Top Management Team: influence upon commitment of MNCs to innovativeness

Top Management Team:

influence upon commitment of MNCs to

innovativeness

Rijksuniversiteit Groningen

Faculty of Management & Organization

Msc International Business and Management

March 2008

By

TETYANA O. VRONSKA

Acknowledgements

I dedicate this paper to my mother - who despite the fact of being a single parent of two children - made sure that I received a high-quality education, a life-time of experiences and freedom to build my future throughout the time spent in The Netherlands. I am extremely grateful for the opportunity to meet people from around the world, to learn different cultures, to travel, and have a great student life abroad.

Also I wish to show appreciation to my boyfriend (Grigorios) - who had to endure me in the stressful times of the whole working process - for being supportive and understanding.

As I finish working on my Master Thesis the time spent at the University of Groningen comes to an end. Therefore I would like to express my gratitude to my supervisor Mr. Lucien Karsten for his insightful suggestions and directions throughout the course of writing this paper.

Tanya Vronska

Top Management Team:

influence upon commitment of MNCs to innovativeness

Abstract

The present paper reveals the main interest in the influence of top management teams (TMTs) upon the commitment of European multinational corporations (MNCs) to innovativeness. The main objective of the study is: to further explore the relationship between the composition of TMTs and firm performance by examining the demographic characteristics of executives (age, firm and industry tenure, educational background), which influence investments into innovative activities. By using organizational and demographic data of 75 European companies (from innovative and non-innovative industries) this study investigates the relationship between TMTs and firm research and development (R&D) intensity.

The significant relationships found for the age and education of top executives suggest that younger managers with fitting educational backgrounds will positively influence MNCs’ commitment to innovativeness. Firm and team tenure resulted in insignificant relationships with MNC innovativeness, which proposes that the length of employment or team membership do not influence the level of innovation a firm will pursue.

Keywords: TMT, MNC, Upper Echelon Theory, Dominant Coalition, innovativeness, corporate

Table of Content

Chapter 1: INTRODUCTION ...5

Problem Statement ...5

Main Research Question...9

Theoretical Idea...10

Chapter 2: THEORETICAL BACKGROUND...11

Literature Review...11

Resource-Based View of a Firm...11

Upper-Echelon Perspective and Dominant Coalition Theory...14

Innovation and Top Management Teams ...17

Sub-questions...20

Framework...21

Hypothesis ...22

Demographic Traits ...22

Age and Innovativeness. ...22

Educational Background and Innovativeness. ...22

Tenure and Innovativeness...23

Organizational Tenure ...23 TMT tenure...24 Chapter 3: METHODOLOGY...26 Research Design...26 Sample Selection ...27 Data Collection...28 Variable Measures...29 Independent Variables ...29 Dependent Variable...31 Control Variables ...32 Reliability of Information ...33 Conceptual Model ...34 Chapter 4: RESULTS ...35

Chapter 5: Conclusion and Discussion...41

Limitations and Further Research ...47

List of Tables and Appendices...48

Glossary ...49

Chapter 1: INTRODUCTION

Problem Statement

One of the most widely studied topics throughout the last several decades remains the phenomenon of top management teams (TMT) and their influence on firm performance (Certo et al. 2006). An important focus of academic research has been to examine the linkages between the demographical characteristics of top organizational managers and their role in shaping a variety of organizational outcomes (Tihanyi et al. 2000). The organizational literature has been enriched by a significant amount of scholarly work that proposes a fundamental role TMTs play in influencing corporate performance (e.g. Hambrick and Mason, 1984; Prahalad and Bettis, 1986; Bantel and Jackson, 1989; Finkelstein and Hambrick, 1990; Hoffman and Hegarty, 1993; Keck, 1997; Carpenter and Fredrickson, 2001; Kor, 2003). The impressive list of authors who dedicated their work to the upper layer of organizational hierarchy clearly demonstrates the significance of executive teams. However, what is a top management team and why has it become so essential for organizational literature?

A broad definition provided by Penrose (1959: pp.46-47) that was quoted by (Kor, 2003: p709) describes a management team as ‘a collection of individuals who had experience of working together, for only in this way can “teamwork” be developed’. Top management of a firm according to Prahalad and Bettis (1986: p.489) should not be viewed 'as a faceless abstraction', but as a 'collection of key individuals' (i.e. a dominant coalition) who have significant influence on the way the firm is managed. This ‘dominant coalition’ of individuals is responsible for setting a firm’s direction (Cyert and March, 1963) it identifies opportunities and threats, filters relevant information, recognizes capabilities and constrains, formulates and implements strategic change (Wiersema and Bantel, 1992). The management team, by working within the firm and with each

other, generates firm specific knowledge and experience that enables them to provide services that are uniquely valuable for the operations of the particular corporation (Kor, 2003).

Two important theoretical developments in organizational studies have initiated the trend to research TMT characteristics. The first was the concept of the dominant coalition, put forward by Cyert and March (1963), which changed the level of analysis from the individual (basis chief executive officer: CEO) towards the entire team. The second development was the increased importance of utilizing the role of demographic characteristics (e.g. age, tenure, experience) and exploring the relationship between these characteristics and organizational outcomes (Pfeffer, 1983; Tihanyi et al., 2000). The explanation of the importance of the demographic approach has been provided in the study of Wiersema and Bantel (1992: p.94) who quoted Pfeffer (1983 p.350): “Demography is an important, causal variable that affects a number of intervening variables and processes and, through them, a number of organizational outcomes". Later the two streams of research were joined in the prominent work of Hambrick and Mason (1984) who asserted the Upper-Echelon Perspective (UE). Their proposition was that ‘organizational outcomes are viewed as reflections of the values and cognitive bases of powerful actors in the firm’ with the main idea that ‘the personal features and experiences of corporate executives influence important strategic decisions enacted by these key corporate actors’ (p.193). A cognitive base has been defined as “assumptions about future events, knowledge of alternatives, and the consequences attached to alternatives” (p.195).

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financial position, and socioeconomic roots. Carpenter et al. (2004: p.750) revisited The Upper-Echelon Theory stating that ‘the managerial characteristics are efficient proxies, which provide reliable indicators of the unobservable psychological concepts’.

of competitive advantage represents strategic change in corporations, which naturally becomes a responsibility of top management teams.

effectively or function productively, which can negatively influence firm outcomes (Kor, 2003: p.710).

Daellenbach et. al. (1999) have also concluded that executive managers with technical education are more prone to lean towards exploring new products and markets, which results in firms having above average R&D intensity. Increase of technologically experienced executives within TMT would mean greater support for technology initiatives and firms’ strategies that emphasize innovation. A supporting view can be found in the research done by Wiersema and Bantel (1992) who claimed that selection of a curriculum of study reflects an individual’s cognitive style and personality and shapes perspectives and outlooks. The argument is based upon work of Hitt and Tyler (1991: p.333) who found that ‘the type of academic degrees executives had influenced their strategic decision-making, since certain academic fields are more change oriented than others’. Technological studies have been suggested as examples of academic fields that highlight progress, invention and improvement. Consequently, firms that set innovativeness as their strategic goal need to fill their TMT positions with managers from technical backgrounds (Daellenbach et. al., 1999).

