Master Thesis for Msc International Business and Management

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NATIONALITY DIVERSITY OF TOP MANAGEMENT TEAMS AND

FIRM R&D ACTIVITIES IN LARGE TRANSNATIONAL

CORPORATIONS: R&D SUCCESS RATE AND INTENSITY

Master Thesis for Msc International Business and Management

Faculty of Economics and Business, University of Groningen

Siyu Xu (S2262304) s.xu.4@student.rug.nl

Supervisor : Dr. Kees van Veen

2013-6-12

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Acknowledgements

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Abstract

This study covers the relationship between nationality diversity of top management team, R&D activities and the moderator managerial discretion. This research is an extension of Upper Echelon theory, which focuses on the role of management teams on firm strategic decisions. Nationality diversity of top management teams is a growing issue in the upper echelon research, but few researchers have studied the relationship between nationality diversity of top management teams and firm R&D activities. We want to fill this gap. Using a sample of largest 100 non-financial transactional corporations worldwide, we find that nationality diversity of top management teams is positively related to R&D intensity, while diversity has no effect on R&D success rate. And managerial discretion has no moderating effect on R&D activities.

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A transnational enterprise is an enterprise that engages in foreign direct investment (FDI) and controls value-added activities in more than one country. Transnational corporations (TNCs) have a great contribution to the world economy in both the developed and the developing countries (Ietto-Gillies, 2001), and they are the leading forces of the globalization process (Claudia & Mihaela, 2012). TNCs account for 15.4% of world total research and development (R&D) expenditures and at least two-thirds of business expenditures on R&D (UNCTAD). They are the drivers of global R&D activities.

There are many researchers studying the R&D activities in TNCs. For example, Wang, An and Luo (2010) found that the host countries' market size and potential are still the main influencing factors of TNCs in making the choice of R&D investment. Li and Li (2010) presented a stage model for theorizing the R&D offshoring process of TNCs. These theories depict an overall picture of firm R&D activities, but intra-firm differences in R&D activities persist even after controlling for the industry, firm size, and performance (Kor, 2006). The continued presence of firm-level heterogeneity in R&D activities remains a fundamental research topic in strategic management (Rumelt, Schendel,& Teece, 1994).

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6 Scholars have found that nationality diversity can help top management teams come up with alternatives to problem-solving, which enhances creativity and the likelihood that innovative strategic decisions will be made (Bantel & Jackson, 1989; Wiersema & Bantel, 1992), because the generation of new products and ideas heavily relies on individual talents and skills from diverse cultural environments. People from different cultures have different ways of thinking because they are affected by different social and natural environments (Hofstede & Hofstede, 2005), and different thinking styles help to develop different ideas, which help a firm in its innovation and R&D activities (Nielsen &Nielsen, 2013). Van Veen and Marsman (2008) found that in 2005, the executive boards of these companies are only marginally diverse in terms of nationalities. However, differences between European countries are substantial. Kaczmarek (2009) found that the companies must reach a certain dimension of internationalization before the positive effects of top management team nationality diversity on firm performance can take place. Zoogah et al (2011) found that strategic alliances coordination strengthens the negative relationship between nationality diversity and team effectiveness. Brandes et al (2009) found that the influence of national diversity on team performance relies on the nature of the fundamental task. In practical level, nationality diversity is an important indicator for the degree of internationalization and the ‘transnational mindset’ of a company, and the presence of intricate knowledge on foreign markets in top management teams is seen as beneﬁcial for MNCs (Van Veen and Marsman, 2008). With the executives of companies searching transcending national borders, the number of non-nationals on top management team of TNCs is steadily increasing (Staples, 2007). The resulting top management team nationality diversity may have important implications as national origin has a profound effect on strategic decision making, team dynamics, and ﬁrm performance (Hambrick et al.,1998).

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7 positive as the proportion of TMT members with offices in the same location increases. Niebuhr (2010) supported the idea that cultural diversity of the labor force has an effect on patent applications; Lin (2011) found that knowledge diversity moderates the effects of R&D investment. Compared to other diversity domains, nationality diversity is less investigated in R&D research. We fill this gap by researching the effect of nationality diversity on R&D intensity and R&D success rate. And we will also analysis what will happen to these two sets of relationships when managers have enough autonomy in making R&D decisions, namely, whether managerial discretion moderates these relationships, to further understand upper echelon theory.

