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The Effect of CEO Characteristics on the
Relationship between Technology Foresight and
Performance Persistence: A Moderation Model
Bachelors Thesis
Victoria Ous
Student Number: 10003213 Thesis Supervisor: Hung-Yao Liu
Academic Year: 2012-2013
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Table of Contents
Abstract 3
Introduction 4
Literature Review & Hypothesis Development 6
Performance persistence 6
Technology foresight and performance persistence 7
Moderating role of CEO characteristics 9
CEO technical experience 10
CEO openness to change 11
Methodology 13
Model 13
Data and Measures 14
Sample characteristics 14 Dependent variable 14 Independent variable 15 Moderators 16 Control variables 17 Methodology 18 Results 18 Discussion 22
Limitations and future research 25
Conclusion 26
Bibliography 27
Appendix 1 33
Figures and Tables
Figure 1.Moderation Model of Technology Foresight and CEO Technical
Experience and Openness to Change on Firm Performance
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Table 1. Descriptives and correlations between the variables 20
Table 2. Regression results of main (model 1) and interaction of R&D
intensity and CEO technical experience and openness to change on firm performance (model 2)
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Abstract
For centuries researchers have been trying to explain why some firms outperform others and what is affecting the performance persistence of the firms. This study looks into the technology foresight as the main driver of performance persistence of a firm by using R&D intensity as a measurement. It tries to investigate how CEO’s technical experience and openness to change moderate the relationship between technology foresight, measured by R&D expenditures, and firm’s performance persistence, determined by Tobin’s Q. It expects that the CEOs technical experience will positively moderate the relationship between technology foresight and performance persistence. Also, it considers openness to change as a proxy of three demographic variables: age, education and organizational background; and expects that age and organizational tenure will negatively moderate the relationship between technology foresight and performance persistence. On the other hand it supposes that the education will positively moderate the main relationship. After investigating 37 pharmaceutical companies from US between 2005 and 2012, the findings show that there is an insignificant negative relationship of technology foresight on performance persistence. Also, it finds no significant moderation effect of CEO technical experience and openness to change on the relationship between technology foresight and performance persistence. Yet, the findings show a positive effect of the CEO technical background, organizational tenure and education and a negative effect of CEO’s age on firm performance after controlling the company size and CEO’s tenure.
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Introduction
A core problem for strategy researchers is why some firms outperform others. Much research in strategic management seeks to find what the advantages and characteristics of firms are that make them do better than others (Barney & Arikan, 2001). Strategy frameworks have been said to share two assumptions: that competitive advantage is created or established through better access to markets, resources, or through organizational opportunities, and that the exploitation of such opportunities mirrors a degree of managerial interpretation of internal and external environmental cues (Cockburn, Henderson & Stern, 2000). Nevertheless, other researches assume that the performance persistence is influenced by core competences of the firm (Prahalad & Hamel, 1990), scope of the firm (Rumelt, 1974), and the type of industry it is in (Chacar & Vissa, 2005). Also, the profitability was attested to be affected by corporate management, which includes managerial ability that manifests itself concretely in managerial plans, and goal setting for the company as a whole and for individual businesses within the corporation (Andrews, 1987; Hambrick & Mason, 1984). Much research was conducted on these variables and they are already proven to influence the performance of the firm. Though, for researchers it is more interesting how companies incorporate them effectively in their strategy in order to benefit a competitive advantage over other firms (Rumelt, 1974). A good strategy is no longer only about using one of these variables, but it’s about combining them while taking into account the new trends in the market, new technologies and the competitors’ next moves (McKelvey & Boisot, 2008).
Moreover, since the early twentieth century foresight has been considered an essential part of the strategic management and performance of the firm (Amsteus 2008, 2011). In fact, foresight has been recognized as crucial in managing the continuously changing environment that the majority of firms face (Freeman and Hannan, 1983). And, because nowadays innovation plays a key role in establishing a competitive position in the market, it is
5 meaningful to analyze the influence of technology foresight on the performance as a device for enhancing innovation and change, in comparison or in contrast to incremental improvements and inertia (Lichtenthaler, 2005). In the literature technology foresight is defined as ‘the process which aims to identify future technological developments and disruptions in order to assist decision making related to future R&D activities’ (Lichtenthaler, 2005). Because developing new products, processes, or technologies are often the driver for future competitive advantage and productivity (Scherer 1984, Ettlie 1998), the ability of the firm to manage its R&D constitutes a source of performance persistence. A firm contributes to its R&D development by allocating enough resources to the department at the right moment.
However, the strategy and the R&D spending of the firm are shaped by the CEO’s choice and deliberation (De Smedt, 2013, Barker & Mueller, 2002). Given that investment decisions are the most important responsibility of the CEO of a firm, it is essential to take into consideration the background and cognitive bases of top managers when analyzing the strategy establishment and allocation of resources (Hambrick & Mason, 1984). Consequently, the CEO plays a decisive role in how technology foresight is being steered and made use of.
