Introduction Local jewelry retail leaders operating in Saudi Arabia

Local jewelry retail leaders operating in Saudi Arabia (S.A.) have been struggling

to revive their companies because their companies’ profits decreased in the first and

second decade of the 21st century (Assad, 2008; World Gold Council, 2012). Global

brands have been capturing a larger share of the S.A. market; this is a strategy that has led

to the foreclosure of many small to medium enterprise (SME) jewelry retail companies in

S.A. (Assad, 2008; World Gold Council, 2012). In this study, I explored the significant

differences among the grouping of countries according to the historical orderof-entry

preference selection to each country by a U.S. company with the grouping of countries

according to each country's attractiveness to global brands (The World Bank, 2012;

Uniworld Publications, 2012). The U.S. company I selected is a leading global, publicly

listed, U.S. jewelry company. I reviewed and selected all 25 countries that the U.S.

company entered from 1972 until 2009 (United States Securities and Exchange

Commission, 2012; Uniworld Publications, 2012). I selected a U.S. company due to (a)

the scarcity of knowledge about the S.A. jewelry industry, and (b) more accessible data

about a publicly listed company (United States Securities and Exchange Commission,

2012; Uniworld Publications, 2012).

The findings of this study have the potential to facilitate leadership strategy

decisions that can help globalize local brands in S.A. and, as a result, enhance their

profitability. I provided in the findings insight into the factors that facilitate successful

globalization processes for SME jewelry brand managers. I provided in Section 1 the

background of the problem, which highlighted the need for globalization in the midst of

the changing factors in the jewelry retail market in S.A. and around the world. I indicated

in the problem statement the need for S.A. business leaders in the jewelry market to seek

a globalization expansion strategy. Section 1 included an articulation of the research

questions that guided the study and a discussion of the nature of the study. Moreover, I

presented in this section relevant literature and the implications of the existing body of

scholarly work in relation to the purpose of the study.

Background of the Problem

The global financial crisis of 2009 revealed the importance of establishing diverse

geographic and business models to increase profitability in the retail industry (Ellaboudy,

2010; "Retail Prospects," 2009). The degrees of the economic decline varied across

different countries. The Saudi jewelry market has been decreasing in the number of gold

tones since 2008 (World Gold Council, 2012). Saudi jewelry retailers should consider the

globalization strategy option for their jewelry brands to contest their challenges of small

Saudi market size, lack of horizontal expansion, vertical integration, and aggressive entry

of large global retail brands (Alanezi, 2012; Alharbi, 2014; Assad, 2008). The trend of

global brands' acceptance by Saudi consumers is possible because of the increasing S.A.

population of those citizens under age 40 (Baqadir, Patrick, & Burns, 2011).

Globalization is an option to support Saudi SME expansion; however, there has

been a lack of knowledge and research about the factors and geographic locations that

would optimize a successful globalization process (Mellahi, Demirbag, & Riddle, 2011;

Sadi & Henderson, 2011). Major globalization theories have addressed Western countries'

globalization processes in developing countries; however, these theories have not

addressed the globalization processes of developing countries’ brands in Western

countries (Pham, 2009; Sadi & Henderson, 2011). Researchers have utilized the Uppsala

model as one of the most used models for internationalization to examine the impact of

external factors under the concept of psychic distance (Johanson & Vahlne, 1977).

Psychic distance is the recognized differences between any brand's country of

origin and the brand's target country for globalization (Sousa & Lages, 2011). Johanson

and Vahlne (1977) used the Uppsala model to demonstrate that the speed of globalization

for a brand relies on the gradual accumulated experience and knowledge of the brand in

its target foreign markets and the capability of the brand's company. Few researchers have

examined the facilitating factors for the success of the globalization process of jewelry

brands in terms of the features that are associated with suitable target countries (Singh,

2011). Therefore, the focus of the study was to present the suitable attraction features in

choosing target countries to facilitate the globalization process for SME jewelry brand

managers in S.A.

Problem Statement

Local retail jewelry leaders of Saudi SMEs who sell original Saudi products have

struggled to survive through declining profits and increasing business foreclosures since

2005 (Al-Asfour & Khan, 2014; Alharbi, 2014; Assad, 2008, "Retail Prospects," 2009).

The struggle to maintain profitability amid declining profits and foreclosures threatens

the sustainability of the Saudi retail sector and the Saudi economy, given that 93% of

Saudi companies are SMEs (Assad, 2008; Sadi & Henderson, 2011; Saudi Arabian

Central Department of Statistics and Information, 2014). The general business problem

was that a globalization strategy to enhance profitability for jewelry retail SMEs in S.A.

is needed, given the limited options for improving profitability (Al-Asfour & Khan, 2014;

Alanezi, 2012; Alharbi, 2014). The specific business problem was that leaders in S.A.

have refrained from developing a globalization strategy because they lack knowledge of

economic attraction features in the globalization process (Bouges, 2013; Mellahi et al.,

2011). Managers of SMEs in the retail jewelry business should identify the economic

attraction features of target countries to make effective globalization strategy decisions

(Bouges, 2013; Eren-Erdogmus et al., 2010).

Purpose Statement

The purpose of this quantitative correlational study using discriminant analysis

was to examine specific countries' economic attraction features in the historical

globalization strategy of a leading U.S. global jewelry company. The examination of the

economic attraction features could facilitate a successful globalization strategy for a

Saudi jewelry SME retail company. Because there has been a lack of knowledge about

how leaders of organizations make decisions on how to globalize in the jewelry market, I

examined a global U.S. jewelry company's globalization process using the Uppsala model

(Couto & Tiago, 2009; Singh, 2011). The Uppsala model of Johanson and Vahlne's

(1977) research is a reliable quantitative model in the globalization process that

researchers have used since the 1990s (Eren-Erdogmus et al., 2010; Singh, 2011). I

applied the Uppsala model, with its stated limitations, to a U.S.-based global jewelry

company. The purpose of this application was to examine the importance of influence of

the target countries' attraction features on the globalization process.

I investigated the effects of six independent predictor variables of 25 target

countries’ economic attractions on the dependent grouping variable, which distinguished

among three order-of-entry groups according to the U.S. company's date of entry in each

country between 1972 until 2009. The findings of the quantitative study have the

potential to facilitate positive change in the Saudi economy. Using these results, I might

assist aspiring jewelry brand managers in S.A. to globalize their brands using the best

globalization strategy practices, as identified from this research.

Nature of the Study

The quantitative correlational study was a positivist research study designed to

examine the factors influencing the globalization process for a U.S. global jewelry

company (Singh, 2011). Jewelry companies' managers from developing countries such as

S.A. could use the results to enhance their companies' profitability. Saudi SME jewelry

company leaders have the potential to transform their successful local jewelry product to

a global brand by using certain well-defined qualifications and parameters identified

during this study. I decided a quantitative approach was appropriate in order to overcome

two main issues in the research topic. The issues were the scarcity of globalized jewelry

brands in developing countries and the lack of existing quantitative research about the

efforts of jewelry brands in developing countries to globalize (Eren-Erdogmus et al.,

2010; Pham, 2009).

Moreover, there has been a lack of experience and knowledge among brand

managers from developing countries on the factors that facilitate the globalization process

(Mellahi et al., 2011; Pham, 2009). Furthermore, I did not choose other research methods

such as qualitative or mixed methods because I could not depend on past research and

experiences of former research participants. Local jewelry retail Saudi SMEs might

benefit from utilizing the findings of a quantitative study to contribute to an

understanding of the positive and negative ramifications of key factors for developing

their globalization strategy.

Research methods vary and relate to subjects of the investigation. Qualitative

researchers require investigative resources such as research participants (Rosas & Kane,

2011). Geographical coverage may be narrower in qualitative studies than quantitative

ones because qualitative researchers do not seek to select samples that are representative

of populations (Rosas & Kane, 2011). The above two features are contrary to the

characteristics of an investigation into globalization procedures.

Mixed methods might be applicable in some circumstances. However, in the case

of this globalization procedure investigation, mixed methods research did not apply. A

mixed method study was not necessary because the purpose of the study was to determine

an individual country’s level of preference and not to explain the preference with rich and

textural data. The qualitative element in mixed methods research would be contrary to the

innate feature of this globalization process study. Therefore, I chose to apply the

quantitative method to this study instead of using qualitative or mixed method research.

Experimental designs, including applied behavioral analysis or single-subject

experiments, would require diverse processes that may not apply to globalization

procedures (Punch, 2013). Therefore, both experimental and quasiexperimental designs

would not have been appropriate for this study. However, causal-comparative research

appeared to be applicable to this study.

I could have used a survey research design in this study if participants' responses

were key to investigating the phenomena. It may not be possible to understand how a

globalized company achieved its objective if qualitative interviews are the only way of

collecting data because of the reliance of the research on the subjective views of the

respondents (Alexander, 2014). In this study, a survey research design could not have met

the requirements for investigating globalization procedures. In this study, I investigated

economic attraction features that managers of a leading U.S. global jewelry company

used to determine their applicability for facilitating the implementation of a successful

globalization strategy for a local Saudi jewelry SME retail company.

Research Questions

The purpose of this quantitative correlational study using discriminant analysis

was to examine the selected target countries' economic attraction features in the

globalization process of a leading U.S. global jewelry company. The findings might

support Saudi jewelry business leaders in developing a globalization expansion strategy

for their local jewelry brands. Although a number of theories and studies could explain

globalization process details and factors, there has been little empirical work on

developing strategies for facilitating factors that support the globalization of jewelry

brands from developing countries (Eren-Erdogmus et al., 2010; Pham, 2009).

Consequently, there was one overarching research question for this study: Which target

countries' economic attraction features should be considered in the globalization strategy

of S.A.’s jewelry brands?

Because of the limited disclosure of public data available from foreign jewelry

companies, I limited this study to a single leading global, publicly traded U.S. jewelry

company. Furthermore, I used a subordinate research question as a guide in the study and

the conducted analyses: What linear combinations of the independent predictor variables

representing six economic attraction feature measures for each country could be used to

predict order-of-entry preference (first third, middle third, or last third)?

Hypotheses

This was a quantitative correlational study. I employed a correlational design and

discriminant function analysis to address the following null and alternate hypotheses:

H01: The target countries' economic attraction features cannot be used to predict the

historical country’s group order of preference (first, second, or third) in the globalization

process of a U.S. jewelry company.

Ha1: The target countries' economic attraction features can be used to predict the

historical country’s group order of preference (first, second, or third) in the

globalization process of a U.S. jewelry company.

I did not select participants for interviews. Therefore, interview questions were

not necessary for this study. I tested the above hypotheses using data available from

public sources to gain the required economic data for addressing the specific business

problem that gave rise to this study.

Theoretical Framework

I used a quantitative methodology for this study. Quantitative methodology is

defined under the positivist theory (Alexander, 2014). Alexander (2014) defined the

theory of positivism to be the search for causal relationships and regularities among the

imperative parts of a subject. Alexander recognized that the positivist paradigm assumes

that objective facts explain behavior by using the persuasion of instrumentation and

experimentation to eliminate bias and error. The positivism concept, which is associated

with the epistemological nature of the quantitative method, tests a deterministic

philosophy in which causes determined outcomes (Alexander, 2014). Positivist

researchers test data to determine causes, which produce the responses and their

outcomes (Alexander, 2014).

Angrist and Pischke (2010) referred to the testing of hypotheses through the

randomization of treatments to experimental units as an experiment, or a

quasiexperiment, in which a randomized allocation of treatments to experimental units is

not selected. According to Alexander (2014), researchers criticized positivists for their

belief in the objectivity of the research, although many research decisions during the

process might be subjective. The latter might be true because the reliability and validity

of a quantitative study should rely on the study's limited assumptions (Alexander, 2014).

Moreover, the reliability and validity should rely on the inclusion of a limited number of

factors, though there are infinite other factors unaccounted and variables that are not

accounted for in any model (Alexander, 2014).

I presented in Figure 1, the general theoretical framework of the study, which I

designed to support examination of the globalization process of a jewelry brand and the

factors that influenced its historical globalization process. Understanding the process of

one company may offer the opportunity to duplicate the process for other brands

interested in globalizing from other countries such as S.A. Factors such as size,

profitability, and sales might be pertinent to understand the importance of both the

historical decision and speed of globalization.

Figure 1. The general theoretical framework for the study.

I examined each target country's unique features that gave strength of

receptiveness to foreign companies and the grouping order-of-entry of importance in the

globalization process. Potential country factors for analyses were geographic proximity,

language and cultural similarity, economic attraction measures, management knowledge

about the target country, country's stability, and country's future growth. S.A. jewelry

companies' leaders could use the results of this study to facilitate the development of

globalization strategies for S.A. SME jewelers by gaining knowledge from historical

experience, thus providing a knowledge base for successful internationalization results.

USA

Country A

Country

Saudi

Country A

Country

•

Geographic

Proximity

•

Language

•

Culture

Similarity

•

Economic

Attraction

•

Economic

Knowledge

•

Country's

Stability

•

Country's

Potential

•

Geographic

Proximity

•

Language

•

Culture

Similarity

•

Economic

Attraction

•

Economic

Knowledge

•

Economic

Attraction

•

Economic

Knowledge

Generic Brand:

•

Size

•

Profitability

•

Sales

Researchers use qualitative and mixed methods methodologies in the empiricism

and social constructivism concepts. Empiricism refers to reliance upon qualitative,

empirical data and methods to ensure objective truth (Alexander, 2014; Punch, 2013).

According to Alexander (2014) and Punch (2013), researchers use empiricism to stress

the correspondence of the research participants' intersubjectivity with the researcher's

inductively developed descriptions of the sample’s culture. Thus, empiricism contradicts

the concept of positivism (Alexander, 2014; Punch, 2013).

Social constructivism requires that the sample be as large as possible to include all

possible participants' intersubjectivity and to minimize the researcher’s role in

interpretation and reliance on empiricism (Alexander, 2014; Punch, 2013). Social

constructivists have argued that each individual holds a subjective view of the world

(Alexander, 2014; Punch, 2013). Researchers should study and include each individual

subjectivity view in the complexity of all divergent views, thus framing a profound and a

comprehensive reflection that results in an objective view (Alexander, 2014). I selected

the quantitative methodology because of the lack of established knowledge and research

on the topic (Eren-Erdogmus et al., 2010; Mellahi et al., 2011; Pham, 2009). Furthermore,

there was a lack of the prerequisite condition of a large number of Saudi businessmen to

serve as participants, which would be necessary in the empiricism and social

constructivism concepts (A. Fakeih, personal communication, December 18, 2009).

Definition of Terms

The measured target countries’ attraction features for the analyses were (a) the

countries' dimension, (b) prosperity, (c) accessibility, (d) language knowledge, (e)

geographic distance, and (f) cultural distance.

Accessibility (Acc): Acc is the population density in concentrated areas or cities

(Couto & Tiago, 2009; Singh, 2011).

Cultural distance (CD): I used the Hofstede index to calculate CD from a formula

that included four components: individualism (IND), uncertainty avoidance (UA), power

distance (PD), and masculinity (MAS) (Couto & Tiago, 2009; Geert-Hofstede, 2012;

Singh, 2011). I used Geert-Hofstede's (2012) website to find the value of the Hosfstede's

country index. Hofstede developed comprehensive indices to evaluate cultures among

countries (Couto & Tiago, 2009; Singh, 2011). Hofstede assigned measurements for each

country’s psychic distance, according to different criteria, which included the valuation of

each measure of individualism, uncertainty avoidance, power distance, and masculinity. I

discuss the use of the measurements in Section 2.

Dimension (Dim): Dim is the size of a country's economy; measured by gross

domestic product (GDP; Couto & Tiago, 2009; Singh, 2011).

Geographic distance (GD): GD is the distance in kilometers between each brand’s

original country's capital and the target country's capital (Couto & Tiago, 2009; Singh,

2011).

Language knowledge (LK): If the managers of the U.S. company expanded the

company in the past in a new foreign country, in which its citizens use the English

language as their first language, the score of the LK measure would be equal to 0;

otherwise the score would be equal to 1 (Couto & Tiago, 2009; Sousa & Lages, 2011).

Prosperity (Pr): Pr is the purchasing power of each country's citizens; measured

by the GDP per capita (GDP PC; Couto & Tiago, 2009; Singh, 2011).

Assumptions, Limitations, and Delimitations

Assumptions

I used eight key assumptions to design the study. The first assumption was that the

attraction features of the target market were accurate measurements of the level and speed

of globalization for the selected U.S. company. The second assumption was that the

publicly disclosed financial and marketing data by the U.S. company from 1972 to 2009

were accurate. The third assumption was that the results of the leading U.S. company's

globalization process could be generalized for any company in the jewelry sector, which

is a part of the luxury goods industry. The fourth assumption was that the leading U.S.

company's globalization process could be considered ideal for generalizing the results for

any luxury goods company around the world, even from developing countries such as

S.A.

Furthermore, the fifth assumption was that the managements’ intellectual and

innovative capabilities were equal among all luxury goods companies, including the

company under study. The sixth assumption was that possible economic depressions and

political turmoil in different countries that might influence financial performance success

and the speed of globalization were ignored. The seventh assumption was that the desire

and speed of the management and board of directors to globalize in each company was

ignored. The eighth assumption was that the leading U.S. company was facing the same

business and industry factors as all of the other jewelry companies.

Limitations

Limitations in the Uppsala model are evident because Johanson and Vahlne (1977)

used the Uppsala model to apply to many countries simultaneously. As a result,

limitations could arise from this quantitative study, including the inability to generalize

the conclusion that the factors that affected a large U.S. global jewelry company would be

the same for local jewelry SMEs or global jewelry brands in S.A. Furthermore, I ignored

other political and economic issues in each region around the world. Moreover, I did not

account for the introduction of new and innovative designs. Other interactions between

variables might not have been captured within the study design. Although the study

focused on one industry and one selective company, the study could be valuable as

foundational research about globalization in the luxury goods market.

Delimitations

I employed this study for the jewelry industry only; thus, the results could not be

generalized to other industries. Furthermore, because of the limited disclosed data of

global jewelry companies, there were no participants in this study. I relied on secondary

data about the selected U.S. company that was publicly traded on the New York Stock

Exchange (United States Securities and Exchange Commission, 2012). This company

represented one of the largest jewelry companies in the world (United States Securities

and Exchange Commission, 2012). Though the inclusion of more companies would add

more reliability and validity to the study, thus also supporting greater generalization to

the results of the study, the study was limited to one company (Alexander, 2014, Punch,

2013). Furthermore, as the study was a correlational study, I did not address cause and

effect implications resulting from the analyses.

Significance of the Study

Contributions to Business Practice

Given that SMEs comprise 93% of Saudi companies, the problem of local jewelry

SME leaders refraining from establishing a globalization strategy has negatively

influenced the Saudi business society, the Saudi GNP, and the sustainable survival of the

Saudi retail sector with local and original Saudi products (Assad, 2008). Researchers

focused on globalization studies covering global trends and success factors for

implementation (Eren-Erdogmus et al., 2010; Pham, 2009). However, few researchers

have focused on how luxury retail brands in developing countries enter western markets

(Eren-Erdogmus et al., 2010; Pham, 2009).

There is a need to investigate the possibility of enhancing profitability for S.A.

SMEs through globalization (Assad, 2008). This investigation cannot occur until the

factors required for developing a successful globalization strategy could be identified

(Eren-Erdogmus et al., 2010; Pham, 2009). Findings from this study provided a

quantitative analysis of potential factors that S.A. jewelers should consider in developing

their globalization strategies. This foundational study was expected to provide initial

insights that did not previously exist in the literature due to a lack of existing research and

the scarcity of information in S.A. (A. Fakeih, personal communication, December 18,

2009; Pham, 2009). From the study results, I gathered insights about the attraction

features of target countries in a globalization strategy that S.A. business leaders could use

as factors when selecting countries in their globalization strategies.

Implications for Social Change

In the last two decades, political leaders in S.A. have struggled to find solutions to

high unemployment, terrorism, corruption, and lack of human rights (United Nations

Development Program, 2010). Globalizing ethnic and local brands is one of the means

Saudis could use to solve some of the human rights' problems and fully participate in the

world community. Because there was little existing research on this topic, the study could

be used to empower local, successful luxury brands in the Middle East to implement their

own globalization expansion process. Furthermore, I plan to open an incubation/venture

capital center to assist local SME brand managers in their quest to globalize their brands.