The purpose of this study is to further explore prior academic work on the nature of the relationship between the composition of top management and firm performance. By examining the demographic characteristics of top management teams (e.g. age, tenure, and educational background) I expect to gain additional insights into the characteristics of top management teams that play a leading role in influencing their firms’ commitment to innovativeness. This study touches upon the management problem of whom to promote into TMTs and what characteristics executives need to possess in order to maintain the core objectives of MNCs, those being either realizing competitive/innovative strategies or avoiding risk-taking activities. Many questions can be raised to provide the answers and suggestions for the issue at stake, in order to grasp the whole idea of this study the main research question is set as follows:

Main Research Question

Theoretical Idea

Chapter 2: THEORETICAL BACKGROUND

Literature Review Resource-Based View of a Firm

Organizational literature is rich on studies about organizations and their processes however not many have asked why a company exists and what is its purpose. Collins and Porras (2000 p.58) have raised this question and came to the conclusion that “a group of people come together and exist as an institution that we call a company so that they are able to accomplish something collectively that they could not accomplish separately- they make a contribution to society”. Other scholars view firms as ‘internal organizations and domains for organizing economic activities in a non-market-like fashion, with patterns of behavior and learning capabilities’, whose collective actions result in contribution to society by managing their internal processes (Teece et al., 1997: p.517). This view originated from resource-based theorists who argue that a firm ‘is best viewed as a collection of sticky and difficult-to-imitate resources and capabilities, which could be physical or intangible’ (Mowery et. al., 1998: p.508). Authors have summarized the focus of the resource-based theory as the one that emphasizes the origins, acquisition, maintenance and erosion of firms’ resources and capabilities. The clearest distinction of resources and capabilities has been adopted from Makadok (2001: pp.388-389) who stated that a resource is ‘an observable (but not necessarily tangible) asset that can be valued and traded- such as a brand, a patent, a license, in-house knowledge of technology, or employment of skilled personnel’. Such assets are said to be firm-specific and difficult to imitate (e.g. trade secrets, certain specialized production facilities and engineering experience) (Teece et al., 1997). A capability is ‘not observable (and hence necessarily tangible), cannot be valued, and changes hands only as part of its entire unit that can enhance the value of a resource’ (Hoopes et. al., 2003: p.890).

insight the resource-based view provides is that firms can reach superior performance not just because of the resources they possess but also due to their effective and innovative management of those resources (Mahoney, 1995). Lately, management itself has been seen as one of the crucial resources for shaping firm’s strategies and sustaining competitive advantage. Kor (2003: p.707) quoted statement of Penrose (1959) who argued that “… a firm’s resources can become unique over time when they are interactively deployed through the processes and routines that the managers, operations as a team, envision, implement and readjust”. Consequently, Kor (2003) concluded that managers play the leading role in choosing a firm’s strategic path, the combination of resources it will use, and the markets it will enter. Kor (2003: p.707) has quoted the view of Carpenter et. al. (2001) arguing that ‘the bundle of managerial experiences executives possess can mirror skills and knowledge as well as the competence of the top management team’. Managerial skills, as a resource1_{, control the information and knowledge a company possesses (Barney et. al.,} 2001) and modes of acquiring and employing skills for generating new resources (Conner and Prahalad, 1996). In Mahoney’s (1995) view firms must continually upgrade their core capabilities2 in order to generate economic value, which can be realized through ‘new combinations of resources as a possible means to achieving sustained competitive advantage’ (Penrose, 1959: p.85). Innovation processes are set as an example of the new combinations viewed as ‘resource transformation processes’ (Mahoney, 1995). Barney et. al. (2001) advanced the view of managerial skills as a resource by claiming that the nature of managerial resources may need to change with the life-cycle of the firm and the industry for rents to be generated. An example can be made by the emphasized significance of top managers to acquire international experience, which represents one of the firm-specific tacit knowledge that is difficult to imitate (Barney et. al., 2001). To decide which strategic course to follow and what kind of resources to acquire, accumulate, maintain and develop it is of crucial importance to identify the ideology and core competencies of organizations. As stated by Collins and Porras (2000) it is much better to understand ‘who you are’ and not ‘where you are going’ because the future will certainly change, but “the truly reliable source of stability are strong inner core values and purpose, set in the core ideology” (2000: p.78). The authors have asserted two ideologies for managing a firm as “clock-builders” or as “time-tellers”. The concept of “clock-builders” stems for an organization with a strong cult-like culture, they look for deeper, more enduring purpose that exceeds reliance on the original visionary

follow a certain way in which presently possessed resources and capabilities are employed, sources of future acquisition are selected and responses to new developments are managed (Conner and Prahalad, 1996).

Upper-Echelon Perspective and Dominant Coalition Theory

The emphasis of the literature covered in the previous chapter is that ‘clock-builders’ accomplish more than just managing resources for a profit: they built an organization that does not become obsolete and prosper far beyond its founder, initial idea and product. These firms do not exist for mere shareholder value maximization, they contribute to societies and become part of their history and cultures.

individuals' (i.e. a dominant coalition) with significant influence on the way a firm is managed. Similarly, West and Anderson (1996: p.680) argued that ‘the most influential group in an organization is the top management team, charged with determining strategy and ensuring organizational effectiveness’.

way in which the concept was portrayed, which made it difficult to be operationalized” (Ray and Chittoor, 2005: p.7). The authors claimed that later work by Bettis and Prahalad (1995) ‘further mystified the concept by giving it an entirely different meaning and taking it to the realm of an emergent property of “organizations as complex adaptive systems” (2005: p.7).

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processes and valuable capabilities, etc (White et. al., 1997; Collins and Porras, 2000). There are also firms that find it more advantageous to hire managers from outside, which are argued to bring companies fresher minds with more risk-taking orientation and creative attitudes (Hambrick and Mason, 1984). The downside is of course the lack of firm-specific experiences, knowledge and skills, as managers have to change and adopt their earlier constructed dominant logic (Bettis and Prahalad, 1995) to new environments due to being introduced from outside.

