Does Porter's diamond hold in the global automotive industry?

ABSTRACT

Michael Porter's Theory of the Competitive Advantage of Nations is commonly referred to as Porter's Diamond, as it comprises 4 key elements that lead to national competitiveness. This paper empirically tests Porter's theory. 50 automotive companies in the automotive industry

headquartered in 8 different nations comprise the sample. The results illustrate that the global automotive industry does in fact support Porter's model that defines national competitive advantage. Relationships that exhibit statistical significance are in the expected directions. Applicability of the model and extensions of this research are discussed.

INTRODUCTION

With the globalization of most industries in the latter half of the twentieth century, the topics of international businesses and competitive advantage have received much attention from business executives, public policy makers and scholars in recent years. This; in conjunction with the rise of global competitors from all parts of the world, has led to the acknowledgement of and search for firms that are both nationally and globally competitive. Practitioners and academics alike have tried to qualify and quantify those characteristics that are present in highly competitive international businesses, with the hope of replicating them in organizations with lower profiles. The research has resulted in numerous rankings, where industries and firms are compared on a global scale to see which are the most competitive. Thus the topics of national competitiveness and global competitiveness have become new additions to the business lexicon.

In recent years, U.S., European and Japanese dominance in many industries has been challenged. This trend has many causal factors, including the rapid rate of technology innovation and dissemination, rising standards of living across the globe, decreasing barriers to trade and the rise of enterprises from big, emerging markets that were formerly considered non-players on the global stage. Nowhere has this been more evident than in the automotive market. Because it is such a universal product and one that has significant national brand recognition and image status connotations, countries and businesses seem to attach special meaning to their ranking within this industry. Because it is so dominant in the minds of businesspeople, economists, government officials and consumers, much data and information exist about the auto industry. This paper will bring together the ideas of worldwide rivalry, global competitiveness and automotive manufacturing in the form of research hypotheses. In so doing, it will evaluate the applicability of one of the seminal models in the area of national competitiveness. Specifically, Michael Porter's (1990) Model of National Competitiveness will be empirically tested using the global automotive market.

LITERATURE REVIEW

One could argue that the topic of global competitiveness occurs at the cross roads between international economics and strategic management. It incorporates many facets of both disciplines, including international trade, global supply and demand, industry structures, levels of competition within industries and strategies utilized by competitors. 1973 Economics Nobel prize winner Wassily Leontief (1953) was one of the first scholars to add an empirical element to the theoretical realm of international trade with his popular paradox of the Heckscher-Ohlin (1919) theory. Later, management scholars adopted the concept of competing globally in their research (Buckley and Casson, 1998; Tsang, 1999). Notably, Hamel and Prahalad (1994) reinforced the concepts of core competencies, industry level analysis and competing for the future. After much research most would agree that we can say that global competitiveness in the aggregate for a nation is not equivalent to global competitiveness at the individual firm level. Yet still today, no universally accepted definition of global competitiveness exists in the literature. The disagreement on the definition is likely due to the multifaceted nature of the construct. Corden (1994) states that there are three major areas of national competitiveness: sectoral or industry competitiveness, cost competitiveness and productivity. All three concepts are captured in the measure of global competitiveness used herein, the Fortune Global 500 Ranking.

Many of Porter's (1990) ideas were shared by earlier scholars. Vernon's early product life cycle (1966) attributed national competitiveness to a nation's technology and capabilities, which are similar to Porter's advanced factors. Hymer's (1976) idea that firms have specific competitive advantages that allow them to overcome the liability of foreignness is similar to Porter's concept of firm-specific advantages that lead to global competitiveness. Caves (1982) discussed the practice of firms transferring knowledge gained in one country to another. If done correctly, this process could offset costs associated with startup in those same countries. He noted that utilizing the right mix of factors of production would lead to probable success, just as Porter does. While each of these men made great contributions, none created a model to assess competitiveness. The Diamond is one of the few models in international business research that illustrates what comprises national competitiveness within a given industry. While Porter's (1990) theory is generally accepted, very few studies have actually tested the concept of national competitiveness based on the model. In light of this fact, this paper attempts to provide some empirical evidence for Porter's Model of the Competitive Advantage of Nations.

