterns and the socioeconomic characteristics of the neighborhoods. The results show
that the percent of vacancies increase in census tracts with high poverty rates, high renter/rental
assistance, and long term vacancies. Also, the percent of abandoned rental properties is present
in census tracts with high concentrations of African Americans, elevated poverty rates and
commercial addresses. The authors conclude that these relationships are unique to older core
cities that are experiencing population and job loss (Silverman, Yin and Patterson 2012).
Research on neighborhood decline transitions into trade area analysis. Century III’s trade
area contains neighborhoods that are going through socioeconomic decline, similar to what is
covered in neighborhood change studies. Therefore understanding the literature on trade area
analysis is important to this study.
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Trade Area Analysis
An important component of shopping mall research involves methods of identifying retail
trade areas. A trade area is a geographic area from which a retail establishment receives most of
its business. Identifying where a majority of the customer base is located is important to
understanding what is causing a mall’s decline. The classic method for determining trade areas,
called customer spotting, is defined in William Applebaum’s article Methods for Determining
Store Trade Areas, Market Penetration, and Potential Sales (1966). Customer spotting involves
surveying in-store shoppers to obtain their addresses and shopping patterns. These addresses are
then plotted on a map, which, in turn, determines the store’s trade area. A trade area can be
divided into primary, secondary and tertiary areas. These subdivisions are based on distance and
percentage of the market base (i.e. the percentage of shoppers). The primary trade area is the
core for the store, which accounts for 60-70% of its customers. The secondary zone is seen as a
transition into the tertiary area and accounts of an additional 15-25%. The tertiary area accounts
for 5-10% of the customer base (Applebaum 1966).
Other scholars have used customer spotting as a basis for in their studies. Hernandez and
Bennison (2000), study the location decisions of retail outlets. Their research finds that distance
to other shopping outlets and their customer base were important factors to consider when
deciding the site of a new retail establishment. Other studies find that shopping experience
(Grewal, Levy and Kumar 2009) (i.e. every interaction the customer has with the business,) and
customer loyalty (Allaway, Gooner, Berkowitz and Davis 2006; Howell and Rogers 2001) are
both factors that impact a retail outlets trade area. Grewal, Levy and Kumar (2009) determined
that there are several ways (i.e., price, promotion and type of merchandise) to reach out to the
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customer and provide excellent customer service. This will result in higher customer satisfaction,
and return visits.
Trade areas can also be identified and analyzed using trend surface mapping (Peterson,
1974). A trend surface is a grid in which patterns can be easily detected. Peterson, administered a
questionnaire to every third shopper at all entrances to his selected mall, asking patrons for their
zip code and home address. The gender of the patrons was also noted. The data were plotted on
an air photo and overlaid with a grid consisting of equal-sized quadrants. The number of
shoppers divided by the number of households in each quadrant was then computed to determine
the number of households that contained a shopper in each quadrant. This technique is known as
market penetration. The benefits of trend surface mapping include the establishment of gross
structural parameters of the trade area (e.g., the fundamental natural period of the area or the
gross external dimensions of the area), the identification of economically diverse portions of
trade areas, and the comparison of multiple trade areas (Peterson 1974).
Other studies use spatial modeling to analyze trade areas. Jones and Simmons investigate
three approaches to trade area analysis: Spatial monopoly, market penetration and dispersed
markets (Jones and Simmons 1990). Spatial monopoly is used in qualitative studies and is often
times a theoretically based model. Generally the five or ten minute isochrones technique is
applied. An isochrone is a line on a map connecting points relating to the same time or equal
times. This technique simply implies that the establishment’s trade area is assumed to extend
within a five or ten minute driving range and includes all households in that isochrone.
Furthermore, a more complex spatial monopoly technique can be done through the use of
Thiessen Polygons. Polygons are constructed in three steps: a) lines are drawn joining each
establishment to the adjacent establishment in an area; b) each of these inter-store lines is then
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bisected to obtain a midpoint of the line; c) each midpoint is then connected with the other
bisectors to establish a polygon, one for each store. The polygon then serves as a theoretical
service area for the store (Figure 8).
Figure 8. Theissen polygon construction process.
Market penetration is the second technique discussed by Jones and Simmons. Customer
spotting and trend surface mapping, which are discussed earlier, are the two main ways to
measure market penetration. Dispersed markets, the final technique discussed in the article is a
technique that is concerned with the life style of the customer. Depending on the location of a
store in a company chain, the products are shifted in order to better accommodate the clientele
for that area. For example, The Shoe Department store in West Mifflin, PA, has shoes geared
more towards an urban customer base (Timberland Boots, Polo Shoes and Fila Shoes). While the
Shoe Department Store in Indiana, PA, has shoes that fit the lifestyle of a rural customer base:
(Work Boots, Camouflage Realtree Shoes and Merrell Shoes).
15
Two other trade area analysis articles deal with spatial distributions and locational factors
associated with customers around shopping centers. The first of these articles used Reilly’s Law
of Retail Gravitation to examine the spatial aspects and patterns of shoppers around the Valley
Shopping Center, located in St. Charles, Illinois. This law states that competition for retail trade
between two cities is directly proportional to the product of their populations and inversely
proportional to the distance from their trade areas (Blount 1964).
Horton (1986) also conducted a quantitative analysis of locational and situational factors
pertaining to retail attractiveness in Waco, Texas. The factors included investment differentials,
locational differentials, contiguity effects, outside competition and other retail establishments.
Horton found that competition from other retailers and investment factors are very important
when attempting to explain the differences in the number of consumers attracted to a retail firm.
Initially the location of competition was defined as driving time to competitive establishments in
one of four zones surrounding the central business district. Zone one was established by
constructing two forty-five degree angles on either side of a line which originates at the central
business district and ends at the establishment. This degree measurement provides four different
zones of equal size. Zone three is the area directly opposite of zone one. Zones two and four are
directed towards periphery competitors (Horton 1986) (Figure 9).
16
Figure 9. Competition analysis as defined by Horton (1986).
