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What is a multinational corporation? Classifying the degree of firm-levelmultinationality

Raj Aggarwal a,1, Jenny Berrill b,2, Elaine Hutson c,3, Colm Kearney d,*aDepartment of Finance, College of Business Administration, University of Akron, OH, United Statesb Business School, Trinity College Dublin, Irelandc UCD Smurfit School of Business, University College Dublin, Irelandd Business School and Institute for International Integration Studies, Trinity College Dublin, Ireland

1. Introduction

The terms multinational company (MNC), multinational enterprise and transnational corporation are widely and often

interchangeably used by international business (IB) commentators and scholars. MNCs are traditionally thought of as

successful firms that have grown over many years into large corporations that are international in their operations, vision

and strategies. This was certainly the case during most of the twentieth century because the prevailing technologies in

communications and transport were associated with economies of scale that curtailed the internationalisation of small and

medium-sized enterprises. Recent technological innovations, particularly the advent of the internet, have removed many ofthese constraints, and scale is no longer a critical requirement for multinationality. The emergence of international new

venture (INV) firms is testament to this phenomenon. In the modern business environment, firms increasingly operate

across national bordersby exporting and importing raw materials and intermediate or finished products; by employing

foreign capital, people and processes; and by organising, coordinating, and controlling resources globally. While MNCs

International Business Review 20 (2011) 557577

A R T I C L E I N F O

Article history:

Received 20 March 2010

Received in revised form 8 October 2010

Accepted 16 November 2010

Available online 17 December 2010

Key words:

Classification systems

Firm multinationality

Internationalisation theory

A B S T R A C T

The degree of firm-level multinationality is a key dimension that spans all theoretical

frameworks, levels of empirical analysis and domains of investigation in international

business research. There is, however, no agreed approach to defining or measuring firm-

level multinationality. This is reflected in inconsistent approaches to sample selection and

empirical testing, and it has curtailed the advancement of the discipline. We propose that

instead of searching for the elusive, all-encompassing definition of an MNC, international

business scholars should instead agree on a classification system for the degree of firm-

level multinationality. We illustrate the advantages of this approach by constructing a

simple classification system that takes into account the firms breadth and depth of

multinational engagements. We illustrate our matrix of firm multinationality by

classifying a novel sample of over 1000 firms from seven countries, and we demonstrate

how it can guide theory development and empirical testing. We also provide examples of

potential future research directions.

2010 Elsevier Ltd. All rights reserved.

* Corresponding author. Tel.: +353 1 896 2688.

E-mail addresses: [email protected] (R. Aggarwal), [email protected] (J. Berrill), [email protected] (E. Hutson), [email protected] (C. Kearney).

URL: http://www.elainehutson.ie/ , http://www.internationalbusiness.ie/1 Tel: +1 330 972 7442.2 Tel.: +353 1 8962632.3 Tel.: +353 1 7168828; fax: +353 1 2835482.

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remain a central focus of IB research, these developments have broadened research agendas and extended the range of firms

that qualify as MNCs.

Although many theoretical and operational definitions of MNC have been proposed, none has become standard.

Researchers have adopted pragmatic approaches to operationally defining the MNC, relying on past usage, data availability

and sub-discipline norms. MNCs have consequently been defined on the basis of characteristics as diverse as the size of the

firm by sales, the proportion of foreign sales or foreign assets, the number of foreign subsidiaries, and the number of foreign

workers. In the internationalisation-performance literature, for example, the ratio of foreign to total sales and the number of

foreign subsidiaries are the most common, but amongst the more unusual, Kwok and Reeb (2000) defined the MNC as a firmwith a foreign assets ratio greater than one percent, and Lecraw (1983) used the FDI level of the firms industry as the

measure of firm-level multinationality.

The absence of an agreed theoretical or operational definition of the MNC is reflected in the lack of consistency in how IB

scholars conduct both high-level and domain-specific theory building and testing. This has hindered the ability to compare

and contrast alternative theoretical frameworks, caused confusion in the interpretation of empirical results, and stymied the

emergence of effective replication studies. This in turn has curtailed the process of validating, refining, and rejecting

prevailing theorywhich according to positivist scientific methodology is necessary for advancement of the discipline

(Kuhn, 1962; Popper, 1978). As IB research agendas broaden and intensify, the need for clarity on this issue is becoming

critical. The complex and dynamic IB landscape, characterised by continual change in the structures and strategies of firms as

they evolve, suggests that a definitive theoretical or operational definition is unlikely to emerge. A better alternative would

be to agree on a classification system that encompasses the current forms of MNC while facilitating the inclusion of new

types as they emerge. In this paper, we propose a simple system for classifying firms by their degree of multinationality, and

we use this to demonstrate the benefits to the discipline of agreeing upon such a system.In the next section, weground our perspective by documentingthe wide variety ofapproachesto definingthe MNCin all393

papers thathave usedempirical MNCsamplesin thefourteen most important international business andmanagementjournals

between 1987 and 2007: Academy of Management Journal (AMJ), Academy of Management Perspectives (AMP), International

Business Review (IBR), International Marketing Review (IMR), Journal of International Business Studies (JIBS), Journal of

International Management (JIM), Journal of International Marketing (JIMR), Journal of Management (JM), Journal of

Management Studies (JMS), Journal of Marketing (JMR), Journal of Marketing Science (JMS), Journal of World Business (JWB),

Management International Review (MIR), and Strategic Management Journal (SMJ). We also document the many and varied

data sources used in these studies, and we show how the multiplicity of approaches to operationally defining the MNC and to

selecting MNC samples has hindered the ability to draw conclusions that can be robustly replicated and verified or refuted.

In Section 3, we construct a simple classification system for the degree of firm-level multinationalitythe matrix of firm

multinationality. We are not advancing this scheme as the optimal system; rather, our purpose is to stimulate debate on the

usefulness of an agreed classification system that facilitates a more coherent and consistent epistemology and methodology

in IB research. We illustrate its use by classifying 1015 firms from the G7countriesBritain, Canada, France, Germany, Italy,Japan and the United States. Using data on the international distribution of each firms sales and subsidiaries, we show that

our sample firms which include many of the worlds largest range from purely domestic (with no international sales or

subsidiaries) to fully global (with sales and subsidiaries in all regions of the world).

In Section 4 we demonstrate the usefulness of a classification system for the degree of firm-level multinationality in

advancing IB research. We first show how such a system can provide insights and perspectives to guide high-level theory

building by anchoring important conceptual norms and traditions, highlighting areas that require theoretical refinement and

renewal, and stimulating new ideas and directions. We then discuss its usefulness for sample selection and hypotheses

testing. In section 5 we point to future research directions. We illustrate how a classification scheme for firm-level

multinationality can be used to improve empirical testing andto help clarify thinking about several topics in IB, including the

relation between firm-level multinationality and performance, and the regional-global debate. Section 6 contains our

concluding comments.

2. Defining MNCs in IB research

The operational definition4 of a word or term provides a clear, concise meaning of a concept to guide measurement and

make it amenable to scientific investigation. Operational definitions should be easily quantifiable and measurable, and they

should point explicitly to what is being measured and how. In conducting empirical research, operational definitions should

be articulated before compiling samples in order to ensure that researchers collect, use and interpret data consistently.

Borsodi (1967) suggested that operational definitions should be clear, distinct, standard and reproducible. He listed four

canons of definition as adequacysufficient to clarify the meaning; differentiationeliminate confusion of the referent with

other terms by including any attributes that distinguish it; impartialitycharacteristics of similar significance should be

included with equivalent emphasis; and completenessall important attributes should be included.

4 Other types of definitions include lexical definitions, whichdescribe a concept in simpleterms to a wide audience; conceptual definitions,which provide

the meaning of a concept in a way that is compatible with a measurable occurrence; and abstract definitions, which are used when the meaning cannoteasily be measured.

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Although IB scholars have long been aware of the complexities involved in arriving at an appropriate definition of the

MNC, the discipline has not succeeded in agreeing on an operational definition that embodies Borsodis (1967) four canons.

The earliest attempt to grapple with the issue of defining the MNC was by Aharoni (1971), whoconsidered three categories of

definition:

Performance definitions, which are based on criteria such as foreign sales and earnings, foreign assets, and the number of

foreign employees.

Structural definitions, such as the number of countries in which the firm operates, the nationality of the firms topmanagement, and the organisational structure of the firm.

Behavioural definitions, which focus on the extent to which management personnel think internationally about strategic

opportunities.