We will use the sample of the largest 100 non-financial TNCs ranked by foreign assets in 2011, reported by UNCTAD. UNCTAD is the principal organ of the United Nations General Assembly dealing with trade, investment, and development issues. Simple linear regression model will be built to test our hypothesis.

The contribution of the paper is that it investigates how the composition, namely the nationality diversity, of top management teams affect the R&D activities in the largest 100 non-financial TNCs, it offers a new insight about nationality diversity of top management teams and firm R&D strategies in TNCs, and test the moderate effect of the autonomy of managers on these relationships, thus strengthen upper echelon theory. As a result, we should be better able to understand the R&D decisions and performance made by the TNCs.

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8 2. LITERATURE REVIEW AND HYPOTHESES

2.1 Literature Review

In the following part we will introduce several theories and new findings relevant to our topic, such as Upper Echelons theory and managerial discretion, R&D intensity and R&D success rate, and nationality diversity.

2.1.1 Upper Echelons Theory and Managerial Discretion

In their upper echelons theory, Hambrick and Mason (1984) pointed out that executive demographic characteristics play a partial role in organizational outcomes, such as strategic choices and firm performance. Executives’ experiences, values, and personalities greatly influence their interpretations of the situations they face and, in turn, affect their choices (Hambrick, 2007). The theory was built on the premise of bounded rationality (Simon,1956), which illustrated that the top executives decide how and why organizations deal with complex and uncertain situations, but they are also limited in their ability and thinking style, and also other environmental factors constrain their decision-making.

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9 discretion might allow more heterogeneous firm strategies, faster firm actions, and more rapid innovation (Crossland& Hambrick, 2011).

Some researchers also further developed the theory of upper echelon. Hambrick, Finkelstein and Mooney (2005) introduced another moderator of upper echelons predictions: executive job demands, which states that executives’ jobs differ widely in how difficult they are. Other concepts are introduced as well, for example, inter-TMT power distributions (Finkelstein, 1992) and TMT behavioral integration(Hambrick, 1994, 1995).

In summary, upper echelons theory is still relevant to strategic management today as it was three decades ago, and top executives really matter to company outcomes as they engage in behaviors and decisions that affect the health, wealth, and welfare of firms. Top executives really matter as much to company outcomes as the theory seems to presume for good and for ill of the organizations (David et al, 2012).

2.1.2 R&D intensity and R&D success rate

Research and development (R&D) investments are important strategic decisions made by a firm. New product development can attract customers and give firms consistent comparative advantages. In 1979, Griliches proposed the R&D Capital Stock Model, which mentioned that R&D activities have a lagged effect on innovations achievement and these innovations subsequently foster ﬁrm performance. Cameron (2000) observed a positive impact of R&D on total factor productivity growth, but the effects varied across industries. Most scholars agree that R&D activities have a positive effect on firm innovativeness, which further improve firm performance.

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10 R&D success rate measures how many technologies has the firm developed successfully. Bringing new products to market is the lifeblood for most organizations, but it is also a complex and difficult task. Aboody and Lev (2000) argued that R&D investments have greater information asymmetry than other investments. On the one hand, R&D projects tend to be unique to the firm which is invested in the projects, and the R&D investment cannot share common attributes across firms, which makes it difficult for investors to derive inferences from other firms. On the other hand, R&D investments are not tradable, so investors can’t gain information on the productivity and value of these investments (Palmon & Yezegel, 2012), which means that it is difficult to initiate a successful R&D project.

2.1.3 Nationality diversity

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11 interpret information and act on strategic opportunities and threats. Nationality diversity will lead to superior ﬁrm performance through access to relevant information and diverse institutionally embedded experiences.

