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The effect of board of directors members’ age on R&D intensity
M. van Wamel; S3540332
Pre-master BA Organizational and Management Control
Faculty of Economics and Business
University of Groningen
18th June, 2018
Supervisor: N.J.B Mangin
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Abstract
The purpose of this paper, by building on the upper echelon theory, is to examine the board of directors’ age and the effect on R&D intensity. Prior researches mainly suggest opposing views or focus solely upon board diversity. In order to examine the relationship a linear regression is used, with R&D intensity as dependent variable and board average age as independent variable. Additionally, several control variables are included in the regression analysis. The results indicate a negative relationship between board average age and R&D intensity. This means, that boards comprised by older members will reduce investing in R&D. The study does not take different strategies from firms into account. Therefore, this research serves as base for future research to investigate if this negative relation is still there when strategies of different firms are taken into account.
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Introduction
R&D investments are the primary source of innovation, it’s critical for many different business sectors and gives a business the ability to stay competitive (Balkin, Markman, & Gomez-Mejia, 2000). Bravo and Reguera-Alvarado (2017) point out that the level of R&D intensity is important for firms regardless the industry where the firms operate. Additionally, prior research suggest that in high-tech firms, like electronic firms, R&D spending is important (David, Hitt, Gimeno, & Insead, 2001). Prior research suggest that innovation such as R&D is of high strategic importance (Midavaine, Dolfsma, & Aalbers, 2016). Furthermore, prior research argue that characteristics such as experience and age enhance strategic change (Goodstein, Gautam, & Boeker, 1994). The board of directors has an important role regarding strategic decisions. Following, Golden and Zajac (2001) argue that board members decisions have considerable impact on the company and the society. With the knowledge that R&D is of high strategic importance, many studies investigate the relationship of the board of directors’ characteristics and R&D investments. These studies focus and examine mostly the relationship gender diversity, age diversity (Midavaine, Dolfsma, & Aalbers, 2016), board tenure and multiple directorships (Bravo & Reguera-Alvarado, 2017). However, these studies do not examine the effect of board of directors’ age on R&D.
4 Hambrick, 1996). These skills tend to be more present by older board members because it takes time to develop these skills. Golden and Zajac (2001) argue that board members decisions have considerable impact on the company and the society. Hence, younger colleagues may lack the confidence or experiences to recommend on strategic change in organizations. As a result, these characteristics are linked to age and therefore age could influence R&D intensity. For firms who want to stay competitive and enhance innovation it’s important to choose a board composition which suits to these visions the best. Prior researches show opposing views. Some studies suggest that younger board members have more physical and mental stamina and more flexibility to engage is change (Child, 1974; Wiersema & BANTEL, 1992). Other studies suggest that younger board members have the lack of confidence and experience to recommend in change (Golden & Zajac, 2001). As a result, the opposing views show a research gap. In order to investigate this research gap, this paper focuses more specifically upon the board of directors’ age and gives us the following research question: “what is the effect of the board of directors average age on the firm’s R&D intensity?”.
For this research linear regression is used in order to test the relationship between board average age and R&D intensity. In addition, board average age is defined as the proportion of older directors above 60. The sample was collected from Proxys statements, BOARDEX, and COMPUSTAT. The focus for this research is high-tech firms. From the database, random sampling is used in order to select 15 high-tech firms, in the period 2013-2016 from North-America.
5 that firm size has a positive effect on R&D intensity and firm leverage has a negative effect on R&D intensity, which are in line with existing theory. However, in order to find a relationship between board average age and R&D intensity this research contributes in several ways. First, the findings show the opposite of what prior research found. Second, the findings show that the psychological researches which are based upon stereotypes are true in the setting of board average age and the relation with R&D intensity. This study gives new insights to the existing theory, since board comprised by older board members enhance less in R&D.
