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Renewing dynamic capabilities globally: an empirical study of the world's largest MNCs.

Abstract This study empirically tests whether MNCs leverage and exploit existing innovative capabilities to penetrate foreign markets by international diversification. MNCs then utilize these international diversification strategies to develop future new knowledge resources and skills being increasingly developed around the globe. The analyses used a sample of the largest MNCs' from Europe, Japan and North America representing nine industries. Results indicate that MNCs initial innovative capabilities are positively related to future international diversification. Our analyses showed that international diversification, in turn were positively and significantly related to the MNCs' future R&D intensity, number of patents, and Technological Impact Index.

Keywords Dynamic capabilities * International diversification * Innovation * Dynamic capability lifecycle * Multinational enterprises * Capability leveraging * Capability building * R&D intensity * Patents

1 Introduction

Firms need to constantly renew and build their capabilities in order to compete over the long haul. One of the distinguishing features of the multinational corporation (MNCs) activities since the late 1980s has been the increasing globalization of research and development (R&D) (Cantwell and Vertova 2004; Gerybadze 2004; Guellec and de la Potterie 2001). Global competition coupled with shorter product cycles and the growing innovative capabilities of non-US companies has led to the location of R&D activities away from MNCs' traditional home countries. With the intensifying global competition, MNCs must continuously innovate to attract and retain customers (Silverman 1999). Innovation allows MNCs to develop capabilities that give them a competitive advantage over their rivals by introducing unique products, goods, processes and services that are hard for competitors to imitate. R&D activities provide the fundamental inputs to MNCs' innovation, leading to patents and ultimately new products (Hagedoorn and Cloodt 2003).

Several scholars have studied the capability leveraging abilities of MNCs in exploiting their technological capabilities into international markets (Bartlett and Ghoshal 1989; Hitt et al. 1997; Kotabe et al. 2002; Morck and Yeung 1991). Other researchers have shown the MNCs build their technological capabilities based on their international diversification activities that then aid their search for new knowledge (Barkema and Vermeulen 1998; Hitt et al. 1997). Helfat and Peteraf (2003) stated that capability lifecycle is an appropriate vehicle in understanding how dynamic capabilities evolve over time. To the best of our knowledge no study has incorporated both capability leveraging and capability building over a longitudinal period. Does asset accumulation lead to increased international diversification that in turn leads to more asset accumulation? The purpose of this paper is to empirically explore whether MNCs first leverage their innovative capabilities through their international diversification activities then in turn utilize their presence in various international market to renew their innovative capabilities over a decade long period.

Teece (2007) states that the dynamic capability perspective is especially relevant to MNCs as they operate in business environments that display certain characteristics. First, MNCs are exposed to international trade and are fully exposed to opportunities and threats associated with rapid technological change. Second, technical change itself is systemic in that multiple inventions have to be combined to create products and services to address customer needs. Third, there exist well-developed global markets for the exchange of goods and services. Lastly, MNCs have to operate in an environment that has poorly developed markets for exchanging technological resources (Teece 2007). In this challenging environment MNCs have to develop resources and capabilities that can be exploited and built within the organization by interacting with the external environment.

This study departs from some earlier research by using data from 13 countries, representing the world's three largest innovation regions. Though the U.S. is one of the major creators of technology (Patel and Pavitt 1998), other countries' contributions have risen in recent years (Cantwell and Vertova 2004; Kotabe 1990). Supply of technology has become a worldwide phenomenon (Cantwell and Vertova 2004; Furman et al. 2002). We measure innovative capabilities in the form of research and development intensity (R&D intensity), patents, and technology impact index (quality of patents) (Ahuja and Katila 2004; Dutta et al. 2005; Hagedoorn and Cloodt 2003). Patents, R&D spending, and technological index are related but independent influences of innovative capabilities. Several researchers have used R&D spending, patents, and patents citations as measures of innovation in the management and economics literatures (Ahuja and Katila 2004; Furman et al. 2002; Helfat 1997; Guellec and de la Potterie 2001; Hagedoorn and Cloodt 2003; Markman et al. 2004; Nerkar and Roberts 2004). Hagedoorn and Cloodt (2003) state that if the overlap between R&D spending, patents, and new product development is high than one indicator is probably fine. However, if the overlap between these three indicators is low then it is appropriate to use separate indicators.

The next section of the paper presents the theory and hypotheses on the relationships between innovation and future international diversification and between international diversification and future MNCs' innovation. It also discusses the effect of international diversification on innovation, using the evolutionary (Nelson and Winter 1982) and dynamic capability perspective of the firm (Teece et al. 1997). Next, the sample and methodology used in this study are described. After the results are presented and explained, the paper discusses their implications for managerial practice and future research.

2 Theory and Hypotheses

MNCs succeed by exploiting a combination of firm-specific advantages that include ownership of intangible and tangible assets, location-specific advantages, and internalizing foreign markets by building a global organization to achieve control over their far flung operations (Dunning 1988). Intangible assets, such as having a well-honed capacity for innovation, can give MNCs competitive superiority over their rivals (Shan and Song 1997). The dynamic capability perspective suggests that superior performance accrues to those MNCs that continuously develop and exploit firm-specific capabilities over a period of time (Teece et al. 1997). Innovation is one of the most crucial capabilities that enable MNCs to sustain and exploit these intangible assets (Leonard-Barton 1995).

The dynamic capability perspective offers insights into resource creation (Teece et al. 1997). It proposes that firms develop the capacity to renew competences appropriate for their business environment. This capacity reflects a firm's business processes, market trends, and expansion paths (Teece et al. 1997). Eisenhardt and Martin (2000) define dynamic capabilities as ... 'the firm's processes that use resources--specifically the processes to integrate, reconfigure, gain and release resources--to match and even create market change'. More recently Teece (2007) provided an more in-depth explanation by stating that dynamic capabilities can be disaggregated into 'the capacity (a) to sense and shape opportunity and threats, (b) to seize opportunities, and (c) to maintain competitiveness through enhancing, combining, protecting, and when necessary reconfiguring the business enterprise's intangible and tangible assets'. Helfat et al. (2007) define dynamic capabilities as 'the capacity of an organization to purposefully create, extend, or modify the resource base' thereby accommodating the previous definitions by Teece et al. (1997) and Eisenhardt and Martin (2000). Helfat et al. (2007) emphasize that the action of dynamic capability is foremost upon the firm's resource base including both intangible and tangible assets and capabilities.

Dynamic capabilities thus are the organizational and strategic routines by which firms achieve new resource configurations as markets enlarge, collide, split, evolve, and die. While dynamic capabilities have been characterized as idiosyncratic in nature due to individual firm histories and path dependencies certain dynamic capabilities exhibit common features that are associated with processes across firms (Eisenhardt and Martin 2000). These include things such as making alliances, product development and knowledge brokering. Commonalities across firms for effective dynamic capabilities have two main implications (Eisenhardt and Martin 2000). First, they imply equifinality. Second, commonalities imply that these routines are more substitutable and fungible across different contexts. Recently, Ellonen et al. (2011) demonstrated the role of dynamic capabilities in developing operational innovation-related capabilities in the publishing industry.

Helfat and Winter (2011) point out the distinction between operational and dynamic capabilities. They state that ordinary capabilities are those that enable a firm to make a living in the present and instrumental in maintaining the status quo. Dynamic capabilities, on the other hand, enable a firm to alter how it currently makes a living that so that it can sustain its competitive advantage in the future. Helfat and Winter explicitly state firms can use dynamic capabilities by altering and modifying existing operational capabilities. Essentially the main difference between ordinary and dynamic capabilities is change. Thus, existing technological resources can be leveraged in international markets to increase profitability by achieving economies of scale, rationalizing production, and amortizing investments over broader market areas and obtaining higher organizational learning (Bartlett and Ghoshal 1989; Hitt et al. 1997). MNCs, in turn, search for new knowledge due to their presence in these international diversification efforts. Both international and product diversification strategies could increase the heterogeneity of MNCs' resources, especially their knowledge stocks that can profoundly affect companies' future investments in and gains from innovation. Frost (2001) exhorted researchers to explore the dynamic capability accumulation of MNCs in the context of corporate diversification. The extent to which international diversification strategies influence MNCs' future innovation remains an empirical question.

Evolutionary theory also suggests that firms build and accumulate capabilities gradually over time. A firm does not only accumulate knowledge but it also engages in a continuous search and selection process to upgrade its technological and organizational knowledge that could improve its performance (Nelson and Winter 1982). Resources and capability are essential to rent generation (Ethiraj et al. 2005). Quasi-rents are associated with capabilities that build and integrate the firm's specialized assets. Evolutionary theory also states that firms are bundles of path-dependent knowledge bases (Nelson and Winter 1982). Over time, firms accumulate knowledge through routines and 'learning by doing'. Routines denote certain capabilities and decision rules and require investments made in routine-specific physical and human capital. Evolutionary theory, therefore, views firms as entities that possess heterogeneous capabilities as a function of routines and search processes. Heterogeneity in capabilities across firms often results in innovation (Knott 2003). Such heterogeneity offers MNCs opportunities to conceive of new markets and products. Firms seeking this heterogeneity often use their international operations to gain access to new but salient knowledge that they could use in their innovation activities. Firm search behavior for knowledge resources that could enhance its strategic variety through access to and control of different types of knowledge has been recently studied in the global chemical industry (Ahuja and Katila 2004), Indian software industry (Ethiraj et al. 2005), and pharmaceutical industry (Nerkar and Roberts 2004).

The essence of both the evolutionary and dynamic capability perspective is that firms build on existing resources to renew and create new resources that allow them to maintain competitive advantage. Following the dynamic capability and evolutionary perspectives, internationally diversified MNCs have major opportunities to exploit and upgrade their intangible assets as they seek to maintain (or exploit) and renew (rebuild) their competitive advantage.

