Firm-level Factors Affecting Foreign Expansion into
Emerging Markets: The Case of Western European
Multinationals
Sonja Kruetzen 10868569 Date: 31/08/2015
MSc. Business Administration: International Management University of Amsterdam
Master’s Thesis
Supervisor: Dr. Niccolò Pisani Second Assessor: Dr. Ilir Haxhi
STATEMENT OF ORIGINALITY
This document is written by Student: Sonja Kruetzen who declares to take full responsibility for the contents of this document.
I declare that the text and the work presented in this document is original and that no sources other than those mentioned in the text and its references have been used in creating it.
The Faculty of Economics and Business is responsible solely for the supervision of completion of the work, not for the contents.
ABSTRACT
This research addresses the question of whether firm-level factors such as technological, human, and financial capital have an effect on the expansion of Western European MNEs into emerging markets. Having assessed 2013 data from the 100 largest companies in a number of Western European countries (the Netherlands, Germany, France, Italy, Spain and the UK), the study found that firm-level factors do not yield a significant influence on expansion into emerging markets. Possible alternative drivers are discussed in the concluding section of the thesis.
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Keywords: Emerging Markets, Foreign Direct Investment, Multinational Enterprise,
CONTENTS
1. INTRODUCTION ... 7
2. LITERATURE REVIEW ... 9
2.1 Foreign direct investment and expansion into emerging markets ... 9
2.1.1 General push and pull factors of foreign direct investment ... 9
2.1.2 FDI in emerging markets ... 10
2.1.3 Risks of FDI in emerging markets ... 11
2.2. Firm-level factors and their effect on internationalization paths ... 12
2.2.1 Technological capital ... 13
2.2.2. Human capital ... 16
2.2.3 Financial capital ... 19
2.3 Research gap ... 21
3. THEORETICAL FRAMEWORK ... 22
3.1 Technological capital of the firm and its effect on expansion into emerging markets ... 22
3.2 Human capital and its effect on expansion into emerging markets... 24
3.3 The moderating effect of financial / tangible resources ... 26
4. METHODS ... 30
4.1. Sample and data collection ... 30
4.2 Measures ... 30
4.2.1 Dependent variable ... 30
4.2.2. Independent variables ... 31
4.2.3 Moderating variable ... 32
4.2.4 Control variables ... 33
4.3 Statistical analysis and results ... 33
4.3.1 Correlations and descriptive statistics ... 33
4.3.2 Hypothesis testing ... 34
5. DISCUSSION ... 40
5.1 Academic relevance ... 40
5.1.1 Discussion of the hypotheses ... 41
5.1.2 Regionalization tendencies among Western European MNEs ... 43
5.1.3 The effect of micro-, meso- and macro level factors on firm expansion into emerging economies ... 44
5.2 Managerial implications ... 47
5.3 Limitations and suggestions for future research ... 48
ACKNOWLEDGEMENTS ... 53 REFERENCES ... 54
LIST OF FIGURES AND TABLES
Figure 1. Conceptual Model………28 Table 1. Descriptive statistics: means, standard deviations and correlations………..38 Table 2. Results of OLS regression……….39
1. INTRODUCTION
Extensive research has been conducted into the pull factors affecting foreign direct investment in emerging markets. However, firm-level push factors which could influence expansion into emerging markets, such as technological and human capital, remain relatively unexplored. Previous research has found various reasons for expansion into emerging economies, such as the phenomenon that some Western European MNEs encounter saturation in their home and regional markets (Du, Lu & Tao, 2008). Hence these MNEs seek new target markets in new geographical regions. A potentially lucrative investment region for Western European MNEs are emerging economies such as Brazil, Russia, India and China, also known as BRIC countries (Ranjan & Agrawal, 2011). According to Ranjan and Agrawal (2011), these countries provide large consumer markets, big land size and fast economic growth. BRIC countries could thus potentially be of high interest for investment-seeking Western European MNEs. In addition, foreign direct investment in emerging economies can bring in knowledge and international experience which helps these emerging countries to integrate into international networks of production and trade (Hanousek, Kocenda & Maurel, 2010).
Most scholars focused on pull factors such as labour arbitrage (Ghemawat, 2003), the attractiveness of large consumer markets (Ranjan & Agrawal, 2011) and tax incentives (Du et al., 2008). However as there is still a lack of research about the push factors for expansion into emerging economies, this thesis aims to find out more about detailed firm-level push factors and what triggers firms to conduct FDI in emerging markets.
This thesis research assesses the patterns of FDI in emerging economies shown by the 100 largest companies in each of the following 6 countries: the Netherlands, Germany, France, Italy, Spain and the UK. Linear regression analysis was used to analyse a number of
Western European MNEs’ firm-level characteristics (technological, human and financial capital) and their effect on the MNEs’ emerging market choices.
The outcome of these empirical tests showed that high levels of technological and human capital do not have a significant effect on expansion into emerging economies among the Western European MNEs. However other push factors at firm micro level such as public listing and company experience were found to have a significant influence on expansion into emerging markets. Furthermore this empirical research found that factors at meso level such as the industry sector electricity services and at macro level the MNEs home country Spain were pushing expansion into emerging markets as well.
The following sections outline other scholars’ findings about firm-level factors, emerging markets and the conceptual framework for research. They also present the methodology employed for analysis, the discussion of the hypotheses, managerial implications, limitations, suggestions for future research and the conclusion.
2. LITERATURE REVIEW
2.1 Foreign direct investment and expansion into emerging markets
2.1.1 General push and pull factors of foreign direct investment
In order to lower costs and increase revenues, firms like to expand their market reach by conducting foreign direct investment (FDI). When conducting FDI, firms can internalize markets outside their home country’s borders (Buckley & Casson, 1976). This market internalization can consist of different types of markets such as products (for trade or FDI), capital, labour, and knowledge markets (Ghemawat, 2003). The number of existing Multinational Enterprises (MNEs) has grown from 40,000 with 270,000 foreign affiliates in 1993 to 103,786 companies with 892,114 foreign affiliates in 2010 (Hyun & Hur, 2013). The goods and services exported by these globally active companies account for one third of all world exports (Hyun & Hur, 2013). However, there are still various cultural, economic and institutional barriers to FDI in foreign countries, especially in developing economies. These barriers can also influence a firm’s decision to expand into emerging markets or not.