Aharonis pioneering contribution to defining MNCs has proved to be an insightful and enduring framework for analysis

in research and teaching in IB. Rather than building on or amending earlier definitions, however, IB scholars have tended to

develop an ever-expanding set of alternative definitions. Panel A of Table 1 lists 19 attributes that have been used as

operational definitions to create empirical samples of MNCs (using Aharonis (1971) headings) in the 393 studies published

during the period 1987 to 2007 inAMJ(10 studies),AMP(4), IBR (45), IMR (7),JIBS(133),JIM(25),JIMR (8),JM(6),JMS(8),JMR

(3), JWB (13), MIR (89), and SMJ(42).5 These studies span a wide range of IB sub-disciplines including culture, government

relations, international finance, international human resources, international management, international marketing,

multinationality and performance, and sourcing strategies and structures. Panel B ofTable 1 lists the most popular sources of

data used in the studies.The first column in Panel A of Table 1 lists the number of studies that have used the particular attribute as the sole

criterionfor operationally defining theMNC; thesecond presents the number of studies that have used theattribute as onein

a multi-attribute definition; and the third adds these together. The overall total of 419 exceeds the number of studies

because many have used multi-attribute definitions. In total, 264 studies used single attribute and 155 used multi-attribute

definitions. Amongst the single-attribute studies, the number of foreign subsidiaries is the most common (163 studies),

followed by foreign sales (62). In the multi-attribute studies, foreign sales (56) is the most common, with foreign subsidiaries

(47) a close runner-up. Other attributes used to operationally define MNCs include foreign listings, assets, employees,

income, and taxation. Panel B ofTable 1 shows that the most popular data source is the Fortune list which has been used in 55

studies, followed by the Directory of Japanese Overseas Affiliates (17), and Compustat (13 studies).

Authors of the multi-attribute studies have in some cases developed rather complex approaches to defining MNCs.

Perlmutter (1969) included four attributes: ownership, organisational structure, the nationality of senior executives, and the

percentage of foreign investment. Sullivan (1994) used Aharonis (1971) performance, structural and attitudinal attributes to

create an index measure of firm multinationality. More recent efforts to construct indexes of multinationality include Gomesand Ramaswamy (1999) and Asmussen (2009). There have been several critiques of the index approach to defining MNC.

Ramaswamy, Kroeck, and Renforth (1996), for example, argued that important information is lost with the aggregation

involved in creating an index, and Allen and Pantzalis (1996) showed that the equal weighting ofSullivans (1994) attributes

is questionable.

With the multiplicity of approaches to operationally defining MNCs that we have documented here, it is not surprising

that the outcome has been inconsistent and even contradictory findings between studieswhich could be largely avoided if

the IB discipline were to agree on a classification system for the degree of firm-level multinationality. We illustrate this by

briefly describing the findings of some of the most well-cited empirical papers on two of the most important topics in IB

research: first, the internationalisation-performance relation, and second, the question of whether investing in home-based

MNCs provides the benefits of international portfolio diversification. For each question, we first show how the issue remains

unsettled; and by detailing the sample selection process in two or three studies, we illustrate how different the samples can

be in the empirical IB literature.

2.1. Does firm-level multinationality affect performance?

The question of whether and to what extent the degree of multinationality adds to firm value is unsettled. Researchers

initially sought evidence on a positive linear relation, and mixed results led to the investigation of various possible nonlinear

relations, such as quadratic, U-shaped, and horizontal S-shaped. Douglas and Craig (1983), Lecraw (1983), Grant (1987) and

Brouthers, Werner, and Matulich (2000) found that the degree of multinationality is associated with rising profitability. In

contrast, Mishra and Gobeli (1998) found that greater multinationality per se does not deliver greater value; Gomes and

Ramaswamy (1999) found that greater multinationality brings performance benefits up to a point beyond which they cease;

and Kotabe, Srinivasan, and Aulakh (2002) found that the benefits of multinationality are moderated by R&D and marketing

capabilities. Grant (1987) created a MNC sample of 304 British-owned manufacturing firms from the Times 500 list of

5

We also examined the Journal of Marketing Science, but found no studies that had compiled MNC samples. Our JIBS list includes studies dating back to1970.

R. Aggarwal et al. / International Business Review 20 (2011) 557577 559


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Britains largest firms that had at least 10 percent of their production abroad. Kotabe et al.s (2002) sample comprised 49 US-

based firms, and the extent of multinationality was measured by the ratio of foreign to total income.

2.2. Do MNCs provide the benefits of international portfolio diversification?

Theory and intuition suggest that investing in locally-listed MNCs should provide international portfolio diversification

benefits. The findings on this question are split down the middle: Hughes, Logue, and Sweeney (1975), Agmon and Lessard

(1977), Mikhail and Shawky (1979), Logue (1982), Errunza, Hogan, and Hung (1999), Cai and Warnock (2004) and Berrill and

Kearney (2010) found that investing in domestically-listed MNCs yields international diversification benefits, but Jacquillat

and Solnik (1978), Senchak and Beedles (1980), Brewer (1981), Fatemi (1984), Michel and Shaked (1986), Mathur, Singh, andGleason (2001) and Rowland and Tesar (2004) found no evidence of international diversification benefits.

Table 1

Defining MNCs in IB research.

Panel A: Variables used to operationally define MNCs

Single-attribute Multi-attribute Total

Performance definitions

Subsidiaries 163 47 210

Sales 62 56 118

Foreign assets 2 6 8Foreign production 3 4 7

Foreign joint ventures 3 2 5

Foreign income 1 3 4

International transactions 2 1 3

Foreign investments 1 2 3

Mergers and acquisitions 1 0 1

Structural definitions

Foreign employees 1 9 10

Foreign exchange listing 1 5 6

Industry details 1 3 4

Foreign equity 2 2 4

Foreign taxation 0 2 2

Global accounts 0 1 1

Behavioural definitions

Research and development 2 2 4International marketing 0 2 2

World mandates 0 1 1

Patents 1 0 1

Unclear 18 7 25

Total 264 155 419

Panel B: Data sources used to classify MNCs

The Fortune List 55

Directory of Japanese Overseas Affiliates 17

Standard and Poors Compustat 13

Dun and Bradstreet International Database 12

International Directory of Corporate Affiliations 12

Moodys Directory of Corporate Affiliations 9

The Forbes List 8

Corporate Families and International Affiliates 7Individual Stock Exchange Publications 7

Directory of American Firms Operating in Foreign Countries 6

Directory of International Affiliations 6

World Directory of Multinatinal Enterprises 5

US Bureau of Economic Analysis 4

Who Owns Whom 4

The Financial Times Global 500 List 3

Centre for Research on Security Prices Database 2

Directory of Foreign Invested Enterprises 2

Electronics Manufacturing Firms in Asia 2

Global Business 1000 List 2

Notes. Panel A lists 17 attributes that have been used to create operational definitions ofMNCs in 393studiespublished during 1987 to 2007 inthe Academy

of Management Journal (10 studies), Academy of Management Perspectives (4), International Business Review (45), International Marketing Review (7),

Journal of InternationalBusiness Studies (133 studies, from 1970 to 2007), Journal of InternationalManagement (25), Journal of InternationalMarketing (8),

Journal of Management (6), Journal of Management Studies (8), Journal of Marketing (3), Journal of Marketing Science (0), Journal of World Business (13),

Management International Review (89), and Strategic Management Journal (42). The total of 419 for this column exceeds the number of studies because

many studies used multi-attribute definitions. Panel B lists 19 data sources that have been used by a minimum of two of these studies.



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A close examination of a couple of these studies reveals the diversity of their samples. Errunza et al. (1999) used the 30

largest US companies in the Fortune 100 list, making the implicit assumption that large firms must be multinational. It is

likely, instead, that their sample includes firms with a broad range of multinationality, from purely domestic to deeply

global. Michel and Shaked (1986) examined Fortune 500 firms in the manufacturing sector, classifying them as MNCs if at

least 20 percent of their sales were foreign and if they had direct investment in at least 6 countries. Domestic firms were

defined as firms with less than 10 percent of sales, profits and assets abroad.

As this brief overview shows, the diversity in approaches to operationally defining MNCs and to compiling MNC samples

limits comparison across studies, and probably explains a large proportion of the disparity in their findings. The absence of anagreed approach to operationally defining or measuring the degree of firm-level multinationality has led to highly dissimilar

firms being included in samples labelled MNC. This has resulted in disparate findings across similar studies, and by making it

hard for researchers to confirm or contradict previous findings, has stymied the development of the discipline. The difficulties

associated withMNC definition wererecognised more than40 years ago by Perlmutter (1969, p. 11) who observed that: Part of

the difficulty in defining the degree of multinationality comes fromthe variety of parameters along which a firm doing business

overseas can be described . . . Another early articulation of the problem was by Sanden and Vahlne (1974, p. 92), who argued

that There is, of course, no sharp demarcation line between multinational and national firms. The essence of our argument is

that given the complexity of the MNC, rather than searching for a single acceptable definition, a better approach would be to

develop a classification system for the degree of firm-level multinationality thatis sufficiently flexible to encompass the known

forms of international enterprise while allowing for new forms that may emerge in the future.

