Technological Educational Institute (T.E.I.) of Crete, School of Management and Economics (S.M.E.),

Accounting Dept., MSc Accounting and Auditing

R&D investments and firm performance: An

Empirical Investigation of the High Technology

Sector (Software and Hardware) in the E.U.

E. Pantagakisa, D. Terzakisb, S. Arvanitisc

Abstract

Purpose – The purpose of this paper is to examine the relationship among

research and development expenses (R&D), market value and firm

performance. Additionally, what is also questioned is, whether the relationship

between R&D and market value of the firm is linear.

Design/methodology/approach – Data analysis was realized through panel

data analysis and the feasible generalized square method (FGLS), using data

from the financial statements of 39 European firms, which activate in the

Software and Hardware Computer sector, for the period 2006-2010. Following

prior studies, our dependent variables are the Market Value and Annual

Return on Assets (ROA).

Findings – Results indicate a positive correlation between R&D investment

and firm performance in the marketplace. In contrast, the above does not

apply in the case of R&D and firm performance, since due to time lag, the

relationship with the ROA results in being negative. Furthermore, a non-linear

relationship between R&D and market value of the firm is verified.

Research limitations/implications – There are limitations because many

firms either they do not accurately calculate or they do not record their R&D

investments in their financial statements.

Originality/value – The verification of the existence of a non-linear

relationship between R&D investments and market value of firms, adds an

innovative character in this research.

Keywords – Research and development (R&D), firm performance, market value, nonlinear, time-lag.

JEL classification – M40, O30, O32

a Correspondence Author: T.E.I. of Crete, S.M.E., Dept. of Accounting, MSc Accounting and Auditing,

postgraduate student. Estavromenos, 71500 Heraklion, Crete, Hellas tel. +30 2810 379612. Email: pantagakis@gmail.com(E.K. Pantagakis) b T.E.I. of Crete, S.M.E., Tourist Dept. & MSc Accounting and Auditing. Estavromenos, 71500

Heraklion, Crete, Hellas, tel. +30 2810 379600. Email: dterzaki@staff.teicrete.gr c T.E.I. of Crete, S.M.E., Dept. of Accounting, MSc Accounting and Auditing. Estavromenos, 71500

Heraklion, Crete, Hellas, tel. +30 2810 379674. Email: starvan@staff.teicrete.gr

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1. Introduction

Nowadays, technological progress is made at an accelerated pace by companies all

over the world in order to improve their position in market, as regards their efficiency

and competitiveness. Firms in order to gain competitive advantage engage in costly

research and development (R&D) activities to develop innovations (Thatcher and

Pingry, 2009). Research and development has been playing a critical role in firms’

productivity, growth and long-run performance (Long and Ravenscraft, 1993; Vivero,

2002).

Empirical studies have shown mixed or even conflicting results. Prior studies

have found that R&D expenditures are positively correlated with firm performance

(Branch, 1974; Tassey, 1983; Erickson and Jacobson, 1992; Long and Ravenscraft,

1993; Hitt et al. 1995). It is also widely adopted that investments in research and

development contribute significantly to sales, productivity and firms’ profits

(Griliches, 1988; Romer, 1990; Geroski, Machin and Van Reenen, 1993; Jones, 1995;

Van Reenen, 1997). Furthermore, several studies have concluded a positive

relationship between R&D investments and market value of the firm (Chan et al.

1990; Doukas and Switzer, 1992; Chauvin and Hirschey, 1993; Szewczyk et al. 1996;

Bae and Noh, 2001; Ho et al. 2005; Bae et al. 2008).

However, some researchers have different empirical results. According to Gou et

al. (2004) and Lin and Chen (2005), the R&D intensity has a significant negative

impact on both firm’s profitability and productivity. It is worth mentioning that

several studies have shown that is required an adjustment period until R&D

investments reduce the production cost and generates profits (Branch, 1974;

Ravenscraft and Scherer, 1982; Hirschey and Weygandt, 1985; Jefferson et al. 2006;

Ding et al. 2007; Coad and Rao, 2008). Therefore, the results many times are not

apparent in the year of investment, as firm performance tends to increase with time

lag.