2.2 Hypotheses

Nationality diversity and R&D intensity

We propose that nationality diversity is positively related to R&D intensity. Resource-based theory proposes that firm’s strategies develop and evolve as a result of the interactions between managers and firm’s resources (Mahoney & Pandian, 1992; Wernerfelt, 1984). According to this view, firm’s entrepreneurial activities and strategic choices are also a function of managers’ experience at the upper ranks (Mahoney, 1995; Penrose, 1959). Resource-based theory emphasizes the attributes of top mangers as a team because strategic decisions are often made and implemented through dynamic processes where managers interact, consult, and debate with each other (Kor, 2006). Nationality diverse top management teams can produce more opinions when interacting because they have different ways of thinking, thus, they have more possibility of coming up with new research and development innovations. Nationality diversity is a source of competitive advantage as a more diverse customer base may be better served by a more diverse workforce that can effectively communicate with customer subgroups, thus the managers can collect more information and thus generate a variety ideas about innovation. In his research, Upadhyay (2012) also found that heterogeneity is associated with higher R&D intensity. Research in organizational behavior which focused on the function and productivity of heterogeneous teams found that heterogeneity brings more resources to problem solving and increases the competitiveness of organizations (William and O’Reilly, 1998).

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12 firm strategic decisions, and nationality diverse team is more adventure because when choosing to working abroad, the managers have to take more risks, thus the managers are more tolerant to risks in their work.

Hypothesis 1: Nationality diversity of top management teams is positively related to R&D

intensity.

Nationality diversity and R&D Success rate

In the second part we argue that nationality diversity of top management teams is negatively related to the success rate of R&D, even though we propose in hypothesis 1 that nationality diversity is positively related to R&D intensity. These two hypotheses are not contradictory. Although R&D intensity and R&D success rate are correlated with each other because the more the firm invest, the higher the possibility of success, intensity refers to an initial R&D spending, while success rate belongs to a result process which measures how many successful new products the firm has landed, they are different sections in R&D activities.

In an R&D project, top managers and R&D managers work as partners to make decisions in deciding what to do and why and when to do it, given the needs of each business and of the corporation. Top managers manage the R&D project in a way that integrates R&D with the rest of the company in order to promote the spirit of collaboration between R&D managers and the functional managers by coordinating plan execution and sharing experiences and information among distribution centers. Top managers have to design the communication networks to ensure a continuum across the R&D spectrum and forward to the market. They have to formulate integrated corporate/business/R&D/technology strategies that take into account trade-offs between projects across businesses and corporate programs (Roussel, Saad & Erickson, 1991). So the role of top managers in an R&D project is fundamental, they are acting as decision-maker, coordinator, communicator, and supervisor.

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project-13 -- decision-maker, coordinator, communicator and supervisor, and nationality diversity discourages team orientation of the top management team itself.

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14 dominant language in a firm, he can easily be distrusted and even be ignored. So the function of communicator is discouraged. Fourthly, the function of supervisor is also hampered, because without trust and team harmony, the managers will find it difficult to work together on the R&D investments, and they may have separate objectives and even use the R&D investments to serve their own interests.

On the other hand, R&D developments constitute risks that require a sense of trust and common understanding among top management teams. Those teams in which managers have high conﬁdence in the ability and credibility of each other are more likely to land successful R&D projects. A strong team orientation not only enables managers to cope well with the uncertainty associated with risky decisions (Bourgeois& Eisenhardt, 1988) but also encourages them to develop positive beliefs about the predictability of the environment (Isabella and Waddock, 1994). Nationality diversity discourages team orientation and team member confidence of each other, so we think that R&D success rate will be low (Kor. 2006).

Hypothesis 2: Nationality diversity of top management teams is negatively related to R&D

success rate.

The moderate role of managerial discretion for nationality diversity and R&D intensity

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15 reducing expenditures that are not essential for the attainment of short-run returns but are critical to the maximization of organizational efficiency in the long run (Baysinger & Hoskisson, 1989). Also, because long-term R&D investment will reduce present net income, managers prefer those short-term projects with quick revenue showed in the accounting book (Dong & Gou, 2010). Even if an R&D project can turn out to be successful, it may be years before the benefits show up. There is a huge variance of outcomes associated with R&D intensity, and it’s possible that managers only share their promised payouts, not any additional profits if the R&D outcome turns out to be significant. So when managerial discretion is high, managers would prefer to reduce the R&D investment to improve short-term financial performance (Baysinger and Hoskisson,1989). Acharya, Amihud, & Litov (2011) also found that managers in firms located in countries with strong creditor rights are more likely to lower cash flow risk, lower leverage, and have fewer innovations to prevent financial distress. So R&D intensity is closely connected to how many power managers have and how much autonomy they are willing to use, further related to managerial discretion.