3. Classifying firms by multinationality

Classification systems are widely used in theoretical and empirical analysis in many disciplines in the arts, humanities,

physical sciences and social sciences. By focusing on agreed sets of characteristic dimensions, classification systems condense

and organise information to facilitate comparison and contrast between object types within and across populations. Many are

well known, such as the Linnaeus hierarchical classification of plants and animals, the periodic table of chemical elementsand

the Dewey library classification system. In business and management, readers will be familiar with the various industry

classification systems (such as the Industry Classification Benchmark (ICB), the North American Industry Classification System

(NAICS), and the Standard International Trade Classification (SITC)), and the Journal of Economic Literature (JEL) system that

organises the business disciplines into 20 main categories. Other well-known business-related classification schemes are

Hambricks (1984) classification of strategy, Pavitts (1984) technical change, McGee and Thomas (1986) strategic groups,

Greenbergs (1987)organisationalstructure,Weatherford and Bodilys (1992)assetyieldmanagement, MillerandRoths(1994)

manufacturing strategies, Archibugi and Michies (1995) technology globalisation, Law, Wong, and Mobeleys (1998)

multidimensional constructs,Earls(2001) knowledgemanagement,and Marks,Mathieu,andZaccaros(2001) teamprocesses.6

Althoughthere is no generally agreed classification system for the degree of firm-level multinationality, a number of authorshave developed high-level typologies of MNCs. Among the best-known and most highly cited of these are Perlmutters (1969)

threefold typology of managerial mindsets as home country-oriented (ethnocentric), host country-oriented (polycentric), and

world-oriented (geocentric); Cavess (1982) threefold typology of multiplant MNCs as horizontal, vertical and diversified;

Bartlett and Ghoshals (1989) fourfold typology of MNC organisational structure as multinational, international, transnational

and global; Dunnings (1993) fourfold typologyof the rationale for FDI as market-seeking, efficiency-seeking, resource-seeking,

and strategic asset-seeking; and Rugmans(2003) fourfold typologyof MNC strategic orientationas home-regional, bi-regional,

host-regional and global. Others include Hill, Hwang, and Kims (1990) foreign market entry modes, Hennarts (1991) control

modes, and Morrison and Roths (1992) industry strategies. Harzing (2000) reviewed firm-level typologies in IB and found that

they relate to variables such as control, human resource practices, organisation design, and strategy and subsidiary behaviour.

The difference between typologies and classification schemes has received considerable attention in the business and

management literature. Doti and Glick (1994) argued that the terms are often confused, and in the process they provided a

good definition of typology:

. . ..a researcher might reasonably conclude that organizational typologies are atheoretical devices that are mainly

useful for categorization.. such a conclusion would be incorrect . . . typologies are complex theoretical statements that

should be subjected to quantitative modeling and rigorous empirical testing . . .. typologies identify multiple ideal

types, each of which represents a unique combination of the organizational attributes that are believed to determine

the relevant outcome(s). (pp. 231-232).

A classification scheme is defined by Chrisman, Hofer, and Boulton (1988) as:

. . .. a system or scheme in order for researchers to arrange entities into taxa [groups or categories] based on their

similarities, differences, and relationships to one another as determined by or inferred from their most fundamental

characteristics. (p. 415).

6

The influenceand usefulness of these contributions is amply demonstratedby their exceptionallyhigh citationrates. At thetime of writing,these papershave more than 250 citations each and together have been cited more than 4800 times on Googles Advanced Scholar search engine.



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Classification schemes categorise phenomena into mutually exclusive and exhaustive groups using discrete decision

rules (Doti & Glick, 1994), and their purpose is to guide theoretical and empirical research. In contrast, typologies are

complex theories that can and should be tested empirically. Classification schemes can in fact be used as a framework for the

empirical testing of typologies. We discuss how our classification scheme could be used to test some of the best-known IB

typologies in section 4.

3.1. A classification system for firm-level multinationality

Our objective in developing a classification system for the degree of firm-level multinationality is to create a scheme that

can encompass the important dimensions of multinationality while at the same time being intuitive and easy to use. This

involves a tradeoff between accuracy and simplicity. A reasonably simple design is critical because multidimensional

classification schemes blow up when dimensions are added.7

Although classifications schemes are not theoretical devices, theory is an important element in guiding the development

of such systems. Indeed, the literature on organisational classification systems (Carper & Snizek, 1980; Chrisman et al., 1988;

McKelvey, 1975, 1978; Rich, 1992) has long recognised that successful systems should capture key characteristics by

drawing on theory while taking into account empirical and practical evidence. In designing a classification system, therefore,

the first decision is to choose the key defining characteristics of the entities to be classified. The second is to determine the

classification systems categories. Chrisman et al. (1988, p. 416) describe five necessary attributes of the categories: they

should be mutually exclusive; internally homogeneous; collectively exhaustive (every organisation must belong to an

existing group); stable (in the sense that the groupings should be fixed over time, and not subject to change with new or

different empirical tests or data sets); and relevantly named (consistent with common usage to ensure effectivecommunication between and within the academic and business communities). Keeping in mind the importance of

simplicity, and informed by Chrisman et al.s (1988) necessary attributes, our classification scheme for firm-level

multinationality captures two defining characteristics: breadththe extent of geographical spread of operations, and depth

the degree of engagement with and exposure to each geographical unit.

3.2. Breadth

We measure breadth as the extent of geographic spread using four broad categories: domestic, regional, trans-

regional and global. To calibrate this, we divide the world into six regions based on the inhabited continents: Africa, Asia,

Europe, North and Central America, Oceania, and South America. Asia includes the Middle East and Turkey, and Europe

includes countries as far east as Armenia, Azerbaijan, Belarus, Ukraine and the Russian Federation. North and Central

America includes Mexico and the other countries of Central America and the Caribbean as well as Canada and the United

States. Oceania comprises Australia, New Zealand and the Pacific islands. Table 2 provides a list of the countries in eachregion.

We use continent-based regions for two important reasons. First, by encompassing all countries of the world, it satisfies

Chrisman et al.s (1988) third necessary attributethat the groupings should be collectively exhaustive. Our delineation is

more inclusive than many of the regional groupings that have been seen in the IB literature. For example, the triad ofOhmae

(1985) the EU, Japan and the United States was advocated on the basis that these regions constituted the worlds three

largest markets and that most large firms were headquartered there. This was extended by Rugman (2003) and Rugman and

Verbeke (2004) to include the expanded EU, Asia and NAFTA, but it still excludes many countries and whole continents

Africa, South America, and Australasia.8 Our continent-based regions provide a framework for examining the activities of

triad-headquartered firms beyond the triad (Flores & Aguilera, 2007), and of firms headquartered in non-triad emerging

economies that internationalise into triad regions and elsewhere (Aggarwal & Agmon, 1990; Aulakh, 2007). Second, our

regions are defined along geographic rather than political lines, because the political map can change over timeas

exemplified by the obsolete regions EFTA and COMECON, used by Hirsch and Lev (1971) and Miller and Pras (1979),

respectively. Our classification scheme therefore satisfies Chrisman et al.s (1988) fourth necessary attributethat thegroupings are stable and not subject to alteration when empirical or other circumstances change. It therefore facilitates the

analysis of changing patterns in international business over time. This is increasingly important because the geographical

distribution of production, investment and consumption are becoming more diverse and dynamic (Dunning, 2009). The

emergence of Eastern Europe from communism in the early 1990s, the development and growth of China and India during

the last two decades, and more recent trends such as the rise of the South American economies and increasing flows of FDI

into Africa and from the Middle East are now on the research agendas of IB scholars.

7 Dess, Newport, and Rasheed (1993) point out that as the number of dimensions of a construct increases arithmetically, the number of combinations

increases geometrically. As we see in the next section, a 2 4 classification matrix of multinationality yields 16 combinations.8 The triad excludes 155 countries, and these are mainly emerging economies in Asia, Eastern Europe, the Middle East, South America and Africa. The

IMFsand theWorld Bankslistof thelargest20 economiesmeasured byGDP in2008 includes Brazil, China,India andRussia(the so-calledBRICs)alongwith

Indonesia and Mexico. These are all outside the triad, as are many of the worlds largest 50 economiesincluding such countries as Argentina, Nigeria,Poland, Saudi Arabia and Turkey.



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3.3. Depth

We measure the depth of market engagement on the basis of the commitments and contractual arrangements that firms

engage in, and the resulting levels of control they obtain together with the risks they face. Depth ranges from the shallow

engagement with international markets associated with exports and imports, to licensing and franchising, operating foreign

offices, forming alliances and joint ventures, through to the commitment of FDIwhich generally involves a deeper

engagement with foreign markets and higher exposures to foreign business, economic and political risks than say exportingor licensing.

In defining depth in this way, we are following several theoretical studies from the entry mode literature,9 which place

the various entry modes on a scale from low engagement to high engagement. Anderson and Gatignon (1986), for example,

defined 17 entry modes from small shareholder to wholly-owned subsidiary (WOS) based on the tradeoff between resource

commitment and control. Erramilli and Rao (1990) developed a 9-point level of involvement scale with licensing and

franchising at the lowest end of the scale and WOS at the other. Hill et al. (1990) typology places entry modes on a scale based

on the extent of resource commitment, control and dissemination risk. As we will shortly illustrate, researchers can use one

or more of the depth dimensions, depending on the issue being studied and the availability of data.

3.4. The matrix of firm multinationality

Combining the breadth and depth dimensions, we create our simple matrix of firm multinationality. A firm whose

business activities take place entirely within its home country is defined as domestic (D), and a firm with business activitiesin the region in which it is headquartered is referred to as regional ( R). R can be further delineated into three categories: R1

(less than one-third of the countries in the region), R2 (between one-third and two-thirds) and R3 (more than two-thirds).