Various forms of distance (e.g. cultural, economic, institutional or geographical) can make it more difficult for firms to operate in foreign markets (Rugman, Verbeke & Nguyen, 2011). This distance is called Liability of Foreignness (LOF) (Rugman et al., 2011; Zaheer, 1995). Johanson & Vahlne (1977) argue that LOF causes firms to have a tendency to move into geographically or culturally close countries. However, research has also found that certain factors like market potential, a skilled work force, low labour costs and relative endowments exert a pull effect on MNEs’ foreign direct investment (FDI) into certain more distant countries (Carstensen & Toubal, 2003), including emerging economies (Ranjan & Agrawal, 2011). FDI is not only advantageous for the investing MNE, but also benefits the host country by fostering local economic development (Cheng & Kwan, 1999). This occurs
when inward FDI creates an amalgamation of technology, marketing, capital and management (Cheng & Kwan, 1999).
A very important element for MNEs to consider before they make expansion plans is the institutional context of the host country they are trying to enter. Du et al. (2008) point out that MNEs from Western countries such as the US are especially likely to prefer investments in geographical areas that provide good protection for intellectual property rights, a lower degree of government corruption, and a low degree of intervention in business operations. In a later study Du et al. (2012) found that the economic institutions of the host countries are also a crucial determinant of whether FDI can or cannot be conducted. These institutions govern the smooth operation of a market economy, which again covers property rights protection, intervention in business operations, contract enforcement and government efficiency. In addition, Du et al. (2012) apply the Uppsala model (Johanson & Vahlne, 1977) to argue that home countries which are psychologically close to a host country are most likely to succeed with FDI in that particular host country. They further argue that FDI also brings access to resources, markets, technology, and training or improvement of human capital (Stiglitz, 2000). Hanousek et al. (2010) point out that these developments encourage a dramatically increased degree of openness where foreign investors participated in various forms of privatization which resulted in new ownership structures which impacted economic performance in varied ways.
2.1.2 FDI in emerging markets
In the past 20-30 years, the propensity to conduct FDI in emerging markets has risen (Luo, Chung & Sobczak, 2009). Strong FDI inflows in transition countries were driven by a real estate boom, massive privatization, reinvested earnings, commodity investments and a strong FDI influx. FDI is a key agent in achieving a transformation towards a market economy through the creation of international production and trade networks (Hanousek et
al., 2010). With respect to the impact of FDI on emerging economies, Hanousek et al. (2010) found that FDI brings know-how and international experience into the host country, and helps emerging economies to integrate into international networks of production and trade. Among the most favourable FDI target markets are Brazil, Russia, India and China. Together, these four countries make up the BRIC states, characterized by a large population, large consumer markets, fast economic growth and big land size (Ranjan & Agrawal, 2011). Looking at larger emerging economies, Luo et al. (2009) argue that it is more favourable for an MNE to establish a Host Country Headquarter (HCHQ) in order to keep control over their subsidiaries’ activities in these larger countries. They consider HCHQs to be the infrastructure from which the MNE can expand geographically. Luo et al. (2009) also consider it crucial to establish a HCHQ in order to move from foreign competitor status to insider status in the host country. In addition, research has found that transition economies or developing countries have been trying to attract foreign direct investment mainly via tax incentives (Du, Lu & Tao, 2008). Zheng (2013) adds to this by pointing out that FDI from developed economies was mainly driven by labour arbitrage and efficiency-seeking purpose, whereas FDI from developing economies had a market seeking purpose.
2.1.3 Risks of FDI in emerging markets
However, conducting FDI in emerging economies is not without risk, which can be significantly higher than when investing into mature economies. Firms that engage in FDI need to consider the potential risks associated with choosing a location. These risks can consist of heavily fluctuating exchange rates, agency problems, and institutional risks (Jimenez & Delgado-Garcia, 2011). Aizenman (2003) argues that if a market is unstable, MNEs tend to diversify their production of intermediate inputs and invest in several emerging markets. Aizenman (2003) also points out that MNEs usually prefer stable over unstable markets.
In this context, Miller and Bromiley (1990) suggest that a firm with above average returns in its industry will have a lesser risk propensity than firms with lower profits. These high profit firms, they argue, will only accept an increased level of risk when the expected return on investment is attractive. Lun, Hung and Shu (2013) add that if the return given on risk is not high enough, MNEs prefer to conduct FDI in developed economies instead, such as the EU, USA or Japan. This view, however, contradicts the view taken by Hyun and Hur (2013) who argue that highly productive MNEs have a higher propensity to invest in risky and volatile markets than less productive firms. Du et al. (2008) found that Western MNEs in particular have a preference for investing in regions that have better protection for intellectual property rights, low levels of government corruption, and lower degrees of government intervention. The authors point out that investment returns depend critically on the protection of property rights and contract enforcement. Again, this runs counter to the findings of Hahn and Bunyaratavej (2010), who suggest that countries with higher levels of Hofstede’s individualism and power distance and lower levels of uncertainty avoidance are still able to attract higher levels of FDI, even when there might be macroeconomic and linguistic risks present.
The next section outlines other scholars’ findings about the effect of firm-level on foreign direct investment.
2.2. Firm-level factors and their effect on internationalization paths
Research has assessed the pull factors for FDI in emerging economies extensively; however, the push factors determining FDI at company level have gone relatively unnoticed. This literature review focuses particularly on the technological, human, and financial / tangible capital of a firm. Resources can help firms to achieve a competitive advantage if these resources are valuable, rare, imperfectly imitable, or non-substitutable (Barney, 1991). The following sections outline the characteristics of the firm-level determinants of
technological capital, human capital and financial / tangible company resources and their potential effect on FDI.
2.2.1 Technological capital
Technological capital is defined by a firm’s R&D activity, IT capacity and its employees’ technological and operational knowledge. Garcia-Vega (2006) found that companies in imperfectly competitive markets must constantly improve their product portfolio in order to stay competitive and to avoid vulnerability. Therefore firms need to diversify their technological base in order to spread their innovative activities over more than one technology (Garcia-Vega, 2006). She adds that firms which diversify their technological assets benefit from new technological options. Diversified companies benefit more from their own research activities because they are able to harvest more of the social benefits of their innovations. This idea is also corroborated by Rugman and Verbeke (2002), who suggest that new knowledge is needed for the international growth of a firm. They add that the replication of existing knowledge in multiple locations can work in a firm’s favour. Salomon and Martin (2003) elaborate on this by pointing out that successful MNEs expand by replicating accumulated knowledge-based assets in foreign locations and are therefore able to out-compete potential rivals. Salomon and Martin (2003) conclude that this may encourage foreign direct investment. Kogut and Zander (1992), however, argue that firms’ expansion propensity depends on the level of tacitness of knowledge. Tacitness is defined as the observance of a set of rules which are not written down or known as such (Kogut & Zander, 1992). Salomon and Martin (2003) suggest that on the one hand tacitness of technological knowledge makes it easier for firms to protect themselves from imitation and increase their know-how, which underpins a firm’s technological knowledge and other knowledge-based assets. On the other hand, however, tacit knowledge can cause problems when a firm attempts to transfer it to new locations, for example due to knowledge miscommunication or
increased costs in the host location. The authors conclude that too high levels of tacit technological knowledge may hinder foreign direct investment.