Hypothesis 3: Managerial Discretion negatively moderates the relationship between nationality diversity and R&D intensity.

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16 management team has more freedom to act in a highly uncertain environment of R&D, and the attributes of divergent thinking, risk-taking, experimentation, improvisation and creative problem-solving from discretion in R&D projects can positively increase R&D success rate(Naveh, 2007).

Wang and Yang(2012) also proposed that as technology and market has become more dynamic, building capabilities for R&D flexibility helps to recognize that uncertainty is the essential core of innovation, and rather than trying to fight it or ignore it, managers should learn how to embrace change and enhance their flexibility. Their proposed flexibility planning procedure is divided into three steps: (1) identifying critical risks, (2) recognizing appropriate options structure, and (3) evaluating project flexibility. Each of the steps requires the managers to react effectively. A nationality diverse team needs enough autonomy to face these challenges, so discretion helps the team to boost the success rate.

Hypothesis 4: Managerial Discretion positively moderates the relationship between nationality diversity and R&D success rate.

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17 3. METHOD

3.1 Data description

Our sample is the largest 100 non-financial transactional corporations worldwide ranked by foreign assets in 2011, reported by United Nations Conference on Trade and Development. As argued before, TNCs are the drivers of global R&D activities, and we want to know the intra-ﬁrm differences in R&D activities of these firms. UNCTAD is the principal organ of the United Nations General Assembly dealing with trade, investment, and development issues, it reports the largest 100 non-financial transactional corporations worldwide every year. The UNCTAD’s World Investment Report 2011 contains two annexes that illustrate the rankings of the largest 100 Transnational Corporations (TNCs) according to their foreign assets, and it also counts for each TNC on the share of foreign employees in total employees and the share of foreign sales in total sales. We collect all the information about the 100 firms, and we find that Walmart has an extreme value of firm size of 2100000 because of its special business area. After doing robustness check, it turns out that this value has interfered the overall results, so we remove Walmart from our sample, thus, our validate sample size is 99.

Firstly, we collect data about number of patents from the database Orbis, Patenten-Octrooicentrum Nederland and NBER. If some data about patent are missing there, we use the USPTO database and European Patent Office database, or website resources such as ‘FreshPatents’(stks.freshpatents.com), ‘Google Patents’(www.google.com/patents)to look for the information. If we still can’t find the information we need, we search for news about research and development activities of these firms to find any clues. It is possible that some firms has no patent during the year because of their specific business domains or other reasons, for example, one company in our sample has no patent in year 2011, which is CITIC Group from China.

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18 (www.topmanagement.net). After collecting all the information about national origins of the executive managers, we use Blau’s index to calculate the result of nationality diversity.

3.2 Control variables

To control for top management team factors, we choose team size as control variable here. To control for firm factors, firm size, firm age, performance and industry are controlled here. To control for the level of firm internationalization, we control foreign sales, foreign assets and foreign employment.

Top management team size: In their research, Haleblian& Finkelstein (1993) found that larger

CEO teams have greater information-processing and decision-making capabilities than smaller teams, which is connected to R&D success rate and R&D intensity. To measure this variable, we counted the number of executive managers in each firm for each year, and find out the average size of the executive teams.

Firm size: Several previous studies demonstrate that company size may be associated with a

higher or lower propensity to innovate, but the effect varies. While Baysinger and Hoskisson (1989) found a positive relationship between firm size and R&D spending, Hansen and Hill (1991) demonstrated a negative association between them. We measure firm size as number of employees. Firm size is calculated as the number of full-time employees in year 2011.

Firm age: Firm age is a well known factor that affects a ﬁrm’s innovative output (Berchicci,

2013). Age is calculated as the logarithmic form of the number of years from the ﬁrm’s establishment.