For example, a British firm that is headquartered in London and operates in one or two countries in the rest of Europe would

be classified as R1. If, however, it has operations throughout Europe (and not elsewhere), it would be classified as R3. I f a fi r m

conducts business in more than one region (but not fully globally) it is defined as trans-regional ( T), and this category is

further subdivided into T2 (two regions), T3 (three regions), T4 (four regions) and T5 (five regions). We classify firms as

global (G) if they conduct business in all six regions. Because our classification scheme includes the full range of geographic

spread, it satisfies Chrisman et al.s (1988) third necessary attribute: that the categories should be collectively exhaustive.

Our scheme is inclusive of all firmsincluding purely domestic firms.

For ease of exposition, we initially work with a basic version of our classification scheme containing the four broad

breadth dimensions: domestic (D), regional (R), trans-regional (T) and global (G). We demonstrate and illustrate the system

Table 2

Country constituents of each region.

Africa (53):

Algeria, Angola, Benin, Botswana, Burkina, Burundi, Cameroon, Cape Verde, Central African Republic, Chad, Comoros, Congo, Congo (Dem. Rep.),

Djibouti, Egypt, Equatorial Guinea, Eritrea, Ethiopia, Gabon, Gambia, Ghana, Guinea, Guinea-Bissau, Ivory Coast, Kenya, Lesotho, Liberia, Libya,

Madagascar, Malawi, Mali, Mauritania, Mauritius, Morocco, Mozambique, Namibia, Niger, Nigeria, Rwanda, Sao Tome and Principe, Senegal,

Seychelles, Sierra Leone, Somalia, South Africa, Sudan, Swaziland, Tanzania, Togo, Tunisia, Uganda, Zambia, Zimbabwe.

Asia (44):

Afghanistan, Bahrain, Bangladesh, Bhutan, Brunei, Burma, Cambodia, China, East Timor, India, Indonesia, Iran, Iraq, Israel, Japan, Jordan,Kazakhstan, Korea, (north), Korea (south), Kuwait, Kyrgyzstan, Laos, Lebanon, Malaysia, Maldives, Mongolia, Nepal, Oman, Pakistan,

Philippines, Qatar, Saudi Arabia, Singapore, Sri Lanka, Syria, Tajikistan, Thailand, Turkey, Turkmenistan, United Arab Emirates, Uzbekistan,

Vietnam, Yemen.

Europe (47):

Albania, Andorra, Armenia, Austria, Azerbaijan, Belarus, Belgium, Bosnia and Herzegovina, Bulgaria, Croatia, Cyprus, Czech Republic, Denmark,

Estonia, Finland, France, Georgia, Germany, Greece, Hungary, Iceland, Ireland, Italy, Latvia, Liechtenstein, Lithuania, Luxembourg, Macedonia,

Malta, Moldova, Monaco, Montenegro, Netherlands, Norway, Poland, Portugal, Romania, Russian Federation, San Marino, Serbia, Slovakia,

Slovenia, Spain, Sweden, Switzerland, Ukraine, United Kingdom, Vatican City.

North and Central America (23):

Antigua & Barbuda, Bahamas, Barbados, Belize, Canada, Costa Rica, Cuba, Dominica, Dominican Rep., El Salvador, Grenada, Guatemala, Haiti,

Honduras, Jamaica, Mexico, Nicaragua, Panama, St. Kitts and Nevis, St. Lucia, St. Vincent and the Grenadines, Trinidad &, Tobago, United States.

Oceania (14):

Australia, Fiji, Kiribati, Marshall Islands, Micronesia, Nauru, New Zealand, Palau, Papua New Guinea, Samoa, Solomon Islands, Tonga, Tuvalu,

Vanuatu.

South America (12):

Argentina, Bolivia, Brazil, Chile, Colombia, Ecuador, Guyana, Paraguay, Peru, Suriname, Uruguay, Venezuela.

Notes: This table places the worlds 193 countries into our 6 continent-based regions. The list includes all countries that are recognised by the

United Nations, and excludes dependencies and territories.

9 Brouthers and Hennart (2007) and Canabal and White (2008) provide reviews of research on foreign entry mode.



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using two depth dimensionstrading (sales) (S) and investment in subsidiaries (I). Putting these together, we derive our 2

4 matrix of firm multinationality shown below, in which the first letter refers to the depth, and the second to the breadth of

multinationality. This allows us to classify firms into categories ranging from purely domestic (SD-ID) thatdo not export and

have no subsidiaries abroad, to deeply global firms (SG-IG) that have sales and subsidiaries in all regions of the world. Inbetween these extremes, firms can be classified into groups depending on the combination of their depth and breadth. Table

3 describes three types of regional firm (numbered 24), five types of trans-regional firm (59) and seven types of global

corporation (1016).

Depth of engagement Breadth of geographical spread

Domestic Regional Trans-regional Global

Sales SD SR ST SG

Investments (subsidiaries) ID IR IT IG

A simple example illustrates the intuition behind our classification scheme. Two hypothetical Canadian firms, Maple Inc

and Rocky Inc, export their products to the United States and Europe. Maples goods are manufactured by a number of

subsidiaries in Canada so it is a ST-ID firm with trans-regional sales and domestic subsidiaries. Rocky is a ST-IT firm withtrans-regional sales and subsidiaries because its products are manufactured by subsidiaries in Brazil and Thailand. If these

two firms were classified by sales the most common approach to operationally defining MNCs in the IB literature they

would be deemed the same. Clearly, however, these firms face dissimilar challenges, costs and risks.

Our matrix of firm multinationality acknowledges location as a pivotal dimension of multinationality. Location has long

played a key part in IB theory and strategy (Dunning, 1977; Ghemawat, 2003, 2005; Losch, 1954; Martin, 1999; Ricart,

Enright, Ghemawat, Hart, & Khanna, 2004; Ronen & Shenkar, 1985; Weber, 1929 ). The sticky places within slippery space

described by Markusan (1996) and Floridas (2005) spiky world provide the basis for international strategy that is lacking in

the flat world ofFriedman (2005). More recently, Dunning (2009) notes that although lower trade costs and barriers have

seen greater geographical dispersion between the location (L) and ownership (O) of production, more reliance on knowledge

and other intangible assets has led to greater concentration in specific clusters, countries and regions. Governments now

compete vigorously for FDI (Barry & Kearney, 2006; Gertler, 2001; Wheeler & Mody, 1992), and the more successful of these

have created innovative regions and clusters like the London square mile, Hydrabad, and Silicon Valley (Casper, 2007; Kenny,

2000; Teece, 2000; Whitley, 2009)that are prime examples of sticky places between wide-open spaces that attract MNCsat different depths of market engagement.

Table 3

Classifying firms by multinationality: 16 firm types.

Category Symbol Description Count %

Purely domestic firms

1 SD-ID Domestic sales and subsidiaries 107 11

Regional firms

2 SR-ID Regional sales, domestic subsidiaries 14 1

3 SD-IR Domestic sales, regional subsidiaries 28 34 SR-IR Regional sales and subsidiaries 14 1

Total regional 56 5

Trans-regional firms

5 ST-ID Trans-regional sales, domestic subsidiaries 17 2

6 ST-IR Trans-regional sales, regional subsidiaries 17 2

7 SD-IT Domestic sales, trans-regional subsidiaries 73 7

8 SR-IT Regional sales, trans-regional subsidiaries 41 4

9 ST-IT Trans-regional sales and subsidiaries 538 53

Total trans-regional 686 68

Global firms

10 SG-ID Global sales, domestic subsidiaries 2 0

11 SG-IR Global sales, regional subsidiaries 0 0

12 SG-IT Global sales, trans-regional subsidiaries 53 5

13 SD-IG Domestic sales, global subsidiaries 4 0

14 SR-IG Regional sales, global subsidiaries 4 015 ST-IG Trans-regional sales, global subsidiaries 87 9

16 SG-IG Global sales and investments 16 2

Total global 166 16

Notes. This table expands the matrix of multinationality into 16 firm types: purely domestic firms ( SD-ID) (number 1), three categories of regional firm

(numbered 24), five categories of trans-regional firm (59), and seven categories of global firm (1016). The two columns on the right apply the

classification system to the 1015 firms from Canada (44), France (91), Germany (91), Italy (107), Japan (156), UK (81) and USA (453), for which both

international sales and subsidiary data are available.



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In addition to location, our classification scheme acknowledges distance as an important dimension of firm-level

multinationality (Nachum & Zaheer, 2005). The gravity model has been applied to study patterns of firm-level

internationalisation, modes of foreign market entry, international strategy, and the effects of culture on human resources,

management and marketing (Ghemawat, 2001; Leamer & Storper, 2001; Slangen, 2006; Tihanyi, Griffith, & Russell, 2005).