Shopping Behavior and Competition
There are alternative explanations for retail decline. Retail establishments like Century
III Mall are declining for reasons other than changes in neighborhood socioeconomic status.
Competition in the form of other retail outlets is a prominent reason, followed by big box
retailers and online shopping. Websites like Amazon and eBay give customers an opportunity to
purchase almost any type of product they want. While also offering swift shipping options, the
clientele can get whatever they want without ever leaving their home. This is extremely
detrimental to traditional retailers. Product availability and convenience is something online
shopping offers that traditional mall shopping doesn’t.
Shopping behavior is an important element to the health of malls. The original retail
function of shopping malls is becoming less prominent in today’s society (Parlette and Cowen
2011; Miller 2014). An example of this can be seen at Morningside Mall, located in Toronto,
Canada. Following the trend of other suburban malls, Morningside became a drop-off location
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for parents wishing to “abandon” their children for the day, an elderly walking path and was a
hot spot for young teens to hang out. These types of activities exist because of the waning of the
core retail function of malls. The population decline of the suburbs mirrors that of the inner city
decades earlier. The demise of Morningside Mall was caused by diminishing sales and customer
patronization in surrounding areas. Also contributing to Morningside’s decline were “category
killers.” These include stores such as Toys R’ Us, Office Depot and Lowes. Their ability to kill
the competition in their consumer category is attributed to the big-box format, which Wal-Mart
pioneered. Malls were once protected from such competition by land use controls, but now this
big box idea is becoming a major problem for malls (Paulette & Cowen 2011).
Miller (2014), conducted a study at a Buenos Aires, Argentina, shopping mall, in which
he surveyed mall shoppers to understand why they were shopping there. In a city that many
called economically unstable or chaotic, a shopping mall provides an outlet to make people’s
lives livable (Anderson 2011; Berlant 2011). Miller received responses from shoppers that were
similar to what Anderson and Berlant received. Respondents of the survey said they come to the
mall when they are “Bored, have nothing else to do, or because shopping makes them feel
satisfied or more complete.” (Miller 2014)
The behavior and motives of mall shoppers change. Nicholls, Li, Kranendonk and
Roslow (2002), study the changes of shopping behavior of today’s mall patrons as opposed to
those in the 1990s. According to the authors there seems to be two different types of shoppers;
those who believe shopping is a job and those who believe shopping is a hobby. Surveys were
conducted in 1993 and 1999 to see if there was any change in shopping behavior. It should be
noted that this was a convenience sampling approach, because the survey was limited to those
people in the mall at the time the survey was given out. The survey asked basic demographic
18
questions as well as some in-depth questions about the shopping patterns of the surveyed
individuals. The following four main questions were asked. What drew people to the mall in
1993 to 1999? What were the changes in shopping destinations between 1993 and 1999? Are
there any changes in shopping patterns between 1993 and 1999? Are there any changes in mall
shopper’s purchasing behavior? Three conclusions were drawn from the survey. First, people
who lived closer to the mall were less likely to make a purchase than those who lived farther
away. Second, shoppers in groups of two or more were more likely to make a purchase than
those who were alone. Lastly, the overall frequency of mall trips declined from 1993 to 1999.
The latter could be a result of the growth of online shopping and competition from other malls
and stores.
Other types of shopping behavior include outshopping (Burns and Warren 1995), and the
psychological processes that consumers go through when making purchases (Howell and Rogers
2001). Outshoppers are those who bypass shopping outlets and malls which are closer to their
homes for other malls which are further away. Burns and Warren, found that dissatisfaction with
the products available locally has led to higher levels of outshopping. Furthermore, the
availability of other, more attractive, shopping alternatives outside of one’s primary shopping
area has also led to an increase in outshopping (Burns and Warren 1995).
Roy Howell and Jerry Rogers (2001) explored issues relevant to research into shopping
mall choice behavior in a medium sized metropolitan statistical area (MSA). Two hundred sixty
middle to upper class women were surveyed and asked to rate the adequacy of two enclosed
shopping malls in the area as well as the downtown area. (It should be noted that the
respondents were asked to rate the attributes of the shopping centers and to report their patronage
only in the context of women’s clothes for themselves or family members). This study is unique,
19
because shopping situation has not been specified in other research on shopping behavior.
Shopping situation is the psychological processes that consumers go through when making
purchasing decisions. This study is concerned with 1) the image components of shopping malls,
2) the measurement of patronage behavior and 3) concerns over aggregation. Four measures of
patronage were combined as a standardized item. They included types of purchases, frequency of
visits, dollars spent, and number of weeks since last visit (as reported by respondents). A
regression analysis was conducted, comparing each of the three shopping centers for patronage
and preference. Atmosphere, personnel, fashion, advertising, convenience, proximity,
accessibility were the predictors of patronage and preference. Results indicated that accessibility
is the best indicator of preference and patronage for all three establishments. Atmosphere and
fashion were also high indicators of preference. Convenience and proximity to other outlets were
low indicators of preference for all three locations. Furthermore, the study found that situation
and preference both must be addressed in order for a mall choice study to prove fruitful.
There are two very different arguments suggested in the literature that may be responsible
for mall decline, ‘neighborhood decline and poverty’ and ‘shopping behavior and competition.’
For the purpose of this study neighborhood decline and poverty will be analyzed with respect to
its impact on Century III Mall’s service area. There are two reasons why this study will analyze
neighborhood decline and poverty as opposed to shopping behavior and competition. First is that
socioeconomic deterioration in the neighborhoods that surround the mall directly impacts
potential mall shoppers. Secondly, time and resources only allowed for a thorough survey of one
mall, Century III Mall. Therefore, a hypothesis was constructed accordingly. Additional time and
assets would have allowed for a detailed survey of multiple malls, which in turn would have
20
allowed investigation into shopping behavior and competition. This will be discussed further in
the prospective research section of the paper.