Altogether, performance persistence is considered to be influenced by technology foresight which represents a strategy to manage innovation in a company. Yet, this relationship depends on the CEO characteristics because the CEO has the ultimate power in taking any decisions regarding the creation of the strategy. That is why, this paper investigates how technology foresight, measured by R&D intensity, influences the company’s performance persistence. Furthermore, it looks into how CEO characteristics influence the relationship between these two variables. The study analyses 37 pharmaceutical companies based in US from 2005-2012. It tries to explain the moderation effect of CEO technical experience, expressed by the number of years worked in the field of product-operation, R&D
6 and marketing and sales, on the relationship between R&D intensity and firm performance. In addition it investigates the CEO openness to change as a proxy of three demographic indicators: age, education and organizational tenure; and how it moderates the relationship between R&D intensity and firm performance.
Literature Review and Hypothesis Development Performance persistence
Considering the resource-based view on competitive advantage, Spanos & Lioukas (2001) found that the firm’s available resources and capabilities are decisive for achieving market performance, and later profitability. Only in case if available resources contain features that are valuable, non-imitable, rare, and non-substitutable, a given strategy will stimulate sustainable performance persistence (Barney, 1991). Dierickx & Cool (1989) suggested that the resources that are acquired on the market cannot constitute sources of competitive advantage since competitors could purchase them as well. Instead, critical resources are built and accumulated inside the firm; and their imitability and non-substitutability are acquired during the engagement process in innovation. Consequently, management should focus on building and sustaining their unique resources and capabilities.
One way of building unique capabilities is by engaging in innovation (Cohen & Levinthal, 1990). Through the investment and development of innovation by scanning the external environment, companies increase their capacity to learn and are able to take advantage of available technological knowledge (Martinez-Ros & Labeaga, 2009). This learning process enables firms to improve their existing products and processes as well as benefit of technological change. Because the development of innovation happens in-house and the end result is a new product or process, the firms develop tacit knowledge which is valuable, rare and non-imitable. As a result, following the reasoning of Spanos & Lioukas
7 (2001) that unique resources are crucial for gaining competitiveness in the market, enhancement of technological knowledge would lead to performance persistence of the firms.
McEvily & Chakravarthy (2002) followed the resource-based theory and examined whether and how the complexity, tacitness and specificity of a firm’s knowledge affect the persistence of its performance advantages. The results indicated that knowledge can extend the firm’s performance persistence. Complexity and tacitness protect large performance gains, though complexity does not affect, and tacitness negatively affects, the persistence of small advantages (McEvily & Chakravarthy, 2002, p. 300). The authors explained these effects by claiming that new technologies are very complex and ambiguous which result in a slower rate of diffusion. Consequently, imitating complex strategies is harder and imperfect imitation can lead to greater accountability.
Furthermore, Rivard, Raymond & Verreault (2006) built up on Spanos & Liouka’s (2001) model and found that information technology (IT) contributes to market performance when aligned with the firm's competitive strategies (p. 43), and it has an indirect and direct effect on firm’s performance when used to leverage firm capabilities (p.44-45). The indirect effect consists of the fact that IT may contribute to fostering the formulation and the implementation of competitive strategies that impact market performance (Rivard et al, 2001, p. 45). When IT is used to support the firm's valued assets, IT has a direct effect on profitability (Rivard et al, 2001, p. 45).
Technology foresight and performance persistence
Canongia, Antunes & Pereira (2004, p. 299) recognize that nowadays in order to beat the competition it is of outmost importance to focus on differentiation, flexibility, speed, cost rationalization and innovation. The authors urge for new management models which would focus on competitive intelligence, knowledge management and foresight. Their research concentrates on the technology foresight as a concept formed of: information- technology-
8 specialists (Canongia et al, 2004, p. 300). Though the authors consider that technology foresight ‘opens a range of opportunities which make desired futures attainable, as well as provides warnings about threats to be avoided in the globalized market’, they take into account that technology foresight provides only ‘glimpses’ of some tendencies which might happen or not (Canongia et al, 2004, p. 300). Nevertheless, these glimpses provide indicators of possibilities which might be attained through strategies or plans of action.
Reger (2001, p. 535) looked into the evolution of technology foresight for the past decades and found that companies which engaged in technology foresight ensured their competitiveness for a sustainable innovation, identified the new trends in technology, anticipated technological discontinuities, and consequently managed to differentiate themselves from their competitors. Reger (2001, p.536) identified 5 core elements of technology foresight (see Appendix 1): (1) activities, (2) science and technology, (3) actors, (4) storage and distribution of information within the corporation, and (5) recommendations or decisions on R&D projects, R&D programs, innovation fields or new businesses. The fifth element is considered to be ‘the bridging interface between technology foresight and the R&D, technology or innovation activities at its core’ (Reger, 2001, p.536).