The paper is structured as follows, first the literature review is presented, followed by the hypotheses development. After that, the method will be described. Then, the results will be presented and briefly analyzed. As last, the conclusion and discussion are presented which also contains implications, limitations and recommendations for future research.
Literature review
The purpose of the paper is to gain more insight into the relationship between the board of directors and R&D intensity. Therefore, this paper investigate the characteristic age related to the board of directors’ age and its effect on R&D intensity. Prior studies have been conducted on board characteristics and the effect on R&D intensity.
Prior researches board characteristics and R&D
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In addition, Bravo and Reguera-Alvardo (2017) show that there was no connection between
board tenure and R&D intensity. Regarding R&D and diversity, Midavaine et al., (2016) researched board diversity on R&D investment, they find that diversity in age and gender have a positive relationship with impulse to invest in R&D. Furthermore, Midavaine et al., (2016) find that diversity in tenure inside a board decreases the likelihood to invest in R&D. Kipkirong and Federico (2014) find that age diversity lessen the firms strategic change and add that age has a critical part in decision-making.
The role of the board of directors and R&D investments
Prior research recognize that R&D investment is a source which can give a company a competitive advantage and is important for long-term achievements (I. H. Lee & Marvel, 2009; Sanchez-Famoso, Maseda, & Iturralde, 2014). However, R&D is a risky investment and can have a negative influence on short-term performance (Baird & Thomas, 1985). Boards are likely to contribute in strategic change through approving and monitoring strategic decisions
7 board of directors’ and their job to monitor and approve strategic decisions such as R&D investments.
Upper echelon theory
This is where the upper echelon theory comes in. According to the upper echelon theory, the top executives have different characteristics, Hambrick and Mason (1984, p. 193) introduce the upper echelon perspective which states that “organizational outcomes are partially predicted by managerial background characteristics of the top level management team”. Hambrick and Mason (1984) mention two groups of upper echelon characteristics; psychological and observable characteristics. In addition, they describe psychological characteristics such as cognitive based and values and observable characteristics such as age, functional tracks and education. Additionally, Harrison, Price, Gavin, and Florey (2002) suggest two different types of characteristics in teams; observable such as age and unobservable such as tenure. Thus, the characteristic age partially predict the organizational outcomes in which are also outcomes related to R&D intensity. Moreover, the board of directors is a team and therefore the characteristics of the upper echelon are applicable on the board of directors.
The effect of age on R&D investments
8 time (Finkelstein & Hambrick, 1996). In addition, Golden and Zajac (2001) argue that board members decisions have considerable impact on the company and the society. Hence, younger colleagues may lack the confidence or experiences to recommend on strategic change in organizations. Reminding, that age enhances strategic change, innovation such as R&D investments is of high strategic importance and that R&D is a risky investment, directors need the confidence, flexibility, the physical and mental stamina and the business and leadership skills to engage in change and therefore recommend and enhance in R&D investments. Hence, these characteristics are linked to age and therefore age could influence R&D intensity.
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Hypotheses development
Considering all above, age is a scarce topic regarding the board members’ age in combination with R&D intensity. However, there are many psychological studies which suggest an increase in age will increase conservatism (M. J. Grant, Ross, Button, Hannah, & Hoskins, 2001; Truett, 1993). Additionally, Cornelis, Van Hiel, Roets, & Kossowska (2009) show findings which claim that the increase of conservatism with increased age is partly explained by personality and motivated cognition under the conditions of openness to experience and the need for closure. However, these suggestions are based on stereotypes. In addition, the board of directors is an elite group of people with interesting careers which mostly differs from people with “normal” careers. Therefore, stereotypes such as increasing conservatism with age, are likely to be less applicable to these kinds of elite groups as the board of directors is.