21
CHAPTER II
METHODS
The three techniques used to establish Century III’s service area included customer
spotting (Applebaum 1966), trend surface mapping (Peterson 1974) and spatial monopoly (Jones
and Simmons 1990). These methods were utilized as follows.
Technique 1 - Customer Spotting
Over a four day period (June 18th 2014, June 29th 2014, July 5th 2014 and July 11th 2014),
mall patrons were asked for their zip codes, but gender and race of the 387 respondents were also
noted. The following is a detailed breakdown of the respondents (Table 2). Of the respondents
186, were white (48.1%), 195 were black (50.4%) and five were Asian (1.3%). There were 138
males (35.7%) and 249 females (64.3%). In terms of gender and race together there were 79
white males (20.4%), 107 white females (27.6%), 57 black males (14.7%), 138 black females
(35.7%), one Asian male (.3%), four Asian females (1%), and one Hispanic male (.3%). This
customer data will be used to aid in the establishment of Century III’s service area. Figures 10
and 11 show the spatial distribution of mall shoppers, according to the survey.
Table 2. Survey Data Results
Gender/Race
White Males
White Females
Black Males
Black Females
Asian Males
Asian Females
Hispanic Males
#
79
107
57
138
1
4
1
Race
White
Black
Asian
Other
Total
Gender
Male
Female
22
#
186
195
5
1
387
#
138
249
Figure 10. Distribution of mall shoppers as whole numbers.
Figure 11. Distribution of mall shoppers as a percentage.
23
The data were plotted on a zip code area map. Outlines were then drawn to identify the
primary and secondary service areas. The primary service area (red shading) included 60 percent
of the customers, while the secondary service area (orange shading) included 20 percent of the
customers
Figures 12 and 13 show the areal extent of the mall service area as defined by customer
spotting. The primary service area map shows that all the zip code areas included in this area are
in close proximity to Century III Mall. When more areas are added, such as the additional 20%
of shoppers for the secondary service area map, two changes occur. First the service area
becomes less contiguous and less compact. The additional zip code areas make the mall service
area less uniform in the secondary service area map. Secondly, as the additional 20% of
customers are added, the service area gets larger. It should be noted that this technique was given
the most weight when determining the final service area of the mall.
24
Figure 12. Primary service area of Century III Mall.
Figure 13. Secondary service area of Century III Mall.
25
Technique 2 – Trend Surface Mapping
Trend surface mapping is a quantitative technique that uses statistical and cartographic
techniques to determine the extent of a trade area. First, the data obtained from the customer
survey was plotted on a map that was divided into equal size grid squares. The size of the
squares was determined by the following formula: 2A/N, where A is the overall size of the study
area (423 sq. miles) and N is the number of individuals surveyed (387 people). This resulted in a
square size of 2.2 square miles. The number of shoppers divided by the number of households in
each square was then computed. The result was the number of shoppers per household in each
quadrant. One issue that needed addressed was how the customer data was going to be
transferred from the zip code areas into the individual squares. This problem was resolved by
establishing a centroid in the middle of the zip code areas. Then, when the grid was overlaid,
whichever square contained a centroid was assigned a value for the household and shopper data
of that zip code. It should be noted that some squares were assigned values from multiple zip
code areas (Figure 14). The final output is a map showing shoppers per household in each square
(Figure 15).
26
Figure 14. Centroid map used in trend surface analysis.
Figure 15. Customers per households map.
27
Technique 3 – The Construction of Theissen Polygons
The final technique used to establish the Century III trade area is called “spatial
monopoly,” and involves the construction of Theissen polygons. The resulting polygons identify
a theoretical trade area, and are constructed as follows. First, all six major regional shopping
malls in the Pittsburgh metropolitan area were located on a map. Secondly, lines were drawn
between each mall and an adjacent mall (Figure 5). Each of these inter-store lines is then
bisected to obtain a midpoint of the line. Finally, each midpoint is then connected to the other
midpoints to establish a polygon, one for each mall. The polygons then serve as a theoretical
trade areas for the malls. The blue star represents Century III Mall, while the yellow dots
represent the five other Pittsburgh Regional Shopping Malls. The red lines are the end results
from the polygon construction technique (Figure 16).
Figure 16. Theissen polygon construction (Google Maps).
28
Final Service Area Evaluation
By using a combination of three techniques, customer spotting (Applebaum), trend
surface mapping (Peterson), and spatial monopoly (Jones and Simmons) the final service area for
Century III Mall was determined (Figure 17). The most weight in the final analysis was given to
Applebaum’s customer spotting method, because of the nature of my data. This technique gives a
more precise approximation of the mall’s service area, because it is based on the actual customer
data from my survey. Trend surface mapping approximates the service area by replacing the zip
code boundaries with grid squares. This method is not as precise as customer spotting, but can
still show generalities of the data. Such as an approximation of where customers are located.
Trend surface mapping would provide more exact results if the data gathered was more precise
(such as addresses as opposed to relative zip codes). Finally the Theissen polygon technique is a
qualitative way of showing a malls service area. This technique was given less weight because it
is not based on any data and only shows a theoretical service area for the mall.
29
Figure 17. Final service area of Century III Mall.
Keeping the mall service area completely contiguous was essential, because accounting
for any noncontiguous areas would skew the results of my statistical tests. Any areas that were
not contiguous were eliminated. Also, counting as many customers as possible, while keeping
the amount of zip codes included as small as possible was also essential. County boundaries and
physical elements (topography, streams, rivers, etc.) were not included in the determination of
the mall’s final service area. The final service area for Century III is comprised of 43
neighborhoods, 42 in Allegheny County and one in Westmoreland County (indicated by red on
figure 17). This area accounts for 294 of the 387 people who were surveyed (76%).
30
Variable Identification
Previous research on neighborhood change and socioeconomic decline identify variables
that could help link neighborhood alterations with external factors, such as declining retail
performance. Based on previous research, nine variables, from three separate time snapshots
(1990, 2000, and 2010) have been identified that are considered to be possible explanatory
variables of neighborhood change. The years were selected based on availability of data and the
timeline of Century III’s decline, which started in the late 1990s. The variables were identified as
follows.