Innovation and Top Management Teams

Considering a resource-based strategy for managing a firm will require organizations to utilize aggressive protection of their valuable resources and capabilities, which are accumulated to gain sustainable competitive advantage (Teece et al., 1997). In Barney’s (1991) view competitive advantage is derived from the resources and capabilities, which can be viewed as collection of tangible and intangible assets, including a firm’s management skills, its organizational processes and routines, and the information and knowledge it controls. Similarly, Teece and Pisano (1994: p.537) claimed that ‘competitive advantage stems from dynamic capabilities that are rooted in high performance routines operating inside the firm, embedded in the firm’s processes and conditioned by its history’. However, pure accumulation of assets does not necessarily grant a company with prosperity (Teece et al., 1997). Powerful competitors and rigid environmental changes have made the pursuit for competitive advantage more difficult and success less sustainable (Lyon and Ferrier, 2002). According to Kor (2003: p.709) to create sustained growth and competitive advantage organizations have to ‘identify the appropriate product opportunities unique to a firm and effectively allocate financial and human resources to seize these opportunities’. The source of sustained advantage is suggested by Teece and Pisano (1994: p.537) as ‘appropriately adapting, integrating, and renewing internal and external organizational skills, resources, and functional competences toward changing business environment’. To be a winner in the global marketplace firms must demonstrate timely responsiveness, quick and flexible product innovation, along with the management capability to efficiently coordinate and redeploy internal and external competences (Teece and Pisano, 1994).

determinant of organizational productivity, competition and survival’. Hence the management of innovation has become a subject of significant research interest (Hitt et. al., 1999).

First and foremost important is to establish a clear understanding of what is meant by ‘innovation’, since surplus of definitions for innovation types has resulted in an ambiguity in the way the terms ‘innovation’ and ‘innovativeness’ are operationalized and utilized (West and Anderson, 1996; Garcia and Calantone, 2002). In theory innovations are separated into technological and product-market innovations, earlier emphasizes research and development, and technical expertise related to new or improved products and process, later comprises product design, market research, and other marketing-related activities, respectfully. However, in practice this distinction is often blurred and brings unnecessary confusion (Lyon and Ferrier, 2002), driving scholars to develop definitions that comprise both elements of innovation. For instance, Garcia and Calantone (2002: p.112) propose that innovation is ‘an iterative process initiated by the perception of a new market and/or new service opportunity for a technology based invention which leads to development, production, and marketing tasks striving for the commercial success of the invention’. ‘Innovativeness’ is usually used as a measure of the degree of ‘newness’ of an innovation, thus ‘highly innovative’ products are seen as having high degree of innovativeness and ‘low innovative’ products are at the opposite extreme of the continuum (2002: p.112; detailed illustration can be seen in Table 3).

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Sub-questions

Considering the rich pool of studies already existent in organizational literature it is clear that there is still place and need for further exploration of the relationship between characteristics of executives and firm performance. Consequently, a number of sub-questions are raised to investigate age, educational background, TMT and organizational tenure of executives in TMTs in relation to the commitment of MNCs to innovativeness.

- What is the influence (if any) of the age of TMT members on MNCs’ commitment to innovativeness?

- What is the influence (if any) of longer or shorter tenured managers on MNCs’ commitment to innovativeness?

- What is the influence (if any) of TMT members with a technical educational background on MNCs’ commitment to innovativeness?

Framework

Independent Variables Control Variables Dependent Variable

Hypothesis Demographic Traits

Age and Innovativeness. In general organizational literature agrees that managerial youth is usually associated with corporate growth (Hambrick and Mason, 1984), greater strategic change and volatility of sales and earnings (Wiersema and Bantel, 1992). Prior research suggests that older managers lack ability to integrate information (Hambrick and Mason, 1984) and are less willing to change and adapt to new idea or behaviours (Bantel and Jackson, 1989). Hambrick and Mason (1984) argued that companies with older managers are more prone to pursue risk-averse strategies, relying on the negative relationship between age and corporate growth but Bantel and Jackson (1989) found no significant relations between age and innovation. Later findings revealed that executives’ age increased rigidity and resistance to change, and decreased flexibility, as financial and career security become of higher importance (Hitt and Tyler, 1991; Wiersema and Bantel, 1992). Tyhanij (2000) claimed that older managers were committed to organizational status quo, as it reflects the strategies they adopted over the years. In a more recent study, Horwitz (2005) stated that younger managers demonstrated greater openness to strategic change and were more inclined to pursue aggressive strategies.

Hypothesis 1

H0: High average age of the top management team will not be negatively associated with MNCs’ commitment to innovativeness.

H1: High average age of the top management team will be negatively associated with MNCs’ commitment to innovativeness.

background had influenced strategic decision making of executives. Certain academic fields are said to be more oriented toward change, for instance engineering is concerned with progress, invention and improvement (Wiersema and Bantel, 1992). Research by Daellenbach et. al. (1999: p.201) showed that ‘executives from technological backgrounds are more likely to focus on and comprehend the technical, operational, and financial implications that proposed investments in innovation would have’. More successful innovators are characterized by top management being scientists or technologists (1999).

Hypothesis 2

H0: TMTs with a high proportion of managers with an educational background in technical fields (engineering, production/operations, R&D) will not be positively associated to the MNCs’ commitment to innovativeness.

H1: TMTs with a high proportion of managers with an educational background in technical fields (engineering, production/operations, R&D) will be positively associated to the MNCs’ commitment to innovativeness.

Tenure and Innovativeness.

shown general agreement that lengthy organizational tenures are associated with ‘passive decision-making approach, increased reluctance to strategic changes and innovations, and increased commitment to established policies and practices (Hambrick and Mason, 1984; Wiersema and Bantel, 1992; Boeker, 1997). Kor et. al. (2006) argued that over the years willingness of managers to undertake risky R&D investments decreases, thus executives may agree to settle for modest levels of R&D intensity, which limits potential losses but does not lead to speedy innovation and high returns. It is suggested that high power in a firm gives executives the liberty to pursue the strategies that may not be profit maximizing. As a result competitive position of a firm may become vulnerable in vibrant industries, where timely adaptation and proactive decision-making are essential (Bantel and Jackson, 1989; Horwitz, 2005). Wiersema and Bantel (1992) found that shorter tenures lead to changes in corporate strategies. In addition, short-tenured managers who desire to be affiliated with and praised for new product successes may push for intense R&D strategies. Executives with less tenure in the firm are said to have fresh and diverse information (Finkelstein and Hambrick, 1990) and to be more willing to take risks and invest in R&D due to pressure to produce results and prove themselves as competent managers (Kor et. al., 2006). Thus, bolder and aggressive investments in R&D increase the likelihood and speedy developments of innovations (2006).

Hypothesis 3

H0: Higher average organizational tenure of the top management team will not be negatively associated to MNCs’ commitment to innovativeness.

