Available online at www.sciencedirect.com
Procedia - Social and Behavioral Sciences 00 (2015) 000–000
www.elsevier.com/locate/procedia
a
b a Murat KARAGOZ, Istanbul, 34363, Turkey b
Dervis Burak ERDEMI, Istanbul, 34363, Turkey
Abstract
R&D activities are carried out by firms in a globalizing and competitive environment in order to override other firms, fulfill changing and increasing customer demands in products and services. The aim of the study is to analyze the effect of R&D investments on firms’ financial performances. For 25 firms which operate in manufacturing and technology sector having regular
R&D investment, the impact of R&D investments on financial performances were analyzed for the years 2000-2013. Sales revenue, net income, return on assets ratio, equity value return on assets and the ratio of sales revenue were used as the indicator of financial performance. Panel data regression analyses determined that R&D investments have significant and positive effect on sales revenue and net profit. Lag effect analysis determined that 3 years of lag effect could increase positive effect on financial performance. Moreover, it was deduced that there was a break on sales revenue and the rate of sales costs in 2008.
© 2015 The Authors. Published by Elsevier Ltd.
Peer-review under responsibility of the International Strategic Management Conference.
Keywords: Research&Development, Financial Performance, Panel Data Analysis, Structural Break
1.
Introduction
Today, product, service and manufacturing quality are changing rapidly. There are numerous reasons including globalization, increasing competition, customer consciousness and expectations, advanced technology attacks to
* Murat KARAGOZ Tel.: +90-533-330-15-51; fax: +90-212-259-42-02.
E-mail address: mkaragoz@yildiz.edu.tr
1877-0428 © 2015 The Authors. Published by Elsevier Ltd.
Peer-review under responsibility of the International Strategic Management Conference.

Author name / Procedia - Social and Behavioral Sciences 00 (2015) 000–000 increment market share, economically sustainable and great target attainment. Firms are innovating on product/service, process, marketing and organizations in order to stay in competition, and gain power and market control. One prominent method is R&D activities. The main goal of this study is to explore and question the impact of R&D expenses of firms regularly investing on such activities to their sales, profits and financial ratios and if there is the magnitude and direction of this relation. Main questions to be addressed in this paper are (1) Existence of causality between R&D investment and financial performance (2) Existence of causality between R&D and net sales/net profit (3) Existence of causality at predetermined sectors based on firm activities (4) How (1), (2), and (3) are motivated when the impact of lagged variables are included (5) Does 2008 global financial crisis have significant structural break the relation between R&D investment and financial performance.
1.1.
Innovation and R&D Spending
Innovation is a new or significantly modified product (good or service), or process; a new marketing method; or an implementation of a new organizational method in business practices, workplace organization or external relations. Collins and Hansen (2013, 245) investigates 290 innovative events to compare types and size of innovations which are made by best and poor performing in industry. OECD describes R&D as “it comprise creative work undertaken on a systematic basis in order to increase the stock of knowledge, including knowledge of man, culture and society, and the use of this stock of knowledge to devise new applications”. (OECD Frascati Manual,
2002).
US is commonly accepted as a dominant power on R&D investments over the globe. However, current trends and indicators on growth and investment predict that China will dethrone the US by 2022. More specifically the trends also predict that the share of top 10 countries in total R&D investments in 2014 will be about 80%. More than half of this share will be taken by US, China, and Japan. Moreover, the predicted jointly share of US, China, Japan, and
EU countries is about 78%. On the other hand, considering economic growth of South Asian countries, the R&D investments is expected to growth increasingly over following 10 years. The R&D investments have increased gradually in the rest leading countries such as Russia, South Korea, and Taiwan in 2014. Notwithstanding that many of the Middle Eastern countries have enjoyed significant GDP growths, other than Israel and Qatar have experienced only limited progress due to their weak R&D infrastructures. Likewise African countries are also expected to leap forward in GDP growth, but scarce R&D activities in other countries than South Africa in the continent limits them
(Batelle, 2013, 4).
