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HOW DOES INTERNATIONAL AND FORMAT DIVERSIFICATION AFFECT THE FINANCIAL PERFORMANCE OF RETAILERS?
By
JEREMY MIANXIN LIM
A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT
OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY
UNIVERSITY OF FLORIDA
2011
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© 2011 Jeremy Mianxin Lim
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To my family, especially my parents, who have supported and encouraged me through all of my endeavors, for which I am eternally grateful
I also dedicate this dissertation to my advisor, Dr. Barton Alan Weitz, whose insights, patience and kindness I am forever indebted to.
Last but not least, I dedicate this dissertation to members of my committee, the marketing faculty at The University of Florida and my fellow PhD students, whose
advice, encouragement and patience I treasure.
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ACKNOWLEDGMENTS
Even prior to me joining the doctoral program in August of 2006, I have looked to
my advisor – Dr. Barton Alan Weitz – for advice and encouragement. I remembered first
meeting him during my campus interview at Florida; his first words were: “What would
you like to know about my PhD program” in a cheerful and genuinely welcoming tone.
Ever since, he has been a source of inspiration both in my academic career and in my
personal life.
He is one of the most determined, successful and benevolent persons I have
ever met and I wished I had spent more time – during my doctoral program – learning
from him. His encouragement and unfaltering support is the reason I am able to
complete this dissertation, for which I will remain eternally grateful.
Next, I would like to acknowledge the support of the acclaimed marketing faculty
at Florida, in particular: Dr. Lyle Brenner, Dr. Steve Shugan, Dr. Joe Alba and Dr. Alan
Cooke, who have provided me with substantial guidance and encouragement over the
course of my studies.
I am also grateful to my colleagues, especially Dr. Melissa Minor, Dr. Xiaoqing
Jing, Dr. Yuying Shi, Dr. Steven Sweldens, Dr. Hyunjoo Oh and Dr. Fang Wang, for the
experiences we have shared.
Last but definitely not least, I would like to acknowledge the incredible amount of
emotional and physical support from my parents that helped me make it through the
enduring hardships of a top doctoral program.
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TABLE OF CONTENTS page
ACKNOWLEDGMENTS .................................................................................................. 4
LIST OF TABLES ............................................................................................................ 8
LIST OF FIGURES .......................................................................................................... 9
LIST OF ABBREVIATIONS ........................................................................................... 10
ABSTRACT ................................................................................................................... 11
CHAPTER
1 INTRODUCTION .................................................................................................... 13
Retail Strategy and Retailers’ Product-Market Portfolio Defined ............................. 13 Evolution in the Retail Climate and the Importance of this Research ..................... 13
International market diversification ................................................................... 14 Format diversification ....................................................................................... 15 Interactive synergies between market and format diversification ..................... 15 Unique aspects of retailers not addressed by other research ........................... 16 Timing............................................................................................................... 17
Intended Contributions ............................................................................................ 17
2 LITERATURE REVIEW .......................................................................................... 20
Underlying Concepts Influencing Firms’ Diversification Decisions .......................... 20 Economies of Scale and Scope ........................................................................ 20 Firm Specific Assets ......................................................................................... 21 Order of Entry and the Learning Curve ............................................................ 22 Risk Reduction ................................................................................................. 23 Market Power ................................................................................................... 24 Other Factors Influencing Diversification Decisions ......................................... 24
Nature of Diversification-Performance Relationship ............................................... 25 Linear Impact of Firms’ Diversification on Their Financial Performance ........... 25 Positive linear impact of diversification on firm performance ............................ 26 Negative linear impact of diversification on firm performance .......................... 26 Non-Linear Impact of Firms’ Diversification on Their Financial Performance ... 27 U-shaped impact of diversification on firm performance ................................... 28 Inverted U-shaped impact of diversification on firm performance ..................... 28 S-shaped impact of diversification on firm performance ................................... 29
Cross-Country Characteristics and their Impact on Successful Diversification ....... 30
3 CONCEPTUAL FRAMEWORK AND HYPOTHESES ............................................. 32
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Conceptual Considerations and Assumptions of the Model .................................... 32 Diversification Implications for Retailers and Manufacturers ................................... 33 Benefits and Costs of Diversification ...................................................................... 35
Diversification benefits...................................................................................... 35 Diversification cost ........................................................................................... 36
International Market and Format Diversification and Retailer Financial Performance ........................................................................................................ 37
International Market Diversification Intensity .................................................... 37 Format Diversification Intensity. ....................................................................... 38 International Market-Format Diversification Interaction .................................... 39
Effects of the Characteristics of the Portfolio of International Market and Format on Retailer Financial Performance ...................................................................... 40
Cultural Dissimilarity ......................................................................................... 40 Economic Dissimilarity ..................................................................................... 41 Format Dissimilarity .......................................................................................... 42
4 METHOD ................................................................................................................ 44
Sample .................................................................................................................... 44 Measures ................................................................................................................ 44
Financial Market Performance .......................................................................... 44 Diversification Intensity ..................................................................................... 45 Format Dissimilarity .......................................................................................... 46 Cultural Dissimilarity ......................................................................................... 47 Economic Dissimilarity ..................................................................................... 48 Summary Statistics ........................................................................................... 49
Estimation ............................................................................................................... 49 Endogeniety Issue ............................................................................................ 49 Method ............................................................................................................. 50 Substantive Issue ............................................................................................. 52 First-Stage Estimation ...................................................................................... 53 Second-Stage Estimation ................................................................................. 55
5 RESULTS ............................................................................................................... 59
First-Stage Results ................................................................................................. 59 Test of Hypotheses (Second-Stage Results) .......................................................... 61
International Market and Formats Intensity of Financial Market Performance .. 61 Cultural, Economic, and Format Diversity and Financial Performance ............. 63
6 DISCUSSION ......................................................................................................... 69
Summary of Results................................................................................................ 69 Directions for Future Research ............................................................................... 71
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APPENDIX
A DATA APPENDIX ................................................................................................... 74
B SAMPLE OF RETAILERS ...................................................................................... 80
C RESULTS FROM FACTOR ANALYSIS OF HOST COUNTRY ECONOMIC VARIABLES ............................................................................................................ 88
D FORMAT DISSIMILARITY SURVEY ...................................................................... 90
LIST OF REFERENCES ............................................................................................... 93
BIOGRAPHICAL SKETCH .......................................................................................... 102
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LIST OF TABLES
Table page 4-1 Summary statistics for main variables ................................................................ 58
4-2 Correlation matrix for main variables .................................................................. 58
5-1 Estimated results: instruments affecting diversification decisions ....................... 64
5-2 Effect of country and format portfolio constituents on Tobin’s Q ....................... 65
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LIST OF FIGURES
Figure page 3-1 Conceptual framework ........................................................................................ 43
4-1 Scree plot from factor analysis of host country economic variables ................... 57
5-1 International diversification and retailers’ Tobin’s Q ........................................... 66
5-2 Format diversification and retailers’ Tobin’s Q .................................................... 66
5-3 Impact of varying levels of market dissimilarity on Tobin’s Q. ............................ 67
5-4 Impact of varying levels of format dissimilarity on Tobin’s Q. ............................. 68
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LIST OF ABBREVIATIONS
Constituents Geographical and/or operational format members in the retailers’ portfolio
D-P Link Link between the effects of firm diversification and corresponding performance
GPR Global Powers of Retailing
Hofstede Scores Cultural constructs developed by Dr. Geert Hofstede
Retailers’ Portfolio The set of markets and formats retailers operate in
WBI World Bank Development Indicators
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Abstract of Dissertation Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy
HOW DOES INTERNATIONAL AND FORMAT DIVERSIFICATION AFFECT THE
FINANCIAL PERFORMANCE OF RETAILERS?
By
Jeremy Mianxin Lim
December 2011
Chair: Barton Alan Weitz Major: Business Administration
A conceptual derivative of the literature on strategic growth management across
firms’ product/market portfolio (i.e. Ansoff’s, Boston Consulting Group’s and McKenzie’s
product/market matrix) –this research strives to provide similar empirically-based
guidance to retailers. Specifically, this research examines the impact of retailers’
portfolio of countries and formats on their financial performance, while accounting for
dissimilarities across the elements of the retailers’ portfolio.
Initially drawing on the methodology and constructs used in studies on market
entry and firm performance in formulating dissimilarity constructs, this study
subsequently departs from the micro aspects of market entry and product launch
sequence in an effort to provide a holistic overview on how retailers’ should strategically
manage their product-market portfolio to achieve optimal financial performance, while
accounting for both the effect of heterogeneous geographical and product markets and
any corresponding higher-order interactive effects (i.e. synergies in particular product-
market combinations).
Based on a sample of the largest global retailers over a five-year period, the
results indicate that the relationship between both the number of counties and formats
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and financial performance is U-shaped. However, due to the negative interaction
between the number of countries and formats, the overall effect on financial
performance is more complex within the range of the observations. Dissimilarities in
retail formats and the cultural and economic characteristics of the countries (in which
the retailer operates in) have significant negative effect on the retailer’s financial
performance, but the impact of these dissimilarities in the retailer’s portfolio is less than
the impact from the number of countries and formats in the retailer’s portfolio. My
research shows that retailers operating a limited number of formats in many countries or
many formats in a few countries have the best financial performance.
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CHAPTER 1 INTRODUCTION
Retail Strategy and Retailers’ Product-Market Portfolio Defined
A fundamental strategic marketing decision facing firms is: how to determine their
product-market portfolio – the set of markets in which a particular firm chooses to
operate in and the product(s) directed toward these respective markets (e.g. Aaker
2005, pp. 30-33; Kerin and Pederson 2007, pp. 7-10). A retailer’s product-market
portfolio is the result of the retailer pursuing two growth strategies: (1) market
diversification – adding new markets to their operating portfolio and (2) format
diversification – launching new formats (Gielens & Dekimpe 2001). Examples of the
former include: McDonalds expanding into China and Wal-Mart expanding into
Argentina, while examples of the latter include: Wal-Mart expanding its operations from
discount stores to include hypermarkets and warehouse clubs and British retailer –
Tesco – expanding its operations to include convenience stores (i.e. Tesco Express).
Market diversification alters the number and nature of markets in which retailers’
operate in (demand-side diversification) while format diversification affects the different
operational resources utilized by retailers (supply-side diversification).
Evolution in the Retail Climate and the Importance of this Research
Faced with maturing markets and increasing competition, retailers – like most
other firms – are faced with two particularly pressing strategic issues: 1) how to adapt to
challenges arising from both globalization and technological advances and 2) how to
ascertain the extent to which retailers should trade-off their core operations [that they
have proven core competencies in] to capitalize on the resulting growth opportunities
that have become ever more so viable.
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In their search for new growth opportunities, retailers face critical decisions
concerning the degree to which they should engage in market and format diversification,
along with the characteristics of the resulting operational portfolio of international
markets and formats that the retailer operates in (Gielens & Dekimpe 2001; Levy &
Weitz 2009)
International market diversification
Many academic and industry observers extol the need for retailers to pursue
international market diversification. For instance, Gielens and Dekimpe (2001) argue
that: “ to avoid a pure market-share game in increasingly saturated domestic markets,
retailers are increasingly forced to also look for new geographical markets” and Retail
Forward, in its outline of the evolving retail landscape, emphasizes that: “Global Scope
[in 2015] will be a necessity, not an option to grow the top line and bolster the bottom
line. Global expansion will be a key avenue for retailers in developed markets to
generate new sources of revenue to offset slower sales growth at home . . . . They
[American retailers] will be late to the game, especially compared with large European
retailers . . .” (Pollack, 2007, p. 11).
Indeed, the evidence suggest that retailers are taking the above sentiments to
heart and devoting proportionally more resources to cross-border operations. For
instance, Higgins (1997) and Mulhern (1997) noted that the world’s 100 largest retailers
are growing twice as fast abroad as they are domestically, with the top-35 largest
retailers averaging one new market entry per year.
However, the returns are unclear given the lack of empirical research on entry
and/or post-entry performance (Feeser & Willard 1990; Gielens & Dekimpe 2001;
Sharma & Kesner 1996). Anecdotal evidence suggest that retailers realize a
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substantially lower [or even negative] return on investment (ROI) on foreign compared
to domestic operations. For instance, Carrefour and Wal-Mart not only maintain
uniformly lower average ROIs on foreign versus domestic operations but have also
sustain losses in many markets (The Economist, 1999).1
Format diversification
Gielens and Dekimpe (2001)
asserts that few international retailers realize comparable margins or break-even
volumes in foreign markets and also suggest that there is a long-run optimal level of
international market diversity for retailers.
The other growth opportunity pursued by retailers is format diversification. Format
diversification is the variety of formats that retailers utilize to offer goods or services to
their target market(s). Format diversity in the retail industry is conceptually similar to
industrial and product diversity constructs used in studies of diversification strategies
engaged in by manufacturing firms. Format diversification, like product and industrial
diversification, focuses on the variety of unique resources and capabilities utilized by
firms. For example, expertise in fashions and merchandise budget planning are key
performance drivers for retailers operating apparel specialty store; while expertise in
supply chain management and cost control are performance drivers for hypermarket
retailers.
Interactive synergies between market and format diversification
Gielens and Dekimpe (2001) noted that format diversification evolved as a more
aggressive competitive response from retailers simply offering new and broader store
assortments, while RetailWire (2009) suggest that new formats [particularly, the more
1 Losses were so heavy in certain markets and necessitated retreat.
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successful formats] are the result of retailers’ evolving to meet changing consumer
habits and current economic conditions. Implicit in the latter report is the emphasis on
retailers focus on customer orientation and delivering formats with a high value
proposition to consumers (i.e. demand-driven product extension) as opposed to the
former justification (i.e. competition/resource [supply] driven product extension).
Unique aspects of retailers not addressed by other research
The benefits of international market and format diversification on firm performance
are unclear. Research examining the affects of international market diversification and
financial performance is mixed. Research has found positive and negative linear
relationships as well as nonlinear relationships (e.g. Annavarjula and Beldona 2000;
Hitt, Tihanyi, Miller and Connelly 2006; Li 2007). Similarly, research on the effects of
product/industry diversification and financial performance has produced inconsistent
results with some research suggesting that product diversification moderates the
international market diversification-performance relationship (Chang and Wang 2007).
But almost all of the extant research has examined the impact of diversification on
the performance of manufacturing firms. For example, Morgan and Rego (2009)
examined the effect of brand portfolios (the number of brands and market segments) on
the financial performance of brand manufactures. Very few studies have focused on the
impact of market and format diversification strategies for firms in service industries and
even fewer for the retail industry.
This lack of research on diversification in service and retail industries is
problematic for two reasons. First, service industries are faster growing than
manufacturing industries worldwide and dominate the business environment in most
developed countries. Second, there are significant differences in the operating
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characteristics of retailers and other service firms compared to manufacturing firms –
differences than can affect the diversification-performance relationships (Dawson 1994,
2007 and Wrigley, Coe and Currah 2005). Due to the lack on research on the
diversification-performance relationship and the unique aspects of retail and other
service industries, several scholars have questioned the generalizability of the extant
manufacturing-dominated research on diversification and have called for studies on the
effects of diversification in service industries (c. f. Agarwal & Ramaswami 1992; Erramilli
& Rao 1993; Ekeledo & Sivakumar 1998).