H1: Higher average organizational tenure of the top management team will be negatively associated to MNCs’ commitment to innovativeness.

patterns, sense of trust and common understanding within it’. Some contradictory views have emerged, as Kor et. al. (2006) highlighted the importance of managers having high confidence in the ability and credibility of each other thereafter such executive teams are suggested to be more likely to initiate risky investments. Subsequently, as team tenures get longer and confidence in each other increases firms will be more likely to invest intensely in R&D. However, other studies suggested that executives by working together for extended periods of time tend to develop similar views and common perspectives, which may result in ‘groupthink’ (Keck, 1997; Tihanyi, 2000; Kor et. al., 2006). Groupthink among top managers may jeopardize organizational adaptation and change. In turbulent industries such tendency can decrease firms’ ability to redefine their productive opportunity, which is necessary to respond to frequently changing economic demands, competition and technology conditions (Kor et. al., 2006). As a team operates over time it becomes less flexible and less receptive to alternative solutions (Keck, 1997). Organizational literature associates long tenures with increased isolation from outside sources of information and members increased resistance to changes in strategic decisions. Other contradictory views exist on promoting from inside or hiring outsiders into top management teams. Hayes and Abernathy (1980 p.77) stated that ‘companies are increasingly choosing to fill new management positions from outside but executives with less firm- and team-specific experience are likely to exhibit non-innovative bias in their choices’ (Daellenbach, 1999: p.200). Hence, concluding that ‘longer careers should enhance a manager’s knowledge and make him/her more open to investments in innovation’ (1999: p.200). In contrast, Hambrick and Mason (1984) argued that long tenures would result in managers focusing on current products and markets rather than new terrains. Supporting view was expressed by Finkelstein and Hambrick (1990) who found that long-tenured top executive teams followed more consistent strategies. Similarly, Michael et. al. (1997) claimed that shorter tenures are associated with strategic change and more innovative focus.

Hypothesis 4

H0: Higher average top management team tenure will not be negatively associated with MNCs’ commitment to innovativeness.

Chapter 3: METHODOLOGY

Research Design

The research process for this study has been applied according to deductive methodological approach (Gill and Johnson, 2002) as it requires initially developing conceptual and theoretical structure and subsequently implementing tests through empirical observations. Consequently, the structure of the study reveals that firstly conceptual structure for the paper has been made up, by identifying the problem statement and management issue at stake, stating research objective and purpose of the study. The next part contains extensive discussion and embedment of scholarly literature, prior theories and theoretical concepts, combination of supporting and refuting arguments, findings and conclusions.

The main objective of this study evidently positions TMT members as the fundamental unit of analysis. The influence of TMT members on innovativeness of multinational organizations is conceptualized as a function of executive characteristics variables. These are the independent variables in this study: age of top executives, educational background, and their TMT and organizational tenures. The dependent variable in this study is the commitment of MNCs to innovativeness, measured by the R&D intensity levels. The size and age of multinational corporations (MNCs) is conceptualized as control variables affecting the relationship between top executives’ demographic characteristics and influence on MNCs’ commitment to innovativeness. Empirical tests are based upon comparing findings within two distinct industries: (1) Low on innovativeness e.g. Oil & Gas, Electricity, or Gas, Water and multi-utilities, etc.; (2) Highly innovative e.g. Aerospace & Defense, Automobiles and parts, Electronic Equipment, Biotechnology, Pharmaceuticals, etc. The decision to choose two industries at both ends of continuum was made upon adoption of the approach from the earlier work of Daellenbach et. al. (1999) and aiming at achieving indiscriminative findings. The industries chosen for the empirical analyses are selected from EUROPEAN COMMISSION ‘The 2006 Industrial Research and Innovation’3_{, ‘The ANNUAL DIGEST of Industrial R&D’ and ‘The 2007 R&D Scoreboard’}4_{. The} Annual Digest of Industrial R&D highlights recent findings on industrial R&D based on a review of publicly-available sources, including official reports and relevant professional and academic literature. The 2007 R&D Scoreboard report contains extensive information on global R&D

3 _{http://iri.jrc.es/documents.htm}

investing companies, which is taken from their audited accounts. According to Eurostat (2003) data four industrial sectors were with highest R&D intensity and accounted for about 55 % of the total R&D expenditure performed in the EU (European Union). These sectors were motor vehicle manufacturing, pharmaceuticals, office equipment, electronics and electrical machinery. Similarly the 2005 EU Scoreboard showed that 67% of R&D investments were concentrated in the following four sectors: automobiles and parts (19%), IT hardware (18.6%), pharmaceuticals and biotechnology (18.2%), and electronic and electrical equipment (11.2%). Oil&Gas industry appeared to be one of the least innovations driven ones.

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Sample Selection

Most researches seek a sample size that ‘ensures a reasonable chance of rejecting null hypothesis involving regression parameters’ (Green, 1991: p.499). Thereafter, sample size can be determined if ‘three values are specified (1) Alpha (α), the probability of incorrectly rejecting the null hypothesis; (2) Power, not rejecting a false null hypothesis; (3) and effect size, the degree to which the criterion variable is related to the predictor variables in the population’ (1991: p.499). An alternative to determining sample size for regression analyses is rule-of-thumb, which indicates that the number of subjects, N, should always be equal to or greater than a constant and a recommended minimum ration of subjects-to-predictors (N>50+8×M , where N is sample size, M is number of independent varibales). Tabachnick and Fidell (1989) suggest that the minimum number of subjects for each predictor or independent variable (IV) in a regression analysis should be 5-to-1. However, having small samples may result in unacceptably low power levels therefore authors indicate that good ratio would be 20-to-1. Following the approach used by Green (1991) the sample size for this study is 75 MNCs out of 17 European Union countries (see Table 6). The sample includes multinational corporations from non-innovative and highly innovative industries, which is, as earlier mentioned, adopted from a study of Daellenbach et. al. (1999) aiming at achieving indiscriminative and comparable findings at both ends of continuum.

Therefore inclusion of multinational companies would secure presence of a TMT and extensive availability of personal data for each executive. MNCs are selected from The EU 2007 Industrial R&D Investment Scoreboard that provides a choice out of one thousand European companies, ranking is based on the R&D intensity level. A selection can be made by country of origin, by yearly sales, by size, by industrial sector, by total rank, by R&D investment or intensity, etc. ---

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Consulting previous studies on top management and dominant coalition of each organization has facilitated defining a TMT and selecting executives for the sample, since Prahalad and Bettis (1986: p.489) stated that ‘top management forms a dominant coalition that exerts a significant influence on the way the firm is managed’. Higgins and Howell (1990) have addressed TMT members as ‘champions’ in their companies and similarly Hambrick and Mason (1984) asserted that TMT is ‘the most influential group’ in an organization. Therefore the most important organizational decision-makers who influence decision-making processes, firm performance and outcomes are included into the sample. Consequently, the sample for this study contains executives, who are part of executive managerial teams or executive boards, which does not include the Board of Directors. In due course of database building demographic information for 447 TMT managers who were part of the Executive Management Teams in 2006 was collected and entered into the SPSS dataset.