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Regarding the relationship between R&D investments and firm performance,

mainly prior empirical studies have shown a linear impact. Researchers may have

ignored several important factors, since if we assume that the relationship is linearly

positive it implies that R&D investments will persistently yield profits to the firm. On the

other hand, when the above relationship is linearly negative it implies that R&D

investments will constantly cause loss to the firm. This can be considered as non-

rational (e.g., Huang and Liu, 2005), because an increase in R&D investments may

generate profits, but it also will increase the firm’s total R&D cost (Shy, 1995), and

secondly, it is impossible for a company to undertake investments in R&D endlessly,

due to the limitations of management capabilities (Penrose and Pitelis, 2009).

We used data from 39 publicly traded high technology companies (hardware and

software) of Eurozone countries for the period 2006-2010. For data processing, used

panel data analysis (panel data analysis) and evaluated models performance of

business (random and fixed effects) with the methods of least squares and generalized

method of least squares, as well with the feasible generalized least squares (FGLS).

The results show a positive correlation between R&D investments and market value

of the firm while the same is not verified for R&D and firm performance, since there

is negative correlation with ROA, due to time lag. Finally, is verified the non-linear

relationship between R&D and market value of the firms.

This paper is organized as follows: The next section briefly reviews some

theoretical background and proposes our research propositions. Section 3 introduces

the data and presents our methodology. Section 4 presents the empirical results. In the

final section, we discuss the findings and limitations.

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2 Literature Review

Investment in R&D is actually an investment in intangible assets that contributes

to the long-term growth of the firm. Firms invest in research and development in

order to enhance their competitiveness and the ability to increase their profits.

Furthermore, spending on research and development can lead to a reduction in

production costs and increase the added value of the firm (Mansfield, 1996).

Correlation and multiple regression analysis have customarily been used to examine

R&D contributions (Connolly and Hirschey, 2005; Huang and Liu, 2005)

Branch (1974), assuming a time lag of four years from the introduction of an

innovation to the registration as industrial property, concluded that the trend of R&D

investments affect positively both the future profits and increase in sales. Used as

basic variables R&D intensity (R&D / sales), profits (earnings after interest and taxes

divided by total assets), growth of sales and the number of patents. It is also important

to mention that the effect on sales growth may be sooner than the increase in profits.

Grabowski and Mueller (1978), showed that R&D investments in companies,

belonging to intense research activity sectors, have high efficiency. Moreover,

concluded that the variability in future profitability of these firms is limited to half

when investments in R&D is capitalized (as assets) rather than calculated as an

expense in the income statement.

Tassey (1983), using the capitalization of R&D as an indicator to calculate the

research activity investigated the effect in various efficiency ratios such as

profitability, sales and Return on Equity (ROE). He concluded that companies which

are operating in high tech industries, their research activity attributes significantly.

Also, Hall and Hayashi (1989), concluded that investing in research and development

is a very important intangible asset, which can lead to higher profits, in greater

duration of time.

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Sougiannis (1994), studied the behavior of profits and stock value in relation

with investments in R&D. The conclusion was that in a 7-year period an increase of

one dollar on research and development expenditures has positive impact on both

profits and market capitalization of the firm, by increasing two dollars the profits and

five dollars the stock value.

Harmantzis et al. (2005), concluded that the market value of the firm and sales

have significant positive relationship with R&D. In addition, researchers found a

positive correlation between cash and R&D expenditures. Ding et al. (2007), verified

that investments in R&D affect both profits of the same and of future period.

Moreover, Ehie and Olibe (2010), examined the relationship between investments in

research and development (R&D) and market value of U.S.A. firms over an

18-period. The researchers concluded that R&D investments affect positively firm

performance, and more specifically the market value. Furthermore, researchers

concluded in a non-linear relationship between R&D and market value. Finally, Zhijie

Lin et al. (2011), found that investments in R&D have a greater impact to the

financial performance of companies, which are activated in the construction of

computer Software in relation with the Hardware companies.

