The geographical sources of competitiveness of multinational enterprises: an econometric analysis

International Business Review 7 (1998) 115–133

The geographical sources of competitiveness of multinational enterprises: an econometric analysis John H. Dunninga,*, Sarianna M. Lundanb b

a Rutgers University, Graduate School of Management, 81 New Street, Newark, NJ 07102, USA The University of Reading, Department of Economics, Graduate Centre of International Business, PO Box 218, Reading RG6 6AA, UK

Abstract This paper presents empirical evidence to assess the extent to which multinational corporations are deriving an increasing share of their competitive advantages from abroad. The evidence is analyzed in the context of the Porter diamond, incorporating both firm, industry and national level variables. A particularly interesting aspect of the results is the extent to which multinationals are increasingly sourcing abroad assets which are knowledge-intensive and appear similar, rather than complementary to their existing activities.  1998 Elsevier Science Ltd. All rights reserved. Keywords: Multinational firms; Foreign direct investment; Competitiveness; Foreign subsidiaries

1. Introduction The purpose of this paper is to assess the extent to which large multinational enterprises (MNEs) perceive they obtain competitive advantages from their foreign operations in relation to a number of explanatory variables. The data for this paper were obtained from a survey of the world’s 500 largest industrial corporations. This survey concluded at the end of 1995, and a preliminary analysis and descriptive statistics were published in Transnational Corporations (Dunning, 1996). The

* Corresponding author. 0969-5931/98/$19.00  1998 Elsevier Science Ltd. All rights reserved. PII: S 0 9 6 9 - 5 9 3 1 ( 9 8 ) 0 0 0 0 1 - 8

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sample obtained in the survey included 147 firms which controlled 40% of the global sales, and nearly two-fifths of the foreign direct investment stake in all primary and secondary activity of the top 500 firms in 1994. The sample consists of a total of 147 responses, which come from 133 different firms. For those firms that provided multiple responses, the statistics on size and degree of multinationality, as well as industry classification, follow those of the largest corporate unit. Outside of these classification variables, all of the multiple responses received were unique, and were treated as individual responses in the analyses. Of the 147 responses to the survey, 111 came from firms that are ranked in the Fortune 500 (based on 1993 sales). The remaining firms were contacted to improve the industrial and/or geographical coverage of the data, and they are all among the largest multinationals of their respective home countries. The survey instrument consisted of a three-page questionnaire which asked respondents to evaluate different types of competitive assets on a seven-point scale on the extent to which they were sourced primarily from domestic or foreign sources. A rank of one would indicate that the capabilities or assets were sourced entirely from the home location, while a rank of seven would indicate that such advantages were derived entirely from foreign sources. A rank of four would indicate, that in the firm’s estimation, the competitive advantage in question stemmed equally from domestic and foreign sources. The field survey was undertaken to gain the opinions of senior business executives about the geographical origin of the kind of firm specific competitive advantages or core competencies identified by the literature by economists, such as Dunning (1993a, Ch. 4), and business strategists, such as Porter (1990) and Peteraf (1993). There has been much debate, in recent years, over the extent to which the competitive advantages of firms stem from the location-bound characteristics of their home countries—some of which may be internalized by the firms themselves; or whether, via foreign direct investment (fdi) and cross-border strategic alliances, they have become increasingly directed to acquiring created assets, and particularly so in the case of firms which have most increased their degree of multinationality and which, in Michael Porter’s terminology have established ‘multiple home bases’ (Dunning, 1997). The increase in multinationality is documented, for example, in the various editions of the World Investment Report (UNCTAD/DTCI, 1992–1993, 1993, 1994, 1995, 1996; UNCTC, 1991, 1992), whereas the most recent US data (Mataloni, 1997) show that, in 1995, the foreign (nonbank) affiliate sales of nonbank US MNEs were 50.5% of their parent sales, which compares with a 1983 figure of 37.3%. The aim of this strategic asset-acquiring fdi is to gain access to assets which protect or advance the acquiring firm’s competitive advantages and/or reduce the competitive advantages of its rivals. In the language of the eclectic paradigm, firms engage in fdi not to exploit existing ownership (O) advantages, but to acquire such advantages, which, when deployed with their existing O advantages, help sustain or further their global competitive positions. Six hypotheses are set up in this paper to explore the geographic sources of competitive advantage. The first hypothesis (H1) links the size of the firm with the importance it assigns to foreign sourcing. The null hypothesis in this case would be that large firms are the least dependent on foreign