Variable 1- Proportion of the Population with a High School Education
Numerous studies have used educational attainment as a variable for their models.
Galster & Quercia (1993) found that education attainment was an explanatory variable in the
identification of neighborhood socioeconomic decline. Crane (1991) used education attainment
as a variable to explain high levels of school dropout rates and high levels of teen child bearing
rates. Schulz et. al. (2008) recognized education as a variable to explain levels of social and
physical stress.
Variable 2- Proportion of Population with Income Above $35,000
Throughout the literature, income has been measured both as an average as well as on a
proportional scale. Crane (1991) used median income levels as one of the variables in his model,
attempting to relate teen child bearing and school dropout rates to neighborhood stress levels.
Galster & Quercia (1993) used the proportion of people with an income above $30,000 in their
study of identifying neighborhood decline. Schulz et al. (2008) used the proportion of people
with an income above $35,000 for their study on neighborhood stress levels. Silverman et al.
(2012) used median income and associated it with vacancy patterns in Buffalo, New York,
31
however their study used median income in three different ways: Total median income, white
median income & black median income. Silverman et. al’s, study on vacancy rates in Buffalo
used income grounded in race (i.e., mean black income and mean white income) to study
vacancy patterns in a major city. It is essential to treat income and race as separate
socioeconomic characteristics, because this study involves identifying specific variables that may
be indicators of mall decline.
Variable 3 - Mean Age of the Population
Literature on socioeconomic decline in neighborhoods identifies mean age as an
important variable for studies on: neighborhood stress levels (Schulz et al. 2008) and teen child
bearing and school dropout rates (Crane 1991). Additional studies have used age grounded in
gender (i.e. proportion of the population between the ages of 10 and 64 who are either male or
female (Galster and Mincy 1993)). Other scholars have discovered that targeting specific age
groups with senior discounts and advertising has little influence on patronization (Gillette and
Schnieder 1978; Lumpkin, Greenberg and Goldstucker 1985). Furthermore, there is an
inconsistency in the literature on defining ‘elderly.’ Fentiman, Tirelli, Monfardini, Schneider,
Festen, Cognetti, and Aapro, (1990), define elderly as age 70 or above, while other researchers
define elderly as age 65 to 84 (Kerrigan, Todd, Della Croce, Lipsitz, and Collins 1998), 70-94
(Jensen and Blichert-Toft, 1971), and 65 to 99 (McItosh 1995). To eliminate the inconsistency in
the literature of defining this age group, mean age was selected as a variable.
32
Variable 4 - Poverty Rate
Poverty rate is one of the most reliable and continuously used explanatory variables in
socioeconomic studies. Poverty rate has been used as an independent variable in studies
investigating teen child bearing and school drop outs rates (Crane 1991), social and physical
stress indicators (Schulz et. al 2008) and socioeconomic change of neighborhoods over time
(Galster and Mincy 1993). Elevated poverty rates cause economic reform in neighborhoods
(Tendulkar and Jain 1995), and economic reform then effects the retail environment (Goetz and
Swaminathan 2006).
Variable 5 and Variable 6 - Proportion of the Population that is White and Non-White
Race has been used as a variable to explain many aspects of retail patronization. Sullivan
and Shaw (2011) found that different racial groups react differently to new retail establishments.
Whites embrace new retail, while African Americans tend to have negative feelings towards new
retail. Other researchers have determined that race influences the behavior and interactions of
customers at shopping malls (Readdick and Mullis 1997; Kim and Kim 2005). These findings
show that cities may be refashioning their neighborhoods and developments in a way that is
hostile to minority groups.
Variable 7- Mean Housing Value
Housing value has been used as a socioeconomic variable in various studies. Cozier,
Palmer, Horton, Fredman, Wise and Rosenberg (2007), classify housing value as an indicator of
health risks among women in the United States. While, Campbell and Cocco (2007) found a
direct positive correlation between housing prices and consumption. Housing value was also
used as a predictor variable for vacancy rates in Buffalo, New York (Silverman 2012), and as a
33
variable in unison with population change to identify processes of neighborhood ascent outside
of gentrification. (Owens 2012).
Variable 8 – Percent Population Change
Population change has been used in longitudinal studies (Galster and Mincy 1993;
Silverman et al. 2012 and Owens 2012). These studies found that socioeconomic variation in
neighborhoods is associated with population change over time. Other scholars have studied
population change and its impact on retail/wholesale employment in a rural setting. Findings
indicate that population change is highly correlated with retail and wholesale employment
(Adamchack, Bloomquist, Bausman and Qureshi 1999). Furthermore, in small communities
population change had a direct relationship with retail sales (Walzer and Schmidt 1977). That is
as population increased, sales increased as well.
Variable 9- Proportion of the Population that is Female
Tauber (1972), found people who shop do it for multiple social reasons including selfgratification, physical activity and learning new trends. He also found that females tend to
identify with these social shopping roles more than males do. In addition, research indicates that
females shop more regularly than do males (Roy Dholakia 1999) although male shopping has
been slowly increasing over the past decade (Otnes and McGrath 2001). My survey data supports
higher female shopping frequency (249) as opposed to male frequency (138).
34
Difference of Means and Difference of Proportions Tests
Hypothesis testing procedures are not limited to situations where there is only one sample
and a statement about a characteristic of the population being examined. In many situations
comparing a quantitative characteristic of two separate populations, such as a mean or a
proportion is very tangible. For example, one may want to compare the average money spent at a
clothing store by males and females. In order to do this a random sample of male shoppers and a
random sample of female shoppers is needed. Then, using the sample means a hypothesis is
tested, where we assume there is no difference between the two groups would be conducted
using the sample means. This is known as a two sample test, because a value within the two
samples is being tested. They could also be referred to as a two population test, because the
fundamental question is do these samples come from the same or different populations (Barber
1988)?