Central to both the dynamic capability and evolutionary perspectives is the notion that existing capabilities can to used generate future capabilities. Helfat and Peteraf (2003) state that capability lifecycle is appropriate in understanding evolution of dynamic capabilities over time. Thus dynamic capabilities have a founding, development, and maturity stages (Helfat and Peteraf 2003). Scholars in international management view the notion of dynamic capabilities can be thought of as having two distinct stages: Capability exploitation and capability building (Tallman and Faldmoe-Lindquist 2002; Luo 2002). The complete cycle of dynamic capability is shown in Fig. 1. Compared to the literature on capability exploitation less has been researched on capability building. Asset accumulation is vital for MNCs' rebuilding or renewing their capabilities in order to sustain their competitive advantage across the long haul. Teece (1986) stated that under conditions of imitation and imperfect markets assets that are complementary to the innovation itself are needed to appropriate rents from new products. Several studies have shown the importance of asset accumulation in the petroleum industry (Helfat 1997), and in the pharmaceutical industry (Thomke and Kuemmerle 2002). The contribution of this paper lies in investigating the capability leveraging and capability building of the world's largest MNCs over a nine-year period. We next discuss the arguments for developing hypotheses for relating technological capability exploitation that leads to MNCs' expansion via international diversification. Subsequently hypotheses are developed for the capability building process that links international diversification to future technological asset accumulation.

2.1 Capability Leveraging: MNC Innovation and Future International Diversification

Miller (2003) defines capability leveraging as the process by which a firm is able to apply resources earned in one situation to serve a different market. Capability leveraging processes are activities that MNCs utilize to gain competitive advantage by exploiting their existing capabilities in the marketplace (Tallman and Faldmoe-Lindquist 2002). Leveraging capabilities developed in either the home market or previous international expansion efforts is extremely useful to MNCs in future international diversification. All firms, particularly MNCs, rely on their existing capabilities to generate profits in order to finance new assets and capabilities (Tallman and Faldmoe-Lindquist 2002). MNCs are able to leverage current capabilities across national borders through international diversification in the quest for developing future capabilities. Wan (2005) stated that MNCs from developed countries are more likely to have market capabilities such as innovation compared to those from developing countries. Since the present study includes the top 200 MNCs from the US, Western Europe, and Japan the selection of technological capabilities is highly appropriate. Leveraging technological capabilities in the form of R&D investments is the key to international competitiveness. Franko (1989) demonstrated that US and UK firms lost global market share to Asian and European rivals due to the lack of investment in new products. Scholars in international business have argued that investments in R&D are a key factor in exports and foreign direct investment (Caves 1982; Kogut 1990). Tallman and Faldmoe-Lindquist (2002) state that technological resources are subject to opportunistic behavior and lead MNCs to appropriate as much of the value-added chain as possible through internalization.

Organizational learning is a critical source of sustained advantage is of which one important form is innovation (Hitt et al. 1994). MNCs are able to identify and maintain competitive standards thereby effectively appropriate value from their innovation through continuous learning (Kogut 1990). Hence, innovation may augment more international diversification in part by boosting the generation of returns from such diversification.

Innovation requires strong knowledge flows. Given the diversity of customer needs in different markets, internationally diversified MNCs need to increase their R&D spending to develop and introduce new products, systems and processes that address these needs. Heavy R&D spending is a key source of this knowledge in the MNCs' central facilities and a major foundation of their global capability (Song 2002).

International diversification allows MNCs to protect their markets by meeting local needs through innovations and by providing technical support (Guellec and de la Potterie 2001; Kogut 1990; Kumar 2001). In a survey 21 large European MNCs, Meyer-Krahmer and Reger (1999) identified three key drivers of the internationalization of R&D. They are developing early linkage to R&D activity to leading innovative clients; gaining access to centers of excellence and coordinating them with the MNC's own R&D unit; and coordinating the firm's production process with and its R&D activities. MNCs also need complementary assets that strengthen their R&D, production and sales (Serapio and Dalton 1999). These activities involve initiating knowledge flows through R&D intensity and creating patents to increase knowledge stocks.

Hypothesis 1: Changes in MNCs' R&D intensity is positively related with changes in future international diversification.

A firm patents its discoveries to protect its discoveries and appropriates rents for its innovative efforts (Nelson 1993). Patents are an objective measure of MNCs' innovative ability (Ahuja and Katila 2004; Cantwell et al. 2004; Pakes and Griliches 1984; Hagedoorn and Cloodt 2003; Nerkar and Roberts 2004) and its knowledge stocks (DeCarolis and Deeds 1999). Patents are a source of revenues and can be swapped to gain access to other firms' innovations. Patents are a measure of inventions and advances in knowledge, serve as an input for the development of new products and processes, and therefore constitute a proxy for MNCs' downstream technical activities (Patel and Pavitt 1997). As a result, patents are useful at the corporate level in measuring the inventive output of formal in-house R&D units. Patents are sometimes the only detailed source of information on the sectoral and geographical composition of innovation capacity of firms and particularly useful in making empirically-based comparisons across industries and across countries (Cantwell et al. 2004). Various studies have shown a strong correlation between R&D intensity and patents (Ahuja and Katila 2004; Hagedoorn and Cloodt 2003; Pakes and Griliches 1984). In a study of 1,194 firms in four high tech industries, Hagedoorn and Cloodt (2003) found that R&D intensity, patents, patent citations, and new product announcements were all highly correlated.

Patents confer firm-specific capabilities to invent, innovate, and discover new wealth-generating opportunities (Markman et al. 2004). The value of patents can be seen from the fact that firms lose 80 % of their revenue to generic substitutes once they expire particularly in the pharmaceutical industry (Barrett et al. 1999). Patents are awarded on the basis of novelty, non-obviousness, and usefulness by the US Patent Trademark Office (USPTO). Patents also bestow first-mover advantages that can be leveraged globally. Given the high level of product diversification in large MNCs they are bound to have a large number of patents. Similar to the discussion earlier on R&D intensity we expect that the relationship between number of patents and future international diversification will be linear in nature.

Hypothesis 2: Changes in MNCs' patents are positively related with changes in future international diversification.

Another measure of innovation according to Business Week, called technological strength is the product of number of patents and its citations reflects the quality of innovation. Patent citations have been widely used in the innovation and R&D literature (Ahuja and Katila 2004; Frost 2001; Hagedoorn and Cloodt 2003; Jaffe et al. 2000; Markman et al. 2004). The patent citation index can be viewed as a measure of its technological significance. Highly cited patents are more likely to be utilized for future market and product expansion. As production becomes more global, the need for appropriate knowledge derived from patents (stock) also increases. As MNCs utilize their knowledge stock they will be able to apportion R&D costs over a wider global basis. The internationalization of developed production through patents also leads to internationalization of knowledge that leads to true global integration of the technology-oriented business.

Kogut and Zander (1993) state that any entry or expansion into a foreign market alters the global knowledge of the firm and increases the chances of recombination of diverse types of technological knowledge and capabilities through the patents performance, particularly in developing new products and markets. Furthermore, Kafouros et al. (2008) empirically demonstrated that a higher degree of internationalization significantly enhances the effects of innovation by affecting innovative capacity as well as a number of appropriability factors.

Hypothesis 3: Changes in MNCs' Technology Impact Index is positively related with changes in future international diversification.

2.2 Capability Building: International Diversification and the MNCs' Future Innovation

Knowledge stocks are the MNCs' non-tradable assets that are rare, unique and cannot be easily imitated (Barney 1991). MNCs gradually accumulate knowledge stocks over time. These stocks include the MNCs' reputations and R&D capabilities. Technological performance is the accomplishment of firms with regard to the combination of their R&D input (R&D expenditures) and R&D output (patents) (Hagedoorn and Cloodt 2003). DeCarolis and Deeds (1999) relate a firm's knowledge measured in terms of stocks and flows to performance, measuring flows in terms of R&D intensity stocks by the number of patents. The dynamic capability perspective and evolutionary theory suggest that international diversification can significantly increase the MNCs' knowledge stocks and flows through capability accumulation and searching behavior, respectively.

Scholars of strategic management have identified innovation as a prominent way of building dynamic capabilities (Eisenhardt and Martin 2000; Nerkar and Roberts 2004; Teece et al. 1997, 2007). Several studies have documented the growing internationalization of R&D activities. Chen (2004) reported that 10 % of all R&D in OECD countries was done by foreign firms. In the Global Benchmark Survey of the strategic management of technology of the largest 209 MNCs, Gerybadze (2004) reported six industries are characterized by high foreign R&D. Serapio and Dalton (1999) observed that R&D spending by U.S. affiliates of foreign companies has increased from $6.5 billion in 1987 to $17.2 billion by 1996. Meyer-Krahmer and Reger (1999) also documented spectacular increases in foreign R&D spending in Europe particularly in the UK (25 %), Spain (50 %), and Ireland (68 %). A study of the 20 largest Swedish MNCs concluded that 22.8 % of their R&D spending took place outside their own country (Hakanson and Nobel 1993). Another study reported that more than 70 % of the patents registered by Belgian and Dutch MNCs originated outside their borders (Dunning 1994). A study of 32 European, Japanese and U.S.-based MNCs found that these companies have created several home-base-augmenting R&D laboratory sites and home-base-exploiting sites, locating them in their foreign markets (Kuemmerle 1997). Foreign augmentation sites absorb knowledge from the local scientific community and transfer it back to their MNCs' central R&D labs. Several studies have investigated foreign R&D in Japanese MNCS as well (Belderbos 2001; Iwasa and Odagiri 2004, 1999; Kumar 2001) as in European MNCs (Cefis and Orsenigo 2001; Le Bas and Sierra 2002; Guellec and de la Potterie 2001).