Timing
Heigtening globalization in the retail industry is intensifying cross-border
competition. This coupled with the compounding effect of the recession on consumer
spending in domestic markets, is forcing many retailers to rethink and restructure
themselves so as to preserve their competitive position and preserve [or even increase]
their value proposition to consumers (Gielens and Dekimpe, 2001; RetailWire 2009;
Sage, 2009).
Indeed, Sage (2009) noted that the shrinking domestic prospects, along with the
market growth in emerging economies, is prompting many retailers to implement not
only market but also format diversification plans at the onset of available cash flow. Wal-
Mart’s plan to open its first cash-and-carry store in India is a very characteristic example
that underscores the relevance of this research.
Intended Contributions
Given the “highly contagious globalization fever” affecting retailers, the recent
recessionary impact on the retail industry and the lack of empirical support and
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guidance on market diversification strategies, specific to retailers, this research is
especially timely and relevant (Gielens and Dekimpe, 2001).
Specifically, this study considers a very specific subset of firms with unique
characteristics, namely service-type firms that have largely been underrepresented by
the extant market diversification literature. In addition, this study provides additional
contributions by questioning whether there exist a long-run optimal level of market
diversification for top performing retailers, while taking into account dissimilarities in
cultural and economic conditions across geographic markets and examining the
interactive impact from pursuing two separate diversification strategies.
It also addresses some inconsistencies in prior research by examining the linear,
nonlinear, and interactive effects of market-format portfolios; correcting for endogeniety
in the estimated effects of diversification intensity on performance; and using a financial
market measure (Tobin’s Q) rather than accounting measures of diversification portfolio
performance.
Another unique aspect of this study is its focus on the operational dissimilarities
across different retail formats and its corresponding impact on retailers’ financial
performance. To the best of my knowledge, this is one of two studies to account for this
important yet under researched dimension – “format familiarity” – as termed by Gielens
and Dekimpe (2001) – in the link between diversification and financial performance.
Furthermore, this research utilizes a global sample and takes a holistic and long
run outlook when examining the effects of both international market and format
diversification specific to the retail context.
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The questions addressed in this research are: How does international market and
format diversification affect the financial performance of retailers? Are there
combinations of countries and formats that yield higher financial performance than
others? What portfolio characteristics should retailers seek to exploit market and
operational synergies and improve financial performance? Does format diversification
improve or degrade the financial performance arising from international diversification?
In the following section, I outline my conceptual framework and hypotheses
concerning the relationship between characteristics of retailers’ diversification portfolios
and their financial performance. Then I describe my sample and methodology for
estimating the relationship between the characteristics of retailers’ diversification
portfolios and their financial market performance. After discussing the results of my
estimation, I review this study’s limitations and provide directions for future research.
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CHAPTER 2 LITERATURE REVIEW
This chapter begins with an interdisciplinary exposition of the relevant concepts
influencing market expansion decisions. Next, the discussion (in the second section)
turns to past research on aggregate expansionary strategies and firm performance,
while highlighting their inconsistencies and shortcomings.
With this background knowledge, I then proceed to review studies examining
differences between cross-country characteristics and their resulting implications in the
third section of this chapter. The fourth section repeats this process for cross-format
characteristics, drawing on the findings from the product diversification literature.
The fifth section highlights important characteristic that are unique to service-type
firms, especially retailers and expose shortcomings and the lack of generalizability of
the extant diversification literature.
Finally, I summarize this review with a synthesis of the literature reviewed and
provide support for the relevance and need for this research in the sixth section.
Underlying Concepts Influencing Firms’ Diversification Decisions
There are several commonly discussed factors motivating firms to diversify, they
include: potential economies of scale and scope, increased market power, the ability to
leverage firm specific assets and the potential for firms to lower risk by hedging across
multiple markets and product lines.
Economies of Scale and Scope
International and format diversification enable retailers to draw on scale
economies to reduce costs. By expanding operations to new markets and using new
formats, a firm’s fixed costs are spread across the increased revenue opportunities
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arising from diversification (Caves, 1996). However, cost savings may not always be
realized due to the increased complexity of managing diversified firms.
As firms’ organizational hierarchy expands, they may encounter greater difficulties
in disseminating information to business units resulting in transmission and coordination
inefficiencies, such as the loss or distortion of information (Hoskisson & Hitt1988;
Williamson 1967). Furthermore, the larger hierarchical structure of diversified firms is
conducive to employee shirking; thereby further incurring cost associated with either
heightened monitoring or decreased worker productivity (Calvo & Wellisz 1978). Hence,
the increased governance costs of operating a diversified firm may even exceed the
benefits of diversification (Hitt et al. 1997; Tallman & Li, 1996).
Firm Specific Assets
These scale and scope opportunities are enhanced for firms possessing valuable
firm specific assets, such as well-known and highly regarded brand names, unique
systems and processes, customer loyalty and managerial skills. These intangible, firm-
specific assets cannot be readily transacted due to market imperfections but can not
only afford firms higher than normal returns in new markets (Markides, 1992) but also
enhance their resource allocation efficiency (Froot et al., 1994; Lang et al., 1995;
Markides, 1992; Palich et al., 2000).
In addition, researchers propose that, in contrast to non-diversified firms,
diversified firms have greater access to both internal and external sources of financing
(Lang & Stulz, 1994) and this expanded access provides diversified firms with privileged
access to less costly financing (Froot et al., 1994; Lang et al., 1995).
However, the transfer of the desired firm specific asset(s) may be met with a range
of obstacles including hostility and excessive restrictions in the host market, currency
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fluctuations and last but not least – deteriorating home market performance (Williams,
2001).
The latter point is especially relevant to service based firms (in particular to
retailers) due to the vast amount of resources needed to establish a retail and
distribution network and retailers may not have sufficient resources to do so (Levy &
Weitz, 2006). Furthermore, this extensive investment has to be undertaken with great
uncertainty since retailers do not have the same luxury of “test marketing” or throttling
productions that is available to manufacturing firms. Indeed, the extensive upfront
investment to establish a retail brand and presence will have to be undertaken long
before retailers can ascertain whether their retail format will ever to “take off” (Golder &
Tellis 1997)
Alexander (1990) and Gielens and Dekimpe (2001) also pointed out that the
transfer of firm specific assets (especially in the case of retailers) may not be readily
accepted by foreign customers and retailers’ management and operational culture – as
well as their retail concept - may not be readily implemented in the host culture.
Implementation is especially pertinent for service industries – especially retailing – due
to the large amount of interpersonal interactions involved.
Order of Entry and the Learning Curve
In addition, diversification can lead to inefficiencies arising from external
challenges of operating in a new environment (Hymer 1976, Zaheer 1995). Some of
these disadvantages, related to diversification, are due to inefficiencies resulting from
the lack of market reputation (Barkema, Bell & Pennings 1996); limited knowledge about
the new environment, which leads to lower efficiencies than native firms (Hyman 1976);
and increased uncertainty from having to operate in a complex and unfamiliar
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environment, characterized by a different set of economic, political and legal factors
(Sambharya 1996).
However, studies examining firm entry outcomes have shown that there is a
learning effect at play. Specifically, incumbent entrants can mitigate the level of
unfamiliarity by entering markets that are similar to their existing markets. Indeed,
successfully diversified firms typically enter markets that are similar to their home
markets or markets that they have experience in (Mitra & Golder 2001).
The above studies show that entry decisions in general are not unilateral but
rather encompass elements of self selection (with regard to size, market potential and
similarities and also the portability and acceptance of the firms’ product and operating
concept in the host country).
Firms that are successful at market and format diversification can then enjoy the
benefits of scope and can match the appropriate format(s) with the needs of the target
market.
Risk Reduction
In a related vein, diversification is also argued to reduce a firm’s risk by reducing
the effects of nonsystematic fluctuations over more business units (Berger & Ofek 1995;
Kim et al. 1993; Lewellen1971; Markham 1973). This risk reduction spawns a feedback
effect, affording firms with reduced risks to realize better external financing terms (Duffie
& Singleton 1997) and improved borrowing ability (Shleifer & Vishny 1992).
Though valid for the manufacturing industry, this particular rationale may not apply
to retailers and may even be detrimental to retailers’ performance due to the extensive
resources needed to establish a retail network in just one additional market. In fact,
Gielens and Dekimpe (2001) and Williams (2001) both suggest that retailers who enter
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new markets (especially markets that are highly competitive) may do so at the expense
of their home market.
Market Power
The ability to cross subsidize internal resources provides yet another benefit to
diversified firms: market power. Larger diversified firms can negotiate better terms with
suppliers and can even demand exclusive relationships, engage in predatory pricing
against competitors (Berger & Ofek 1995; Markham 1973) and may even deter entry by
threatening to respond with a pricing war (Salone, 1987).
Indeed, Gielens and Dekimpe (2001) found evidence that the retail chains that
have the best long run performance are those “who enter early, with substantial scale . .
. while offering a store format that is both new to the host market and familiar to the
parent firm”.
Other Factors Influencing Diversification Decisions
As noted in an earlier subsection, diversified firms may be afforded privileged
financing and are further able to improve their performance by exploiting tax advantages
associated with increased borrowing (Melicher and Rush, 1973; Shleifer and Vishny,
1992).
Williamson (1964) and Jensen (1986) discuss the agency problems that arise from
hierarchical versus market governance. Specifically, managers have an incentive to
over diversify since their compensation is often tied to the revenues generated (Murphy
1985). In addition, revenue growth creates new positions for which these managers
may be promoted to (Barker 1986). The tendency to over diversify is further
exacerbated in mature firms with substantial “free cash flow” (Jensen 1986; Mueller
1972).
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This observation further strengthens the case for this research; in particular, this
research can serve as a guiding principle as to what level of diversification is optimal
resulting in better decisions that are guided by empirical evidence as opposed to
managerial incentives.
Nature of Diversification-Performance Relationship
The benefits of international market and format diversification on firm performance
are unclear. Research examining the affects of international market diversification and
financial performance is mixed. Research has found positive and negative linear
relationships as well as nonlinear relationships (e.g. Annavarjula and Beldona 2000;
Hitt, Tihanyi, Miller and Connelly 2006; Li 2007, Palich et al. 2000). Specifically, Palich
et al. (2000) – in their synthesis of three decades of research on diversification and firm
performance – conclude that “this area of inquiry falls far short of consensus”.
Similarly, research on the effects of product/industry diversification and financial
performance has produced inconsistent results with some research suggesting that
product diversification moderates the international market diversification-performance
relationship (Chang and Wang 2007).
Linear Impact of Firms’ Diversification on Their Financial Performance
Earlier research focused on the benefits or costs of diversification and proposed
either a positive or negative linear relationship between international and
product/industry diversification and performance. Consistent with basic marketing
guiding principles that stress core competencies and market orientation, a survey of the
literature revealed that proportionally more studies found positive linear as opposed to
negative linear relationships between diversification and performance (Hitt et al., 2006).
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Positive linear impact of diversification on firm performance
Support for this model stems largely from theories of market power and internal
market efficiencies (Gort, 1962; Grant, 1998; McCutcheon, 1991; Scherer, 1980).
Specifically, diversified firms are able to use their asymmetric financial strength (i.e.
cross-subsidization across other business units) to engage in predatory pricing driving
their more focus rivals out of the market and thereafter reaping gains from higher pricing
strategies (Saloner, 1987).
Market power can also yield a positive linear relationship for diversified firms as
they are able to secure favorable buying and selling arrangements from other firms that
are both suppliers and customers of the diversified firm (Schrer, 1980; Sobel 1984).
Implicit in these justifications is that a certain degree of market efficiency exists.
However, these theories may not be suitable in global and mature markets where
competitive intensity is high or in retail markets where margins are sufficiently low .
Negative linear impact of diversification on firm performance
While the agency justification provided in the proceeding section can also lead to
spurious diversification decisions and negative firm performance, the extant literature
focuses largely on the governance cost justification. Specifically, diversified firms incur
increase governance cost, which detracts from their performance (i.e. excess returns).
Vermeulen and Barkema (2002) in their study found that the speed of
internationalization, spread of the geographic and product markets and irregularity in
expansion patterns can further negatively moderate the diversification – performance
relationship.
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Another study by Siegel, Omer, Rigsby & Theerathorn (1995) found that increased
international diversification is also associated with increased total risk to the firm,
resulting in a negative relationship between diversification and performance.
Non-Linear Impact of Firms’ Diversification on Their Financial Performance
More recent research, considering both the costs and benefits of diversification,
has proposed and empirically investigated complex, non-linear relationships such as
inverted U-shaped, U-shaped, or S-shaped (Hitt et al. 2006).
These models posit that moderate levels of diversification is better than none
(Palich et al., 2000) and the curvilinear models stem from an array of moderators, such
as: geography (Li and Qian, 2005, Nachum, 2004, Vermeulenn and Barkema, 2002),
munificence in the home country (Wan and Hoskisson, 2003), speed of
internationalization, spread of product markets entered (Hitt, Hoskisson and Kim, 1997,
Vermeulenn and Barkema, 2002), competitive intensity (Martin, Swaminathan and
Mitchell, 1998, Sakar, Cavugsil and Aulakh, 1999), level of intangible assets (Lu and
Beamish, 2004), firm size (Dragun, 2002, (Qian, Yang and Wang, 2003), R&D intensity
(Kotabe, Srinivasan and Aulakh, 2002, Qian, Yang and Wang, 2003), marketing
intensity (Kotabe, Srinivasan and Aulakh, 2002), level of international experience
(Bloodgood, Sapienza and Almedia, 1996, Daily, Certo and Dalton, 2000, Ramaswamy,
1995), novelty and differentiability of the firm’s product offerings over those found in the
host countries (Bloodgood, Sapienza and Almedia, 1996), commonalities between
markets entered (Riahi-Belkaoui, 1996), mode of entry and investment intensity upon
entry (Gielens and Dekimpe, 2001).
28
U-shaped impact of diversification on firm performance
One rationale for a U-shaped relationship considers both governance costs and
learning effects. Initially the governance costs reduce firm performance, but over time,
firms learn how to operate new formats and in new markets, reducing the governance
costs, and performance begins to increase. However, as diversification continue to
increase the governance costs increase to a point at which they can not be effectively
dealt with and performance decreases.
Another rationale for this relationship stems from the use of predatory pricing by
the incumbent entrant. Initial performance may be negative due to deliberate pricing
strategies and intense competition from established firms in the host country.
Subsequent performance improves as the entering firm mitigates losses by increasing
prices and gains market share (Berger & Ofek 1995; Markham 1973).
Firm performance may also subsequently improve yielding an inverted U-shaped
diversification - performance relationship as: their product/concept gains acceptance in
the host market(s) (Bloodgood, Sapienza and Almedia, 1996, Gielens and Dekimpe,
2001), they increase their market presence (i.e. retail network) or improve their image in
the host country (Gielens and Dekimpe, Kotabe, Srinivasan and Aulakh, 2002, Sakar,
Cavugsil and Aulakh, 1999) and when they improve their global market position and
image (Barkema, Bell & Pennings 1996; Bloodgood, Sapienza and Almedia, 1996).
Inverted U-shaped impact of diversification on firm performance
The theoretical rationale for the inverted U-shaped relationship between
diversification and performance stresses the initial positive effects of diversification up to
a point at which the governance and agency costs arising from further diversification out
weight the benefits.
29
Several studies have found support for this particular form diversification –
performance proposition (Hitt, Hoskisson and Kim, 1997; Gomes and Ramaswamy,
1999). Specifically, Hitt et al., (1997) finds that while firms can enjoy substantial growth
opportunities in new markets (Markides, 1992, Hitt et al. 2006) the cost of coordination
increases with increased operating scope.