Table 1 presents the shares of R&D investments over the globe. Some eminent countries, especially US and China, are clearly distinguished among other in the table. Also R&D appears as one of the locomotive powers of economic progress as technology-advanced countries like Japan and Germany are located at the top of the table ranking.
Table 1.
R&D Investment Shares over the Globe (%)
Continents and Countries
America(21)
USA
Asia(20)
China
Japan
India
Europe (34)
Germany
The Rest of Countries (36)
2012
34,5
32,0
37,0
15,3
10,5
2,7
23,1
6,1
5,4
2013
34,0
31,4
38,3
16,5
10,5
2,7
22,4
5,9
5,3
2014
33,9
31,1
39,1
17,5
10,2
2,7
21,7
5,7
5,3

Author name / Procedia - Social and Behavioral Sciences 00 (2015) 000–000
US clearly exceeds other countries in the table both in GDP and in R&D investments. However its share in globe follows an inert trend over last 3 years. In contrast, China has experienced an enormous growth in GDP beside an average annual 0.1% increase in “R&D/GDP” rate during these 3 years. If this trend continues, then China will dethrone US in near future. On the other hand, Turkey is located at 20th place in this ranking. Its GDP was $1.14bn in 2012, $1.185bn in 2013, and $1.227bn in 2014. This gradual growth does not affect the “R&D/GDP” rate at all, because this ratio has remained at 0.9% through these 3 years. This ratio indicates that R&D investments in 2013 are about $11bn. As our country targets to acquire a leading role in global economy and technology, it should follow more progressive policies to catch countries like Germany, Japan, Switzerland, Singapore, and South Korea whose have high technologic influence in the world.
1.2.
Literature Survey
Looking at product innovations of the literature regarding the impact on overall financial performance, we see a positive relationship. For example Damanpour and Evan (1984) have found a positive relationship between innovation and performance. Kleinschmidt and Cooper (1991) reported a positive relationship between innovation and firm profitability. Similarly, Subramanian and Nilikanta (1996) have demonstrated innovations positive effect on financial performance by measuring it with return on assets.
Lag effect between innovation performance and financial performance is situated in the literature. It may take a certain period of time to see the reflection of the positive impact on the financial performance that originated from innovation performance. (Zahra, Sidhartha, 1993; Teece, 1988). Accepted performance criteria for innovative performance are, R&D investments, the number of owned patents or patentable processes and products and new product releases to market. (Alpkan et al ., 2005, 175). Common measurement criteria for performance are company growth, sales growth that is effective on market share and firm productivity. In addition, the growth prospects for future sales and revenues are important factors in terms of company performance. (Bird, Beechler, 1995, 23). 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). 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.
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 Tatar (2010, 63), there is a positive relationship between innovative operations and financial performances of enterprises based on the result of analysis of the data obtained from 43 firms which operate in manufacturing sector. Toprak (2010, 120) posits that out of 526 firms sampled in the study which represent the private health sector, % 88,4 (465 firms) of them do not exhibit innovative activity whereas % 11,6 (61 firms) of them display innovative activity, % 73,38 (386 firms) of them have good financial performance whereas 140 firms
(% 26,62) have bad financial performance. Furthermore, % 7,4 of innovative firms (39 firms) have good financial performance whereas % 66 of the firms which do not show innovative activities (347 firms) have good financial performance. In a study which comprise the period between 2004-2007, relationship and impact level of product, process, marketing and organizational innovation on financial performance with a sample of 184 firms from
Turkish manufacturing sector. Outcome of the study suggests that innovation performance directly affects the marketing performance positively (Gunday et al., 2011, 662).