As emphasized earlier, the development of the firm’s innovativeness represents an essential activity of a company if it wants to meet the challenges of fast changing environment and strengthen its competitive advantage (Reger, 2001). Much research proved that investments in R&D have been the primary source of innovation and superior returns in a firm (Branch, 1974; Hill and Snell, 1988; Chauvin and Hirschey, 1993; David, Hitt, & Gimeno, 2001). Cohen & Levinthal (1989, p. 593) discovered that ‘firms invest in R&D not only to pursue directly new process and product innovation, but also to develop and maintain their broader capabilities to assimilate and exploit externally available information’.
9 Thus, technology foresight represents a great way to increase the competitiveness of the firm by spreading insights about the future technological trends across the organization. This leads to the hypothesis:
Hypothesis 1: Technology foresight, measured by R&D intensity, will positively affect the firm performance persistence.
The moderating role of CEO characteristics.
Many scholars tried to explain how CEO’s characteristics influence the firm performance for over a century. Most of them came to the conclusion that top management has a direct effect on the performance at all levels. For example, Barnard (1938) demonstrated that management creates a collective rationale which unifies the employees in the company. Selznick (1984) and Schein (1992) deduced that upper echelons set the firm’s values and build the firm’s culture. And more recently, Tichy (2009) argued in his book that CEOs are the ones who decide the firm´s course of action and establish the overall strategy of the company especially in the fast changing environment. All these effects of leadership influence the entire company resulting in a big impact on firm´s performance (Mackey, 2008). On the other hand, other academics like Pfeffer (2003) considered that CEO has very little influence over the firm performance due to environmental, organizational and legal constraints and that CEO is playing a symbolic role in the firm and has humble effect on performance. This study considers the most recent view that CEO characteristics influence the firm outcomes and is based on the Mackey’s (2008, p.1358) research which showed that, in fact, CEOs have a substantial impact and explain as much as 29.2 percent of the variance in a firm’s performance.
Additionally, the R&D investment decisions are taken by the top management which acts as ‘filtering mechanism’ interpreting the information through their cognitive base and values. Barker & Mueller (2002) discovered that CEO characteristics explain a significant
10 proportion of a firm’s relative R&D spending even after controlling for corporate strategy, ownership structure and other firm characteristics. The authors base their research on three assumptions. First, given that R&D spending represents a long-term investment that is considerably risky and the top management will monitor it very closely, they supposed that R&D spending is an investment that top executives have control over (Barker & Mueller, 2002, p. 783). Secondly, due to the fact that CEO is often the central strategic decision maker, Barker & Mueller (2002, p. 783) assumed that CEOs have the greatest organizational power to influence R&D spending. Thirdly, they inferred that a CEO’s preferences for various levels of R&D spending are associated with visible CEO characteristics such as age, tenure, education, career experiences, and stock ownership (Barker and Mueller, 2002, p. 783). Their research showed that ‘R&D spending is greater at firms where CEOs are younger, have greater investments in firm stock and significant career experience in marketing and/or engineering/R&D’ (Barker and Mueller, 2002, p. 797). And Kor (2006) found that management team characteristics have a direct effect on R&D investment intensity.
Considering the findings of previous research that CEO directly influences the firm’s course of action, R&D investments and performance separately, this paper explores the effect of the CEO characteristics on the relationship between technology foresight, determined by R&D intensity, and performance persistence, evaluated by firm performance. No previous research has been noticed to look into this moderation effect. Therefore, this research tries to reveal how CEO characteristics moderate the effect of R&D intensity on firm performance.
CEO technical experience. Lefebvre & Lefebvre (1992) conducted a research and looked into
how CEO personal characteristics, attitudes and personal traits, and characteristics of decision-making process affect firm innovativeness. They found out that functional experience of the CEO in engineering and production is positively and significantly related to the degree of innovativeness. The authors explained the phenomenon with the fact that
11 technological know-how of the founders of high technology based firms is positively associated with the technological sophistication of the firm’s products (Lefebvre and Lefebvre, 1992, p 256). Furthermore, they found out that the favorable attitude towards risk and proactive attitude of the CEO are associated with higher degree of firm innovativeness (Lefebvre and Lefebvre, 1992, p 257). Also, Daellenbach, McCarthy & Schoenecker (1999) established a positive relationship between the technical background of the CEO and R&D expenditures. Beal & Yasai-Ardekani (2000) found a positive effect of CEO’s R&D, sales, marketing or engineering experience on the R&D and firm performance.
Since past research showed that CEO’s technical background, like engineering, R&D, operations, marketing and sales, has a positive effect on firm’s commitment to innovation as well as R&D expenditures which eventually will affect the performance persistence of the firm (Daellenbach et. al., 1999; Beal & Yasai-Ardekani, 2000). Thus, the paper hypothesizes:
Hypothesis 2: CEO’s technical experience will positively moderate the relationship between technology foresight and firm performance.