Therefore, the study is beneficial for its theoretical and academic value and practical for

business implementation and the derivative benefits of catalyzing business growth and

employment.

A Review of the Professional and Academic Literature

The purpose of this quantitative study using discriminant analysis was to examine

the countries' economic attraction features in the globalization strategy of a leading

United States global jewelry company that could facilitate the implementation of a

successful globalization strategy for a local Saudi jewelry SME retail company. The

references' section contains 100 references related to the research topic. Of these, 86

references are from 2010 or later and were peer-reviewed or dissertations, representing

86% of all references for this study. Authors of these resources emphasized the need for

the development of managerial skills, company capabilities in technology development,

human and financial resources, product features, and careful selection of target countries.

I used the literature review to pursue gradual steps from general to specific topics

in researching the study. I gathered information in the Orientation section to understand

the overall status, structure, players, forces, and challenges of the global retail brand. The

challenges included barriers to entry and market risks among all other aspects of the

branded retail industry and SMEs in the world and S.A. In the Significance and

Motivation for Globalization section, I addressed the necessity for globalization and the

consumption pattern of luxury goods in S.A. and emerging markets.

In the third section of the literature review, I presented counterarguments to

globalization, with emphasis on the challenges and barriers to entry in target markets. I

also presented the original research and supporting research to explain the Uppsala model

details and its limitations. In the Globalization Methodologies section, I explored the

elements, concepts, techniques, and strategies of the globalization process by presenting,

in chronological order, quantitative studies related to these topics. Furthermore, I

presented, in chronological order, qualitative and mixed method case studies that had

focused on the internationalization of luxury goods retail brands. Finally, I presented case

studies that had focused on the globalization of specific Western brands and brands in

emerging markets.

Orientation

I started the literature review by investigating the global brand phenomenon. I

identified the trends affecting the luxury goods retail brands' industry. Furthermore, I

identified the trends and features of the retail industry in S.A.

Global brand. The globalization of luxury goods' brands might not be an absolute

necessity. However, corporations sometimes notice demand for their products by

consumers in foreign markets. Under the latter circumstances, globalization might

become one strategy option for a corporation to consider. Danziger (2005) dissected the

global luxury market landscape and the trends emerging in the global consumer

purchasing habits. Danziger explained the rising power of global brands in contrast to

local brands and the influence of the instant gratification that characterizes the

millennium generation and the price-sensitive Generation X on the world luxury market.

Kim and Jang (2014) also found that Generation Y continued its appetite for global

brands as substitutes for local brands.

Key drivers of retail globalization success from different regions around the world

were discussed, including valuable analysis of current trends, forecasts of future

globalization factors, and segmentation of the global retail market (Danziger, 2005).

Using the discussed features in the globalization construct, the need that appeared to exist

in the Saudi jewelry market was strengthened by the limited scholarly work discussing

the process by which globalization occurs (Pham, 2009). According to Danziger (2005),

researchers explored how the largest retailers around the world focused on factors that

supported the success of sustainable globalization strategy and confirmed other findings

that stressed the increasing powers of globalization and the need for retailers to globalize

their brands.

Danziger (2005) confirmed the need, features, and diverse consumer demands

across markets, as well as the importance of the process of luxury goods globalization. As

a result, Saudi jewelry brands that witness demands from other countries might follow the

steps of their predecessors in pursuing the globalization of other global brands. If the

Saudi brands exhibit quality features that match other global luxury products, then local

Saudi companies making branded products might make efforts to globalize their brands

using strategies that have been successful for other brands.

Based on some of the assumptions underlying the research study, small jewelry

firms operating within S.A. were incapable of going global given their current

constraints. This assumption warranted a review by Dimitratos, Plakoyiannaki,

Pitsoulaki, and Tüselmann’s (2010) stratification of small firms that were global in

nature. Dimitratos et al.'s presentation warranted further understanding of how firms

could be global, while also being small. While S.A.’s jewelry industry is comprised of

large and small companies, those companies that have filled international orders might,

therefore, already be global in nature without an official strategy to operate in those

foreign lands.

Dimitratos et al. (2010) distinguished the global smaller firm that is seeking to

globalize in leading countries of its industry from newborn global ventures that are not

present in leading countries in their industries. A researcher could use Dimitratos et al.'s

study to organize small firms in S.A. that are interested in globalizing their brands by

their respective abilities to tolerate risks. This specific distinction supports findings from

10 case studies conducted by Dimitratos et al. of Greek small gold and silversmith firms.

Dimitratos et al. found that the global smaller firms possessed a stronger entrepreneurial

course.

If the assumption of the entrepreneurial strength of the global small firm would be

immediately tested, results could point in any direction. The results could show either that

small Saudi jewelry makers and sellers possess the entrepreneurial prowess to match the

Greek gold and silversmith small firms, or that the essential ingredients for successful

globalization are lacking in those Saudi jewelry firms. Moreover, Dimitratos et al. (2010)

verified their perception of the global smaller firm's needed strength in its

entrepreneurship capabilities in terms of seeking international opportunities, approaching

risk, and innovating, which Saudi jewelry firms have been assumed to lack. Dimitratos et

al. also stressed that the global market selection and speed to globalize might be pertinent

to the globalization strategy and process. This also means that the global market selection

and speed to globalize might not be pertinent to the strategy and process.

If Dimitratos et al.’s (2010) comments are valid, then Saudi jewelry firms that

seek to globalize should ascertain their standing in the context of the two factors: market

selection and speed, and globalization strategy and process. Under circumstances of

ascertainment, Saudi jewelry firms could try to understand the characteristics of the

Greek silver and goldsmith foreign markets' selection and globalization process speed to

foreign markets, as well as their globalization strategy and process. I also needed an

understanding of the Saudi retail market to distinguish its characteristics from other

potential markets for globalization.

The retail trends and features of S.A. The globalization strategy decision for

Saudi luxury goods retail brand leaders has been affected by Saudi market dynamics and

challenges to increase profitability, and both warranted research about Saudi retail trends

and features (Assad, 2008; Opoku, 2012). I researched the market trends and challenges

to understand the future of the Saudi jewelry retail market.

The World Gold Council (2012) estimated that the world jewelry market was

$98.633 billion United States Dollars (U.S.D.) in 2011, and the Saudi jewelry market was

$2.783 billion U.S.D. Since 2011, the world jewelry market has been regaining its value,

which had declined since 2008, though its growth was still negative. The world jewelry

market's growth rate in gold demand in tons was -3% in 2011, compared to its growth

rate of -6% in 2008 (World Gold Council, 2012). Furthermore, the Saudi jewelry market

has been decreasing in size, with growth rates in gold demand in tons decreasing 17% in

2011, compared to a -11% decrease in 2008 (World Gold Council, 2012). The rise of

consumerism and the power of the internationalized brand in developing countries have

raised awareness for S.A. jewelers of all sizes and types to seek a globalization strategy to

increase their local brand sales and profitability (Assad, 2008; Chan, Finnegan, &

Sternquist, 2011; Danziger, 2005; Hertog, 2010; World Gold Council, 2012).

Saudi jewelers need to consider the globalization strategy option for their jewelry

brands and overcome their small Saudi market size, lack of horizontal expansion, and

vertical integration (Alanezi, 2012; Alharbi, 2014). Horizontal expansion in the Saudi

market is not feasible because of the small market size, and the Saudization policy

prevents brands from following a vertical integration strategy (Al-Asfour & Khan, 2014;

Alanezi, 2012; Alharbi, 2014; Sadi & Henderson, 2010). A successful globalization

strategy should address these challenges.

Saudization is a job localization program managed and mandated by the Saudi

government; the program is required of all private sector Saudi industries, and has been

subject to increasingly stringent laws since 1995 (Alanezi, 2012; Sadi, 2013; Sadi &

Henderson, 2010). The S.A. government implemented this policy to reduce rising levels

of unemployment in S.A. (Alanezi, 2012; Ramady, 2013; Sadi, 2013; Sadi & Henderson,

2010). Although official unemployment estimates among Saudi men stood at 12% in

2013, unemployment estimate for the population between 20 to 25 years old was as high

as 40% (Saudi Arabian Central Department of Statistics and Information, 2014).

A lack of horizontal expansion options is present in S.A., along with a decrease in

the markets of leading local jewelry brands around the world, including the Middle East

(“Retail Prospects,” 2009). The decline of the local market for jewelry brands in S.A. is a

result of the aggressive entry of large global retail brands in the first and second decade

of the 21st century in developing countries (Danziger, 2005). These large brands use their

name recognition and large economies of scale and scope in their entry in developing

countries (Danziger, 2005). As these larger brands gain market share, declining popularity

of local brands is expected to intensify in the second decade of the 21st century

(Danziger, 2005). This trend is possible because of the increasing S.A. population of

those citizens under age 40, who constitute 78% of the population and tend to prefer

global brands (Danziger, 2005; Baqadir, Patrick, & Burns, 2011).

Moreover, Opoku (2012) emphasized the rise of consumerism and influence of

globalization in Saudi Arabia. Opoku conducted a quantitative survey of 200 university

students in S.A. to examine the influence of peer-pressure on young men. Opoku found

that peer-pressure was high and could be dictated by culture. I concluded from Opoku's

findings the opportunity for international brands to succeed in targeting S.A. and

influencing Saudi young consumers by strong marketing campaigns.

Syed (2012) used a mixed method design study on 177 micro and SME Saudi

entrepreneurs to understand the difficulties they were facing in their businesses in Saudi

Arabia. Syed found that the difficulties were in the lack of financial funding, bureaucracy,

and unfavorable business environment, lack of government support, unexpected policy

changes, and lack of training for labor. Although economic challenges are prevalent in all

sectors of the Saudi market, the need to diversify and globalize certain sectors, such as

the retail sector, was prioritized for the sustainability of the Saudi economy (Assad,

2008). Assad (2008) and Opoku (2012) emphasized the need for S.A. business leaders to

understand the threats of foreign and global brands, and the need to enhance profitability

that would lead to the need to globalize. As a result, Saudi retail brand managers should

use globalization to survive in the changing and competitive global retail brand industry.

Nevertheless, scarce research existed about the globalization decision-making

process of Saudi SME retail brand leaders. Moreover, further research was needed to

understand the significance and motives for SME retail brand leaders in other markets to

pursue a globalization strategy in their efforts to enhance their brands' profits. I could also

consider the need to understand the facilitating factors for globalization success when

analyzing the decision-making process of Saudi jewelry brand leaders as they choose a

globalization strategy.

Significance and Motives for Globalization

Hynes (2010) studied country preference process, motives, and challenges of

internationalization. Hynes conducted interviews with 80 Irish SME managers. Hynes

found that the notable internationalization motive was the absence of Irish market

opportunities. Hynes also found that among the challenges of internationalization was a

lack of knowledge about foreign markets and customer research. Hynes stressed the need

to develop research on features of an SME company that is tied to the type and level of

internationalization. Hynes also stressed the need for future research to include

internationalization in SME core growth strategy.

Schweizer, Vahlne, and Johanson (2010) considered the strategy and

entrepreneurship drive of a company and its management research. Schweizer et al.

examined whether globalization should be considered as a result of an organization's

effort to enhance its networks or as an entrepreneurial achievement. Schweizer et al.

investigated three theories with a case study. As a result, Schweizer et al. proposed

modifications to Johanson and Vahlne's (2009) internationalization process model and the

Uppsala internationalization process model, by stressing the entrepreneurial feature of the

globalization process.

Papadopoulos and Martín Martín (2011) reviewed literature about the decision to

globalize highlighting the complex decisions of international market selection and

segmentation. Papadopoulos and Martín Martín argued that the research subject is

fragmented and could result in divergent streams of perspectives. Papadopoulos and

Martín Martín categorized different directions for future research, including factors for

the globalization decisions.

Aklamanu (2014) presented a framework to explore reasons for failure in

globalization strategy. Aklamanu introduced the factors of the institutional environment

of a foreign target country that could influence the success of a globalization strategy.

The institutional environment factors were (a) regulative, (b) normative, (c) cognitive,

and (d) constituents (Aklamanu, 2014).

Regulative factors were related to pressure of foreign governments’ business laws

and regulations (Aklamanu, 2014). Normative factors were related to consumer, supplier,

competition, human resource, and public pressure (Aklamanu, 2014). Cognitive factors

were related to business ownership structure and retail showrooms structure (Aklamanu,

2014). Finally, constituents' factors were related to legitimacy pressure from constituents

to serve them in all of the regulative, normative, and cognitive factors stated above

(Aklamanu, 2014). Aklamanu concluded by the suggesting future researchers should test

the factors with empirical studies. I concluded from Aklamanu's study the complexities of

the factors that are involved in selecting countries for a globalization strategy and the

need for researchers to study globalization strategy factors in detail.

With the acknowledged lack of research on globalization for SMEs in developing

countries, researchers focused on motives and processes of globalization for SME

entrepreneurs in developing countries. Sadi and Henderson (2011) investigated the reason

and level of interest of Saudi SME leaders in franchising. Although I did not study

franchising as a mode of globalization in this study, any research about Saudi retailer

SMEs is considered valuable because there is a of lack of research on this subject (Sadi &

Henderson, 2011).

Sadi and Henderson (2011) distributed a survey to 179 Saudi retailers and

integrated secondary research to recognize attitudes toward franchising global brands in

S.A. Sadi and Henderson found that retailers preferred adapting global franchises in S.A.

based on the scarcity of management knowledge, capital, and human resources to expand

or globalize local brands. I concluded from the study that the danger of the widespread

acceptance of global brands by local retailers is that the acceptance might jeopardize the

long-term existence of local Saudi SME brands. Knowledge, human, and capital

resources are essential for globalizing local brands for SME entrepreneurs (Sadi &

Henderson, 2011).

Dahan and Peltekoglu (2011) argued that global brands had a negative impact on

local SME brands and rapidly captured local market shares. Dahan and Peltekoglu

examined the negative effect of the global brand Zara on 50 Turkish SMEs in the Turkish

clothing market. According to Dahan and Peltekoglu, SME leaders should globalize their

brands and elevate their brands' offerings to global standards if they wish to compete with

the growing market share that is occupied by global brands. My conclusion about the

benefits of globalization was supported by Elango, Talluri, and Hult (2013). Elango et al.

examined 584 global operating service companies from the U.S. to see if their

globalization processes reduced their risk of failure in their globalization strategies.

Elango et al. found that careful globalization process with global diversification would

reduce risk-adjusted performance for the companies in the study.

Nevertheless, Mollá-Descals, Frasquet-Deltoro, and Ruiz-Molina (2011) warned

SME leaders from expected financial losses early in the globalization processes and

increased global competition. Mollá-Descals et al. (2011) examined the performance of

64 small to large Spanish global retail chains and compared their globalization processes

to their performances. Mollá-Descals et al. found that most companies suffered losses

early in the globalization processes. However, the companies' increased economies of

scope and scale facilitated success later in the globalization process. Mollá-Descals et al.

emphasized the importance of a well-planed globalization strategy and culturally-

knowledgeable company leaders.

Other important motives for globalization in developing countries were

government financial support and networking capabilities in home countries such as the

case in China (Liang, Lu, & Wang, 2011). Liang et al. examined 553 Chinese private

companies to understand the influence of the factors of resource endowment, foreign

investments in China, and organizing capability in China on the companies' globalization

strategy. Liang et al. found the factors increase the likelihood of selecting high-risk

globalization strategies.

Researchers such as Arndt, Buch, and Mattes, (2012) Korsakienơ and

Tvaronaviþienơ (2012), and Sekliuckiene (2013) analyzed companies' internal and

external motives and barriers to globalization. Arndt et al. (2102) examined German

companies' exports and foreign direct investment decisions in light of companies' sizes,

financial constraints, and German labor market restrictions. Arndt et al. found that

companies' sizes and labor market restrictions were important factors and motives,

respectively, for internationalization. However, financial constraints were not deterrent

factor for internationalization.

Korsakienơ and Tvaronaviþienơ (2012) investigated quantitatively the

globalization process of 300 Lithuanian and Norwegian SMEs to understand the motives

and barriers of globalization. Korsakienơ and Tvaronaviþienơ found that limited local

market size and increasing competition were the main external motives. Furthermore,

internal motives were the need for mitigating risk and absence of skilled labor. Barriers

for Lithuanian companies were lack of economies of scope and scale.

Korsakienơ and Tvaronaviþienơ (2012) emphasized the success of the

globalization process of the Norwegian companies was based on the advanced

entrepreneurship skills of the Norwegian leaders when compared to the Lithuanian

leaders. Korsakienơ and Tvaronaviþienơ concluded that lack of international knowledge

and entrepreneurship skills were important barriers to the success of the companies'

globalization strategies. Korsakienơ and Tvaronaviþienơ's findings were related to this

study because they emphasized the need for knowledge of foreign factors that would

facilitate the success of a globalization strategy.

Sekliuckiene (2013) continued to analyze motives and barriers of

internationalization by interviewing managers from 34 Lithuanian companies, which

globalized in Brazil, Russia, India, and China (BRIC). Sekliuckiene argued that motives

could vary from target market size, competition, product appeal, and other

businessrelated motives. Moreover, Sekliuckiene found internal barriers could include a

company's size and resources as well as managers lacking international experience.

External barriers could include regulations within the target country, differences in

culture and language, and geographic distance (Sekliuckiene, 2013). Sekliuckiene's study

was important to accumulate all the different factors that would affect the globalization

process.

Counter Positions

A few researchers have disagreed with the decision to globalize rapidly, including

Dimitrova, Rosenbloom, and Andras (2014). Dimitrova et al. investigated the relationship

between the degree of retail internationalization involvement and company performance

using an exploratory quantitative study. Dimitrova et al. measured the degree of retail

internationalization involvement (DRII) by the measuring the number of regions around

the world that each company entered.

Furthermore, Dimitrova et al. measured company performance by measuring sales

per square meter in each store for each region. Dimitrova et al. found that, in general,

DRII was negatively related to companies' sales because of different cultural and legal

features in different markets. Companies' sales in fewer geographic areas exceeded

companies in more geographic areas (Dimitrova et al., 2014). I concluded from

Dimitrova et al.'s study that entering large number of countries in the globalization

process could not compensate for selecting few suitable countries. As a result, I

concluded from the findings the importance of country selection in the globalization

strategy.

Although Dimitrova et al. concluded that the gradual expansion into

geographically close markets increased company sales. However, leaders of companies

also increased their companies' sales in the globalization process if they enter in a distant

market first thus accumulate knowledge to enter a few geographically close markets

(Dimitrova et al., 2014). Dimitrova et al. argued that expanding to a few important distant

markets assisted leaders of companies in investing resources to increase sales, thus

succeeding in the globalization process. I concluded that researchers agreed on the

strategic benefits of globalization to retail companies but differed on the timely selection

of countries.

Globalization Methodologies

I used secondary research to understand different business globalization strategies.

I researched globalization strategies in terms of the expansion order of target countries

and the marketing, organizational, and financial structures the brands need to succeed in

their globalization processes. I researched the different methods of past studies to

understand the factors affecting the globalization process.

Uppsala model. According to Singh (2011), one of the respected globalization

models used has been the Uppsala model of Johanson and Vahlne (1977). Johanson and

Vahlne (1977) introduced the Uppsala model to understand the internationalization

process of Swedish companies in the 1970s. The model is based on the hypotheses that

the internationalization process of any company should start with gradual expansion

(Johanson & Vahlne, 1977). The gradual expansion should start in near markets, then

move to distant markets through exports (Johanson & Vahlne, 1977). Further, as

experience is gained about distant markets through the process, companies commit to

more investments; as a result, they enter full operation without local equity partners

(Johanson & Vahlne, 1977).

I perceived the attractiveness of markets to rely on geographic distance and

psychic distance from a company that is globalizing in this market (Johanson & Vahlne,

1977). According to Johanson and Vahlne (1977), psychic distance is defined as the total

of circumstances blocking the exchange of business transactions from and to a market.

Examples of psychic distance can include dissimilarity in language, education, business

norms, and culture. The less experience and knowledge a company has about a market,

the larger the psychic distance, and the greater the risk for the company to enter this

market (Johanson & Vahlne, 1977).

A company may gain gradual and sequential globalization process knowledge

based on the knowledge that is gained from the cumulative experience in past entries in

near and distant markets (Johanson & Vahlne, 1977). The Uppsala model is based on two

components: state factors related to market commitment and knowledge and change

factors related to allocation of resources and current operations (Rubaeva, 2010).