International diversification centers on entry into new foreign markets. This entry is motivated by several factors that frequently relate to technology acquisition. First, international diversification enables MNCs to tap into the global specialized technology market easily by virtue of being in that location. This is important because technological activity is more internationally dispersed than ever and MNCs could use cross-border knowledge flows to strengthen their competitive lead (Bartlett and Ghoshal 1989; Cantwell and Piscitello 2000). Technological activity in any industry is locationally differentiated in accordance with the varied participation of MNCs in different national systems of innovation whose patterns of technological competence are fairly stable (Cantwell 1991; Patel and Pavitt 1991). Indeed, there are significant economies of agglomeration in the geographical locations where innovation is clustered (Audretsch and Feldman 1996; Baptista and Swann 1998; Cantwell 1991; Dosi 1988). Knowledge diffusion is geographically bounded, involving different characteristics of innovation in each country. This means that MNCs can acquire an important source of competitive advantage by geographically dispersing streams of their new knowledge creation and then integrating them at a corporate level (Kogut and Chang 1991; Cantwell 1991).

International diversification also gives MNCs the opportunity to capitalize on the diversity of national endowments of technological resources, knowledge and skills (Anand and Kogut 1997). Countries specialize in particular technologies (Bartholomew 1997; Kogut 1990; Nelson 1993; Porter 1990). The technological knowledge that exists in different countries is unique and is not easily transferred (Szulanski 1996). International diversification also takes MNCs closer to foreign centers of innovation, making possible the learning of new skills that could increase innovation.

Exposure to different national innovation systems can also improve MNCs' learning (Cantwell and Harding 1998). This learning is an important intangible resource for companies (Teece et al. 1997), as it allows MNCs to exploit existing skills or develop new ones. This process requires MNCs to increase their R&D spending and adapt existing products for sales in different national or regional markets. Heavy R&D investments also enable MNCs to develop radically new products for existing or new markets. Original R&D research is costly (Mansfield 1988), and internationally diversified MNCs need these innovations to sustain their competitive positions or enter new markets. Thus, if the unique knowledge gained from international diversification ignites the quest for radical innovation, strong R&D intensity makes it possible.

Second, MNCs also diversify internationally to develop and enhance their absorptive capacity, a key determinant of technological learning (Tallman and Faldmoe-Lindquist 2002). Absorptive capacity refers to the ability of the firm to acquire, assimilate, and exploit new knowledge (Cohen and Levinthal 1990). Recently, it has been has been viewed as a dynamic capability that enables firms in knowledge creation and utilization, thereby upgrading a firm's ability to gain and sustain its competitive advantage (Zahra and George 2002). Cohen and Levinthal (1990) identify two characteristics of absorptive capacity that are worth noting: (1) accumulating absorptive capacity in one period leads to more efficient accumulation in the next and (2) the possession of related expertise allows the firm to better understand and evaluate technical advances in its current or adjacent fields. R&D spending therefore not only generates new knowledge but contributes to the firm's absorptive capacity due to the properties of cumulativeness and its effect on expectation formation (Cohen and Levinthal 1990).

Third, another mechanism by which MNCs build and enhance their innovative capabilities is by establishing centers of excellence abroad (Frost et al. 2002). Birkinshaw and Hood (1998) state that parent driven FDI as one of the main avenues by which subsidiaries build capabilities that subsequently lead to an expanded path within the MNC. RBV theorists such as Dierickx and Cool (1989) and Barney (1991) note the importance of sustained investment over time that leads to the development of capabilities and positions that eventually help in achieving competitive advantage. In a survey of 99 Canadian MNCs, Frost et al. (2002) found that international diversification was a significant factor in the establishment of centers of excellence.

To summarize, Kogut and Zander (1993) state that any entry or expansion into a foreign market alters the global knowledge of the firm and increases the chances of recombination of diverse types of knowledge. This can fuel discovery that intensifies patenting. As MNCs acquire and build knowledge stocks by adding patents from subsidiaries in lead markets they are likely to indulge in cross-border transfer of learning. As production becomes more global, the need for appropriate knowledge derived from patents (stock) also increases. As MNCs utilize their knowledge stock, they will be able to apportion R&D costs over a wider global basis. The internationalization of production leads to internationalization of knowledge (stocks) that leads to true global integration of the MNC. These observations suggest the following hypothesis:

Hypothesis 4: Changes in international diversification is positively related to changes in MNCs' future R&D intensity.

The number of patents, which are a key measure of the innovative capacity of a firm and an important source of competitive advantage in global markets (Patel and Pavitt 1991, 1997; Hagedoorn and Cloodt 2003), and knowledge stocks (DeCarolis and Deeds 1999). Patents indicate a firm's success in discovery and innovation (Griliches 1984; Hagedoorn and Cloodt 2003) and represent a reliable proxy of a firm's progress in developing strong technological capabilities (Narin et al. 1987; Patel and Pavitt 1998).

Based on its technological strength a firm's commitment to supporting R&D activities enable a firm to develop new products that can be one of major incentives to expand the scope of global markets. As such, the firm's internal capability residing in R&D investment is one of the key predictors of a firm's capability to generate inventive outputs (Cardinal and Hatfield 2000) for market diversification. Given that international diversification is conducive to higher R&D spending, it can further increase the firm's patents. Consequently, internationally diversified firms are likely to innovate with greater frequency than less diversified firms, a process that generates more patents. Furthermore, multinational companies are increasingly driving to the development of international networks through the corporate patenting in order to exploit the differentiated diversification of their foreign markets of excellence (Cantwell and Piscitello 2000).

Hypothesis 5: Changes in international diversification is positively related to changes in MNCs' future number of patents.

A criticism of patents is that they do not represent the economic value of innovation and that not all patents lead to innovations. Even though R&D intensity, patents, and patent citations are highly correlated measures of innovation (Hagedoorn and Cloodt 2003) it is important to distinguish patents by looking at the quality of patents. Technological strength has been defined as the product of patent citation and number of patents (Businessweek 1992, 1993). Patent citations are a good indicator of the impact patents have on the technological landscape. High citation rates are associated with distinct innovation (Jaffe and Lerner 2001; Jaffe et al. 2000). Patent citations are associated with stock performance (Hall et al. 2005), localization of technological spillovers (Jaffe et al. 1993), technological trajectories (Stuart and Podolny 1996), and are a source of foreign subsidiary innovation (Almeida 1996; Frost 2001). Firms renew patents with higher citation rates (Harhoff et al. 1999). Thus, those MNCs that are keen on building dynamic capabilities and utilizing search behaviors for upgrading and developing their technological knowledge base will be characterized by developing patents that have higher citations.

Hypothesis 6: Changes in international diversification is positively related to changes in MNCs' future Technological Impact Index.

3 The Empirical Method

3.1 Sample

The sample of firms was initially started from both The Directory of Multinationals: The World's Largest Global Enterprises (Stopford 1992) and The Directory of Multinationals: The World's Largest Global Enterprises (Timbrell and Tweedie 2001) with respect to geographical and product analysis data for diversification measures, which profiled the world's top 450 largest industrial corporations, with consolidated sales of over US$1.5 billion and overseas sales in excess of US$750 million during 1999/2000, and had significant international operations (Timbrell and Tweedie 2001). Although there is not standard definition of the term 'multinational corporations/enterprises', the Directory of Multinationals provides a comprehensive and up to date profiles on the world's top global enterprises, particularly in which these companies account for a significant proportion of the foreign direct investment in the world (Geringer et al. 2000; Carpenter et al. 2001; Stafford et al. 1989). As such, we adopted Stopford's (1989) criterion that a firm is multinational if the firm's sales or business operations are generated from at least three foreign countries and also the corporations account for a significant proportion of the foreign direct investment in the world (Tallman and Li 1996; Timbrell and Tweedie 2001). Furthermore, an average of sales revenue of listed firms was not statistically different from that of The Fortune Global 500 during that period under study.

However, finance related firms (SIC 6000-6999) and government and special service related firms were initially eliminated to maximize the generalizability and reliability of the study since these industries are not directly applicable for technology projection. In addition, this initial sample was followed up (or matched by) through patent scoreboard reported in Businessweek (1992, 1993) and the CHI Research Inc. that Businessweek had originally used in their patent scoreboard. Starting with this group of companies, we developed the measures of the study's variables. More firms with respect to R&D intensity and other control variables were also eliminated because of insufficient data (i.e., missing data) and incompatibility of the study. The relatively long time frame of nine years (1992-2000), coupled with the lack of data on several companies or variables reduced the initial samples to 210 leading MNCs. All other variables including R&D expenditure and control variables were selected from Compact-D World Scope for the corresponding period.

The present study encompasses three different time periods both involving lags. The first part of the study deals with investigating the impact of capability leveraging (innovation) on international diversification. Thus the independent and control variables were measured over a 3-year period 1992-1994 whereas the dependent variables were measured over the periods 1995-1997. The second part of the study looks the effects of the capability building (international diversification) on technological asset accumulation. Thus the independent and control variables were measured over the period 1995-1997 and the dependent variables over the time period 1998-2000. The use of 2-5 years lags is commonly in the innovation literature (Ahuja and Katila 2004; Hagedoorn and Cloodt 2003; Markman et al. 2004; Pakes and Griliches 1984).

According to Ployhart and Vandenberg (2010) change is the most appropriate thing to use when doing longitudinal research. They define longitudinal research as research that emphasizes change and contains at minimum three repeated observations on at least one of the substantive constructs of interest. Furthermore, Ployhart and Vandenberg (2010) state that explanatory longitudinal research seeks to identify the cause of change process by the use of one of more substantive predictor variables. Thus we used the average of the year-to-year changes for the relevant time periods for all variables. The use of changes has been used in diversification (Bergh and Holbein 1997) and innovation studies (Mudambi and Swift 2011). Thus for the purposes of this study we used the effect of changes in the independent and control variables on the changes in the dependent variables.