CEO openness to change. Musteen, Barker and Baeten (2010) investigated the effect of
CEOs’ attitudes toward change on the emphasis on innovation in competitive strategies. They found out that the CEO’s openness to change has a strong, systematic impact on firm’s focus on innovation (Musteen et al, 2010, p. 374). They explained this discovery by reasoning that transformational leaders always choose for growth oriented strategies which finally lead to a greater level of innovation. Also, Daellenbach et. al. (1999) demonstrated in their research that CEO’s openness to change is positively related to firm’s commitment to innovation.
Past research has associated individual demographic characteristics to firm innovativeness and persistence rather than relating to the integrative construct, CEO openness to change. The commonly used proxies for the openness to change were age, educational
12 background and organizational tenure (Daellenbach et. al., 1999, Musteen et al, 2010, Datta, Rajagopalan & Zhang, 2003).
Age has been associated with low openness to change (Datta et.al., 2010, Daellenbach et. al., 1999). Hambrick & Mason (1984) argued that age is influencing the CEO’s attitude towards risk making older CEOs are more risk-averse than the younger ones. Wiersema & Bantel (1992) showed that older managers have a greater commitment to past strategies and limited exploration of new trends and alternatives which lead to less strategic change. Given that technology foresight involves risk and novelty, CEO age could negatively influence the relationship between technology foresight and firm performance. Thus, the paper theorizes:
Hypothesis 3: CEO’s age will negatively moderate the relationship between technology foresight and firm performance.
Educational background has been proven to be positively related to openness to innovation and strategic change (Hambrick & Mason, 1984, Wiersema & Bantel, 1992, Datta et. al., 2003). Hambrick & Mason (1984) emphasized influence of educational background and experience of top managers on their decision making process and demonstrated that these two varaibles constitute the main determinants of a person’s values, cognitive base and biases. Becker (1970) showed that higher education level is associated with greater tolerance for uncertainty, increased ability to handle new alternatives and greater openness to change. As a result, the higher educational level the CEO has, the greater the technology foresight the CEO would develop which would lead to higher firm performance; so that the paper assumes:
Hypothesis 4: CEO’s educational background will positively moderate the relationship between technology foresight and firm performance.
Organizational tenure was argued to be negatively associated with openness to change (Datta et. al., 2003, Daellenbach et. al., 1999). Tushman & Romanelli (1985) provided evidence that CEOs with higher levels of organizational tenure are likely to get stuck within
13 firm’s routines and processes and avoid changes. Consequently, insider successions are associated more with perpetuation of existing strategies which will negatively influence the firm’s innovation as well as performance persistence. Accordingly, the paper hypothesizes:
Hypothesis 5: CEO’s organizational tenure will negatively moderate the relationship between technology foresight and firm performance.
Methodology Model
A lot of research was made in analyzing the direct effect of firm innovativeness and R&D spending on performance persistence (Rivard et. al., 2006, Canongia et. al., 2004), as well as, the effect of CEO characteristics on R&D spending, firm innovativeness or firm performance (Lefebvre & Lefebvre, 1992, Daellenbach et al, 1999, Barker & Mueller, 2002, Kor, 2006). Building up on the literature review, this paper looks how CEO’s technical experience moderates the relationship between technological foresight, measured by R&D intensity, and firm’s performance persistence, measured by the past firm’s performance (Figure 1). It also investigates the CEO openness to change as a proxy of three demographic indicators: age, education and organizational tenure; and how it moderates the relationship between technology foresight and firm performance. By combining the four variables in a model, the paper tries to find out the strength of the CEO experience and openness to change on the relationship between technology foresight of the company and its performance.
Technology Foresight as a proxy of R&D intensity Performance Persistence measured by past firm
performance CEO Characteristics - technical experience; - openness to change: age; educational background; organizational tenure.
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Figure 1 .Moderation Model of Technology Foresight and CEO Technical Experience and
Openness to Change on Firm Performance
The model is based on the assumptions Barker & Mueller (2002) considered in their research: (1) top executives have control over the investment in R&D; (2) CEO has the biggest power to influence the innovativeness of the firm; (3) CEO preferences for various levels of firm’s innovation vary according to his characteristics. In addition, it supposes that the technology foresight is mainly determined by R&D activities the company engages in (Reger, 2001).
Data and Measures
Sample characteristics. At first, the data was taken from Compustat from 2005 till 2012 and
incorporates 294 pharmaceutical companies. The data included information about the total assets, short-term debt, long-term debt, market value, preferred stock, revenue and R&D spending of the firms. The sample was narrowed down to 86 companies due to missing data related to R&D spending, total assets or preferred stock.
Secondly, the companies from the sample were check through People Intelligence, a database which includes information about the firm’s CEO name, title, compensations, age, gender etc. After finding out the names of the CEO’s, they were checked through BoardEx, a database which contains biographical information about senior executives around the world, for information regarding CEO educational background, previous work experience, number of years in the company and number of years being in the function of CEO. After this step the sample got reduced to only 37 companies because: BoardEx contains information only about most recent board members so no information was found for 26 companies, 12 companies got delisted from BoardEx, and 11 companies were deleted due to missing information regarding CEO education or previous work experience.