10 the inclination towards strategic change, based on the argument that board members need to have sufficient capabilities, confidence and experience. Thus, these capabilities, experience and confidence are expected to be more present in boards comprised by older board members. The theoretical background suggest that older board members are more likely to possess sufficient capabilities, confidence and experience, younger board members lack confidence and experience to engage in R&D. Therefore, the following hypotheses is formulated:
H1: There is a positive linear relation between boards comprised by older board members and R&D intensity
Method section
In order to test hypotheses, the available data of Proxys statements, BOARDEX, and COMPUSTAT is used. Bravo and Reguera-Alvarado (2017) point out that the level of R&D intensity is important for firms regardless the industry where the firms operate. Prior research suggest that in high-tech firms, like electronic firms, R&D investments is important (David et al., 2001). In this research only the effect of board average age on R&D intensity is investigated. Previous researches which examined the board of directors on R&D focus mostly on high-tech firms. Therefore from the database, random sampling is used in order to select 15 high-tech firms, in the period 2013-2016 from North-America.
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Measurements
Dependent variable
In prior research, the dependent variable RD INTENSITY is measured in multiple ways, mostly one or two different measurements are used (Bromiley, Rau, & Zhang, 2017). RD INTENSITY can be measured by using the actual R&D spending or as a ratio of R&D spending divided by sales. This ratio is chosen because the sample contains different firm sizes in which the R&D investments differ too much. R&D spending is formulated in US dollars without inflation and the ratio of R&D spending to sales is used for this research. The data is collected from COMPUSTAT as a matter of course in the same time period 2013-2016 as the independent variable.
Independent variable
The independent variable is collected by BOARDEX and through proxy statements over the time period 2013-2016. In order to calculate the proportion of older directors, the method of Golden and Zajac (2001) is used. However, in this research the boundary is higher >60 years. In the research of Golden and Zajac (2001) the variable BOARD AVERAGE AGE is calculated with the boundary of >50 years. The range of the age of the board of directors varies mostly between the 45 and 80 years, therefore in this research a higher boundary is used. The independent variable is calculated as the directors above 60 divided by the total board members. This measurement is used because this number indicates the proportion of older directors better than a normal average of board’s age.
Control variables
12 obtained from COMPUSTAT besides BOARD SIZE which is obtained from BOARDEX. The first control variable is FIRM SIZE. According to Midavaine et al., (2016) bigger firms seems to be more conscious of the importance of R&D. Moreover, according to Barker and Mueller (2002) larger firms are prospected to have adequate competences and resources to engage in strategic change. Thus, due to the adequate competences and resources bigger firms may develop more sustainable R&D investments. Therefore, FIRM SIZE is measured by the natural logarithm of the total assets of a firm (P. M. Lee & O’Neill, 2003).
The second control variable is FIRM AGE, older firms seems to be conscious of the importance of (Midavaine et al., 2016). However, according to Lin, Song, Lin and Li (2011) suggested that older firms have a lower motivation to engage in innovation. However, these studies show that FIRM AGE may affect the level of R&D. Therefore, FIRM AGE needs to be controlled and is measured as the total number of years a firm is founded.
The third control variable is FIRM PERFORMANCE, studies suggest that underperforming firms have less discretion to invest tickly in R&D (Daellenbach, McCarthy, & Schoenecker, 1999). Based on the argument that R&D expenditures generally are the first to be cut when firm performance needs to be improved (Daellenbach et al., 1999). Thus, poor performing firms relative to their competitors is likely to lessen the commitment to R&D investments (Daellenbach et al., 1999). That’s why FIRM PERFORMANCE needs to be controlled. FIRM PERFORMANCE is measured by the return on equity (ROE).
13 term R&D projects with the reason of raising the present cash flow for debt services (Barker & Mueller, 2002). Therefore, FIRM LEVERAGE is calculated as the total debt scaled by the total assets.