In a study which is publish in 2009 and examined 449 firms operating in Australian manufacturing industry, financial performance is evaluated under organizational performance and two ratios, namely growth of sales and expected growth of sales are used as financial performance variable. A positive relationship is revealed between innovative activities and financial performance (Liao, Rice, 2009, 117). According to a study which is published in
2010 and analyzed absorptive capacity which is the process of the skill of defining the value of new information, internalization of this new information and transforming this new information into a commercial commodity, it is

Author name / Procedia - Social and Behavioral Sciences 00 (2015) 000–000 revealed that external and complementary in nature flow of information has a positive relationship with absorptive capacity of the firms, absorptive capacity of the firms has a positive impact on innovative performance and this positive impact on innovative performance affects financial performances of the firms positively (Kostopoulos et al .,
2011, 1335).
In a study conducted with 100 small and mid-sized enterprises which operate in Czech Republic, by using
Revenue based firm value variable, the effect of technical innovations on the financial performance is investigated.
Sampled firms are divided into three industries, namely manufacturing industry, service industry and commercial activity industry. Revenue based firm value which will measure the financial performance is evaluated for 5 years in a way to include t, t-2 and t+2 where t is the year which technical innovation is realized (Tabas, Beranová, 2014,
695). In the study of Tabas, Beranová, Martinovičová (2012, 43), although number of sampled company and the method of sectoral grouping is same with the above study, they employed interval strategy to investigate the impact of product innovation on financial performance in small and mid-sized enterprises. The most positive and lasting impact is observed in the manufacturing sector.
In a study published in Australia, product innovation of the firms which use their budget as a planning mechanism facilitate the increase of financial performance, whereas usage of the budget as a control mechanism in the firms removes the impact of product innovation on the financial performance (Dunk, 2011, 102). In relation to delayed impact of innovative investments on financial performance, new short-term investments and internal resource usage may lead to initial losses. However, in the long-run, innovative performance will have positive impact on marketing and financial performance. Lawless and Anderson (1996) stated that adaption to new technologies which involve innovation comes with a prices at the beginning. Similarly, Damanpour and Evan (1984) emphasized that observing the positive impact of innovations on the firm performance requires some time in general. The study which is conducted with 461 Greek firms shows that in the case of neglecting the effect of delay on the study of absorptive capacity, innovation and financial performance, it is revealed that there is no significant impact of innovation on financial performance (Kostopoulos et al ., 2011, 1335).
2.
Panel Data Analysis
2.1.
Data and Variables
The population of the research is industrial enterprises operating in the Istanbul Stock Exchange (BIST). When selecting sampling from BIST examining activities showing companies are determined. Regular research and development expenses from the income statement of the companies’ are the indicator factor to select sampling from
BIST. The companies are divided into designated sector groups in which they operate. The financial data of the research is derived from the Consolidated Income Statement. Also helping of the balance sheet and income statement archive in FINNET Financial Analysis program, years of research is extended. Exchange traded among companies who invest in research and development between the years 2000-2013 in Istanbul Stock Exchange, grouped 25 companies are based on their respective sectors. 25 companies which operate in the manufacturing and technology sectors are divided into 6 groups according to their operating areas. The balance sheet and income statement have been created using data set of the years from 2000 to 2013. When income statements examined in years of 2005, 2006 and 2007, it is faced with the problem shown in the statement of income for some companies items in which the research and development expenses do not belong to. When this situation causes are investigated it is seem that the respective companies held their expense research and development in the financial statements in the specified year and they do not declare as a separate item included in research and development activities.
2.2.