This paper will contribute to the literature on the topic of national competitiveness in several ways. Much research in this area has occurred at the macroeconomic level (Davies, 2001; Storper 1995), focusing on trade patterns and government policies; here firms and industries will constitute the level of analysis. Other research has focused on improving the competitiveness of firms from developing nations (Fitzgerald, 2002; Lall, 2001); here, the focus is on firms that are the leaders in world markets, regardless of their origin. Still others have used theoretical papers and qualitative arguments to advance knowledge (Papanastassou and Pearce, 1999; Veiyath and Zahra, 2000). These are necessary and useful, but need to be augmented with empirical research studies. This paper achieves this objective.

Porters' Competitive Advantage of Nations

Porter's model includes four key elements. The model is depicted as a diamond, where the four forces jointly constitute a firm's global competitiveness in a given industry. Figure 1 depicts the model. Although not illustrated in the formal model, Porter also acknowledges the role that governmental forces and luck can play in national competitive advantage.

[FIGURE 1 OMITTED]

The first of the four elements is known as demand conditions. Demand conditions describe the level of domestic demand that a firm faces. Demand conditions depend both on the quantity of demand as well as the sophistication level of consumers in a home market. Generally, demand conditions are associated with a country's level of economic development. Porter's model indicates that a primary source of competition for firms in a given industry comes from domestic demand. Very demanding consumers create an awareness in firms that causes them to focus on the needs and preferences of the consumer base. Also, quantities of demand drive firms to higher levels of efficiency and productivity. Thus, high levels of demand in a nation would drive the firms in that industry to become globally competitive. Some examples are the French preference for luxury goods (i.e. cosmetics and fashion) and the Swiss insistence upon precision (i.e. banking and watchmaking). Therefore, we expect that:

Hypothesis 1: More demanding consumers in the home market will positively impact a firm's global competitiveness.

The second element of the model is known as factor conditions. According to Porter, factor conditions include any factors of production that a firm uses in its businesses. These include the traditional factors of production, such as land, labor, capital and also naturally occurring raw materials. Other factors of production can include manmade structures that facilitate commerce, including basic infrastructure systems such as roads, water systems and telecommunications. Other factors are those that are specific to the firm, such as entrepreneurship and innovation. Still other factors would be educational and legal systems. Porter classifies these factors into five major categories: human resources, physical resources, knowledge resources, capital resources and infrastructure. The more advanced these factors are, the more they will enhance the success of businesses located in the country. These factors provide needed inputs and systems that businesses use to gain competitive advantages over their rivals. Without them, firms would have to expend their own resources to provide such structures for commerce and transactions. Therefore, we expect that:

Hypothesis 2: More advanced factor conditions in the home market will positively impact a firm's global competitiveness.

The third element of the model is known as related and supporting industries. This aspect of the model includes the importance of enterprises that indirectly or directly affect a given industry. Porter describes these ancillary businesses needed by firms as related and supporting industries. These most often encompass suppliers or distributors that serve the industry at hand. However, they could also include consulting companies, contractors or even outsourcing ventures. The model proposes that the stronger these industries are, the stronger the focal industry will be. The underlying assumption is that highly competitive supporting industries will drive the focal industry to be more competitive. For example, the use of state of the art technologies or globally progressive human resource practices in the related industries would likely mean that the focal industry is also using such techniques, which would lead to a competitive advantage in the marketplace. Similarly, supporting industries that exhibit unique practices will create competitive advantages for the firms they serve, which will add value for the end consumer. Therefore, we expect that:

Hypothesis 3: Strong and dynamic related and supporting industries in a firm's home market will positively impact the firm's global competitiveness.