According to the above we posit the following two hypotheses:

Hypothesis 1: Investment in research and development affect positively the

market value of the firm.

Hypothesis 2: Investment in research and development affect positively the firm

performance.

According to previous studies, if the relationship between R&D investments and

firm performance is linearly positive, it implies that investment in R&D will

persistently yield profits to the firm. However, because of the limitations that exist in

management capabilities in every company (Penrose and Pitel, 2009), and because

innovations from R&D may be easily duplicated by competitors (Huang and Liu,

2005), we doubt the case that R&D investments can definitely create sustainable

competitive advantage and yield profits endlessly. On the other hand, if the

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relationship between R&D investments and firm performance is linearly negative, it

implies that R&D investments will constantly cause loss to the firms.

Firstly investments in R&D will be charged at great cost the firm making them.

An increase in R&D can bring profits, but simultaneously will increase the overall

cost of the enterprise in R&D (Shy, 1995). Therefore, firm performance cannot be

considered that it is depended linearly with R&D investments. Secondly, based on the

theory of the growth of a firm, it is impossible for a company to undertake itself

endlessly investments in R&D due to the limitations of management capabilities

(Penrose and Pitelis 2009).

Based on above arguments, investments in R&D, in the early stages may have a

positive impact on firm performance. However, after reaching the optimal point, the

continued investments in R&D will eventually lead to a negative correlation between

R&D investments and firm performance.

The third research hypothesis is formulated as follows:

Hypothesis 3: There is a non-linear relationship between investments in research

and development and the market value of the firm.

3 Data and Methodology

The data were drawn from database Thomson-One (thomsonone.com). The

sample is consisted with companies which publish in their financial statements

investments in research and development (R&D) for the period 2006-2010, and

belong to the subsectors of Computer Software and Hardware. In addition, firms

belong to 17 countries, members of the Eurozone, and are publicly traded. The

companies which publish to their financial statements R&D expenditures are 73 and

amounted to 46.5% of all companies. Our final sample is consisted of 39 companies,

since in order to to have a strongly balanced data set we had to exclude companies

that they have not available data for all years.

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Based on the previous literature review, this study has two major themes: (1) to

explore the relationship between R&D investments and financial performance; (2) to

explore the relationship between R&D investments and market performance.

The conceptual framework of this study is shown in Figure 1:

Figure 1: The conceptual framework of this study

The main variable of our study is the intensity of firms in R&D, which is

calculated as the ratio of R&D expenditures to total net sales of the company. Several

researchers have argued the need to capitalize R&D, since it represents an intangible

asset of the company. However, because there is an inability to determine a

depreciation rate (Hirschey and Weygandt, 1985; Ho et al. 2005), we use the

expenditure on R&D to net sales instead of capitalized R&D. Many previous studies

have attempted to investigate the linear relationship between R&D investments and

firm performance, which usually adopted the R&D intensity (e.g., Erickson and

Jacobson, 1992; Finkelstein and Boyd, 1998; Henderson and Fredrickson, 2001).

As dependent variables used for firm’s performance the annual return on asset

(ROA), while for the market value of the company we use the MARKET

CAPITALIZATION. The market value of the firm equals to the closing price by the

number of shares outstanding to total sales of the company and reflects the market

perception of the firm value. According to Aboody et al. (1999), the market price,

sums up not only investors' assessments of the value of firm assets, but also the

R&D investments Firm performance

Financial

performance

Market performance

Control variables

Firm characteristics

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outcomes of investment decisions. Also, performance measures based on the market

are used to estimate the return of R&D investments by Chauvin and Hirschey (1993)

and Bae et al. (2008). On the other hand, ROA, is a measure of accounting firm

performance and has been widely adopted by many studies (e.g., Gedajlovic and

Shapiro, 1998; Anderson et al. 2000; Tosi et al. 2000; Henderson and Fredrickson,

2001; Coombs and Gilley, 2005; Hogan and Lewis, 2005; Kato et al. 2005).