Johanson and Vahlne (1977) used Figure 2 to represent the essential apparatus of the

globalization process with its two components, state and change factors.

Figure 2. State and change factors (Johanson & Vahlne, 1977, p. 26).

The brand commitment component was further explored by Rubaeva (2010).

Rubaeva claimed that a commitment of a brand to remain in a market relates to the level

of risk associated with the market, and the amount of investment in marketing, human

resources, and other economic and political factors associated with this market. Rubaeva

explained that market knowledge refers to the experimental gradual knowledge about a

foreign country's culture and customers. Rubaeva elaborated that the allocation of

resources in a market refers to the company's commitment decision to transfer resources

to this market based on the accumulated gradual experience of operating in this market or

foreign markets. Rubaeva also stressed that the company's activities depend on the

company's capabilities and skills of its employees. In order to understand details of my

study, I reviewed studies by researchers who explored the globalization of SME retail

brands using quantitative, qualitative, and mixed method studies.

Existing quantitative studies. In this study, I used the results of prior quantitative

research to identify the factors that influence the globalization process of a local brand

from emerging markets. I also reviewed quantitative studies that have focused on the

globalization of luxury goods brands. I conducted my review to understand the latest

research and models that would have supported this study.

Johanson and Vahlne (2006) stressed that while learning and commitment

building in the Uppsala model were imperative to reducing uncertainty; learning and

commitment building could be viewed as opportunity development procedures. Johanson

and Vahlne explained that the model might not be deterministic; that is, commitment

might not be a direct consequence of experience. Johanson and Vahlne emphasized that

the incremental internationalization process exploits opportunities based on experience

gained. However, opportunity exploitation might be marginal to present internalization

actions (Johanson & Vahlne, 2006). Couto and Tiago (2009) used the Uppsala model to

quantitatively test the effect of different factors against the decision to globalize. The

factors were the knowledge of culture, geography, and language; the attractiveness of the

target market; target market economy of scope and scale; and management internal

capabilities (Couto & Tiago, 2009). Couto and Tiago, through their findings, accepted the

positive effect of the factors mentioned above, except for the language factor, which did

not prove significant in the internationalization process. Couto and Tiago also found that

importance should be directed toward the forces of organizational culture, leadership,

strategic direction, logistics knowledge, cost reduction, and the economy of scope as the

significant factors in subsequent internationalization steps of the process.

Johanson and Vahlne (2009) revisited their research in their Uppsala model

(Johanson & Vahlne, 1977) with a novel approach. Johanson and Vahlne contested the

importance of the psychic distance as the cause of uncertainty in international business

practices. Alternatively, Johanson and Vahlne proposed that outside networking,

trustbuilding, and knowledge creation will be more powerful influences than psychic

distance in years to come, especially for established international firms.

Between 2010 and 2014, researchers focused on factors that might influence

globalization success, such as the Ninan and Puck's (2010) study. Ninan and Puck (2010)

extended the Uppsala model by investigating the internationalization process of 109

Austrian companies in Central and Eastern Europe from 1989 to 2008. Ninan and Puck

used a longitudinally-designed study to compare past and current internationalization

processes. The study was used to highlight the importance of the collective learning

perspective within and among firms in the Uppsala model, and introduced two different

strategy types of changing entry modes overtime as new dependent variables.

Sanguanpiyapan and Jasper (2010) applied research to test motives for luxury goods

purchases at retail jewelry shopping outlets. Moreover, Sanguanpiyapan and Jasper

identified factors influencing shopping preferences, and found that jewelry shoppers were

more affected by functional motives than nonfunctional motives.

One of the few studies focusing on globalization in the jewelry retail industry was

the study by Simoni, Rabino, and Zanni, (2010). Simoni et al. quantitatively examined

the success of the globalization process in the U.S. for four SME Italian and three SME

Indian jewelry companies. Simoni et al. conducted intensive interviews with the

managers of the Italian and Indian companies. Simoni et al. found that Indian companies

were more successful in their globalization process because their products and marketing

campaigns were more adaptive to U.S. culture needs.

Furthermore, Indian companies used outsourcing in manufacturing to limit cost

and were more strategic than Italian firms (Simoni et al., 2010). However, managers of

the Italian companies insisted in marketing their companies' Italian identity and their

marketing campaigns were reactionary rather than strategic. I concluded from Simoni et

al.'s article the need to adapt to the cultures of target foreign markets. Other researchers

such as Cleveland, Papadopoulos, and Laroche (2011) continued focusing on the

importance of culture in the globalization process.

Cleveland et al. (2011) examined the relationship between strong ethnic identity

(EID) and globally-oriented disposition (cosmopolitanism: COS) to understand how

stable the EID-COS relationship across cultures varied according to demographic

variables in each culture. Cleveland et al. surveyed 2800 graduate and undergraduate

students from seven countries: Greece, Hungary, Sweden, Mexico, Chile, Canada, Korea,

and India, and found that customers incorporated their EID preferences with their COS,

and demographic and psychological variables were important factors across products and

countries. The findings emphasized the importance of culture and demographics in the

globalization process.

Jung and Shen (2011) emphasized the importance of Hofstede’s four dimensions

as factors in the globalization process. The dimensions were collectivism, power distance,

uncertainty avoidance, and status consumption. Jung and Shen used a quantitative survey

of 50 female college students in the U.S. and their counterparts in China to test the

students' reactions to 10 global brands. Jung and Shen found cultural differences between

the U.S. and the Chinese consumers in all of the four dimensions, except for brand equity.

Jung and Shen recommended that researchers and business leaders should include other

factors in the globalization process such as brand features, cultural differences, and

demographic disparities, and test different age groups.

Among the studies focusing on factors for globalization was Chan et al.’s study

(2011). Chan et al. investigated company and country level factors that influence retail

companies' performance in the globalization process. Furthermore, Chan et al. used a

regression analysis on 200 global retailers. Country factors consisted of each country's

economic attractiveness features. Economic features consisted of public policy laws and

practices, economic development, political risk factors, social and cultural environment,

and retail market characteristics including size and growth prospects.

Chan et al. explored company factors using the International Market Portfolio

Management (IMPM) and Retail Portfolio Management (RPM) capabilities and firm size.

IMPM capabilities relate to the experience of management in selecting foreign markets

and expansion strategies by incremental learning from each misstep in the globalization

process. The RPM capability refers to international experience in each country and

subsequent rate of expansion in other countries based on incremental learning.

Chan et al. found that the factors explained sales growth, but not return on

investment (ROI). Sales growth had no relation with population and country risk factors.

However, low level of development and high country income was associated with sales

growth. While the factors had no significant relationship with ROI, retailers with higher

sales growth are likely to select strategies that include limited retail outlets, few countries

of operation, high income countries, and faster speed of expansion. Chan et al.

emphasized the need for research to construct a framework to understand the detailed

relationships between globalization strategies and countries' features.

The validity of the Uppsala model has been tested quantitatively by Singh (2011).

Singh performed a quantitative reappraisal of the Uppsala model on U.S. companies in

the manufacturing and service industries over three time periods between 1965 until

2009. Singh found the relevance of the Uppsala model has not diminished over time.

Companies have relied on the model’s emphasis on psychic distance and its subsequent

sequence of entry.

The importance of psychic distance between countries of origin between the

manufacturer and the consumer in the globalization of a brand was also examined by

Carvalho, Samu, and Sivaramakrishnan (2011). Carvalho et al. conducted two

quantitative studies on 39 undergraduate Canadian students to examine the different

combinations of factors related to brands' countries of origin and products' features effect

the success of brands' globalization. Carvalho et al. found in their first study that different

combinations of country of origin and country of manufacturer lead to the successful

brand globalization. Moreover, when information of product attributes was shared

between managements in the brand's country of origin and county of manufacturer; the

brand was more successful in the globalization process. Furthermore, when customers

perceived countries of origin and manufacturer positively; the brand was successful in the

globalization process. The findings highlighted the importance of psychic distance

between countries, and the need to classify countries' attractiveness features in the

globalization process.

Nevertheless, Cuervo-Cazurra (2011) argued that management knowledge and

success in local markets, industry, and foreign businesses alliances were important factors

in internationalization. Cuervo-Cazurra examined the internationalization process of 602

Moroccan companies. Cuervo-Cazurra found the Moroccan companies started their

internationalization process in countries other than the Middle East or France because

management possessed industry knowledge, business competitiveness' capabilities, and

alliances with foreign companies. Cuervo-Cazurra raised concerns about the validity of

psychic distance and the sequential model of globalization.

Lin, Liu, and Cheng (2011) argued in their investigation of 164 Japanese SMEs

the significant factors of foreign direct investments, exports, and foreign alliances, in the

success of the globalization strategy. Although, profits for the companies suffered in the

first years of foreign operation, the companies were successful in later years (Lin et al.,

2011). Lin et al. also found that foreign alliances with knowledge about foreign cultures

were essential in light of SMEs limited resources. Furthermore, Tang (2011) argued that

the extent of foreign business alliances was a determinant factor for globalization success.

Tang examined quantitatively 210 Chinese SMEs to understand the relationships among

networking, resources, and globalization strategies. Tang found that foreign business

alliances were more important than foreign networks.

Aliouche et al. (2012) used an integrated quantitative model to predict an

attractiveness grouping of 143 for U.S. and Australian firms. Aliouche et al. based their

model on the Uppsala model, the electric paradigm model, and the transaction cost

analysis model. Aliouche et al. found the top and bottom five countries in the grouping

order-of-entry preference are the same for U.S. and Australian firms.

However, when Aliouche et al. compared the model's grouping results with the

actual international expansion practices of Australian franchise firms, they found mixed

results. The significant results were the emphasis of Australian firms to globalize

according to geographic and cultural distance factors regardless of foreign market

opportunities. Nevertheless, U.S. firms' grouping preference of countries according to the

model and historical order of entry were similar.

The importance of the geographic and cultural distance in the globalization

process of Australian firms was in accordance with the importance of the Uppsala model,

with its psychic distance concept in the globalization process (Aliouche et al., 2012).

Aliouche et al. wondered if the lack of determinant factors in the globalization process

influenced the grouping preference of countries' attractiveness. Aliouche et al.

emphasized the need for industry-level and firm-level research from different countries,

which were also discussed in the study by De Beule and Duanmu (2012).

De Beule and Duanmu (2012) analyzed 121 and 531 acquisitions by Chinese and

Indian companies in foreign countries quantitatively to determine how the factors of

country, industry, and company features effect the location choice of acquisition in the

globalization process. While De Beule and Duanmu found that the factors of regulatory

quality and control of corruption in foreign countries were determining factors in India's

acquisitions, technologically-advanced foreign countries were determining factors in

China's acquisitions. However, the results varied across industries in both countries.

Companies in both countries did not invest in politically unstable countries. I concluded

from the findings the need to investigate the countries, industries, and companies' success

features along with the companies' globalization strategies.

Assaf, Josiassen, Ratchford, and Barros (2012) tried to understand the different

relationships between the factors influencing globalization decisions in the globalization

process, and the level of companies' performance success for different globalization

strategies. Assaf et al. (2012) used organizational learning theory to test the relationship

of four variables with the performance of international companies. Assaf et al. used the

four variables because they might affect the transfer of organizational learning in the

internationalization process. The sample of the tests consisted of large international

supermarkets that have a presence in multiple countries in Europe and the U.S.

Assaf et al. (2012) found that the relationship between the companies'

internationalization and performance is a U-shaped curve. As leaders of companies

internationalize their companies, companies perform well the first few years and then

face difficulties before succeeding again. Assaf et al. reasoned the U-shaped relationship

curve existed because companies tend to internationalize in similar markets first, and thus

tend to succeed in the early internationalization process. However, as companies

internationalize in different countries, the implementation of the accumulated learning

process requires more time; thus companies are likely to underperform for some years

before succeeding again.

Assaf et al. (2012) found that mergers and acquisitions (M&A), companies' age at

entry to international markets, and country of origin have a relationship with the

performance of the company in the internationalization process. However, Assaf et al.

revealed the economic similarities between the target foreign country and country of

origin do not have a relationship with companies' performance. Assaf et al. concluded

that companies would be successful in the international market if these companies were

younger, entered in few international markets, and acquired knowledge through M&A.

Moreover, Assaf et al. (2012) found companies from developed countries would

benefit more than companies from Western countries in the internationalization process.

Assaf et al. explained the findings because of the smaller market size in developed

countries when compared to Western countries. As a result, companies from developed

countries would increase their sales and profits by targeting Western countries. Assaf et

al. highlighted in the study the importance of selecting similar foreign countries and

urged researchers to study the different factors of selected countries that might have

relationships with the success of the internationalization process.

Researchers such as Childs and Jin (2014) began challenging the Uppsala model’s

applicability for different industries. Childs and Jin examined if the Uppsala model was

applicable in the fashion industry, an industry characterized by strong brand images and

abundant resources targeting niche markets. Childs and Jin examined the success of the

Uppsala model in predicting the globalization process of three global fashion retail

companies: H&M, New Look, and Zara. Childs and Jin quantitatively examined the

companies' globalization speed, economic distance, geographic distance, and cultural

distance.

Childs and Jin (2014) found that the companies initially followed the Uppsala

model in choosing gradual globalization to countries with geographic and economic

proximity, and culturally similar countries to the companies' countries of origin.

However, the leaders of the companies did not follow the gradual expansion in later

stages in the globalization process. In fact, the leaders of the companies grew their

companies in other markets rapidly. Childs and Jin attributed the success and failure of

the Uppsala model in the early stages and late stages, respectively, to the strength of the

global brand established through the global media. Childs and Jin recommended that

researchers study a large sample of companies in each fashion industry and include new

variables such as company strategies and companies' economies of scope and scale.

Researchers started developing new collective measures as factors such as the

country distance measure (COD), which was introduced by Martín Martín and

Drogendijk (2014). Martín Martín and Drogendijk included in the measure

socioeconomic, geographic, cultural, and historical distances. Martín Martín and

Drogendijk used the measure to analyze its accuracy in predicting globalization country

selection decisions. Martín Martín and Drogendijk used a sample of 170 Spanish SMEs

exporting to countries around the world. Martín Martín and Drogendijk found that

cultural and historical differences were significant thus important factors. Martín Martín

and Drogendijk emphasized the importance of the concept of psychic distance and

recommended that researchers investigate the COD with the Hofstede index.

Nevertheless, Biçakcioğlu, Özgen, and Bakar (2014) found in their studydifferent

psychic distance factors influenced with different degrees the selection of foreign

countries. Biçakcioğlu et al. surveyed 123 global Turkish SMEs and found that psychic

distance factors was important in the initial stage of globalization and slowly became not

important in later years. Furthermore, the factors of political, business, and legal

similarities among countries were more important than religious, life-style, historical

similarities in the globalization process. Business similarities such as financial incentives

were important finding in the study. Biçakcioğlu et al. recommended future researcher

should study different countries with larger samples and incorporate in their studies

management cultured capabilities.

Existing qualitative studies. Between 2010 and 2014, scholars reevaluated the

past 20 years of research in the field of retail internationalization (Alexander & Doherty,

2010). Alexander and Doherty (2010) reviewed the challenges and development of retail

internationalization research. They proposed a framework for future research

emphasizing the focus on a global agenda that encompasses standardized global factors

for a globalization strategy. I concluded that Alexander and Doherty's framework could

provide a suitable reference to standardize the process of globalization strategies for

different industries from different countries.

Etgar and Rachman-Moore (2010) examined the effectiveness and efficiency of

two international retail expansion strategies. While the first strategy included expanding

into regional countries close to the home market, the second included expanding globally

into diverse and distant markets (Etgar & Rachman-Moore, 2010). Etgar and

RachmanMoore used the data of the 2007 Deloitte survey of 250 large-scale global

retailers to conclude that international retailers use both strategies evenly. Etgar and

RachmanMoore also found that the success of the globalization strategy should be more

effective than the proximate regions-only strategy, when measured and focused on sales

volume generation.

Guercini and Runfola (2010) presented diverse theoretical perspectives on the

aspect of business networks and their role in the internationalization process. Guercini

and Runfola conducted a case study for a vertically integrated company that implemented

branding and globalization in foreign markets in the fashion supply chain. The case

analysis was a longitudinal study that investigated the influence of business relationships

as a learning context involving opportunities/obstacles on the internationalization process

(Guercini & Runfola, 2010). Moreover, Guercini and Runfola posed questions for further

research and highlighted the relationship between the specific business model, the

subsequent international process, and business relationships.

McAuley (2010) analyzed research on the internationalization of SMEs from 1999

to 2009, comparing the findings to a previous review from 1989 to 1998 to see what

recommendations from prior research had been followed, and what challenges could be

anticipated for the future. McAuley used content analysis to compare past research

conceptual, empirical, and methodological approaches. McAuley found that progress has

been made in some areas, such as global and cross-cultural coverage, multisector, and

multimethod approaches. However, other areas need development, such as relevance to

policy makers and longitudinal studies (McAuley, 2010).

The validity of the Uppsala model has been also tested for different sectors and

company sizes. Kontinen and Ojala (2010) performed a case study of four Finnish

family-owned SME manufacturing companies operating in France. Kontinen and Ojala

used open-ended interviews with managers from the four companies.

They found that the companies followed the gradual steps approach, which relied

on the psychic distance, emphasizing the validity of the Uppsala model. The leaders of

the Finnish companies chose to follow the Uppsala model in expanding in geographically

close markets. I concluded that other companies from developing countries should also

use the gradual steps approach of the Uppsala model instead of expanding rapidly in

distant markets.

Stehr (2010) presented 30 diverse cases of the development of local German

market leaders to global market leaders, thus presenting actual examples of SMEs’

entrepreneurship lessons in internationalization. Tavoletti’s (2011) case study of a large

Italian fashion company found a fit between strategy set by the company leaders and the

globalization structure outcome. Tavoletti found that the globalization structure was an

evolving process and was not predetermined by a strategy. Nevertheless, Tavoletti found

that the Uppsala model was a suitable reference. The Uppsala model was flexible,

evolved within the globalization process, and encompassed the strategy of company

leaders.

Researchers including Lu, Karpova, and Ann (2011) studied the factors affecting

retail internationalization relating to firm-specific and country-specific factors. Lu et al.

used a case study to present a framework based on existing and past theories for retailers

in the fashion industry to select their entry mode to foreign countries in their

internationalization process. Lu et al. found that the influential factors were related to

companies, countries, and markets. Lu et al. found that company-specific factors to be

asset specificity, brand equity, financial capacity, and international experience. Lu et al.

also found that country-specific factors were country risk, cultural distance, and foreign

government restrictions. Market-specific factors were market potential and competition.

Lu et al. concluded the study by emphasizing the need for future research to develop a

systematic empirical analysis of the determinant factors.

As a result of the need for empirical analyses, researchers explored the

measurements and tests to understand the different levels of influence of the Uppsala

model and psychic distance factors on the globalization process. Sousa and Lages (2011)

developed a new measurement scale to assess psychic distance (the PD scale). Sousa and

Lages also examined the impact of the PD scale on the implementation and adaptation of

international marketing strategies. Sousa and Lages questioned 301 export firms and used

structural equation modeling analysis for the results. Sousa and Lages determined that

psychic distance was a construct of two dimensions: country distance and people

distance. Moreover, Sousa and Lages indicated, through the research findings, that the PD

scale was positively correlated with cultural distance and the new development of

product, promotion, pricing, and distribution strategies suitable to the foreign country.

Other researchers continued to examine the applicability of the model to other

specific situations, such as the study by Costa e Silva, Pacheco, Meneses, and Brito

(2012). Costa e Silva et al. used secondary research to examine the importance of

secondhand knowledge such as the building of trust, knowledge about a foreign market,

and opportunity creation in the success of the Uppsala model in the globalization process

of a European textile company in China. While Costa e Silva et al. found the importance

of second-hand knowledge in the Uppsala model, they conceded the limitation of the

study because it focused on one company, a single entry mode, and China. Costa e Silva

et al. recommended that researchers should study several countries with different entry

modes for different industries.

As more variables and different methods of globalization similar to the bornglobal

method became visible, researchers started testing the differences between the born-

global method and the Uppsala model; Kalinic and Forza's (2012) study is one example.