Dependent variable. Chacar & Vissa (2005, p. 937) define the performance
15 current period, and measure it with the normalized returns and firm specific rent. Morbey & Reithner (1990) consider measuring it by sales growth, profit margin or return on assets. According to Tidd (2001), stock market value is more appropriate indicator than return on investments or profits because it would show quicker the influence of innovation on firm performance. Other researches use return on assets, expenses to revenues or return on equity (Michel & Hambrick, 1992). However, these indicators contain limitations since they use net income to value profitability which is restricted by tax systems (Hokkanen, 2006).
This paper considers Tobin’s Q as the best proxy for book-to-market value. Furthermore, it applies the approximation of Tobin’s Q as elaborated by Chung & Pruitt (1994), since it requires only the data from Compustat. Chung & Pruitt (1994, p. 74) developed a formula which has higher accuracy than the original formulation. The Tobin’s Q is calculated as following (Chung & Pruitt, 1994, p. 71):
𝑇𝑜𝑏𝑖𝑛′𝑠𝑄 = (𝑀𝑉𝐸 + 𝑃𝑆 + 𝐷𝐸𝐵𝑇)/𝑇𝐴
where, 𝑀𝑉𝐸 represents the market value of shareholders equity, 𝑃𝑆 is the value of the firm’s outstanding stock, 𝐷𝐸𝐵𝑇 is the value of the firm’s short-term liabilities net of its short-term assets plus the book value of the firm’s long term debt, and 𝑇𝐴 is the book value of the total assets of the firm.
Independent variable. In the analyses the technology foresight is brought to the
research model by measuring the innovation intensity. As discussed previously, much research was made on the influence of R&D intensity on firm performance where it was assumed that the greater investment in R&D the higher levels of innovation and gains from the investment (Del Monte & Papagni, 2003). Yet, it is very difficult to measure the inputs and outputs of innovation and their relation to firm performance (Hokkanen, 2006).
Some researches consider that patents are indicators of the effectiveness of investments allocated to innovation as well as technological performance (Dodgson and
16 Hinze, 2000). They use patents to track the R&D intensity and direction of knowledge flows. Pakes (1985) found in his studies that there is a relationship between R&D and number of patents across firms. Raw patent counts are generally accepted as one of the appropriate indicators that enable researchers to evaluate the innovativeness of the firm as a matter of new technologies, new products and new processes (Hagedoom & Cloodt, 2003, p. 1368). Nevertheless, raw counts of patents represent a purely quantitative measure. Patent citations are a more appropriate measure since it takes into consideration the quality of the patents. The validity of patents as a measurement of productivity of R&D is a matter of doubt in many studies since there is always a discrepancy between the R&D investment, patent application and patent approval (Hokkanen, 2006, p. 32). Consequently, it is very difficult to match the R&D effect on profitability by only using patents.
The most commonly used measurement of R&D intensity in several previous research studies is by looking at R&D expenses. The ratio of R&D expenditures to total sales has been widely used as a measure of R&D intensity (Morbey & Reithner, 1990; Lee & Shim, 1995, Del Monte & Papagni, 2003). Also, the ratio of R&D expenditures to total assets is applied often since some researchers consider that R&D activity should not be related to the way funds are acquired (Xu & Zhang, 2004). Other approaches of measuring R&D use profits, growth or total factor productivity as an output besides other normal factors (Hokkanen, 2006, p. 34). The ratio of R&D expenditures to total sales is chosen because it has been the most used in previous studies and is considered to be a standard measure of R&D intensity (Morbey & Reithner, 1990; Lee & Shim, 1995 and Del Monte & Papagni, 2003).
𝑅𝐷𝑖,𝑡 =
𝑅𝐷𝐸𝑖,𝑡 𝑆𝑖,𝑡
where 𝑅𝐷𝑖,𝑡 represents the R&D intensity of firm 𝑖 at year 𝑡; 𝑅𝐷𝐸𝑖,𝑡 is the R&D expenditures of firm 𝑖 at year 𝑡, and 𝑆𝑖,𝑡 is the sales of firm 𝑖 at year 𝑡.
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Moderator. In similar studies which examined CEO characteristics-strategy alignment,
CEO experience in a particular functional area was measured as a continuous variable reflecting a CEO’s number of years of experience in that area (Daellenbach et. al. 1999; Beal & Yasai-Ardekani, 2000). Functional tracks are divided into 9 as defined by Daellenbach et. al. (1999, p. 203) and Michel & Hambrick (1992, p. 22): (1) production-operations, (2) research & development (including engineering), (3) finance, (4) accounting, (5) law, (6) administration, (7) general management, (8) marketing-sales, and (9) personnel and labor relations. As Daellenbach et. al. (1999, p. 203) defined in their research, categories (1), (2) and (8) are defined as technical areas, whereas the other categories are defined as support areas. The functional experience was measured as the average years of experience in the respective functional areas of interest (Beal &Yasai-Ardekani, 2000, p. 743). The technical experience variable represents a dummy of 1 if the CEO has experience in (1), (2) or (8) areas.