Data Collection

In order to build the dataset it is essential to choose reliable sources for extracting the required data. First of all, a decision was made to base the analysis of this study upon the multinational corporations from two distinct industries, one that is located at the high-end and the other on the low-end of scale of commitment to innovativeness. By consulting ‘the European Commission 2006 Industrial Research and Innovation’ and ‘the Annual Digest of Industrial R&D’ and ‘the 2007 R&D Scoreboard’ two industries are identified as the most (Pharmaceutical) and least (Oil&Gas) innovative. This conclusion is based upon the R&D intensity value, which is explained further in the Variable Measures chapter.

of European companies operating in different industries. The databases are accessible by industrial sector, rank, R&D expenditures or R&D intensity, etc. To have a representative sample size it was concluded that about 75-80 MNCs need to be included into the dataset for this study. Therefore the first 37-40 MNCs from each industry are selected from the total list for further research. However, Oil&Gas industry comprised only five companies, which is an insufficient number for the purpose of this study. A decision was made to include other non-innovative industry in order to achieve acceptable sample size. For this reason MNCs from Electrical and Gas&Water industries were added to the initial sample, which share a similar dependence on the infrastructural basis with the oil&gas industry.

Thirdly, the personal demographic data of the TMT members was gathered from the web-sites of the MNCs and their annual reports, as well as Amadeus database. To obtain the least possible percentage of missing data alternative search engines were used in case company websites or annual reports did not provide complete demographic information (e.g. Google). In case of large percentage of missing data for executives that particular MNC was excluded and replaced by another firm.

Variable Measures

By means of empirical research the relationship between the demographic characteristics of top management team members and the commitment of multinational corporations to innovativeness is hypothesized and examined in this study. The relationship is hypothesized between the age, educational background, team and organizational tenures of executives as independent variables and MNCs’ commitment to innovativeness as dependent variable.

Independent Variables Age

greater openness to strategic change and were more inclined to pursue aggressive strategies. Values for each team member were composed and later a mean was taken for the whole team.

Educational Background

Early researches have noted that inclusion of the educational backgrounds of executives has been limited primarily to studies attempting to predict innovation. Kimberly and Evanisko (1981) examined the type of educational curriculum and found no association with the adoption of innovations. Later work of Hitt and Tyler (1991: p.333) concluded that type of degrees did influence executives’ strategic decision making since ‘certain academic fields are more oriented toward change than others’. According to Daellenbach et. al. (1999) formal educational background shapes person's preferences with respect to openness to innovation. The curriculums of executives are divided into: (1) Business and Management (BA, trade, commerce, sales, marketing; (2) Finance and Accounting; (3) Economics; (4) Law Studies; (5) Engineering and Technology; (6) Human Resource Management (HRM); (7) Pharmacy and Biology (pharmacology, bio/chemistry); (8) Physics; (9) Political Science (journalism); (10) Medical Degree; (0) General. Later, based on the educational specialization of team members a dummy variable was developed to detach top management teams that are dominated by executives with educational backgrounds in technical fields (engineering, technology, science, etc).

Organizational Tenure

TMT Tenure

Findings of the influence and relationship between TMT tenure and firm innovativeness have generated a number of diverse conclusions. Finkelstein and Hambrick (1990) found that tenure impacted organizational outcomes, and long-tenure had negative influence upon commitment to innovation. Wiersema and Bantel (1992) supported their view that long-tenured teams tended to develop homogeneous opinions for that reason time of entry is an important determinant of communication patterns within teams. However, West and Anderson (1996) concluded that long tenures do not have negative influence but no positive either, also they stated that team tenure was unrelated to innovation. TMT tenure was measured by summation of the total years spent in top management team including 2007 and the mean value for the whole team was taken.

Dependent Variable

Commitment to Innovativeness

In the following regression analysis the dependent variable, commitment to innovation, is measured by the R&D Intensity, which is a common proxy for measuring innovativeness of companies (Levine et. al., 1985):

R&D intensity has been used as a substitute measure for innovativeness levels by researches from both industrial management and strategic management studies (Hoskisson et. al., 1993). To reduce the chance of getting irregular values for R&D intensity in a single year that would bias final results, a two-year average (2005-2006) is used (Hosskison et. al., 1993; Daellenbach, 1999). Patents are not used as a measurement for innovativeness in this study since it has been put forward by scholars that effectiveness of patents is quite limited because they can be adopted from competitors, copied and ‘invented around’, which does not represent the size of investment made and rents that are collected from output (Cohen and Klepper, 1992; Vossen, 1999).

R&D Expenditures R&D Intensity =

Control Variables MNC Age

Generally, scholars researching strategic and organizational change seem to argue that older firms tend to be more static than younger firms5 (Boeker, 1997). ‘As firms age, numbers of routines, programs, and structures increase and become more internally consistent’ (1997: p.161). Contra views quoted by Boeker (1997: p.161) included statements that ‘younger firms are at disadvantage due to many uncertainties and little experience, therefore are less prepared to introduce changes that can disturb still new and fragile relationship with customers, suppliers or other stakeholders (Hannan & Freeman, 1989). Given the uncertainty of the relationship between age and firm performance ‘MNC Age’ is included as a control variable. The variable is entered into the dataset as the total number of years MNC has existed since it has been found.

MNC Size

Boeker (1997) in his work has presented two competing theories regarding the effect of firm size on strategic change: (1) small firms change more easily and tend to do so less as they grow larger, more bureaucratic and static (Hannan & Freeman, 1989); (2) larger firms have control of more extensive resources, which makes it easier to initiate and sustain change (Haveman, 1993). According to Wiersema and Bantel (1992) the likelihood of changes in corporate strategy decreases as companies grow larger, for the reason that larger size adds complexity and eventually creates resistance to essential changes. This topic has been looked at from a point of view of ‘centralized’ and ‘decentralized’ structures, which has produced contradicting conclusions6 (Chandler, 1997; Collins and Porras, 1997).

Carpenter (2002) stated that rich amount of resources and bureaucratic structures are reflected in firm performance as well as firm size affects the relationship between executive characteristics and organizational outcomes. Work of Cohen and Klepper (1992: p.793) asserts that firm size plays an important role in conditioning firm R&D spending, ‘the larger the firm grows, the greater are the

5 _{Issue, which is widely studied and discussed by scholars, however practical and accepted conclusion is yet to be}

found, when looking at big multinationals it seems that having well-established business, broader experience and in a sense being older player helped companies with their innovative efforts. However, this is not the main interest of the current paper, therefore in-depth discussions of this particular issue are excluded, and ‘MNC Age’ is entered as a control variable.

6 _{The topic of centralizing or decentralizing organizational structures is an essential issue for a discussion on the}

returns it earns per unit of R&D and, therefore the more R&D it will conduct’. In general scholars tend to control for the size of MNCs since smaller companies can have minor budgets to allocate to R&D activities however compared to their sales a smaller company can still have higher R&D intensity value. Therefore, in this study no hypothesis is made about the effect of company size on innovativeness, but organizational size is included as a control variable and is measured by taking the logarithm of total employees in 2006. This measure is the established method to justify the differences in firm size when researching organizational outcomes (Wiersema and Bantel, 1992).

Reliability of Information

Due to gathering data for the research from secondary sources and from the internet it is of crucial importance to carefully choose the source that is used for this study and if possible to consider alternative way of acquiring necessary data.