Kalinic and Forza argued that specific strategic focus is more important factor than the

gradual globalization, which is based on the factors of the accumulation of international

experience in the Uppsala model. Kalinic and Forza conducted a qualitative research on

five Eastern European countries and found that the companies were able to succeed in

their rapid globalization efforts by following an adaptable strategy, an entrepreneurial

spirit of problem-solving, and different levels of commitments in each foreign country.

Kalinic and Forza suggested research from different regions around the world and testing

the born-global method in different pints in history with different company sizes. Finally,

Kalinic and Forza warned of the stress consequences of the bornglobal method on the

companies and possibility of failure.

McCann and Acs (2011) examined a feature in target countries in the globalization

process, which is population density. McCann and Acs explored the relationship between

the sizes of the foreign countries, cities in them, and companies globalizing in those

foreign countries. McCann and Acs argued that global companies expanded in cities that

were multinationals but did not follow the population density index for countries.

McCann and Acs questioned the value of size and population density of countries and

recommended integrating cities instead of countries in globalization research.

Parmentola (2011) conducted qualitative research on six Chinese

telecommunication equipment manufacturing companies to understand the factors for

globalization success. Parmentola found that the level of competitiveness in the local

market and the socioeconomic department of the destination country were the most

important factors for globalization success. Although Parmentola sample of companies

was not from the retail sector, Parmentola's findings could be generalized to other

industries including retail.

By 2012, researchers focused on the features of the global SME such as

Hutchinson and Quinn's (2011) study. Hutchinson and Quinn examined nine British retail

SMEs in the luxury market using a case study and secondary research. Hutchinson and

Quinn found that five characteristics were evident in all of the nine companies. Each

company had a strong brand image, an opportunistic policy of preserving a niche strategy,

an aggressive expansion strategy in local and international markets, an involvement of the

company founder or owner, and a vertical integration strategy (Hutchinson & Quinn,

2012). I concluded from the findings the need for a retail SME to establish itself locally

with a strong brand, a strong management, and a solid business model before globalizing.

Existing mixed methods studies. Gammeltoft, Pradhan, and Goldstein (2010)

presented a framework of determinants and outcomes for the selection of target foreign

countries in the globalization process of emerging multinationals. Gammeltoft et al. used

a conceptual approach with statistical analyses and secondary research. Gammeltoft et al.

found the changing trends and features of foreign direct investment (OFDI) from

emerging countries, and compared between them specifically from Brazil, Russia, India,

and China.

Researchers in the field of retail internationalization have tailored their research to

specific sizes, sectors, regions, and recently developed countries, such as in the study of

Filippov (2010). Filippov studied the rise of Russia's international companies and

analyzed their reasons and processes of globalization. Filippov used secondary research

to highlight the challenges Russian companies faced than other countries’ global

companies, such as China. Filippov also emphasized the importance of more detailed

research on Russian companies, because Chinese and Indian companies have been

studied more than companies in other countries. In order to hone down my research, I

explored studies specific to single Western brands, and I used my findings to continue

exploring studies specific to emerging countries markets. The distinction between

Western brands and emerging countries' companies could assist in understanding the

differences of brands globalizing from Western markets and brands globalizing from S.A.

Single Western Brands’ Studies

In order to understand the globalization strategies of a Saudi brand, I researched

past studies of globalization methodologies of successful Western retail brands to provide

insight into the needed factors by brand managers in their globalization strategy

implementation. I included the needed factors in building my quantitative model. By the

middle of the last decade, new large luxury goods retail brands emerged strongly in the

global luxury market with encompassing globalization strategies, an example was the

Spanish luxury goods retailer Zara.

Bhardwaj, Eickman, and Runyan (2011) studied Zara extensively. Bhardwaj et al.

applied aspects of retail internationalization models and theories, such as the psychic

distance and resource-based theory, to understand and learn from the success of Zara.

Bhardwaj et al. found that Zara built on its early psychic distance experience in every

target country, which enabled it to expand rapidly, resembling a global-born model of

globalization. Bhardwaj, Kumar, and Kim (2010) investigated the reasons for the success

of the global brand Levi's in India when compared with local brands. Bhardwaj used

repeated measures ANOVA for 411 college students and found that Levi's were able to

capture market shares in India easily because of Levi's brand equity and global standards'

appeal.

New global jewelry SME brands emerged, which required investigation of the

reasons and processes of globalization success. One of the studies concerning the

internationalization process of a successful jewelry SME brand was conducted by

Rubaeva (2010). Rubaeva explored the internationalization processes of the Metro Group

jewelry company into the Russian market. Rubaeva examined the factors that determined

the success of internationalization processes. As a result, Rubaeva analyzed different

internationalization theories, including the Uppsala model to understand the Metro

Group's internationalization process and provide recommendations to apply the Uppsala

model for other jewelry companies in other countries.

Researchers such as Diallo (2012) focused on large international brands in large

emerging markets and attempted to understand the differences and similarities among

foreign and local companies' success in local countries. Diallo (2012) focused on the

globalization strategy of Carrefour and Extra in Brazil. Carrefour, which is a global

French supermarket chain-store company, is the largest grocery retailer in Europe. Extra,

which is a Brazilian retailer, is the second largest retailer in Latin America (Diallo, 2012).

Diallo (2012) conducted a case study using in-depth interviews with store and

department managers from both companies. Diallo compared the key success factors of

the foreign company Carrefour with the local company Extra in Brazil. Among the

marketing strategy theories, Diallo considered the core competencies theory and the

organizational culture theory. Diallo found that store format, localization, core business

competitive advantages, and organizational culture were the factors for success among

the two companies. The challenge for leaders of Extra was to compete with other

executives from Carrefour, given Carrefour's economy of scale and scope. Although

Extra and Carrefour's success factors in Brazil were similar, Diallo posited the possibility

to generalize the importance of some factors as imperative success elements in any

retailers' local or international expansion.

Other researchers such as Jianguo (2013) started examining successful

globalization strategies of Chinese brands such as Giordano. Jianguo conducted

secondary research about Giordano from its inception in 1981. Jianguo concluded that

Giordano followed the Uppsala model theme by expanding in Asia first then in Europe

and other regions around the world. Jianguo found that franchising was more beneficial

to Giordano than joint venture or wholly- owned operations. Furthermore, Jianguo argued

that globalization strategy success for Giordano hindered on (a) choosing the right

foreign partner, (b) bridging cultural gap by recruiting qualified cultured staff, and (c)

expanding gradually internationally. I concluded from Jianguo's findings the importance

of understanding foreign target cultures as a determinant factor in the globalization

strategy.

Emerging Market Brand Globalization Studies

As my understanding of the factors of globalization strategy decisions and

processes emerged, a literature review was warranted about the globalization studies of

SME retail brands in emerging markets. Business leaders of SME retail brands in

emerging markets might consider different problems and factors in selecting and

implementing a globalization strategy from their counterparts of SME retail brands in

Western markets. I researched past globalization studies about SME retail brands from

developing countries to understand significant factors in successful globalization

processes. Despite entrepreneurship skills, government networks, and experience in local

markets, retail SME brand leaders from emerging countries were hesitant about selecting

a globalization strategy (Pham, 2009).

Knowledge of foreign markets was stressed as an important factor in selecting a

globalization strategy by Pham (2009). Pham presented a new dimension to the Uppsala

model from the perspective of emerging markets. The Uppsala model was based on the

internationalization process of the developed Western countries (Pham, 2009). Pham used

hypotheses to test the importance of downstream and upstream factors for 226

Vietnamese firms. Some of the downstream competitive factors were staff proficiency in

foreign languages, conduct of business trips, sales staff with international experience, use

of Internet for day-to-day business, collaboration with other firms, use of governmental

linkages, and use of formal business networks (Pham, 2009). Pham argued that

investment in either downstream or upstream competitive factors produce the same

strategic high returns. Although Pham focused on upstream or vertical integration, Pham

provided additional factors to consider as salient to the success of the internationalization

process.

By the end of 2009, managers of local retail brands started searching for reasons

to increase their profits (Eren-Erdogmus et al., 2010). As a result of the managers' needs

to enhance their brands' profitabilities, some retail brand managers decided to enter the

lucrative foreign markets (Eren-Erdogmus et al., 2010). Eren-Erdogmus et al. (2010)

studied the latter phenomenon using eight exploratory case studies of Turkish retailers.

Eren-Erdogmus et al. investigated the different internationalization strategies of retail

companies from developing countries, which differ from Western-established strategic

theories. Eren-Erdogmus et al. posited that the main motive for internationalization was

economic pressure in the country of origin. Moreover, Eren-Erdogmus et al. found that

the success factors in internationalization to be product differentiation, branding,

government and social networking, and management capabilities and skills.

Researchers continued to focus on networking as one of the most important

factors in SME globalization such as Mohamed and Alexandre Rocha (2010). Mohamed

and Alexandre Rocha investigated qualitatively the influence of entrepreneurship and

networking relationship on the globalization strategy of SMEs from Brazil. Mohamed

and Alexandre Rocha examined three global manufacturing companies from the

manufacturing industries. Mohamed and Alexandre Rocha argued entrepreneurship

capabilities and foreign network were some of the most important factors for successful

globalization strategy.

As emerging retail SME brands decided to target foreign countries and knowledge

about foreign countries was imperative in the globalization decision and implementation,

a different selection processes emerged. Demirbag, Tatoglu, and Glaister (2010)

investigated the targeted countries' selection process for globalization of subsidiaries of

522 global Turkish companies, using the institutional and transaction cost theories.

Demirbag et al. used secondary research from official sources to accumulate data for

regression variables to test a number of hypotheses between these variables and location

expansion selection. Demirbag et al. argued the selection of target countries for

globalization was affected by politics, infrastructure in the foreign target country,

subsidiary concentration, industry R&D, and subsidiary size.

Demirbag et al. (2010) found no support for the influence of subsidiary ownership

and the group affiliation on location choice for the companies' subsidiaries. Although the

study was limited to Turkish companies, and thus might not be generalized, the Demirbag

et al. found the Turkish companies aimed to increase their global competitiveness when

they entered developed countries. The latter was in contrast to the reason for targeting

emerging countries, which was to take advantage of the companies' specific strengths

(Demirbag et al., 2010).

Researchers focused on the determinants of globalization success of SMEs from

developing countries such as the study by Amal and Freitag Filho (2010). Amal and

Freitag Filho conducted a qualitative study on three Brazilian SME companies from the

manufacturing industry to analyze the factors for globalization success. Amal and Freitag

Filho found that the entrepreneurial capabilities of the companies' management and

innovative networking relationships were important factors in the success of the

globalization process. Amal and Freitag Filho recommended future research should focus

on cross-country quantitative analyses with different cultures and public policies for

different countries. Khavul, Benson, and Datta, (2010) also argued that human capital

capabilities in SMEs were significant factors for successful globalization strategies.

Among the scarce studies about globalization methods from Arabian countries, Al

Qur'an (2010) attempted to analyze the drivers of globalization and explore the factors

that contribute to the selection of foreign countries in the globalization strategy from S.A.

Al Qur'an conducted a single case study on a leading S.A. company in the construction

industry. Al Qur'an argued that the drivers of globalization consist of firm financial

strength and foreign target countries' quantitative and qualitative factors.

Al Qur'an (2010) argued that the qualitative factors related to the cost of raw

material and natural resources in the foreign target country, the infrastructure, political

and economic stability, and geographic proximity. Al Qur'an conceded a concern

regarding time limitations that may have influenced the study's validity and the need to

study other industries and to explore other firms' factors.

As researchers developed literature about the globalization strategies of SME

retail brands from emerging countries, more studies for specific countries emerged for

Mexican brands (Vargas Hernández, 2011). Vargas Hernández (2011) analyzed the

globalization increase of New Mexican emerging multinational enterprises. Vargas

Hernández reviewed literature about the theoretical perspectives, explaining the

emergence of the globalization phenomena of Mexican multinationals. Vargas Hernández

then analyzed the enterprises' globalization strategies, implementations, and their

performance; thus, profiles of enterprises were examined. Vargas Hernández concluded

that the Mexican enterprises who survived the process of creative destruction (p.1) were

transformed into sustainable, innovative enterprises capable of fending off new, future

challenges.

Researchers also focused on globalization strategies of rising Asian developing

countries (Chang, 2011). Chang examined 115 international companies from different

industries from Hong Kong, South Korea, Taiwan, and Singapore from 2003 until 2006.

Chang attempted quantitatively to understand how did specific factors changed because

of the globalization strategies of the selected Asian companies. The factors were company

performance, degree of internationalization, global market growth rate, domestic growth

rate, R&D investment, size, debt ratio, and new plant and equipment.

Chang found that global market growth rate, domestic growth rate, and investment in R&

D were the most important factors for the selection of foreign countries and the success of

the globalization strategy.

Javalgi and Todd (2011) examined 150 Indian SMEs from different industries and

found that their entrepreneurial orientation, management commitment, and human capital

were positively related to their degree of internationalization. Javalgi and Todd also

supported Johanson and Vahlne's (1990) findings that knowledge and experience in

globalization were factors in predicting the degree of a company's internationalization.

Javalgi and Todd concluded that Indian SMEs should invest in fostering a culture of

knowledge sharing and entrepreneurship to succeed in their globalization strategies.

Different models evolved and were established to explore the globalization

process of retail SME brands from emerging countries. Yeoh (2011) analyzed the

globalization strategies of two Indian pharmaceutical companies. Using the Ownership,

Location, and Internationalization (OLI) framework, the Uppsala model, and the

accelerated internationalization perspective, Yeoh investigated three questions. First:

How are the two companies' competitive advantages affected the country selection?

Second: How do the globalization reasons of seeking new resources, new markets, better

efficiency, and implementing strategic vision differ between the two companies in their

country selection? Third: How do the patterns of globalization for the two companies

differ from each other?

Yeoh (2011) used a longitudinal case-study approach and secondary research to

understand the globalization pattern of the two companies. Yeoh argued that the

globalization process of the two companies could be understood by mainstream

internationalization models (Yeoh, 2011). Yeoh reported that each company's existing

knowledge in the early stages of globalization affected the company's initial globalization

efforts. However, the emerging internationalization models, such as the

LinkageLeverage-Learning (LLL) framework and accelerated internationalization, were

more effective in describing narrative knowledge flows in each company's later stages of

globalization (Yeoh, 2011).

Karabulut (2013) used a mixed method study to examine 267 Turkish SMEs in

terms of the characteristics of the SME, the SME's entrepreneur, and the globalization

process. Karabulut found that the selected Turkish companies follow the Uppsala model

of gradual globalization. Karabulut argued that the selected Turkish companies could

succeed faster in their globalization strategies if they would invest in foreign business

alliances and increase their knowledge about foreign markets. Furthermore, Loo and

Hackley (2013) found through their case study of 32 Malaysian fashion brands that

business knowledge, location, language, networks and management systems were

important for successful globalization strategy.

Bouges (2013) used the Uppsala model theme to conduct a case study to

investigate the successful globalization strategy of three Saudi family business leaders.

The interviews focused on (a) the features of family businesses to succeed in its

globalization strategy, (b) the features of target foreign markets for a globalization

strategy, and (c) the features of suitable internationalization opportunities for a

globalization strategy. Bouges found that Saudi family business leaders should have a

planned globalization strategy, financial and human resources, a strong governance

systems, and globally-competitive products.

Bouges (2013) concluded that Saudi family business leaders should target foreign

markets that are stable, hospitable to foreign investors, and protected by strong

governance and regulations. Furthermore, Bouges found that international opportunities

should have close psychic distance to Saudi Arabia. In addition, family business leaders

should establish business connections and the companies' products should be timely and

in demand in the targeted foreign markets.

In summary, knowledge of foreign markets and capabilities of the brands seem to

be the predominant factors to consider in selecting a globalization strategy. However, a

need existed for research about the options to find the factors that support globalization

success and select the suitable target countries for expansion for a Saudi SME in the

jewelry retail market. Business leaders and researchers could use the results of this study

to understand the most significant factors in successful globalization processes for a

Saudi jewelry SME retail brand.

Transition and Summary

This first section of the doctoral study presented the foundation of the study and

included the problem statement, the purpose statement, the nature of the study, the

research questions and hypotheses, and a review of professional and academic literature. I

investigated the key factors of economic attraction features of target foreign countries for

affecting a successful globalization strategy. I used the literature review to reveal the need

for a quantitative study to test the research questions under study, and the questions were

designed to identify the globalization success factors in selecting suitable target countries

for globalization. The findings of this study could be applied on the S.A. SME jewelry

branded sector, to facilitate the development of the globalization strategy decisions for

S.A. local SME jewelry leaders. I outlined in Section 2 the selected methodology for this

study and the procedure that I implemented for data collection and analysis.

Section 2: The Project

In this quantitative correlational study, I examined the potential factors that

support globalization in the Saudi jewelry retail sector. The research plan was to gather

data on the globalization process of a selected U.S.-based global jewelry company from

1972 to 2009. I utilized the data to learn whether or not the attraction features of the

target countries were important when compared with the actual historical country

preference order of entry in the globalization process by the U.S. company's

management. The process and findings could be generalized for other countries similar to

S A. I outlined in this section the methods that I used for the study.

Purpose Statement

The purpose of this quantitative correlational study using discriminant analysis

was to examine countries' economic attraction features within the context of the

globalization strategy of a leading U.S. global jewelry company that could facilitate the

implementation of a successful globalization strategy for one or more local Saudi jewelry

SME retail companies. Saudi jewelry business leaders could use the findings of the study

to facilitate their efforts in selecting a globalization strategy. I designed the quantitative

correlational study to utilize the Uppsala model, build on past quantitative studies, and

utilize publically available data from a leading U.S. company's globalization process to

answer the research questions that guide this study (Singh, 2011; United States Securities

and Exchange Commission, 2012; Uniworld Publications Inc., 2012).

I combined the quantitative study with the Uppsala model to examine a U.S.

company that was, at the time of this research, one of the world’s largest companies in

the jewelry market (United States Securities and Exchange Commission, 2012). I

identified all foreign countries the selected U.S. company entered using Uniworld's

Directory of American firms operating in foreign countries (Uniworld Publications Inc.,

2012). I excluded franchisees, representatives, and noncommercial entities. I used the

study data to identify which, if any, economic factors were significant in identifying

target markets as attractive to the U.S. company in their efforts to globalize in new

markets.

The independent predictor variables were the economic attraction of target

countries, language knowledge, geographic distance, and cultural distance. I measured

the economic attraction of target countries according to dimension, prosperity, and

accessibility (Couto & Tiago, 2009). I measured dimension by the GDP, prosperity by the

GDP PC, and accessibility by the population density (Couto & Tiago, 2009; Singh, 2011).

Other independent predictor variables included geographic distances and cultural

differences between each brand's country and targeted countries (Couto & Tiago, 2009;

Singh, 2011).

The dependent grouping variable was the historical, chronological entry

preference grouping (first third, middle third, and last third) for a foreign country for

globalization. I reviewed a U.S. company's globalization historical order of entry in

foreign countries from 1972 to 2009, using consecutive editions of the Uniworld

Directory (Uniworld Publications Inc., 2012). I categorized the countries in three groups

according to the order-of-entry in each country. The first group represented the period

from 1972 until 1989, the second group represented the period from 1990 until 1999, and

the third group represented the period from 2000 until 2009.

Theoretical Considerations

According to Alexander (2014), researchers use the theory of positivism as a

philosophy in their research to influence business behaviors of corporations and

individuals. Alexander argued that researchers use positivism in their research to learn

about human events in the areas of removing barriers to business growth and

development. Couto and Tiago (2009) and Singh (2011) relied on the theory of positivism

to chart the course of business expansion in their Uppsala modeling. From the point of

view of contemporary positivists, the use of scientific methods to uncover the

ramifications of international business globalization in the modern era builds on the

works of Couto and Tiago and Singh. It also seeks to align modern business growth

development in globalization with circumstances of countries in which the globalizing

businesses exist vis-a-vis those of the countries to which the businesses are expanding.

I verified the sequence of date of entry in each country by the U.S. company from

reliable sources (United States Securities and Exchange Commission, 2012; Uniworld

Publications Inc., 2012). The Uppsala model and past research were the components of

the study’s theoretical framework (Couto & Tiago, 2009; Eren-Erdogmus et al., 2010;

Singh, 2011). I catalyzed positive social and economic change in the Saudi retail

economy. I used the order-of-entry modeling process in assisting entrepreneurs with

successful retail jewelry and luxury goods brands in S.A. to globalize and thereby create

jobs and contribute to the Saudi economy.