In order to accumulate new knowledge, firms need to conduct research and development (R&D) activity to add to their existing technological knowledge (Chang & Lu, 2012). Garcia-Vega (2006) suggests that companies who limit their R&D activity to a smaller number of technological fields can benefit from the specialization of their research activities. This specialization can encourage economies of scale, which can facilitate a learning process and hence ease the transfer of knowledge between core technologies in a firm. This can result in the firm having a technological comparative advantage. Banlieva and Dhanaraj (2013) add that these specialized firms can become technology leaders in their field and ultimately function as trailblazers for other firms to follow into new international markets. This proprietary knowledge gives firms the competitiveness and the resources to expand into all kinds of regions and lets them benefit from scale economies created by their global plant system (Banlieva & Dhanaraj, 2013). Kirca et al. (2011) point out that R&D propensity has an especially strong impact on multinational performance when it is taking place in manufacturing and high-technology-based industries. However, Garcia-Vega (2006) disagrees with this view, pointing out that only half of the R&D projects a firm undertakes are successful. In addition, increasing competition in innovative markets, the risk of imitation and rapid technological change can all be sources of economic depreciation or obsolescence for firms and their technologies (Garcia-Vega, 2006). Still, Banlieva and Dhanaraj (2013) argue that firms’ technological advancements will help them to combine their knowledge on a global scale, reduce the high costs of new product development and find more efficient global distribution channels.
R&D is not the only requirement for increasing a firm’s capacity, however Luo et al. (2012) suggest that that the wider use of information technology (IT) encourages business
transformation and enhances a company’s ability to lower costs and improve its efficiency, making it a crucial element of firms’ technological capital. Mithas et al. (2012) add to this and point out that IT investments can allow firms to achieve revenue growth and cost savings. Advanced IT systems also enable firms to create new value propositions for their customers (Mithas et al., 2012). In particular, many sales and marketing measures to reach customers are IT driven (Cheng & Nault, 2007). Mithas et al. (2012) argue that advanced IT systems are needed to support R&D measures in research-intensive internationally-operating industries, as IT enhances communication between virtual research teams at the firms’ various global locations. The authors add that IT investments may be far more profitable than R&D or advertising investments and should be considered when a firm is evaluated financially by investors.
However, in-house R&D or IT advancements are not the only way of helping companies to become more technologically proficient. A firm’s technological capital can also be fostered by positive spill over effects (Damijan, Rojec, Majcen & Knell 2012) when firms accumulate more knowledge by learning from other companies in their sector. This usually happens when firms agglomerate in the same region (Damijan et al., 2012). In addition to this, firms can also transfer technological knowledge to their subsidiaries or affiliate partners by conducting foreign direct investment or licensing, allowing the transfer of resources to take place under the firm’s ownership and control. This stance, however, is criticized by Garcia-Vega (2006), who points out that the spill over effect can also be negative, as firms may start copying each other. This outcome may be an indicator that their technological knowledge is simpler and more generic. This idea is also partially endorsed by Banlieva and Dhanaraj (2013), who argue that if technological advantage is low, MNEs are forced to fend off other more advanced MNEs who settle in the same region as well as fighting local competition in the host country. There are many contradicting views about a firm’s
technological capital and its effect on FDI. However, most researchers agree that the higher the level of technological advancement of the firm, the higher the propensity to invest abroad will be.
2.2.2. Human capital
In order to steer the technological capital of a firm, highly skilled labour (human capital) is needed. The term “human capital” can be defined as the knowledge, skills and abilities (“KSAs”) embodied in people in an organization (Coff, 2002). In the light of the resource-based view (RBV), Barney (1991) points out that human capital is a key factor in enabling firms to outperform their competitors. The resource-based view suggests that the unequal distribution of human capital among different firms is the major determinant for performance differences (Barney, 1991). Human resources with specific skills cannot be easily transferred to or duplicated by other firms, so that firms with human capital gain a competitive advantage over firms which do not possess skilled human resources (Barney, 1991). The reason for this uneven distribution is that skilled managers and individuals are often in short supply (Crook, Combs, Ketchen, Todd, Woehr, 2011). Chi (1994) adds to this by stating that firm-specific human capital is tied semi-permanently to the firm and therefore difficult to exchange or trade. Jin, Hopkins, and Wittmer (2010), referring to Barney (1991), point out that human capital is an imperfectly imitable resource, as the tacit knowledge developed by social interactions is not easy for other firms to obtain or copy. The development of human capital is unique to each individual in the firm, as it includes elements such as an employee’s background and experience (Jin et al., 2010).
Rompho and Sienthai (2012) point out that the business environment has shifted to a “knowledge economy” and that intellectual capital has become an important determinant in creating competitive advantage and value for companies. In addition to being valuable, rare, imperfectly imitable and non-substitutable, Parent (2002) points out that human capital
contributes to firms’ competitive advantage by providing flexibility. Highly skilled human resources provide adaptive performance, which increases the company’s overall flexibility. This is represented for example in workers’ skill in applying knowledge and capabilities more quickly to newly emerging tasks and in finding new approaches to old tasks. People skills within the human resources department can increase open communication which may decrease resistance to change within a company (Parent, 2002). These people skills are strongly determined by the managers’ and supervisors’ ability to work effectively with people. If the managers succeed, they are able to foster group collaboration, inspire individuals to excel at their work and facilitate communication (Jin et al. 2010). This also requires managers’ conceptual skills, which enable them to view the company from a wider perspective in order to oversee and understand the relationships between departments and their relationships with suppliers and customers (Jin et al. 2010). The managers have to understand that many decisions made in various individual departments will have a direct or indirect effect on other departments in the organization, and therefore require constant monitoring (Jin et al. 2010). In the technological field in particular, human capital is measured by the degree of managers’ and workers’ conceptual, technical and analytical potential, which are important for solving technical problems and making the right decisions (Schulz, Chowdhury & van de Voort, 2013). Barney (1991) points out that by facilitating talents within a firm’s staff and by focusing on core competencies, organizations are enabled to align these skills with its organizational strategy. If implemented successfully, these human resources practices have a direct positive effect on operational performance and firm profitability (Crook et al., 2011). In addition to this, high levels of human capital enhance the likeliness that firms will retain customers and build up long-term customer relationships (Pennings, Lee & Wittelloostuijn, 1998) which ultimately help to build a loyal client base and help to attract new clients.