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sourcing owing to the cumulative impact of their already established global networks. Alternatively, it could be argued that large MNEs are likely to be more dependent on foreign sourcing if additional foreign investment creates more opportunities for tapping into the assets of foreign firms and countries. The second hypothesis (H2) argues that firms that have a high degree of multinationality are more dependent on foreign sourcing than their relatively more domestic counterparts, to the extent that size and multinationality correlate with each other. The alternative hypothesis under H1 and the null hypothesis under H2 will point in the same direction. However, to the extent that size and multinationality are not significantly correlated with each other, as is, indeed, the case in our sample, H1 and H2 may be expected to have different signs. Hypothesis three (H3) suggests that firms in high technology sectors are more likely to augment their domestic assets from foreign sources than firms in medium or low technology sectors. Hypotheses four and five (H4 and H5) relate the country level variables of the size and stage of development of the MNE to its dependence on foreign sourcing. H4 suggests that firms from larger home countries will be less dependent on foreign sourcing, and similarly, H5 suggests that firms from more developed home countries will be less dependent on foreign sourcing. Finally, hypothesis six (H6) suggests that, at the industry level, more highly R&D intensive sectors are likely to be less dependent on created assets, but more dependent on resource assets, found abroad.

2. The double diamond of competitive advantage As the analytical basis for our study, we shall use the concept of the ‘double’ diamond of competitive advantage, as developed by Alan Rugman and his colleagues over the past three or four years, resulting in the special issue of Management International Review edited by Alan Rugman (1993), and Rugman et al. (1995). Essentially, the Rugman approach extends the concept of Porter’s ‘single’ diamond of competitive advantage first set out in Porter (1990)—which argues that a firm’s competitive advantages are essentially a function of its domestic economic environment—whether or not the attributes of this environment are owned by the firm or available to it. Rugman and others, including one of the authors of this paper, Dunning (1993b), have argued that the deepening structural integration of the world economy, and the burgeoning of alliance capitalism (Dunning, 1995) are widening the geographical scope for creating or augmenting competitive advantages, and that any attempt to identify the geographical sources of such advantages must embrace the diamonds of other countries, and particularly those with which the home country firms have the most dealings, by way of trade, foreign direct investment and nonequity cooperative ventures. The present study aims to offer some new evidence to that recorded in the initial analysis of the survey findings along with a number of country case studies, including those of Australia, Canada, Denmark, Korea, Mexico and New Zealand, which, in the main, have confirmed the Rugman/Dunning hypothesis. The present contribution

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can be said to contribute to this body of literature in five important ways. First, it obtains the information directly from firms themselves. Second, it attempts to classify the competitive advantages identified in the literature into a number of groups, and, in doing so, uses the four components of Porter’s diamond as the framework. Third, it aims to relate its findings to a number of critical firm-specific variables, e.g. size of firm, degree of multinationality, industry-level technological intensity, and country specific variables e.g. the size and degree of development of the home economy. Fifth, it ranks the significance of the three main modes of involvement of the sample firms in acquiring competitive advantages from a foreign location.

3. Variable definitions The independent variables in this study utilize firm, industry, and country specific measures (see Exhibit 1). The first of the three firm-level measures is the size of the firm, which is measured by global sales. Although all of our sample firms were large or very large companies, their global sales in 1993/4 varied from $0.17 billion to $138.2 billion. The first of the two measures of multinationality was obtained by averaging out (for each firm) the percentage of their global assets and global employment accounted for by their foreign affiliates, or one of these when data on either employment or assets were given. The second measure of multinationality, viz. the degree of foreign R&D activity, was similarly based on self-reported values by the companies, and if missing, was replaced by the first measure of multinationality in the full sample. The industry classification based on technological intensity utilized the classification provided by the National Science Foundation (1990), which was extended to all firms in the sample. The GNP and GNP per capita data for 1992 were obtained from the World Development Report (World Bank, 1994). The dependent variables are all firm specific, and are postulated to link the four facets of Porter’s diamond, namely the availability, price and quality of natural and created assets, the level and characteristics of consumer demand, the structure and intensity of inter-firm rivalry, and the extent and pattern of linkages with related firms and institutions in adding to a firm’s core competencies. Essentially, the Porter hypothesis is that high technology firms will use their home bases as platforms for creating their core assets (e.g. innovatory capacity), and that medium or low technology firms (which, in general, are more likely to be natural resource intensive, or influenced by the characteristics of consumer demand) will tend to assign lower rankings to these and related variables. Such firms will also tend to be more internationally oriented; hence, their predicted higher ranking of cross-border, vis a` vis domestic, competitiveness-enhancing activities. The contrasting hypothesis, set out by Rugman, Dunning and other scholars researching into the behavior of MNEs, is that, as firms become more multinational and globally integrated in their value added activities, they are likely to derive an increasing proportion of their core assets from outside their national boundaries and, indeed, may deliberately seek out foreign assets which they perceive will help augment or complement their home based competencies.