In addition, selective entry of markets based on market potential can also result in
an inverted U-shaped diversification-performance relationship as firm experience
diminishing excess returns and increased competition with each subsequent entry
(Gielens and Dekimpe, 2007).
In particular, Ramaswamy (1995) finds that diversification-performance is
positively related to the level of control and coordination within the firm and the ability to
exercise a high level of control is decreasing with firm size (Hoskisson & Hitt1988;
Williamson 1967). The case for an inverted U-shape diversification-performance
relationship is further strengthened by combining the latter theory with the selective
entry hypothesis as coordination and control become increasingly difficult in markets
that are increasingly unrelated (Mitra and Golder, 2001, Riahi-Belkaoui, 1996,
Sambharya 1996).
Yet another rationale supporting an inverted U-shaped diversification-performance
relationship is the diminishing excess returns that firms receive from new markets over
time (Markides, 1992).
S-shaped impact of diversification on firm performance
Finally, the rationale for the S-shaped relationship suggests that some level of
diversity is needed before learning effects take hold, but when they do, performance
30
increases to a point at which there are diminishing returns to additional learning and
diversification.
Cross-Country Characteristics and their Impact on Successful Diversification
A survey of the entry literature provides support for the selective entry hypothesis
(Gielens and Dekimpe, 2007, Mitra and Golder, 2001). In particular, these studies
suggest that firms stage their entry decisions to target enter markets that are most
similar to their home market and/or markets they are familiar with (Riahi-Belkaoui, 1996)
and subsequently use the knowledge acquired from existing markets to guide future
entry decision (Mitra and Golder, 2001).
Despite this background, Palich et al. (2000) noted that few studies spanning
strategic management, economics and finance (with the exception of : Bettis, 1981;
Christensen and Montgomery, 1981, Markides and Williamson, 1994, Nayyar, 1992,
Rumelt, 1974, 1982, Galai and Masulis, 1976, Higgins and Schall, 1975, Levy and
Sarnat 1970, Lewellen 1971) have considered the moderating impact of related versus
unrelated diversification on firm performance. Even fewer have taken into account the
level and type of diversification on the diversification-performance relationship resulting
in “no clear consensus” on how commonalities across diversified operations affect the
diversification-performance relationship (Paclich et al. 2000).
On the other hand, several authors argue that differences in markets can provide
firms with access to substantial growth opportunities in foreign countries (Hitt et al.
2006, Markides 1992) and can also accentuate their existing core competencies and
gain unique knowledge (Hitt et al. 2006).
31
Of the studies that do account for similarities across countries, the findings are
mixed. In particular, Riahi-Belkaoui (1996) in his survey of 31 French multinational
organizations finds that performance gains are greater with unrelated
32
CHAPTER 3 CONCEPTUAL FRAMEWORK AND HYPOTHESES
My conceptual framework for examining the effects of international market and
format diversification on retailer financial performance is illustrated in Figure 3-1.
This framework suggests that the financial performance of retailers is affected by
two characteristics of their international market portfolios – the intensity of international
market diversification and the cultural and economic dissimilarity (lack of synergy) of the
international markets in which the retailer operates. Similarly, financial performance is
also affected by the intensity of format diversification and the dissimilarity of the formats
operated by the retailer. Finally, I propose an interaction between international market
and format diversification intensity. The dashed feedback paths in the framework
represent the endogenous confounds in the diversification decision making.
Specifically, the retailer’s financial performance affects its decisions to diversify as well
as diversification affecting financial performance.
In the following section, I discuss the requisite assumptions as well as several
unique characteristics of this framework. Next, I argue that the effects of diversification
on financial performance are different for retailers and manufacturers. Then I review the
costs and benefits of diversification as a background for the rationales I used to support
the hypotheses illustrated in my framework.
Conceptual Considerations and Assumptions of the Model
As previously mentioned, this research is distinct from studies that focus on entry
and performance as it abstracts from micro market considerations based on the
assumption of aggregate rationality prevailing over stochastic variations in the long run
and across the cross section. Specifically, this model assumes that retailers make
33
rational choices to maximize their expected return of investment when evaluating
subsequent growth opportunities and their calculus involves a joint optimization across
expected market and expected format potential
,[ (format expertise, international expertise, control variables)]i tE π and the corresponding
risk based on their unique constraints – denoted by , (.)i tC and information set at time t
(denoted by tΩ ).
Specifically, I assume that retailers’ enter markets that are the most lucrative,
given their existing market-format portfolio and will continue to do so in descending
order of the expected marginal products and cross products.
1
, 1, t
, , 1
Max [ (format expertise, international expertise, control variables) | ]
s.t. (.) and (.)i t
i t
i t i t
E
C Cπ
π+
+
Ω
Specifically, retailers are faced with the following constrained optimization:
(3.1)
Based on this assumption, the aggregate count variables, especially the
interaction between the country and format counts are highly endogeneous while the
dissimilarity measures can be considered pre-determined conditional on the information
set (Davidson & MacKinnon 1993).
Diversification Implications for Retailers and Manufacturers
Two critical differences between retailers and manufacturers that can affect the
portfolio diversification-performance relationship are: (1) the local nature of retailing and
(2) the greater complexity of retailing compared to manufacturing operations (Dawson
1 This assumption is supported by research examining McDonald’s expansion strategy; see Lafontaine & Leibsohn (2004).
34
1994 2007; Finn and Quinley 2010; Wrigley et al. 2005). Even though retailers may
have extensive global operations, their offering to consumers is predominantly made
through their stores and the market for these stores is local. Most consumers
patronized store in close geographic proximity to where they work and live. Retailers,
and other consumer service providers, need to satisfy the needs of local markets and
compete against other retailers in these local markets. Thus, as they diversify
internationally, retailers typically need to have operations (stores) in many more
locations and be more sensitive to local cultural differences compared to manufacturers.
For example, there is significant worldwide heterogeneity in preferences for food offered
by supermarkets and restaurants, but little heterogeneity in the use of microprocessors.
Because a key benefit offered by retailers is an assortment of products, retailers
typically interact with more suppliers than manufacturing firms (Capar & Kotabe 2003;
Quinn & Fally 2010). Even a relatively small retail chain will operate stores in over 500
locations, deal with a thousand suppliers, and hundreds of thousands of customers
(Alexander & Myers 2000). The complexity of managing this extensive network of
customers, suppliers, and locations are dramatically heightened when retailers engage
in international or format diversification. In addition to the challenges of managing
employees in a large number of remote locations, there are significant supply chain
management challenges in getting the right products to the right places at the right time.
Indeed, the difference in delivery requirements for manufacturing firms versus
service firms (especially retail firms) gives rise to greater uncertainty for service firms
ex-ante, which may lead to different post entry outcomes (Lafontaine & Leibsohn 2004).
35
Benefits and Costs of Diversification
The intensity of international market and format diversification, the extent to
which a retailer operates in multiple markets with multiple formats, affects the retailer’s
revenues, costs, and subsequent financial performance. In this section I briefly review
the costs and benefits of diversification.
Diversification benefits
Some of the benefits of diversification are: economies of scale and scope, more
effective asset management, opportunity to use market power, and risk reduction.
International market and format diversification enable retailers to draw on scale
economies to reduce costs. By expanding operations to new markets and using new
formats, a retailer’s fixed costs are spread across the increased revenue opportunities
arising from diversification (Caves, 1996). These scale and scope opportunities are
enhanced for retailers possessing valuable firm specific assets, such as well-known and
highly regarded brand names, unique systems and processes, and managerial skills.
Diversified retailers, in contrast to non-diversified retailers, can have greater
access to both internal and external sources of financing (Lang & Stulz, 1994) and this
expanded access provides diversified firms with privileged access to less costly
financing (Froot et al., 1994; Lang et al., 1995). Diversification also reduces a retailer‘s
risk by spreading the effects of nonsystematic fluctuations over more business units (i.e.
Berger & Ofek 1995; Kim et al. 1993;). This risk reduction produces a feedback effect
affording retailer’s with reduced risks to realize better external financing terms (Duffie &
Singleton 1997) and improved borrowing ability (Shleifer & Vishny 1992).
36
The ability to cross subsidize internal resources provides yet another benefit to
diversified retailers: market power. Larger diversified retailer’s can negotiate better
terms with suppliers and can even demand exclusive relationships, engage in predatory
pricing against competitors and may even deter entry by threatening to respond with a
pricing war (Berger & Ofek 1995; Markham 1973).
Diversification cost
Some of the costs of diversification are due to increased management
complexity, agency issues, and familiarity. Perhaps the primary cost related to
diversification is the increased complexity of managing diversified firms. As retailers
diversify, their management hierarchy expands and they encounter greater difficulties in
disseminating information to business units, resulting in transmission and coordination
inefficiencies and the loss or distortion of information (i.e. Hoskisson and Hitt 1988;
Williamson 1964). Furthermore, the larger hierarchical structure of diversified retailers is
conducive to employee shirking; thereby further incurring cost associated with either
heightened monitoring or decreased labor productivity (Calvo and Wellisz 1978).
Williamson (1964) and Jensen (1986) discuss the agency problems that arise from
hierarchical versus market governance. Specifically, managers have an incentive to
over diversify since their compensation is often tied to the revenues they generate
(Murphy 1986). In addition, revenue growth creates new positions to which these
managers may also be promoted. This tendency to over diversify is further exacerbated
in mature firms with substantial “free cash flow” (Jensen 1986; Mueller 1972).
In addition to these increased internal governance costs, diversification can lead
to inefficiencies arising from external challenges of operating a new format or being in a
new international market (i.e. Hymer 1976, Zaheer 1995). Some of these
37
disadvantages, related to diversification, are due to inefficiencies resulting from the lack
of market reputation (Barkema, Bell and Pennings 1996); limited knowledge about the
new environment, which leads to lower efficiencies than native firms (Hyman 1976); and
increased uncertainty from having to operate in a complex and unfamiliar environment,
characterized by a different set of economic, political and legal factors (Sambharya
1996).
International Market and Format Diversification and Retailer Financial Performance
In this section, I discuss how these benefits and costs associated with
diversification affect the financial performance of retailers.
International Market Diversification Intensity
Early research on international market diversification focused primarily on either
the benefits or costs and proposed either positive or negative linear relationships
between international market diversification and the performance of manufacturers.
Earlier research focused on the benefits or costs of diversification and proposed either a
positive or negative linear relationship between international and product/industry
diversification and performance. More recent research, considering both the costs and
benefits of international market diversification, has proposed and empirically
investigated complex, non-linear relationships such as inverted U-shaped, U-shaped, or
S-shaped (Hitt et al. 2006).
The theoretical rationale for the inverted U-shaped relationship between
international market diversification and financial performance stresses the initial positive
effects of diversification up to a point at which the governance and agency costs arising
from further diversification out weight the benefits. The rationale for a U-shaped
38
relationship considers both governance costs and learning effects. Initially the
governance costs reduce firm performance, but over time, firms learn how to operate
new formats and in new markets, reducing the governance costs, and performance
begins to increase. Finally, the rationale for the S-shaped relationship suggests that
some level of diversity is needed before learning effects take hold, but when they do,
performance increases to a point at which there are diminishing returns to additional
learning and diversification.
As discussed previously, most of the prior empirical research on international
diversification is based on samples of manufacturing firms, but there are significantly
greater governance costs (managing many remote locations and suppliers) associated
with the international diversification of retailers compared to manufacturers as
discussed previously. Thus, I propose that retailer with a modest number of
international markets in their portfolio will have the highest governance costs as a
percent of sales and the lowest financial performance. Retailers operating in a small
number of international markets will have lower relative costs and higher performance
because they have sufficient slack to manage the governance issues in a limited set of
markets. The performances of retailers with many international markets in their
portfolios have lower relative costs and higher performance because they have learned
to effectively manage many remote locations. Thus, I propose that:
H1: The relationship between the intensity of international market diversification and retailer financial performance is U-shaped.
Format Diversification Intensity.
Using a similar logic, due to the complexity of retail operations, the governance
costs incurred by launching a new format are substantial. However, as retailers
39
develop a larger portfolio of formats, they learn how to manage the multiple formats for
efficiently and extract the economies of scale and scope from operating many formats.
In addition, with many format, retailers are better able to match the format with the
needs of different markets. Thus,
H2: The relationship between the intensity of format diversification and retailer financial performance is U-shaped.
International Market-Format Diversification Interaction
Many manufacturing firms, as they diversify internationally, also diversify their
product portfolios as well. Prior research suggests that product diversification (the
manufacturing equivalent of format diversification for retailers) moderates the
international diversification-performance relationship (Hitt, Hoskinsson, & Kim, 1997;
Kim, Hwang.& Burgers1989). However, the empirical evidence on the moderating
effects between product and international diversification is mixed (Chang and Wang
2007). Some research has reported a positive moderating effect while other studies
have found a negative moderating effect. The rationale for the positive moderating
effect is that the experience gained from dealing with a diversified product portfolio
enables manufacturers to better deal with the complexities associated with international
market diversification. In addition, the access to diversified portfolio of products enables
firms to better tailor their offering to the unique needs of different countries. The
rationale for the negative effect is that product diversification, when coupled with
international diversification, dramatically increases the complexity of the management
tasks and reduces efficiency, and further decreases performance. In light of the
heightened demand and supply side complexity of retailing compared with
manufacturing, I propose that:
40
H3: The intensity of international market and format diversification interact with each other producing a negative effect on retailer financial performance.
Effects of the Characteristics of the Portfolio of International Market and Format on Retailer Financial Performance
The extant international diversification research discussed in the previous sections
simply examines the effects of the intensity, or degree, of international market and
format diversifciation on performance but does not consider the characteristics of the
diversified portfolio. However, a number of researchers have suggested that the degree
to which the products [or in the case of retailers, the formats] and international markets
are related affects the diversification-performance relationship (i.e. Cavusgil 1983). For
example, Rumelt (1982), in his classic diversification research study, found differences
in financial performance between firms pursuing related and un-related product
diversification strategies. Rumelt speculated that the performance premium afforded to
firms with related product diversity stems from their ability to exploit core competencies
and create unique and defensible product-market positions, a view echoed by marketing
scholars (Davison 1983, Jaworski & Kohli, 1993; Hunt & Morgan, 1995). In this
research, I examine the following three factors affecting the degree to which the
international markets and formats in a retailer’s diversification portfolio are unrelated: (1)
the cultural dissimilarity of the countries in the retailer’s portfolio, (2) the economic
dissimilarity of the countries in the retailer’s portfolio, and (3) the operational dissimilarity
of the formats in the retailer’s portfolio.
Cultural Dissimilarity
Increased challenges arise when a retailer’s portfolio consists of international
markets with dissimilar cultures because the retailer must develop an understanding of
41
a number of different norms and values for both its customers and employees. To
effectively satisfy the needs of customers and employees, the retailer has to develop a
wide variety of merchandise assortment, formats, and human resource policies and
procedures. Managers in culturally diverse countries are also less able to take
advantage of economies of scale and scope involving joint purchasing, advertising,
brand building programs, distribution, and systems for workload planning, visual
merchandising, and employee performance appraisal. Thus cultural dissimilarity in a
retailer’s portfolio of countries reduces the retailer’s ability to develop scale economies,
exploit synergies, and realize the benefits of increased revenues from diversification
(Palich & Gomez-Mejia, 1999, Gomez-Mejia & Palich 1997). Thus,
H4: The degree of cultural dissimilarity within a retailer’s international market portfolio decreases the retailer’s financial market performance of.