Human capital can often be measured by employees’- and managers’ salary levels, which relate directly to their level of education and training (Crook et al. 2011). This idea is also backed by Felli and Harris (1996) who point out that wages increase with job market experience. However, employees must match job descriptions to their skills in order to be valuable for the company (Felli & Harris, 1996). Groysberg, McLean and Nohria (2006) differentiate on this point between task-specific human capital and non-task specific human capital. Non-task specific human capital entails experience in tasks that are not widely applicable or relevant to the current job. This type of capital might for example be knowledge about company culture, procedures and policies, formal and informal reporting relationships and organizational systems. Task-specific human capital includes task-based training, level of education in the field relevant to the task, and experience relevant to the task at hand (Groysberg et al., 2006). Groysberg et al. (2006) also found that employees with greater task experience receive higher financial compensation than ones with lower levels of task experience. Parent (2002) adds that the more experience employees collect on the job over the years, the more their salary will rise. This idea is also supported by Schulz et al. (2013) who point out that employee compensation increases with longer organizational tenure as employees develop more skills and knowledge in performing tasks related to the firm’s operations. Schulz et al. (2013) point out that human capital theory is defined by the idea that employees who acquire high levels of general and / or firm-specific human capital will receive higher financial compensation from the firm. In their view, general human capital is applicable in multiple organizational settings, whereas firm-specific human capital has value for only one firm. Task-specific knowledge and a higher education thus seem increasingly desirable as the world turns more and more into a “knowledge society” where human and social capital are crucial for organizational performance and survival (Pennings et al., 1998). However, it is not only workers’ and managers’ skills and expertise which are important to a
business. Pennings et al. (1998) point out that that CEO human capital also enhances organizational performance as the CEO is the one making the ultimate decisions within a company.
In order to measure the level of human capital in a firm, performance measurement systems are implemented (Rompho & Sienthai, 2012). In addition to measuring productivity, salary and product / service output, performance measurement also investigates employee satisfaction. Rompho and Sienthai (2012) consider employee satisfaction as highly important for firms’ productivity, especially in knowledge-intensive industries. According to the two-factor theory put forward by Herzberg (1964), “hygiene two-factors” such as decent working conditions, supervision, and co-workers, appropriate remuneration, fair policies / procedures and job security are essential for keeping up employee morale and productivity. If these factors are lacking or are only insufficiently present, employee dissatisfaction arises (Herzberg, 1964). This can ultimately lead to decreased productivity or even company failure (Herzberg, 1964). Human capital is one of the strongest determinants of a firm’s success. It drives the firm’s productivity, its relationships with clients and the quality of its products and services (Jin et al., 2010). Hence human capital is a core determinant for all further company actions.
2.2.3 Financial capital
Something that is often overlooked in wider research is the role of financial capital and its relation to a company’s FDI behaviour, as well as the influence of firm-level factors such as technological and human capital. Conducting FDI would not be possible without sufficient financial and tangible assets. Assets such as plants, land, stock and equipment are crucial to a firm’s ability to conduct business regionally and globally. Access to financial capital is particularly important, as it increases a firm’s ability and propensity to make foreign investments (Forssbaeck & Oxelheim, 2008). Financial resources give firms more leeway to
experiment with their investments and test their investments in various target markets (Oxelheim, 2001). Forssbaeck and Oxelheim (2008) extend their idea further and state that financial strength generates advantages that can be exploited through foreign direct
investment activity. Financial strength can also be considered as an ownership advantage
under Dunning’s (2000) eclectic paradigm, or ownership - internalization – location - advantages matrix. A firm’s ability to conduct FDI depends on it having developed a sufficient specific ownership advantage (good financial standing). Wagner (2004) adds that a firm’s financial costs in its home country, especially its labour costs, can be a push-factor to move into lower labour cost countries / emerging economies. Western European countries like Germany face particularly high labour costs due to obligatory social security charges which come on top of salaries and wages (Wagner, 2004). The financial standing of a firm is also strongly determined by the strategy taken by the board of directors and by the CEO (Leng & Mansor, 2005). CEOs exert an influence on the strategic steering of the firm, which can ultimately affect its profits (Leng & Mansor, 2005). In turn, profits determine a company’s liquidity and ultimately its equity capital. In order to assess the concept of financial capital further, it is necessary to look at the level of firms’ gearing. The higher the equity capital and the lesser the long term liability, the better a firm’s gearing level is (Devan & Danbolt, 2002). High levels of gearing reduce financial flexibility because the utilization of current borrowing capacity will result into less availability in the future (Moradoglu & Sivaprasat, 2012). Moradoglu & Sivaprasat also argue that it is possible to distinguish between firm and industry gearing. Industry gearing measures the firms’ financial risk in the industry. Some capital-intensive industries normally have higher levels of gearing. Companies that make use of higher gearing levels to finance their assets pose a higher risk to equity holders. The financing choices a firm makes depend partially on its financing structure as well as the industrial sector it belongs to (Moradoglu & Sivaprasat, 2012). Kale and Sharur
(2007) argue that firms in highly concentrated supplier and customer industries are more likely to have higher debt ratios. However, the general tendency is for firms to have a preference for low gearing to avoid bankruptcy and retain a line of credit which grants them financial flexibility (Moradoglu & Sivaprasat, 2012). Thus the lower a firm’s gearing percentage is, the more likely it is to spend its excess money on new investment projects such as foreign direct investment (Forssbaeck and Oxelheim, 2008).
2.3 Research gap
The review of the literature suggests that the direct effect of firm-level factors on firms’ expansion propensity into emerging markets remains relatively unexplored. Many researchers focus on the country-level pull factors for investment in emerging markets, such as low wages (Ghemawat, 2003), tax incentives and attractive institutional environments (Du et al., 2008) and a large consumer market (Ranjan & Agrawal, 2011). However, only a few researchers have investigated how a firm’s human and technological capital can increase its foreign direct investment and the expansion propensity. This empirical research therefore aims to find out more about how and if human and technological capital drive foreign direct investment into emerging markets. In addition, it aims to assess whether firms’ financial standing has an effect on the relationship between firm-level factors and expansion into emerging markets.
3. THEORETICAL FRAMEWORK
3.1 Technological capital of the firm and its effect on expansion into emerging markets
Technological capital can help a firm to achieve success and work more efficiently (Chang & Lu, 2011). Successful MNEs expand by making use of their technological capital, which they can also accumulate in foreign locations (Salomon & Martin, 2003). By doing so, they are able to outcompete rivals in the host locations and gain a competitive advantage (Salomon & Martin, 2003). Garcia-Vega (2006) adds that firms can use their technological research and development programmes as “competitive weapons” against business rivals. Firms with higher levels of technological capital invest more into R&D in order to diversify their research portfolio. Technological diversification can help to prevent a negative “lock-in” effect into one particular technology. In addition, it can help the companies to refocus faster when certain technologies become out dated or useless (Chang & Lu, 2011). Garcia-Vega (2006) points out that particularly in R&D intensive industries such as the pharmaceutical industry, technological diversification will foster innovation and company expansion.