According to Daellenbach et. al. (1999, p. 203), CEO openness to change is measured by CEO´s company and industry experience, dominant functional track, and educational background. Datta, Rajagopalan & Zhang (2003, p. 107) consider this variable a proxy of the three indicators: age, organizational tenure, and educational level. However, this research combines the two measurements and defines the CEO openness to change as a variable formed of age, the number of years from birth to the year of succession, organizational tenure, the number of years the CEO has been employed in the firm, and educational background.
Educational background of the CEO is identified by the highest degree the CEO earned. The education background is classified in four levels: (1) high school, (2) bachelors degree, (3) masters degree, and (4) Phd and/or MBA (Daellenbach et. al., 1999, p. 203).
Control variables. Research shows that the more power CEO has, the bigger influence
18 why, the paper includes the CEO tenure as a control variable measured by number of years the CEO has been the top executive of the firm. (Haynes & Hillman, 2010, Le, Walters, & Kroll, 2006).
Previous empirical findings indicate that firm size has an influence on the R&D spending (Haynes & Hillman, 2010, Barker and Mueller, 2002). The larger firms may have greater resources to exploit R&D programs and innovation than smaller firms (Barker and Mueller, 2002). Consequently, firm size is taken as a controlled variable and is measured by the logarithm of market capitalization of the firm (Haynes & Hillman, 2010, p. 1155).
Methodology. In order to measure how CEO’s technical experience and openness to
change influences the relationship between R&D expenditures and firm performance, the paper applies a moderation model on the main relationship. Linear regression analysis is used to investigate the hypotheses. The firm performance, which is measured by Tobin’s Q, is regressed on the following variables: R&D intensity, CEO technical experience which is represented by the functional experience in technical area, CEO openness to change determined by age, organizational tenure, and educational background, considering the CEO tenure and firm size. Such regression models have been used earlier by Beal & Yasai-Ardekani (2000)who have tested similar alignment hypotheses. The equation which is used can be represented as follows:
𝑇𝑜𝑏𝑖𝑛′𝑠𝑄𝑖,𝑡 = 𝛼1 + 𝛼2𝑅𝐷𝑖,𝑡 + 𝛼3𝑇𝑒𝑐ℎ𝐸𝑥𝑝 + 𝛼4𝑂𝑝𝑒𝑛𝐶ℎ + 𝛼5𝑅𝐷𝑖,𝑡𝑇𝑒𝑐ℎ𝐸𝑥𝑝
+ 𝛼6𝑅𝐷𝑖,𝑡𝑂𝑝𝑒𝑛𝐶ℎ + 𝛽1𝐶𝐸𝑂𝑇𝑒𝑛𝑢𝑟𝑒 + 𝛽2𝑆𝑖𝑧𝑒 + 𝜀
where 𝑇𝑜𝑏𝑖𝑛′𝑠𝑄𝑖,𝑡 is the Tobin’s Q of firm 𝑖 at year 𝑡; ; 𝑅𝐷𝑖,𝑡 represents the R&D intensity of firm 𝑖 at year 𝑡; 𝑇𝑒𝑐ℎ𝐸𝑥𝑝 is CEO technical experience; 𝑂𝑝𝑒𝑛𝐶ℎ is CEO openness to change; 𝐶𝐸𝑂𝑇𝑒𝑛𝑢𝑟𝑒 is CEO’s number of years in the executive function; and 𝑆𝑖𝑧𝑒 stands for firm 𝑖 size.
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Results
Table 1 presents the means, standard deviations and inter-correlations between the variables. The results revealed that 92% of the CEOs are male with an average age of 56. Most of them finished an MBA or PhD and occupy the CEO position for an average of 5 years. As expected, most of the executives are mainly specialized in technical areas.
CEO technical experience is significantly related to the firm performance but not to the R&D intensity. The CEO’s age is significantly negative correlated to firm performance as well as his technical experience. The education is significantly rellated to the CEO technical experience since most of them finished at least Bachelors or MBA. The CEO’s organisational tenure is notably associated with his age and technical experience. On the other hand CEO tenure is strongly correlated to his organizational tenure which is explained by the amount of time the CEO worked for the company. The fact that the firm size is significantly negative correlated to the firm performance can be explained by the amount of debt it holds.