Majority of the company data is selected from the EU 2007 Industrial R&D Investment Scoreboard7, which is published annually by the European Commission (JRCIPTS/ DG RTD) as part of its Industrial Research Investment Monitoring (IRIM) activity. Information comprised in Scoreboard is extracted from audited annual reports and accounts of companies, using thorough financial reporting practice certification processes. The companies included in the sample are those which have an R&D activity and which either have their accounts publicly available for free (e.g. on the internet or upon request) or at low cost (e.g. at the company registry). The market capitalization data was drawn from both the Financial Times London Share Service and Reuters reflecting company data at the close of trading on 24 August 2007. The database is supplemented by a feed service from Standard & Poor's Compustat Global Vantage database to identify potential new entrants to the ranking. The industry sectors are based on the ICB Industry Classification System.

For developing dataset of the TMT personal demographic information company web-sites and annual reports are used, which are presumed to be valid sources of information. In case of missing

7 _{Background Information and Methodology for The 2007 EU Industrial R&D Investment Scoreboard:}

http://iri.jrc.ec.europa.eu/research/docs/2007/methodology.pdf

data Amadeus database (or other alternative search engines e.g. Google) will be used to fill in the missing data, which is also recognized as reliable source.

Conceptual Model

Creating a conceptual model gives writers an opportunity to see their idea of a research as one graphical picture of the relationships between certain variables and their constructs. The model below illustrates the assumed influence of Independent Variables upon the Dependent Variable. It also shows two Control Variables that are said to influence the relationship between IV and DV therefore need to be acknowledged to avoid discrepancies in further findings.

Independent Variables Control Variables Dependent Variable

Chapter 4: RESULTS

The analysis of the collected data has been conducted in a step-wise manner to identify the appropriate type of statistical technique to use that would enable providing answers to the main research question of this paper. As all variables in the dataset are numeric and the value of the dependent variable is continues the regression test is used for analyzing the relationship between dependent and independent variables. Prior to performing regression analysis it was important to follow and satisfy the conditions of assumptions of the linear regression. This part of the paper provides an explanation of the statistical analysis and steps taken to obtain the final results. In the early stage of regression analysis the data have to be checked for normality and linearity of distribution between dependent and independent variables (Norusis, 2006: p.523). The next step is an analysis of missing and extreme values (outliers) and multicollinearity, which all are the assumptions of linear regression that have to be satisfied (Anderson et. al., 2007: p.577;589-592). In case violations of assumptions are present steps have to be undertaken to improve the distribution of the data, as Boeker (1997) suggested the most common is a logarithmic transformation of the variables to improve normality or linearity. Table 1 presents the descriptive statistics, which is an overview of the sample and includes means, standard deviations, and skewness and kurtosis statistics.

Table 7: Descriptive Statistics of Sample Characteristics

N Min Max Mean

Std.

Deviation Skewness Kurtosis

Statistic Statistic Statistic Statistic Statistic Statistic

Std. Error Statistic Std. Error AGE 75 41,60 64,00 50,7216 4,02645 ,217 ,277 ,935 ,548 Control_Age 75 2 166 57,88 40,432 ,685 ,277 -,188 ,548 EDU 75 0 1 ,63 ,487 -,535 ,277 -1,762 ,548 LogSize 75 1,95 5,38 3,8346 ,85402 -,217 ,277 -,771 ,548 Org_Tenure 75 1,67 34,50 13,0254 7,62656 ,728 ,277 -,052 ,548 RD_Int 75 ,012 49,867 7,21319 10,157773 2,015 ,277 4,524 ,548 TMT_Tenure 75 1,60 21,50 5,4874 3,33189 2,159 ,277 7,071 ,548

relationship can distort results of regression analysis, one of the solutions can be logarithmic transformation in order to improve the relationship between variables in the sample and to perform reliable regression test (Boeker, 1997; Rummel, 1970: p.283). Before any transformations take place data are checked for other assumptions that are expected to provide better view of the sample’s distribution.

Normality of distribution can be observed by looking at skewness and kurtosis statistics that show that data approaches normality if values fall within the -1 and +1 range (Groeneveld and Meeden, 1984: p.391). For kurtosis range of +3 and -3 is also used. Initially it is possible to conclude that variables ‘age’, ‘control_age’, ‘logsize’ and ‘org_tenure’ do not experience problems with skewness or kurtosis. However, ‘rd_intensity’ and ‘tmt_tenure’ have both skewness and kurtosis relatively higher values. Table 8 clearly shows the improved values of skewness and kurtosis after these two variables have been transformed.

Table 8: Descriptive Statistics of transformed variables

Minimum Maximum Mean Std. Deviation Skewness Kurtosis Statistic Statistic Statistic Statistic Statistic Error Std. Statistic Error Std. RD_Int _{,012 49,867 7,21319 10,157773 2,015 ,277 4,524 ,548}

log_rd -4,42 3,91 ,5383 2,07856 -,262 ,277 -1,104 ,548

TMT_Tenure 1,60 21,50 5,4874 3,33189 2,159 ,277 7,071 ,548

log_TMT _{,47 2,74 1,5354 ,50975 -,013 ,279 -,382 ,552}

In order to perform a reliable regression test variables also have to be controlled for normality of the residuals by looking at the normal P-P plot of the residuals. This will show the relationship between the predicted and observed probability of residuals, if the distribution of residuals is approximately normal, values should fall between -2 and +2 (Norusis, 2006: p. 498). If sample represents normal distribution dots in the P-P plot are expected to line up on a relatively straight line (diagonal) (Huizingh, 2007: p.299). As can be seen from the results of the normality test (see Appendices B1-B6) most of the variables present enough support to accept normality assumption. As a next step variables have been also controlled for intercollinearity, which is ‘a descriptive measure of the strength of linear association between two variables’ (Anderson et. al., 2007: p.508). Values of the correlation coefficient are always between -1 and +1, indicating perfect negative and positive linear relation, respectively. Results of the correlation statistics indicate that most of the independent variables are significantly correlated with one another, which means violation of the collinearity assumption (see Appendix C1). However, according to often used rule of thumb of the collinearity diagnostics ‘VIF (Variance Inflation Factor) values that are greater then 10 signal multicollinearity’(Huizingh, 2007: p.309). Miles and Shevlin (2001) suggested that maximum acceptable level of the VIF value is 4, accepting values up until 10 would mean allowing a higher degree of standard error. The strength of the linear relationship among the independent variables is measured by ‘tolerance’. “Tolerance for each independent variable is the proportion of variability of that variable that is not explained by its linear relationships with the other independent variables in the model” (Norusis, 2006: p.533). Values close to 1 show that little variability is explained by another independent variable and values close to 0 indicate that a variable is almost a linear combination of the other independent variable (2006: p. 533). Results of the collinearity diagnostics show that VIF values are smaller than both 10 and 4 (see Appendix C2), which indicates that existing correlations between independent variables are acceptable and do not represent a violation of the multicollinearity assumption (Anderson et. al., 2007: p.578). The same table shows that tolerance values are closer to 1 indicating that little variability is explained by other independent variables, therefore multicollinearity assumption is rejected.