3.2 Measures of Key Variables

3.2.1 Dependent and Independent Variables

International Diversification was measured using an entropy index (Hitt et al. 1997). It was calculated using the formula: International diversification = [summation][[P.sub.i] x ln (1/[P.sub.i])]. [P.sub.i] was the sales reported by a given world market region i. Ln (1/P,) was the natural logarithm of that region's sales and was the weight assigned to that region.

R&D Intensity was measured as the MNC's allocations to this activity as a percent of its annual sales (Hitt et al. 1997). R&D data were obtained from Compact Disclosure. R&D spending is an input indicator that firms hope leads to innovation in the future in the form of patents (Griliches 1984; Hitt et al. 1997). R&D spending can also indicate the innovative capabilities of firms that are related to organizational performance particularly in high-tech industries (Duyster and Hagedoorn 2001; Henderson and Cockburn 1994). Raw patent counts have been overwhelming accepted in the economics and management literatures as the most appropriate measure of innovation (Ahuja and Katila 2004; Dutta et al. 2005; Cefis and Orsenigo 2001; Le Bas and Sierra 2002; Markman et al. 2004; Patel and Pavitt 1997; Silverman 1999).

Patents were measured by the number of patents granted to an MNC by the U.S. Patent Office. This number excluded design and other special case patents. Though incomplete, the number of patents obtained in the U.S. is a valid proxy for a firm's overall patenting activities (e.g., Patel and Pavitt 1991, 1997, 1998). The attractiveness of the U.S. market encourages patenting of inventions that are of significant commercial importance. Raw patent counts have been overwhelming accepted in the economics and management literatures as the most appropriate measure of innovation (Ahuja and Katila 2004; Cefis and Orsenigo 2001; Le Bas and Sierra 2002; Markman et al. 2004; Patel and Pavitt 1997; Silverman 1999). Still, it should be recognized that patents do not fully capture a firm's innovation. Some technological knowledge is tacit and cannot be fully captured in patents. Also, some MNCs may not patent their inventions to prevent the flow of know-how to their competitors. Finally, patent counts overlook the qualitative differences that exist between patents. However, Businessweek (1992, 1993) suggests that the number of patents developed by CHI Research is reliable. Narin et al. (1987) also provide extensive evidence of the validity of this measure.

Technology Impact Index (TII) indicates the impact of a company's patents on the latest technological developments. The Technology Impact Index is a measure of how often the previous five years of a companys' patents are cited by patents issued in the most recent year, relative to all U.S. Patents. A TII of 1.0 shows that the last five years of a company's patents are cited as often as expected. A TII of 1.1 indicates 10 % more citations per patent than expected, and so forth. Data for the later time period 1992-2000 was bought from the CHI research firm. Patent citations have been widely used in the innovation and R&D literature (Ahuja and Katila 2004; Dutta et al. 2005; Frost 2001; Hagedoorn and Cloodt 2003; Jaffe and Lerner 2001; Jaffe et al. 2000; Markman et al. 2004).

3.2.2 Control Variables

Nine variables were also used as statistical controls in the analyses. Data for the control variables were obtained from the Compact Disclosure Worldscope database.

Product Diversification was also measured using an entropy measure (Jacquemin and Berry 1979; Palepu 1985). This measure, which has been used in prior research (e.g., Hitt et al. 1997; Sambharya 1995), was calculated as follows: Product diversification [summation][Pi x ln (1/Pi)]. Pi was the sales reported by a given business segment i. Ln (1/Pi) was the natural logarithm of a given segment's sales and was the weight given to that segment. Prior research found that this measure had better construct validity than other similar measures (Hitt et al. 1997).

Firm Size was measured by the natural log of the company's total assets, as done in past research (e.g., Hitt et al. 1997). Firm size affects a firm's innovation (Chaney and Devinney 1992) and both international- and product diversification (Tallman and Li 1996).

Liquidity was measured by the firm's current ratio, calculated by its current assets divided by its current liabilities (e.g., Hitt et al. 1997). Liquidity influences the slack resources available for R&D (Baysinger and Hoskisson 1989). It also influences diversification efforts (Hoskisson and Hitt 1994).

Debt Leverage was measured by an MNC's long-term debt divided by its total debt plus equity. Debt leverage affects a company's performance (Tallman and Li 1996) and its support for R&D intensity (Hoskisson and Hitt 1994).

Past Performance was measured by the MNC's past return on assets (ROA). ROA was measured by earnings before income taxes divided by total assets (Hitt et al. 1997; Sambharya 1995). Past performance influences R&D intensity (Chaney and Devinney 1992; Doi 1985).

Intangible Resources were measured using Tobin's q (Wernerfelt and Montgomery 1988), calculated by dividing the market value of the MNC's equity plus the liquidation value of its preferred stock plus the value of its total debt by total assets, Tobin's q was expected to positively influence the firm's R&D (Chaney and Devinney 1992).

Business Strategy was the final control used in the study. Porter (1985) stated that firms compete on the basis of cost leadership or product differentiation. We used four variables to capture operations and market strategy variables in business such as cost of goods (cost of goods sold/sales revenue), to measure the manufacturing/ operating cost efficiency, plant and equipment (net amount of plant and equipment/ sales revenue) to measure the usage of plant and equipment for sales, inventory turnover (cost of goods sold/average inventory), and account receivable (average accounts receivable x 365/sales revenue) to indicate the average collection period in market promotion.

Country Effects were captured using dummy codes. To do this, companies were assigned to one of three groups based on the country where their headquarters were located: North America, Japan and the European Union. MNCs could derive major advantages from establishing their headquarters in a given location (Cantwell and Harding 1998), which would shape these firms' international operations (Porter 1990). Countries differ also in their innovation (Kotabe 1990), technological resources (Anand and Kogut 1997; Nelson 1993); R&D intensity (Porter 1990); the types of innovations developed (Kotabe 1990); speed of product development (Mansfield 1988); use of internal vs. external sources of technology (Chiesa 1996; Mansfield 1988); and patents (Kotabe and Cox 1993).

We used changes in all the control variables for the various corresponding time periods for the independent and independent variables.

Industry Type effects were captured using dummy codes for the nine groups represented in the sample (e.g., Hitt et al. 1997; Tallman and Li 1996). These groups were defined based on the two-digit standard industrial classification (SIC). Industries represented in the sample were: Chemical (n = 22); drugs (n = 18); electronics (n = 36); machinery (n = 24); precision and measurement (n = 13); transportation (n = 27); petroleum (n = 17); prime metals (n = 10); and other SICs (n = 24). Industries differed in their R&D intensity (Patel and Pavitt 1998) and diversification (Doi 1985; Ramanujam and Varadarajan 1989).

3.3 Data Analyses

Tables 1 and 2 report the means, standard deviations, and the intercorrelations for the study's variables for the three time periods. Specifically Table 1 shows the intercorrelations between the average of the year-to-year changes between the control and independent variables (1992-1994) and the change in international diversification ([DELTA]ID) (1995-1997). These correlations relate to the capability exploitation of MNC in the dynamic capability asset accumulation cycle under scrutiny in this study. Table 2 indicates the intercorrelations between the average of the year-to-year changes in the control and independent variables of [DELTA]ID (1995-1997) and the three innovation dependent variables ([DELTA]R&D intensity, [DELTA]number of patents, [DELTA]Technological Impact Index). These correlations correspond to the capability building aspect of the dynamic capability aspect MNC.

Variable inflation factors indicated that multicollinearity was not a problem in the statistical analyses since they are all below the acceptable number ten (Ito and Pucik 1993).

3.3.1 Testing MNC Exploiting Capability

To test the study's Hypotheses 1, 2, and 3, we used multiple regression analyses for the dependent variable: Change in international diversification ([DELTA]ID). We calculated the average of the year-to-year change in values of all the independent and control variables over the 1992-1994 and the change in values for [DELTA]ID for the period 1995-1997 (except the country and industry dummy variables). Ordinary least square regression analyses were then used to test the three hypotheses using the following four models. In model 1, the dependent variable was regressed on the control variables. The addition of change in R&D intensity in model 2 enabled us to test Hypothesis 1. In models 3 and 4 the independent variables, changes in number of patents and changes in Technological Impact Index were added to test Hypotheses 2 and 3 respectively. Thus the models tested were as follows:

Model 1: [DELTA]ID = f (changes in control variables).

Model 2: [DELTA]ID = f ([DELTA]control variables, [DELTA]R&D intensity).

Model 3: [DELTA]ID = f ([DELTA]control variables, [DELTA]R&D intensity, [DELTA]number of patents).

Model 4: [DELTA]ID = f ([DELTA]control variables, [DELTA]R&D intensity, [DELTA]Technological Impact Index).

3.3.2 Testing MNC Capability Building

To test the study's Hypotheses 4, 5, and 6, we used multiple regression analyses for the dependent variables: [DELTA]R&D intensity and [DELTA]Technological Impact Index. Negative binomial regression analysis was employed when the dependent variable was number of patents a count variable (Ahuja and Katila 2004). We calculated the average of the year-to-year changes in values of all the independent and control variables over the 1995-1997 and the for three independent variables [DELTA]R&D intensity, [DELTA]number of patents, and [DELTA]Technolog- ical Impact Index for the period 1998-2000 (except the country and industry dummy variables). The various regression analyses then used to test the three hypotheses using the following three models. In model 1, the dependent variable was regressed on the control variables. In model 2, [DELTA]ID was added to test Hypothesis 4.

Model 1: [DELTA]R&D intensity = f (changes in control variables).

Model 2: [DELTA]R&D intensity = f (changes in control variables, [DELTA]ID).

Similar models were used for the other two dependent variables: [DELTA]number of patents and [DELTA]Technological Impact Index to test Hypotheses 5 and 6 respectively.