20 Mean Standard deviation 1 2 3 4 5 6 7 8 9 10 11 1. Tobin´s Q 1.33 4.41 2. RD intensity .074 .81 -.008 3. CEO Technical Experience .72 .45 .196** .052
CEO Openness to Change
4. age 56.46 8.13 -.300** -.087 -.194** 5. education 3.80 .40 .086 .041 .149* -.107 6. organizational tenure 8.74 6.47 -.092 -.055 .135* .325** .064 Interaction Terms 7. RD*TechExp .072 .81 .009 .934** .056 -.086 .040 -.053 8. RD*Age 3.64 37.56 .008 .986** .055 -.071 .042 -.049 .986** 9. RD*Education .29 3.25 .956** .852** .052 -.087 .042 -.055 .795** .986** 10. RD*OrgTenure .36 3.12 .003 .928** .068 -.022 .051 .026 .928** .960** .928** Control Variables 11. Firm Size 15.77 2.59 -.274** -.078 .066 .223** -.033 .266** -.080 -.075 -.078 -.046 12. CEO Tenure 5.55 3.78 -.010 -.045 .006 .099 .133* .470** -.042 -.044 -,045 -.045 -.046 Note: N=296, *p<.05, **p<.01, ***p<0.001
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model 1 explains 20 percent of the variance in comparison to the second model which shows 20.6 percent. Consequently since model 2 shows more variance, it can be considered a more appropriate model. Nevertheless, the model 2 does not show a significant level of interaction between R&D intensity and CEO technical experience and openness to change on firm performance.
Table 2. Regression results of main (model 1) and interaction of R&D intensity and CEO
technical experience and openness to change on firm performance (model 2)
Model 1 Model 2 SE Beta SE Beta Constant 3.1 3.21 RD intensity .285 -.025 10.89 -2.146 CEO Technical Experience .539 .278*** .548 .288***
CEO Openness to Change
age .032 -.335*** .032 -.342*** education .587 .088* .590 .090* organizational tenure .046 .164* .048 .158* Control Variables Firm Size .100 -.244*** .101 -.238*** CEO Tenure .075 -.109 .076 -.110 Interaction Terms RD*TechExp 8.615 1.961 RD*Age .056 .115 RD*Education .114 .043 RD*OrgTenure .309 .051 R squared .200 .206
Note: Dependent variable is Tobin´s Q, N= 296, *p<.05, **p<.01, ***p<.001
Considering Table 2, in model 1 R&D intensity has a negative effect on firm performance, although it is insignificant. Therefore, the hypothesis 1 is rejected. Nevertheless, CEO technical experience has a significant positive effect of firm performance, as well as, CEO’s education and organizational tenure. However, the age and firm size affect significantly negative the firm performance.
22 Alternatively, in model 2 after introducing the interaction terms, there is no robust evidence which would prove a connection between interaction terms and the firm performance. This leads to the conclusion that there is no significant moderation effect of CEO technical experience, age, education background and organization tenure on the relationship between the R&D intensity and firm performance even if their effect is positive. As a result, the hypotheses 2, 3, 4 and 5 are rejected.
Discussion
The paper investigates 37 pharmaceutical companies in US with the most recent data from 2005 to 2012 regarding firm performance, R&D spending, and CEO characteristics. It strives to explain the moderation effect of CEO characteristics on the relationship between technology foresight, a proxy of R&D intensity, and performance persistence. The study reveals that R&D intensity has an insignificant negative effect on firm performance which means that technology foresight does not affect positively the performance persistence of the firm. Further, this study does not show any significant moderation effect of CEO’s technical experience, age, educational background and organizational tenure on the interaction between technology foresight and performance persistence even after holding CEO tenure and firm size as control variables.
There are several implications that can be concluded from this analysis in parallel with the previous literature. In the paper technology foresight was considered to be determined only by R&D intensity and, normally, R&D intensity requires a big investment which represents a big percentage out of the earnings of the firm. Since the performance persistence was calculated by using Tobin’s Q, the analysis captured an insignificant negative relationship between the R&D intensity and firm performance. Because technology foresight is seen as a proxy of R&D intensity which represents a snapshot of a specific year rather than a process, it does not include the value of technology knowledge which the firm acquires during the
23 process of getting involved in innovation. Canongia et. al. (2004) underlined that the greatest benefit of technological foresight is the process of gathering and analysis of strategic information, the creation of multiple interpretations and the implementation of expectations about the future combined with lessons learnt from the past into the present. The knowledge which the firm obtains in this process is more important rather than the numbers themselves. As Lichtenthaler (2005) pointed out, systematical engagement and an accurate implementation of technology foresight result in the creation of valuable, rare and non-imitable resources which contribute to the enhancement of firm’s performance persistence.
In addition, past research suggests that R&D expenses do not immediately affect the company profitability. There are studies which consider a time lag between the R&D investment and its actual outcome, though this paper considers the immediate return (Hokkanen, 2006). Morbey and Reithner (1990), for example, used the average R&D intensity from four years to analyze its impact on the following year’s firm performance using one year time lag. Especially in the pharmaceutical industry, the R&D benefit can be perceived with a delay of at least 2 years (Del Monte and Papagni, 2003). This might be another reason why an insignificant effect of R&D intensity on firm performance persistence was detected.