While the locational choice and multinationality-performance literatures often allude to many factors other than purely

geographical distancecultural, economic and psychic, for examplegeographical distance can be seen as a reasonable

approximation of these. In their review of country groupings, Aguilera, Flores, and Vaaler (2007) argued that physical

contiguity the most straightforward approach to defining regions is also the most appropriate because geographicalproximity correlates closely with other properties such as culture. Consistent with this, Goerzon and Beamish (2003) found

that MNCs international asset dispersion (their purely geographical measure of international scope) dominates the political

and cultural diversity of MNCs foreign operations. Although our system does not explicitly include any metric for distance, it

implicitly acknowledges that activities that are trans-regional or global will generally be more distant from the

internationalising firms home base than domestic or home regional activities.

3.5. Illustration of the classification scheme

To illustrate how the matrix of firm multinationality can work in practice, and to clarify how it differs from a typology,we

construct a sample of firms from Canada, France, Germany, Italy, Japan, the United Kingdom and the United States that are

the constituent firms of these countries main stock market indexes: the TSX 60, the SBF 120, the HDAX 110, the MIB-SGI 174,

the Nikkei 225, the FTSE 100 and the S&P 500. We compiled this list from the websites of each countrys stock exchange in

2006. The geographical spread of subsidiaries was obtained from Dun and Bradstreets Who Owns Whom 2005/06, whichspecifies the location by country of each subsidiary. We obtained data on the geographic spread of each firms sales from

Worldscope, which is drawn from company accounts for the year ending 31 December 2005. Of our initial data set of 1289

firms, data on sales and subsidiaries were available for 1143 and 1155 companies, respectively.

As well as being much larger than the Fortune 500 (which is the most commonly-used data source in research on MNCs, as

detailed in Panel B ofTable 1), our data set has more non-US firms and it includes smaller firms from a more eclectic range of

industries. Our sample comprises firms in 10 broad ICB industry categories: industrials (237 firms), financials (231),

consumer services (195), consumer goods (174), technology (109), basic materials (106), health care (88), oil and gas (65),

utilities (62) and telecommunications (22). The mean incorporation date is 1922, with firm age ranging from more than 5

centuries (Banca Monte dei Paschi dates from 1472) to less than 5 years. The largest firm is Exxon Mobil, and average size by

sales is US$14 billion.

The two right-hand columns in Table 3 show the number and percentage of our sample firms in each multinationality

category for the 1015 firms for which both sales and subsidiary data are available. Our classification scheme does not impose

thresholds; if a firm has any sales or a minimum of one subsidiary in a particular country or region, it is considered to have apresence there. It is important not to use thresholds for two main reasons. First, the average firm in a large and relatively

closed country will usually have a smaller proportion of sales and assets abroad than a similar firm in a more open country

(Eden, 2008; Glaum & Oesterle, 2007). Using thresholds will therefore result in a large number of firms from closed economy

countries being excluded from the sample, resulting in selection bias. Second, countries vary greatly in their market size,

wealth and price levels, rendering sales thresholds inappropriate. For example, if a firm from a developed country sells its

products in Africa, that market will most likely constitute a small fraction of its overall sales. This does not mean, however,

that the firm considers its presence in Africa irrelevant to its operations and strategies.

There are 107 (11 percent of the sample) domestic SD-ID firms with no foreign sales or subsidiaries, and 56 (5 percent)

regional firms. Trans-regional firms are the most numerous; 686 or 68 percent fall into this category. Most of these (53

percent) are ST-ITfirms that are trans-regional in both sales and subsidiaries. Lastly, 16 percent of the sample firms have a

global reach in either their sales or investments. The fact that some of our categories are rather underpopulated there are

few regional firms, for example, and few firms are categorised as types 10, 11, 13 and 14 does not imply that they are

redundant. Classifying a different set of firms would undoubtedly result in a different distribution of firm types. The keypoint is that our matrix of firm multinationality is easy to implement and can be applied to different samples of firms.

Table 4 provides comprehensive details on the largest 100 firms10 in our G7sample. We rank the firms by size (column 1),

and provide the company name and industry sector (2 and 3), the date of formation (4), the size measured by sales in US$

billions (5), and the country in which the firm is headquartered (column 6). In columns (7) and (8) we categorise the firms

using our 10-point breadth dimension (D, R1, R2, R3, T1, T2, T3, T4, T5 or G) for the sales and subsidiaries depth dimensions,

respectively; and in column 9 we categorise each firm as one of the 16 firm types as described in Table 3. For the 100 largest

listed firms in the G7countries, there are relatively few at either end of the multinationality spectrum: 7 category 1 (SD-ID)

purely domestic firms, and 4 category 16 (SG-IG) fully global MNCs. The most common firm type is category 9 (ST-IT) (trans-

regional sales and trans-regional investments) with 40 constituent firms, and there are 38 category 15 (ST-IG) firms with

trans-regional sales and global investments.

10 The classifications for all of the sample G7 firms are available on request from the authors.



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Table 4

The 100 largest G7 firms.

(1) (2) (3) (4) (5) (6) (7) (8) (9)

Rank Firm Industry Age Size Location Sales Subs Category

1 Exxon Mobil Oil & Gas 1870 328.21 US T2 G 15 ST-IG

2 Wal-Mart Consumer Goods 1962 312.43 US T2 T4 9 ST-IT

3 BP Oil & Gas 1908 250.32 Britain T3 G 15 ST-IG

4 General Motors Consumer Services 1908 192.60 US T4 G 15 ST-IG5 Toyota Motors Consumer Goods 1937 191.31 Japan T4 G 15 ST-IG