The firm's performance can be influenced by many other factors except R&D.

Thus, we used as control variables, firm size, capital structure, and growth of sales.

Firm size is calculated by the natural logarithm of total assets (Finkelstein and Boyd,

1998). Leverage is defined as the ratio of total liabilities to total assets and the growth

of sales, as the current period's sales minus last year sales to sales of the previous

period.

In the empirical verification or not of the above assumptions, we estimate the

following models.

MARKETCAP= ( ) ( ) ( ) ∑ ( ) (1)

MARKETCAP= ( ) ( ) ( ) ∑ ( ) (2)

ROA = ( ) ( ) ( ) ( ) (3)

The test to add time dummies to the models showed that is required as p <0.05.

In addition, tests for autocorrelation of residues (Pesaran's test and Wooldridge test) at

contemporaneous correlation and correlation in panel data show that in both cases

there is autocorrelation problem. Also, test for heteroscedasticity (Modified Wald test)

showed that there is serious problem in all three models (p <0.05). Finally, there is no

multicollinearity problem since the average Variance Inflation Factor (VIF) ranged

from 1.06 to 4.27. A VIF value greater than 10 is an indication for multicollinearity

problems.

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It is worth mentioning that all the models of our study have been estimated with

random and fixed effects. For selecting the most appropriate model for interpretation,

Hausman test shows that these are the fixed effects except the third, which was

chosen for interpretation that of random effects (p>0.05). Because of the serious

heteroscedasticity and autocorrelation problems in our data in the estimation of

models we cannot accept any of them for the interpretation of estimates that obtained.

For this reason, we will move into the methodology of panel data analysis, but

we will estimate the models by using the method of feasible generalized least squares

(FGLS), which assumes the existence of heteroscedasticity and autocorrelation in our

data. In particular, the analysis of panel data models will be estimated with the

feasible generalized least squares method, which besides the existence of

heteroscedasticity assumes common first-degree autocorrelation (AR1) between the

panels.

4 Empirical Results

Tables 1 and 2 present the descriptive statistics and Pearson correlation of the

variables used in our models. The data show that the average value of firms’

MARKETCAP is about 1.7, while the average R&D intensity is about 0.17. This

means that on average our sample firms invest in research and development

approximately 17% of their revenues. The average annual return on assets is 0.062

and the average annual sales growth is amounted to 8.3%. Finally, the average

percentage of total liabilities to total assets is 0.42 and the natural logarithm of firm

size is 4.18.

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Table 1: Descriptive Statistics

Variables Mean Std. Dev. Min Max Observations

MARKCAP overall 1,686 1,324 0,219 8,071 N = 195

between

1,085 0,315 4,185 n = 39

within 0,776 -0,924 5,589 T = 5

R&D overall 0,167 0,102 0,004 0,595 N = 195

between

0,100 0,007 0,567 n = 39

within

0,025 0,071 0,258 T = 5

(R&D)2 overall 0,038 0,054 0,000 0,354 N = 195

between

0,054 0,000 0,322 n = 39

within 0,012 -0,015 0,104 T = 5

ROA overall 0,062 0,106 -0,275 0,470 N = 195

between

0,077 -0,156 0,243 n = 39

within

0,073 -0,188 0,378 T = 5

SIZE overall 4,179 1,597 1,411 9,431 N = 195

between

1,606 1,730 9,289 n = 39

within 0,153 3,712 4,608 T = 5

LEVERAGE overall 0,420 0,164 0,042 0,832 N = 195

between

0,154 0,058 0,734 n = 39

within 0,063 0,254 0,690 T = 5

GROWTH overall 0,083 0,158 -0,384 0,599 N = 195

between

0,073 -0,049 0,255 n = 39

within 0,141 -0,316 0,592 T = 5

The correlation coefficients show that there is not very strong correlation

between the independent variables, and will not address multicollinearity problems in

the estimation of models with the method of combined cross sectional and

longitudinal analysis. The only variables which showing significant correlation, are

the variables R&D and (R&D)2.