Table 9 presents results of the regression analysis. The hypotheses were tested using multiple regression analysis. Results of the test show the overall predictive power of the model and the (in)significant relations between dependent and independent variables. Correlation matrix with significant correlations between dependent and independent variables has been included in the Appendix D1.

Table 9: Regression Analysis Results¹

Unstandardized Coefficients

Standardized Coefficients

Model B Std. Error Beta t Sig. (Constant) _{8,044 1,263 6,370 ,000} Control_Age _{,017 ,002 ,334 7,341 ,000**} logSize -1,358 ,103 -,581 -13,209 ,000** AGE _{-,076 ,025 -,135 -3,020 ,003**} EDU ,516 ,158 ,126 3,269 ,001** Org_Tenure _{-,006 ,016 -,021 -,355 ,722} 1 log_TMT ,262 ,204 ,066 1,285 ,200 ¹ Dependent Variable: log_rd

R = 0.623 R² = 0.389 * p < 0.05 ** p < 0.01

Table 9 provides information about the (un)standardized beta coefficients and standard error estimates, T-statistics and significance of the regression analysis. For drawing conclusions about the test performed we examine the standardized beta coefficients, which indicate the contribution of the variable to the overall model and lie within the range of -1 and +1. The above mentioned results show which hypotheses have been supported or rejected by examining the significance levels. The variables in the final model explain 38.9% of the variance in commitment to innovativeness.

Both control variables are significantly related to R&D intensity with significance of .000 indicating that indeed age and size of firms matter for the innovative behavior and degree of investments in research and development activities.

conclusion, there is a significant negative relationship between the age of executive managers and innovation-oriented activities in multinational corporations, H1 is supported. This result goes inline with findings of Hambrick and Mason (1984) who claimed that age had a negative significant relationship to innovation. Also work of Hitt and Tyler (1991) produced support to the effects of executives’ age, as well as the more recent study by Horwitz (2005). On the other hand results of this study stand contrary to findings of Bantel and Jackson (1989: p.120) who concluded that executives’ age was not related to innovation. Based on their argument Daellenbach et. al. (1999) have decided to exclude managerial age as predicting and influencing variable in their study.

The field of education chosen by executive managers resulted in a positive regression coefficient .126 with a significance level (p=.001). However, it is not possible to reject null hypothesis, since this result is valid for the non-technically educated executive teams. As a result this study lands support to earlier findings that education has a significant relationship with innovativeness of MNCs, however it rejects hypothesis that TMTs with high proportion of managers with an educational background in technical fields (engineering, production/operations, R&D) are positively associated to the MNCs’ commitment to innovativeness. On one hand results of the analysis performed support findings of Bantel and Jackson (1989) of positive relationship between education and receptivity to innovation. Wiersema and Bantel (1992) and Hitt and Tyler (1991) proved that the type of managers’ field of study had influenced their strategic decision-making. On the other hand this study does not support arguments in favour of including executives from technological backgrounds in top management teams that were asserted by Daelenbach et. al. (1999). This study is also contrary to Kimberly and Evanisko (1981) who found no associations between educational curriculum and the adoption of organizational innovations.

(1999) also obtained results that did not support their hypothesis of industry and company tenure influence upon innovativeness of firms.

However, these findings stand contrary to arguments of Finkelstein and Hambrick (1990) who proved that tenure had a significant effect on organizational performance and outcomes, with long-tenured teams following more persistent strategies and having negative influence upon commitment to innovation. Wiersema and Bantel (1992) have also concluded that organizational and team tenure is related to change in corporate strategies.

Chapter 5: Conclusion and Discussion

The present study has been conducted to further explore scholarly literature on the nature of the relationship between the composition of top management and commitment of MNCs’ to innovativeness. The goal of this paper was to extend work of Daellenbach et. al. (1999) by means of enlarging the sample size, including ‘age’ of top executives as one of the predictive variables and creating a separate hypothesis for influence of technologically educated managers upon innovativeness of MNCs. It was of interest to follow up arguments of Daellenbach et. al. (1999) who strongly suggested populating TMTs with technologically experienced executives in order to get greater support for technology initiatives and firms’ strategies that emphasize innovation. The present study has raised a question of whom to promote into TMTs and what characteristics executives should possess so that MNCs prosper. Through examining the demographic characteristics of top management teams (e.g. age, tenure, and educational background) this paper was expected to provide additional insights into the characteristics of top management teams that play a leading role in influencing firms’ commitment to innovativeness. By means of empirical research the relationship between the top management team members and the commitment of multinational corporations to innovativeness was hypothesized and examined in this study.

After reviewing prior scholar literature related to the topic at stake and hypothesizing the relationship of dependent and independent variables a sample was selected and an original database of firm and executive data was generated. Conducted statistical analyses have shown that the average age and education of TMT members significantly influence MNCs’ commitment to innovativeness in terms of R&D intensity in European firms performing within the industries covered by this paper.

markets, creating new products, and making risky investments (Hambrick and Mason, 1984). Authors argued that executives’ innovative behavior can also depend on the proportion of the income tied to the performance of a firm (Hitt and Tyler, 1991). Naturally, this may result in risk-averse behavior to sustain financial secure position. Findings of this study showed that MNCs operating in pharmaceutical sphere have on average younger (49 years old) managers, than other non-innovative industries (51 years old), even though the difference is not essential.

One aspect that seems to attract much attention among scholars is the type of industry a firm operates in. Miles and Snow (1978) are the supporters of the theory that industry affects the types of managers present in top management teams. In their view the slow growth of an industry results in little mobility of the executive personnel, hence, top teams tend to be populated by older executives with long tenure lengths. The industry environment can similarly effect the staffing decisions; authors supported their argument by findings in the banking industry where it is required for bank presidents to have significant banking experience. This serves as a constricting factor for the choice of who can be considered for a top position, thus, resulting in top management teams with highly homogeneous educational backgrounds. Supporting view was presented by Harris (1979) whose findings showed that railroad executives were older and were more likely to rise from within the organization and the electronics industry was populated by younger executives with short firm tenures. The author argued that studies attempting to find differences in organizational outcomes of diverse industries as a cause of managerial backgrounds tend to mask the fundamental phenomenon of the industry dynamism. Later studies supported the earlier views arguing that executives are chosen for top positions because they poses exactly the necessary demographic and individual characteristics in order to fulfill the requirements and expectations of the board of directors (Hambrick and Mason, 1984). Supporting point of view was presented by Finkelstein and Hambrick (1990) who found that in the computer industry executives mattered greatly in contrast to the natural-gas industry were managers had less influence on organizational outcomes.