Model
Research and development expenses are the independent variables and their effects are examined on the dependent variables for each model. In the model i is indicating the firm, t is the time index. Here 𝛽
0
, 𝛽
1
are constant values of regression parameters to be estimated. ε is a random error term. The model is,

Author name / Procedia - Social and Behavioral Sciences 00 (2015) 000–000
𝑌 𝑖𝑡
= 𝛽
0
+ 𝛽
1
𝑋 𝑖𝑡
+ 𝜀 𝑖𝑡
𝑖 = 1,2, … ,25 𝑡 = 2000, 2001 ,2002, 2003, 2004, . , . , . 2008, 2009, 2010, 2011, 2012, 2013
Having the R&D expenditures as the independent variable, to investigate its effect on the active profitability we construct
Model 1: 𝐴𝑃 𝑖𝑡
= 𝛽
0
+ 𝛽
1
𝑅𝐷 𝑖𝑡
Other models for different sectors can be defined in a similar manner as follows
+ 𝜀 𝑖𝑡
Model 2: 𝐸𝑃 𝑖𝑡
Model 3: 𝑆𝑅 𝑖𝑡
Model 4: 𝑁𝑃 𝑖𝑡
Model 5: 𝑆𝐶 𝑖𝑡
= 𝛽
0
= 𝛽
0
= 𝛽
0
= 𝛽
0
+ 𝛽
1
𝑅𝐷 𝑖𝑡
+ 𝛽
1
𝑅𝐷 𝑖𝑡
+ 𝛽
1
𝑅𝐷 𝑖𝑡
+ 𝛽
1
𝑅𝐷 𝑖𝑡
+ 𝜀 𝑖𝑡
+ 𝜀 𝑖𝑡
+ 𝜀 𝑖𝑡
+ 𝜀 𝑖𝑡
We have defined a separate dummy variable in order to measure the sectoral differences regarding the R&D and selected variable relationships. D dummy, f being the index for specific sector, the dummy variables 𝐷 𝑓
are defined as,
𝐷
𝑊𝐺
= {
0,
1,
if white goods industry not included
if white goods industry is included
𝐴𝑃 𝑖𝑡
= 𝛽
0
+ 𝛽
1
𝑅𝐷 𝑖𝑡
Fort the value of 𝐷
𝑊𝐺
= 1 the regression model becomes
+ 𝛽
2
𝐷
𝑊𝐺
+ 𝜀 𝑖𝑡
𝐴𝑃 𝑖𝑡
𝐴𝑃 𝑖𝑡
= 𝛽
0
= (𝛽
0
+ 𝛽
1
𝑅𝐷 𝑖𝑡
+ 𝛽
2
+ 𝛽
) + 𝛽
1
2
𝑅𝐷
(1) + 𝜀 𝑖𝑡
+ 𝜀 𝑖𝑡 𝑖𝑡
In the same manner other dummy variables can be defined accordingly.
We would like to study the lag effect of R&D expenditures on financial indicators as well. In the literature, there is an allowance of time for the maturity of R&D expenditure to have its effect on financial indicators. To this end we can consider 1, 2, 3 or more lags for the R&D expenditures. Lag models for active profitability are
𝐴𝑃 𝑖𝑡
𝐴𝑃 𝑖𝑡
= 𝛽
0
𝐴𝑃 𝑖𝑡
= 𝛽
0
= 𝛽
0
+ 𝛽
+ 𝛽
1
𝑅𝐷 𝑖𝑡
+ 𝛽
1
𝑅𝐷 𝑖𝑡
+ 𝛽
11
1
𝑅𝐷 𝑖𝑡
+ 𝛽
𝑅𝐷
11 𝑖,𝑡−1
+ 𝛽
+𝛽
11
12
𝑅𝐷
𝑅𝐷 𝑖,𝑡−1 𝑖,𝑡−2
+ 𝜀 𝑖𝑡
𝑅𝐷 𝑖,𝑡−1
+ 𝛽
12
𝑅𝐷 𝑖,𝑡−2
+ 𝛽
13
+ 𝜀
𝑅𝐷 𝑖𝑡 𝑖,𝑡−3
+ 𝜀 𝑖𝑡
Similar lag effect models can be constructed for other dependent variables as well.
3.
Results
3.1.
Descriptive Statistics and Panel Unit Root Tests
Considering descriptive statistical values of all the variables in the model for 25 firms between 2000 – 2013, the average spending on the R&D for selected 11 periods is TL 10,256,556 and the highest R&D expenditure is TL 145
889 919. We determine that the highest R&D expenditure among the companies that operate in this area in 2013 was made by Ford Otosan. For the same data set the average sales revenue was TL 1,184,103,954 and the average net profit amounted to TL 52,271,572. The average return on assets ratio was 4.061, while return on equity ratio stood at 1,460. Finally, the ratio of cost of sales to net sales was calculated as 73.67%.