Model 1: [DELTA]Number of patents = f (changes in control variables, [DELTA]R&D).

Model 2: [DELTA]Technology Impact Index = f (changes in control variables, [DELTA]ID).

4 Results

4.1 MNC Capability Exploitation (Hypotheses 1, 2, and 3)

The results of testing MNCs exploiting their innovative capabilities are shown in Table 3. Model 1 shows the effects of all the control variables on future international diversification. Several control variables were significant including Tobin's q, firm size, ROA, inventory turnover, and product diversification. In model 2 AR&D intensity was added and it was significant thus supporting Hypothesis 1. The change in number of patents was added in model 3 and it was highly significant leading to a 7 % increase in the variance. Thus, Hypothesis 2 was highly significant. Finally, in model 4, ATechnological Impact Index was added and it too was highly significant upholding Hypothesis 3.

4.2 MNC Capability Building

MNC capabilities are measured by three innovation measures ([DELTA]R&D intensity, [DELTA]number of patents, [DELTA]Technological Impact Index). We discuss the results of the regression analyses relating to these dependent variables next.

4.2.1 Future R&D Intensity (Hypothesis 4)

The results of regression analyses for the first dependent variable future R&D intensity appear in Table 4. The control variables shown in models 1 contributed 18 % of the variation in the [R.sup.2] for this dependent variable. The industry effects were significant only for petroleum and technology services and the country effect for Japan. Among the financial variables, the strongest effects originated from Tobin's q, and firm size. The effects for [DELTA]product diversification variables were not significant. In model 2, [DELTA]ID was entered and was positively and significantly related to the dependent variable, [DELTA]R&D intensity. The [R.sup.2] increased dramatically to 34 %. Thus, Hypothesis 4 was strongly supported.

4.2.2 Number of Future Patents (Hypothesis 5)

Table 5 presents the results of the negative binomial regression analyses for the dependent variable MNCs' [DELTA]number of patents. Negative binomial regression analysis is the appropriate test when the dependent variable--number of patents--is a count variable (Ahuja and Katila 2004). Note that past [DELTA]R&D intensity has been added to the control variables. Industry effects shown in model 1 were strong for electronics, machinery, metals, and transportation measurement the industries. [DELTA]R&D intensity was entered as a control variable and not surprisingly was highly related to [DELTA]number of patents as well as was the variable [DELTA]PD. International diversification was introduced in Model 2 it was strongly and significant related to [DELTA]number of patents at the p = 0.05 significance level thereby strongly supporting Hypothesis 5.

4.2.3 Technological Impact Index (Hypothesis 6)

Table 6 shows the results of OLS regressing MNC [DELTA]Technological Impact Index on the [DELTA]international diversification. Model 1 shows the effect of the control variables. As seen the earlier equations industry effects were strong for electronics, petroleum, and the measurement industries. [DELTA]R&D intensity was entered as a control variable and not surprisingly was [DELTA]Technological Impact Index.

[DELTA]international diversification introduced in model 2 in the technological

strength equation was significantly related (p < 0.001) to MNC [DELTA]technological strength thus supporting H6.

Overall all the six hypotheses were supported thus lending strong support to the dynamic capabilities possessed by MNCs. MNCs are able to exploit their existing innovative capabilities and expand into international markets. The three hypotheses relating innovative capabilities in terns of [DELTA]R&D intensity, [DELTA]number of patents, [DELTA]Technological Impact Index was all highly related to future [DELTA]ID. The three hypotheses regarding capability building effects of these [DELTA]ID on all the three dependent variables: [DELTA]Future R&D intensity, [DELTA]number of patents, and [DELTA]technological strength were strongly supported. Thus, the entire model of MNC dynamic capability lifecycle was strongly supported in the present study.

5 Discussion

Today's multinationals have to innovate in order to survive and succeed in global markets. Past studies have been limited as they either have examined MNC capability exploitation or capability building separately. For example, studies had reported that MNCs were able to exploit their innovative capabilities in international markets (Bartlett and Ghoshal 1989; Hitt et al. 1997; Kotabe et al. 2002; Morck and Yeung 1991). Other studies have shown that MNCs build their innovative capabilities based on their presence in various foreign markets due to their international diversification strategies (Barkema and Vermeulen 1998; Hitt et al. 1997). The main contribution of the present study is that it looks at the dynamic capabilities from a capability lifecycle perspective. More specifically we looked at how the world largest MNCs exploit dynamic capabilities and rebuild capabilities as a result from knowledge gained by their international diversification activities from a longitudinal perspective.

The essence of the 'dynamic' capability perspective is that a firm's has the capacity to renew competencies (Teece et al. 1997). Dynamic capabilities can also be conceptualized in terms of positions, paths, and processes (Teece et al. 1997). Positions can be seen as the specific set of resources available in a firm that could be technological, reputational, or financial in nature (Schreyogg and Kliesch-Eberl 2007). The external side refers to the specific market position of the focal firm. The current position of a firm determines to a certain extent the future decisions a firm can reach and realize. Innovative routines are equivalent to a MNCs internal position that can be matched to its international diversification activities as the external position. The capability exploitation aspect of MNCs in this study was anchored in the evolutionary and dynamic capability perspectives in which innovative capabilities lead to international expansion. The results of the present study indicate that MNCs are able to exploit their innovative capabilities into international expansion and build on international diversification to generate and/or renew more innovation capabilities over a nine year period. Various firms can take different paths (Eisenhardt and Martin 2000) to match their internal position (technological assets) to their external environment and reach the appropriate international diversification posture depending on their strengths and experience. The results of the capability exploitation aspect of this study is consistent with the literature on international diversification (Bartlett and Ghoshal 1989),

Following the evolutionary theory (Nelson and Winter 1982) and dynamic capability perspectives (Teece et al. 1997), Hypotheses 4, 5 and 6 suggested that international diversification will be positively associated with the MNC's future R&D intensity, patents, and MNC technological impact. The results strongly support H4, H5 and H6 and are consistent with evolutionary theory and dynamic capability perspectives (Ahuja and Katila 2004; Nerkar and Roberts 2004) and prior findings (Hitt et al. 1997; Kotabe 1990). International diversification encourages MNCs to invest heavily in R&D to support innovations that allow them to develop new products and customize their offerings for different markets (Hypothesis 4). MNCs indulge in search behavior by looking at markets worldwide in the form of international diversification that enables them to identify and get access to innovation in key markets. The positive and significant relationships observed between international diversification and number of patents (H5) also show that international diversification can provide major opportunities for future innovation by exposing MNCs to different systems of innovation (Nelson 1993) and centers of excellence around the world (Porter 1998). International diversification can also enhance learning from the MNCs' global partners and customers (Hitt et al. 1994, 1997). Given that increases in international diversification encourage higher future R&D intensity, international diversification can also increase the MNCs' future patents and technological strength and therefore its absorptive capacity. These results confirm that MNCs are continuously building and developing their dynamic capabilities by investing in firm-specific knowledge assets. Consequently, those MNCs that use international diversification can innovate with greater frequency than less geographically diversified MNCs, a process that generates more patents.

In summary, the results for Hypotheses 4, 5 and 6 are consistent with the recent discussion in the literature of the role of international diversification in organizational learning (Barkema and Vermeulen 1998). International diversification may facilitate MNCs' access to networks, enabling them to acquire new knowledge that spurs innovation (Cantwell and Harding 1998; Nobel and Birkinshaw 1998).

The contributions of this study's results can be underscored in the context of the dynamic capability perspective and evolutionary theory. Teece et al. (1997) define 'dynamic' as the capacity to renew competencies so as to achieve consistency with their changing business environments. This process requires innovation to capitalize on changes in the MNCs' markets, competitors and technologies. Teece and colleagues identify technological assets as one of the major positions any firm can acquire. The path to developing these assets is determined by the MNCs' previous R&D intensity and other strategic choices (Bartlett and Ghoshal 1989). Some MNCs search globally for technological knowledge in order to enhance their innovation by making investments in many countries and products simultaneously. Evolutionary theory, on the other hand, focuses on the firm's response to developing heterogeneous resources to idiosyncratic situations and gradually acquired capabilities through functional and product-market experiences. Viewed in this light, international to significantly increase investments in R&D, patents, and technological strength that MNCs gain.

One of the study's key findings is that developing innovative capabilities has to be seen on a long-term basis. Results support the capability lifecycle approach to understanding dynamic capabilities. MNCs particularly need to nurture and plan their innovative and intellectual property in strategic terms. The current trend of establishing centers of excellence in various parts of the world indicates that MNCs are acutely aware of tapping in pockets of knowledge in host countries. This is part of the capability building process that is increasingly becoming more important to worldwide competitiveness. The motives for international diversification have to be viewed increasingly not only just for innovative capability exploitation and to augment and build existing innovative capability towards an enhanced one. The managerial challenge is for MNCs to be able to harness these new sources of knowledge into their innovation routines and practices.

5.1 Limitations

A weakness of research on diversification is over-reliance on cross-sectional research designs (Dess et al. 1995). While this study represents an improvement over past research by incorporating lagged designs, the results may not apply to longer time frames than the nine-year period analyzed. Another limitation of the study is the reliance on data from three major world regions in examining the technology-diversification-technological capability relationship. While these three regions are the most important worldwide centers of innovation, other influential centers of innovation exist elsewhere (Porter 1998). Finally, because the MNCs we examined are among the most powerful in their industries and markets, the results may not apply to smaller or service MNCs. With these limitations in mind, it is important to discuss the contributions of the study to managerial practice and future research in this area.