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Table 2: Pearson Correlation

MARKCAP RD (RD) 2 ROA SIZE LEVERAGE GROWTH

MARKCAP 1.0000

RD 0.4292 1.0000

(RD) 2 0.3533 0.9242 1.0000

ROA 0.3400 -0.0848 -0.0398 1.0000

SIZE 0.1272 -0.1621 -0.0570 0.3132 1.0000

LEVERAGE -0.3017 -0.2495 -0.2089 -0.3335 0.0990 1.0000

GROWTH 0.2375 0.1272 0.1630 0.2373 0.0828 -0.0679 1.0000

Table 3 presents the empirical results of our models with dependent variables

MARKETCAP and ROA. Model 1, includes R&D intensity and market value of the

firm, show that R&D intensity does positively affect the market value of the firm.

Also firm size is positively correlated with market value, while the leverage has a

negative impact. It is worth mentioning that we observe a strong negative relationship

between time dummies and market value of the firm (MARKETCAP). The dummy

for 2006 is omitted, so that the effect of time on the value of the firm is with reference

year 2006.

Specifically, with reference year 2006, we observe a continuous decline in the

market value of the firm with more intense the years 2008 and 2009 (p = -10.95 and

p = -5.86 respectively, start of economic crisis in the Eurozone) and progressive

retreat in 2010 (p = -4.12). In other words, the market value of high tech firms

(software & hardware) in the Eurozone has declined in relation with the reference

year 2006, 1.04% in 2008, 0.63% in 2009 and 0.47% in 2010.

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Table 3: Panel data analysis – FGLS estimator

t statistics in parentheses * p < 0.05,

** p < 0.01,

*** p < 0.001,

a p < 0.1

This study further adds the squared term of R&D intensity in Model 2, which

shows that R&D investments have a significantly positive correlation with market

value of firm, while the squared term of R&D investments has a negative correlation

to market value. Hence, there is an inverted U-shaped non-linear relationship between

R&D investments and market value. As in the first model firm size is positively

correlated with market value, while the leverage has a negative impact.

Model 1

MARKET

CAPITALIZATION

FGLS Hetero. –

Common AR(1)

Model 2

MARKET

CAPITALIZATION

FGLS Hetero. –

Common AR(1)

Model 3

ROA

FGLS Hetero. –

Common AR(1)

RD

4.264***

9.996***

-0.267***

(R&D)2 -12.19

**

(-2.59)

SIZE 0.241***

0.292***

0.0188***

(4.49) (6.07) (8.17)

LEVERAGE -1.932***

-1.952***

-0.215***

(-5.88) (-6.25) (-7.56)

2007.YEAR -0.305***

-0.324***

(-4.21) (-4.32)

2008.YEAR -1.039***

-1.044***

(-10.95) (-10.88)

2009.YEAR -0.625***

-0.640***

(-5.86) (-6.04)

2010.YEAR -0.468***

-0.481***

(-4.12) (-4.32)

GROWTH 0.112***

(5.62)

_CONS 1.113***

0.434 0.100***

(3.31) (1.28) (4.82)

N 195 195 195

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On the other hand, the results of model 3, which has as dependent variable the

annual return on assets (ROA), show that R&D intensity does negatively affect ROA.

Because of the high degree of uncertainty, R&D investments do not necessarily create

profits in a current year. Regarding the control variables, size and growth of sales are

positively correlated with the annual return on assets, while leverage has a negative

impact.

If differentiating the non-linear part of the second model, we gain an optimum

R&D intensity level of 41%4. This indicates that when firms spend about 41% of their

revenues as R&D expenditures, the firm's market value will reach the optimum level.

In other words, R&D investments make a positive contribution to market value of the

firm at the beginning. However, when R&D investments reach an optimum level,

market performance will decline following continuous R&D spending.