Performance: Previous studies have found mixed relationship between performance and R&D as

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management is at using its assets to generate earnings. So it is additionally relevant in this paper

compared to other measurements.

Managerial Discretion: We will use the result of Crossland & Hambrick (2011)’s measurement

of managerial discretion in country level. There is an array of proxy measures to measure discretion because managerial discretion is intangible and not directly observable. In their research, Crossland & Hambrick (2011) asked an expert panel to rate the degree of managerial discretion in various settings. The advantage of an expert panel is that panelists can provide informed ratings of discretion in multiple, comparative contexts and with relative objectivity. Crossland & Hambrick (2011) carefully selected 8 prominent, long-tenured managers of international equity mutual funds as the panelist. Each panelist was asked to rate, on a 1–7 scale, the degree of discretion available to CEOs in 15 countries. In our sample of the 99 non-financial transactional corporations firms, most of them come from the 15 countries. But some countries are not on Crossland and Hambrick (2011)’s list, such as Luxembourg, Belgium, Hong Kong, China, Brazil, Denmark, Finland, Israel, Malaysia, and Mexico. According to some research, countries were clustered into recognizable patterns based on commonalities in language and religion, shared geographic borders, and a history of socio-political interaction over centuries. For example, Anglo (e.g., Ireland, New Zealand, the United Kingdom, the United States), Germanic (e.g., Austria, Germany, Switzerland) and Nordic (e.g., Denmark, Norway, Sweden). So we rating those countries which are not rated by Crossland and Hambrick(2011) according to the rate of their neighbor countries or formal colonial countries which are rated already, and finally we get all the results. Then we use robustness checks to see whether the values of these not rated countries have an effect on the results. We found that the overall result is not affected, so our way of rating turns out to be reasonable. For some firms which have two home countries such as Royal Dutch Shell, we take the mean value of the two countries.

Foreign assets: Firms operating in an international market are more likely to develop innovative

products than those active in a domestic market due to stronger competition (Basile, 2001). So we will control internationalization level. Foreign assets is a fundamental determinant of external sustainability. It is measured as the assets in foreign countries.

Foreign sales: Although Bloodgood, Sapienza, and Almeida (1996) found that the extent of

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20 related to sales growth, foreign sales remains to be an important factor of internationalization(Sullivan, 1994). Foreign sales is the volume of sales generated in foreign countries.

Foreign employment: Except for foreign assets and foreign sales, UNCTAD (1995) also used

the index of foreign employment as a measurement of firm internationalization. Foreign employment captures the location side of the firm (Ietto-Gillies, 1998). Foreign employment is the number of employees who work overseas.

Industry: The industry in which the company operates could explain a significant part of the

R&D activities. The validity of indicators across industries may vary. We have three sets of industry indicator. Organizational industry indicator 1 includes organizations in agriculture, construction, manufacturing or mining. Organizational industry indicator 2 includes organizations in communication, electric, gas, retail, transport or wholesale. Organizational industry indicator 3 includes organizations in administration, finance, insurance, or service. (OSHA, 2010).

3.3 Dependent variables

R&D intensity: It is measured as the ratio of R&D expenses to total revenues for the

organization in the year 2011(Krishnan, Tadepalli &Park, 2009).

R&D success rate: There are many definitions of “success”. If one only look at whether the

R&D resulted in a working product, the success rate would be high (Keller &Mullett.1984). But from an investor’s perspective, we test success rate as R&D inputs into tangible outputs, which is the number of patents authorized to the firm in year 2011.

3.4 Independent variables

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21 disagreement, opposition, or dissimilarity in a particular attitude or value. Secondly, within-unit diversity may indicate variety: differences in kind or category, primarily of information, knowledge, or experience among unit members. And finally, within-unit diversity may indicate disparity: differences in concentration of valued social assets or resources such as pay and status among unit members.