5.2 Research Implications

The results also suggest several avenues for future research. Notably, there is a need to validate the results using other samples, perhaps with different time periods and different lag structures. Future studies can improve our understanding of the relationships that might exist across different time frames and different lag effects. International and product diversification may affect the firm's innovation differently at periods of economic recession vs. expansion, and differently across various time horizons (short vs. long term). It is important also to explore the effect of industry variables on the relationship between international diversification and future innovation. The motives for, and modes of, international diversification may also differ by industry conditions (Hennart and Park 1993). Perhaps, the effect of international diversification on MNCs' innovation differs between growing vs. declining industries. Researchers would benefit from examining the direct effects of industry variables on the relationship between international diversification and MNCs' innovation.

A complex issue to be explored in future research is the length of the time lag between international diversification and the MNCs' future innovation. Previous research has not addressed this issue, focusing mostly on one-year time lags (Barkema and Vermeulen 1998; Hitt et al. 1997). Given that R&D intensity and patents may exhibit different lags with international and product diversification, this issue should also be examined in future research.

The relationship between MNCs' innovation and diversification is dynamic in nature. Innovation can influence MNCs' strategic choices and vice versa. Innovation can also encourage MNCs to broaden their international expansion activities (Silverman 1999). Future studies, therefore, would benefit from exploring this dynamic link between international and product diversification and innovation using alternative research designs and methods. Cross panel designs and time series analyses can also help to improve our understanding of these dynamic relationships.

One of the key assumptions of this and prior studies is that companies gain new knowledge as a consequence of international diversification (Hennart and Park 1993; Hitt et al. 1997). The results, and those reported in earlier studies (Barkema and Vermeulen 1998; Hitt et al. 1997), offer some support for this assumption. Still, do firms actually learn as a result of international and product diversification? How is knowledge transferred from leading subsidiaries to other parts of the MNC's network? Does this learning differ by the nature of the diversification strategy used (international vs. product diversification) and the type of diversification effort (related vs. non-related)? What kinds of skills do MNCs learn from international and product diversification? How do MNCs convert these skills into products? These questions deserve close attention in future studies.

DOI 10.1007/s11575-013-0199-7

Received: 7 February 2012/Revised: 21 November 2013/Accepted: 17 December 2013/


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R. B. Sambharya ([mail])

Rutgers University-Camden, Camden, New Jersey, USA


J. Lee

William G. Rohrer College of Business, Rowan University, Glassboro, New Jersey, USA

Table 1 Descriptive statistics and correlations (1992-1994 &

Variables                          Mean     S.D.     1       2

 1. Dummy: USA                      0.460    0.500
 2. Dummy: Japan                    0.288    0.454   -0.59
 3. Dummy: EEC                      0.251    0.435   -0.54   -0.37
 4. [DELTA]R&D intensity            0.008    0.005   -0.02    0.04
 5. [DELTA]Number of patents       10.463   27.084   -0.05    0.07
 6. [DELTA]Technology impact        0.279    0.167    0.04   -0.03
 7. [DELTA]Firm size: In            0.074    0.117    0.03    0.13
 8. [DELTA]Return on assets         0.509    2.560    0.14   -0.15
 9. [DELTA]Tobin's q                0.165    0.539    0.08   -0.06
10. [DELTA]Debt leverage           -0.025    0.701   -0.09    0.06
11. [DELTA]Cost of goods           -0.003    0.049   -0.03    0.01
12. [DELTA]Plant and equipments    -0.006    0.022   -0.09    0.25
13. [DELTA]Account receivable       1.409   15.415    0.07   -0.05
14. [DELTA]Inventory turnover       0.083    1.200    0.12   -0.10
15. [DELTA]Product divers           0.005    0.086   -0.13    0.04
16. [DELTA]Int'l divers             0.038    0.119   -0.13   -0.03

Variables                          3       4       5       6

 1. Dummy: USA
 2. Dummy: Japan
 3. Dummy: EEC
 4. [DELTA]R&D intensity           -0.02
 5. [DELTA]Number of patents       -0.01    0.21
 6. [DELTA]Technology impact       -0.01    0.15    0.25
 7. [DELTA]Firm size: In           -0.17    0.15    0.14   -0.13
 8. [DELTA]Return on assets         0.00    0.17    0.07    0.12
 9. [DELTA]Tobin's q               -0.03    0.25    0.26    0.23
10. [DELTA]Debt leverage            0.03   -0.02   -0.18   -0.02
11. [DELTA]Cost of goods            0.02   -0.09   -0.14   -0.12
12. [DELTA]Plant and equipments    -0.16    0.15   -0.03   -0.15
13. [DELTA]Account receivable      -0.03   -0.04   -0.13    0.04
14. [DELTA]Inventory turnover      -0.04    0.02    0.01   -0.02
15. [DELTA]Product divers           0.10    0.08    0.01    0.15
16. [DELTA]Int'l divers             0.18    0.27    0.18    0.47

Variables                          7       8       9       10

 1. Dummy: USA
 2. Dummy: Japan
 3. Dummy: EEC
 4. [DELTA]R&D intensity
 5. [DELTA]Number of patents
 6. [DELTA]Technology impact
 7. [DELTA]Firm size: In
 8. [DELTA]Return on assets         0.01
 9. [DELTA]Tobin's q               -0.02   -0.12
10. [DELTA]Debt leverage           -0.07   -0.18    0.04
11. [DELTA]Cost of goods            0.07   -0.14   -0.12    0.07
12. [DELTA]Plant and equipments    -0.04    0.17   -0.15   -0.04
13. [DELTA]Account receivable      -0.04   -0.13    0.04    0.10
14. [DELTA]Inventory turnover       0.02    0.01   -0.02   -0.03
15. [DELTA]Product divers           0.08    0.01    0.15    0.01
16. [DELTA]Int'l divers             0.27    0.18    0.38   -0.02

Variables                          11      12      13     14      15

 1. Dummy: USA
 2. Dummy: Japan
 3. Dummy: EEC
 4. [DELTA]R&D intensity
 5. [DELTA]Number of patents
 6. [DELTA]Technology impact
 7. [DELTA]Firm size: In
 8. [DELTA]Return on assets
 9. [DELTA]Tobin's q
10. [DELTA]Debt leverage
11. [DELTA]Cost of goods
12. [DELTA]Plant and equipments     0.17
13. [DELTA]Account receivable       0.11    0.01
14. [DELTA]Inventory turnover       0.27    0.17   0.12
15. [DELTA]Product divers          -0.15   -0.08   0.02   -0.13
16. [DELTA]Int'l divers             0.02    0.04   0.02   -0.20   0.31

n = 210. Correlation coefficients > 0.135 are (in absolute value)
significant at p < 0.05; those greater than 0.18 are significant at
p < 0.01

Technology Impact Index measures showcases the broader significance
of a company's patents by examining how often its U.S. patents from
the previous 5 years are cited as "prior art" in the current year's
batch. A value of 1.0 represents average citation frequency, so 1.4
would indicate a company's patents were cited 40 percent more often
than the average, and so on

International Diversification indicates the change between the year
of 1995-1997

* Industry dummy variables are not shown because of space limitation

Table 2 Descriptive statistics and correlations (1995-1997 &

Variables                   Mean     S.D.     1       2       3

 1. Dummy: USA               0.461    0.500
 2. Dummy: Japan             0.288    0.454   -0.59
 3. Dummy: EEC               0.251    0.435   -0.54   -0.37
 4. [DELTA]R&D intensity     0.010    0.020    0.03   -0.17    0.14
 5. [DELTA]No. of patent    56.819   98.875   -0.04   -0.10    0.15
 6. [DELTA]Technology        0.135    0.231   -0.05   -0.05    0.11
    impact index
 7. [DELTA]Firm size: In     0.068    0.135    0.19   -0.24    0.23
 8. [DELTA]Return on         0.329    2.464    0.02   -0.14    0.12
 9. [DELTA]Tobin's q         0.250    0.831    0.20   -0.17   -0.05
10. [DELTA]Debt              0.039    0.562    0.12   -0.04   -0.10
11. [DELTA]Cost of goods    -0.014    0.045    0.03    0.05   -0.10
12. [DELTA]Plant and         0.006    0.039    0.10   -0.02   -0.10
13. [DELTA]Account           0.472    5.556    0.18   -0.16   -0.04
14. [DELTA]Inventory         0.149    2.664    0.14   -0.09   -0.07
15. [DELTA]R&D               0.001    0.011    0.09    0.01   -0.12
16. [DELTA]Product           0.043    0.125    0.13   -0.15    0.00
17. [DELTA]Int'l             0.140    0.180    0.11   -0.20    0.08

Variables                   4       5       6       7       8

 1. Dummy: USA
 2. Dummy: Japan
 3. Dummy: EEC
 4. [DELTA]R&D intensity
 5. [DELTA]No. of patent     0.59
 6. [DELTA]Technology        0.14    0.20
    impact index
 7. [DELTA]Firm size: In     0.40    0.29
 8. [DELTA]Return on         0.15    0.19    0.22    0.21
 9. [DELTA]Tobin's q         0.06    0.05   -0.04    0.35    0.17
10. [DELTA]Debt              0.15    0.12    0.03    0.18    0.28
11. [DELTA]Cost of goods    -0.06   -0.14    0.02   -0.04   -0.10
12. [DELTA]Plant and         0.25    0.15    0.10    0.22    0.29
13. [DELTA]Account           0.27    0.18    0.10    0.22    0.25
14. [DELTA]Inventory         0.00    0.02   -0.17    0.19    0.08
15. [DELTA]R&D               0.25    0.20    0.21    0.20    0.22
16. [DELTA]Product           0.51    0.48    0.19    0.39    0.25
17. [DELTA]Int'l             0.44    0.38    0.45    0.42    0.19