According to the literature, CEO technical experience has a positive effect on the relationship between R&D spending and firm performance (Daellenbach et. al., 1999, Beal & Yasai-Ardekani, 2000, Kor, 2006). Moreover, as Beal & Yasai-Ardekani (2000) showed in their research, CEOs with a marketing and sales background have a positive effect on the connection between R&D spending and firm performance. This study supports these affirmations by showing a significant positive effect of CEO technical experience on firm performance. Though, it does not show a significant moderation effect of CEO technical experience on the relationship between R&D intensity and firm performance.
24 Daellenbach et. al. (1999) and Datta et. al. (2003) demonstrated in their researches that CEO’s openness to change is positively related to R&D expenditures and firm performance. This paper confirms that education is positively affecting the firm performance. It proves the Barker and Mueller (2002) position that the type of higher education is important in predicting the R&D spending as well as firm performance. The higher level the CEO has the greater is his attitude towards innovation. Still, no significant moderation effect of the education has been captured on the relationship between R&D and firm performance. Also, as in previous research the age of the CEO has a negative influence on performance of the firm which can be explained by the fact that younger managers are more predisposed to higher R&D spending (Wiersema & Bantel, 1992, Datta et. al., 2003, Hambrick & Mason, 1984, Becker, 1970).
In contrast to the findings of Tushman & Romanelli (1985), this study revealed that longer CEO tenure appears to be associated with a more active approach to innovation. This result is counterintuitive since the increasing tenure was associated in the literature with the preservation of the status quo and resistance to change (Tushman & Romanelli, 1985, Datta et. al., 2003, Daellenbach et. al., 1999). Musteen et. al. (2010) encountered the same finding and explained this deviation with the fact that CEO tenure might depend on firm’s environment. Henderson, Miller and Hambrick (2006) found that the relationship between the CEO tenure and firm performance was strongly affected by industry dynamics. Wu, Levitas and Priem (2005) analyzed biopharmaceutical companies and revealed that in a more technologically stable environment a longer tenure involved more innovation while shorter tenure with a higher number of patents. Considering the context of different environments, this study shows the longer the CEO has worked in the company the greater the innovativeness of the firm which might be due to the CEO experience and knowledge he
25 acquired in-house, although CEO tenure does not affect significantly the relationship between technology foresight and firm performance persistence.
This paper brings managerial implications for the companies which are focusing on innovation as the main competitive advantage. Managers can apply the findings when hiring or changing the CEO. According to the findings, the best candidate should be young, have at least a Master degree, and has been working for the company for some time. Moreover, the person should have previous experience in the one of the following fields: R&D, production and operations, or marketing and sales.
Limitations and future research. Due to limited research into technology foresight, the
paper considers R&D intensity as the main determinant of technology foresight. This constitutes the research major limitation since the technology foresight has 4 more core elements (Reger, 2001). Further research should be conducted in analyzing what is the influence of CEO characteristics on the other elements.
Besides, this paper analyzes technology foresight in a snapshot and it does not take into consideration the entire process it implies. Amsteus (2008, 2011) and Reger (2001) describe technology foresight as a process of systematic identification of new technologies, evaluation of their potential and their usage in enhancing the competitiveness of the company. That is why, a good suggestion for future research would be undertaking a longitudinal study analysis of technology foresight and the implications of CEO characteristics on the process.
In addition, the study does not take into consideration the effect of executive board characteristics. Since the CEO does not take all the decisions by himself but he discusses them first with the board of directors, the board characteristics should be taking into account as well. Future researcher should account the influence of the board of directors’ composition and characteristics when analyzing the CEO decision-making process.
26 Also, the paper makes use of the three demographic variables: age, education and organizational tenure, as a proxy for the CEO openness to change construct which can imply that the analysis might not fully captured the cognitive variables that tap into the openness to change concept. Future studies should look more into how to define openness to change more broadly in order to capture its entire significance and find more appropriate measures of executive perceptions and beliefs next to control and contextual variables.
Along with the measurement of openness to change, this paper is based exclusively from archival secondary data. Nevertheless, future research which is looking into the cognitive characteristics of the CEO should use alternative research designs and data-collection methods which would analyze more deeply the psychological mechanisms through which CEOs influence the firm’s performance persistence.
Conclusion
The paper investigates how CEO characteristics moderate the relationship between technology foresight and firm’s performance persistence. It considers technology foresight as determinant of R&D intensity and it looks into how CEO technical experience and openness to change, as a proxy of CEO’s age, education and organizational tenure, influences the R&D intensity and firm performance. After analyzing 37 pharmaceutical companies in US in the period of 2005-2012, it finds an insignificant negative relationship between technology foresight and performance persistence which is explained with the fact that R&D intensity does not include the technological knowledge the company acquires during the process of foresight. Also, it finds no moderation effect of CEO technical experience and openness to change on the relationship between technology foresight and performance persistence. Even so, the findings show a positive effect of the CEO technical background, organizational tenure and education and a negative effect of CEO’s age on firm performance after controlling the firm size and CEO’s tenure.
27
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Appendix 1: Core elements of technology foresight