According to these definitions, nationality diversity belongs to the category of variety, because a nationality diverse management team differs in the composition, they belong to different categories of attribute, and they command unique information. Blau’s (1977) index is the most commonly employed measurement for variety, it is a useful tool to figure the level of nationality diversity among a group of individuals, and we will use it to measure nationality diversity. The index is defined as B = [1 −∑(Pi)²], where Pi is the percentage of category i in the group. The index ranges from zero to (k-1)/k. A perfectly homogeneous group would get a score of 0, whereas a perfectly heterogeneous group would get the maximum score of (k-1)/k. Thus, the maximum value is a function of the number of categories. The more qualitatively different categories we have, the higher the upper limit for Blau’s index.

4. RESULTS

We will use simple linear regression model to test our hypotheses:

Y1 = a1+ b1 x + e1 Y2 = a2+ b2 x + e2

Where, Y1 denotes R&D success rate, a1 is the constant term, b1 is the coefficient parameter. X

represents the independent variable nationality diversity, and e1 is the residual term of the

regression equation.

Similarly, Y2 denotes R&D intensity, a2 is the constant term, b2 is the coefficient parameter. X

represents the independent variable nationality diversity, and e2 is the residual term of the

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22 Simple linear regression is a linear regression model analyzing a variable Y changes with the pace of variable X. In this model, the slope of the fitted line is equal to the correlation between y and x corrected by the ratio of standard deviations of these variables.

The effect of nationality diversity on R&D intensity and on R&D success rate will be investigated with this cross-sectional data analysis. In the following part, we will firstly have a look at the descriptive results, then have a look at the correlation tables to see how the variables relate to each other. Afterwards we will use the regression analysis to see whether the four hypotheses are supported. Finally we will do a series of tests to verify the quality of the data.

Table 1: Descriptive results

variables N Minimum Maximum Mean Std.Dev.

Patents 99 0 8660 761.35 1628.98 R&D intensity 99 0.01 18.87 3.12 4.17 Nationality diversity 99 0 0.82 0.42 0.23 Managerial discretion 99 3 6.6 5.02 1.2 Firm size 99 6472 492714 135162.7 106138 ROA 99 -3.31 30.38 7.99 6.67 Team size 99 3 30 11.2 4.96 Industry indicator 1 99 0 1 0.1 0.3 Industry indicator 2 99 0 1 0.88 0.33 Industry indicator 3 99 0 1 0.02 0.14 Foreign assets 99 29783 502612 77791.61 67506.31 Foreign sales 99 9366 316686 56435.36 55760.09 Foreign employment 99 4495 318301 83419.27 72803.89 Firm age 99 5 348 84.21 57

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23 countries like Japan and Spain, nationality diversity is 0, which means the top management team is entirely occupied by local citizens. The mean number of patents in 2011 is 761, while the stand deviation is 1628.98. R&D intensity has a mean number of 3.1, while the stand deviation is 4.1. Managerial discretion varies from 3 to 6.6. And we can see some descriptive data about the control variables. The mean number of ROA is 7.99, of team size is 11.2. The max number of team size is 30, while in some teams the size is only 3. ROA of firms ranges from -3.31 to 30.38, we can see a big difference. The number of foreign assets ranges from 29783 to 502612, the number of foreign sales ranges from 9366 to 316686, foreign employment ranges from 4495 to 318301, firm age ranges from 5 to 348. In our data, firm size varies from 6472 to 492714. Because of this wide distribution, a logarithmic transformation reduced the scale effect. Also we will take natural logarithms of firm age to reduce the scale effect.

Table 2: Correlation analysis

Then we will have a look at the correlation table 2. Nationality diversity and managerial discretion are correlated with a result of 0.18. Nationality diversity and R&D success rate are not significantly correlated with a result of 0.07, nationality diversity and R&D intensity are correlated with a result of 0.18. Number of employees and team size are significantly positively correlated, we assume that usually a bigger firm with more employees need a larger management team to manage the firm. Most correlations are between -0.2 and + 0.2, indicating little correlation between the variables. The most significant correlation is between number of employees and foreign employment (0.90), which is not surprising since they are closely

patents R&D intensenation.diversitymana.discretionN.employeesROA team sizeindustry F. AssetsF.Sales F.employment firm age

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24 correlated. We can use this correlation analysis to show that these variables are related in a linear sense. The results also give us an indication of whether there is a relationship between variables that regression analysis in later stages can elaborate upon.