These tests are relevant to this study, because comparing the variables identified in the
literature for the mall’s service area to Allegheny County as a whole will show which variables
in the defined service area are significantly different from the same variables in the county itself.
For example, is the average housing value of Century III’s service area in 1990 the same as the
average housing value of Allegheny County in 1990 (i.e., are they from the same population?) or
are they significantly different from one another (i.e., are they from different populations?)? It is
hypothesized that the variables selected will be significantly different in Century III’s service
area when compared to Allegheny County as a whole. If any of the variables have a significant
standard score as a result of these tests, they will be assessed as possible indicators of mall
decline, and considered for further testing.
35
In order to interpret the analysis results, it is important to understand the parameters of
the tests. A confidence level refers to the percentage of all possible samples that can be
anticipated to include the actual population parameter. We assume all possible samples were
selected from the same population, and a confidence interval was computed for each sample. A
95% confidence level implies that 95% of the confidence intervals would include the true
population parameter. For this study a 95% confidence level was used. The critical Z values for
these tests are -1.96 and +1.96. This means that any variables that have a Z-score between those
values is not statistically significant. Any variables that have a Z-score outside of the critical
range is statistically significant. Furthermore, statistical significance is attained when the p-value
(another statistic used for measuring significance) is less than the significance level (Krzwinski
and Altman 2013; Cumming 2012). In a two-tailed test, the rejection region for a significance
level of α=0.05 is partitioned to both ends of the sampling distribution and makes up 5% of the
area under the curve (Figure 18).
Figure 18. SignificanceInterpretation
regions for difference
of means and
of Results
difference of proportions results.
36
When evaluating the results, the Z-scores are particularly important. A positive Z-score
indicates Allegheny County has a higher value than the service area. A negative Z-score
indicates Allegheny County has a lower value then the service area. The only variable that was
significant in 1990 was income, which was significantly higher in Allegheny County (Z = 4.21).
In 2000, income (Z = -2.818), age (Z = -1.975) and housing value (Z = -2.650) were significantly
lower in Allegheny County. In 2010, income (Z = -2.500) and age (Z = -2.302) remained
significantly lower in Allegheny County. Table 3 displays the variables that were examined.
Tables 4, 5 and 6 show the results of the difference of means and difference of proportions tests,
and table 7 is a descriptive analysis of these results.
Table 3. Variables Used in Difference of Means and Difference of Proportions Tests
V1
V2
V3
V4
V5
V6
V7
V8
V9
Proportion of population with a highschool education
Proportion of population with income over 35K
Mean Age of the population
Poverty Rate
Proportion of the population that is white
Proportion of the population that is non-white
Mean Housing Value
Population Change
Proportion of the population that is female
37
Table 4. Difference of Means and Difference of Proportions Tests 1990 Results
Service Area/ Allegheny 1990
Variable Z/T-Score P-Value Sig. Level Result
V1
0.739
0.459
0.05 Not Sig.
V2
4.2104
0
0.05 Is Sig
V3
-0.5598
0.0575
0.05 Not Sig
V4
0.0805
0.936
0.05 Not Sig
V5
0.3149
0.756
0.05 Not Sig
V6
-0.3149
0.756
0.05 Not Sig
V7
1.3729
0.17
0.05 Not Sig
V8
N/A
N/A
N/A
N/A
V9
0.711
0.8952
0.05 Not Sig
Notes
means test
means test
Service Area/ Allegheny 2000
Variable Z/T-Score P-Value Sig. Level Result
V1
0.3339
0.7414
0.05 Not Sig.
V2
-2.8184 0.00587
0.05 Is Sig.
V3
-1.9752
0.05 Is Sig.
V4
-0.5599
0.575
0.05 Not Sig.
V5
-1.503 0.13362
0.05 Not Sig
V6
1.503 0.13362
0.05 Not Sig.
V7
-2.65
0.05 Is Sig.
V8
1.2475
0.089
0.05 Not Sig
V9
0.3167 0.67448
0.05 Not Sig
Notes
means test
means test
Service Area/Allegheny 2010
Variable Z/T-score P-Value Sig. Level Result
V1
0.2644
0.79
0.05 Not Sig
V2
-2.5
0.012
0.05 Is Sig
V3
-2.302
0.05 Is Sig
V4
-0.1299 0.89656
0.05 Not Sig
V5
-1.6749
0.0949
0.05 Not Sig
V6
1.6749
0.0949
0.05 Not Sig
V7
1.2388
0.05 Not Sig
V8
1.3277 0.18352
0.05 Not Sig
V9
0.0244 0.98404
0.05 Not Sig
38
Notes
means test
means test
Table 7. Difference of Means and Difference of Proportions Test Descriptive Analysis
Allegheny
(%)1990- Whole Numbers 1990
V1 (Education)
79.30%
1059804
V2 (Income over 35k)
39.40%
53761
V3 (Mean Age)
36.6
V4 (Poverty Rate)
11.50%
153692
V5 (White Population)
87.10%
1164047
V6 (Non-White Pop)
12.90%
172402
V7 (Housing Value)
$56,800.00
V8 (Population Change) -7.90%
723019
V9 (Female Population) 54.10%
1990-2000 1990-2000
2000-2010 2000-2010
(%) 2000 Whole Numbers 2000 (%) 2010 Whole Numbers 2010 % Change Whole Number Change (%) Change Whole Number Change
86.50%
1108641
92.60%
1132820
7.20%
48837
6.10%
24179
54.10%
6393381
63.90%
781719
14.70%
6339620
9.80%
-5611662
41
40
4.4
-1
11.20%
143547
12.70%
155365
-0.30%
-10145
1.50%
11818
84.30%
1080444
82.00%
1003145
-3.70%
-83603
-2.30%
-77299
15.70%
201222
18.00%
220203
2.80%
28820
2.30%
18981
$97,200.00
$
120,300.00
40400
23100
-4.10%
1281666
-4.50%
1223348
1281666
-58318
53.00%
679283
52.00%
636140
1.10%
-43736
-1.00%
-43143
Whole Numbers 1990(%) 2000 Whole Numbers 2000 (%) 2010 Whole Numbers 2010 % Change Whole Number Change(%) Change Whole Number Change
Century III Service Area
V1 (Education)
78.80%
530610 87%
405947
92.20%
541310
8%
-124663
5.20%
135363
V2 (Income over 35K)
31%
208743 61.30%
371411
69.80%
409799
30.30%
162668
8.50%
38388
V3 (Mean Age)
37.7
42.1
43.9
4.4
1.8
V4 (Poverty Rate)
11.30%
76090 11.30%
68466
12.90%
75736
0%
-7624
1.60%
7270
V5 (White Population)
86.60%
583132 87%
527125
85.30%
510781
0.40%
-56007
-1.70%
-16344
V6 (Non-White Pop)
13.40%
90231 13%
78766
14.70%
86304
-0.40%
-11465
1.70%
7538
V7 (Housing Value)