Economic Dissimilarity
Increased operational costs also arise when a retailer has a portfolio of countries
at different stages of economic development. For example, a key competitive asset of
Wal-Mart’s is its systems and skills in supply chain management. However, this
capability was developed in develop in the United States with its well-developed
infrastructure. Wal-Mart may not be able to exploit this asset when operating in less
developed countries with limited infrastructure. Prior research supports this contention
that economic dissimilarity can impact on firms’ performance (Baffoe-Bonnie & Khayum
2003; Beckerman 1956; Davidson 1983; Dunning 1973)
H5: The degree of dissimilarity in the economic development of the countries in diversification the financial market performance of retailers.
42
Format Dissimilarity
Different skills and resources are required to effectively operate different formats.
For example, brand building, merchandise selection, pricing, and store design and
visual merchandise skills, skills related to generating demand, are needed to effectively
operate apparel specialty stores that typically target fashion-oriented customers. On the
other hand, cost control factors such as supply chain management are more important
in the effective management of discount store that typically target value-conscious
customers. By operating different formats, retailers can provide an attractive offering to
different market segments but their abilities to exploit operational efficiencies and scale
economies decreases. However, the benefit of operating a number of different formats
is moderated by the degree to which the formats require similar operating skills. Thus,
one would expect that a retailer operating discount stores and warehouse clubs, two
formats focusing on cost control skills would have better performance than a retailer
operating apparel specialty stores and warehouse clubs.
H6: The dissimilarity in the operational characteristics of different formats within a portfolio reduces the financial market performance of retailers.
43
Figure 3-1. Conceptual framework
Financial Performance
of Retailer
Intensity of International
Market Diversity in Retailer’s
Portfolio
Intensity of Format
Diversity in Retailer’s Portfolio
Dissimilarity of Cultural and
Economic Market Conditions in
Retailer’s Portfolio
Dissimilarity of Formats in
Retailer’s Portfolio
44
CHAPTER 4 METHOD
In this section, I describe the sample, measures for the constructs, sources of the
data, and econometric model used to estimate the effects of the degree of international
and format diversity and portfolio characteristics on financial performance.
Sample
The sample of retailers used in this study was the world’s largest global retailers
as identified in the annual Deloitte Touche Tohmatsu’s Global Powers of Retailing
reports published from 2002 to 2007 (Deloitte 2002, 2003, 2004, 2005, 2006, 2007).
These reports, also published as addendums to the January issues of Stores magazine,
provided data on the 200 largest retailers from 2002 to 2004 and on the 250 largest
retailers from 2005 to 2007. However, some retailers in these reports were not be
included because financial market performance measures of retail performance were
not available (i.e. they were not publically traded or were part of larger
diversified/holding firms), In addition, Ieliminated retailers that were either automobile,
food, and hotel franchisors or franchisees. These restrictions, along with firms entering
and exiting the largest retail list, resulted in data set composed of 762 annual
observations for 170 retail firms.
Measures
Financial Market Performance
As noted by Hoskisson, Hitt, Johnson and Moesel (1993), market-based financial
measures of firm performance are superior to accounting measures because they are
more closely related to the firm’s objective of maximizing stockholder and less
45
susceptible to managerial discretion (Barney 1997), making them more suitable for
studying diversification-performance linkages (Palich et al., 2000).
In this study, I used Tobin’s Q as the measure of financial market performance.
Tobin’s Q is the ratio of a firm’s market value to the cost of replicating the firm’s assets.
It is a proxy for the average return on a firm's capital anticipated by the market. Hence, it
is a preferred measure of firm performance because it is forward looking and provides
an unbiased comparison across firms (Wernerfeldt and Montgomery 1988, Srinivasan
and Hanssens 2009). Particularly, under the assumption of financial market efficiency,
changes in future earnings and costs will be capitalized into share prices. Thus, if
diversification is beneficial, past expansion should be related to higher share value (i.e.
high Q), not high current returns to shareholders. Further, because investors require a
return related to the riskiness of an asset, comparing returns without adjusting for
riskiness yields biased results. In contrast to stock return comparisons or accounting
performance measures, no risk adjustment is required to compare Q across firms.
Tobin’s Q was operationalized as the retailer’s market capitalization at the end of the
fiscal year divided by firm’s total assets.
Diversification Intensity
In prior research, the most commonly used measures of international
diversification are the firm’s percentage of overall sales, profit, assets, or employees in
non-domestic markets. However, these measures have been criticized for not capturing
the actual nature of heterogeneity in international diversification (Vachani 1991). Firms
can have the same level of diversity if their percent of international sales are the same
but they operate operating in few or many countries.
46
In a similar manner, product diversity, the manufacturing industry’s parallel to
retail format diversity, is typically operationalized as the number of SIC categories in
which the firm operates or an entropy measure based on the percentage of sales or
profit generated in each SIC category. The SIC categories are particularly poor at
characterizing the operating characteristics of services firms, especially the operating
characteristics of the various operating formats. I used a simply count of the number of
countries that retailer have operated in to measure international diversity and a simple
count of the number of formats to measure format diversity 1
Format Dissimilarity
. These counts were based
on data provided in the Global Powers in Retailing reports (Deloitte 2002 - 2007). These
count measures provide a richer portrayal of the level of diversification than a simple
percent of international/product category sales or profits.
I used expert judges to assess the similarity of the retail formats in each retailer’s
portfolio. Ten experts (academic scholars, senior retail executives and consultant to the
retail industry) were asked to indicate the degree to which each retail format pair were
similar in terms of their operations using a seven-point Likert scale [very dissimilar = 1
and very similar = 7].
The experts made these judgments for the 78-pairs of formats, derived from the
following 13 retail formats: apparel specialty, catalogue or electronic retailer,
1 Another commonly used measure of diversification is an entropy y measure because it considers both the number of countries in which a firm operates in and the proportion of total sales or profits in each country (see Palepu 1985). The entropy measure is calculated by summing the percentage of sales in each countries weighed by the natural log of one divided by the percentage of sales in the country. I was not able to use this measure of diversification because firm sales or profit by country and by retail format are not readily available using secondary sources.
47
convenience store, departmental store, drug store, electronic specialty, hard goods
discounter, hypermarket, home improvement, non-apparel/other specialty, soft goods
discounter, supermarket and warehouse club. Eight experts responded and the
reliability of their responses was acceptable [Alpha = 0.81]. The average of the mean
expert rating for each pair in a retailer’s portfolio was used to create a format
dissimilarity index. A copy of the survey questionnaire is included in Appendix D.
Cultural Dissimilarity
Our measure of cultural dissimilarity of a retailer’s portfolio of countries was based
on the four cultural dimensions developed by Hofstede (1984): individualism
masculinity, power distance, and uncertainty avoidance. The cross-cultural scores,
available for 79 countries, have been widely used in studies of consumer innovation
(Steenkamp, ter Hofstede & Wedel 1999), consumer taste (Craig, Greene & Douglas
2005), decision marking (Roth 1995), ethics (Franke & Nadler 2008; Waldman, de
Luque, Washburn, House, Adetoun, Barrasa, Bobina, Bodur, Chen, Debbarma,
Dorfman, Dzuvichu, Evcimen, Fu, Grachev, Duarte, Gupta, Den Hartog, de Hoogh,
Howell, Jone, Kabasakal, Konrad, Koopman, Lang, Lin, Liu, Martinez, Munley,
Papalexandris, Peng, Prieto, Quigley, Rajasekar, Rodriguez, Steyrer, Tanure, Thierry,
Thomas, van den Berg & Wilderom 2006), managerial functions (Costigan, Insinga,
Berman, Ilter, Kranas & Kureshov 2006; Smith, Dugan & Trompenaars 1996; van
Oudenhoven, Mechelse & de Dreu 1998), negotiations (Campbell, Graham, Jolibert &
Meissner 1988; Graham 1985) and retail image (Bianchi & Ostale 2006; O'Grady &
Lane 1997).
To calculate cultural dissimilarity, I created a composite index that is a variant of
the index used by Kogut and Singh (1988). I first divided the country rating for each of
48
the four Hoefstede dimensions by its standard deviation. Then I calculated the average
dimension dissimilarity between all countries in the retailer’s portfolio. Next, I took the
average of the squared average dimension dissimilarity across the four dimensions. In
the few cases, where cultural distances scores were not available, I either made the
appropriate substitutions or dropped the observation point altogether. For instance,
cultural distances for former and present territories or colonies were substituted with
scores of the governing country.
Thus, this index is:
2 2, ,Cultural dissimilarity = (( ( ) / ( 1) / 2!) / ) / 4i k i j ik ji
H H n n σ<ΣΣ − −∑
where:
,i jH denotes the value of the ith Hofstede dimension for the jth country.
i = 1 .. 4 denotes the ith original Hosftede dimension; and,
k, j = 1 .. n index the country pairs for which the firm has operations in;
n denotes the number of countries in the firm’s portfolio for that particular year.
Economic Dissimilarity
This index was developed using a similar approach to the development of the cultural
dissimilarity index using factors assessing the economic climate and technological
development of the countries rather than Hofstede’s culture scores. As noted earlier,
variations in economic orientation across countries has been shown to affect the
internationalization decisions of firms and is also likely to affect a firm’s performance. To
account for this effect, I obtained various economic measures that would later comprise
the economic distance construct and instrumental variables. A total of 86 relevant World
Bank Indicators [WBI] time series for the dynamic variables [for the years 1997 to 2006]
49
and 13 relevant Central Intelligence Agency’s World Factbook 2007 entries for the static
[or reasonably static over the analysis period] variables were obtained for the relevant
countries and their corresponding descriptions can be found in Appendix D.
The factors scores for four dimensions (development status and infrastructure
technological advancement and efficiency, international orientation and market
potential) were based on an exploratory factor analysis of 62 economic indicators from
the World Bank (WDI 2007). Factor analysis results are provided in Appendix D and the
Scree plot is shown below.
Summary Statistics
The means, standard deviations, and correlation matrix for the variables are
shown in Table 1-1 and 1-2 respectively. The sources of the data used in the study are
in the Appendix A.
Estimation
Endogeniety Issue
A critical issue that has not been addressed in most diversification-performance
studies is the endogenous link between a firm’s decision to diversify its portfolio of
countries and formats and its performance. While diversification can improve firm
performance, improved performance can also generate more resources that motivate
and enable firms to expand into new markets and launch new formats. Li (2007)
emphasizes that the causality of the international diversification-performance
relationship remains ambiguous. He refers to Tallman and Li (1996, p.181) who say “…
evidence suggests that firms with significant performance advantages tend to be
multinationals but that the direction of the causal relationship may well run from high
levels of firm-specific capabilities to higher performance to international diversification,
50
rather than from capabilities to multi-nationality to higher performance.” Li (2007) then
goes on to say that there are virtually no studies that have examined the two-way
relationship between international diversification and firm performance simultaneously.
An assumption necessary for regular estimation is that ,[ ] 0E =Ti,t i tx u . This
condition, referred to as the sequential conditional exogeneity condition, is likely to have
been violated based on theories on how firms sequentially choose to enter markets
(Mitra & Golder 2002; Montgomery & Wernerfelt 1988; Wernerfelt & Montgomery 1988),
survivor bias (Denrell 2003) and other heterogeneous firm characteristics (e.g. size,
structure, industry) affecting diversification decisions (Campa & Kedia 2002).
Method
Simultaneous equations is an econometric technique that is used to estimate
multiple interdependent variables of interest in a series of equations involving the
interdependent variables of interest and other exogenous variables and includes as
examples: substitution into reduced form equations, IV estimation, 2SLS, 3SLS and
seemingly unrelated regression models (Wikipedia 2006). The latter is a technique for
enhancing the efficiency of regression systems with correlated error terms (Wikipedia
2008b) and the remainder, except for substitution into reduced form, are manifestations
of IV-type techniques. In particular, 3SLS is 2SLS with the addition of an extra step that
enhances efficiency thru estimation of the error components using the residuals from
the second stage (Sola 2004).
The Tobit model is used for dealing with latent variables that are prone to
inconsistent estimates when using traditional estimation techniques due to issues that
arise from censoring (Wikipedia 2008c). Another model, the Heckman selection model,
51
does not appropriately deal with the issue at hand. Specifically, the Heckman selection
model is used to estimate parameters when the sample is prone to self-selection (i.e.
estimating wage relationships when wages are only available for working individuals)
and corrects for this self-selection by the use of a Probit model to estimate the
probability of being employed and subsequently using this result to weight the ensuing
regression/ estimation (Hopkins 2005; Wikipedia 2008a). Hence, while this method
corrects for the sample selection problem, it does not correct for the inherent
endogeneity between performance and level of diversification (the endogeneous
relationship between the dependent and independent variables). It may be beneficial to
implement the Heckman correction with another technique that corrects for the
endogenous relationship, thereby accounting for 1) the self selecting sample of
companies that diversify and 2) the endogeneous relationship amongst those who
diversify. However, the first issue may very well be equally addressed by the use of
dummy variables to account for heterogeneous sample characteristics and estimating
separate equations for the different sample groups.
Perhaps the most well-known techniques used in econometrics for the correction
of endogeneous relationships between the dependent and independent variables are
those of the IV-type, specifically: 2SLS and 3SLS. The use of such techniques will help
in isolating the exact performance premium attributed solely to diversification levels and
NOT to a mix of diversification levels and other unobserved correlated constructs (e.g.
size, managerial efficiency, TMT diversity, etc.) that seems to plague many of the
existing diversification studies. It is perhaps for this reason that researchers have not
yet reached a consensus on the exact diversification relationship that seems to change
52
with each passing sample or the varying relationships across different firm
characteristics, such as size (see: Dragun 2002).
A review of the literature revealed that a handful of the many existing studies used
the following endogeneity correction method(s): 2SLS (Campa & Kedia 2002) and
Heckman Correction (Campa & Kedia 2002; Graham, Lemmon & Wolf 2002; Hyland &
Diltz 2002; Lang & Stulz 1994). However, a subset of these studies look at a totally
different issue: the self selection into diversification rather the level of diversification
(Campa & Kedia 2002; Hyland & Diltz 2002). Hence, while it is appropriate for those
studies to correct for endogeneity using a Heckman approach, it is not appropriate in
our case to solely rely on that method.
Substantive Issue
The key issue is to derive “non-endogeneous country and format” count variables
for use in predicting the relationship between level of diversification and performance for
a sample of retailers. Focusing on the first stage analysis, the analysis in which we use
various instruments to “tease out” the bidirectional effect from the respective count
variables, we require that our instruments be correlated with the problematic variable
(the count variables) but not correlated with the error term (the residual after regressing
performance on level of diversification, distance moderators and their interaction terms).
This implies that the instruments need to be uncorrelated not only with the current
period’s firm performance construct but also that of previous lags. This is due to the
possibility that firms make entry/ expansion decisions in periods of good performance
that manifest in future periods but firms can also abort such plans intermittently if
performance starts to head south (e.g. Toyota delays plans to open additional plants in
the US).
53
The following is a suggested list of instruments types, listed in an order that
depicts decreasing correlation with the error terms (i.e. ranging from better to worse
instruments).
• Historic variables (e.g. firm founding year, continent firm was originally established
in, etc.)
• Relatively static home country specific characteristic variables (e.g. uncertainty
index, social index, etc.)
• Relatively dynamic home country variables (e.g. infrastructure expenditure, tourism
rates, etc)
• Industry specific variables (e.g. competition index for a certain industry)
• Firm specific variables (e.g. firm size, alternative performance index, etc.)