Firm strategy, structure and rivalry is the fourth element in the model. This point on the diamond refers to several key strategic factors that characterize a firm. Strategy describes the types of actions firms utilize to achieve both long-range and short-range goals. These are often either low-cost, differentiation, focus strategies or some combination thereof. Other common strategies include growth, maintenance or restructuring activities. Growth strategies would be associated with higher competitiveness because the ability to pursue growth internally or externally would be indicative of overall business health. Structure refers to the industry composition. This describes the degree to which an industry is concentrated or dispersed, competitive or monopolistic, or global or domestic. A more crowded structure would indicate multilevel competition and therefore greater competitiveness. Rivalry indicates both the number of players and the level of competition among firms in an industry. This could be heated, mid-range, non-rivalrous or somewhere in between. Greater rivalry in an industry would lead a firm to higher levels of competitiveness visa vis its rivals. Rivalry is thought to be the most comprehensive of the three factors, as it often indicates the underlying strategy and structure of the competitors. Thus, a greater number of firm actions as well as a greater number of competitor responses in the focal industry lead to greater competitiveness of the firm. Therefore, we expect that:

Hypothesis 4: Greater rivalry within a firm's home market will positively impact the firm's global competitiveness

METHODS

Data for this study came from several sources. The 2002 Fortune Global 500 and the 2002 Fortune 500 were used to obtain a list of the 50 most successful companies in the automotive industry in the world based on 2001 revenues. Home countries represented for these firms include Canada, Germany, Italy, France, Sweden, Japan, South Korea and the United States. The goal for the study was to evaluate a multinational sample across a single industry in order to evaluate the applicability of the model. Automotive manufacturing is one of the most global industries in the world and therefore it was chosen as the focal industry. Previous research in the area of national competitiveness has often been survey-based (Papanastassou and Pearce, 1999). Criticisms of this methodology include small sample sizes, subjectivity and self-reporting bias. In an attempt to avoid these issues, this research includes a larger sample utilizing objective data. In this way, it contributes to the stream of literature on national competitiveness. Data on country conditions and statistics came from the Economist Pocket World in Figures 2002. Other economic and industrial indicators came from the CIA World Factbook 2002. In addition, information on recent merger, acquisition and divestment activity was gleaned from the 2001 and 2002 Directory of Corporate Affiliations as well as company websites.

MEASURES

The dependent variable was firm performance. Performance has been used extensively in business research as a measure of how a firm is achieving its stated goals and objectives (Christensen and Montgomery, 1981; Hambrick and Mason, 1984). In this case, performance will be measured using data from the Fortune listings. 2001 profits as a percent of revenues, or Return on Sales (ROS) and 2001 profits as a percent of assets, or Return on Assets (ROA) will be used to operationalize performance. There are 4 independent variables. Each represents one tip of the Diamond. Demand conditions are measured as a composite score which includes both consumer sophistication and size of home market demand. First, consumer sophistication was measured using the percent of the population enrolled in higher education in the home market. Education levels have been suggested as a proxy for consumer sophistication in much of the marketing literature (Barnes and McTavish, 1983; Titus and Bradford, 1996). Second, the automotive competitor revenues within the home country as a percentage of total global automotive manufacturing revenues were measured. Similar percentage of total measures have been used in previous research (Banerji and Sambharya, 1996; Luo, 1998). These two elements were transformed to percentages, weighted equally and then combined to create the composite demand conditions score, similar to the technique employed by Brouthers and Yu (2002).

Factor conditions include naturally occurring factor endowments as well as man-made ones. Porter states that advanced factors are more significant than basic ones and specialized factors are more relevant than general ones. The competitive advantage of firms is usually based on these types of factors. To capture both advanced and specialized factors, Porter's concepts of physical infrastructure and knowledge resources were used. First, the number of internet hosts per 1,000 people in the headquarters location of the focal firm was used as a proxy for communications infrastructure. This is considered to be a more advanced indicator than the number of telephone lines and has been utilized by other researchers (Bandyopadhyay, 2001). Secondly, U.S. patent filings were used. They have been used to assess technological innovation in the international business literature research (Bresman, Birkinshaw and Nobel, 1999). Here, the number of patents the firm had established with the U.S. Patent Office as of the year 2001 was combined with the number of internet hosts in an equally weighted equation to create a the composite score for factor conditions.