A high degree of technological knowledge also lowers a company’s reluctance to expand into unknown regions, as their proprietary knowledge equips them with the intellectual resources and competitive advantages to enter new markets (Banlieva & Dhanaraj, 2013), which This confidence can be advantageous when expanding into emerging economies: emerging economies, with their large and unsaturated consumer markets (Ranjan & Agrawal, 2011), are in particular need of new products and technologies. Kirca et al. (2011) add that these fast-growing economies do not necessarily have enough local firms with sufficient technological knowledge and capabilities to cater for their fast domestic economic growth. Therefore the presence of foreign firms in these emerging economies is considered highly desirable (Kirca et al., 2011).
Increasing technological advantage is one way in which highly productive Western firms seek to expand their market reach (Garcia-Vega, 2006). They also invest more in R&D activities because the diversification in their research portfolio tends to reduce the risks that come along with expanding R&D projects. A proxy for R&D activity is the number of patents a firm applies for nationally and internationally. Especially when filing for patents internationally, firms have to assess the institutional context of potential host countries (Garcia-Vega, 2006). If a host country offers favourable conditions and firms considering entering that country have high levels of R&D activity, then there is a chance that the firms will move into this country or region, either through sales of their products or by partial or full relocation of their R&D activity to that particular country (Kedia and Mukherjee, 2009). This is especially true for firms which plan to move their R&D activities into emerging economies. Temouri, Diffield and Higon (2010) suggest that emerging economies such as Russia, the Czech Republic or China have a lot of young, highly educated citizens, many of whom hold engineering degrees. These countries are therefore deemed very attractive R&D locations for Western firms. They ground this in the idea that this highly skilled work force is capable of providing value-adding activities at a lower price than Western counterparts in Germany, UK or Spain (Temouri et al., 2010). Consequently, Western firms with high levels of technological capital are more likely to move into these markets due to labour arbitrage (Ghemawat, 2003).
High technological capital can enhance firms’ ability to make accurate risk assessments before entering potentially volatile markets such as emerging economies. Firms’ sophisticated technological structures allow them to spot inefficiencies early on and equip them with various options to respond to these problems (Garcia-Vega, 2010). Hence firms which possess high levels of technological capital have a higher tolerance level for experimenting with expansion into new geographic markets.
The interconnection between seeking new markets, R&D off-shoring and the need for high-level technology in emerging economies leads to the assumption that the higher the technological capital of a firm, the more likely it is to expand into prosperous emerging markets. Hence:
H1a: Ceteris paribus, firms’ technological capital is positively related to international
expansion into emerging markets.
3.2 Human capital and its effect on expansion into emerging markets
Human capital describes the skill level of company employees (Coff, 2002). Coff (2002) considers KSAs (knowledge, skills and abilities), including the experience, education and training managers bring into the firm, as drivers of its strategy and performance. Human capital goes hand in hand with a firm’s the level of technological capital. When knowledge projects are closely tied to a unique technology, organizational division or strategic area, firm-specific human capital which is trained on the technology or project is of high value to the firm (Mayer, Somaya & Williamson, 2012). In addition, Mayer et al. (2012) point out that a firm requires industry-specific human capital in order to be able to execute a knowledge-based project accurately. Knowledge work requires a trade-off between various task elements and requires an excellent understanding of the application domain. Knowledge about the market position, complementary technologies and competitive background of a product or service are therefore crucial (Mayer et al., 2012).
Gattai (2011) points out that firms which possess higher levels of human capital are more likely to operate abroad via FDI. She explains this finding by suggesting that firms need to have knowledgeable, internationally experienced staff in order to understand and adapt to culturally and institutionally distant markets. Especially when it comes to setting up foreign subsidiaries, senior management in the company’s headquarters has to decide whether to hire
local or expatriate staff (Bouquet, Hebert and Delios, 2004). This study also found that foreign firms usually staff their subsidiaries with a certain number of expatriate managers from their home country in order to maintain the firm’s brand identity, which is linked to the host country. A further advantage is that this measure aims to ensure the subsidiary operates in the way the headquarters wish it to operate.
The expatriates who are sent into the host location have to possess a certain level of education and training in order to perform accurately in line with the firm’s rules and regulations (Bouquet et al., 2004). Bouquet et al. also argue that, as suitably trained employees are rare and expensive. These employees are considered to possess high levels of human capital, which is regarded as highly valuable to the firm. Anand and Delios (1997) add that expatriate managers are required to manage the subsidiary and show flexibility in the face of uncertainty and volatility in the new host location. Ultimately, they will be held responsible for the subsidiary’s success or failure, and hence need to be highly skilled and experienced in order to handle the high level of responsibility and to deal with market and institutional conditions which, are often uncertain in emerging economies (Anand & Delios, 1997). In addition to bringing in their existing knowledge, expatriate managers are also expected to accumulate new knowledge in the host location. They are one of the most important elements of the human capital of the firm, as the firm’s expansion success depends critically on the success of the management of its subsidiaries (Schulz et al., 2013). This is in line with the argument made by Felli and Harris (1996), who point out that human capital is only valuable if workers match the specific tasks that are assigned to them. Expatriate managers’ skill and knowledge will ultimately increase the firm’s productivity and long term success in the host location (Bouquet et al., 2004). Expatriate managers have to show a high level of self-motivation and knowledge in order to manage a more productive workforce infrastructure with minimal layers of senior management available (Jin et al. 2010). This
holds true especially for the management of subsidiaries trying to expand into emerging markets. High levels of skill and knowledge are required in order to make accurate expansion decisions into emerging markets.
Therefore I argue that the higher the level of human capital is in a firm, the more likely the firm is capable to invest into unknown emerging markets. Hence:
H2: Ceteris paribus, firms’ human capital is positively related to international expansion in
emerging markets.