Role of the Researcher

My role was limited to selecting the U.S. jewelry company, collecting the data,

identifying and conducting the statistical tests, monitoring and analyzing the results,

reporting the findings and implications, and validating the data and model findings. I used

public data from the U.S. company's available public information (United States

Securities and Exchange Commission, 2012; Uniworld Publications Inc., 2012) to

accumulate a full understanding of the historical preinternationalization and

postinternationalization public data for the company from 1972 to 2009. I also used

periodicals such as Business Week and Barron's to validate the data.

Furthermore, I used reliable and credible sources such as World Development

Indicators to collect the data required from each target country (The World Bank, 2012).

My relationship with the topic arises from having worked in jewelry markets around the

world. However, I do not have any relationship with the selected U.S. company in this

study. I selected the company through a search of the New York Stock Exchange and

other relevant sources to determine which company met the study's criteria.

Participants

I used a public U.S.-based, global branded jewelry retail company as the focus of

this study because of five reasons. The reasons were (a) scarcity of knowledge about the

S.A. jewelry industry, (b) scarcity of research about globalization processes of local

brands from developing countries because only a few SME or born-global brands from

developing countries have experienced globalization, (c) more accessible data that could

be gathered about a U.S. publicly-listed company, and (d) scarcity of large U.S. global

jewelry retail companies in the world (Eren-Erdogmus et al., 2010; Pham, 2009; United

States Securities and Exchange Commission, 2012).

The data included six attraction features for each of 25 countries that were entered

by the U.S. company, to constitute a data set of 150 items. Although the U.S. company

had franchises and agencies in many other countries, I selected countries in which the

company had a fully-owned operation. I gathered data from public information provided

through Uniworld Publications, Securities and Exchange databases, and reports and

public information to document their globalization during the period of 1972 to 2009

(United States Securities and Exchange Commission, 2012; Uniworld Publications Inc.,

2012). I used a quantitative correlational methodology. I examined the potential factors

that supported the U.S. company’s globalization strategy from the company's financial

reports and disclosed information.

I selected purposeful homogeneous sampling for this study to facilitate the use of

the desired type and size of organization for the study (Punch, 2013). An investigation of

a phenomenon occurring within an echelon that only few companies attain does not

require mass participation. Furthermore, some companies that achieve that level of

success and fame may not possess all the attributes necessary for a successful

investigation of the phenomenon.

Research Method and Design

I used the quantitative correlational study to examine the relationship of economic

attraction features among target countries on the U.S. company’s globalization process.

S.A. jewelry SME leaders could use the findings of the study to enhance profitability for

their companies by transforming a successful local Saudi SME jewelry brands to global

brands by using well-defined qualifications and parameters to guide their efforts (Punch,

2014). I planned the quantitative study to follow a scientific method to enhance the

business decision-making process of the study (Punch, 2013). Quantitative researchers

implement the method by the use of five steps: (a) framing the problem, (b) developing

the hypotheses and questions to be tested or answered, (c) accumulating and testing data,

(d) interpreting results, and (e) making decisions (Punch, 2013). The method was (a)

conservative, (b) objective, (c) efficient, and (d) effective (Punch, 2013).

Method

I used quantitative methodology to make the appropriate adjustments in the

analysis to compensate for the assumptions that the participants' depth of knowledge,

experience, and a large sample number would be required in empiricism and social

constructivism concepts. Other considerations were the apparent scarcity of the number

of globalized brands in developing countries, and the lack of existing quantitative

research supporting the efforts of developing countries to globalize (Eren-Erdogmus et

al., 2010; Pham, 2008). In addition, a lack of experience existed on the part of managers

who consider the globalization of their brands (A. Fakeih, personal communication,

December 18, 2009).

Quantitative research is useful in providing an opportunity for broad research that

would encompass industries, markets, or geographies (Rosas & Kane, 2011). For this

reason, the quantitative research method was appropriate for the study. Qualitative

research often requires in-depth investigations with many participants, especially for

interviews (Rosas & Kane, 2011). Therefore, a qualitative methodology would not have

been applicable to this research because of the lack of experienced and knowledgeable

participants from S.A. Mixed methods research would not have been inapplicable

because of the qualitative component within the method. Considering the above features

of research methods, the quantitative method possesses the features that were necessary

for an effective execution of this research.

Research Design

I used a correlational design with discriminant analysis in this study. Researchers

use discriminant analysis to classify individuals into groups on the basis of one or more

measures, or to distinguish groups based on a linear combination of measures (StatSoft,

2013). I examined the relationship between the independent predictor variables of

countries' economic attractions and the dependent grouping variable distinguishing

among three order-of-entry groups. I used six measures of countries' attraction features as

the values of the six independent predictor variables to develop a discriminant function

that determined which, if any, of the independent predictor variables may be used to

predict grouping date of entry preference. The dependent grouping variable distinguished

among the three order-of-entry groups according to the date range of entry in each foreign

country between 1972 and 2009.

Other potential quantitative designs include a preexperimental design, a

trueexperimental, a single-subject design, and a quasiexperimental study, which includes

a control and an experimental group of participants, but participants are not randomly

assigned to groups (Punch, 2013). In a preexperimental design, a single group is studied,

and an intervention is provided during the study (Punch, 2013). In a true-experimental

design, the participants are randomly assigned to treatment and control groups (Punch,

2013). In a single-subject design, the researcher observes the behavior of participants

over time (Punch, 2013). I selected a correlational design for this study because of the

need for purposeful homogeneous sampling and my reliance on publically available

information about the company under study.

I based this study on the Uppsala model and the internationalization process’s

gradual penetration in foreign countries described by Johanson and Vahlne (1977).

Researchers and business leaders have recognized the Uppsala model as an optimum

quantitative model for globalization since 1990 (Couto & Tiago, 2009; Eren-Erdogmus et

al., 2010; Singh, 2011). Johanson and Vahlne (1977) developed the Uppsala model to

reflect how the speed of the globalization process of a company is based on the

accumulated historical experience that is gathered from entering foreign countries.

Singh (2011) used public data of global U.S.-based service and manufacturing

industries to test the application of the Uppsala model as a determinant of country

preference for globalization based on the target countries’ attraction features and

strengths. I used a similar method to examine a leading U.S. international jewelry

company in the luxury goods market and its internationalization efforts from 1972 to

2009 from Uniworld, databases and reports from the Securities and Exchange

Commission, and several other reliable sources (United States Securities and Exchange

Commission, 2012; Uniworld Publications Inc., 2012).

I examined the degree to which the target countries’ economic features predict the

grouping of entered countries according to their order of entry by the U.S. jewelry

company. I defined the values of the independent predictor variables as the economic

attraction features of the target countries according to dimension, prosperity, and

accessibility (Couto & Tiago, 2009; Singh, 2011). I measured dimension using the GDP,

prosperity using the GDP PC, and accessibility using the population density (Singh,

2011). Other independent predictor variables included geographic, cultural, and economic

differences between each brand's original country and target foreign countries (Couto &

Tiago, 2009; Singh, 2011).

The set of data for the dependent grouping variable distinguished among three

preference groups of the 25 foreign countries according to the date of entry by a leading

U.S. jewelry company. The first preference group represented the countries entered by the

U.S. company between 1972 and 1989. The second preference group represented the

period between 1990 and 1999. The third preference group represented the period

between 2000 and 2009. I used public sources of databases and reports of the selected

U.S. company and the 25 countries, which the U.S. company's leaders had chosen to

enter (United States Securities and Exchange Commission, 2012; Uniworld Publications

Inc., 2012).

Population and Sampling

I selected purposeful homogeneous sampling for this study to facilitate the use of

the desired type and size of company for the study. I used only one U.S.-based, global

branded jewelry retail company because of the lack of research about globalization

process from developing countries and the scarcity of global brands in the jewelry market

(Eren-Erdogmus et al., 2010; Pham, 2009; United States Securities and Exchange

Commission, 2012). Based on Couto and Tiago’s (2009) selection criteria, I used five

criteria for selecting the study’s U.S. jewelry company among 33 publicly traded jewelry

companies in the New York’s stock exchange (United States Securities and Exchange

Commission, 2012).

First, I limited the company to the jewelry retail industry. Second, the company

should have operated globally in at least one other foreign country so the study could

benefit from each company's entry experience (Couto & Tiago, 2009). Third, I restricted

the selection of the company to U.S. public companies in order to access their public

information easily (United States Securities and Exchange Commission, 2012; Uniworld

Publications Inc., 2012). Fourth, I designed the study to focus on a company that was

profitable (Couto & Tiago, 2009). Fifth, I selected a company that had survived for

decades, holding leading market share positions in markets around the world and has not

failed or otherwise withdrawn from any international foreign market that it has entered

(Couto & Tiago, 2009).

The data included six attraction features for each of 25 countries that were entered

by the U.S. company, to constitute a data set of 150 items. I used the 150 items to

perform a quantitative methodology with a correlational design using discriminant

analysis. Researchers and business leaders could use the study to generalize its findings

for a branded jewelry retail local company from S.A. and other developing countries.

Ethical Research

Because individuals were not interviewed, nor were they given survey

questionnaires for completion, the study did not require consent forms, incentives, or

processes for assuring anonymity or data confidentiality. The company under study had

publicly disclosed the needed information pertaining to its organization for the study;

thus the global business and academic community may use the company's information.

Therefore, the issue of participants withdrawing from the research also did not apply. I

did not provide incentives to the company or its representatives because contacts were not

established between me and the company or its agents.

I maintained data pertaining to this research on a compact disc, in my personal

safe box. I will destroy the disc after 5 years to protect the identity the company, even

though no negative effects were anticipated as a result of this study. The company's name

will be identified in the study and archived data as the U.S. company for the benefit of the

reading public.

I did not use a specific intermediate specialized organization to collect data from

the company. Instead, I used publicly disclosed data. Therefore, the need to deploy an

agreement did not arise, and I did not need to obtain a permission agreement from the

company. I conducted the research under the IRB approval from Walden University

(approval # 01-03-14-0198953).

Data Collection

Instruments

I conducted secondary research using the tool Globalization Trail and Search

Protocol to collect data from different web sites, and to gather the numerical values for

the needed measures of the variables in the quantitative model. I identified all foreign

countries entered by the U.S.-based company under study with dates of entry by the U.S.

company through Uniworld's Directory of American Firms Operating in Foreign

Countries (Uniworld Publications Inc., 2012). I also used the disclosed information

reported by the company in the U.S. Securities and Exchange Commission reports

(United States Securities and Exchange Commission, 2012), removing franchisees and

representatives from the population sample because I was interested in investigating the

fully-owned operations of the company in foreign countries.

The collected data pertained to the dependent grouping variable and the

independent predictor variables. I used the dependent grouping variable to distinguish

among the three order-of-entry preference groups. The first preference group was

between 1972 and 1989. The second preference group was between 1990 and 1999. The

third preference group was between 2000 and 2009. The independent predictor variables

were the competitive target countries’ demographic and economic features. The

competitive target countries' features constituted the data that were explained below with

associated explanations of data collection.

Competitive target countries' features. The economic features for countries

were divided into economic attractions, language knowledge, geographic distance, and

cultural distance (Couto & Tiago, 2009; Singh, 2011). Economic attractions were based

on dimension, prosperity, and accessibility (Couto & Tiago, 2009). I defined dimension

as the size of the countries' economies, which is essential for an expansive brand, and was

measured by GDP for each country (Couto & Tiago, 2009). Prosperity was based on the

purchasing power of the countries' citizens, which was measured by the GDP PC (Couto

& Tiago, 2009; Sigh, 2011). Data for GDP was adjusted for inflation within each country

and converted to 2011 U.S. dollar numbers. Accessibility referred to the population

density in concentrated areas or cities, which facilitates marketing efforts for brands

(Couto & Tiago, 2009; Singh, 2011).

I based the cultural distance variable on Geert-Hofstede’s (2012) country index

measures. Professor Hofstede developed a comprehensive indices, which is recognized as

the best measure to evaluate cultures among countries (Couto & Tiago, 2009; Singh,

2011). Hofstede assigned measurements for each country’s psychic distance, according to

different criteria. Psychic distance was defined as the recognized differences between a

company’s home country and the country's host country in the globalization process

(Sousa & Lages, 2011). The differences included culture, language, and level of

development (Sousa & Lages, 2011). According to Geert-Hofstede (2012), cultural

distance could be measured by four components. The four components were (a)

individualism, (b) uncertainty avoidance, (c) power distance, and (d) masculinity.

The measure of Hofstede’s individualism referred to the preference of the

individual in a culture to prioritize her/his need before the collective need of the society.

The opposite of individualism was collectivism, in which individuals would promote

loyalty for others while expecting their relatives and others to care for them in time of

need. Uncertainty avoidance measured the ease of a culture toward ambiguity and future

uncertainty. A high uncertainty avoidance measure revealed strong and fixed cultural

beliefs in norms and traditions. By contrast, a low uncertainty avoidance measure

indicated flexible and tolerant attitudes toward ideas and change.

The measure of power distance referred to the equality of the distribution of rights

and powers among the people of a certain culture. The higher the measure, the more

rights that would be given, and justice would prevail as a code of conduct within a

society. The masculinity measure was related to the perception of a culture toward

successful and heroic achievers. The higher the masculinity measure, the more

competitive the culture would be. By contrast, femininity referred to the acceptance of a

culture to value modesty, cooperation, and empathy for the needy.

Couto and Tiago (2009) and Singh (2011) considered geographic distance a

significant factor in the gradual internationalization process. The geographic distance was

measured by the distance in kilometers between each brand’s original country's capital

and the target country's capital (Couto & Tiago, 2009; Singh, 2011).

I presented cultural knowledge using the single variable of language (Couto &

Tiago, 2009; Singh, 2011). Furthermore, I considered the historical penetration of another

country of the same language of the target country as an indication of cultural awareness

of the target country. The variable of language could have one of two values: the value of

1 if the brand would originate from the home country with the same language as the

target country, and the value of 0 otherwise (Couto & Tiago, 2009; Singh, 2011).

I used the data sheet instrument without the infrastructure factor and tables

developed by Couto and Tiago (2009) and Singh (2011), which were summarized in

Table 1, to develop values for the predictor independent variables for each candidate

country. The Expected Sign in the table represented the expected type of effect of the

measure on the preferential grouping of a target country in the globalization strategy.

Table 1

Factors, Their Corresponding Variables, and Six Measures in the Model

Factors Variables

Measures

Expected

sign

Economic attraction

Prosperity

Gross Domestic

Product (GDP)

Target country GDP

Negative

Accessibility

GDP per capita

(GDP PC)

Target country GDP

Negative

Dimensions

Population density

Target country

population density

Negative

Language knowledge

Experience in a past

country with the same

language (1). No

experience in a past

country with the same

language (0)

Negative

Geographic distance

Distance between

countries' capitals in

Kilometers

Positive

Country of origin natural culture

Hofstede country index

Hofstede index based

on individualism,

uncertainty avoidance,

power distance, and

masculinity

N/A

Note. Adapted from “The Internationalization Process of Fashion Retailers,” by Couto

and Tiago, 2009, The Business Review, 13(1), pp. 278-286.

Data Collection Technique

I collected data for each country from multiple sources including World

Development Indicators (The World Bank, 2012). Data for the U.S. company from

Uniworld's Directory of American Firms Operating in Foreign Countries database and

other reliable databases (United States Securities and Exchange Commission, 2012;

Uniworld Publications Inc., 2012). I used prior research in the development of the

Uppsala model and determining the independent and dependent variables (Couto &

Tiago, 2009, Singh, 2011). I based my analysis on historical preinternationalization and

postinternationalization public data for the selected U.S. company from 1972 to 2009.

Data consisted of years of entry in each of the 25 countries and economic and cultural

information for each country. Economic and cultural data consisted of GDP for each

country, GDP PC, population density, language, geographic distance, and Hofstede index.

A pilot study was not necessary because I utilized only archival data.

Data Organization Techniques

I organized data in tables according to the analysis of the different categories of

variables. The dependent grouping variable's value was 1, 2, or 3, reflecting the date

range of the three actual historical country preference groups. The independent predictor

variables' values were the economic values for each country the U.S. jewelry company

decided to enter. Using the described structure, keeping track of data and emerging

understandings were accomplished through sequential recording in tables and saving the

emerging data. The data tables served as logs with which to provide the foundation for

the analysis of pertinent data. I also used the tables to enable me to stay within the

framework, to prevent any inadvertent data entry errors. Furthermore, I have locked the

research and results of the study in a compact disc, in my personal safe box, for the next 5

years and then will destroy the disc to protect the identity the company.

Data Analysis Technique

Because of the nature of this study, interview questions were not required. I

calculated the overall classification into groups for each country in terms of chronological

date of entry in one data set. I classified the countries among three groups and referred to

them as country grouping order-of-entry preference 1, country grouping order-of-entry

preference 2, and country grouping order-of-entry preference 3. The dependent grouping

variable distinguished among the three groups according to date of entry for each country.

I conducted discriminant analysis, which included grouping data for the dependent

variables and economic data for the independent predictor variables. The independent

predictor variables constituted the six different measures of the psychic distance

described in Table 1. The six measures for each country were countries' (a) dimension, (b)

prosperity, (c) accessibility, (d) language knowledge, (e) geographic distance, and (f)

cultural distance.

I divided economic attractions into (a) prosperity, (b) acceptability, and (c)

dimensions, measured by GDP, GDP PC, and population density, respectively. I used

Hofstede’s uncertainty avoidance measure described in Table 1 in calculating cultural

distance. Moreover, I used language as a variable in this study to determine if the first

language of a foreign country is the English language or not. Because the company in this

study was a U.S. company, the globalization process would be facilitated in a foreign

country if English is its first language. I measured geographic distance as the distance

between the capitals of the two countries in kilometers.

Because of the nature of the study’s premise, the assumption of normality and

independence were crucial considerations. There were three main assumptions related to

the significance tests for the underlying discriminant analysis. The first assumption was

that the independent variables should be normally distributed in which each variable was

normally distributed ignoring other variables. Furthermore, each variable should be

normally distributed at all combinations of other variables'

The second assumption was that the variances and covariances among the

dependent variables were equal. If the variances and covariances were unequal, the p

values produced invalid results. I tested the assumption of homogeneity of the

variancecovariance matrices using Box' M statistic. The third assumption was related to

the randomness of the selection of participants and the independence of variables, in

which the measure of each variable was independent of the measure of another variable.

I conducted discriminant analysis to test if continuous independent predictor

variables of 25 target countries' economic attraction features, which were represented by

six measures, could be used to predict group membership of the dependent variable of

historical countries' preference. I distinguished among three groups in the dependent

variable of the historical country preference according to the date of entry in each of the

25 foreign countries by the U.S. company. The variables in the data set were presented in

Table 2.

Table 2

Variables in Discriminant Analysis

Independent variables:

gdp Gross Domestic Product of the target country

gdp_capita

pop_dens

lang_know

geog_dist

hofstede

Gross Domestic Product per Capita of the target country

Population density of the target country

Experience in a past country with same language(1), No

experience in a past country with same language(0)

Distance between countries' capitals in Kilometers

Hofstede country index

Dependent Variable:

count_categ

Countries classified into one of three preference categories

based on order-of-entry:

1 = Countries entered between 1972 and 1989

2 = Countries entered between 1990 and 1999

3 = Countries entered between 2000 and 2009

I collected data for analysis from Uniworld's Directory of American Firms

Operating in Foreign Countries database (Uniworld Publications Inc., 2012), the public

domain, and data that were publically available from the selected U.S. company (United

States Securities and Exchange Commission, 2012). Data analysis included the level of

significance (p) between each selected factor and the country preference grouping

according to order of entry (Green & Salkind, 2008). I utilized SPSS software to analyze

the data by developing discriminant functions to examine the degree to which the scores

of the economic and cultural features of target countries could predict the preference of

countries.

Variables

Definition

The results included an explanation and analyses for the findings addressing the

principal research question and the derivative hypotheses for this study. The analysis

included charts and graphs of the descriptive statistics as well as the F test, and the p

value, the Eigenvalue, and the Wilks's lambda (λ; Green & Salkind, 2008). I transformed

each set of data for countries' measures in the SPSS software without any modifications

or partial deletion (Green & Salkind, 2008). Furthermore, I analyzed the results of the

original analysis to understand the strength of significance effect of each variable in the

model.