Author name / Procedia - Social and Behavioral Sciences 00 (2015) 000–000
The most widely used methods for testing the stationarity of the series are Dickey and Fuller (1979), Expanded
Dickey and Fuller (ADF) (1981) and Phillips-Perron (PP) (1988) tests. The null hypothesis of these tests is that there is a unit root in the series. If there is no unit root then the series are stationary. In this case, the null hypothesis can be rejected and classic regression method can be used. To examine the stationarity, we use "Levin, Lin, Chi test" here. The null hypothesis in this test is that there is no stationarity in the series; alternative hypothesis indicates that the series are stationary.
Table 2 . Panel Unit Root Test Results
Variable
RD
SR
NP
AP
EP
Statistic
-4,9398
-6,6989
-8,9666
-9,7834
-8,7775
Probability
0.0000
0.0000
0.0000
0.0000
0.0000
Cross-section
25
25
25
25
25
Observation
242
243
241
240
240
SC -10,602 0.0000 25
Note: (1) Method: Levin, Lin & Chu t* (2) Years: 2000 – 2013
243
Table 2 shows the results of the panel unit root tests estimated by Levin, Lin&Chu method. The probability values of all the variables such as research and development expenses, sales revenue, net profit, return on assets, return on equity and the ratio of the cost of sales to the net sales is less than 0.05 at the 5% significance level.
Therefore, we reject the null hypothesis stating that “there is no stationarity in the structure of the series” and conclude that given series are stationary.
The pooled data model collects all observations in a pool ignoring the sections and the effect of the time dimension. For this reason, it is not preferred for this application. Different models can be obtained from the panel data analysis by making different assumptions about the variability of characteristics and coefficients of error terms.
Such models which are derived using different assumptions are known as ''Fixed Effects Model '' or ''Random
Effects Model”. In order to determine an appropriate model we use Hausman test statistics.
3.2.
Panel Data Regression Results
In order to determine the regression model based on the relationship between R&D expenses and sales revenues we first conduct the Hausman test. The null hypothesis indicates that there is no correlation between the individual effects of research and development expenses; alternative hypothesis is that there is a correlation between them.
Hausman test results are given on Table 3. Hausman test reveals that the probability value 0.7015 is higher than 0.05 and significant at 5% significance level, and indicates that the null hypothesis cannot be rejected. In this case, both estimators are expected to have very little difference between the fixed and random effects. Therefore, we take random effects panel data estimator for random effects model because we consider it to be more effective.
Table 3.
Hausman Test Results
Dependent Variable
Sales Revenue
Net Profit
Active Profitability
Equity Capital Profitability
Chi-Square Statistic
0.146881
0.183723
0.022629
0.146881 d.f.
1
1
1
1
Probability
0.7015
0.6682
0.8804
0.7015

Author name / Procedia - Social and Behavioral Sciences 00 (2015) 000–000
Sale Cost Net Sales 0.076498 1 0.7821
Panel data analysis performed by the random effects model is given on Table 4. Accordingly, the R-squared of the regression explains 59.3% of the response by change in sales revenue (dependent variable) when the R&D expenses change (independent variable). The correlation between the variables in the model is 81.3%, which is positive and strong. According to the null and alternative hypotheses based on the investigation of the relationship between the R&D expenses and sales revenue: there is no impact of the R&D expenses on the sales revenue; R&D expenses affect the sales revenue.
In testing the model at the 5% significant level, we find that the probability value of 0.0000 < 0.05. This indicates that the conducted model is significant. The Durbin-Watson autocorrelation test result is observed as 1.848. This proves that there is no autocorrelation problem in the model. Based on these findings H0 hypothesis is rejected and
H1 hypothesis is accepted. It indicates that the R&D expenses has positive and strong effect on the sales revenue.