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3.1. Exhibit 1 3.1.1. Dependent variables (variable names in parentheses) These variables measure the extent to which the sourcing of specific advantages is dependent on foreign resources and capabilities. Facets of Porter’s diamond of competitive advantage: 1. Natural assets (Natural) 2. Created assets (a) Technology based (Createch) (b) Organizational (Creamgr) 3. Consumer demand (Consumer) 4. Inter-firm rivalry (Rivalry) Additional factors: 5. Linkages (Linkages) 6. Modality (Modality) 3.1.2. Independent variables Firm level: 1. Size of firm (SALES) 2. Degree of multinationality (a) Average share of foreign capital and employment (MULTINAT) (b) Share of foreign R&D (RD) Industry level: 3. R&D intensity (RDSHARE) Country level: 4. Size of home country of the MNE (GNP) 5. Stage of development of home country of MNE (GNPCAP)

4. The regression models In the first stage of the analysis, each explanatory variable was regressed with each of the dependent variables to assess its individual explanatory power. In the second stage, stepwise regressions were run with the full set of explanatory variables to arrive at the best model in terms of its adjusted R2. The residuals from these models were then analyzed to identify any outlying observations. Up to five observations were removed from the models, and no systematic pattern could be detected in the deleted observations.

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The stepwise regressions were then re-run with the reduced data set, and these produced the first group of models that are set out in Tables 6 and 7 in this paper. Since the entry criteria for the stepwise procedure was set at 0.85, independent regressions were run to see whether any of the independent variables with significance levels less than 0.95 could be dropped from the model. No variables were eliminated as a result. The models suggested by the stepwise analysis were then re-run with various diagnostic options to assess the appropriateness of the model. A simple correlation analysis of all the independent variables revealed one cause for concern, which was the relatively high coefficient of correlation (r = + 0.67) between variables MULTINAT and RD, which were the two firm level measurements of multinationality used in the study. To assess whether this correlation contributed to multicollinearity in the sample, variance inflation factors (VIF) were calculated for each variable in each model. With the exception of MULTINAT and RD, all of the values for VIF are close to 1, indicating a high degree of independence between the variables. The VIF values for MULTINAT and RD, when the two appear in the same model, lie in the range of 2–2.5, indicating that the standard errors for these parameter estimates are about twice as large as they would be without any multicollinearity in the sample. Although not negligible, relying on the interpretation of VIF values provided in Neter et al. (1990), it was felt that these levels of VIF did not require the elimination of either variable from the models. To assess goodness-of-fit, residuals were plotted against each of the regressors to assess, on one hand, whether the residuals followed any obvious pattern suggesting a departure from the linear model and, on the other, the homogeneity of error variance (heteroscedasticity). Overall, the residual plots did not indicate a departure from the linear model, however, and as is the case with many kinds of economic data, two of the variables, namely SALES and GNP, exhibited residuals with decreasing variances, which produced the typical cone-shaped plot of the residuals against the independent variable. Since both of these variables display a wide range of values, with considerable gaps within that range, a natural logarithmic (ln) transformation of the data was called for. While this is not a typical remedy for decreasing error variance, it did improve the fit of most models, and, in most cases, alleviated the problem of nonconstancy of variance. The results of the ln-transformation on the original models are set out in Tables 8 and 9. In addition to analyzing the residuals, the normality of the error terms was also assessed by means of normal probability plots, as well as the Shapiro–Wilk test for a normal distribution. None of the errors exhibited any significant deviations from normality, and consequently, remedial measures to address both non-normality and non-constancy of error variances, such as a Box– Cox transformation of the response variable, were not undertaken. 5. Hypothesis testing In this section, five tables are presented which provide data to assess the six hypotheses set out in Exhibit 2. Each of these tables provides parameter estimates

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for the regression of each independent variable against each dependent variable, as well as the contribution of each independent variable to the final models chosen for each of the six dependent variables. As regards the individual contribution of each regressor, the variables MULTINAT and RD have the highest predictive power on their own. All of the individual regressions were run with the largest available number of observations with no influential observations removed. However, for some variables, namely SALES and GNPCAP, a reasonable level of predictive power could only be obtained by looking at their impact within a threeband classification of multinationality, in which firms with a level of multinationality of 60% or higher were placed in the high multinationality group, those between 30% and 60% to the medium multinationality group, and the remaining firms to the low multinationality group. In addition to these tables, four sets of regressions are presented in Tables 6–9, which represent the best regression models found for each dependent variable with a reduced data set in the first table, and a full set in the second table. Additionally, Tables 8 and 9 present results following the ln-transformation of two of the independent variables. The reduced set utilizes only genuine observations for the variable RD, i.e. the actual share of foreign R&D as reported by the respondents on the questionnaire. As there are only 102 observations with valid values for RD, the use of the stepwise procedure effectively reduces the data set to the lowest number of observations available for any of the independent variables, whether or not these are included in the final model. However, to make use of as many observations as possible, the value for MULTINAT could be substituted for RD as both of these variables measure the degree of multinationality of the corporation. The limiting factor in this instance becomes the number of observations for the variable MULTINAT, which has 133 observations, thus increasing the data set by almost a third. Although one could usefully restrict the analysis to the largest available data set, the reduced set is quite interesting, as the overall results seem to indicate that there is something of a qualitative difference between the 102 observations in the reduced set and the full data set. Comparing Tables 6 and 7 and Tables 8 and 9 with each other, it is quite apparent that in both instances the explanatory power of the models is higher in nearly all cases in the reduced set. Two possible explanations readily come to mind. The first explanation is that contrary to one’s expectations, MULTINAT is not a substitute for the variable RD, and the poorer fit of the models with the substituted observations is due to the inferior quality of the 31 added observations. While, to some extent, this may still be the case, the fact that the variable RD appears in both sets, and is often not significant in either, would seem to negate this proposition. The other explanation is that, instead of (or perhaps in addition to) the RD variable itself being of reduced quality, other responses provided by respondents who identified their share of foreign RD are of higher quality than the remaining observations. While the exact cause is impossible to pinpoint, it is worthwhile to consider both sets in the analysis to obtain a fuller understanding of the underlying data.