3.3 The moderating effect of financial / tangible resources
The effect of firm-level factors such as technological capital and human capital on expansion into emerging economies will vary by the availability of the firms’ financial and tangible resources (Garcia-Vega, 2006; Crook et al., 2011; Oxelheim, 2011). Financial and tangible resources are critical determinants that allow firms to conduct FDI in the first place. Even when the levels of human capital and technological capital are high, firms will have major difficulties expanding abroad without financial capital and tangible assets. Forssbaeck and Oxelheim (2008) argue in the light of Dunning’s (2000) eclectic paradigm that a firm’s financial / tangible resources have to be transferrable and of sufficient magnitude to compensate for any trade barriers or extra costs while doing business abroad. In a later study, Forssbaeck and Oxelheim (2011) add that the financial resources of a firm become especially important when target markets are incomplete and lack international financial integration, which is often the case for emerging economies. They further suggest that financial advantages are more important for firms in knowledge-intensive industries. Oxelheim, Randoy and Stonehill (2001) argue that firms are most likely to engage in FDI when they have cross-listed their stock in a larger and more liquid equity market, when they have access to competitively priced equity, when they are able to obtain a strong investment grade credit
rating, and when firms are able to acquire reduced taxation or even attract subsidies. This idea is supported by Du et al (2008), who point out that favourable financial standing enhances company expansion into host countries with low tax levels. They add that some emerging economies, such as China, keep tax rates and exchange rates artificially low in order to attract FDI from Western economies.
However, there are also downsides to firms which aim to invest in foreign trade possessing high levels of financial capital. Chen and Chen (2012) point out that the more companies diversify their investments abroad, the more conflicts could potentially arise among human resources, for example through disagreements between headquarters and foreign subsidiaries. However, the majority of researchers emphasize that financial strength has a positive effect on foreign direct investment. A firm may face volatility in demand and unstable institutional environments, especially when investing into emerging economies (Jimenez & Delgado-Garcia, 2011). Companies therefore need high levels of financial capital and general liquidity to outbalance fluctuations in income, especially in the first start-up years before a break-even point is reached (Devan and Danbolt, 2002). Oxelheim (2001) adds to this by pointing out that financial resources give firms more financial leeway to invest into new and unknown target markets. A further indicator of the moderating effect of high levels of financial capital on the relationship between firm-level factors and the propensity to invest in emerging markets is the notion that high financial capital is directly related to high levels of human and technological capital. Without financial capital, companies would not be able to attract high-profile employees and pay them high salaries (Rompho & Sienthai, 2012). In addition, high levels of financial capital are needed to create technological capital by pursuing research and development activities, which ultimately enable companies to develop or purchase high-tech gadgets or systems for their work (Crook et al. 2011).
Therefore I suggest that firms with high levels of financial capital and high levels of tangible assets will have higher risk propensity and stronger willingness to invest into financially more volatile markets. I hypothesize that more financial resources will grant firms more capacity to deal with potential financial risks and losses in emerging market investments. Hence:
H3: Ceteris paribus, the availability of financial/ tangible resources positively moderates the
relationships hypothesized in H1 and H2.
The hypotheses discussed in the previous chapter yield the following conceptual model:
Accordingly, I hypothesize that firm-level factors such as high levels of technological capital (H1) will have a positive effect on FDI propensity into emerging markets. Furthermore, I hypothesize that higher levels of skilled human capital (H2) will encourage firms to expand into emerging markets. In addition to this, I assume that the effect of both firm-level factors
H2 H1
H3 H3
Technological
Capital of the Firm
Human Capital of
the Firm
Expansion into
Emerging Markets
Financial / Tangible Resources
on expansion into emerging markets is positively moderated by high levels of financial and tangible capital (H3).
4. METHODS
4.1. Sample and data collection
The sample analysed for this research consists of the 100 biggest companies of Germany, the U.K., France, Italy, The Netherlands and Spain as listed in 2013. In total, data from about 711 companies was collected. In order to select this sample from the wider population of Western European companies, the database Orbis was consulted. Orbis provides information about the location, industry sector and financial standing of companies worldwide. The Western European MNEs in Orbis were ranked according to country and annual revenue, from which a non-probability sample was drawn. In order to find further information on the MNEs’ sales regions and their spending on administration, R&D, advertising and labour costs, the annual reports of 2013 of the firms were retrieved from their websites. The majority of the company data collected from Orbis comes from multinational corporations, which by definition produce and sell their products / services nationally and internationally (Dunning, 2009). The MNEs analysed in this sample belong to a large variety of industries; the sample can therefore give a good insight into the influential effect of industry level on expansion into emerging markets.
In total 711 companies were analysed. They originated from France (19.1%), the UK (18%), Germany (16.2%), the Netherlands (15.9%), Spain (15.5%) and Italy (15.3%).
4.2 Measures
4.2.1 Dependent variable
The dependent variable in this thesis research is expansion into emerging markets. To determine if a firm invests in emerging markets I assessed the data about the firm’s geographical investment regions. The established geographical sales regions at the point of data collection were Europe, North America, South and Central America, Asia, Oceania, and
other regions. As many emerging economies are located in Asia and in South / Central America (Ranjan & Agrawal, 2011), I established these two geographical regions as the potential emerging market sales locations for the Western European MNEs. The use of sales regions to analyse firms’ international geographic activity is in line with previous studies (Tan & Meyer, 2011). To operationalize emerging market sales activity, I compared the number of sales in these particular emerging markets with the companies’ total sales in other sales regions. The higher the sales ratio in emerging markets is compared to other sales regions, the more expansion into emerging markets is taking place. After eliminating for incomplete observations in the variable expansion into emerging markets, data could be retrieved from 185 firms.
4.2.2. Independent variables
The independent variables used in this thesis research are the firm-level factors. The concept of firm-level factors is determined by the firms’ technological- and human capital.
In order to analyse the concept of human capital, the cost of employee remuneration was obtained. This is in line with the study conducted by Crook et al. (2011) who point out that human capital can often be measured by the level of salary of employees / managers, which relates directly to their level of education and training. In order to operationalize the concept of human capital, I calculated a ratio by dividing the cost of employee remuneration by 100. After eliminating for incomplete observation, it was possible to generate data from 501 firms in the data set.
In order to assess technological capital of a firm, R&D spending of the various Western European MNEs was collected. In this study I calculated a ratio of R&D spending divided by total sales. Doing so made the overall quantity of R&D spending in relation to total sales visible. This ratio is used as an indicator for the independent variable technological
capital of the firm. Data about the technological capital of the Western European MNEs is
available for 216 firms.
The two ratios of human capital and technological capital of the firm serve to operationalize the firm-level factors as independent variables in this research. Based on this information, this study tested the effect of these concepts on the dependent variable MNEs’
expansion into emerging markets.
4.2.3 Moderating variable
To operationalize the concept of financial / tangible resources of a firm, the level of gearing of the companies was collected. Gearing represents the level of equity capital and long term / short term liabilities of a firm (Devan and Danbolt, 2002). This means that gearing shows how much the company is funded by the owners in relation to how much it is funded by creditors. A high level of gearing is usually a negative sign, as companies will have to invest most of their earnings to pay off their debt instead of being able to invest their capital into new projects such as business expansion (Muradoglu & Sivaprasad, 2012). Additionally, a high level of gearing could put a company at financial risk, as the company is likely to become too dependent on loans (Devan and Danbolt, 2002). The gearing of a firm is normally stated as a percentage. In this research I subtract the gearing percentage from 1 and divide it by 100 in order to make the level of gearing measurable as a ratio for the empirical tests for this research [1 – (gearing / 100)]. This research establishes that the lower the level of gearing is, the more positively the relationship between the independent variables (firm-level factors: technological and human capital) and the dependent variable (expansion into emerging markets) is moderated by it. After eliminating for incomplete observations in the variable financial capital, information about 554 firms could be generated.