And we can see the correlation of R&D intensity and R&D success rate is 0.29, it means that these two variables are related. Given the fact that R&D intensity has an effect R&D success rate because if the company invest more in R&D, the R&D investments will have a higher chance to succeed, we will control R&D intensity when do regression analysis of R&D success rate. The results of regression analysis are presented in table 3. The first two hypotheses predicted the the effect of nationality diversity on R&D intensity and R&D success rate. We found support for hypothesis 1 (p<0.05), but not for hypothesis 2. We can see that nationality diversity is positively and significantly related to R&D intensity, but it does not significantly affect R&D success rate. Hypothesis 3 was also not corroborated. The interaction of nationality diversity and managerial discretion with the effect of R&D intensity showed a significant, but positive relationship with the dependent variable ( p< 0. 05), which is consistent with the idea that managerial discretion moderates the relationship between nationality diversity and R&D intensity, but in a positive way, which is on the contrary to our hypothesis. Hypothesis 4 is not supported. No significant effect was found for the moderating effect of managerial discretion on R&D success rate.

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25 Table 3: Regression analysis

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R&D intensity patents R&D intensity patents R&D intensity R&D intensity

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26 We find that most of the control variables have no effect on the dependent variables. We only find that number of employees and foreign employment affect R&D intensity, and R&D intensity affects R&D success rate, which means that if we remove the other control variables, our results would not change. But the control variables such as top management team size, firm size, firm age, performance, firm internationalization level and industry are frequently controlled in previous R&D and diversity studies with sufficient theory support (eg. Haleblian& Finkelstein, 1993; Baysinger& Hoskisson, 1989; Berchicci, 2013; Harrison & Wicks, 2013; Basile, 2001), and these variables turned out to be significant in previous research, so we still control them in our research. The insignificance in our research may be caused by having chosen imprecise proxies for the underlying variables or caused by incorrect modeling. For example, effects are assumed to be linear, as we used a linear regression model, but possibly the effects are curved. After conducting the regression analyses, we do several tests of the data to gain confidence in the robustness and explanatory power of our results.

Firstly, we will test whether our residuals are distributed normally in Table 4. We use Skewness/Kurtosis in Stata, and we find that our data is not normally distributed (Prob=0). Our results fail to reject the hypothesis that our residuals are distributed normally. But the number of our sample is large enough to prove that our data is consistent.

Table 4: Normally distribution test

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27 Table 5: Heteroskedasticity test

Table 6 is the multicollinearity check. We would expect that there may be collinearity among the data because R&D success rate and intensity are closely connected. We test for multicollinearity using variance inflation factor (VIF) analysis. From the table we can see that none of the value is exceeding 10, so we think that there is no multicollinearity does exist in our data.

Table 6: Multicollinearity check

5. DISCUSSION AND CONCLUSION

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28 possibility that the firm will succeed. But on the other hand, R&D intensity refers to the initiate spending, while R&D success rate refers to the results of innovation.

Most of the prior researches on R&D depicted an overall picture of firm R&D activities, and they ignored intra-ﬁrm differences in R&D activities. We try to understand firm R&D activities using the upper echelon theory, namely, how top management teams affect the R&D activities. Among the limited related research, education diversity, functional diversity and tenure diversity have gained much attention, but none of the researchers focus on nationality diversity. Our research is a response to the gap.

Our sample is the largest 100 non-financial transactional corporations worldwide ranked by foreign assets in 2011, reported by United Nations Conference on Trade and Development. We collect all the information about the 100 firms, and we find that Walmart has an extreme value of firm size of 2100000 because of its special business area. After doing robustness check, it turns out that this value has interfered the overall results, so we remove Walmart from our sample, thus, our validate sample size is 99.

Seen from the standpoint of upper echelon theory, we firstly argue that nationality diversity of top management teams is positively related to R&D intensity. This is because nationality diverse top management teams have more possibility of coming up with new research and development innovations, which is contribute to their different ways of thinking, and a nationality diverse top management team may have more tolerance to risk to facing the uncertainty of R&D activities. The result of the regression shows a positive and significant relationship between nationality diversity and R&D intensity, which is in line with the proposed theory.