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5.1. Exhibit 2: hypotheses and expected signs

H1: Larger firms are less likely to be dependent on foreign sourcing ( − ) H2: More multinational firms are more likely to be dependent on foreign sourcing (+) H3: Firms in high technology sectors are more likely to be dependent on foreign sourcing (+) H4: Firms from larger home countries are less likely to be dependent on foreign sourcing ( − ) H5: Firms from more developed countries are less likely to be dependent on foreign sourcing ( − ) H6a: Firms in high technology sectors are less likely to be dependent on created assets abroad ( − ) H6b: Firms in high technology sectors are more likely to be dependent on natural assets abroad (+) 5.2. H1: size of firm The first independent variable—which to some extent is correlated with the degree of multinationality—is the size of firm, although the (Pearson) correlation coefficient between size and multinationality is not significant (r = + 0.13, p ⬍ 0.18). While it is not obvious why size itself should be related to the geographical sourcing of competitive-enhancing assets, the literature (see, e.g. Dunning, 1993a, Ch. 6) does suggest that large firms are more likely to engage in fdi than smaller firms, and that multinationality itself helps a corporation to maintain, or increase, its share of global markets. At the same time, it might be hypothesized that medium size firms are likely to be more specialized in their portfolio of global assets and, hence, more reliant on foreign sources to enhance or complement this portfolio. However, in recent years, the evidence suggests that mega-firms are just as likely to engage in merger and acquisition (M&A) activities as are their smaller counterparts. Finally, it might be predicted that the portfolio of foreign competitive-enhancing assets sought by medium size firms relative to larger firms, may have more to do with gaining access to specialized resources, capabilities and markets, and/or establishing linkages with local foreign firms or research consortia, although they may be expected to engage in less competition with foreign owned firms than their larger counterparts. (Also remembering that all the firms in our sample are large, relative to the universe of firms, but that some are much larger than others.) The results for the first hypothesis (see Table 1) have the expected sign, and, with the exception of the group of low multinationality firms, all of the coefficients are negative, confirming that larger firms tend to be less dependent on foreign sourcing. These results are also naturally consistent with the previous results from the same survey (Dunning, 1996), which showed that size of firm is only of marginal importance in affecting the sourcing of most categories of competitive advantage, although,

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Table 1 Results for Hypothesis 1 Equation number

Dependent variable

N

1-1 1-2 1-3

Rivalryb Modalityc Linkages

46 41 92

1-4

Creamgr

90

Parameter estimate for SALES/LSALES

Prob > |t|

Other independent variables in model

−0.018400 0.015900 −0.201166

0.0648 0.0547 0.0091

− 0.197973

0.0183

– – MULTINAT**, GNPCAP* MULTINAT**, RD, GNPCAP**, RDSHARE**

R2 (adjusted)

0.0737 0.0892 0.4460 0.4457

a

High multinationality. Medium multinationality. c Low multinationality. *0.01 level of significance. **0.05 level of significance. b

apart from access to innovatory capacity, the smaller sized firms tended to perceive access to foreign assets and markets as more important than their larger counterparts (although this was not the case for the third of four categories of size, the Very Large firms). 5.3. H2: degree of multinationality Perhaps the most frequently cited hypothesis about the propensity of firms to derive competitive advantages from their foreign operations is that the former will be positively related to the extent and the depth of the latter, relative to that of their domestic operations. In this part of our paper, we relate the two measures of multinationality earlier identified—viz. the average of the (i) percentage of global assets and employment employed outside the home country and (ii) the average percentage of global R&D expenditure undertaken outside the home country to the values placed on Porter’s four facets of competitive advantages. The results of the previous study (Dunning, 1996) showed quite clearly that a greater degree of multinationality of firms is likely to be associated with the perception that an increasing proportion of global competitive advantages are derived from foreign sources. It would also appear from these results, that for created assets, the largest rise in the significance of foreign operations for global competitiveness occurs when the average degree of multinationality (of assets and employment) is 30% or above. However, for innovatory capacity, it is only where the degree of multinationality exceeds 60%, that the (average) ranking exceeds 3.00 (on a seven point scale); while, as far as the influence of consumer demand and inter-firm rivalry is concerned, it approaches or exceeds 5.00. The distinction between the sources of