$49,930.00
$101,749.00
$103,319.00
51819
1570
V8 (Population Change) N/A
-2.80%
605891
-3.20%
587104
N/A
605891
-18787
V9 (Female Population) 52.90%
356209 52.10%
315669
51.80%
304120
-0.80%
-40540
-0.30%
-11549
39
This section will briefly discuss the significant results of the difference of means and
difference of proportions tests as they relate to the study’s proposed hypothesis. After analyzing
literature on neighborhood socioeconomic change, nine variables were identified as possible
explanatory elements of retail decline. A small number of these variables were documented as
significant following the tests. The outcomes of these tests reveal a counterintuitive result to
what was hypothesized at the outset of this study. Compared to Allegheny County, the defined
study area seems to be economically and socially healthier than the county, with respect to the
variables identified in the literature. Again, this is the counter to this study’s hypothesis, which
suggests that the negative socioeconomic conditions of neighborhoods in Century III’s service
area largely explain the mall’s decline.
Tables 4, 5 and 6 yield the following results. In 1990 the proportion of the population
with an income over $35,000 was significantly lower in the study area than it was in Allegheny
County. However, by 2000 the proportion of the population with an income over $35,000 was
significantly higher in the study area compared to Allegheny County. Finally in 2010 the
proportion of people with an income over $35,000 remained significantly higher in the study
area than in Allegheny County. Also, mean housing value in 2000 was significantly higher in the
study area compared to Allegheny County. With the exception of the income variable in 1990,
these results do not support the original hypothesis that the neighborhoods in the study area are
declining and therefore negatively impacting the mall’s performance.
In 2000 and 2010 the proportion of people with an income over $35,000 is higher in the
study area then it is in Allegheny County. This finding is interesting because while the income
levels of the study area were stable during these years, the mall that serves it was and still is
40
failing. Furthermore, mean housing value in 2000 was reported as significantly higher in the
service area, compared to Allegheny County (Table 8 shows national mean housing values). This
is also contradictory to my original statement about Century III Mall’s decline being related to its
service areas socioeconomic status. This is evidence that the decline of Century III Mall is
related to something other than the socioeconomic status of the defined service area.
Table 8. National Mean Housing Values
National
1990
2000
2010
Housing Value
$78,500
$119,600
$179,200
The only variables that had significant results consistent with my initial hypothesis are
mean age in 2000 and 2010. Mean age in Century III Mall’s service area was significantly higher
than it was in Allegheny County, during these years. This is particularly important because
Allegheny County is one of the oldest counties in the United States. The University of
Pittsburgh’s Center for Social and Urban Research reports that 17.8% of Allegheny County was
over the age of 65, compared to 12.4% nationally (Musa 2014). According to the difference of
means and difference of proportions test, this percentage is even higher for Century III Mall’s
service area.
There are many ways that age can negatively impact a mall’s service area, accessibility
being one of them. Studies have found that accessibility to malls is a good indicator of patronage
(Howell and Rogers 2001). This is vital, because, as individuals get older, their mobility
decreases, making it harder to access places such as shopping malls. Increasing age can also be
detrimental to malls from a customer loyalty standpoint. Scholars have discovered that targeting
specific age groups with discounts (e.g., senior discounts) and advertising (product exposure) has
41
little influence on patronization (Gillette and Schnieder 1978; Lumpkin, Greenberg and
Goldstucker 1985). In fact, elderly shoppers do not like being targeted with such age based
advertisements and often times act unfavorably towards them. Also, older consumers prefer to
shop in groups as opposed to being alone (Tongren 1988). These conditions clarify the difficulty
in keeping elderly individuals engaged in retail patronization.
In conclusion, statistical results (with the exception of age) conflict with my original
hypotheses regarding the impact of the socioeconomic circumstances of a mall’s service area on
its performance. My research largely does not support the idea that Century III Mall’s decline
can be directly related to the socioeconomic degeneration of the neighborhoods that make up its
service area. It seems that some other phenomena are responsible for Century III’s demise. These
elements will be discussed in the following chapter.
42
CHAPTER III
RESEARCH DISCUSSION AND SUMMARY
Chapter three will begin with a summary of the research process, followed by a
discussion of the strengths and weaknesses of the study. Suggestions of alternative explanations
for the decline of Century III Mall will conclude this paper.
Summary
Customer data was collected at the zip code level, through a one question survey that was
conducted at Century III Mall in West Mifflin, Pennsylvania. I asked mall patrons for zip codes
because most people know their zip code and the census collects data at the zip code level..
Three methods were used to help establish Century III Mall’s service area: customer spotting
(Applebaum 1966), trend surface mapping (Peterson 1974) and spatial monopoly (Jones and
Simmons 1990). Customer spotting was included because of the accurate results the technique
provided to this study. Trend surface mapping was used because of the quantitative components
(grid squares, numbers of shoppers, and numbers of households) that were used to establish the
service area. Finally, spatial monopoly was used because it provided a qualitative technique to
the study. The final service area included 76% of my survey sample (294 out of 387
respondents).