First-Stage Estimation
The orthogonality conditions needed for least squares to produce unbiased
estimates was potentially violated for a variety of reasons. For instance, correlations
could arise due to heterogeneous firm characteristics (e.g. size, structure, industry) of
highly versus lowly diversified retailers (Campa & Kedia 2002), sequential entry
decisions (Mitra & Golder 2002; Montgomery & Wernerfelt 1988; Wernerfelt &
Montgomery 1988) and survivor bias (Denrell 2003).
To correct for the potential orthogonality violation and endogeneity bias, I used a
two stage estimation model with panel autoregressive disturbance. However, standard
two-stage estimation techniques were not suitable for my model since both my first
stage dependent variables were count variables. Standard Instrumental Variables
method utilize normal distribution estimation techniques for the first stage and is hence
54
not appropriate for count variables that follow either a Poisson or Negative Binomial
distribution.
My modified estimation technique involved first regressing two sets of instrumental
variables (IVs) on each of the two decision variables: retailers’ country count and format
count. Intuitively, this step can be thought of as a screen for the endogenous confounds,
that drops out with the other unexplainable confounds in the error term from the first
stage.
The first-stage regression is aimed at “teasing out” the bi-directional causality
between firm performance and the explanatory variables. I assume that endogeneous
effects lie predominantly in the aggregate count variables for countries and formats
based on conjectures and some evidence that firms diversify in a strategic manner
(Mitra & Golder 2002; Montgomery & Wernerfelt 1988; Wernerfelt & Montgomery 1988).
Appropriate instruments should correlate well with the count variables but not
with performance related constructs (Davidson & MacKinnon 1993; Wooldridge 2002);
hence, we chose to use 9 firm specific variable and 11 three-year lagged country WBI
specific variables since evidence suggest that it takes approximately that amount of lag
for international responses (Flowers 1976) and in implementing managerial actions
(Williams, Hoffman & Lamont 1995). Theoretical and empirical rationales drove the
selection of IVs.
The IVs used in the first stage were variables that depicted the characteristics of
the retailer’s home country influencing diversification interest such as infrastructure,
tourism, industry competition, and continent and characteristics of the retailer such as
founding date, sales, and number of employees.
55
These IVs – used in the first stage to estimate the number of countries and
formats in a retailer’s portfolio – were lagged by three years to reflect the retailer’s
planning horizon. Research suggests that managers often make involved
entry/expansion decisions for the mid to distant future (Alexander & Myers 2000;
Dawson 2001; Rugman & Girod 2003).
Justification for my use of a three-year lag can be found in cases involving
decisions on foreign direct investment (Flowers 1976), by examining the substantially
different firm characteristics (R&D-to-Asset ratio) evident three- to four-years preceding
firms’ diversification attempt(s) (Hyland and Diltz 2002) and even in micro managerial
issues, such as the persistence of managerial-employee relational conflict (Klass and
Denisi 1989). The sources of the data for the IVs is described in Appendix A.
Next, I transformed and re-coded the data when necessary, and windsorized the
data symmetrically at the 5 % end points. I then estimated the first stage models with
two negative binomial regressions.
Second-Stage Estimation
The predicted values for the decision variables – retailers’ country counts and
format counts – from the first stage model were then transformed in accordance with
main, interactive, and non-linear terms of my model used to estimate the factors I
hypothesize to explain retailers’ financial performance (Tobin’s Q).
Tobin’s Qi,t = β0 + β1,i,t x number of countries (4-1)
+ β2,i,t x number of formats
+ β3,i,t x number of countries2
+ β4,i,t x number of formats2
56
+ β5,i,t x number of countries x number of formats
+ β6,i,t x cultural dissimilarity
+ β7,i,t x economic dissimilarity
+ β8,i,t x format dissimilarity
I estimated the model described by Equation 4-1 using Stochastic Moderated
Regression (SMR), a method proposed by Gatignon and Vosgerau (2006) for handling
collinear interaction terms.
SMR allows for random moderating relationships, thereby enabling other
variables [other than the hypothesized moderators] to influence the stochastic
relationship. This is particularly useful when the exact nature of moderating relationship
is: (1) random, (2) not fully understood or (3) misspecified. Additional benefits of SMR
include: achieving more efficient estimates in the presence of multicolinearity, (2)
determining the direction of the moderating causality and (3) not detecting a spurious
moderating effect when none exists, particularly when the main effects are moderately
high to highly correlated. The latter is relevant since correlation between the format
distance variable and format count variable is 0.747 and the correlation between
physical, economic and cultural distance and the country count variable are 0.5881,
0.5869 respectively.
Using SMR, estimation of Equation 4-1 is done by estimation of Equation 4-2 –
Equation 4-4 jointly. However, there are still two issues remaining before we can
proceed further.
, ,0 ,1 , ,2 , ,PERF FORMAT COUNT COUNTRY COUNTi t i i t i t i t i t i tuβ β β, ,= + + + (4-2)
, ,1 , , , ,2 , ,AVG FORMAT DIST COUNTRY COUNTt i i i t i i t i tιβ γ γ γ ε0 1= + + + (4-3)
57
, ,2 , , ,1 , , ,2 ,
,3 , ,2
AVG CULTURAL AVG ECONOMICFORMAT COUNT
t i i t i t i t i t
i i t
ι
ι
β α α α
α ε0= + +
+ +, (4-4)
First, while cross sectional time series data provides for a richer and more in
depth analysis (Gujarati 2003), it also requires additional assumptions about the error
structure. Specifically, firms have varying resources and characteristics, which will likely
result in heterogeneous outcomes and error terms. In addition, these firms are also
likely to demonstrate temporal dependence and variability. Since subsequent tests
indicated no presence of the latter effect, we decided to use a heteroskedastic and
robust covariance matrix to mitigate the former concern and to attain more efficient
estimators. Thus, I specified a heteroskedastic and robust covariance estimator.
Figure 4-1. Scree plot from factor analysis of host country economic variables
58
Table 4-1. Summary statistics for main variables
MEAN S.D. MIN MAX
TOBIN'S Q-RATIO 1.01 0.85 0.15 3.33
COUNTRY COUNT 5.16 6.49 1 23
FORMAT COUNT 2.36 1.64 1 6
AVG. CULTURAL DISSIMILARITY 0.71 0.94 0 5.93
AVG. ECONOMIC DISSIMILARITY 5.29 21.99 0 378.58
AVG. PHYSICAL DISTANCE 2468.11 2905 0 11426.71
AVG. FORMAT DISSIMILARITY 2.57 2.42 0 6.14 Table 4-2. Correlation matrix for main variables
TOBIN'S Q-RATIO
COUNTRY COUNT
FORMAT COUNT
AVG. CULTURAL DISSIMILARITY
AVG. ECONOMIC DISSIMILARITY
AVG. PHYSICAL DISTANCE
AVG. FORMAT DISSIMILARITY
TOBIN'S Q-RATIO 1
COUNTRY COUNT 0.01 1
FORMAT COUNT -0.22 0.2 1
AVG. CULTURAL DISSIMILARITY -0.09 0.44 0.26 1
AVG. ECONOMIC DISSIMILARITY -0.13 0.08 0.16 0.43 1
AVG. PHYSICAL DISTANCE 0.06 0.59 0.13 0.53 0.13 1
AVG. FORMAT DISSIMILARITY -0.27 0.14 0.75 0.2 0.12 0.06 1
59
CHAPTER 5 RESULTS
First-Stage Results
The estimated coefficients for the first stage are shown in Table 5-1. The Durbin-
Wu-Hausman test was conducted to determine the potential impact of endogeneity in a
regression estimated via instrumental variables (IV). The null hypothesis that an
ordinary least squares (OLS) estimator of the same equation would yield consistent
estimates was rejected for the the country and format equations
(21Pr ( 13.03) 0.0003ob χ > = and
21Pr ( 3.33) 0.0679ob χ > = respectively. Thus ignoring
endogeneity among the regressors would have had deleterious effects on OLS
estimates.
While the estimated coefficients for the IVs were used to correct for the
endogeneity bias, there some interesting results supporting my a priori hypotheses
about the effects of the IVs. As expected, the size of the retail firm as measured in
annual sales had a significant positive effect on both the number of countries in a
retailer’s portfolio as well as the number of format utilized. As retailers grew larger they
sought growth opportunities outside their home country and from operating different
formats.
In addition, a number of retailers’ home country characteristics affected their
interest in diversifying their portfolio of countries and formats. The ratio of tourist arrivals
to departure was a surrogate measure of the brand image of the countries. Retailers
with headquarters in countries with a strong brand image were prone to exploit this
advantage by entering more countries.
60
Retailers from home countries with high GDP per capita and population were in a
better position to expand internationally and operate different formats since they were
more likely to have greater access to the resources needed to drive such expansion. On
the other hand, these retailers also benefited from being situated in countries with large
domestic markets and thus were more likely to have significant domestic growth
opportunities. my results suggest that retailers in home countries with high GDP per
capita and populations focused on their growth opportunities in their home countries.
The prevalence of computers in a retailer’s home country was a surrogate
measure of home country technology and infrastructure development. The significant
positive coefficient indicated that technology and infrastructure depth in the retailer’s
home country were enablers of international expansion and format diversification.
Retailers that were headquartered in the European or North American continents
(compared to those from other continents) and operated specialty stores formats
operates diversified into more countries. Perhaps the intensity of retail competition and
scale economies due to increasing consolidation resulted in North American and
European retailers having a greater tendency to diversify internationally. In addition, the
greater international diversity of European retailers may be due to the limited size of
their domestic markets, greater regulation in their home countries, and/or their proximity
to other countries for potential expansion.
Specialty store retailers were more prone to expand internationally while
supermarket retailers were less prone. These results may be due to the substantial
heterogeneity of food taste globally and relative homogeneity for merchandise sold in by
specialty store retailers.
61
The period from 1961 to 1985 is characterized as the era of internationalization
(Nelson & Wright 1992; O'Rourke, Taylor & Williamson 1996). Thus the business
climate might have stimulated retailers operating prior to 1961 that had developed the
operational skills to internationalize.
Test of Hypotheses (Second-Stage Results)
The second stage results outlining the effects of international and format
diversification and portfolio characteristics on financial market performance are shown
in Table 5-2.
International Market and Formats Intensity of Financial Market Performance
The number of countries and formats in a retailer’s portfolio had a significant effect
on the retailer’s financial performance (Tobin’s Q). The estimated coefficient for the
number of countries in a retailer’s portfolio was positive and significant (β1,i,t = 0.397,
p<.01), while the estimated coefficient for number of formats in a retailer’s portfolio was
negative and significant (β2,i,t = - .745, p<.01). These simple main effects suggested
that retailers operating in more countries and retailers operating fewer formats have
better financial market performance. However, the number of countries squared (β2,i,t
= 0.366, p<.01) and the squared number of formats (β4,i,t = 0.781, p<.01) were also
significant and positive. The significance of these nonlinear terms and signs of the
coefficients indicates that the relationship between the number of countries and formats
in a retailer’s portfolio and its financial market performance is U-shaped supporting H1
and H2. In addition, there was a significant negative coefficient (β5,i,t = - 0.731, p<.01)
for the interactions of number of formats and number of countries supporting H3. This
interaction coupled with significant squared terms suggested a complex relationship
between retailer’ financial performance and the international market and format
62
diversifications of their portfolios. The nature of these relationships is illustrated in
Figures 5-1 and 5-2.
In Figure 5-1, the estimated Tobin’s Q as a function of international market was
plotted for retailers with the average levels of cultural and economic dissimilarity at
several levels of format diversification. At low levels of format diversification (number of
formats less than four), international diversification had a positive relationships with
financial market performance (i.e. Tobin’s Q). But at higher levels of format
diversification (number of format great that 4), the relationship between international
diversification and financial market performance was just the opposite. The estimated
Tobin’s Q decreased as the retailer operates in more countries. At modest levels of
format diversification (number of countries) was weaker. Note that the relationship
between international market diversification was basically U-shaped, but only a portion
the U is shown with the range of the data – 1 to 30 countries – because the number of
formats shifted the U horizontally. The more countries in which a retailer operated, the
higher was its estimated
Figure 5-2 illustrates the effects on retailers’ Tobin’s Q as a function of format
diversification. The number of formats in the retailer operates was plotted for retailers
with the average levels of cultural and economic dissimilarity at several levels of
international market diversification. In this case, the relationship between Tobin’s Q and
the number of formats operated was consistently U-shaped with the range of data,
however, the minimum level of Tobin’s Q increased as the number of countries in which
the retail operates increased. Note that when a retailer operates one format, its financial
performance basically increased as the retailer’s contains more countries. On the other
63
hand, when a retailer operates eight formats, its financial performance decreased when
its portfolio included more countries.
Cultural, Economic, and Format Diversity and Financial Performance
The results in Table 5-2 also provided support of propositions H4 and H5. Both the
cultural (β6,i,t = -0.0107, p<.10) and economic (β7,i,t = -0.03302, p<.01) dissimilarity
of the countries in a retailer’s portfolio had a significant effect on the retailer’s Tobin’s Q.
Figure 5-1 illustrates the effect of these market dissimilarities on the estimated Tobin’s
Q. In Figure 5-3, the estimated Tobin’s Q is plotted as a function of the number of
countries for three levels of cultural and economic dissimilarity – retailers with high
market dissimilarity (75th percentile for bother cultural and economic dissimilarity),
median levels of market dissimilarity (50th percentile), and low levels of market
dissimilarity (25th percentile). Other variables affecting Tobin’s Q (format dissimilarity
and number of formats) were set at the mean levels for the sample. Figure 5-3
illustrates that the estimated Tobin’s is higher for retailers that operate a portfolio
involving similar versus dissimilar markets.
Finally, my analysis indicates that format dissimilarities in a retailer portfolio had a
significant negative effect on financial market performance (β8,i,t = -0.107, p<.01)
supporting proposition H6. In Figure 5-4, the estimated Tobin’s Q was plotted as a
function of the number of format for three levels of format dissimilarity – retailers with
high format dissimilarity (75th percentile), median levels of format dissimilarity (50th
percentile), and low levels of format dissimilarity (25 percentile). Other variables
affecting Tobin’s Q (cultural and economic dissimilarity and number of countries) were
set at the mean levels for the sample. Figure 5-5 illustrates that the estimated Tobin’s
64
was higher for retailers that operated a portfolio involving similar versus dissimilar
formats.