Related and supporting industries were identified for the automotive industry from the Fortune list of global industries. These were oil and gas, chemicals, metals, mining and crude oil production, insurance, electronics, building materials and glass, banks and petroleum refining. This construct was also computed using a composite score. For the year 2001, total revenues were calculated for each of the 9 industries, and each nation's percentage of total revenues was determined from each industry. For example, South Korea had I company out of the 11 on the list within petroleum refining. This company had revenues of $33,008 million. Total petroleum refining revenues were $1,204,811 million. South Korea therefore received a score of 33,008/1,204,811 or .0274 for petroleum refining. Each of the 9 industries was summarized and given a total score for each nation. This score was used as the measure for related and supporting industries. Similar aggregate sales measures have been used by Shrader et al. (2000) and Autio et al. (2000).

Firm strategy, structure and rivalry indicates strategic actions taken by the players in an industry as well as the amount and type of competition within an industry. This construct was also measured using a composite score to incorporate multiple elements of the concept. First, firm strategy was assessed by evaluating the three year period 1999-2001 for merger, acquisition and divestiture activity for the firms in the sample. A Likert scale of -1 to +2 was used, where acquisition activity received a score of +2, merger activity received a score of +1, no change in the scope of activity resulted in a score of 0 and divestment activity received a score of -l. Similar ranking scales have been used by Elsbach (1994) and Burpitt and Rondinelli (1998). Then, the structure of the industry was ascertained by using a count measure. Industry structure was calculated as the number of domestically headquartered competitors within a country. For example, Japan has 9 companies listed within the top 50, so it received a score of Count measures such as these have been used by other scholars to indicate levels of activity by firms (Hitt, Hoskisson and Kim, 1997; Khanna and Palepu, 2000). These two scores were transformed to percentages, weighted equally and combined to create firm scores for strategy, structure and rivalry.

ANALYSIS

To test the hypotheses, several regression equations were used. The final model specification is presented below.

PERF = [[beta].sub.0] + [[beta].sub.1]DEMCON + [[beta].sub.2]FACCON + [[beta].sub.3]RELIND + [[beta].sub.4]STSTRIV + [epsilon]

Where PERF is firm performance, DEMCON is demand conditions, FACCON is factor conditions, RELIND is related and supporting industries, STSTRIV is strategy, structure and rivalry and [epsilon] is the random error term. Both curvilinear and interactive specifications of the model were run but showed no statistical significance and therefore they are not included in the analysis.

RESULTS

The results of the analyses follow. Descriptive statistics for the sample are presented first, and then the findings from the hypothesis testing are given. Table 1 presents the means, standard deviations and intercorrelations among the variables. The correlations among the variables present no problems of multicollinearity.

Validity of the independent variables was evaluated using the 2002 Economist Pocket Worm in Figures national competitiveness rankings as well as using the 2002 World Economic Forum's Global Competitiveness Report. Competitiveness scores from both of these publications confirmed that the measures used here as proxies for competitiveness are valid. Reliability of the findings was deemed good based on a separate sample conducted on other firms in the 2002 Fortune Global 500 Ranking.

Ordinary least squares multiple regressions were chosen since both dependent and independent variables were numeric. Table 2 gives the results for the Revenues model and Table 3 gives the results for the Assets model. Both analyses tested all 4 hypotheses. The findings for each analysis were similar, so they will be discussed together.