6 Daimlerchrysler Consumer Goods 1903 186.37 Germany T5 G 15 ST-IG

7 Chevron Oil & Gas 1879 184.92 US T2 T5 9 ST-IT

8 Ford Motor Consumer Services 1903 177.09 US T3 G 15 ST-IG

9 Conocophillips Oil & Gas 1875 162.41 US T3 T4 9 ST-IT

10 Total Oil & Gas 1924 152.58 France T5 T2 9 ST-IT

11 General Electric Industrials 1879 148.02 US T5 G 15 ST-IG

12 Citigroup Financials 1812 120.28 US T3 G 15 ST-IG

13 Volkswagen Consumer Goods 1937 118.55 Germany G G 16 SG-IG

14 Allianz Financials 1890 115.97 Germany T4 G 15 ST-IG

15 AIG Financials 1919 109.29 US T3 T5 9 ST-IT

16 AXA Financials 1817 106.75 France T4 T5 9 ST-IT

17 Generali Financials 1831 95.69 Italy Rl D 2 SR-ID

18 Siemens Industrials 1847 93.88 Germany T5 G 15 ST-IG

19 Hsbc Hldgs Financials 1865 92.84 Britain T4 G 15 ST-IG

20 Carrefour Consumer Services 1957 92.70 France T4 T4 9 ST-IT21 ENI Oil & Gas 1953 91.68 Italy G T5 12 SG-IT

22 IBM Technology 1888 91.13 US T3 G 15 ST-IG

23 Honda Motor Consumer Goods 1948 90.11 Japan T4 G 15 ST-IG

24 Mckesson Health Care 1833 88.05 US T2 T3 9 ST-IT

25 Hewlett-Packard Technology 1939 86.70 US T2 T5 9 ST-IT

26 Hitachi Industrials 1910 86.07 Japan T4 G 15 ST-IG

27 Nissan Motors Consumer Goods 1932 85.74 Japan T4 T5 9 ST-IT

28 Bank Of America Financials 1928 85.06 US T5 T5 9 ST-IT

29 Valero Energy Oil & Gas 1980 82.16 US T2 Rl 6 ST-IR

30 BNP Paribas Financials 1966 81.98 France G G 16 SG-IG

31 Credit Agricole Financials 1894 81.62 France G D 10 SG-ID

32 Home Depot Consumer Services 1978 81.51 US Rl T2 8 SR-IT

33 Cardinal Health Health Care 1971 81.36 US T2 T5 9 ST-IT

34 JPMorgan Chase Financials 1799 79.90 US G G 16 SG-IG

35 HBOS Financials 1695 76.60 Britain T2 T4 9 ST-IT

36 Deutsche Bank Financials 1870 75.82 Germany T5 G 15 ST-IG37 Verizon Telecommunications 1885 75.11 US T2 T2 9 ST-IT

38 Prudential Financials 1848 74.83 Britain T3 G 15 ST-IG

39 Deutsche Telekom Telecommunications 1871 74.17 Germany T3 T5 9 ST-IT

40 Aviva Financials 1696 72.75 Britain T3 T3 9 ST-IT

41 RBOS Financials 1727 71.89 Britain T3 T4 9 ST-IT

42 Tesco Consumer Services 1919 71.79 Britain T2 T2 9 ST-IT

43 Peugeot Consumer Goods 1882 70.02 France T3 T5 9 ST-IT

44 Metro Consumer Services 1964 69.34 Germany T3 T3 9 ST-IT

45 Altria Group Consumer Goods 1860 68.92 US T3 G 15 ST-IG

46 Procter & Gamble Consumer Goods 1837 68.22 US T2 G 15 ST-IG

47 Societe Generate Financials 1864 64.53 France G G 16 SG-IG

48 EOn Utilities 1929 64.52 Germany T3 T4 9 ST-IT

49 EDF Utilities 1946 63.54 France T2 T2 9 ST-IT

50 France Telecom Telecommunications 1988 61.02 France T2 G 15 ST-IG

51 Kroger Consumer Goods 1883 60.55 US D D 1 SD-ID

52 Muenchener Ruck Financials 1880 58.91 Germany T5 T4 9 ST-IT53 Marathon Oil Oil & Gas 1887 58.60 US T3 T2 9 ST-IT

54 Fiat Consumer Goods 1899 57.92 Italy T4 G 15 ST-IG

55 Toshiba Industrials 1875 57.69 Japan T4 T5 9 ST-IT

56 Legal & General Financials 1836 56.39 Britain T2 T2 9 ST-IT

57 Dell Technology 1984 55.91 US T4 T5 9 ST-IT

58 Nippon Oil Oil & Gas 1888 55.64 Japan T4 T4 9 ST-IT

59 Deutsche Post Industrials 1490 55.49 Germany T5 G 15 ST-IG

60 Lloyds TSB Financials 1765 55.47 Britain D T4 7 SD-IT

61 Boeing Industrials 1916 54.84 US G T4 12 SG-IT

62 Amerisourcebergen Health Care 1907 54.58 US D D 1 SD-ID

63 Vodafone Group Telecommunications 1983 53.40 Britain T3 T5 9 ST-IT

64 Basf Basic Materials 1865 53.19 Germany T5 G 15 ST-IG

65 Costco Consumer Goods 1983 52.94 US T2 T3 9 ST-IT

66 Target Consumer Services 1962 52.62 US D T3 7 SD-IT

67 Thyssenkrupp Industrials 1860 52.34 Germany T3 G 15 ST-IG

68 Morgan Stanley Financials 1935 52.08 US T4 T4 9 ST-IT



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4. Implications for theory and testing

The literature on organisational classification (Carper and Snizek, 1980; McKelvey, 1975, 1978; Rich, 1992) has long

recognised that a successful classification scheme should be consistent with prevailing theory while also reflecting the world

as it is perceived. This generates confidence in its applicability which in turn promotes widespread acceptance and use by

researchers, teachers and practitioners. Taking the lead from the designers of the influential business and management

classifications mentioned in the previous section, classification schemes inform theory building in five main ways. First, they

provide a framework within which researchers can think about ideas and form opinions; stimulating reflection on related

topics that might previously have seemed unconnected, and they provide direction in refining existing theories and

developing new theory. Second, classification schemes emphasise the conceptual traditions from which theory develops.

This helps to ensure that concepts are applied consistently with the existing body of theory; and by identifying trends in thedevelopment of the literature, classification schemes help to shed light on areas that need new theory building, replication or

validation studies, or new empirical approaches. Third, classification schemes assist in identifying the common and disparate

elements of alternative theories and in clarifying the value of specific contributions. By enhancing clarity and reducing

confusion, they help researchers to focus on promising directions while avoiding sterile debates. Fourth, classification

schemes support the compilation of complete and systematic data sets to test the various theories, and this spurs the

development of the discipline. Finally, classification schemes caution scholars that because of the richness and variety of

business forms, no single high-level theory is likely to capture all their complexity, no matter how elegant the theoretical

framework.

These insights apply to our classification system of firm-level multinationality. The rich ecology of international business,

inhabited by firms of different age, size, industry, country of origin and degree of multinationality, means that each firm will

have a unique combination of objectives, strategies, opportunities and constraints. Given their financial, management and

knowledge resources, internationalising firms choose a level of engagement with foreign markets that maximises their risk-

adjusted expected returns net of expected costs. We should therefore expect to observe many alternative patterns ofinternationalisation, and the agenda of IB research is rightly focussed on assessing whether a small, manageable set of high-

Table 4 (Continued )

(1) (2) (3) (4) (5) (6) (7) (8) (9)

Rank Firm Industry Age Size Location Sales Subs Category

69 Suez Utilities 1858 51.63 France T5 G 15 ST-IG

70 Renault Consumer Goods 1899 51.44 France T5 G 15 ST-IG

71 Pfizer Health Care 1849 51.30 US T3 G 15 ST-IG

72 Johnson & Johnson Health Care 1886 50.51 US T5 G 15 ST-IG

73 RWE Utilities 1990 50.42 Germany T4 G 15 ST-IG

74 Barclays Oil & Gas 1690 49.98 Britain T4 G 15 ST-IG75 Merrill Lynch Financials 1914 47.78 US T5 T5 9 ST-IT

76 Dow Chemical Basic Materials 1897 46.31 US T3 T5 9 ST-IT

77 United Health Health Care 1977 45.36 US D D 1 SD-ID

78 Sojitz Industrials 1892 45.22 Japan T4 T5 9 ST-IT

79 Wellpoint Health Care 1900 45.15 US D D 1 SD-ID

80 Microsoft Technology 1975 44.28 US T2 G 15 ST-IG

81 Metlife Financials 1914 44.07 US T2 T4 9 ST-IT

82 Mitsubishi Industrials 1870 43.90 Japan T4 G 15 ST-IG

83 Nee Corporation Industrials 1899 43.88 Japan T2 G 15 ST-IG

84 AT&T Telecommunications 1983 43.86 US D T4 7 SD-IT

85 Time Warner Consumer Services 1990 43.65 US T4 G 15 ST-IG

86 Fujitsu Industrials 1935 43.57 Japan T5 G 15 ST-IG

87 Goldman Sachs Financials 1869 43.39 US T4 T3 9 ST-IT

88 CNP Assurances Financials 1857 43.27 France T2 Rl 6 ST-IR

89 Lowes Consumer Services 1946 43.24 US D D 1 SD-ID

90 United Parcel Service Industrials 1907 42.58 US T2 G 15 ST-IG

91 United Technologies Industrials 1929 42.28 US T4 G 15 ST-IG

92 Walgreen Consumer Goods 1901 42.20 US D D 1 SD-ID

93 Japan Tobacco Consumer Goods 1949 42.18 Japan T3 T3 9 ST-IT

94 Aeon Consumer Services 1758 40.29 Japan T3 T4 9 ST-IT

95 Tyco International Industrials 1962 39.73 US T4 T2 9 ST-IT

96 Glaxosmithkline Health Care 1902 39.41 Britain T3 G 15 ST-IG

97 Intel Technology 1968 38.83 US T4 T5 9 ST-IT

98 Safeway Consumer Goods 1915 38.42 US Rl Rl 4 SR-IR

99 Medco Health Care 1668 37.87 US D D 1 SD-ID

100 Mitsui Industrials 1947 37.43 Japan T5 G 15 ST-IG

Notes. This table illustrates the application of the classification scheme to the largest (by sales) 100 firms in our G7sample, ranked by sales (column 1). The

columns provide the followinginformation:companyname(2), industry(3), date of formation(4), size measured by sales in US$billions(5), thelocation of

headquarters (6), the geographic categorisation of sales (7) and investments (subsidiaries) (8), and the number category and description of degree of firm-

level multinationality (as in Table 3) (9).



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internationalisation entrepreneurial experiences of INV firms is also encompassed within our system as it includes domestic

firms at one end of the spectrum of multinationality. Our main point here is that categorising firms by their degree of

multinationality yields insights into prevailing theories of firm-level internationalisation patterns, while also providing the

framework for conceptualising new combinations and patterns.

Hull (1965) describes how biological classifications prior to Darwin were static insofar as species were classified into

fixed groups on a permanent basis. Darwins evolutionary theory showed how species can evolve within and across groups,and this stimulated the emergence of phylogenic (or evolutionary) classification systems. McKelvey (1982) and others have

reinterpreted Darwinian evolutionary theory to describe how organisations can evolve across groups within phylogenic

classes, and Rich (1992) illustrates how theory-builders can use phylogenics as a lens to look into the past in order to explain

the present. Although the traditional Darwinian model sees evolution occurring slowly over long periods, revolution can

sometimes occur when external shocks create jumps in evolutionary adaptation. Similarly, in IB we have seen the

revolution that is the INV; the shock being fast-developing innovations in communication and transportation technology.

Our matrix of firm multinationality facilitates longitudinal examination of the broad range of internationalisation patterns

that we now observe, from the slowly evolving large firm to the dynamic and ambitious INV.

Our classification scheme is neutral with respect to the typologies of MNCs managerial intentions, marketing strategies

and entry modes. It can facilitate analysis of Dunnings (1993) why firms engage in FDI, Hill et al.s (1990) how firms enter

new markets, Perlmutters (1969) managerial orientations, and Bartlett and Ghoshals (1989) organisational forms. It can be

used to describe and analyse, for example, howand why a Chinese mining firm engages in resource-seeking FDI in Africa (a la

Hill et al., 1990 and Dunning, 1993), and how it manages the operation either by retaining full control in China (a laPerlmutters home-country ethnocentric orientation), or by decentralising and delegating responsibilities to its African

SD-ID SR-ID ST-ID SG-ID

SD-IR SR-IR ST-IR SG-IR

SD-IT SR-IT ST-IT SG-IT

SD-IG SR-IG ST-IG SG-IG

Example 1: Shallow internationalisation in stages

Example 2: Balanced internationalisation in stagesExample 3: Deep internationalisation in stages

Example 4: Rapid shallow internationalisation

Example 5: Born trans-regional

Deeperengagement

Broader geographical spread

Fig. 1. Possible internationalisation paths.