Variables                   9      10     11      12     13

 1. Dummy: USA
 2. Dummy: Japan
 3. Dummy: EEC
 4. [DELTA]R&D intensity
 5. [DELTA]No. of patent
 6. [DELTA]Technology
    impact index
 7. [DELTA]Firm size: In
 8. [DELTA]Return on
 9. [DELTA]Tobin's q
10. [DELTA]Debt             0.16
11. [DELTA]Cost of goods    0.04   0.04
12. [DELTA]Plant and        0.15   0.30   -0.02
13. [DELTA]Account          0.23   0.46    0.05   0.30
14. [DELTA]Inventory        0.22   0.03    0.17   0.01   0.01
15. [DELTA]R&D              0.21   0.35   -0.02   0.29   0.32
16. [DELTA]Product          0.25   0.25   -0.05   0.33   0.34
17. [DELTA]Int'l            0.39   0.13   -0.15   0.25   0.30

Variables                   14      15     16

 1. Dummy: USA
 2. Dummy: Japan
 3. Dummy: EEC
 4. [DELTA]R&D intensity
 5. [DELTA]No. of patent
 6. [DELTA]Technology
    impact index
 7. [DELTA]Firm size: In
 8. [DELTA]Return on
 9. [DELTA]Tobin's q
10. [DELTA]Debt
11. [DELTA]Cost of goods
12. [DELTA]Plant and
13. [DELTA]Account
14. [DELTA]Inventory
15. [DELTA]R&D               0.06
16. [DELTA]Product           0.04   0.21
17. [DELTA]Int'l            -0.12   0.31   0.30

n = 210. Correlation coefficients >0.135 are (in absolute value)
significant at p < 0.05; those greater than 0.18 are significant at
p < 0.01

R&D intensity, no. of patents and Technology Impact Index for the
change between the year of 1998-2000

Technology Impact Index measures showcases the broader significance
of a company's patents by examining how often its U.S. patents from
the previous 5 years are cited as "prior art" in the current year's
batch. A value of 1.0 represents average citation frequency, so 1.4
would indicate a company's patents were cited 40 percent more often
than the average, and so on

* Industry dummy variables are not shown because of space limitation

Table 3 Result of hierarchical regression analysis for changes in
international diversification (1995-1997)

Variables                         Model 1             VIF

(Constant)                         0.040 (0.02) *
Dummy: USA                        -0.005 (0.04)       1.445
Dummy: Japan                       0.057 (0.02) ***   1.301
Dummy: chemicals                  -0.002 (0.02)       1.414
Dummy: electronics                 0.013 (0.02)       1.548
Dummy: machinery                   0.014 (0.02)       1.609
Dummy: transportation              0.004 (0.03)       1.309
Dummy: metals                     -0.005 (0.03)       1.337
Dummy: measurement                -0.003 (0.03)       1.273
Dummy: petroleum                  -0.018 (0.01) *     1.375
Dummy: tech, service               0.075 (0.04) *     1.214
[DELTA]Firm size: Ln (sales)       0.170 (0.06) **    1.281
[DELTA]Return on assets            0.003 (0.00)       1.264
[DELTA]Tobin's q                   0.093 (0.01) ***   1.251
[DELTA]Debt leverage              -0.002 (0.01)       1.120
[DELTA]Cost of goods               0.399 (0.15) **    1.238
[DELTA]Plants and equipments       0.621 (0.33) (+)   1.252
[DELTA]Account receivable          0.000 (0.00)       1.140
[DELTA]Inventory turnover         -0.023 (0.01) ***   1.202
[DELTA]Product diversification     0.311 (0.08) ***   1.224
[DELTA]R&D intensity
[DELTA]Number of patents
[DELTA]Technology impact index

Adjusted [R.sup.2]                 0.3621
F-ratio                            7.3982 ***

                                  Hypothesis 1

Variables                         Model 2             VIF

(Constant)                         0.040 (0.02) *
Dummy: USA                         0.013 (0.00) *     1.456
Dummy: Japan                      -0.042 (0.02) *     1.324
Dummy: chemicals                   0.191 (0.07) **    1.494
Dummy: electronics                 0.021 (0.02)       1.442
Dummy: machinery                   0.042 (0.02) (+)   1.541
Dummy: transportation              0.014 (0.02)       1.549
Dummy: metals                      0.191 (0.07) **    1.494
Dummy: measurement                 0.003 (0.03)       1.329
Dummy: petroleum                   0.005 (0.00) *     1.381
Dummy: tech, service               0.031 (0.04)       1.244
[DELTA]Firm size: Ln (sales)       0.166 (0.06) **    1.300
[DELTA]Return on assets            0.002 (0.00)       1.332
[DELTA]Tobin's q                   0.095 (0.01) ***   1.310
[DELTA]Debt leverage              -0.002 (0.01)       1.121
[DELTA]Cost of goods               0.401 (0.15) **    1.239
[DELTA]Plants and equipments       0.625 (0.33) (+)   1.254
[DELTA]Account receivable          0.000 (0.00)       1.154
[DELTA]Inventory turnover         -0.024 (0.01) ***   1.202
[DELTA]Product diversification     0.306 (0.09) ***   1.248
[DELTA]R&D intensity               0.238 (0.08) **    1.250
[DELTA]Number of patents
[DELTA]Technology impact index

Adjusted [R.sup.2]                 0.3601
F-ratio                            7.0102 ***

                                  Hypothesis 2

Variables                         Model 3             VIF

(Constant)                         0.056 (0.02) **
Dummy: USA                        -0.011 (0.02)       1.444
Dummy: Japan                       0.075 (0.02) ***   1.232
Dummy: chemicals                  -0.001 (0.03)       1.420
Dummy: electronics                 0.014 (0.03)       1.523
Dummy: machinery                   0.038 (0.02) (+)   1.553
Dummy: transportation              0.003 (0.04)       1.321
Dummy: metals                     -0.008 (0.03)       1.383
Dummy: measurement                 0.000 (0.04)       1.336
Dummy: petroleum                   0.077 (0.04) (+)   1.215
Dummy: tech, service               0.001 (0.03)       1.528
[DELTA]Firm size: Ln (sales)       0.225 (0.08) **    1.271
[DELTA]Return on assets           -0.001 (0.00)       1.407
[DELTA]Tobin's q                   0.106 (0.02) ***   1.463
[DELTA]Debt leverage              -0.005 (0.01)       1.135
[DELTA]Cost of goods               0.478 (0.21) *     1.492
[DELTA]Plants and equipments       0.603 (0.41)       1.420
[DELTA]Account receivable          0.001 (0.00)       1.146
[DELTA]Inventory turnover         -0.031 (0.01) ***   1.278
[DELTA]Product diversification     0.296 (0.09) ***   1.259
[DELTA]R&D intensity               0.218 (0.07) **    1.203
[DELTA]Number of patents           0.003 (0.00) *     1.108
[DELTA]Technology impact index

Adjusted [R.sup.2]                 0.4327
F-ratio                            8.0693 ***

                                  Hypothesis 3

Variables                         Model 4             VIF

(Constant)                         0.041 (0.02) *
Dummy: USA                         0.012 (0.00) *     1.444
Dummy: Japan                       0.077 (0.02) ***   1.230
Dummy: chemicals                  -0.027 (0.03)       1.579
Dummy: electronics                 0.010 (0.03)       1.529
Dummy: machinery                   0.020 (0.03) *     1.591
Dummy: transportation             -0.009 (0.04)       1.341
Dummy: metals                     -0.017 (0.03) *     1.381
Dummy: measurement                -0.009 (0.04)       1.324
Dummy: petroleum                   0.057 (0.04)       1.242
Dummy: tech, service              -0.006 (0.03)       1.485
[DELTA]Firm size: Ln (sales)       0.209 (0.08) **    1.283
[DELTA]Return on assets           -0.002 (0.00)       1.437
[DELTA]Tobin's q                   0.103 (0.02) ***   1.477
[DELTA]Debt leverage              -0.005 (0.01)       1.134
[DELTA]Cost of goods               0.416 (0.21) *     1.516
[DELTA]Plants and equipments       0.595 (0.41)       1.420
[DELTA]Account receivable          0.000 (0.00)       1.161
[DELTA]Inventory turnover         -0.027 (0.01) ***   1.333
[DELTA]Product diversification     0.258 (0.09) **    1.286
[DELTA]R&D intensity               0.226 (0.07) **    1.216
[DELTA]Number of patents           0.004 (0.00) *     1.123
[DELTA]Technology impact index     0.152 (0.05) **    1.417

Adjusted [R.sup.2]                 0.4453
F-ratio                            8.2536 ***

n = 210. Unstandardized regression coefficients are shown. Standard
errors are in parentheses

VIF indicates variance inflation factor to detect multicollinearity
(VIF >10 is considered unsatisfactory)

Technology Impact Index measures showcases the broader significance
of a company's patents by examining how often its U.S. Patents from
the previous 5 years are cited as "prior art" in the current year's
batch. A value of 1.0 represents average citation frequency, so 1.4
would indicate a company's patents were cited 40 percent more often
than the average, and so on

Significance level (+) p < 0.1, * p < 0.05, ** p < 0.01, *** p < 0.001

Table 4 Results of hierarchical regression analysis for changes in
R&D intensity

Hypothesis 4

Independent variables                   Model 1              VIF

(Constant)                               0.005 (0.002) **
Dummy: USA                               0.013 (0.006) *     1.346
Dummy: Japan                             0.002 (0.001) *     1.305
Dummy: chemicals                         0.003 (0.009)       1.713
Dummy: electronics                       0.002 (0.001) *     1.628
Dummy: machinery                        -0.001 (0.009)       1.577
Dummy: transportation                    0.004 (0.002) **    1.213
Dummy: measurement                       0.001 (0.009)       1.471
Dummy: petroleum                        -0.004 (0.002) *     1.939
Dummy: metals                            0.001 (0.009)       1.576
Dummy: tech, service                    -0.003 (0.001) **    1.355
[DELTA]Firm size: Ln (sales)             0.200 (0.083) *     1.212
[DELTA]Return on assets                 0.014 (0.005) **    1.253
[DELTA]Tobin's q                        -0.042 (0.018) *     1.191
[DELTA]Debt leverage                     0.001 (0.000)       1.074
[DELTA]Cost of goods                    -0.018 (0.014)       1.133
[DELTA]Plant and equipments              0.089 (0.021) ***   1.364
[DELTA]Account receivable                0.001 (0.000)       1.137
[DELTA]Inventory turnover               -0.015 (0.006) **    1.274
[DELTA]Product diversification           0.002 (0.006)       1.161
[DELTA]Int'l diversification
Adjusted [R.sup.2]                       0.2842
F-value                                  4.2312 ***
[DELTA] in adjusted [R.sup.2]
Partial F due to [DELTA] in [R.sup.2]