Table 5-1. Estimated results: instruments affecting diversification decisions
COUNTRY COUNT
FORMAT COUNT
LN (SALES) .1853*** .1565*** PERCENTAGE OF POPULATION LIVING IN URBAN SETTINGS -.0091** -.00245
POPULATION DENSITY (PPL / SQ KM) .00042*** .00018*** LN (POPULATION) .2593*** LN (GDP PER CAPITA) -.4945*** -.4724*** COMPUTER PREVELANCE (PER 100 PEOPLE) .0112*** .00594** TOURIST ARRIVAL-DEPARTURE RATION .02622 -.01256 FOUNDED PRIOR TO 1961? (F=0, T=1) -.4344*** .1078** EUROPEAN? (F=0, T=1) 1.994*** .578*** N. AMERICAN? (F=0, T=1) .308** .1286 SPECIALTY-TYPE RETAILER? (F=0, T=1) .5368*** SUPERMARKET-TYPE RETAILER? (F=0, T=1) -.3115*** CONSTANT -3.225*** 1.572** LN(ALPHA) CONSTANT -.675*** N 822 822 LN-LIKELIHOOD -2060 -1368 CHI2 960 499.6 AIC 4149 2756 BIC 4215 2803 * p < .1, ** p < .05, *** p < .01
65
Table 5-2. Effect of country and format portfolio constituents on Tobin’s Q
Tobin’s Q
Estimated Coefficient Standardized Coefficient
Number of Countries 0.0746*** 0.397***
Number of Formats -0.768*** -0.745***
Number of Countries Squared 0.00205*** 0.366***
Number of Formats Squared 0.125*** 0.761***
Number of Countries x Number of Formats -0.0307*** -0.731***
Cultural Dissimilarity -0.0116* -0.0107*
Economic Dissimilarity -0.00159*** -0.0332***
Format Dissimilarity -0.044*** -0.107***
Constant 2.089***
N 737
Chi-Square (8) 2270.98
Pseudo-R2 0 .127
* p< .10, ** p<.05, *** p<.01
66
Figure 5-1. International diversification and retailers’ Tobin’s Q
Figure 5-2. Format diversification and retailers’ Tobin’s Q
67
A
B
Figure 5-3. Impact of varying levels of market dissimilarity on Tobin’s Q; A) Across multiple countries.; B) Across multiple formats.
68
A
B
Figure 5-4. Impact of varying levels of format dissimilarity on Tobin’s Q; A) Across multiple countries.; B) Across multiple formats.
69
CHAPTER 6 DISCUSSION
As discussed previously, the prior research on international market and product
diversification for manufacturing firms has reported a wide variety of relationships –
negative, positive, inverted U-shaped, U-shaped, and S-shaped --between
diversification intensity and financial performance. Some of these differences may be
due to differences in methodology such as failure to consider potential endogeniety bias
or the different measures used to assess performance and diversification. However,
these differences might also be due to the industries sampled.
Summary of Results
My results support an inverted U-shaped relationship consistent within my premise
that retail operations are more complex than manufacturing operations and thus the
scale and scope economies are realized after retail firms have considerable learning
from diversification experiences.
The inverted U-shaped relationship in my results for retail firms is consist with
Porter’s (1985) business strategy dictum that getting “stuck in the middle” is a
prescription for below average financial performance. In the context of international
market and format diversification, “stuck in the middle” is pursing both forms of
diversification with middling intensity. My results suggest the highest levels of financial
performance are achieved when retailers either operate one format in many
international markets or multiple formats in one market.
While the estimated coefficients indicate an inverted U-shaped relationship, my
research findings suggest that international and format diversification have significant
interactive and nonlinear effects on performance. Due to these interactive and
70
nonlinear effects, the nature of the functional relationships between international
diversification varies for different levels of format diversification. Thus, the relationship
between diversification and performance can appears to take various functional forms
with in the range of the data.
The managerial implication of this research is that are retailers seek growth
through diversification, the two approaches that lead to strong financial performance are
to operate a large number of formats in a few countries or use a small number of
formats in many countries. The first approach exploits the retailer’s assets related to
operations, while the second approach exploits the retailer’s assets related to home
market. The lowest level of financial performance arises when a retailer has a portfolio
that involves operating a median level of formats in a median level of countries. For
retailers with this type of portfolio the cost of diversification outweigh the benefits.
When looking at diversification within the retail sector, it appears that
diversification intensity, the number of countries in which a retailer operates and the
number the formats utilized, has a greater impact on financial performance than the
similarities between of countries and format within a retailer’s portfolio. However,
weaker estimated effects of format and country dissimilarity on financial market
performance may be due to the quality of the dissimilarity measures. Assessing the
number of countries in which a retailer operates and the number of formats used in
straight forward. However, there is probably more error in assessing portfolio
dissimilarities, particularly cultural and economic dissimilarities. Both of these measures
require transformations of data to form an index. In addition, even though the
measures of cultural characteristics used in this research are widely used to assess
71
culture, they are based on dated, small, convenience sample of people living in the
countries.
Directions for Future Research
As indicated, this research is one of the few to examine the effects for
diversification for service business, specifically retailers. While my research findings do
differ somewhat from previous diversification research, these differences might be due
to different methods and measures rather than the differences in industries. Thus a
direction for future research would be to examine the potential differences between
manufacturing and services industry using the same methodologies with samples of
firms in both industry sectors.
Another critical direction for future diversification research is to examine the
moderating effects of managerial actions on the diversification-performance
relationship. For example, the effects of operating portfolios with many international
market and/or formats might be mitigated or exasperated by the activities the retailers
choose centralize versus decentralize and the ownership structure of the operations in
the retailer’s portfolio. Ahold chooses to decentralize most decision making and even
uses differ names for chains in the same and different countries. It also uses a variety of
on ownership ranging from cooperatives to direct foreign investment other hand, Wal-
Mart takes a more centralized decision making approach and use direct foreign
investment. Which operating and ownership approaches are more effective or does the
effectiveness of the approach depend on the number of countries and formats in the
retailer’s portfolio?
Due to data limitations, I was unable to study the moderating effect of network size
or the effect of selective cross country format implementation on performance.
72
However, past research has shown that retail investment intensity during entry can
impact retailers’ long-run post entry performance (Gielens and Dekimpe 2001),
presumably due to information asymmetries, pioneer advantage and market power.
Hence, future research can include the moderating effect of retail network size on
retailers’ financial performance. Along those lines, one could also examine whether
there exist any systematic impact on retailers’ performance based on how aggressively
and uniformly retailers distribute their existing formats over each geographic retail
network cluster.
Given the present hype about opportunities in emerging markets, one can extend
this research to study the differential impact of retailers who predominantly maintain
either a portfolio of developed or developing geographic market constituents. Such an
endeavor may reveal interesting findings regarding the transferability and
implementation of specific retail concepts and expertise under varied market conditions
while taking into consideration the size of the retail network. For instance, while
emerging markets may afford retailers with higher market potential and growth, these
markets may also prove more volatile to retailers trying to market and implement their
retail concept and this risk is often not priced in the decision making process ex-ante.
Accounting for the intangible cost and volatility of operating in emerging markets may
lead retailers to re-think their diversification portfolio strategy.
Several authors posit that retailers can engage in selective entry and utilize
knowledge gained from increasingly diverse markets to guide sequential expansion
decisions. Given this learning justification, a final recommendation for future research is
to split the sample into four groups: 1) retailers from developed home markets who are
73
primarily confined to developed host markets [compact developed markets portfolio], 2)
retailers from developed home markets who are primarily confined to emerging host
markets [varied emerging market portfolio], 3) retailers from emerging home markets
who are primarily confined to developed host markets and [varied developed markters
portfolio] 4) retailers from emerging host markets who are primarily confined to
emerging host markets [compact emerging markets portfolio]. A comprehensive
examination across these subgroups could reveal interesting findings on the differential
efficacy of the “learning” process and overall portfolio performance.
74
APPENDIX A DATA APPENDIX
Construct Classification Variable Transformation Source
First Stage - Company Variables Performance Retail Sales NA Global Powers of Retailing 2002-07 Descriptive Information Founding Year NA
Hoovers (http://premium.hoovers.com); Google Finance (http://finance.google.com)
Country List NA Global Powers of Retailing 2002-07 Format List NA Global Powers of Retailing 2002-07 First Stage - Country Variables Country Metrics
Urban Population (% of total)
(Instrumented/Factor Rotated)
World Bank Development Indicators, 2007 (http://publications.worldbank.org/WDI/indicators)
Population (Instrumented/Factor Rotated)
World Bank Development Indicators, 2007 (http://publications.worldbank.org/WDI/indicators)
Population Density (Instrumented/Factor Rotated)
World Bank Development Indicators, 2007 (http://publications.worldbank.org/WDI/indicators)
GDP (per capita) (Instrumented/Factor Rotated)
World Bank Development Indicators, 2007 (http://publications.worldbank.org/WDI/indicators)
Personal Computers (per 1000 individuals)
(Instrumented/Factor Rotated)
World Bank Development Indicators, 2007 (http://publications.worldbank.org/WDI/indicators)
International Tourist Arrivals/Departures
International Tourist Arrival/International Tourist Departure (Instrumented/Factor Rotated)
World Bank Development Indicators, 2007 (http://publications.worldbank.org/WDI/indicators)
Second Stage
Performance Tobin's Q
(Share Price x Shares Outstanding)/Total Assets
Wharton Research Data Services - Compustat (http://wrds.wharton.upenn.edu/)
Share Price (Adjusted) NA
Wharton Research Data Services - Compustat (http://wrds.wharton.upenn.edu/); Google Finance (http://finance.google.com)
Shares Outstanding NA
Wharton Research Data Services - Compustat (http://wrds.wharton.upenn.edu/); Google Finance (http://finance.google.com)
Total Assets NA
Wharton Research Data Services - Compustat (http://wrds.wharton.upenn.edu/); Google Finance (http://finance.google.com)
Diversification level Country Count NA Global Powers of Retailing 2002-07 Format Count NA Global Powers of Retailing 2002-07 Cultural Dissimilarity Power Distance Index NA
Hofstede Scores (http://www.geert-hofstede.com/hofstede_dimensions.php)
Individualism NA Hofstede Scores (http://www.geert-hofstede.com/hofstede_dimensions.php)
Masculinity NA Hofstede Scores (http://www.geert-hofstede.com/hofstede_dimensions.php)
Uncertainty Avoidance Index NA
Hofstede Scores (http://www.geert-hofstede.com/hofstede_dimensions.php)
Economic Dissimilarity
Economic Dissimilarity Factor Scores Weighted Average See enclosed factor analysis results in Appendix.
75
Scientific and Technical Articles (per 1000 individuals)
(Instrumented/Factor Rotated)
World Bank Development Indicators, 2007 (http://publications.worldbank.org/WDI/indicators)
Merchandize Exports (Instrumented/Factor Rotated)
World Bank Development Indicators, 2007 (http://publications.worldbank.org/WDI/indicators)
Working Age Population (ages 15-64)
(Instrumented/Factor Rotated)
World Bank Development Indicators, 2007 (http://publications.worldbank.org/WDI/indicators)
Dependents to Working Age Population Ratio
(Instrumented/Factor Rotated)
World Bank Development Indicators, 2007 (http://publications.worldbank.org/WDI/indicators)
Information and Telecommunication Expenditure
(Instrumented/Factor Rotated)
World Bank Development Indicators, 2007 (http://publications.worldbank.org/WDI/indicators)
Real Interest Rate (Instrumented/Factor Rotated)
World Bank Development Indicators, 2007 (http://publications.worldbank.org/WDI/indicators)
Domestic Credit Provided by Banking Sector
(Instrumented/Factor Rotated)
World Bank Development Indicators, 2007 (http://publications.worldbank.org/WDI/indicators)
Gross Domestic Savings (% of GDP)
(Instrumented/Factor Rotated)
World Bank Development Indicators, 2007 (http://publications.worldbank.org/WDI/indicators)
Physical Distance
Geographical Coordinates NA
World Factbook, 2007 (https://www.cia.gov/library/publications/the-world-factbook)
Format Dissimilarity
Format Dissimilarity Survey Items Weighted Average See enclosed survey questions in Appendix.
Misc. Variables
Performance Revenue NA
Wharton Research Data Services - Compustat (http://wrds.wharton.upenn.edu/); Thompson One Analytics (http://www.thomsononeim.com); Hoovers (http://premium.hoovers.com); Google Finance (http://finance.google.com)
Net Income NA
Wharton Research Data Services - Compustat (http://wrds.wharton.upenn.edu/); Thompson One Analytics (http://www.thomsononeim.com); Hoovers (http://premium.hoovers.com); Google Finance (http://finance.google.com)
Descriptive Information Cost of Goods Sold NA
Wharton Research Data Services - Compustat (http://wrds.wharton.upenn.edu/); Thompson One Analytics (http://www.thomsononeim.com); Hoovers (http://premium.hoovers.com); Google Finance (http://finance.google.com)
Average Inventory (Inventory[t] + Inventory[t-1])/2
Wharton Research Data Services - Compustat (http://wrds.wharton.upenn.edu/); Thompson One Analytics (http://www.thomsononeim.com); Hoovers (http://premium.hoovers.com); Google Finance (http://finance.google.com)
* Data may not be exclusive to the listed category.