It is a high percentage, but is influenced by the fact that our sample is consisted

by firms which are with high intensity in R&D. Also, the above rate is verified by the

descriptive statistics of the data, and specifically with the average (0.17) and

maximum (0.6) value of R&D intensity. This result is consistent with the prior

literature’s argument that a high degree of R&D investments does not necessarily

result in higher firm performance. Figure 2 shows the non-linear relationship between

R&D intensity and market value.

4 The optimal point implies the point where the marginal costs of R&D intensity become equal to their

marginal benefits. Differentiating the R&D intensity of Model 6, we have the following results:

marketcap/rd=0 9.996rd-12.19

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Figure 2: The relationship between R&D intensity and MARKETCAP

5 Conclusions

In our study we undertook an empirical investigation using panel data

methodology for 39 Computer Software and Hardware firms of the Eurozone for the

period 2006-2010. This study confirms the positive impact of R&D in market value

for the 39 companies forming our sample (Chan et al. 1990; Doukas and Switzer,

1992; Chauvin and Hirschey, 1993; Bae and Noh, 2001; Ho et al. 2005; Bae et al.

2008; Ehie and Olibe, 2010). Moreover, we conclude that the relationship between

investments in research and development and the market value of the firm is non-

linear. Hence, investments in R&D will have positive effect on the market value of

the firm until the optimum point.

In our research we showed that when firms invest 41% of their revenues in R&D,

then the company's market value is maximized and reaches the optimum point. After

this optimum point, the relationship between R&D and market value of the firm is

negative. In a similar research of Cheng-Jen Huang and Shu-Yun Chen (2010), have

41%

0

0,5

1

1,5

2

2,5

3

0 0,1 0,2 0,3 0,4 0,5 0,6 0,7 0,8

RD intensity

MARKETCAP

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found that firms can reach the optimum ratio of ROA and of Tobin's q5 ratio when

they spend about 20% and 13% of their revenues in R&D respectively. As regards the

control variables in our research are statistically significant. More specifically, the

firm size and the growth of sales are positive related to dependent variables, while the

leverage is related negative.

In contrast is no verified the positive impact between research and development

expenditures and firm performance, and more specifically with the annual return on

assets of the same year. Several studies observe time lag between the impact of R&D

and firm performance (Branch, 1974; Ravenscraft and Scherer, 1982; Hirschey and

Weygant, 1985; Cockburn and Griliches, 1988; Lev and Sougiannis, 1996; Lev and

Aboody, 2001; Ding et al. 2007; Coad and Rao, 2008).

The production costs tend to increase in the short run, because new product

development need time to show results, since the innovation of the methods

introduced into the production processes creates commotion during the adjustment

period. The firms need time to assimilate the changes and reduce production costs in

order to increase firm performance, since the innovation of the methods introduced to

production processes requires a learning period. That explains the negative relation of

R&D to firm performance of the investment year. Therefore, firms need to have

consistency in investments in research and development because many times the

results are not apparent in the year of investment.

Although we have results in accordance to the theory, further research should be

done to investments in research and development and the time lag which is required to

have a positive effect on firm performance would be particularly important. Finally, it

is important to further research in the future, because many firms do not accurately

calculate their investments in R&D in their financial statements. Several managers

calculate investments in research and development as an expense and do not capitalize

in order to raise or lower firm’s profits (Lev, 2003).

5 Tobin's q ratio is defined as the quotient of the sum of market value and total liabilities of the business

(debt), to total assets (Chung and Pruitt, 1994).

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6 Limitations

While this research has found some new findings, we acknowledge some

limitations. First, the non-recording of research and development expenses in the

financial statements of companies limited our sample from 157 to 73 companies. In

addition, several companies had no data on our variables for the period 2006-2010,

with result our sample be limited even further. This limitation of our sample may have

affected the results of the study. Furthermore there is difficulty on modeling such a

research because many of the R&D expenditures are calculated in the income

statements as production costs and not specifically as an R&D figure. This is due to

the fact that many companies do not accurately calculate their R&D investments in

their financial statements in order to raise or lower their profits.

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