Table 2 shows the results of the model where Return on Sales is the measure of firm performance. It is evident that three of the four points on the diamond, demand conditions (p< .05), factor endowments (p < .05) and related and supporting industries (p< .01) show statistical significance. Each of the three parameters is positive and in the expected direction. Thus the data confirm support for Hypotheses 1, 2 and 3. As Porter predicted, more advanced quantities of these points were related to greater firm performance. However, the fourth point on the diamond, firm strategy, structure and rivalry, did not show any statistically significant relationship with performance. Thus no support was evinced for Hypothesis 4. The model as a whole is significant with an F score of 13.90 at the p < .01 level. It explains over half of the variation in ROS, with an adjusted [R.sup.2] score of .51.

The second model showed similar results. Table 3 presents the ROA model. The same 3 elements of the model explain performance. Demand conditions are positively related to firm performance (p < .05), thus supporting Hypothesis 1. Factor conditions are marginally significant at p < .10, and so support Hypothesis 2. Related and supporting industries do exhibit strong statistical significance at p < .01, thus providing support for Hypothesis 3. Again, firm strategy, structure and rivalry shows no relationship to performance, giving no support for Hypothesis 4. For assets, the entire model is significant where F = 9.91 (p < .01) and it explains somewhat less variation in firm performance with an Adjusted [R.sup.2] of .42.

DISCUSSION AND CONCLUSION

This study provides empirical evidence in support of Porter's model describing the Competitive Advantage of Nations. This is noteworthy, as few studies have achieved this result in the international business literature. A few comments are in order about the operationalizations of the constructs. This study is considered exploratory research. As such, the facets of the diamond had to be constructed. Not every construct incorporates every concept from the theory. With such multifaceted constructs, it would be impossible to include every such item. Instead, this analysis incorporates the most comprehensive and assessable items within the four elements of the model. In the future, through the development of this stream of research, we can expect the constructs to be refined and solidified.

The findings here show very strong support for Hypotheses 1, 2 and 3. This is noteworthy, as previous iterations of this research yielded similar results using different operationalizations. In addition, similar testing the model using the banking industry confirmed the positive relationship between demand conditions and firm performance and the positive relationship between factor conditions and firm performance. These results reflect positively on this specification of the model. First, the data here support the assertion that strong, demanding consumers in a country will serve to increase that country's national competitiveness. In fact, this parameter estimate was more than twice as large as the next estimator, thus influencing performance to a large degree in both models. Certainly, there is anecdotal evidence from Japan, Germany and the United States to substantiate this. It is customary for most, if not all, high-end automotive producers choose to introduce their products in these markets to satisfy the most demanding customers in the world. Additionally, there is automotive industry research to support these results (Boudette, 2003). This finding bolsters this claim and provides evidence for firms that may relocate their businesses to become more competitive.

Secondly, factor conditions, as expected, do positively influence national competitiveness in the automotive industry. This estimator had the smallest impact on performance for both the revenues and the assets model, perhaps because factor conditions are indirectly related to firm success. This finding gives support to the argument that governments can and do play a large role in their own industrial global competitiveness. Historically, this result has been true, as many developed nations committed to an advanced business infrastructure and an emphasis on innovation through the development of proprietary intellectual property have been those with leaders in the field of automotives. Advanced factors are the combined result of private business innovations and concerted governmental efforts to improve and maintain underlying infrastructures. This is significant and should be considered by nations trying to improve or maintain their global rankings in business.

The issue of strong related and supporting industries advancing and strengthening primary industries has historically been true for the automotive industry. Linked businesses in South Korean chaebols and Japanese keiretsu have been a large factor in the success of these firms globally. The United States' emphasis on building the tire, steel, glass, rubber and chemical industries over the 20th century has also supported this finding. It points to the need for companies and governments to encourage and support ancillary industries to enhance global competitiveness. In both models, this parameter estimate was the second largest and the most statistically significant. This underscores the importance of related and supporting industries in the context of global competitiveness. Without a network, firms can not hope to be worldwide leaders.