4.2.4 Control variables
To make sure that no other factors influence the causality hypothesized between firm-level factors and expansion into emerging markets, this research controls for the effect of industry, home country, company experience, public listing, firm size and family ownership or public ownership. To control for industry, the standard industry code (SIC) of every company in the data set was collected. The experience of the company was operationalized by company age. This empirical research was also controlled for public ownership and for the Western European MNE’s country of origin by means of the country code. In addition, control variables were established for family ownership and state ownership.
4.3 Statistical analysis and results
4.3.1 Correlations and descriptive statistics
This section presents the statistical analysis and results. The descriptive statistics for the independent, dependent and control variables are presented in Table 1. The variables are analysed to see whether multicollinearity (Pallant, 2010) is taking place. Multicollinearity might influence or corrupt the outcome of multiple regression tests, as it considers highly correlated variables as undesirable (Pallant, 2010). As it can be seen in the table, none of the variables correlate higher than .7 with each other; hence it can be assumed that no collinearity or multi-collinearity is taking place.
When looking at the descriptive statistics in Table 1, it can be seen that the average Western European MNE is 51 years old and employs 40,592 employees. The average sales of Western European MNEs in emerging markets range around 15.35% (2,645.72 mil. USD) of the total sales. In this research, emerging markets are defined as Asia and South / Central America. There is barely any difference in sales between the two regions. Average sales in
South and Central America account for 12.8% (2,208.74 USD million) and average sales in Asia rank slightly higher at 13.3 % (2,286.98 USD million) of the total sales.
When looking at the internationalization patterns of Western European MNEs, this research found that the average sales outside the home country in relation to total sales inside the home country rank at 63.93%. However the MNEs’ regional sales are still slightly higher, at 53.17%, when compared to remaining global sales at 46.83%. More than half (63%) of the Western European MNEs are publicly listed on their local stock exchange. However, only 20.9% of the MNEs are listed on foreign stock exchanges. When looking at ownership status, one can see that 22.3% of the companies are family owned and 9.1% are owned by the state.
The average gearing level of the Western European MNEs ranges around 134%. Firms spend an average of 2,058.95 USD million on research and development projects and an average of 17,232.74 USD million on labour expenses. The major industry sectors of the largest Western companies are holding firms, which make up 9% of all companies of the data set. These are followed by insurance carriers (4%), electric services (3%), commercial banks, motor vehicles and motor equipment (2%), computer programming (2%), heavy construction (2%) and air transportation (1%).
4.3.2 Hypothesis testing
After controlling for company size, foreign listing, state ownership, family ownership, company age, industry level and home country, a hierarchical multiple regression was performed to investigate the effect of the firm-level factors technological capital and human capital on expansion into emerging markets. To prepare the data for testing, all the variables were transformed into z-scores in order to standardize them. This measure lowers the skewness of the data and creates a normally distributed model where the standard deviation is 1 (Pallant, 2010). As the dependent, independent and moderator variable are continuous, this
research assumes a linear relationship, and therefore chose to apply linear regression analysis. To test the effect of the moderator variable, interaction terms were computed by calculating the product of each of the independent variables (Technological and Human Capital) and the moderating variable (Financial Capital).
The regression analysis was conducted in three steps. First the control variables were tested against the dependent variable to observe their effect. In the second step the independent variable was added to test it and the control variables against the dependent variable. In the third test the interaction term and the moderating variable were added to the model and tested together with their corresponding independent variable and the control variables to look for a moderating effect on the relationship between the independent and the dependent variable. The last two steps were performed for each H1 and H2, so that the testing procedure produced 5 models. Table 2 presents the results of the regression analysis. The beta coefficient indicates the change in the dependent variable in relation to the independent variable. The significance value shows whether the results found are reliable and whether they support the hypotheses. The R² measures the goodness of fit of the model and to which extent variance is explained.
The results of the hierarchical regression analysis indicate that most of the factors included have no additional explanatory power as their significance is over the .05 level.
The first step, of the analysis in Model 1 tests for the influence of the control variables on the dependent variable Expansion into emerging markets. Looking at the influence of the control variables on expansion into emerging markets revealed significant influence from the variables Public listing (b = .287; p = .000), Company experience (b = .103; p = .025) and
Home country Spain (b = .149; p = .008). Further significance at the 10% level was found for
services (b = .074; p = 095). The effect of the control variables on the independent variable in
Model 1 were found to be highly significant at the p = .000 level.
After controlling for these factors, Hypothesis 1 was tested and the first independent variable, Technological Capital, was introduced in Model 2. Hypothesis 1 states that firms’ technological capacity is positively related to expansion into emerging markets. Introducing the independent variable Technological Capital to Model 2 barely makes any improvement: the model barely moves by .001 from R² = .134 to R² = .135. The coefficient of the effect of technological capital of Hypothesis 1 was found to be insignificant (b = .040; p = .353) in Model 2. It can be concluded that the firm-level factor Technological Capital has no influence on expansion into emerging markets. Hence Hypothesis 1 can be rejected.
Hypothesis 3 states that financial capital positively moderates the relationship between firm-level factors and expansion into emerging markets. Introducing the moderator
Financial Capital and its interaction term, only changed R² by .001 from R² = 0.135 in Model
2 to R² = 0.136 in Model 3. When testing for the moderating effect of financial capital on the relationship between the firm-level factor technological capital and expansion into emerging
markets in Model 3, no effect could be found. The moderating variable Financial capital (b =
-.005; p = .915) and its corresponding interaction term Financial capital x Technological
capital (b = .036; p = .600) were added in Model 3, but the variables were found to be
insignificant. Hence it can be concluded that Hypothesis 3 can be disconfirmed.
Hypothesis 2 states that the firm-level factor Human capital has an influence on the variable Expansion into emerging markets. The R² value does not change at all when adding the second independent variable Human Capital to the control variables in Model 4, as the R² value remains at .134. The coefficient of the independent variable Human capital was also
found insignificant in Model 4 (b = -.002; p = .963). It can therefore be concluded that that Hypothesis 2 is disconfirmed.