76
Home Country Economic Variables GINI index Income share held by highest 20% Poverty headcount ratio at national poverty line (% of population) Age dependency ratio (dependents to working-age population) Birth rate, crude (per 1,000 people) Mortality rate, under-5 (per 1,000) Net migration Population ages 15-64 (% of total) Population density (people per sq. km) Population growth (annual %) Population in urban agglomerations > 1 million (% of total population) Population, female (% of total) Population, total Survival to age 65, female (% of cohort) Survival to age 65, male (% of cohort) Urban population Urban population (% of total) Urban population growth (annual %) Foreign direct investment, net (BoP, current US$) Foreign direct investment, net inflows (% of GDP) Foreign direct investment, net outflows (% of GDP) Exports of goods and services (BoP, current US$) Goods exports (BoP, current US$) Goods imports (BoP, current US$) Imports of goods and services (BoP, current US$) Net income (BoP, current US$) Service exports (BoP, current US$) Service imports (BoP, current US$) Trade in services (% of GDP) Average number of times firms spent in meetings with tax officials Average time to clear exports through customs (days) Broadband subscribers (per 100 people) Business disclosure index (0=less disclosure to 10=more disclosure) Business entry rate (new registrations as % of total) Container port traffic (TEU: 20 foot equivalent units) Cost of business start-up procedures (% of GNI per capita) CPIA building human resources rating (1=low to 6=high) CPIA business regulatory environment rating (1=low to 6=high) CPIA debt policy rating (1=low to 6=high) CPIA economic management cluster average (1=low to 6=high) CPIA efficiency of revenue mobilization rating (1=low to 6=high) CPIA equity of public resource use rating (1=low to 6=high) CPIA financial sector rating (1=low to 6=high) CPIA fiscal policy rating (1=low to 6=high) CPIA gender equality rating (1=low to 6=high) CPIA macroeconomic management rating (1=low to 6=high) CPIA policies for social inclusion/equity cluster average (1=low to 6=high) CPIA policy and institutions for environmental sustainability rating (1=low to 6=high) CPIA property rights and rule-based governance rating (1=low to 6=high)
77
CPIA public sector management and institutions cluster average (1=low to 6=high) CPIA quality of budgetary and financial management rating (1=low to 6=high) CPIA quality of public administration rating (1=low to 6=high) CPIA social protection rating (1=low to 6=high) CPIA structural policies cluster average (1=low to 6=high) CPIA trade rating (1=low to 6=high) CPIA transparency, accountability, and corruption in the public sector rating (1=low to 6=high) Credit information availability index (0=less info to 6=more info) Daily newspapers (per 1,000 people) Ease of doing business index (1=most business-friendly regulations) Firms offering formal training (% of firms) Firms that do not report all sales for tax purposes (% of firms) Firms using banks to finance investment (% of firms) Firms with female participation in ownership (% of firms) Fixed line and mobile phone subscribers (per 100 people) High-technology exports (% of manufactured exports) High-technology exports (current US$) Households with television (%) IDA resource allocation index (1=low to 6=high) Information and communication technology expenditure (% of GDP) Information and communication technology expenditure (current US$) Information and communication technology expenditure per capita (US$) International Internet bandwidth (bits per person) International Internet bandwidth (Mbps) International tourism, expenditures (% of total imports) International tourism, number of arrivals International tourism, number of departures International voice traffic (minutes per person) International voice traffic (out and in, minutes) Internet users (per 100 people) ISO certification ownership (% of firms) Legal rights of borrowers and lenders index (0=less credit access to 10=more access) Losses due to theft, robbery, vandalism, and arson (% sales) Management time dealing with officials (% of management time) Micro, small and medium enterprises (per 1,000 people) Mobile phone subscribers (per 100 people) New businesses registered (number) Passenger cars (per 1,000 people) Patent applications, nonresidents Patent applications, residents Personal computers (per 100 people) Price basket for Internet (US$ per month) Price basket for mobile (US$ per month) Price basket for residential fixed line (US$ per month) Private credit bureau coverage (% of adults) Procedures to build a warehouse (number) Procedures to enforce a contract (number) Procedures to register property (number) Public credit registry coverage (% of adults) Pump price for diesel fuel (US$ per liter) Rail lines (total route-km) Railways, goods transported (million ton-km)
78
Researchers in R&D (per million people) Rigidity of employment index (0=less rigid to 100=more rigid) Roads, goods transported (million ton-km) Scientific and technical journal articles Secure Internet servers (per 1 million people) Start-up procedures to register a business (number) Tax payments (number) Technicians in R&D (per million people) Telecommunications investment (% of revenue) Telecommunications revenue (% GDP) Telephone average cost of call to US (US$ per three minutes) Telephone faults (per 100 mainlines) Telephone mainlines (per 100 people) Time required to build a warehouse (days) Time required to enforce a contract (days) Time required to obtain an operating license (days) Time required to register property (days) Time required to start a business (days) Time to prepare and pay taxes (hours) Time to resolve insolvency (years) Total businesses registered (number) Total tax rate (% of profit) Trademarks, nonresidents Trademarks, residents Unofficial payments to public officials (% of firms) Value lost due to electrical outages (% of sales) Vehicles (per 1,000 people) Vehicles (per km of road) Consumer price index (2000 = 100) GDP deflator (base year varies by country) Inflation, consumer prices (annual %) PPP conversion factor, GDP (LCU per international $) Real effective exchange rate index (2000 = 100) Wholesale price index (2000 = 100) Bank capital to assets ratio (%) Bank nonperfoming loans to total gross loans (%) Domestic credit to private sector (% of GDP) Lending interest rate (%) Listed domestic companies, total Net foreign assets (current LCU) Risk premium on lending (%) Highest marginal tax rate, corporate rate (%) Highest marginal tax rate, individual rate (%) Social contributions (% of revenue) Taxes on exports (% of tax revenue) Taxes on goods and services (% of revenue) Taxes on international trade (% of revenue) Exports as a capacity to import (constant LCU) Exports of goods and services (% of GDP) GDP per capita (constant 2000 US$) GDP per capita growth (annual %) Gross savings (% of GDP)
79
Household final consumption expenditure (constant 2000 US$) Imports of goods and services (annual % growth) Net income from abroad (constant LCU) Trade (% of GDP) Death rate, crude (per 1,000 people) Employment in agriculture (% of total employment) Employment in industry (% of total employment) Employment in services (% of total employment) Employment to population ratio, ages 15-24, total (%) GDP per person employed, index (1980 = 100) Labor force participation rate, total (% of total population ages 15-64) Labor force with primary education (% of total) Labor force with secondary education (% of total) Labor force with tertiary education (% of total) Labor force, female (% of total labor force) Labor force, total Life expectancy at birth, total (years) Literacy rate, youth total (% of people ages 15-24) Long-term unemployment (% of total unemployment) Unemployment, male (% of male labor force) Unemployment, total (% of total labor force) Unemployment, youth total (% of total labor force ages 15-24) Agricultural raw materials exports (% of merchandise exports) Agricultural raw materials imports (% of merchandise imports) Computer, communications and other services (% of commercial service exports) Computer, communications and other services (% of commercial service imports) Export quantum/quantity index (2000 = 100) Export value index (2000 = 100) Import quantum/quantity index (2000 = 100) Import value index (2000 = 100) Manufactures exports (% of merchandise exports) Manufactures imports (% of merchandise imports) Merchandise exports (current US$) Merchandise imports (current US$) Merchandise trade (% of GDP) Travel services (% of commercial service exports) Travel services (% of commercial service imports)
80
APPENDIX B SAMPLE OF RETAILERS
RETAILER COUNTRY OF ORIGIN GPR OBSERVATION YEARS
FINANCIAL DATA AVAILABLE?
7-ELEVEN (SOUTHLAND) USA 2002 2003 YES 84 LUMBER USA 2005 NO A&P USA 2002 2003 YES ABERCROMBIE USA 2007 NO ADVANCE AUTO USA 2003 2004 2005 2006 2007 NO AEON JAPAN 2002 2003 2004 2005 2006 2007 YES ALBERTSONS USA 2002 2003 2004 2005 2006 2007 YES ALDI GMBH GERMANY 2002 2003 2004 2005 2006 2007 NO
ALIMENTATION COUCHE_TARD CANADA 2005 2006 2007 NO ALTICOR INC USA 2002 2003 2004 2005 2006 2007 NO AMAZON.COM USA 2002 2003 2004 2005 2006 2007 YES AMES USA 2002 2003 YES AMWAY USA 2002 NO ARCADIA GROUP UK 2002 2003 2004 2005 2006 2007 YES ARMY & AIR FORCE EXCHANGE USA 2002 2003 2004 2005 2006 2007 NO AS WATSON/ HUTCHINGSON WHAMPOA HONGKONG 2004 2005 2006 2007 NO ASBURY AUTOMOTIVE USA 2003 2004 2005 NO AUTOGRILL ITALY 2005 NO AUTONATION USA 2002 2003 2004 2005 YES AUTOZONE USA 2002 2003 2004 2005 2006 2007 YES AVON USA 2002 2003 2004 2005 2006 2007 YES AXFOOD AB SWEDEN 2002 2003 2005 2006 2007 YES BARNES & NOBLE USA 2002 2003 2004 2005 2006 2007 YES BAUGUR GROUP ICELAND 2007 NO BAUHAUS GERMANY 2006 2007 NO BED BATH AND BEYOND USA 2002 2003 2004 2005 2006 2007 YES BEIJING GOME HOME APPLIANCE CHINA 2005 NO BELK, INC. USA 2003 2005 2006 2007 NO BERKSHIRE-HATHAWAY USA 2005 2006 2007 NO BERTELSMANN AG GERMANY 2002 2003 2004 2005 2006 2007 YES BEST BUY USA 2002 2003 2004 2005 2006 2007 YES BEST DENKI CO. JAPAN 2002 2004 2005 2006 2007 YES BIC CAMERA JAPAN 2005 2006 2007 NO BIG LOTS USA 2002 2003 2004 2005 2006 2007 YES BILL HEARD ENTERPRISES USA 2003 NO BJ'S WHOLESALE USA 2002 2003 2004 2005 2006 2007 YES BL-LO HOLDINGS USA 2007 NO BLOCKBUSTER USA 2006 2007 NO BOOTS GOUP UK 2002 2003 2004 2005 2006 2007 YES BORDERS GROUP USA 2002 2003 2004 2005 2006 2007 YES BRINKER INTERNATIONAL USA 2005 NO
81
BURLINGTON COAT USA 2003 2004 2005 2006 2007 NO C&A BELGIUM 2002 2003 2004 2005 2006 2007 NO CAINZ HOME JAPAN 2006 2007 NO CANADIAN TIRE CANADA 2002 2003 2004 2005 2006 2007 YES CAPRABO, SA SPAIN 2005 2006 2007 NO CARMAX USA 2005 NO CARREFOUR FRANCE 2002 2003 2004 2005 2006 2007 YES CASA BAHIA BRAZIL 2006 2007 NO CASEY'S GENERAL USA 2005 2006 2007 NO CASINO GUICHARD FRANCE 2002 2003 2004 2005 2006 2007 YES CBRL GROUP USA 2005 NO CCA GLOBAL USA 2005 NO CELESIO AG GERMANY 2005 2006 2007 NO CENCOSUD S.A. CHILE 2007 NO CENTRES DISTRIBUTEURS E LECLERC FRANCE 2002 2003 2004 2005 2006 2007 NO CHARMING SHOPPES USA 2004 2005 2006 2007 NO CIRCUIT CITY USA 2002 2003 2004 2005 2006 2007 YES COLES MYER AUSTRALIA 2002 2003 2004 2005 2006 2007 YES COMCAST/QVC USA 2002 2003 2004 YES
COMPANHIA BRASILEIRA DE DISTRIBUICAO SA GRUPO PAO DE ACUCAR BRAZIL 2002 2003 2004 2005 2006 YES COMPASS UK 2002 2003 2004 2005 YES COMPUSA USA 2003 2004 2005 2006 2007 NO CONAD CONSORZIO ITALY 2002 2003 2004 2005 2006 2007 NO CONTROLADORA COMMERICAL MEXICANA MEXICO 2002 2003 2004 2005 2006 2007 YES COOP ITALIA ITALY 2002 2003 2004 2005 2006 2007 NO COOP KOBE JAPAN 2002 2005 2006 NO COOP NORDEN AB SWEDEN 2004 2005 2006 2007 NO COOP NORWAY NORWAY 2002 2003 NO COOP SWITZERLAND SWITZERLAND 2002 2003 2004 2005 2006 2007 YES COOPERATIVE GROUP UK 2002 2003 2004 2005 2006 2007 NO CORA FRANCE 2002 2003 NO COSTCO USA 2002 2003 2004 2005 2006 2007 YES CVS USA 2002 2003 2004 2005 2006 2007 YES DAIEI JAPAN 2002 2003 2004 2005 2006 2007 YES DAIRY FARM HONGKONG 2002 2003 2004 2005 2006 2007 YES DAISO SANGYO JAPAN 2005 2006 2007 NO DALIAN DASHANG GROUP CO. LTD. CHINA 2006 NO DANSK SUPERMARKED DENMARK 2002 2003 2004 2005 2006 2007 YES DARDEN RESTAURANTS USA 2002 2003 2004 2005 YES DEBENHAMS PLC UK 2004 2005 2006 2007 NO DECATHLON GROUP FRANCE 2006 2007 NO DEFENSE COMM USA 2004 2005 2006 2007 NO DELHAIZE GROUP BELGIUM 2002 2003 2004 2005 2006 2007 YES DELHAZIE AMERICA (FOODLION) USA 2002 YES DELL USA 2003 2004 2005 2006 2007 NO DICK'S SPORTING USA 2007 NO
82