The fifth control variable is BOARD SIZE, according to Chen (2012) having more directors on the board is having more expertise in the board and are linked to higher levels of connections to the external environment. Because there is more knowledge and resources in a bigger board, they are better able to collaborate to improve the quality of R&D investment (Chen, 2012). This is from a resource dependency perspective, but it is influencing RD INTENSITY. BOARD SIZE is measured as total members of the board of directors. Board tenure and R&D intensity have no association according to Bravo & Reguera-Alvarado (2017), that’s why in this research board tenure is excluded.
Statistical test
For this research a linear regression is used in order to test the relationship between the independent and dependent variable. The equation of the model is as follows:
- R&D_INTENSITY= β0+ β1Board average agei, + β2Firm leveragei,+ β3Firm performancei, + β4Firm agei, + β5Board sizei, + β6Firm size (log)i, + εi
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Results section
Descriptive statistics
Table 1 shows the descriptive statistics of the used variables. For the dependent variable RD INTENSITY the mean is 0.2122 and the standard deviation is 0.4518. Comparing to the research of Bravo & Reguera-Alvardo (2017) the standard deviation seems high. As a result, this shows that there is enough variation. According to Cohen (2010) and Shefer and Frenkel (2005) different firm sizes have different innovation output. This difference may due to the sample composition, probably bigger firms are used in this research. The independent variable of interest is BOARD AVERAGE AGE which has an average of 0.6214 and a SD of 0.2109. Comparing to the research of Golden and Zajac (2001) average as well as the SD are similar. The SD and average of Golden and Zajac (2001) were 0.260 and 0.533. The difference in this number may due to the higher boarder of age >60 of old board members used in this research, against >50 in the research of (Golden & Zajac, 2001).
Table 1 Descriptive statistics
Variables Mean SD Median Maximum Minimum Range
1. RD INTENSITY .2122 .4513 .0673 3.006 .013 2.990
2. BOARD AVERAGE AGE .6214 .2109 .6938 1.000 .200 .8000
3. FIRM LEVERAGE .4518 .1949 .4657 .8209 .074 .7500
4. FIRM PERFORMANCE -.1933 1.002 .051 .3433 -6.686 7.030
5. FIRM AGE 53.90 39.06 52.50 128.0 3.000 125.0
6. BOARD SIZE 8.550 3.100 7.500 18.00 5.000 13.00
15 Distribution
To categorize the data for this research, the normality test is conducted. For RD INTENSITY the test showed insignificance and shows that the data is non-normal distributed. In addition, Table 1 shows that the median of RD INTENSITY is lower than the average which means that the data is skewed right. Besides, the high standard deviation shows that there is a greater spread of data. For BOARD AVERAGE AGE the normality test shows that the data is non-normal. Table 1 shows that the median is higher than the average, which means that the data is skewed left. In order to stabilize the findings, the independent variable and dependent variable are standardized. In the appendix, the histograms and box plots of before and after the standardization are shown. Despite the standardization of the variables, the data is still non-normal.
For all the control variables except FIRM LEVERAGE the normality test shows significance, which indicates again that the data is non-normal distributed. Nevertheless, FIRM LEVERAGE is insignificant in the normality test which indicates that this variable is normally distributed and therefore, linear distributed. The distribution of FIRM LEVERAGE and FIRM PERFORMANCE are skewed left. Additionally, FIRM SIZE, BOARD SIZE and FIRM AGE are skewed right.
Correlation
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Table 2 Descriptive statistics Pearson correlation
Variables 1 2 3 4 5 6
1. RD INTENSITY
2. BOARD AVERAGE AGE -,539**
3. FIRM LEVERAGE -,184 ,026 4. FIRM PERFORMANCE -,339** ,446** -,149 5. FIRM AGE -,678** ,389** ,275* ,321* 6. BOARD SIZE -,143 ,175 ,421** ,128 ,584**
7. FIRM SIZE LOG -,161 ,165 ,364** ,307* ,655** ,875** **. Correlation is significant at the 0.01 level (2-tailed).
*. Correlation is significant at the 0.05 level (2-tailed).