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subsidiary (a la Bartlett and Ghoshals multinational firm). Our classification scheme can also be used to study a born trans-

regional Norwegian software firm that operates in four regions, selling its products via the internet, and establishing a

transnational organisational structure with dispersed and interdependent teams of software developers (a la Bartlett and

Ghoshal).

Our classification scheme can also be used as a framework for consistent empirical testing of MNC typologies. In his

critique of typologies of strategy, Miller (1996) argues that they are rarely tested empirically, and when they are, they are

usually found to fall short. This is in part

. . .

.because approaches vary greatly among studies: different variables, operationalizations and samples are used totest the same typologies. Moreover, conflicting findings are rarely resolved as researchers build too little on each

others work. Many scholars, for example, have tested [various typologies of strategy] using ambiguous and divergent

operationalizations and sample definitions. So results remain inconclusive after many years of work.

[Miller, 1996, p. 506]

In short, Miller (1996) points out problems with empirical tests of typologies that are similar to our critique of the body of

empirical work on MNCsinconsistent operational definitions and sample selection across studies.

4.1. Implications for sample selection

We have seen how the MNC has been operationally defined using a diverse and sometimes eccentric collection of

characteristics, which contributes to inconsistent and often contradictory findings. This problem is exacerbated by the use of

samples that are too general and broadly specified to yield meaningful results that can reliably support or reject prevailingtheory. McKelvey (1978) highlighted this problem more than 30 years ago with an insightful analogy:

Consider a typical study on four organizations: a gear manufacturer, a large engineering firm, a small retail store, and a

social welfare agency.The investigatorwould typically claim thatfindings fromsuch a sample (andpopulation) would be

more broadly applicable than if the study were limited to,say, smallstores.This sample is akin to a biologists wanting to

make broad statements about heartbeat rates based on a sample of one elephant, one tiger, one rabbit, and an alligator.

Obviously, no meaningful biological population is defined by such a sample, and surely no one would believe the

researchers statements about heartbeats. They would probably wonder if the researchers elephant was at all

representative of other elephants perhaps it wasolder, more vigorous, or biggerthan the average elephant, and so on.

[McKelvey, 1978; pp. 1437-1438].

McKelvey advocates narrowing the selection process to create samples of firms with similar characteristics. The

resulting loss of generalisability would be . . ..offset by gains in the definitiveness of the findings, the levels of variance

explained, and the applicability of the results to the population. In short, solid findings about a narrower population arebetter than marginal findings of questionable generalizability to a broadly defined population. (McKelvey, 1978: 1438). A

valid alternative to this approach is to gather a large, diverse sample and to control for different characteristics. Our

classification system facilitates the selection and compilation of both of these types of samples. In the next section, we

provide examples of how this can be done.

5. Future research directions

In addition to providing a framework for empirical testing of high-level internationalisation theories and typologies, our

classification system provides fresh perspectives on current debates and opens up the possibility of new research directions.

Some examples are provided here.

5.1. Standardised marketing strategies and MNC performance

The question of whether MNCs use standardised or localised approaches in foreign markets to branding, pricing, product

mix, promotion, and distribution channels, and which of these delivers superior performance, yields mixed results that are

difficult to generalise and reconcile (Griffith, Chandra, & Ryans, 2003; Cui & Lui, 2005; Han & Kim, 2003; Krum & Rau, 1993;

Liu & Pak, 1999; Theodosiou & Katsikeas, 2001; Xu, Cavusgil, & White, 2006 ). This arises largely because of inconsistent

approaches to the operational definition of MNC. Liu and Pak (1999), for example, studied 35 firms with venture locations in

Beijing and Shanghai on the basis of personal knowledge of the management team, whereas Griffith, Chandra, and Ryans

(2003) used a random sample of US Fortune 750 firms with Indian operations, and Han and Kims (2003) sample comprised

the 200 largest Korean exporters to China with head offices in Seoul. Because of the widely disparate approaches to sample

selection, the findings from these studies cannot easily be compared to related studies, and this makes it difficult for

researchers to build on prior work.

International marketing researchers could use our classification scheme as a framework for constructing samples that

are more consistent across studies, facilitatingmore effectivecomparison. By thinking carefully about thetype of firm that

is to be included in a sample defined by such features as the country of its headquarters, the range of its internationalexperience, and the markets in which it operates our classification scheme also assists researchers to balance the



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tradeoffs between narrowing the sample in order to address very specific research questions, and obtaining generalisable

conclusions.

5.2. Firm-level multinationality and foreign exchange exposure

Firms become more directly exposed to exchange rate movements as they internationalise, but highly internationalised

firms with assets and earnings denominated in many currencies are naturally hedged insofar as exchange rates are

imperfectly correlated. Using a data set of 220 US firms from the Fortune 500 list, Pantzalis, Simkins, and Laux (2001) definedMNCs as firms with at least oneforeign subsidiary, and they used two measures of firm-level multinationality: the number of

foreign countries in which the firm had subsidiaries, and the number of foreign subsidiaries in its top two foreign countries

(as a measure of concentration). Consistent with theory, and confirming the importance of the extent of the firms

international operations on its exchange rate risk, they found that the greater the number of foreign subsidiaries the lower

the foreign exchange exposure, and that greater concentration is associated with higher foreign exchange exposure. Using a

sample of over 3000 firms from 23 OECD countries, Hutson and Stevenson (2010) found that the more open the economy, the

greater the degree of firm-level exposure. This, they argue, is due to firms in open economies being more exposed to indirect

foreign exchange exposurethat is, the exposure arising from the competitive environment in which the firm operates

(Bodnar, Dumas, & Marston, 2002).

Future research could usefully use our classification scheme to provide a more consistent approach to sample selection

together with a superior measure of the degree of firm-level multinationality. For example, researchers could examine

whether purely domestic firms those that are internationally undiversified experience greater foreign exchange exposure

than highly internationalised firms. Researchers could also compare the exposure experience of firms with varying degreesof breadth and depth of multinationalitysuch as type 10 SG-ID firms with global sales and no foreign subsidiaries versus

type 13 SD-IG firms with no foreign sales but with global subsidiaries.

5.3. Firm-level multinationality and performance

We saw in section 2 how divergent approaches to operationally defining the MNC has contributed to disparate

findings and ongoing debate on whether firm-level multinationality delivers enhanced performance. Having analysed

more than 100 empirical studies spanning 3 decades, Contractor (2007) concluded that the multinationality-performance

literature is mixed, confusing and contradictory, and asked What exactly do we mean by internationalisation or degree

of internationalisation? At the very least it is incumbent on authors to state the exact construction of their DOI [degree of

internationalisation] variable in their papers. (p. 469). Bausch and Krist (2007) argued that since there is no universal

internationalisation-performance relation, rather than looking for generalisations, future research should develop more

finely-grained modelsdifferentiating between firms of different age, size, country of origin, industrial sector, productdiversification, and R&D intensity. Hennart (2007) concurred, suggesting that empirical studies have been undertaken at

too high a level of aggregation, and that rather than building ever-more sophisticated measures of multinationality,

researchers should focus on the key dimensions of multinationality such as the size of foreign sales versus their

dispersion.

Our classification scheme addresses these concerns and provides an appropriate framework for analysing the

multinationality-performance issue. Using the scheme, Berrill and Kearney (2010) showed that more internationalised

MNCs deliver superior risk-adjusted equity returns after controlling for size, industry and country effects. Subsequent

research could usefully generalise this approach. For example, researchers could build large cross-country databases to

examine whether firms that expand domestically exhibit different performance trajectories to firms that expand

internationally, controlling for country, regional and other effects. A related research question is, is expansion within the

home region associated with different performance vis-a-vis expansion across regions? Further, does this pattern depend on

the depth dimension of internationalisation? For example, are there differences in performance between type 5 ST-ID firms

with trans-regional sales but no foreign investments and type 9 ST-IT firms with trans-regional sales and investments?

5.4. The regional/global debate

The extent to which the worlds largest MNCs have a truly global outlook remains a hotly debated topic. Some scholars

(Govindarajan & Gupta, 2008; Yip, 2002) argue that global strategy is paramount, while others such as Doremus, Keller,

Pauly, and Reich (1998) and Ghemawat (2001, 2003, 2005) argue the case for semiglobal strategy. Rugman (2000, 2003,

2005), Rugman and Hodgetts (2001), Rugman and Verbeke (2003, 2004) and Collinson and Rugman (2008) go further in

arguing that the worlds largest MNEs operate mostly within their home regions, that very few are global, and that global

strategy is a myth. Rugman and Hodgetts (2001, p. 341), for example, conclude that the CEOs of MNCs should ..encourage all

[their] managers to think regional, act localand forget global. Understandably, this has not gone unchallenged. Aharoni

(2006), Asmussen (2009), Westney (2006), Dunning, Fujita, and Yakova (2007), Flores and Aguilera (2007), Osegowitsch and

Sammartino (2008), Tallman (2007), Vives and Svejenova (2007) and Eden (2008) have introduced refinements to Rugmans

approach to categorising firms and demonstrated that the evidence in favour of regionalisation over globalisation is notpersuasive.