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competitive advantages perceived to be derived from fdi and cross-border strategic alliances is particularly noticeable in the case of firms with low and high degrees of multinationality. Additionally, bi-variate correlation coefficients between the two measures of multinationality and six indices of competitive advantage suggest, that for the first measure of multinationality, the coefficients are positive and significant at a 99% level or above, and that the most likely benefits of increased multinationality are likely to arise from an access to foreign organizational capacity and managerial expertise, and the linkages forged with foreign firms. The results for the second measure of multinationality, the foreign R&D ratio, were virtually identical to those obtained for the first measure, although in the case of sourcing of technological assets the R&D ratio was found to have higher explanatory power (Dunning & Wymbs, 1997). Except for the correlation with natural assets, all of the coefficients were positive and significant at the 99% level or above. In this analysis, all of the coefficients for H2 had the expected sign, indicating that the more multinational a company is, the more likely it is to be more dependent on foreign sources of technology and other assets (see Table 2). The only exception occurs in the case of the response variable Creamgr, which measures the extent of sourcing of created organizational or managerial assets in foreign locations, where the degree of multinationality of the corporation is negatively related to its propensity to source such assets abroad. Table 2 provides similar results for using R&D as a measure of multinationality. The relationship is generally positive, except for the sourcing of natural resource-based assets (including low-skilled labor), which is negatively related to the degree of foreign R&D of the corporation. 5.4. H3: technological intensity The results presented in Table 3 confirm the hypothesis that firms in high technology sectors are more dependent on the foreign sourcing of competitive advantages and particularly of ‘created’ technological and organizational assets. 5.5. H4: size of home country economy H4 postulated that firms from larger home countries would be less dependent on foreign sourcing. As is indicated in Table 4, the signs of all of the parameter estimates are negative as expected, although the size of the home economy is significant only for the benefits derived from inter-firm rivalry abroad, and even then only for the sub-set of firms which are highly multinational. 5.6. H5: stage of home country development Similar to H4, H5 suggested that firms from more developed home economies, as measured by GNP per capita, would be less dependent on foreign sourcing. This is not confirmed in Table 5 for any other dependent variables except for the sourcing of natural assets, which is negatively related to the level of development of the

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125

Table 2 Results for Hypothesis 2 Equation number

Dependent variable

N

2a-1 2a-2 2a-3 2a-4 2a-5 2a-6 2a-7

Natural Createch Creamgr Consumer Rivalry Linkages Createch

122 129 131 128 131 128 91

0.018856 0.017470 0.023010 0.019227 0.019411 0.023734 0.015188

0.0001 0.0001 0.0001 0.0001 0.0008 0.0001 0.0039

2a-8

Creamgr

90

− 0.019083

0.0020

2a-9 2a-10

Rivalry Linkages

95 92

0.016110 0.028439

0.0189 0.0001

2a-11

Natural

89

0.037849

0.0001

98 100 999 100 98 91

Parameter estimate for MULTINAT

Parameter estimate for RD 0.019720 0.023987 0.024265 0.022202 0.024698 0.010990

Prob > |t|

2b-1 2b-2 2b-3 2b-4 2b-5 2b-6

Createch Creamgr Consumer Rivalry Linkages Createch

0.0001 0.0001 0.0001 0.0031 0.0001 0.0641

2b-7

Creamgr

90

0.012559

0.0510

2b-8

Natural

89

− 0.014859

0.0754

Other independent variables in model

R2 (adjusted)

– – – – – – RD RDSHARE* LSALES*, RD, GNPCAP**, RDSHARE** LGNP LSALES**, GNPCAP* RD, GNPCAP**

0.1065 0.1333 0.1895 0.1147 0.0753 0.2303 0.2952

– – – – – MULTINAT** RDSHARE* LSALES* MULTINAT** GNPCAP ** RDSHARE ** MULTINAT** GNPCAP **

0.1401 0.1547 0.1556 0.0756 0.2063 0.2952

0.4457

0.0871 0.4460 0.2361

0.4457

0.2361

MNE’s home economy. For the purposes of the present analysis, we did not consider the influence of particular home or host countries on the results, but descriptive statistics to that effect are provided in Dunning (1996). 5.7. H6a: technology and created assets In the previous survey, the business executives of the low technology firms claimed to obtain a higher proportion of their created assets from foreign sources than did those of medium or high technology firms, and particularly so in the case