by shorter tenures and younger managers, which suggests that like ‘time-tellers’ they are prone to hire talented, experienced and already successful executives from outside the firm. On the other hand we have companies from non-innovative industries with longer tenures and older managers, which allows us to compare these firms to builders’. According to this ideology ‘clock-builders’ continuously develop exceptional managerial talent in order for core values and purpose of a firm to be preserved at the top through multiple generations. Another parallel can be seen in the fundamental ideas of corporations in each industry. Pharmaceutical firms keep on innovating and creating new products, which allows them to exploit timely market opportunities and ride the growth curve of an attractive life cycle until the next better creation comes on to the market. Such description falls under the ideology of the ‘time-tellers’ (Collins and Porras, 2000). On the other hand firms in oil & gas industry (e.g. Shell) are famous for having deep-rooted values and organization-specific cultures, which have been developed and continued through generations. Norburn and Birley (1988: p.226) argued that studies, similar to the present one, within an industry pattern need to implicitly mention that results are valid “within an industry”. For this reason when drawing a conclusion that promoting younger managers in TMTs will result in higher innovation levels it is important to mention that this result can be generalized for pharmaceutical industry when compared to oil, gas, electricity and water industries.

communication, exchange and participation. However, an additional research would have to be conducted to find out if indeed the size of top teams in the industries and firms of this study had influenced the obtained findings.

By its nature both pharmaceutical and oil and gas are old industries with decades of historical developments. The differential point between these industries stems in the fact that oil and gas industry has always been government controlled and quite monopolistically established. On the other hand pharmaceutical industry has always been innovation-driven and competitive with numerous research-induced institutions. Contrary views have been asserted by scholars arguing that performance of a firm does not only depend on the industry structure but also on the path a firm followed through history to arrive where it is (Barney, 1991). If a firm obtained valuable resources in the past it is presumed to have historically predetermined competitive advantage over its rivals and perform better. The earlier theory of Miles and Snow (1978) is supportive of the above statements by suggesting that strategies are self-reinforcing. The authors discussed an example of an innovative prospector strategy, which would require competencies, structures and processes that would support the firm’s continuing search for new products and markets. In the future executives are said to favorite innovative alternatives, which is as much a reflection of the embedded character of the prospector strategy as of the personal preferences of the executives (Miles and Snow, 1979).

from the sample tenure for pharmaceutical teams becomes smaller than that of oil, gas, water and electricity industries. These residuals have appeared as a result of very young firms present in the sample, which naturally have shorter tenures if compared to old multinationals. Nonetheless, findings of this study are similar to those of West and Anderson (1996) who did not find any relationship between tenure of executives and innovation. Authors, however, proved that presence of innovative individuals (innovators) in teams would influence innovation. Their point of view was based upon prior research in 13 oil company teams by Burningham and West (1995) who found that individual tendency to innovate was superior to that of a team. Authors built this statement upon the assumption that generating new ideas is a cognitive process within individuals, thereafter the innovation process begins with individuals. Hence, proportion of innovative executives determines the extent of innovative activities and firm behavior present and supported by each multinational.

The present study was initiated as an extension to the research of Daellenbach et. al. (1999) therefore it seems only essential to underline similar findings obtained by the scholars. In their research of the relationship between company/team tenure and commitment to innovativeness no empirical support was found for this proposition. Authors have explained their finding to be a result of the limited impact of the knowledge and experience after passing a threshold of five or ten years, as was found by Gimeno et. al. (1997) for entrepreneurial firm performance (1999: p.204-206).

Limitations and Further Research

During the process of conducting this study some limitations have emerged that could have affected the final results. The initial and very important limitation of this study is the picture view (snapshot) of the current TMT composition, which has its own negative implications on the conclusions drawn about the studied topic. Followed observation of top teams’ composition and change of membership over a longer period of time (longitudinal study) would provide a researcher much better view and more efficient understanding of the causal relationship between the variables at stake. A following limitation is the restriction of the sample to large MNCs, therefore findings cannot be generalized and applied to all firms in pharmaceutical or oil&gas industries. Importance of management and its influence on decision-making may be stronger in smaller firms as organizational structure is simplified. Furthermore the data gathered for the executives has presented some complications for the analysis performed. The sample contains many residual values, which are due to essential difference between the companies in innovative and non-innovative industries, age of firms, number of employees, generated revenues, founding history etc. In older companies you could observe tenure range of 1-30 years and younger MNCs employed managers below their 40s in its TMT. Older companies with well established market share and much higher profits could investment millions into innovative strategies, which could still look minuscule in comparison to small research oriented firms that invest almost half of their earning into R&D activities. The type of industries chosen for this study could to some extent be considered as a limitation, since the pharmaceutical industry is of highly competitive character and oil industry is considered old and stable. This naturally brings a competitive sphere and environment to pharmaceutical firms thereafter strive for innovativeness.

List of Tables and Appendices

Tables:

Table 1: Strategic Choice under Conditions of Bounded Rationality 6

Table 2: TMT and Corporate Performance 16

Table 3: Operationalization of innovativeness 18

Table 4: 2006 R&D Investment Intensity by industry within EU 27 Table 4.1: 2007 R&D Investment Intensity by industry within EU 27 Table 5: 2007 Industrial R&D Investment Scoreboard by sector 28

Table 6: List of companies in the sample 27

Table 7: Descriptive Statistics of Sample Characteristics 35

Table 8: Descriptive Statistics of transformed variables 36

Table 9: Regression Analysis Results 38

Appendices:

Appendix A1: Boxplot R&D Intensity variable 37

Appendix A1.1: Transformed R&D Intensity variable 37

Appendix A2: Boxplot Age 37

Appendix A3: Boxplot TMT_Tenure 37

Appendix A3.1: Transformed TMT_Tenure variable 37

Appendix A4: Boxplot Org_Tenure 37

Appendix A5: Boxplot Control_Age 37

Appendix A6: Boxplot logSize 37

Appendix B1: Normal Probability Plot of RD_Intensity variable 37

Appendix B1.1: Normal Probability Plot of transformed RD_Intensity variable 37

Appendix B2: Normal Probability Plot of Age variable 37

Appendix B3: Normal Probability Plot of TMT_Tenure variable 37

Appendix B3.1: Normal Probability Plot of transformed TMT_Tenure variable 37

Appendix B4: Normal Probability Plot of Org_Tenure variable 37

Appendix B5: Normal Probability Plot of Control_Age variable 37

Appendix B6: Normal Probability Plot of LogSize variable 37

Appendix C1: Correlation Statistics of the Sample 36

Appendix C1.1: Correlation Statistics of the Sample with transformed data 36

Appendix C2: (Multi) Collinearity Diagnostic 37

Appendix C3: Descriptive Statistics of Transformed Data 37

Glossary CEO - Chief Executive Officer

EU - European Union

MNC – Multinational Corporation R&D – Research and Development ROI - Return on Investment