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Our classification scheme provides a useful framework to contribute to this debate. In Table 5 we provide a breakdown of

multinationality for our data set by sales and subsidiaries separately (in Panels A and B, respectively), and also by country. Asubstantial proportion of the firms are purely domestic (20 percent using sales as the measure of multinationality and 17

percent using subsidiaries); there are relatively few regional firms (4 percent using sales and 11 percent using subsidiaries);

and most are trans-regional (73 percent using sales and 59 percent using subsidiaries). While only 3 percent of our firms

have global sales, 13 percent have global subsidiaries. When the trans-regional firms are added together with the global

firms, it is clear that the vast majority operate beyond their home regions874 firms (76 percent) by sales and 838 (72

percent) by subsidiaries.

In contrast to the findings of Rugman and his co-authors, therefore, our classification scheme suggests that in the G7data

set there are relatively few regional firms, the vast majority are trans-regional, and there are a substantial proportion of

global firms. Why are the findings so different from those of Rugman and co-authors? First, our classification scheme

includes all countries in the world. Second, with the exception of Collinson and Rugman (2008), all of the Rugman studies

used sales as the sole depth measure of multinationality. It is clear from our sample that large firms based in the G7 countries

have a greater global reach in subsidiaries than in sales. Third, we impose no activity thresholds. Our findings suggest that

rather than being a myth, global strategy is a reality of modern international business. As this debate continues, researcherscould use our classification scheme to construct larger and broader datasetsto include, for example, listed firms from

emerging regions. It could also be used to examine the issue in a narrower way; for example, to examine the international

reach of SMEs based in a particular country or region, or to look more carefully at the extent of multinationality of firms in

particular industries.

5.5. Business in developing and emerging markets

Developing andemergingmarketsnow capture increasing proportions of worldwide inward and outward FDI and trade,

and areconsequently thefocus of much recentresearch (Arregle,Beamish,& Hebert,2009; Buckley, Clegg, Forsans,& Reilly,

2001; Buckley & Ghauri, 2004; Cuervo-Cazurra, 2008; Flores & Aguilera, 2007; Gupta & Wang, 2009; Kali & Reyes, 2007;

Khanna, Palepu, & Sinha, 2005; Wu & Strange, 2000). MNCs from the triad increasingly pursue strategies to overcome the

resource deficiencies and other limitations of doing business in developing countries, and firms from developing and

emerging economies analogously devise strategies to compete in their own and developed markets (Agular et al., 2006;Aulakh, Kotabe, & Teegen, 2000; Hoskisson, Eden, Lau, & Wright, 2000; Seelos & Mair, 2007). Guillen and Garcia-Canal

Table 5

Are the G7 firms regional or global?

Canada France Germany Italy Japan UK US Total G7

Panel A: Sales

D 13 (23) 6 (6) 5 (5) 46 (30) 16 (9) 7 (8) 136 (29) 229 (20)

R1 7 (12) 8 (7) 3 (3) 7 (5) 5 (6) 10 (2) 40 (4)

T2 17 (30) 16 (15) 26 (27) 37 (24) 33 (20) 22 (24) 110 (23) 261 (23)

T3 10 (18) 23 (21) 19 (19) 16 (11) 45 (27) 16 (18) 78 (17) 207 (18)

T4 7 (12) 27 (25) 15 (15) 20 (13) 49 (29) 19 (21) 78 (17) 215 (19)

T5 3 (5) 21 (20) 24 (24) 17 (11) 25 (15) 14 (15) 48 (10) 152 (13)

T 37 (65) 87 (81) 84 (85) 90 (59) 152 (91) 71 (78) 314 (67) 835 (73)

G 6 (6) 7 (7) 9 (6) 7 (8) 10 (2) 39 (3)

Total 57 107 99 152 168 90 470 1143

Panel B: Subsidiaries

D 5 (12) 19 (18) 5 (5) 50 (36) 20 (9) 6 (7) 87 (18) 192 (17)

R1 20 (20) 13 (13) 31 (23) 6 (3) 13 (15) 34 (7) 117 (10)

R2 1 (1) 3 (3) 4 (3) 8 (1)

R 21 (21) 16 (16) 35 (26) 6 (3) 13 (15) 34 (7) 125 (11)

T2 16 (37) 9 (9) 19 (19) 19 (14) 29 (14) 11 (12) 75 (16) 178 (15)

T3 11 (25) 15 (15) 17 (17) 13 (10) 58 (28) 12 (14) 70 (15) 196 (17)T4 8 (19) 14 (14) 9 (9) 6 (4) 53 (25) 14 (16) 67 (14) 171 (15)

T5 2 (5) 5 (5) 7 (7) 4 (3) 28 (13) 13 (15) 85 (18) 144 (12)

T 37 (86) 43 (42) 52 (52) 42 (31) 168 (80) 50 (57) 297 (63) 689 (59)

G 1 (2) 19 (19) 26 (27) 10 (7) 16 (8) 19 (21) 58 (12) 149 (13)

Total 43 102 99 137 210 88 476 1155

Notes. In this table we categorise the G7 firms using our classification system, reporting figures for the sales (Panel A) and subsidiaries (Panel B) depth

dimensions separately, with percentages in brackets. Of the 1289 G7 firms in the initial sample, data were available on the geographic spread of sales for

1143 and of subsidiaries 1155. The breadth dimensions are D, R1, R2, T2, T3, T4, T5 and G. Weadd together data forregional firms (R) and trans-regional firms

(T) overall; adding together the numbers in bold gives the total for each column. (There are no R3 firms in subsidiaries, and all of the regional (R) firms in

sales are R1).



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(2009) reviewed research on the variously-labelled new, emerging and unconventional MNCs from emerging markets,

and how their rationales, paths and speeds of internationalisation differ from those of the more traditional MNCs. These

new MNCs must deal with thedisadvantage of beinglatecomers inadditionto the liability of foreignness, butthisis offset

by specific skills such as project execution, networking, and dealing with institutional weakness and political instability

(Aulakh, 2007; Campa & Guillen, 1999; Goldstein, 2007; and Cuervo-Cazurra & Genc, 2008). Our classification system

provides a framework for examining a range of issues related to the regional, trans-regional and global strategies of firms

beyond the worlds developed economies. For example, researchers could use our classification scheme to study the extent

to which new MNCs in Africa, Asia, and Latin America internationalise within and beyond their regions. It could also beused to investigate the pre-internationalisation strategies of small, young firms as well as the patterns of

internationalisation of emerging market INVs (Matthews & Zander, 2007). Researchers could also use our scheme to

inform the construction of firm-level samples with greater granularity in order to examine entry mode choices and effects

in developing and emerging markets.

5.6. Longitudinal studies

The prevailing theories of firm-level internationalisation, together with the many patterns of internationalisation that

are traced by firms of different age, size, industry and home country, suggest that there is no standard or optimal path of

international expansion (Buckley & Chapman, 1997; Fillis, 2001). Whether firms internationalise slowly according to one

or more of the stages theories of internationalisation, or rapidly according to INVT, the common dimension is time. It

seems surprising, therefore, that there have been relatively few longitudinal studies of internationalisation. We believe

that this will soon change. Contractor (2007) and Hennart (2007) suggest that IB researchers should diversify away froman almost exclusive reliance on cross-sectional data and embrace longitudinal studies as the best approach to achieving a

more complete understanding of the evolution of the MNC. Glaum and Oesterle (2007) advocate the benefits of a

longitudinal approach on the relation between firm-level multinationality and performance, and Brouthers and Hennart

(2007) and Canabal and White (2008) suggest that longitudinal data should be used in studies of entry mode. As

illustrated in section 4.1 with three examples of firms that follow different paths of internationalisation our

classification scheme is well placed for use as a framework for longitudinal analysis of firm internationalisation.

Researchers can use the scheme to construct appropriate samples and to trace the processes and paths

internationalisation as firms proceed across the matrix of firm multinationality. Researchers could also use our

framework to study the patterns, processes and consequences of de-internationalisation. Finally, given its usefulness for

studying patterns of international business over time, our classification scheme provides a framework for historical

analysis of MNCs (Jones & Khanna, 2006).

6. Concluding comments

A distinguishing feature of the IB discipline is the use of alternative theoretical frameworks to approach empirical

questions at different levels of analysisthe industry, the firm, its management, and other stakeholders (Buckley & Lessard,

2005). The domain of questions spanned by the discipline includes but is not restricted to the activities, strategies, and

structures of MNCs, and the interactionsbetween them and other firms, governments and civil society. In this paper, we have

focussed on one level of analysisthe firm. From a comprehensive review of all papers published in the top international

business and management journals that ha

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17-What is a Multinational Corporation