I used significance tests to determine how many discriminant functions should be

interpreted. I conducted follow-up significance tests to evaluate strength-of-relationship

statistics. Other follow-up tests included computing coefficients for the discriminant

functions, group centroids, group classification, and Kappa to assess classification

accuracy. I used the results of the tests to answer the research question and hypotheses of

the study in investigating the importance of each measure in the grouping order-of-entry

in the globalization process. Brand managers in S.A. could use the findings of this study

to understand and prioritize the most significant factors with measures for each foreign

country, and apply the results in the globalization process for S.A. retail jewelry brands

Reliability and Validity

Reliability refers to the consistency of the data sampling, collection, analysis, and

findings through research and over time, which decreases the proportion of expected data

loss to arrive at decisions (Punch, 2013). Validity refers to the accuracy, meaningfulness,

and usefulness of the research findings (Punch, 2013). Reliability and validity could be

established in quantitative research by testing the data for reliability, construct validity, and

internal and external validity (Punch, 2013).

Reliability

I performed the following procedures to support the reliability of the study: (a) the

introduction of a full account of ideas in every research phase such as the detailed

description of data collection sources, (b) establishing a protocol in conducting the tests,

in which I based the foundation of every test on the results prior tests, (c) abiding by

predefined codes such as the use of different codes for each measure term, and (d)

recording all information gathered periodically (Punch, 2013).

Validity

There are two types of validity threats in a quantitative study: internal and

external (Punch, 2013). Threats to internal validity include time relevancy, which affects

data suitability and participant maturity, regression irregularities, sampling selection, and

inconsistency in instrumentation (Punch, 2013). Time relevancy and participant maturity

might be relevant because the independent predictor variables depended on the economic

attractions of each country, which could change over time.

Threats to external validity were assumed to include the inflexibility of the

research findings for generalization to other industries, regions around the world, and

different global economic circumstances (Punch, 2013). Generalization might not have

been be achieved in later years because of the global economic crisis in 2009, which

might reflect different readings and relationship than normal years. As a result, I use in

my research the period from 1972 to 2009, which is prior to the start of the global

economic crisis. However, leaders of companies who have made analogous decisions for

internalization of their companies’ brands could use the overall modeling process with

current economic data in their brands' internationalization process.

Transition and Summary

I offered in Section 2 a review of the purpose of the study, defined the quantitative

methodology of the study, and provided a detailed description of the correlational design.

Moreover, I presented descriptions of the rationale for choosing the U.S. jewelry

company, population, data collection, and analyses. I explained the envisioned use of

discriminant analysis for the correlational design. Section 2 concluded with a discussion

of the processes for ensuring the study's reliability and validity. In Section 3, I used the

collection of the data and the data analysis to provide the findings and recommendations

of the study.

Section 3: Application to Professional Practice and Implications for Change

I described in Section 3 the process of the study, the results of my research, the

application of the findings to professional practice, and the implications of these findings

for social change. I also included recommendations for action and scope for future

studies, and I concluded with my reflections about the research process.

Overview of Study

The purpose of this quantitative correlational study using discriminant analysis

was to examine specific countries' economic attraction features in the historical

globalization strategy of a leading U.S. global jewelry company. The findings could

facilitate the implementation of a successful globalization strategy for a local Saudi

jewelry SME retail company. I applied the Uppsala model to a U.S.-based global jewelry

company to examine the relative importance and influence of the target countries'

attraction features on the globalization process.

I investigated the effects of six independent predictor variables of 25 target

countries’ economic attractions on the dependent grouping variable, which distinguished

among three order-of-entry groups according to the U.S. company's date of entry in each

country between 1972 and 2009. I considered six countries’ attraction features for the

analyses: (a) the countries' dimension, (b) prosperity, (c) accessibility, (d) language

knowledge, (e) geographic distance, and (f) cultural distance. I considered these attraction

features for each country for globalization as the discriminating or predictor variables.

Accessibility (Acc): Acc is the population density in concentrated areas or cities

(Couto & Tiago, 2009; Singh, 2011).

Cultural distance (CD): I used the Hofstede index to calculate CD from a formula

that included four components: individualism (IND), uncertainty avoidance (UA), power

distance (PD), and masculinity (MAS; Couto & Tiago, 2009; Geert-Hofstede, 2012;

Singh, 2011). I used Geert-Hofstede's (2012) website to find the value of Hosfstede's

country index, which included the valuation of each measure of individualism,

uncertainty avoidance, power distance and masculinity.

Dimension (Dim): Dim is the size of a country's economy measured by GDP

(Couto & Tiago, 2009; Singh, 2011).

Geographic distance (GD): GD is the distance in kilometers between each brand’s

original country's capital and the target country's capital (Couto & Tiago, 2009; Singh,

2011).

Language knowledge (LK): If the managers of the U.S. company expanded the

company in the past in a new foreign country in which its citizens use the English

language as their first language, the score of this measure would be equal to 0; otherwise

the score would be equal to 1 (Couto & Tiago, 2009; Sousa & Lages, 2011).

Prosperity (Pr): Pr is the purchasing power of each country's citizens measured by

the GDP PC (Couto & Tiago, 2009; Singh, 2011).

I collected data for each country from multiple sources, including The World

Development Report and World Development Indicators (The World Bank, 2012). I

collected data for the U.S. company from Uniworld's Directory of American Firms

Operating in Foreign Countries database and other reliable databases (United States

Securities and Exchange Commission, 2012; Uniworld Publications Inc., 2012). I used

prior research in determining the independent and dependent variables (Couto & Tiago,

2009; Singh, 2011).

I conducted the analysis based on historical preinternationalization and

postinternationalization public data for the selected U.S. company from 1972 to 2009. I

categorized the countries that the U.S. company entered in three groups according to the

order-of-entry in each country. The first group represented the period from 1972 to 1989,

the second group represented the period from 1990 to 1999, and the third group

represented the period from 2000 to 2009. The dependent grouping variable was the

historical, chronological entry preference grouping (first third, middle third, and last

third) for a foreign country for globalization. The independent predictor variables' values

were economic values for each country the U.S. jewelry company decided to enter.

I used discriminant analysis to verify if the classification of the target countries

into the three groups was correct. Thus, there was one main research question formulated

for this study: Which target countries' economic attraction features should be considered

in the globalization strategy of Saudi’s jewelry brands? Because of the limited disclosure

of public data available from foreign jewelry companies, I limited this study to a single

leading, publicly traded U.S. global jewelry company. As a result, I used a subordinate

research question as a guide in the study and the analyses: What linear combinations of

the independent predictor variables representing six economic attraction feature measures

for each country could be used to predict the order- of-entry preference (first third,

middle third, or last third)? Based on the research question, the null hypotheses statement

was formulated as follows:

H01: The target countries' economic attraction features cannot be used to predict

the historical country’s group order of preference (first, second, or third) in the

globalization process of a U.S. jewelry company.

I failed to reject the null hypothesis on the basis of statistical evidence displayed

by Wilks's lambda (λ), Fischer’s tests of significance, chi-square tests, and supporting p

values. I failed to reject the null hypothesis because I found that only 47.6% of original

grouped sizes were correctly classified. As a result of loading the predictor variables on

the grouping of the countries entering, I found that except for the Hofstede index, no

other variable had a significant role in the classification of the countries.

Presentation of the Findings

I performed statistical analyses on the data following the techniques described in

Section 2. I used SPSS to perform the basic statistical tests on the independent variables,

to conduct a discriminant analysis, and to present the graphical outputs of the data. I

described in this section the results for each statistical test in sequence. I presented in

Table 3 the data on six attraction features from countries entered by the company from

1837 on.

Table 3

List of All 25 Countries Entered

GDP per Distance

Year GDP

Capita Population Hofstede from

No. Country of ($billion)*

($billion)/ Density Index Washington Entry 1000

1000 D.C. (KM)

1 USA 1837 1,024.80 25,509.52 22.39 .00 16,206.89

2 Japan 1972 312.74 17,834.51 231.83 .00 10,873.00

3 U.K. 1986 570.43 20,831.00 222.61 3.50 557.00

4 Germany 1987 1,256.26 23,287.76 163.63 1.50 6,341.00

5 Switzerland 1987 178.58 31,613.06 2.12 2.25 15,988.00

6 Australia 1994 325.86 24,727.16 127.78 .25 6,859.29

7 China (PRC) 1995 728.01 1,849.15 269.55 10.25 11,014.00

8 Guam 1995 5,907.97 12,805.00

9 Hong Kong

(PRC) 1995 144.23 28,813.46 456.73 15.00 12,976.00

10 South Korea 1995 517.12 15,761.32 .00 9.25 11,078.00

Taiwan

11 (ROC) 1995 12.50 12,549.00

12 U. A. E 1995 65.74 68,201.39 108.74 1.50 11,025.00

13 France 1999 1,456.43 27,395.54 69.60 7.25 5,852.00

14 Malaysia 2000 93.79 10,618.97 20.62 5.75 15,130.00

15 Italy 2000 1,104.01 27,717.07 193.61 1.50 6,908.00

16 Brazil 2001 553.58 7,898.11 3.42 1.75 7,659.00

17 Canada 2003 865.87 33,639.98 98.50 5.00 551.00

18 Austria 2006 324.95 34,688.34 348.35 6.00 6,814.00

19 Belgium 2007 459.62 33,529.87 18,061.04 12.25 59.50

20 Macau 2007 18.06 47,551.60 56.18 .00

21 Mexico 2007 1,035.93 12,415.32 6,650.14 5.75 3,358.00

22 Singapore 2007 168.43 49,952.29 63.24 22.25 15,349.00

23 Ireland 2008 263.65 39,674.73 91.33 9.50 5,129.00

24 Spain 2008 1,593.36 28,353.89 487.13 .75 5,783.00

25 Netherlands 2009 793.43 36,520.08 204.64 13.50 5,879.00

Note. Year of Entry data are adapted from "American Firms Operating in Foreign Countries," by Uniworld

Business Publications, 2012, https://www.uniworldbp.com/search.php. GDP, GDP per Capita, Population

Density, and Geographic Distance data are adapted from "World Development Indicators," by The World

Bank, 2012, http://econ.worldbank.org. Hofstede Index data are adapted from "Geert Hofstede Cultural

Dimensions" by Geert-Hofstede, 2012, http://www.geert-hofstede.com.

* Japan - entered in 1972, but GDP per capita & PPP available since 1980.

* USA started in 1837 - data are available since 1970.

Analysis of Outliers

The analysis of outliers varied depending on the type of variable. Initially, I had

considered 25 countries for the analysis, as shown in Table 3. I removed Guam from the

list of countries because I did not find sufficient data on GDP, GDP PC, and Hofstede

index. Similarly, Taiwan (ROC) was removed because I could not find data on GDP,

GDP PC, and population density from that country. I was also unable to calculate the

Hofstede index of Macau. As a result, Macau was also removed from the list. In the end,

21 countries were selected for the analysis.

Figure 3 represents the GDP distribution among the three groups of countries. The

21 countries were grouped into three groups; Group 1, 2, and 3 are represented on the X

axis. I plotted the values of the variable GDP on the Y axis. Examining the mean GDP

values represented by a cross line inside each box indicated that Group 1 countries had a

higher mean GDP, while Group 2 countries had the lowest mean GDP.

Figure 3. Box plot of GDP.

Figure 4 represents the box plot of GDP PC values. In the second group there was

an outlier, 9, which was United Arab Emirates (UAE). Group 3 had the highest mean

GDP PC. The mean value for Group 2 would have been much lower if I had removed

UAE. However, I included UAE because I assumed UAE had many other favorable

attraction features for its touristic attractions.

Figure 4. Box plot of GDP per capita.

Figure 5 depicts the box plot of population density. Country 7 (Hong Kong) in

Group 2 and Country 18 (Singapore) in Group 3 had outlying population density values,

as shown in Figure 5. However, I decided to include these two countries because

population density is an important attraction feature.

Figure 5. Box plot of population density.

Figure 6 depicts the box plot of the Hofstede index. The mean Hofstede index for

Group 1 was the lowest. In Group 3, Country 18 (Singapore) was an outlier.

Nevertheless, I included them in the study because of other favorable features.

Figure 7 represents geographic distance of first showroom cities in thousands of

kilometers. In Groups 2 and 3, there were outlier values. The outliers were Country 5

(Australia) and Country 10 (France) in Group 2, and Country 11 (Malaysia), Country 18

(Singapore), and Country 14 (Canada) in Group 3. However, these countries were all

included because of other favorable economic attraction features.

Figure 7. Box plot of geographical distance.

Exploratory Data Analysis

The purpose of the exploratory data analysis was to obtain the descriptive

statistics that provide a richer understanding of the data spread and variability. The data

spread and variability were related to the assumption of normality of the independent

variables. Therefore, I analyzed each variable separately by checking the normality of

variables using the probability–probability (P-P) plots before getting the descriptive

statistics. The most important condition for discriminant analysis is that all independent

variables should be normally distributed. Figure 8 illustrates the P-P plots for predictor

variables GDP, GDP per capita, population density, and Hofstede index. Normality

testing was not required because language knowledge is a dichotomous variable with 0 or

1 values in the case of four countries in Group 1 (N1 = 4), six countries in Group 2 (N2 =

6), and 11 countries in Group 3 (N3 = 11). For plotting the P-P plots, the scale factor was

set at 1.00 and location factor was set at 0.00. In each case, the data points were in close

proximity to the normality line; thus, I assumed normality of variables in all cases, and I

verified that I had chosen the right variables for the discriminant analysis.

I had a small problem in the P-P plots of population density as shown in Figure 8,

because I included Hong Kong and Singapore in the list of countries, although they were

outliers in the population density data. If I had removed the population density values of

these two countries, I would have gotten a normally distributed data set. However, I used

the original data on population density including the data from Hong Kong and Singapore

because population density is an important attraction feature in the globalization strategy

for target countries.

Figure 8. Normal P-P Normal P-P plots of predictor variables.

I computed the descriptive statistics for each independent variable as shown in

Table 4. I included kurtosis because it measures peakedness of the probability

distribution of a random variable. Kurtosis describes the shape of a probability

distribution and its value varies from 1 to positive infinity (Doane & Seward 2011).

Kurtosis is the degree of peakedness of a distribution, defined as a normalized form of

the fourth central moment µ4 of a distribution, and µ2 is the second central moment,

which equals the variance (Doane & Seward 2011). The reference standard is a normal

distribution, which has a kurtosis of 3 (Doane & Seward 2011). As a result, often the

excess kurtosis is presented: excess kurtosis is simply (kurtosis−3).

A normal distribution has a kurtosis equals to 3 and excess kurtosis equals to 0

(Doane & Seward 2011). Any distribution with kurtosis ≈ 3 (excess kurtosis ≈ 0) is

called mesokurtic (Doane & Seward 2011). A distribution with kurtosis < 3 (excess

kurtosis < 0) is called platykurtic (Doane & Seward 2011). Compared to a normal

distribution, platykurtic distribution’s central peak is lower and broader, and its tails are

shorter and thinner (Doane & Seward 2011). A distribution with kurtosis > 3 (excess

kurtosis > 0) is called leptokurtic (Doane & Seward 2011). Compared to a normal

distribution, leptokurtic distribution’s central peak is higher and sharper, and its tails are

longer and fatter (Doane & Seward 2011).

Table 4

Descriptive Statistics of the Independent Variables

Descriptive Statistics

Mean

Std.

Deviation

Variance

Skewness

Kurtosis

Ratio of

Skewness

Std. Std. its Std

Statistic Statistic Statistic Statistic Statistic Statistic

Error Error Error

values

gdp

$.6101 $.45862

.210

.781

.501

-.402

.972

1.56

gdp_capita

$27.3963 $14.81734

219.554

.815

.501

1.810

.972

1.63

pop_dens

7.4478 18.48877

341.834

2.966

.501

7.646

.972

5.92

lang_know

.5238 .51177

.262

-.103

.501

-2.211

.972

.21

geog_dist

8.5915 4.76318

22.688

.127

.501

-.848

.972

.25

hofstede

6.4167 5.81396

33.802

1.132

.501

1.167

.972

2.26

Valid N

I found kurtosis values less than 3 for all cases, except population density, as

shown in Table 4. The kurtosis value was high for population density because of the

large variance in the population density data set. The high values of population density

in Hong Kong and Singapore resulted in a high value of kurtosis (7.646). Therefore, the

excess kurtosis of 4.646 (7.616 – 3) indicates a non-normal leptokurtic distribution.

Similarly, skewness provides insight into the distribution of data. Large values of

skewness indicate a large variance (Doane & Seward 2011). I presented skewness values

in Table 4. Skewness value, which is more than twice its standard error’s value, is taken

to indicate a departure from symmetry (Doane & Seward 2011). Thus, the ratio of

skewness to its standard error can be used as a test of normality, so I can reject normality

if the ratio is less than -2 or greater than +2 (Doane & Seward 2011). In Table 4, I have

shown the ratios of skewness to standard error. For population density and the Hofstede

index, the ratios were 5.92 and 2.26 respectively, and hence these two variables did not

follow normal distribution strictly. The variation from normality in the case of the

Hofstede index was not significant. I have proved this by the normality plots shown in

Figure 8.

The comparatively low values of kurtosis of less than 3 and skewness to standard

error ratios in Table 4 indicated that the variances in these data were insignificant. As a

result, I considered all variables as normal except population density and the Hofstede

index. However, I decided to continue using population density and the Hofstede index

in the model because of their assumed importance as key factors influencing purchasing

power.

ANOVA and Correlational Analysis

An important prerequisite for discriminant analysis is that the predictor variables

should not be highly correlated to each other (StatSoft, 2013). I used the bivariate

correlations procedure in SPSS to compute the Pearson's correlation coefficient, which is

a measure of linear association of variables, as I indicated in Table 5. Because the results

included the primary conditions required for performing discriminant analysis, I used

discriminant analysis for the classification of countries into three groups.

Table 5

Correlation Matrix

Correlations

gdp

gdp_capita

pop_dens

lang_know

geog_dist

hofstede

gdp

p (2-tailed)

-.257

.261

-.325

.151

-.077

.741

-.581**

.006

-.255

.265

gdp_capita

p (2-tailed)

-.26

.261

.278

.223

.342

.129

.039

.866

.214

.351

pop_dens

p (2-tailed)

-.33

.151

.278

.223

.29

.202

.389

.082

.732**

lang_know

p (2-tailed)

-.08

.741

.342

.129

.29

.202

-.069

.767

.225

.326

geog_dist

r p (2-

tailed)

.581**

.006

.039

.866

.389

.082

-.069

.767

.15

.515

hofstede

p (2-tailed)

-.26

.265

.214

.351

.732**

.225

.326

.15

.515

**. Correlation is significant at the .01 level (2-tailed).

From the results, I found that there was a significant positive relationship

between the Hofstede index and population density, with r = .732, p < .001. There was a

negative but insignificant correlation, between geographic distance and GDP, with r = -

.53 and p =

.02. No other variable pairs were significantly related (see Table 5).

Cultural distance and density might be two major drivers for globalization.

Consequently, I included the Hofstede index, which is a measure of culture, in the

model.

On the other hand, Singh (2011) commented that population density is a dynamic factor

in the economic growth of a country. Therefore, I included population density in the

model (see Table 18).

Discriminant Analysis

I started discriminant analysis by testing the equality of means and covariances of

the groups and predictor variables. Referring to Table 6, I found that some variables

display significant differences in means and standard deviations across all groups of

country categories. The differences in means of GDP (M = 539.57 to 659.69), GDP per

capita (M = 23391.583 to 28637.15) and language knowledge (M = .5 to 0.5455) were

insignificant. However, the differences in means might have been significant in the case

of population density (M = 228.3 to 1105.79) and Hofstede index (M = 1.8125 to 7.63).

Therefore, I could not conclusively decide that I had selected the right predictor

variables for classification.

I used the Wilks's lambda (λ) to verify the significance of the predictor variables

(StatSoft, 2013). Wilks's lambda (λ) is a variable selection method for stepwise

discriminant analysis to help the researcher choose variables for entry into the equation

on the basis of how much they lower Wilks's lambda (λ; StatSoft, 2013). At each step,

the variable that minimizes the overall Wilks's lambda (λ) is entered (StatSoft, 2013).