17 if there is occurrence of multicollinearity. In table three the VIF is calculated, in order to find multicollinearity or not.
Table 3 Multicollinearity (VIF)
Variables VIF 1. FIRM LEVERAGE 1,303 2. FIRM PERFORMANCE 1,35 3. FIRM AGE 1,83 4. BOARD SIZE 4,861
5. FIRM SIZE LOG 5,578
(Dependent variable BOARD AVERAGE AGE)
18 Regression analysis
In model 1 all the control variables are included which show significance for several control variables.
Tabel 4 Regression analysis
Variables Model 1 Model 2
Control variables FIRM_LEVERAGE -,430* -,409* (,223) (,213) FIRM_PERFORMANCE -,120*** -,071 (,044) (,047) FIRM_AGE -,011*** -,010*** (,001) (,001) BOARD_SIZE -,007 ,006 (,027) (,026) FIRM_SIZE_LOG ,247*** ,198*** (,074) (,074) Independent variable BOARD_AVERAGE_AGE - -,638** - (,259) Adjusted R ,626 ,658 Change in adjusted R - ,032
N=60 for all models unstandardized coefficients are reported; the figures in parentheses are standard errors, *p<0.1; **p<0.05; ***p<0.01.
19 and RD INTENSITY for H1. Model 2 shows that there is significant negative relationship between BOARD AVERAGE AGE and RD INTENSITY (β= -0.638. p<0.05). Unfortunately, the relationship is negative and therefore hypotheses 1 “There is a positive linear relation between boards comprised by older board members and R&D intensity” is rejected. However, the results show that there is negative linear relationship between BOARD AVERAGE AGE and RD INTENSITY.
Discussion and conclusion
The purpose of this paper was to gain more insight into the relationship between the board of directors and R&D intensity. Building up on the upper echelons theory, the observable characteristic age in terms of board of directors’ members is used. The way of measuring board average age concludes whether indicates if it’s a young board or an old board. The research approach consist out of a regression analysis and is conducted with the dependent variable R&D intensity, independent variable board average age and the control variables. The findings show that hypotheses 1 “There is a positive linear relation between boards comprised by older board members and R&D intensity” is not supported. This indicates that board average age has a negative linear relationship with R&D intensity.
However, this relationship could be explained by the psychological theory. Psychological researches suggest that and increase in age will increase conservatism (M. J. Grant et al., 2001;
Truett, 1993). Additionally, Cornelis et al., (2009) added and found that the increase of
conservatism with increased age is partly explained by personality and motivated cognition
under the conditions of openness to experience and the need for closure. Furthermore, Child
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However, in order to find the relationship between board average age and R&D intensity this research contributes in several ways. First, the findings show the opposite of what prior research found. Since, strategic change partly consist of R&D the results argue against the positive relationship between positive relationship of board average and strategic change of Golden and Zajac (2001) found. Hence, this raises questions towards the relationship between board average age and the advising on risky decisions/investments. Second, the findings show that the psychological researches which are based mostly on stereotypes are true in the setting of board average age and the relation with R&D intensity. Hence, with these results, this research raises questions regarding the relationship Golden and Zajac (2001) found, but gives at the same time clearance how the board of directors’ age relates to R&D intensity.
Implications and limitations
Furthermore, R&D is important for gaining a competitive advantage and for strategic change. However, investing in R&D comes with risks. Based upon the results, firms whom are seeking a competitive advantage which can be achieved by investing in R&D, should choose a board comprised by young board members because this choice would enhance R&D investments and therefore partly strategic change.
21 than cost leadership does, which relies more on low costs. Therefore, these strategies have an effect on R&D investments.
Future research
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Appendix
Figure 1 Box plot dependent variable R&D intensity (extreme values)
26 Figure 3 Independent variable Board average age Histogram (non-normal) distribution
27 Figure 5 Box plot independent variable board average age after standardizing (extreme values)