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29 hinders the communication in an R&D project, which discourages the role of coordinator; Cross-cultural interaction appears to have considerable potential for misunderstandings and other kinds of conflicts, which discourages the role of communicator; The function of supervisor is also hampered, because without trust and team harmony, the managers will find it difficult to work together on the R&D investments, and they may have separate objectives and even use the R&D investments to serve their own interests. Although some literature propose that diversity helps to build effective teams (Camelo-Ordaz, C, Fernández-Alles, M& Valle-Cabrera, R, 2008), we think that the disadvantage of diversity outpaces the advantage. Furthermore, those teams in which managers don’t have high conﬁdence in the ability and credibility of each other are less likely to land successful R&D projects. Lacking of a strong team orientation discourages the managers to develop positive beliefs about the predictability of the environment. But the regression results did not support our theories, the effect was found to be insignificant. This result is surpring because other diversity (culture, functional, ect) attributes were supported to have some relations with R&D success rate (Niebuhr, 2010). One reason of this insignificant result may be that top management teams affect R&D success, but the effect is not obvious enough to be investigated because top managers do not involve in the research process directly. And there may be some problems relate to our data collection. Compared to other studies, our data collection of R&D success rate only focuses on year 2011, while other research collected data for successive 3 years or more (eg, Falk, 2012). There is always time interval between applying for a patent and the approval of the patent because of the procedures of data application, so one-year observation can not fully explain the R&D success of one company.

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30 have more autonomy, they are more aggressive in making decisions and they tend to increase firm investment (Hambrick& Mason, 1984), thus R&D investment will increase as well.

Finally we propose that managerial discretion positively moderates the relationship between nationality diversity and R&D success rate. When discretion is high, managers have more alternatives to develop the R&D project, they have more latitude to pursue R&D options in reaction to environmental pressures, and they can learn how to embrace change and enhance their flexibility. But we find the role of the moderator to be insignificant. In hypothesis 2 we found no relationship between nationality diversity of top management teams and R&D success rate, so it’s natural that managerial discretion does not moderate their relationship.

A related problem is the insignificance of variables that were found to be of significant influence in other studies, such as top management team size, firm size, firm age, performance, firm internationalization level and industry. The insignificance of these control variables in our research may be caused by imprecise proxies we choose for the underlying variables or caused by incorrect modeling.

In summary, the results obtained from the the largest 100 non-financial TNCs ranked by foreign assets in 2011 generally support our first proposition, which means that nationality diversity of top management teams helps to increase R&D intensity. But the second, third and fourth propositions are not supported. Managerial discretion positively moderates the relationship between nationality diversity of top management teams and R&D intensity, and we have not found a significant relationship between nationality diversity and R&D success rate, and managerial discretion has no moderating effect.

6. LIMITATIONS AND FUTURE RESEARCH

Although we believe this study has enhanced our understanding of upper echelon theory and the R&D activities in transactional corporations, our study is far from conclusive, and has some limitations.

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31 gender diversity and educational diversity may also have a significant impact on firm R&D activities, according to upper echelon theory. It would be interesting to see what influence the other dimensions as well as the complete set of diversity attributes together would have in the same kind of mode. Further research may incorporate other diversity attributes into this line of research.

The second group of limitations is a result of the empirical method applied to test the model and data collection. The data collected was limited to the 100 non-financial transactional corporations worldwide. Future research could test the hypotheses of the study using a wider data, not restricted to 100 TNCs, but to more multicultural corporations, and research them in different contexts, such as Anglo-Saxon companies, where firm characteristics such as organizational culture patterns are completely different than those of Dutch firms. On the other hand, we collect data of year 2011, and one-year data is not sufficient to test the relationship, because it always takes several years for an R&D project to succeed. Further research could use data from more year observations to test the hypotheses. Furthermore, we use managerial discretion as the moderator, but we can not find data about discretion on firm level. We use country level discretion data from Crossland & Hambrick(2011)’s research, but some data in our research is not addressed in theirs, so our data is incomplete. Further study can fix this problem by finding better ways to measure managerial discretion.

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