Wilks's lambda (λ) varies between 0 and 1, and values closer to zero indicate larger

dispersion of groups (StatSoft, 2013). I presented the results of Wilks's tests of equality

of group means in Table 7. I found that Wilks's lambda (λ) values for all predictor

100

variables were close to 1. The Wilks's lambda (λ) results indicated that group means

were very close to each

other and the contribution of the predictor variables that I chose in discriminating the

three groups of countries was poor.

101

Table 6

Group Statistics

Valid N (Listwise) count_categ

M SD

Table 7

Tests of Equality

of Group Means

Tests of Equality of Group Means

Wilks's Lambda (λ)

df1

df2

Unweighted

Weighted

gdp

gdp_capita

pop_dens

lang_know

579.503

23391.583

228.313

.500

479.578

5917.238

52.767

.577

geog_dist

8439.750

6568.594

hofstede

1.813

1.463

gdp

gdp_capita

pop_dens

lang_know

539.565

27791.337

1105.790

.500

510.515

22186.849

2358.210

.548

geog_dist

11358.648

3369.075

hofstede

7.250

5.570

gdp

gdp_capita

pop_dens

lang_know

659.693

28637.150

735.676

.546

463.991

13201.231

1967.307

.522

geog_dist

6601.773

4937.593

hofstede

7.636

6.397

Total

gdp

gdp_capita

pop_dens

lang_know

610.096

27396.334

744.782

.524

458.620

14817.341

1848.877

.512

geog_dist

8310.971

5088.822

hofstede

6.417

5.814

Group Statistics

102

gdp

.986

.132

.877

gdp_capita

.981

.171

.844

pop_dens

.973

.25

.781

lang_know

.998

.019

.982

geog_dist

.83

1.84

.187

hofstede

.844

1.661

.218

I found that the sample size of 21 countries out of the 193 countries was

sufficient for the study. Furthermore, changing the significance level lowers the Wilks's

lambda (λ) value or raises the F value marginally. I used alpha of .05 for the analyses.

High values of Wilks's lambda (λ = .844 to .998), low values of F statistic (F = .019 to

1.840), and p values greater than .05 (p = .216 to .982) categorically point to the fact that

all of the selected predictor variables had insignificant roles in discriminating the

country groups.

Therefore, the group means of predictor variables across the three groups were the same.

Based on the SPSS outputs, I fail to reject the null hypothesis that the target

countries' economic attraction features do not predict the historical country’s group order

of preference (first, second, or third) in the globalization process of a U.S. jewelry

company. However, to enhance confidence in the results, additional statistical analyses

related to discriminant analysis were conducted.

Covariance matrices and Box’s M test. Table 8 displays the SPSS output for

the covariance matrix. Covariances of predictor variables varied significantly across the

groups in some cases. For example, covariances of GDP and population density ranged

from 1719.507 to - 446813, GDP per capita, and the Hofstede index ranged from

103

3126.851 to – 60974; all of these displayed significant differences. As a result, the large

differences in covariances of independent variables disqualified them as predictor

variables in discriminant analysis. The finding of the inequalities of covariances across

the groups supported failing to reject of the null hypothesis.

To verify the equality of the covariance matrices, I conducted Box’s M test,

which is used for testing the homogeneity of population covariances across the groups

(see Table 9). For moderate to small sample sizes, an F approximation is used to

compute its significance (StatSoft, 2013). The results indicated that there were

significant differences in the covariance matrixes across groups (p = .004). Therefore,

my decision to fail to reject the null hypothesis that the target countries' economic

attraction features do not predict the historical country’s group order of preference (first,

second, or third) in the globalization process of a U.S. jewelry company was supported

by the discriminant analysis, Box’s M test, and other tests.

Table 8

Covariance Matrices

count_categ

gdp

gdp_capita

pop_dens

lang_know

geog_dist

hofstede

gdp

gdp_capita

pop_dens

229994.702

-620262.844

1719.507

-620262.844

35010000.000 -

300854.198

1719.507

-300854.198

2784.319

-136.665

1886.965 -

19.568

-1674730.556

22980000.000 -

122737.342

27.102

3126.851

-44.219

lang_know

-136.665

1886.965

-19.568

.333

-111.500

.708

geog_dist

-1674730.556

22980000.000

-122737.342

-111.500

43150000.000

-4584.729

hofstede

27.102

3126.851

-44.219

.708

-4584.729

2.141

gdp

gdp_capita

pop_dens

260625.289

-4775619.705

-446813.163

-4775619.705

492300000.000 -

344491.593

-446813.163 -

344491.593

5561152.680

61.565

-487.570

540.676

-1324979.902 -

2437766.895

1755235.550

335.406

-60974.741

9381.996

104

lang_know

61.565

-487.570

540.676

.300

191.789

.150

geog_dist

-1324979.902

-2437766.895

1755235.550

191.789

11350000.000

-4215.996

hofstede

335.406

-60974.741

9381.996

.150

-4215.996

31.025

gdp

gdp_capita

pop_dens

215287.813

-1014078.740

-318004.470

-1014078.740

174300000.000

14580000.000

-318004.470

14580000.000

3870295.723

-28.228

4801.620

284.633

-1060737.024 -

1952021.792

5585812.564

-1622.556

57202.957

9944.129

lang_know

-28.228

4801.620

284.633

.273

-592.514

.993

geog_dist

-1060737.024

-1952021.792

5585812.564

-592.514

24380000.000

9383.472

Total

hofstede

-1622.556

57202.957

9944.129

.993

9383.472

40.917

gdp

gdp_capita

pop_dens

210331.923

-1743990.593

-275174.540

-1743990.593

219600000.000

7610431.640

-275174.540

7610431.640

3418345.463

-17.982

2592.983

274.323

-1224721.602

953030.854

3538633.088

-679.551

18445.661

7870.657

lang_know

-17.982

2592.983

274.323

.262

-307.765

.671

geog_dist

-1224721.602

953030.854

3538633.088

-307.765

25900000.000

2446.776

hofstede

-679.551

18445.661

7870.657

.671

2446.776

33.802

a. The total covariance matrix has 20 degrees of freedom.

Table 9

Box’s M Test

Test Results*

Box's M

62.984

Approx.

2.307

df1

df2

419.533

Sig

.004

Tests null hypothesis of equal population covariance

a. Some covariance matrices are singular and the usual

procedure will not work. The non-singular groups will be tested

against their own pooled within-groups covariance matrix. The

log of its determinant is 48.647

105

Significance tests and strength of relationships statistics. I presented the

output from significance tests and strength of relationships’ statistics for the discriminant

analysis in Table 10. I conducted a series of chi-square significance tests in the Wilks's

lambda (λ) table. These tests assessed whether there were significant differences among

groups across the predictor variables, after removing the effects of any previous

discriminant functions.

The results indicated there were no significant differences among groups across

the six attraction features, Λ = .811, χ2 (5, N = 21) = 3.245, p = .662. The Wilks's lambda

(λ) test was insignificant at the .05 level and indicated that there were no differences

among groups across the six attraction features after removing the effects across the

effects associated with Function 1.

Table 10

Significance Tests and Strength of Relationship Statistics for Discriminant Analysis

1 .333a 58.9 58.9 .5

2 .233a 41.1 100 .435

a. First 2 canonical

disc

riminant functions were used i

n the analysis.

Wilks's Lambda

Test of

Function(s)

Wilks's

Lambda

Chisquare

Sig.

1 through 2

.608

7.701

.808

.811

3.245

.662

Eigenvalues

Function

Eigenvalue

% of

Variance

Cumulative

Canonical

Correlation

106

A series of statistics associated with each discriminant function were displayed in

the table of eigenvalues, which provided information about the relative effectiveness of

each discriminant function. Function 1 had an eigenvalue of .333 and a canonical

correlation of .500. By squaring the canonical correlation for Function 1 (.5002 = .25), I

found the eta squared index that would result from conducting a one-way ANOVA on

Function 1. Eta squared index is defined as the proportion of variance of the test variable

that is a function of the grouping variable (StatSoft, 2013). Eta squared value ranges

from 0 to 1, in which a value of 1 indicates perfect replication. Perfect replication means

that there are no differences on the dependent variable measures within each of the

groups (StatSoft, 2013). The eta squared values of .01, .06, and .14 are considered small,

medium, and large effect sizes, respectively (StatSoft, 2013).

Accordingly, the differences among the three country groups accounted for 25%

of the variability of the scores in Function 1. Function 2 had an eigenvalue of .233 and a

canonical correlation of .435. Therefore, .4352 = 18.9% of the variability of the scores

for the Function 2 was accounted for in the classification. Because Function 1 and

Function 2 were insignificant, I should fail to reject the null hypothesis.

Standardized canonical discriminant function coefficients. Table 11 presented

the standardized discriminant function coefficients and the pooled within groups

correlations for discriminant analysis (coefficients in a structure matrix). I named each

discriminant function by determining which variables were strongly related to it. I

assessed strength of relationship by the magnitude of standardized coefficients for the

107

predictor variables and the correlation coefficients between predictor variables and

function within a group.

Table 11 indicated that the Hofstede index had the largest positive coefficient

1.514 in Function 1, and geographical distance had the largest positive coefficient 1.262

in Function 2. I proceeded to determine the prediction capability of each of the variables.

Language knowledge and GDP per capita had weak coefficients for both discriminating

functions. Population density had the largest negative coefficients in both functions.

Therefore, I concluded in Function 1 that the Hofstede index was the most

effective predictor, and language knowledge and GDP were the least effective predictors.

Similarly in Function 2, geographical distance was the most effective predictor, and GDP

and population density were the least effective predictors. Thus, Function 1 is

predominantly culture-oriented, while Function 2 is predominantly geography-oriented.

On the basis of the standardized function and structure coefficients, I named the first

discriminant function culture and the second function geography.

Table 11

Standardized Coefficients and the Pooled Within Groups Correlations for Discriminant

Analysis

Standardized Canonical Discriminant Function Coefficients

Function

gdp

.48

.404

gdp_capita

.372

.056

pop_dens

-.948

-.335

lang_know

-.081

.133

108

geog_dist

.324

1.262

hofstede

1.514

.189

Structure Matrix

Function

hofstede

.744*

.009

gdp_capita

.238*

-.029

pop_dens

.230*

.209

geog_dist

-.09

.931*

gdp

.075

-.234*

lang_know

.046

-.077*

Pooled within-groups correlations between discriminating variables and standardized canonical

discriminant functions

Variables ordered by absolute size of correlation within function.

* Largest absolute correlation between each variable and any discriminant function

SPSS output for group centroids. Centroids are the mean discriminant scores

for each group (StatSoft, 2013). Table 12 displays SPSS output for group centroids. The

values labeled group means were the mean values of the discriminant functions for the

three groups.

Table 12

Group Centroids for Discriminant Functions

Function

count_categ

Unstandardized canonical discriminant functions

evaluated at group means

-1.098

-.078

.19

.688

.295

-.347

Functions at Group Centroids

109

Based on the interpretation of the discriminant functions, the countries that were

entered by the U.S. company from 2000 to 2009 (Group 3), had the largest positive mean

score of .295 on dimension culture. Countries that were entered by the U.S. company

during 1990 to 1999 (Group 2) had a score of .688 on the geography dimension. Cases

with scores close to particular group-centroids were predicted to belong to that particular

group. The pattern of the means for the discriminant functions aligned with my

interpretation of the two functions.

SPSS output for group classification. I presented the group classification

results in Table 13. I could determine how well I could predict group membership by

using the classification function. The top part of the table, which was labeled original,

indicated how well the classification function predicted grouping date of entry

preference for the

21 countries. Correctly classified cases appear on the diagonal of the classification table.

For example, of the three date-of-entry groups considered, 1 out of 4 countries (25%)

that were entered by the U.S. company during 1972-1989, 1 out of 6 countries (16.7%)

that were entered by the U.S. company during 1990 -1999, and 8 out of 11 countries

(72.7%) that were entered by the U.S. company during 2000-2009 periods were correctly

classified.

Of the total countries in all of the three groups, 10 (= 1+1+8) out of 21 countries

(47.62%) that were entered by the U.S. company over the period from 1972 to 2009

(Group 1) were correctly classified. The significance of the group classification result

led me to conclude that the classification was highly effective during the period from

110

2000 to 2009 (Group 3), while the classification was most ineffective from 1990 to1999

period

(Group 2).

The bottom part of Table 13, labeled as cross-validated, was generated by

choosing the leave one out option within the classification dialogue box of SPSS. In

general, cross validation is more stringent than original classification. Here the

classification functions are derived on the basis of all cases except one, and then the

leftout case is classified (StatSoft, 2013). The process is repeated N times until all cases

have been left out once and classified based on classification functions for the N cases.

Therefore, as shown in Table 13, one country from Group 1, no country from Group 2,

and seven countries from Group 3 were correctly classified. Overall, 38.1% of countries

were correctly classified.

Table 13

Classification Results

Total

Original

Count

16.7

83.3

100

9.1

18.2

72.7

100

Cross-validateda

Count

Classification Results

b,c

count_categ

Predicted Group

Membership

111

100

9.1

27.3

63.6

100

a. Cross validation is done only for those cases in the analysis. In cross validation, each case is

classified by the functions derived from all cases other than that case.

b. 47.6% of original grouped cases correctly classified.

c. 38.1% of cross-validated grouped cases correctly classified.

Computing kappa (K) to verify accuracy of classification. I found the results

of correct classification to be 47.6%, which might have been a chance result. Cohen's

kappa K is a statistical index that corrects chance agreements (StatSoft, 2013). K is a

more vigorous measure than simple percent agreement calculation, because K takes into

account the agreement occurring by chance (StatSoft, 2013). I have presented the output

of K computation in Table 14 to verify the accuracy of the classification of countries into

Groups 1, 2, and 3.

Table 14

Results of Kappa Analysis

Symmetric Measures

Value

Asymp.

Std.

Errora

Approx. Tb

Approx.

Sig.

Interval by Interval

Pearson's R

.157

.253

.691

.498c

Ordinal by Ordinal

Spearman

Correlation

.113

.24

.495

.626c

Measure of Agreement

Kappa

.053

.168

.347

.729

N of Valid Cases

a. Not assuming

the null hypothesis.

b. Using the asymptotic standard error assuming the null hypothesis.

c. Based on normal approximation.

112

Values of K range from -1, which indicates wrong prediction, to +1, which

indicates perfect prediction (StatSoft, 2013). K > 0 indicates better than chance-level

prediction, K = 0 indicates chance prediction and K < 0 indicates poorer than chancelevel

prediction (StatSoft, 2013). In my analysis, I found the value of K = .053, which was

much lower than +1, but slightly above the middle value 0. Because I found K > 0, the

predictions that I had made were better than chance-level prediction (StatSoft, 2013).

Mapping of discriminant functions. I selected the scatter plots option in

discriminant analysis for representing the graphical mapping of the relationship between

predicted groups and discriminant functions. Figure 9 displays the scatter plot of the

combined groups. The plot illustrates the relative location of the boundaries of the

different categories. Culture represented by Function 1, and geography represented by

Function 2, were taken as the X and Y axes respectively. I combined the axes with the

structure matrix results to present scatter plots of the centroids of group and boundaries

Figure 9.

113

Figure 9. Separation of groups on discriminant functions.

Figure 9 represented the separation of groups on the two discriminant

dimensions. The small shaded rectangles in the picture marked centroids, around which

the three groups of countries were grouped. The centroids in Figure 9 were not close.

The dispersion of the centroids indicated that the separation of the groups was not

insignificant. The closer the group centroids, the more errors of classification would be.

Discriminant function 1 represented culture. As can be seen from the graph, Group 2 and

Group 3 countries are richer in culture than the Group1 countries. Similarly, Group 2

countries are far ahead of Group 1 and Group 3 countries in the geography dimension.

Summary of discriminant analysis. I conducted a discriminant analysis to

determine whether the six attraction features GDP, GDP per capita, population density,

Hofstede index, geographical distance, and language knowledge could predict the

grouping of countries according their order-of-entry by a U.S. company. The overall

114

value of Wilks's lambda (λ) was insignificant, λ = .608, χ2 (12, N = 21), p > .05,

indicating that the overall prediction variables did not differentiate significantly among

the three groups. In addition, the residual Wilks's lambda (λ) was insignificant, λ = .808,

χ2 (5, N = 21), p > .05. The residual Wilks's lambda (λ) test indicated that the prediction

variables did not differentiate significantly among the three groups. Because Wilks's

lambda (λ) and residual Wilks's lambda (λ) tests were insignificant, there was no need

for interpreting the discriminating functions. Nevertheless, I chose to proceed with the

analysis.

In Table 15, I have presented the within-groups’ correlations between the

prediction variables and the discriminant functions as well as the standardized weights.

Based on these coefficients, the Hofstede index variable had a strong relationship with

the first discriminant function (.744), while GDP, geographic distance, GDP and

language knowledge showed weak relationships. On the other hand, geographic distance

showed the strongest relationship with the second discriminant function (.931). GDP and

GDP per capita demonstrated negative relationships with the second function. On the

basis of the results presented in Table 15, I labeled the first and second discriminant

functions culture and geography respectively.

Table 15

115

Standardized Coefficients and Correlations of Predictor Variables with the Two

Discriminant Functions

Prediction variables

Correlation coefficients

with discriminant functions

Standardized Canonical

Discriminant Function

Coefficients

Function 1

Function 2

Function 1

Function 2

Hofstede

.744

.009

1.514

.189

GDP per Capita

.238

-.029

.372

.056

Population density

.230

.209

-.948

-.335

Geographic distance

-.090

.931

.324

1.262

GDP

.075

-.234

.480

.404

Language knowledge

.046

-.077

-.081

.133

The means of the discriminant functions were consistent with this interpretation.

Group 1, which was between 1972 and 1990 (M = -1.098), had the highest mean on the

culture dimension (the first discriminant functions). Group 3, which was between 1999

and 2009 (M = .295), and Group 2, which was between 1999 and 2009 (M = .190), had

lower means than Group 1. On the other hand, Group 2 (M = .688) had the highest mean

on the geography dimension, Group 3 (M = - .347) had the next highest mean, and

Group 2 (M = - .078) had the lowest mean score.

When I tested the classification prediction of the countries, which were entered in

Group 1, which was between 1972 and 2009, I found that only 47.6% of the original

grouped cases were correctly classified. When the leave-one-out technique or cross

validation was employed, I found that the classification prediction of the countries was

116

reduced to 38.1%. I concluded that the selected prediction variables were ineffective in

classification of the countries.

In order to take into account the possibility of a chance agreement, I computed

the kappa coefficient and found a low value of .053, which indicated that the predictions

that I had made were better than chance-level prediction. Because I found that the

discussed discriminant model was weak in discriminating among the countries, I decided

to explore other model scenarios to arrive at a better discriminant model.

Refining the Model

Using SPSS dialogue box features, I eliminated geographic distance from the list

of predictor variables. Table 16 presents the classification function coefficients. I found

the Hofstede index having the highest coefficient in culture, while language knowledge,

GDP, and GDP per-capita did not have significant roles in culture.

Table 16

Standardized Coefficients and Correlations of Predictor Variables with the Two

Discriminant Functions without Geographical Distance

Correlation coefficients with

discriminant functions

Standardized Canonical

Discriminant Function

Coefficients

Function

Function 1

Function 2

Function 1

Function 2

Hofstede

.734*

-.576

1.417

.090

GDP per Capita

.240*

-.090

.344

.099

Population density

.191

-.793*

-.813

-.868

GDP

.114

.642*

.353

.449

Language

knowledge

.058

.195*

-.123

.433

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Population density, GDP, and GDP per capita had the largest coefficients in

Function 2, while the Hofstede index had a weak effect on Function 2, with a coefficient

value of .090. Therefore, the attributed names to Function 1 (culture) and Function 2

(geography) were appropriate. The classification results presented in Table 17 indicate

that the classification efficiency of the model increased from 47.6% to 61.9% of the

original grouped cases. Furthermore, using the leave-one-out technique, 38.1% of the

original grouped cases were correctly classified.