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Table 3 Results for Hypotheses 3, 6a and 6b Equation number

Dependent variable

3-1 3-2 3-3

Other R2 (adjusted) independent variables in model

N

Parameter estimate for RDSHARE

Prob > |t|

Naturalc Createch

38 91

0.011678 0.060913

0.0503 0.0201

Creamgr

90

0.086525

0.0035

N

Parameter estimate for GNP/LGNP

Prob > |t|

Other independent variables in model

R2(adjusted)

145 95 42

− 0.000198 − 0.247938 − 0.000276

0.0017 0.0704 0.0288

– MULTINAT** GNPCAP

0.0600 0.0871 0.1714

– MULTINAT** RD LSALES* MULTINAT** RD GNPCAP**

0.0997 0.2952 0.4457

a

High multinationality. Medium multinationality. c Low multinationality. b

Table 4 Results for Hypothesis 4 Equation number

Dependent variable

4-1 4-2 4-3

Rivalry Rivalry Rivalrya

a

High multinationality. Medium multinationality. c Low multinationality. b

of innovatory capacity. Inter alia, this finding tends to support the proposition that at least part of the competitive advantages of firms obtained from foreign sources is likely to be different from (and complementary to) those obtained from domestic sources. Nevertheless, the results also strongly suggested that, for each of the advantages identified, a multiple (or at least a dual) location of value added activities was perceived to yield positive gains. Consequently, it was hypothesized in H6a that companies in high technology sectors, as measured by the percentage of sales accounted for by R&D, would be less dependent on foreign sourced created assets. The results in Table 3 indicate that contrary to expectation, the sourcing of created assets, both technological and organizational, is positively related to the technological intensity of the industry. The result is, however, consistent with the pattern of strategic asset acquiring investment of MNEs over the past decade and a half, and of the tendency of firms in high technology sectors to engage in foreign R&D to augment, as well as to exploit, their

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Table 5 Results for Hypothesis 5 Equation number

Dependent variable

N

Parameter estimate for GNPCAP

Prob > |t|

5-1 5-2 5-3 5-4 5-5

Creamgr Linkages Creamgrc Modalityb Natural

145 140 41 45 89

0.000048972 0.000039352 0.000047400 0.000061560 − 0.000058424

0.0010 0.0045 0.0580 0.0340 0.0072

5-6

Creamgr

90

0.000048144

0.0040

5-7

Linkages

92

0.000028857

0.0462

Other R2 (adjusted) independent variables in model – – – – MULTINAT** RD LSALES* MULTINAT** RD RDSHARE** LSALES** MULTINAT **

0.0667 0.0498 0.0869 0.0981 0.2361 0.4457

0.4460

a

High multinationality. Medium multinationality. c Low multinationality. b

home based competencies, as set out in Dunning (1995), Kuemmerle (1996) and Almeida (1996). 5.8. H6b: technology and natural assets As might be reasonably predicted, firms from high wage economies, e.g. the US and smaller European countries, notably Sweden and Switzerland, rank the foreign sourcing of unskilled labor relatively more highly than those from low wage and, particularly, developing countries. Likewise, and consistent with the principle of comparative advantage, is the above average reliance of Japanese firms on foreign natural resources, and the below average reliance of ‘other’ developed country firms on such resources, as in our sample survey, these were all from resource rich countries, viz. Canada, Australia and New Zealand. However, as regards the sourcing of natural assets, results for testing H6b (see Table 3) could only be obtained for a subset of the firms identified as low multinationality, and consequently the expected (and observed) positive relationship between technological intensity and sourcing of natural assets is in fact particular to firms with relatively low levels of multinationality.

6. Best overall models Overall, the results for each dependent variable are presented in Tables 6–9 for the reduced and full sets, and with and without a logarithmic transformation on two

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Table 6 Reduced models Equation number

N

Dependent variable Independent variables in model

6-1

91

Natural

6-2

93

Createch

6-3

92

Creamgr

6-4 6-5

94 97

Consumer Rivalry

6-6

94

Linkages

6-7

92

Modality

RD, MULTINAT**, GNP, GNPCAP* SALES*, MULTINAT**, RD, RDSHARE* RD,RDSHARE**, SALES*, MULTINAT**, GNPCAP** MULTINAT** MULTINAT**, GNP** MULTINAT**, GNPCAP, SALES MULTINAT

R2 (adjusted)

0.2436

0.3327

0.4586

0.2832 0.1012 0.4465 0.0140

Table 7 Full models Equation number

N

Dependent variable Independent variables in model

7-1

121

Natural

7-2

126

Createch

7-3

126

Creamgr

7-4 7-5

126 131

Consumer Rivalry

7-6 7-7

125 137

Linkages Modality

MULTINAT**, GNP*, GNPCAP* MULTINAT**, RD, RDSHARE RD,RDSHARE*, SALES*, MULTINAT**, GNPCAP* MULTINAT** MULTINAT**, GNP** MULTINAT** GNPCAP*, RDSHARE**