Independent variables                   Model 2              VIF

(Constant)                               0.002 (0.001) **
Dummy: USA                               0.017 (0.007) **
Dummy: Japan                             0.003 (0.001) **
Dummy: chemicals                         0.001 (0.008)
Dummy: electronics                       0.003 (0.001) **
Dummy: machinery                        -0.001 (0.008)
Dummy: transportation                    0.005 (0.002) **
Dummy: measurement                       0.002 (0.008)
Dummy: petroleum                        -0.004 (0.002) *
Dummy: metals                            0.000 (0.009)
Dummy: tech, service                    -0.002 (0.001) (+)
[DELTA]Firm size: Ln (sales)             0.215 (0.083) **    1.230
[DELTA]Return on assets                  0.013 (0.005) *     1.272
[DELTA]Tobin's q                        -0.001 (0.001)       1.365
[DELTA]Debt leverage                     0.000 (0.000)       1.075
[DELTA]Cost of goods                    -0.009 (0.013)       1.143
[DELTA]Plant and equipments              0.070 (0.019) ***   1.391
[DELTA]Account receivable                0.000 (0.000)       1.139
[DELTA]Inventory turnover               -0.017 (0.006) **    1.331
[DELTA]Product diversification           0.002 (0.005)       1.161
[DELTA]Int'l diversification             0.024 (0.004) ***   1.427
Adjusted [R.sup.2]                       0.3362
F-value                                  5.7553 ***
[DELTA] in adjusted [R.sup.2]            0.0521
Partial F due to [DELTA] in [R.sup.2]    6.2630 **

n = 210. Unstandardized regression coefficients are shown. Standard
errors are in parentheses

VIF indicates variance inflation factor to detect multicollinearity
(VIF >10 is considered unsatisfactory)

Significance level (+) p < 0.1, * p < 0.05, ** p < 0.01, *** p < 0.001

Table 5 Results of negative binomial regression analysis for changes
in the number of patents

Variables                         Model 1                VIF

(Constant)                           3.302 (0.274) ***
Dummy: USA                           0.496 (0.219) *
Dummy: Japan                        -0.453 (0.298)
Dummy: chemicals                     0.187 (0.304)
Dummy: electronics                   0.970 (0.314) **
Dummy: machinery                     1.255 (0.334) ***
Dummy: transportation                0.724 (0.317) **
Dummy: measurement                   0.569 (0.582)
Dummy: petroleum                    -1.346 (0.388) ***
Dummy: metals                       -1.982 (0.983) **
Dummy: tech, service                 0.563 (0.376)
[DELTA]Firm size: Ln (sales)         1.276 (0.536) **    1.421
[DELTA]Return on assets              0.070 (0.041) (+)   1.311
[DELTA]Tobin's q                     0.126 (0.171)       1.316
[DELTA]Debt leverage                 0.124 (0.090)       1.498
[DELTA]Cost of goods                -7.166 (3.040) *     1.149
[DELTA]Plant and equipments         -7.326 (3.850) *     1.408
[DELTA]Account receivable           -0.015 (0.010)       1.598
[DELTA]Inventory turnover           -0.130 (0.069) (+)   1.213
[DELTA]Product diversification       1.286 (0.538) **    1.436
[DELTA]R&D intensity                 2.123 (0.834) *     1.344
[DELTA]Int'l diversification
Chi square                         121.469 ***
Log likelihood                    -831.178

Variables                         Model 2                VIF

(Constant)                           3.277 (0.272) ***
Dummy: USA                           0.610 (0.224) **
Dummy: Japan                        -1.359 (0.569) *
Dummy: chemicals                     0.206 (0.302)
Dummy: electronics                   1.110 (0.324) ***
Dummy: machinery                     1.478 (0.351) ***
Dummy: transportation                0.723 (0.325) **
Dummy: measurement                   0.538 (0.587)
Dummy: petroleum                    -1.174 (0.395) **
Dummy: metals                       -1.158 (0.586) **
Dummy: tech, service                 0.626 (0.378) (+)
[DELTA]Firm size: Ln (sales)         0.954 (0.489) *     1.498
[DELTA]Return on assets              0.063 (0.041)       1.313
[DELTA]Tobin's q                     0.313 (0.199)       1.430
[DELTA]Debt leverage                 0.142 (0.091)       1.511
[DELTA]Cost of goods                -8.119 (3.100) **    1.158
[DELTA]Plant and equipments         -7.533 (3.910) *     1.416
[DELTA]Account receivable           -0.018 (0.010) (+)   1.612
[DELTA]Inventory turnover           -0.167 (0.074) *     1.314
[DELTA]Product diversification       1.342 (0.623) **    1.438
[DELTA]R&D intensity                 2.248 (0.883) **    1.434
[DELTA]Int'l diversification         1.230 (0.484) *     1.554
Chi square                         126.901 ***
Log likelihood                    -828.462

n = 210. Unstandardized regression coefficients are shown. Standard
errors are in parentheses R&D intensity for the year between

VIF indicates variance inflation factor to detect multicollinearity
(VIF >10 is considered unsatisfactory

Significance level (+) p < 0.1, * p < 0.05, ** p < 0.01, *** p < 0.001

Table 6 Results of hierarchical regression analysis for changes in
Technology Impact Index

Hypothesis 6

Variables                                Model 1              VIF

(Constant)                                0.023 (0.010) **
Dummy: USA                                0.022 (0.011) **
Dummy: Japan                              0.034 (0.016) *
Dummy: chemicals                          0.068 (0.193)
Dummy: electronics                        0.119 (0.090) *
Dummy: machinery                         -0.169 (0.194)
Dummy: transportation                     0.078 (0.035) **
Dummy: measurement                        0.042 (0.028) **
Dummy: petroleum                         -0.120 (0.058) *
Dummy: metals                             0.041 (0.200)
Dummy: tech, service                     -0.090 (0.198)
[DELTA]Firm size: Ln (sales)              0.019 (0.009) *     1.525
[DELTA]Return on Assets                   0.021 (0.006) ***   1.316
[DELTA]Tobin's q                         -0.064 (0.019) ***   1.462
[DELTA]Debt leverage                      0.015 (0.028)       1.529
[DELTA]Cost of goods                      0.641 (0.309) *     1.166
[DELTA]Plant and equipments               0.073 (0.388)       1.429
[DELTA]Account receivable                 0.000 (0.003)       1.621
[DELTA]Inventory turnover                -0.012 (0.006) *     1.335
[DELTA]Product diversification            0.289 (0.123) *     1.439
[DELTA]R&D intensity                      0.035 (0.017) *     1.413
[DELTA]Int'l diversification
Adjusted [R.sup.2]                        0.3283
F-value                                   5.7612 ***
[DELTA] in adjusted [R.sup.2]
Partial F due to [DELTA] in [R.sup.2]

Variables                                Model 2              VIF

(Constant)                                0.023 (0.011) **
Dummy: USA                                0.019 (0.010) *
Dummy: Japan                              0.028 (0.014) *
Dummy: chemicals                          0.050 (0.181)
Dummy: electronics                        0.110 (0.050) *
Dummy: machinery                         -0.156 (0.182)
Dummy: transportation                     0.054 (0.025) **
Dummy: measurement                        0.032 (0.018) *
Dummy: petroleum                         -0.124 (0.057) *
Dummy: metals                             0.009 (0.188)
Dummy: tech, service                     -0.087 (0.186)
[DELTA]Firm size: Ln (sales)              0.021 (0.006) ***   1.322
[DELTA]Return on Assets                   0.019 (0.006) ***   1.326
[DELTA]Tobin's q                         -0.048 (0.018) **    1.506
[DELTA]Debt leverage                      0.018 (0.027)       1.530
[DELTA]Cost of goods                      0.608 (0.310) *     1.170
[DELTA]Plant and equipments               0.187 (0.365)       1.435
[DELTA]Account receivable                -0.001 (0.003)       1.634
[DELTA]Inventory turnover                -0.013 (0.005) *     1.344
[DELTA]Product diversification            0.319 (0.115) **    1.443
[DELTA]R&D intensity                      0.033 (0.016) *     1.415
[DELTA]Int'l diversification              0.303 (0.118) **    3.078
Adjusted [R.sup.2]                        0.3671
F-value                                   6.3214 ***
[DELTA] in adjusted [R.sup.2]             0.0388
Partial F due to [DELTA] in [R.sup.2]     6.2529 **

n = 210. Unstandardized regression coefficients are shown. Standard
errors are in parentheses R&D intensity for the year between

VIF indicates variance inflation factor to detect multicollinearity
(VIF >10 is considered unsatisfactory

Significance level (+) p < 0.1, * p < 0.05, ** p < 0.01, *** p < 0.001
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Title Annotation:RESEARCH ARTICLE; multinational corporation
Author:Sambharya, Rakesh B.; Lee, Jooh
Publication:Management International Review
Geographic Code:1USA
Date:Mar 1, 2014
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