Table 17

Classification Results with Geographical Distance Removed From Prediction Variables

Country_Catg

(Period of Entry)

Countries

Entered

between

1972 and

1989

Countries

Entered

between

1990 and

1999

Countries

Entered

between

2000 and

2009

Total

1972-1989

Count

1990-1999

4 6

100

Cross-

validateda 1972-1989 25 0 75

% 1990-1999 33.3 0 66.7 100 2000-2009 18.2 27.3 54.5 100

Original

2000-2009

1972-1989

1990-1999

16.7

66.7

2000-2009

9.1

90.9

Count

1972-1989

1990-1999

2000-2009

Classification Results

a,b,c

Predicted Group Membership

118

a. Cross validation is done only for those cases in the analysis. In cross validation, each case is

classified by the functions derived from all cases other than that case.

b. 61.9% of original grouped cases correctly classified.

c. 33.3% of cross-validated grouped cases correctly classified.

I attempted other scenarios, but found that the model related to the removal of

only geographic distance was the best model. I presented in Table 18 a summary of

model scenarios. Model 17 was the original model with six prediction variables.

Table 18

Various Scenarios

Cross-

$billion)

Model 1

61.9

33.3

Model 2

57.1

38.1

Model 3

57.1

33.3

Model 4

52.4

42.9

Model 5

52.4

42.9

Model 6

52.4

38.1

Model 7

52.4

38.1

Model 8

52.4

38.1

(table continues)

Model

gdp

(

$billion)

gdp_

capita

/1000

(

pop_

dens.

lang.

know.

geog_

dist (/1000 km)

Hof-

stede

Correctly

Classified

validated

group_

cases

Correctly classified during the period

Rank

Correctly classified (%)

1972-89

1990-99

2000-09

Out of

gdp

cases

Model 9

52.4

33.3

Model 10

52.4

33.3

Model 11

52.4

33.3

Model 12

52.4

33.3

Model 13

47.6

42.9

Model 14

47.6

42.9

Model 15

47.6

38.1

Model 16

47.6

38.1

Model 17

Yes

47.6

38.1

Model

(

$billion)

gdp_

capita

(

/1000

$billion)

pop_

dens.

lang.

know.

geog_

dist

(

/1000

km)

Hof-

stede

Correctly

Classified

Cross-

validated

group_

Correctly classified during the period

Rank

Correctly classified (%)

1972-89

1990-99

2000-09

Out of

122

Model 1 in Table 18 illustrates the results found when geographic distance was

removed from the list of the six prediction variables. Of the original grouped variables,

61.9% were correctly classified; only 47.6% were found to be correctly classified when

geographic distance was included in the model. I found the next best model when

language knowledge and geographic distance were removed. Of the original grouped

cases, 57.1% were correctly classified, and 38.1% grouped cases were correctly

classified under cross validation.

Alternative Analyses

Logistic regression and multiple regression analyses were also options in addition

to discriminant analysis to classify individual countries into groups. I used a linear

multiple regression module to get the classification results.

Linear multiple regression. Table 19 displays the SPSS output from this

analysis, in which the regression coefficients and ANOVA table were presented. I found

in the ANOVA presented in Table 19 that the F value of .716 was low and p value of

.643 was high, which indicated that the regression model was insignificant.

Table 19

Results of Linear Multiple Regression

Coefficientsa

95.0%

Unstandardized

Confidence

Coefficients

t p Interval for B

Model

Lower

Bound

Upper

Bound

123

1 (Constant)

1.809

.964

1.877

.082

-.258

3.876

gdp

.180

.530

.340

.739

-.956

1.316

gdp_capita

.007

.014

.507

.620

-.023

.037

pop_dens

-.014

.017

-.807

.433

-.050

.023

lang_know

-.075

.404

-.186

.855

-.942

.792

geog_dist

-.020

.054

-.368

.718

-.135

.095

hofstede

.083

.049

1.693

.113

-.022

.187

ANOVAb

Model

Sum of

Squares

Mean

Square

Sig.

1 Regression

2.973

.496

.71

.643

Residual

9.693

.692

Total

12.667

Applications to Professional Practice

The study has the potential to generate interest in different business fields, such as

entrepreneurship, marketing, leadership, and finance. Young entrepreneurs thinking of

establishing born-global brands or globalizing their local brands could use the results of

this study to aid in their planning. Interested entrepreneurs might use the results of the

study to recapitalize their companies, establish new companies, or open branches in

foreign target countries. Because the variables in this study were not significant,

entrepreneurs could consider other variables such as economies of scope and scale for

their perspective companies (Schweizer et al., 2010; Sun & Xu, 2010). Marketing and

brand managers should consider investigating the most suitable countries for their

globalization strategies. Marketing and brand managers should investigate other variables

124

that correlate with cultural distance such as purchasing habits, given the similarities of

successful different marketing tools between the country of origin and target countries

(Danziger, 2005).

Chief executive officers (CEOs) and managers could use the results to

marginalize the value of the variables that I introduced and to introduce new variables

that are related to the capabilities of their companies thus facilitate successful

globalization strategies (Lu et al., 2011). CEOs might consider the influence of cultural

variables when hiring potential new leaders for globalization projects. Some of the

variables CEOs could consider when hiring new recruits and targeting new countries

include (a) knowledge of target countries' public policy laws and practices

(CuervoCazurra, 2011); (b) economic development, (c) political risk factors, (d) social

and cultural environments, and (e) retail target market characteristics, including size and

growth prospects (Chan et al., 2011). Chief financial officers (CFOs) and financial

managers could also use the study to develop financial solutions to enhance profitability

by globalization instead of cutting costs or investing more money in the Saudi market.

CFOs could enhance their knowledge of target companies by considering acquisition of

companies in target companies or entering target countries with other forms of strategy

that provide synergies such as agency, franchising, joint ventures, and licensing (Assaf et

al., 2012).

Implications for Social Change in Business and Other Disciplines

Saudi Arabia suffers from unemployment, terrorism, and corruption; the

conditions lead to injustices and lack of human rights (United Nations Development

125

Program, 2010). As a Saudi, I was surprised to find Saudi Arabia, despite scoring high in

GDP per capita, scored one of the lowest ratings among world countries in the Human

Development Report and Gender Human Development Index, and Human Well-being

Index (United Nations Development Program, 2010).

Saudis could contribute positively to the world community by pursuing business

dreams. Although the Middle East has a rich culture of ethnic products, little research and

knowledge exists on how to globalize Middle Eastern products. Business leaders could

use the results of this study to give impetus for successful luxury brands in the Middle

East to understand the benefit and framework of the globalization process. Although I

found the variables, which I used in this study were not significant, other researchers and

business leaders could use the study to focus on other variables, such as companies'

economies of scope and scale, and countries' political and economic strength.

The results of this study also hold value for developing countries as they seek

research for public policy and business standards for globalizing their brands. Thus, I

decided to use Social Science Research Network (SSRN.com) links and web sites to

introduce the study. In addition, the results of this study may be eligible for publication in

professional journals such as the Journal of International Entrepreneurship,

International Journal of Entrepreneurship, Journal of Retailing & Consumer Services,

Journal of

Small Business and Enterprise, Journal of Fashion Marketing and Management, Journal

of International Business Studies, Journal of Small Business and Enterprise Development,

and International Marketing Review, among others.

126

Recommendations for Action

First, although the classification of the countries using the economic attraction

features into the three groups was insignificant, I found from the kappa statistical test that

the correct classification that I conducted at the level of 47.6% was not a chance

occurrence. Therefore, I concluded that the variables I used are important attraction

features in globalization. Leaders from local brands might consider the methods and

results of this study, and use the variables I chose with other new variables to formulate

and implement global strategies for their brands. I recommend variables such as trade

openness of the country, domestic financial development, and country size (Chan et al.,

2011). Other variables might include knowledge of target countries' public policy laws

and practices; economic development; political risk factors; social and cultural

environment; and retail target market characteristics, including size and growth prospects

(Chan et al., 2011). Nevertheless, the attraction features that I chose might not exhibit

perfect separation. Differences that distinguish among variables might not clearly exist,

such as the differences between GDP and GDP per capita, population density and

population size, and income level and GDP per capita. Therefore, I recommend an

indepth analysis of these different prediction variables for discrimination.

Second, I have arrived at an important conclusion that culture is more influential

than geographic distances, demographic features, and economic attraction features in

globalization strategies. The effect of cultural differences between target countries and

the company’s country of origin is aligned with the strength of the Uppsala model with

its basis on the psychic distance, which was discussed in the study. Because I found that

127

the Hofstede index predominantly influenced the classification, any future action related

to globalization should be based on the prevailing culture in target countries. I considered

four dimensions in computing the Hofstede index. These dimensions were power

distance index (PDI), individualism (IDV), uncertainty avoidance index (UAI), and

masculinity (MAS; Geert-Hofstede, 2012). However, I recommend that company leaders

consider all the original dimensions of Hofstede while planning to go global, thus

including long-term orientation (LTO) and indulgence versus restraint (IVR; Geert-

Hofstede, 2012).

Third, I found that geographic distance was the least important feature, which is

understandable because of the globalization factors of media, products, and connectivity.

As a result, geographic distance should not be an important factor. Although population

density had no significance in the model, the importance of population density is

obvious, because it influences the availability of a concentrated market in any country.

Leaders of companies should consider other factors with population density such as

political and economic strength (Chan et al., 2011).

Fourth, I did not address or explore the competitive advantages of companies and

brands in this study. Competitive advantages of companies could include economies of

scale and scope in terms of depth of financial and human resources (Arndt, 2012). Depth

of financial resources includes sales and profitability ratios, and the abundance of

financial liquidity. Depth of human resources includes depth of employees’ quantity,

diversity, and experiences in different business-related competencies (Cuervo-Cazurra,

2011).

128

Fifth, I found that the predicting variables were classifying countries in Group 3,

which were entered as target countries by the U.S. company between 2000 and 2009,

were better than classifying countries in the other two earlier time groups’ periods. The

improvement of the results in Group 3 might have been influenced in part by the relative

political and economic stability in most countries in the 1990s compared to the 1980s and

1970s in most countries around the world. Future researchers might include other

political and economic variables in their models to explore this possibility or to affirm

the results. Furthermore, the small sample size of the study might have been a deciding

factor in the study; thus, I suggest using a larger sample size in future research.

Sixth, taking the period of entry in target countries as the grouping variable is, in

effect, considering time as a discrete variable, which might have been wrong. Therefore I

suggest that future researchers modify the method by using time as a continuous variable

instead of using time periods as a grouping variable based on period of entry in target

countries.

Finally, I recommend extensive data collection on the demographic variables that

influence globalization strategies. In addition to population density, GDP per capita, and

language knowledge in my analysis, gender, educational level, income level, religious

beliefs, and ethnic diversities are important features that might influence globalization

strategies of any company. Any business opting for globalization might use these

demographic variables as an important input while assessing target countries’ profiles.

129

Therefore, I highlight the importance of conducting a comprehensive questionnaire

survey in all of the target countries in future studies. The surveys might be expensive, but

they are likely to provide valuable insights.

Recommendations for Further Study

I recommend that researchers use the Uppsala model with its psychic distance

theory and explore other cultural and demographic factors that distinguish between target

countries and companies’ countries of origin. Researchers could use the discriminant

analysis model with more variables and varying scenarios. Even though I found that the

variables that I used were ineffective in classifying the countries entered, the results of

the 17 scenarios may provide additional insights if they are explored in alternate

combinations, and possibly with other variables. As a result, I recommend an application

of combinatorial optimization for arriving at an optimal classification using alternate

variables combinations. Researchers could use multiple regression with continuous

variables. The researchers could use combination of the variables, introduced in this

study such as GDP and population density, with other new variables as literacy, domestic

financial development, country size, and political and economic strength, among other

potential variables (Chan et al., 2011; Schweizer et al., 2010).

I recommend that researchers be careful when choosing attraction variables that

might be correlated with each other to avoid multicollinearity. One of the highly

correlated variables should be removed from the list to avoid duplicating the results. In

my study, population density and the Hofstede index were correlated significantly to each

other (r = .73, p < .001). However, I included both variables as individual attraction

130

features because I assumed they were independently influential in the globalization of

companies based on past studies (Couto & Tiago, 2009; Singh, 2011; Sousa & Lages,

2011). The Hofstede index is a measure of culture, which is a major driver for

globalization (Couto & Tiago, 2009; Singh, 2011; Sousa & Lages, 2011). Furthermore,

population density is a dynamic factor in the economic growth of a country (Singh,

2011). As a result, one might assume they were independent of each other.

Because of changing socioeconomic and political situations in all of the countries

that were entered by the U.S. company, I recommend that the data be updated when any

researcher wishes to perform the grouping. I also recommend researching globalization

strategies for global brands for shorter and more recent periods; so the results would be

more significant and applicable to current challenges. Furthermore, I recommend

elimination of qualitative data as much as possible, because it could impair the accuracy

of the results.

Multidimensional scaling. My final recommendation is that researchers should

try alternative multivariate analysis techniques such as multiple regression,

multidimensional scaling (MDS), and cluster analysis (Jain, 2010). Although MDS is a

scaling technique, I could also find advantages in using it for grouping of countries.

Discriminant analysis is best suited when dependent variable is categorical and

independent variables are metric (StatSoft, 2013). Multiple regression is best suited for

interval data. Similarly both dependent and independent variables should be metric

(measurable) in nature (StatSoft, 2013). Furthermore, if there are two or more categorical

values, multiple regression would be an appropriate choice (StatSoft, 2013).

131

Multidimensional scaling (MDS) would be best suited when there are many dependent

and independent variables (Jain, 2010; StatSoft, 2013).

Researchers use MDS to handle multiple dimensional positioning of products,

objects, and countries by mapping on to two-dimensional forms (StatSoft, 2013).

Researchers could position various competing product brands (the grouping variables) in

the market on to a two-dimensional plot, based on conflicting customer requirements,

which are the independent variables (StatSoft, 2013). Researchers could use MDS to

reduce multiple dimensions into two dimensions, step-by-step, each time introducing a

stress (StatSoft, 2013). Because I had six dimensions in my study, the grouping was more

complex than a study with less dimensions. As a result, MDS might be more suitable for

this study. Future researchers could use MDS to present a visual display of the grouping

of the countries on a two-dimensional map (StatSoft, 2013).

Researchers could use MDS to plot the countries from a six dimensional space

onto a two dimensional plane. Researchers could use MDS to perform the mapping of

countries so that the countries that are perceived to be similar are placed next to each

other, and the countries that are perceived to be dissimilar are placed far away from each

other on the map. I presented a group plot from SPSS analysis module of MDS in Figure

10. I used MDS to place all the countries in the list in four quadrants of the

twodimensional plot, which is reduced from a six dimensional space.

I used discriminant analysis to derive two dimensions: culture and geography (see

Table 15). Similarly, I assigned the two dimensions of culture and population density in

the MDS plot in Figure 10. As an alternative, I also tried using two other dimensions,

132

prosperity and geographical distance. I used MDS to place each country into the four

quadrants, which were formed by these dimensions. The original dimension 1 and

dimension 2 form the reference plane.

Researchers could use the MDS plot to find a better grouping by inspection. A

researcher could perform the inspection by rotating the dimensional axes clockwise or

anticlockwise and by inspecting which countries fall in the rotating quadrants each time

the researcher makes a rotation. By trial and error, the researcher could fix the best

orientation for the coordinate system, which could explain the grouping of the countries

in the study. Using the coordinates of population density and culture, I found a distinct

grouping among the countries in the study (see Figure 10).

When I tried a second coordinate system with two other dimensions, prosperity

and geographical distance, I found that countries with large geographic distances were

grouped into the third and fourth quadrants. Similarly, I found that countries with a high

value of GDP per capita were grouped into the third and second quadrants. As result,

future researchers could use MDS as a reasonable grouping method for the selected

countries.

Hierarchical cluster analysis. I also recommend that future researchers should

perform a cluster analysis using SPSS. Cluster analysis is a multivariate analysis method

similar to discriminant analysis, which researchers use for the classification of variables

(Jain, 2010; StatSoft, 2013). Researchers use hierarchical cluster analysis to identify

relatively homogeneous groups of cases (or variables) based on selected characteristics,

133

using an algorithm that starts with each case (or variable) in a separate cluster and

combines clusters until only one is left (Jain, 2010; StatSoft, 2013).

Researchers could use the method for the grouping of data to arrive at a

meaningful interpretation of the data or data summarization (StatSoft, 2013). According

to Qi, Tang, Wu, Guo, Fuller, and Zhang (2014), researchers could use hierarchical

cluster analysis to present a visual display of the grouping of the countries in the form of

a diagram called a dendrogram plot (see Figure 11). I presented in Table 20 a summary

comparison of the MDS and clustering analysis outputs.

134

Figure 10. Multidimensional scaling (Euclidean distance model) of the countries entered.

* * * * * * * * * * * * * * * * * * * H I E R A R C H I C A L C L U S T E R A

N A L Y S I S * * * * * * * * * * * * * * * * * * *

Dendrogram using Average Linkage (Between Groups)

Rescaled Distance Cluster Combine

C A S E 0 5 10 15 20 25 Label

Num +---------+---------+---------+---------+---------+

Italy 12 -+

Spain 20 -+

135

Germany 3 -+

France 10 -+

England, U.K. 2 -+-+

Switzerland 4 -+ |

Australia 5 -+ +-+

Belgium 16 -+ | |

Netherlands 21 -+ | |

Austria 15 -+-+ |

Ireland 19 -+ +-----------------+

Canada 14 -+ | |

Brazil 13 -+ | |

Mexico 17 -+-+ | |

South Korea 8 -+ | | +-------------------------+

Malaysia 11 -+ +-+ | |

Japan 1 -+ | | |

China (PRC) 6 ---+ | |

United Arab Emirates 9 -----------------------+ |

Hong Kong PRC 7 -------+-----------------------------------------+

Singapore 18 -------+

Figure 11. Dendrogram of clustered countries using cluster analysis.

Table 20

Comparison of MDS and Hierarchical Cluster Analysis Results

Groups

Multidimensional Scaling

Hierarchical Cluster Analysis

Group I

England, Belgium, Italy, Spain,

Germany, Mexico

England, Switzerland, Australia,

Belgium, Netherland, Austria

Group II

France, Ireland, Netherlands,

Austria, Canada, UAE

Italy, Spain, Germany, France,

Ireland, Canada, Brazil, UAE

Group III

Hong Kong, Singapore,

Switzerland, Australia,

Hong Kong, Singapore

Group IV

Japan, South Korea, Malaysia,

China, Brazil

Mexico, South Korea, Malaysia,

Japan, China

Reflections

Because I have worked in the jewelry industry for the past 20 years and I was

raised in a family business in the jewelry industry, I had prior expectations about the

results of my study. Nevertheless, I have done my best to create a fusion between my

academic and professional experience. Although my traditional business background

136

influenced my selection of globalization for a jewelry company and selecting a jewelry

company as the topic of my study, I applied my academic knowledge to arrive at the

study results.

The results of the study were different from my original expectations about the

selected variables. I found that the accuracy of classification of the countries entered by

the U.S. company was only 47.6% of the originally grouped cases, and 38.1% of the

cross validated grouped cases. However, the results were not accidental; this was

confirmed by the kappa (K = .053) test results. Furthermore, my experimentation with

diverse combinations of prediction variables and the use of multidimensional scaling and

cluster analysis reinforced my conclusion that the selected prediction variables were

ineffective in classifying the countries.

I concluded from the study results the understanding of some of the countries’

attraction features that might have influenced the globalization strategy of a major global

U.S. jewelry company since 1972. I found that the leaders of the U.S. company’s

globalization strategy were not influenced by the countries’ attraction features. Instead,

the leaders of the U.S. company relied on cultural similarities between the U.S. and their

foreign target countries. The importance of culture and employees’ competency changed

my understanding toward investments in human resources as a gateway for a successful

globalization strategy. Other leaders of small local brands could start their globalization

process by targeting countries with similar cultures, regardless of their distance from

their brands’ original countries.

137

Summary and Study Conclusions

I emphasized in the study results the importance of cultural differences using the

Uppsala model with its psych distance theory. Nevertheless, researchers should explore

different and current cultural and demographic variables that distinguish among

countries, thus adding value to the Uppsala model. Finally, researchers should add

companies’ competencies in addition to countries’ features to arrive at a comprehensive

understanding of the changing factors of the Uppsala model that would influence the

success of a globalization strategy of a jewelry retail company.

Introduction Local jewelry retail leaders operating in Saudi Arabia

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