Following the establishment of Century III Mall’s service area, difference of means and
difference of proportions tests were implemented. These tests measure the quantitative
characteristics of one population to the same characteristics of a different population. The two
populations tested were Allegheny County and Century III Mall’s service area. Comparing the
socioeconomic status of the service area to the socioeconomic status of Allegheny County
revealed which elements of neighborhood change were significantly different in the service area
43
compared to the county as a whole. Undertaking statistical testing of variables identified from
the literature was designed to help answer the question of whether the socioeconomic status of
the neighborhoods in Century III Mall’s service area are related to the decline in performance of
Century III Mall.
The literature offers two arguments that could be used to explain mall decline. The first is
neighborhood decline and poverty. This literature highlights the economic and social distress that
neighborhoods are going through. Teen pregnancy (Crane 1991), elevated poverty (Galster and
Mincy 1993), racial segregation (Schulz, Zenk, Israel, Mentz and Galea 2008), and high school
dropout rates (Crane 1991) are all indicators of neighborhoods going through socioeconomic
distress. The second literature relates to shopping behavior and competition. This literature
discusses the motives and intentions of shoppers as well as the impacts of other retail (i.e., other
malls, online shopping). These two different explanations will be revisited in the latter portions
of this chapter.
This study focused on theories of neighborhood change and attempted to relate them to
Century III Mall’s decline. It was hypothesized that the neighborhoods in Century III Mall’s
service area have undergone socioeconomic decline, which has directly impacted the health of
Century III Mall. The results of the difference of means and difference proportions tests were
counter to this proposition however. In chapter one, two different research arguments were laid
out through the literature. The first group of literature was neighborhood decline and poverty,
second was shopping behavior and competition. This study tested neighborhood decline and
poverty. The ultimate hypothesis was that the declining socioeconomic status of the
neighborhoods in Century III Mall’s service area are responsible for the occupancy decline in the
mall. Results from the difference of means and difference of proportions tests indicated that
44
Century III’s service area was socially and economically healthier than Allegheny County.
Proportion of the population with an income over $35,000 was significantly higher in the service
area in 2000 and 2010. Mean housing value was also significantly higher in the service area in
2000. Age was the only variable that was consistent with what was hypothesized, being
significantly higher in the service area in 2000 and 2010. Based on these results we would reject
this study’s hypothesis that the socioeconomic decline of Century III Mall’s service area is
responsible for the decline in the mall’s occupancy. These findings may be counterintuitive to
what was hypothesized, however, based on the literature they are still instructive and provide an
explanation as to what is happening in Century III Mall’s service area compared to Allegheny
County. With the exception of age, the significant variables indicate that the service area is doing
socially and economically better than Allegheny County is.
The mall service area delineation was constructive and offers the most credible finding of
the study. Three practical techniques were employed to help delineate Century III Mall’s service
area. Customer Spotting (Applebaum 1966) provided a result based highly on the customer data
that was collected. This technique also provided a very specific result with no generalities or
simplifications. Trend surface mapping (Peterson 1974) also provided a result based on the
customer data that was collected. The result of this method was less specific, but still did provide
an informative outcome. Spatial monopoly (Jones and Simmons 1990) was the final service area
analysis technique used. Though this method was not based off of any customer data, it did
deliver a qualitative result for Century III’s service area. This method provided an alternative
view on how a service area could be established. These three techniques offered a solid
foundation to help delineate the mall’s service area. The final service area included 76% of my
sample (294 of 387 respondents).
45
Data availability for this study was difficult. The nine variables identified in the
literature provided a solid framework for statistical testing, but the data itself was not readily
obtainable. The United States Census provided data for the years examined in the difference of
means and difference of proportions tests (1990, 2000, and 2010). However this left a gap of
unattainable data for the intercensal years. The difference of means and difference of proportions
tests provided a small number of significant explanatory variables. This is due to the limited
amount of data that was obtained for this study. More data, especially for intercensal years,
would have been beneficial to the statistical analysis section of this study. If additional data was
incorporated into the statistical testing, this may have yielded a higher number of significant
results, thus more explanatory variables. This would, in turn provide more evidence for improved
interpretation of results.
Economic Restructuring Revisited
Literature on the economic geography of Pittsburgh provides two differing viewpoints on
what is occurring in and around the city. This section of chapter three will discuss the economic
revitalization efforts of Pittsburgh, Pa and the ramifications on the city’s economy as studied by
Bluestone and Harrison (1988), Beeson and Tannery (2004), and Deitrick (1999). Pittsburgh lost
its steel base in the 1980’s, forcing the city into a partnership along consensual lines. New, nonprofit, community based organizations were incorporated into the revitalization efforts. By the
1990’s the region’s economy had weakened. Manufacturing decline continued, as the service
sector began to gain importance to overall regional employment.
Pittsburgh’s postindustrial transformation has led to an impact on the region as a whole.
According to Deitrick (1999) in 1990 the average annual service sector wage was $24,442. This
is considerably lower compared to an average annual manufacturing wage of $36,989. Deitrick
46
furthers this argument of overall regional wage shrinkage by comparing the region’s wages to
national wages. In 1979 Pittsburgh workers hourly wages were 18% greater than the national
median. By 1989, the hourly wage had dropped 20% and dipped below the state and national
median (Deitrick 1999).
According to Beeson and Tannery (2004) the 1980’s were a remarkable period for
Pittsburgh’s economy. In 1979 the region’s unemployment rate was 5.1%. In 1983, this number
more than tripled to 17.1%. By 1990 the region’s economy recovered gradually alongside the
nation’s economy. This resulted in shifts from industrial employment to unskilled service sector
employment. The impacts of this type of employment shift has received attention as a source of
earnings inequality (Bluestone and Harrison 1988). Beeson and Tannery’s study found that
earnings had dropped in the Pittsburgh region overall between 1978 and 1989. Shifts of
employment accounted for most of the reduced earnings in lower income divisions, but had little
impact in the higher income divisions. Thus, it was concluded that restructuring plays a major
role in the explanation of wage inequality in the Pittsburgh region (Beeson and Tannery 2004).