DILLARDS USA 2002 2003 2004 2005 2006 2007 YES DIRK ROOSSMANN GERMANY 2007 NO DISTRIBUCION Y SERVICIO CHILE 2006 2007 NO DM-DROGERIE GERMANY 2004 2005 2006 2007 NO DOHLE-HANDELSGRUPPE GERMANY 2004 2005 2006 2007 NO DOLLAR GENERAL USA 2002 2003 2004 2005 2006 2007 YES DOLLAR TREE STORES USA 2005 2006 2007 NO DOUGLAS HOLDING GERMANY 2005 2006 2007 NO DSG INTERNATIONAL UK 2002 2003 2004 2005 2006 2007 YES EAST JAPAN RAILWAY JAPAN 2006 2007 NO EDEKA ZENTRALE AG GERMANY 2002 2003 2004 2005 2006 2007 NO EDGARS CONSOLIDATED SAFRICA 2007 NO EDION JAPAN 2005 2006 2007 NO EL CORTE INGLES SPAIN 2002 2003 2004 2005 2006 2007 NO EL PUERTO DE LIVERPOOL MEXICO 2007 NO ESSELUNGA ITALY 2002 2003 2004 2005 2006 2007 NO ETS FRANZ COLRUYT BELGIUM 2004 2005 2006 2007 NO EUROMADI SPAIN 2002 NO FA ANTON SCHLECKER GERMANY 2002 2003 2004 2005 2006 2007 NO FAMILY DOLLAR USA 2002 2003 2004 2005 2006 2007 YES FAMILYMART CO., LTD. JAPAN 2006 NO FAST RETAILING JAPAN 2003 2004 2005 2006 2007 NO FDB DENMARK 2002 2003 NO
FEDERATED DEPARTMENT STORES USA 2002 2003 2004 2005 2006 2007 YES FEMSA COMERCIO MEXICO 2007 NO FINIPER S.P.A. ITALY 2006 2007 NO FLEMING USA 2002 YES
FOCUS WIKES GROUP LTD. UK 2005 2006 NO
FOODLAND ASSOCIATED LTD. AUSTRALIA 2004 2005 2006 NO FOOT LOCKER USA 2002 2003 2004 2005 2006 2007 YES FOOTSTAR USA 2003 NO FUJI CO. JAPAN 2005 2006 2007 NO GAMESTOP USA 2007 NO GAP, INC USA 2002 2003 2004 2005 2006 2007 YES GATEWAY USA 2003 NO GIANT EAGLE USA 2002 2003 2004 2005 2006 2007 NO GIB BELGIUM 2002 YES GIGAS K'S DENKI JAPAN 2006 2007 NO GLOBUS HOLDING GERMANY 2002 2003 2004 2005 2006 2007 NO
GREAT UNIVERSAL STORES UK 2002 2003 2004 2005 2006 2007 YES GROUP 1 AUTOMOTIVE USA 2002 2003 2004 2005 YES GROUPE AUCHAN FRANCE 2002 2003 2004 2005 2006 2007 NO GROUPE GALERIES LAFAYETTE FRANCE 2002 2003 2004 2005 2006 2007 YES GRUPO CARSO MEXICO 2002 YES GRUPO EROSKI SPAIN 2002 2003 2004 2005 2006 2007 NO GRUPO GIGANTE MEXICO 2002 2003 2004 2005 2006 2007 YES
83
GRUPPO PAM S.P.A., GECOS S.P.A. ITALY 2005 2006 2007 NO GS RETAIL CO. SKOREA 2006 2007 NO H&M SWEDEN 2002 2003 2004 2005 2006 2007 YES HACHETTE FRANCE 2006 2007 NO HANKYU DEPARTMENT JAPAN 2002 2003 2004 2005 2006 2007 YES HE BUTT GROCERY USA 2002 2003 2004 2005 2006 2007 NO HEIWADO CO. JAPAN 2002 2003 2004 2005 2006 2007 YES HENDRICK AUTOMOTIVE GROUP USA 2003 2005 NO HMV GROUP PLC UK 2005 2006 2007 NO HOLMAN ENTERPRISES USA 2003 NO HOME DEPOT USA 2002 2003 2004 2005 2006 2007 YES HORNBACH GERMANY 2006 2007 NO HUDSON'S BAY CANADA 2002 2003 2004 2005 2006 2007 YES HY-VEE, INC USA 2002 2003 2004 2005 2006 2007 NO IAC/INTERACTIVE USA 2005 2006 2007 NO ICA AB SWEDEN 2006 2007 NO IFA SPAIN 2002 NO IKEA SWEDEN 2002 2003 2004 2005 2006 2007 NO INDITEX SPAIN 2003 2004 2005 2006 2007 NO INSIEME ITALY 2002 NO INTERDIS ITALY 2002 NO INTERMARCHE FRANCE 2002 2003 2004 2005 NO INTERMEDIA 90 ITALY 2002 NO INTIMATE BRANDS USA 2002 2003 YES ISETAN JAPAN 2002 2003 2004 2005 2006 2007 YES ITM DEVELOPPEMENT INT FRANCE 2006 2007 NO ITO-YODADO CO. LTD. JAPAN 2002 2003 2004 2005 2006 YES IZUMI CO. LTD. JAPAN 2002 2003 2004 2005 2006 YES IZUMIYA CO. JAPAN 2002 2003 2004 2005 2006 2007 YES J SAINSBURY UK 2002 2003 2004 2005 2006 2007 YES JC PENNY USA 2002 2003 2004 2005 2006 2007 YES JEAN CROTU GROUP CANADA 2004 2005 2006 2007 NO JERONIMO MARTINS PORTUGAL 2005 2006 2007 NO JIM PATTISON GROUP CANADA 2005 2006 2007 NO JOHN LEWIS UK 2002 2003 2004 2005 2006 2007 YES JOSHIN DENKI JAPAN 2006 2007 NO KARSTADTQUELLE GERMANY 2002 2003 2004 2005 2006 2007 YES KATZ GROUP CANADA 2006 2007 NO KESA ELECTRICALS UK 2005 2006 2007 NO KESKO FINLAND 2002 2003 2004 2005 2006 2007 YES KINGFISHER UK 2002 2003 2004 2005 2006 2007 YES KINTETSU DEPARTMENT JAPAN 2003 2004 2005 2006 2007 NO KMART HOLDING CORP. USA 2002 2003 2004 2005 2006 NO KOHL'S CORP USA 2002 2003 2004 2005 2006 2007 YES KOJIMA CO. JAPAN 2002 2003 2004 2005 2006 2007 YES
KONINKLIJKE NETHERLANDS 2002 2003 2004 2005 2006 2007 YES
KOOPERATIVE FORBUNDET GROUP SWEDEN 2002 NO
84
KOTOBUKIYA JAPAN 2002 YES KROGER USA 2002 2003 2004 2005 2006 2007 YES
LAURUS N.V. NETHERLANDS 2002 2003 2006 2007 YES LAWSON, INC. JAPAN 2006 NO LEROY MERLIN FRANCE 2002 2003 2004 2005 2006 2007 NO LIBERTY MEDIA USA 2005 2006 2007 NO LIFE CORPORATION JAPAN 2002 2003 2004 2005 2006 2007 YES LIMITED BRANDS USA 2002 2003 2004 2005 2006 2007 YES LINENS 'N THINGS USA 2005 2006 2007 NO LITHIA MOTORS USA 2005 NO LITTLEWOODS SHOP UK 2002 2003 2004 2005 2006 2007 NO LOBLAW CANADA 2002 2003 2004 2005 2006 2007 YES LONGS DRUG USA 2002 2003 2004 2005 2006 2007 YES LOTTE SHOPPING SKOREA 2003 2004 2005 2006 2007 NO LOUIS DELHAIZE BELGIUM 2004 2005 2006 2007 NO LOWE'S USA 2002 2003 2004 2005 2006 2007 YES LUXOTTICA GROUP ITALY 2005 2006 2007 NO LVMH FRANCE 2002 2003 2004 2005 2006 2007 YES MANOR SWITZERLAND 2005 NO MARKS & SPENCER UK 2002 2003 2004 2005 2006 2007 YES MARUI CO. JAPAN 2002 2003 2004 2005 2006 2007 YES MASSMART HOLDINGS SAFRICA 2005 2006 2007 NO MATSUMOTO KIYOSHI JAPAN 2005 2006 2007 NO MATSUZAKAYA CO. JAPAN 2002 2003 2004 2005 2006 2007 YES MAUS FRERES SWITZERLAND 2002 2003 NO
MAXEDA/ROYAL VENDEX KBB NETHERLANDS 2002 2003 2004 2005 2006 2007 YES MCDONALDS USA 2002 2003 2004 2005 YES MEIJER USA 2002 2003 2004 2005 2006 2007 NO MENARDS USA 2002 2003 2004 2005 2006 2007 NO MERCADONA SPAIN 2002 2003 2004 2005 2006 2007 NO MERVYN'S, LLC USA 2006 2007 NO METCASH GROUP AUSTRALIA 2007 NO METCASH TRADING SAFRICA 2004 2005 2006 2007 NO METRO AG GERMANY 2002 2003 2004 2005 2006 2007 YES METRO-RICHELIEU INC CANADA 2002 2003 2004 2005 2006 2007 YES MICHAELS STORES USA 2003 2004 2005 2006 2007 NO
MIGROS-GENOSSENSCHAFTS BUND SWITZERLAND 2002 2003 2004 2005 2006 2007 YES MILLENNIUM RETAILING JAPAN 2005 2006 2007 NO MITCHELLS&BUTLERS UK 2005 NO MITSUKOSHI JAPAN 2002 2003 2004 2005 2006 2007 YES MUSGRAVE GP IRELAND 2005 2006 2007 NO MYCAL JAPAN 2002 YES NEIMAN MARCUS USA 2002 2003 2004 2005 2006 2007 YES NEXT PLC UK 2003 2004 2005 2006 2007 NO NORDSTROM USA 2002 2003 2004 2005 2006 2007 YES NORGESGRUPPEN NORWAY 2002 2003 2004 2005 NO
85
NORMA LEBENSMITTELFILIALBETRIEB, GMBH & CO. KG GERMANY 2005 2006 2007 NO
ODAKYU ELECTRIC RAILWAY CO. LTD. JAPAN 2002 2005 2006 YES OFFICE DEPOT USA 2002 2003 2004 2005 2006 2007 YES OFFICEMAX USA 2002 2003 2004 2005 2006 2007 YES
ORGANIZACION SORIANA S.A. DE C.V. MEXICO 2002 2003 2004 2005 2006 2007 YES OTTO GROUP GERMANY 2002 2003 2004 2005 2006 2007 YES OUTBACK STEAKHOUSE USA 2005 NO PATHMARK STORES USA 2002 2003 2004 2005 2006 2007 YES PAYLESS SHOESOURCE USA 2002 2003 2004 2005 2006 2007 YES PEPBOYS USA 2002 YES PEPKOR SAFRICA 2002 2003 YES PETSMART,INC. USA 2003 2004 2005 2006 2007 NO PHONES4U UK 2006 2007 NO PICK N PAY RETAILERS SAFRICA 2004 2005 2006 2007 NO PPR GROUP FRANCE 2002 2003 2004 2005 2006 2007 YES PRAKTIKERBAUUND GERMANY 2007 NO PRESIDENT CHAIN STORE TAIWAN 2006 2007 NO PUBLIX SUPERMARKETS USA 2002 2003 2004 2005 2006 2007 YES QUIKTRIP CORP. USA 2006 2007 NO RACETRAC PETROLEUM USA 2006 2007 NO RADIOSHACK USA 2002 2003 2004 2005 2006 2007 YES RALEY'S INC. USA 2002 2003 2004 2005 2006 2007 NO REITAN HANDEL NORWAY 2003 2004 2005 2006 2007 NO RETAIL VENTURES USA 2005 2006 2007 NO REWE-ZENTRAL AG GERMANY 2002 2003 2004 2005 2006 2007 NO RINASCENTE ITALY 2002 YES RITE AID USA 2002 2003 2004 2005 2006 2007 YES ROSS STORES USA 2002 2003 2004 2005 2006 2007 YES ROUNDY'S, INC. USA 2005 2006 2007 NO RUDDICK/ HARRIS TEETER USA 2003 2005 2006 2007 NO S GROUP FINLAND 2002 2003 2004 2005 2006 2007 NO S.A.C.I. FALABELLA CHILE 2006 2007 NO SAFEWAY UK 2002 2003 2004 2005 YES SAFEWAY USA 2002 2003 2004 2005 2006 2007 YES SAKS USA 2002 2003 2004 2005 2006 2007 YES
SAVE MART SUPERMARKETS USA 2006 2007 NO SCHNUCK MARKETS USA 2005 NO
SCHWARZ UNTERNEHMENS GERMANY 2002 2003 2004 2005 2006 2007 YES SEARS USA 2002 2003 2004 2005 2006 2007 YES SEARS CANADA CANADA 2002 2003 YES SEIBU DEPARTMENT JAPAN 2002 2003 2004 YES SELEX ITALY 2002 NO SEVEN & I HOLDINGS JAPAN 2007 NO SHANGHAI FRIENDSHIP CHINA 2005 NO SHEETZ, INC. USA 2006 2007 NO SHERWIN-WILLIAMS USA 2002 2003 2004 2005 2006 2007 YES
86
SHIMAMURA JAPAN 2005 2006 2007 NO SHINSEGAE SKOREA 2003 2004 2005 2006 2007 NO SHOPKO STORES USA 2002 2003 2004 2005 2006 2007 YES SHOPPERS DRUG CANADA 2004 2005 2006 2007 NO SHOPRITE HOLDINGS SAFRICA 2002 2004 2005 2006 2007 YES
SHV MAKRO NETHERLANDS 2004 2005 2006 2007 NO SIGNET GROUP UK 2006 2007 NO SIRIO ITALY 2002 NO SKYLARK JAPAN 2002 2003 2004 2005 YES SOBEYS CANADA 2002 2003 2004 2005 2006 2007 YES SOMERFIELD GROUP UK 2002 2003 2004 2005 2006 2007 YES
SONAE/MODELO CONTINENTE PORTUGAL 2002 2003 2004 2005 2006 2007 YES SONIC AUTOMOTIVE USA 2002 2003 2004 2005 YES SPAR JAPAN JAPAN 2002 NO SPAR OESTERREICHISCHE WARENHANDELS AUSTRIA 2002 NO SPAR OSTERREICHISCHE AUSTRIA 2003 2004 2005 2006 2007 NO SPIEGEL USA 2002 2003 2004 YES STAPLES USA 2002 2003 2004 2005 2006 2007 YES STARBUCKS USA 2003 2004 2005 NO STATER BROS. USA 2002 2003 2004 2005 2006 2007 YES SUPERVALU USA 2002 2003 2004 2005 2006 2007 YES SYSTEME U FRANCE 2002 2003 2004 2005 2006 2007 NO TAKASHIMAYA JAPAN 2002 2003 2004 2005 2006 2007 YES TARGET USA 2002 2003 2004 2005 2006 2007 YES TCHIBO HOLDING GERMANY 2002 2003 NO
TENGELMANN VERWALTUNGSUND BETEILIGUNGS GERMANY 2002 2003 2004 2005 2006 2007 NO TESCO PLC UK 2002 2003 2004 2005 2006 2007 YES THE BIG FOOD GROUP (ICELAND) UK 2002 2003 2004 2005 2006 YES THE CARPHONE UK 2006 2007 NO THE DAIMARU JAPAN 2002 2003 2004 2005 2006 2007 YES THE GOLUB CORP. USA 2004 2005 2006 2007 NO THE MARUETSU, INC. JAPAN 2002 2004 2005 2006 2007 YES THE MAY DEPARTMENT STORES CO. USA 2002 2003 2004 2005 2006 YES THE PANTRY USA 2002 2003 2004 2005 2006 2007 YES THE SEIYU LTD. JAPAN 2002 2003 2004 2005 2006 YES THE SPORTS AUTHORITY USA 2006 2007 NO TJX USA 2002 2003 2004 2005 2006 2007 YES
TOKYU DEPARTMENT STORE JAPAN 2002 2003 2004 2005 2006 2007 YES TOKYU STORE CHAINS JAPAN 2005 NO TOY R US USA 2002 2003 2004 2005 2006 2007 YES TRICON RESTAURANTS USA 2002 NO UNITED AUTO GROUP USA 2002 2003 2004 2005 YES UNY JAPAN 2002 2003 2004 2005 2006 2007 YES V.T.INC USA 2003 2004 2005 NO
87
VALUE CITY USA 2004 NO WALGREEN USA 2002 2003 2004 2005 2006 2007 YES WALMART USA 2002 2003 2004 2005 2006 2007 YES WALMART-MEXICO MEXICO 2002 2003 YES WAWA, INC. USA 2007 NO WEGMANS FOOD USA 2002 2003 2004 2005 2006 2007 NO WENDYS USA 2005 NO WESTFARMERS AUSTRALIA 2005 2006 2007 NO WH SMITH UK 2002 2003 2005 2006 2007 YES WHOLE FOODS USA 2004 2005 2006 2007 NO WILLIAMS-SONOMA USA 2005 2006 2007 NO WINN DIXIE USA 2002 2003 2004 2005 2006 2007 YES WM MORRISON SUPERMARKETS UK 2002 2003 2004 2005 2006 2007 YES WOOLWORTHS AUSTRALIA 2002 2003 2004 2005 2006 2007 YES WOOLWORTHS UK 2004 2005 2006 2007 NO YAMADA DENKI JAPAN 2002 2003 2004 2005 2006 2007 YES
YODOBASHI CAMERA CO. LTD. JAPAN 2002 2003 2004 2005 2006 2007 NO YORK-BENIMARU JAPAN 2006 2007 NO YUM! BRANDS (TRICON) USA 2003 2004 2005 NO ZALE USA 2005 NO TOTAL 200 200 200 250 250 250
88
APPENDIX C RESULTS FROM FACTOR ANALYSIS OF HOST COUNTRY ECONOMIC VARIABLES
Component Matrix(a)
Component
1 2 3 4
URBAN INDEX .806 9.644E-02 .444 .268
SCIENCE INDEX .804 -4.136E-02 .522 .142
MERCHANDISE EXPENDITURE INDEX -.794 -7.353E-02 .442 -.168
SAVINGS INDEX -.637 -.136 .219 .323
URBAN RATIO INDEX -.550 .256 .496 -.233
TOURISM INDEX .547 .328 .349 -.145
DOMESTIC CREDIT INDEX .397 -.820 .188 .104
ADULT GROWTH RATIO -7.857E-02 .749 .176 .386
89
ADULT RATIO .393 .626 -.341 -.389
INTEREST RATE INDEX -.342 .196 .568 .174
INFRASTRUCTURE INDEX -.140 .165 -.455 .754
Extraction Method: Principal Component Analysis.
a 4 components extracted.
Rotated Component Matrix(a)
Component
1 2 3 4
URBAN INDEX .949 -.131 -7.634E-02 6.877E-02
SCIENCE INDEX .939 -.106 -.194 -9.418E-02
MERCHANDISE EXPENDITURE INDEX .649 -6.372E-02 .264 -.232
SAVINGS INDEX -.401 .804 -6.926E-02 -.217
URBAN RATIO INDEX -.137 .723 .241 -.263
TOURISM INDEX .141 .691 7.327E-02 7.076E-02
DOMESTIC CREDIT INDEX -.316 .595 -.207 .283
ADULT GROWTH RATIO .283 -.201 -.860 -.124
ADULT RATIO .135 -.478 .723 -.209
INTEREST RATE INDEX .263 .328 .610 .444
INFRASTRUCTURE INDEX -.177 -.130 7.574E-02 .876
Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization.
a Rotation converged in 8 iterations.
90
APPENDIX D FORMAT DISSIMILARITY SURVEY
91
92
93
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BIOGRAPHICAL SKETCH
Born in 1981 to parents Tow Seng Lim and Hui Phing Low, Jeremy Mianxin Lim
attended The University of Florida from 2006 to 2011 and received his PhD in Business
Administration in the fall of 2011. Prior educational experience include: a Master of Arts
in Economics from Queen’s University in 2005 and a Bachelor of Science from The
University of Michigan – Ann Arbor in 2002.
Jeremy Mianxin Lim now resides in Oakland Township, Michigan.