R2 (adjusted)

0.1795 0.2182 0.3114

0.1665 0.1179 0.2990 0.0909

of the independent variables. Firstly, the dominance of multinationality in these models is apparent. This variable is included in all models, and its explanatory power is very seldom below the 99% level of significance. In addition to multinationality, whose coefficient is positive, the level of development of the MNEs home economy is negatively linked to the sourcing of natural assets. For created assets, multi-

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129

Table 8 Reduced models with ln-transformation Equation number

N

Dependent variable Independent variables in model

8-1

80

Natural

8-2

82

Createch

8-3

81

Creamgr

8-4 8-5 8-6

83 86 83

Consumer Rivalry Linkages

8-7

82

Modality

MULTINAT**, LGNP, GNPCAP** MULTINAT**, RD*, RDSHARE* LSALES*, MULTINAT*, RD**, GNPCAP**, RDSHARE** MULTINAT** RD*, LGNP LSALES*, MULTINAT**, GNPCAP*, RD –

R2 (adjusted)

0.2567 0.3190 0.4854

0.3037 0.0909 0.4756

0.0000

Table 9 Full models with ln-transformation Equation number

N

Dependent variable Independent variables in model

9-1

89

Natural

9-2

91

Createch

9-3

90

Creamgr

9-4 9-5

92 95

Consumer Rivalry

9-6

92

Linkages

9-7

90

Modality

MULTINAT**, RD, GNPCAP** MULTINAT**, RD, RDSHARE* LSALES*, MULTINAT**, RD, GNPCAP**, RDSHARE** MULTINAT** MULTINAT**, LGNP LSALES**, MULTINAT**, GNPCAP* –

R2 (adjusted)

0.2361 0.2952 0.4457

0.2763 0.0871 0.4460

0.0000

nationality and the technological intensity of the industry are predictably positively linked to the sourcing of technological assets. However, in the case of managerial assets, the relationship for multinationality and size of firm is negative, while it is positive for the share of foreign R&D of the corporation, its technological intensity and the level of development of its home economy. For the influence of foreign consumers, as well as inter-firm rivalry, multinational-

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ity is the only significant (positive) determinant. For linkages with foreign firms, the size of firm has a negative relationship, while multinationality and degree of development of the home economy have a positive relationship. Table 10 presents additional evidence to assess the relative importance of the three modes of foreign sourcing, namely (a) foreign direct investment, (b) non-equity cooperative agreements, e.g. strategic alliances, management contracts, licensing and franchising agreements, etc., and (c) arm’s length transactions (in both intermediate and final goods and services). The hypothesis here is that ‘deeper’ forms of crossborder structural integration, e.g. (a) and (b), are more likely to result in an addition of competitive advantages to the home company (and country) than ‘shallower’ forms of transactions, e.g. arm’s length trade. It might also be postulated that firms are more likely to wish to internalize the cross border market for their assets (or the rights to these assets) in sectors which are technology intensive, than those which are not. A related proposition is that non-equity cooperative ventures are likely to be ranked higher as a modality for competitive advantages in less technology intensive sectors. Because of their needs to integrate their global operations, it might be expected that the most multinational of firms may be expected to rank fdi relatively higher than firms whose foreign operations are less significant to their overall prosperity. The results from the first analysis (Dunning, 1996) indicated that while the low and medium technology firms behaved as expected, in other words, fdi was perceived to offer the most advantages while arm’s length transactions would offer the least, and that arm’s length trade would be relatively more important to low technology firms and fdi to medium technology firms, the high technology firms actually derived more advantages from cooperative alliances than from fdi. Also somewhat contrary to the initial expectations, the medium–low and medium–high multinationalization firms actually derived more advantages from all three modalities of foreign involvement than did firms in the high multinationalization group. More in line with expectations, the gains from fdi accrued predominantly to the largest firms, whereas the benefits from cooperative alliances or trade did not exhibit any obvious pattern. In summary, although fdi was found to be the preferred route by which the domestic and foreign diamonds of competitive advantage are linked, the relative significance Table 10 Results for variable modality Equation number

N

Dependent variable

Independent variables in model RDSHARE**, GNPCAP* SALES**, MULTINAT** MULTINAT**, RDSHARE** GNP

10-1

137

Modality

10-2

127

FDI

10-3

127

10-4

140

Non-equity alliances, licensing Trade

R2 (adjusted)

0.0909 0.1127 0.1790 .0028

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of this route is greatest in the case of (large) firms which are (a) medium to high technology and (b) moderately to highly multinational. In the present analysis, the very low levels of R2 observed for the Modality variable, which is a weighted average of the importance of the modalities of fdi, nonequity cooperation and trade, led to the partitioning of this variable into its component parts, the results of which are shown in Table 10. These results indicate that the use of fdi is positively (and significantly) related to the size and multinationality of the corporation, while the sourcing of foreign assets through non-equity alliances is significantly negatively related to the degree of multinationality of the firm, but positively related to its technological intensity.