No significant effect was found by introducing the moderating variable Financial
Capital (b = .010; p = .840) and the interaction term Financial Capital x Human Capital (b =
-1.276; p = .419) in Model 5. Hence it can be concluded that Hypothesis 3 can also be regarded as disconfirmed in Model 5. Once more the R² value only changes by .001 from R² =.134 in Model 4 to R² =.135 in Model 5.
Human capital .3091 4.66484 1 Technological capital .0291 .37723 .352** 1 Expansion into emerging markets .0553 .12083 -.014 .057 1 Financial capital -.3422 1.44128 -.077 -.039 -.049 1 Company size 40592.08 69378.585 .026 .003 .124** -.041 1 Company experience 51.43 52.590 .047 .076* .103** .098* .113** 1 State owned .09 .288 -.010 -.011 -.110** -.052 -.016 -.004 1 Family owned .22 .417 -.023 -.030 -.007 .047 -.048 -.075* -.170** 1 Public listing .63 .483 -.043 .023 .291** .078 .199** .190** -.216** -.184** 1 Foreign listing .21 .407 -.020 .078* .110** -.023 .250** .106** -.077* -.103** .374** 1 Homecountry Spain .15 .362 -.016 -.018 .074* -.181** -.108** -.105** .000 .120** -.058 -.004 1 Homecountry Germany .16 .368 .086* .023 .061 .007 .121** .147** -.014 -.052 .085* -.008 -.188** 1 Homecountry France .19 .394 -.019 -.024 -.004 .037 .151** .077* .101** .051 -.014 -.027 -.208** -.214** 1 Homecountry Italy .15 .361 -.017 .072 -.084* -.056 -.133** -.051 -.013 .148** -.172** .185** -.182** -.187** -.207** 1 Homecountry Netherlands .16 .366 -.019 -.023 -.078* .118** -.147** -.020 .024 -.150** -.069 -.091* -.186** -.191** -.211** -.185** 1 Holding companies .09 .288 -.015 -.023 -.044 -.080 -.005 -.108** .001 .171** -.236** -.077* .148** -.086* -.092* .082* .022 1 Insurance carriers .04 .198 -.013 -.016 -.071 .c -.036 .202** .011 -.092* .016 .080* .010 .083* .026 -.009 -.031 -.065 1 Electricity services .03 .177 .204** .062 -.056 -.103* -.045 -.053 .164** -.080* .042 .049 -.012 -.016 -.069 .099** .029 -.058 -.038 1 p†<0.10; *p<0.05; **p<0.01; ***p<0.001.
Table 2 - Results of OLS Regression
Controls Model 1 Model 2 – H1 Model 3 – H3 Model 4 – H2 Model 5 – H3
Dependent variable: Expansion into emerging markets
Beta Sig. Beta Sig. Beta Sig. Beta Sig. Beta Sig
Control variables Homecountry Spain .149 .008** .148 .008** .147 .009** .149 .008*** .150 .008** Homecountry Germany .072 .186 .072 .190 .073 .186 .072 .186 .068 .221 Homecountry France .015 .790 .015 .783 .015 .790 .015 .790 .014 .807 Homecountry Italy .047 .441 .044 .471 .041 .505 .047 .443 .050 .421 Homecountry Netherlands .001 .982 .002 .975 .001 .987 .001 .983 .001 .987 Motor vehicles and equipment .032 .463 .032 .458 .032 .465 .032 .464 .033 .453 Electricity services .074 .095† -.076 .086† -.071 .120† -.074 .109† -.078 .092† Commercial banks -.055 .194 -.055 .197 -.054 .201 -.055 .194 -.053 .213 Insurance carriers -.071 -.061† -.070 -.061† -.071 -.062† -.071 -.061† -.069 -.061† Holding firms -.003 .938 -.003 .945 -.003 .941 -.003 .938 -.003 .939 Computer programming -.034 .428 -.034 .425 -.034 .431 -.034 .429 -.032 .452 Heavy construction -.023 .595 -.022 .602 -.022 .606 -.023 .596 -.023 .592 Air transportation -.034 .434 -.033 .439 -.034 .436 -.034 .435 -.037 .397 Company size .054 .260 .054 .255 .054 .255 .054 .260 .063 .203 State owned -.029 .534 -.027 .550 -.029 .536 -.029 .533 -.023 .618 Family owned -.020 .655 -.019 .684 -.019 .688 -.021 .654 -.021 .644 Public listing .287 .000*** .287 .000*** .285 .000*** .286 .000*** .295 .000*** Foreign listing -.028 .572 -.031 .534 -.031 .533 -.028 .572 -.033 .512 Firm experience .103 .025* .098 .033* .099 .032* .103 .025* .104 .024* Independent variables Technological capital .040 .353 .067 .323 Human capital -.002 .963 -1.275 .419 Moderating variable Financial capital -.005 .915 .010 .840 Interaction terms
Interaction term financial capital x Human capital -1.276 .419 Interaction term financial capital x Technological capital .036 .600
Constant 0.109 0.114 0.114 0.11 0.085 R² 0.134 0.135 0.136 0.134 0.135 Adjusted R² 0.102 0.102 0.099 0.1 0.098 Change in R² 0.134 0.002 0 0 0 F-Statistic 4.239 1 4 0.002 0.332 P Value 0*** 0.353 0.87 0.963 0.718 p†<0.10; *p<0.05; **p<0.01; ***p<0.001.
5. DISCUSSION
The hypotheses and the theoretical framework of this empirical research have shown to be incorrect throughout this study. None of the hypotheses could be upheld. However, interesting findings relative to other potential factors influencing Western European MNEs expansion into emerging markets emerged from the analysis. Various control variables on micro firm level such as firms’ public listing and firm experience yielded significant effects on expansion into emerging markets. Furthermore the meso factor such as the MNEs’ industry sector electrical services and macro factors such as the MNEs’ home region Spain seemed to have a significant influence as well.
While previous research mainly focused on the pull factors of FDI into emerging markets, this research aimed to find out more about the firm-level push factors that drive FDI into emerging economies. In this study I hypothesized that high levels of human capital (Hypothesis 1) and technological capital (Hypothesis 2) have a positive effect on Western European MNEs’ propensity to expand into emerging markets. Both hypotheses have been disconfirmed by the empirical findings, as none of the coefficients had a significance level below .05. Hypothesis 3 which suggests that financial capital has a positive moderating effect on the relationship between the two firm-level factors and expansion into emerging markets was also found to have no significant effect. The academic relevance, managerial implications, limitations and suggestions for future research are outlined in the next sections.
5.1 Academic relevance
The research presented in this thesis has made surprisingly new findings and adds to the existing literature of firm-level factors being push factors for expansion into emerging markets. The initial hypotheses about firm level factors human and financial capital having an effect on expansion into emerging markets have turned out to be insignificant. However other