This type of economic polarization can impose consequences such as changing business cycle
recoveries and permanent job loss (Jaimovich and Siu 2012).
While Deitrick contends that Pittsburgh as a region overall had declined because of the
employment shift from industry to unskilled services, Beeson and Tannery take a different
approach. They claim that earnings inequality had occurred in and around the city, causing more
harm to the lower income sectors, than the middle and upper class. While both of these
approaches have merit the findings of this study on Century III Mall’s service area favors Beeson
and Tannery.
47
Their findings of wage inequality impacting lower income classes more so than middle
and higher level income classes, can be explained through the results of this studies statistical
testing. Results from the difference of means and difference of proportions tests indicate that in
2000 (Z= -2.8184) and 2010 (Z= -2.500), the proportion of people with an income over $35,000
was significantly higher in the mall’s service area than it was in Allegheny County. This means
that the neighborhoods in Century III Mall’s service area were less impacted by wage inequality,
caused by the postindustrial shift than other neighborhoods in the Pittsburgh area. According to
Beeson and Tannery’s findings, the neighborhoods in Century III Mall’s defined service area are
above the lower income levels that are most adversely impacted by wage inequality.
Prospective Research
The literature on shopping behavior and competition provides additional alternative
explanations for retail decline. The motives and techniques of mall patrons are an ever changing
phenomena. Nicholls, Li, Kranendonk, and Roslow (2002) found that overall frequency of mall
trips declined between 1993 and 1999. Also, those that live closer to the mall are less likely to
make a purchase than those that travel further. The decline in mall visit frequency could be a
result of online shopping and competition from other retail outlets.
Furthermore, big box retailing has been cited in the literature as a source contributing to
the decline of malls. Morningside Mall in Toronto, Canada has become a mall with a waning
core retail function. The demise of this mall was caused by diminishing sales and customer
patronization. Also contributing to Morningside’s decline are these big box stores such as, WalMart, Target and K-Mart. These types of stores are able to kill the competition by providing a
one stop shop for customers (Paulette and Cowen 2011).
48
In addition to online shopping and big box retailers, another possible culprit could be
outshopping. Outshoppers are those who bypass shopping outlets and malls that are closer to
their homes for other establishments which are further away. Dissatisfaction with the products
available locally and more attractive shopping alternatives can lead to outshopping (Burns and
Warren 1995).
Research on shopping behavior and retail competition (online shopping and big box
stores) have provided possible explanations for why retail establishments like Century III are
declining. This study did not test the explanations listed above so they should be looked at as
possible explanatory elements of retail decline. One way to address these potential explanations
would be to conduct a detailed survey with more thorough questions. Asking mall patrons
questions such as: how frequently they shop at this location?, how often do they shop online?, do
you have a budget when shopping? Also conducting surveys at multiple malls could be
beneficial to understanding what kind of and how many shoppers patronize certain
establishments. This type of survey would generate much more detailed data compared to what
was collected during this study.
A possibility for future research is the analysis of relationships between changes in
Century III Mall’s occupancy rates and changes of selected socioeconomic variables using
correlation analysis. The technique attempted in this study used Spearman’s correlation, a
nonparametric test which measures the strength of relationship between dependent and
independent variables. Basically, correlation analysis measures the strength of the relationship
between two variables, and is expressed as the “correlation coefficient.” When an increase in
the value of one variable results in an increase in the value of another variable, the correlation is
49
said to be positive (+). On the other hand, if the value of one variable increases, while the value
of the other variable decreases, the correlation is said to be negative (-).
One aspect of this research was to determine which socioeconomic characteristics are
causing Century III Mall to decline. Using data from the Directory of Major Malls, occupancy
rates (dependent variable) of Century III Mall were calculated for 2005 to 2015. The service
area independent variables thought to be associated with Century III’s occupancy rate decline
included proportion of the population with income over $35,000 in 1990, proportion of the
population with income over $35,000 in 2000, mean age in 2000, mean housing value in 2000,
mean age in 2010, and proportion of the population with income over $35,000 in 2010. The latter
six variables were included because of their significance level in the difference of means and
difference of proportions tests. Furthermore, six additional variables were added to the
correlation tests, they include: mean income in 2011, 2012 and 2013, and mean age in 2011,
2012, and 2013. The results of the Spearman correlation analysis showed that none of the above
variables are significantly correlated with Century III’s occupancy rate between 2005 and 2015.
This result seemingly means that none of the selected variables are significant indicators of the
occupancy decline of Century III Mall between 2005 and 2015.
It should be noted that in Century III Mall’s case, not all factors could be analyzed using
Spearman’s correlation, which is why it wasn’t included in the analysis section of the study.
Furthermore, as is the case of many socioeconomic analyses, there is a limited number of
socioeconomic variables available in the intercensal years between 2000 and 2010. A future
avenue of research would involve identifying causal variables associated with Century III’s
occupancy decline using a questionnaire survey of individual shoppers.
50
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56
APPENDIX A
SPEARMAN’SCORRELATION RESULTS
Table 9: Spearman's Correlation Results
57
APPENDIX B
STORE DATA FOR CENTURY III MALL
Table 10: Store Data for Century III Mall
# Spaces # Stores
2000
191
2001
190
2002
190
2003
190
2004
190
2005
190
131
2006
190
133
2007
180
142
2008
180
138
2009
166
138
2010
166
94
2011
161
85
2012
161
90
2013
161
119
2014
161
119
2015
161
91
Occupancy
73%
65%
64%
75.30%
74%
71.20%
75%
75%
64%
60%
38%
58
sq ft
sq ft
GLA Area Occupied
1289115
1289115
1289115
1289115
1289115
1280995
931283.365
1254795
811852.365
1255166
798285.576
1255166
945139.998
1265916
941841.504
1265916
901332.192
1195506
896629.5
1193000
898329
1193000
763520
1193000
719379
1193000
456919

Declining Retail Establishments: The Case of Century III Mall

Transcription

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