7. Conclusions The initial findings of this study indicated that some of the world’s leading industrial corporations perceive they derive notable (and often increasing) benefits from their foreign based activities. This paper has sought to expand on these findings by linking firm, industry, and country level variables to the sourcing of the specific types of advantages identified in the Porter diamond. Overall, the signs of all three firm level variables were as expected, namely, that size is negatively related, while multinationality and technological intensity are positively related to foreign sourcing, although, as could be predicted, some of the results are specific to particular types of advantages. Of the country level variables, the negative relationship between foreign sourcing and the size of the home economy was confirmed, whereas the predicted negative relationship between the level of development of the home economy and foreign sourcing was not confirmed, possibly owing to the under-representation of developing countries in the sample. The results indicate an increasing propensity for multinational corporations to source productive assets abroad, and this tendency seems to be true both of the more traditional complementary assets of natural resources and unskilled labor, and of the sourcing of foreign technological assets. It was also found, that while size and multinationality are related to the sourcing of foreign assets via fdi, technological intensity (along with multinationality) is positively linked to the use of cooperative alliances. Additionally, it was predicted that firms in high technology sectors would be less dependent on created assets, but more depended on natural assets found abroad. This was indeed true in the case of natural assets, but not for created assets, which raises a few interesting issues with implications for the theory of foreign investment as well as for policy. From a theoretical perspective, the observed patterns are consistent with the strategic asset seeking investment undertaken by MNEs since the 1980s, and these results build on the evidence already accumulated on the internationalization of the R&D activities of multinational firms. Specifically, the results are in line with studies such as Cantwell & Piscitello (1997), whose analysis of patent data in the United States found that foreign multinationals were deriving an increasing share of technological advantages from outside of their home country, and that of Pearce & Singh (1992),

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who discovered that for foreign affiliates located in the United Kingdom, their share of corporate R&D was on the increase. Additionally, these results also reflect the trend in recent years of foreign direct investment being undertaken through mergers and acquisitions rather than via greenfield investment, and the concomitant trend of growth in the numbers of non-equity cross border alliances, particularly in knowledge-intensive sectors (Hagedoorn, 1996). It seems likely that multinationals are increasingly sourcing assets that are similar to those they already possess abroad, and that their preferred, and quite possibly only, means of acquiring such assets is either through mergers and acquisitions or by way of strategic alliances. From a policy perspective, some important questions are raised concerning the extent to which the sourcing of such technologically-intensive assets is linked to the increasing importance of locationally specific assets, such as those found in Silicon Valley in California, Silicon Glen in Scotland or any number of industrial and science parks around the world. Such mini-regions benefit from externalities within a specialized industry which arise from the creation and maintenance of non-market institutions that facilitate information exchange and the creation of new knowledge. To the extent that such externalities are unique to the area, mobile capital will be attracted to such locations. It could even be argued that coordinated competitiveness policies in a given region, such as ‘strategic’ investment in education and infrastructure, afford similar externalities to a more diversified group of firms in the area. Consequently, whether in Singapore or Baden–Wu¨rttenberg, such a (policy-induced) environment can also offer a unique location-bound resource for a multinational firm. In both of these cases, the inward direct investment is attracted by a complex set of institutional features. If these features are genuinely locationally unique, i.e. nonimitable by other regions, the likelihood that the foreign investment becomes integrated into the underlying economy would be increased. Consequently, the kind of foreign investment identified in this study, which is increasingly directed at the sourcing of technologically-intensive assets abroad, is also potentially more likely to generate the kinds of spillovers which nations and regions seeking inbound investment hope to benefit from. In this admittedly best case scenario, the benefits of mobile capital, such as the transfer of technology and management practices, are coupled with a moderate level of stability, owing to the specificity of the locational assets sourced by MNEs. References Almeida, P. (1996). Knowledge sourcing by foreign multinationals: patent citation analysis in the U. S. semiconductor industry. Strategic Management Journal, 17 (Winter). Cantwell, J. A., & Piscitello, L. (1997). The emergence of corporate international networks for the accumulation of dispersed technological competencies. Discussion Papers in International Investment and Management No. 238, The University of Reading. Dunning, J. H. (1993a). Multinational enterprises and the global economy. Wokingham, Berkshire: Addison Wesley. Dunning, J. H. (1993). Internationalizing Porter’s diamond [special issue]. Management International Review, 33(2), 7–15.

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