Finally, the lack of evidence for Hypothesis 4 is noteworthy. No relationship between firm strategy, structure, rivalry and national competitiveness was indicated by the data. This finding may indicate that high levels of domestic rivalry do not necessarily make global firms more competitive. Perhaps this may be due to the fact that those firms operating in a highly competitive atmosphere spend most of their efforts fending off domestic competitors and therefore are unable to expend efforts on globalization. Or, it may be that global competitors do not waste time in highly-rivalrous domestic industries. Thus there may be a separation of global and domestic competitors. It might also mean that domestic industry structure does not play a large role in firm competitiveness, possibly due to the fact that industries today are largely global, and so domestic industry structure is irrelevant. An alternate explanation here is the possibility that strategy, structure and rivalry is not represented accurately. This finding points to the need to reconceptualize this construct to better incorporate firm strategy into the model. It is highly unlikely that firm strategy and rivalry are unrelated to firm performance. Thus, capturing the strategic actions of a firm would most likely lead to a revealing link between strategy and performance. This possibility should be investigated in future research.

Porter's consideration of the impacts that governmental forces and luck have on global competitiveness should be addressed. Governments no doubt play a substantial role in a firm's rise to worldwide competitiveness. While not an explicit part of the model, governments are indirectly included in all four tips of the diamond through their administration of regulation, legal proceedings and infrastructures. They may be more clearly defined in future editions of the model.

Luck is essentially the wild card factor that would be difficult to identify and operationalize. It represents the stochastic element within the model. Porter obviously saw how the impacts of timing, location and chance could be highly beneficial to firms with this recognition. It may be possible to model this factor and to include it in future work.

In summary, Porter's model depicting the Competitive Advantage of Nations is illustrated quite well by the global automotive industry. Certain aspects of the data do not accord to the model precisely, but the model does identify the key elements of national competitive advantage which lead to global competitiveness among leading automotive manufacturers around the world. Further evidence from other industries will lend additional verification of the model as one of the building blocks of international business research. Such additional research will lead to new theoretical models describing the dynamic international business climate of today. The global winners in the future will be those that learn from those that have come before them and such models will help facilitate that process.

The author wishes to thank several anonymous reviewers for their insightful comments and suggestions which improved the paper significantly.

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Sally Sledge (ssledge@cnu.edu) is Assistant Professor of Management, Department of Management & Economics, Christopher Newport University, 1 University Place, Newport News, VA 23606. Sally Sledge is currently an Assistant Professor of Management at Christopher Newport University. She received her Ph.D. in International Business and Strategic Management from Old Dominion University. Her research interests are in the fields of services, diversification, multinationals and global competitiveness.

TABLE 1

Pearson Correlation Coefficients

   Variable            Mean  s.d.   1.   2.    3.    4.   5.

1. Profits-Revenues    8.27  4.01
2. Profits-Assets      7.76  3.91  .90
3. Factor Conditions   1.57  1.12  .45  .40
4. Demand Conditions    .35   .18  .13  .23  -.01
5. Related Industries  4.87  1.29  .59  .48   .25  -.27
6. Structure/Rivalry   2.03   .98  .60  .51   .36  -.03  .34

N = 50

TABLE 2

Profits as a Percentage of Revenues

Independent Variables

Intercept             11.41(1.26) **
Demand Conditions              2.82(1.20) *
Factor Conditions              1.01(.39) *
Related Industries             1.35(.41) **
Structure/Rivalry     .82(.61)

[R.sup.2]                      .55
Adjusted [R.sup.2]             .51
F                               13.90 **

Coefficient (Standard Error)
+ p < .10
* p < .05
** p < .01

TABLE 3

Profits as a Percentage of Assets

Independent Variables

Intercept             9.93(1.50) **
Demand Conditions              3.89(1.32) *
Factor Conditions              .87(.42)+
Related Industries             1.33(.52) **
Structure/Rivalry     .46(.69)

[R.sup.2]                      .47
Adjusted [R.sup.2]             .42
F                              9.91 **

Coefficient (Standard Error)
+ p < .10
* p < .05
** p < .01