Table 4.
Panel Data Regression Results
Dependent Variable
Sales Revenue
Independent
Variables
C
R&D
Coefficient
3.87E+08
77.75454
Std. Error
1.93E+08
3.904457 t Statistics
2.006674
19.91430
Net Profit
Active
Profitability
C
R&D
C
R&D
13.579.477
3.772426
3.665379
3.86E-08
14.056.492
0.387932
1.084670
3.21E-08
0.966064
9.724458
3.379256
1.202724
Equity Capital
Profitability
C
R&D
-1.081253
2.48E-07
4.593335
1.42E-07
-0.235396
1.739007
Sale Cost C 73.15868 4.084642 17.91067
Net Sales R&D 5.02E-08 3.89E-08 1.291009
Notes: (1) Method: Panel EGLS (Random Effects) Hausman Test (2) Number of periods: 11
Probability
0.0458
0.0000
0.3349
0.0000
0.0008
0.2301
0.8141
0.0832
0.0000
0.1978
Hence the regression equation for Sales Revenues can be quantitatively written as:
SR = 386.610.217+77.754* R&D + ε
According to the equation TL 1 R&D expenditure will increase annual sales revenue by TL 77.754.
In the second step, we separated the samples into the groups according to their areas of activity and analyzed the relationship between R&D and sales revenue based on the random effects model at the separated group level. The results of the conducted panel analysis are provided on Table 5. According to the sectors’ probability values, all sectors are shown to be statistically significant. The highest correlation between the R&D spending and sales revenue is identified in the home appliances with the rate of 94.5%. The model also reveals that the highest explanatory power of this relationship is seen again in home appliances with the rate of 83.6%. The contribution of a unit R&D investment to the sales revenues is the highest in the food sector with the amount of TL 149.4. Another striking finding in this analysis is weak and negative correlation rate of the given variables with the rate of -8.8% in the information technology group. Although the investigation of the reasons of this relationship is the subject of a separate study, it can be suggested that the R&D expenditures can increase costs in the first periods, while sales remain unchanged. Therefore, it would be more useful to investigate this relationship including lagged variables into the model.

Author name / Procedia - Social and Behavioral Sciences 00 (2015) 000–000
3.3.
The Analysis of Lag Effect
According to the literature and the findings it can be concluded that a time lag is needed to pass for the emergence of the effect of R&D on the sales revenues. The impact of this maturation process is known as “lagged effect”. The previous section conducts the panel analysis without taking into account the effect of the delay. In this section, 0 base case (no inclusion of lags here, the starting point) is referenced with respectively 1, 2 and the 3-year delay effect and seen with its impacts on R, R2, β and probability (sig.). If we look at the Table 6 to see the relationship between the R&D and revenues, we can find that the reference model and the delay effect models are significant at 5% level of significance.
Table 5.
R&D – Sales Revenue Lag Effect
Lag R R-square 𝜷 Probability
0
1
2
0.813
0.782
0.763
0.593
0.597
0.589 𝛽
1
= 77,754 𝛽
1
= 64,785, 𝛽
2
= 13,651 𝛽
1
= 58.98, 𝛽
2
= 7.316, 𝛽
3
= 12.097
0.000
0.000
0.000
3 0.792 0.609 𝛽
1
= 41.135, 𝛽
2
= 15.149, 𝛽
3
= 7.843, 𝛽
4
= 37.097 0.000
Decrease in correlation value is observed in the 1st and 2nd year but with the 3rd year the correlation increases.
R2 value shows a slight fall in the first and the second year but in the third year it exceeds the reference value. The sum of the coefficients of the independent variables in the regression equation (β) is 77.754 in the reference case but starts rising with each year and reaches a total value of 101.24 by the third year. In this case, while the impact of
R&D affects the sales revenue by TL 77.754 without delay effect, its impact is TL 101.24 on a 3 year delay effect model. This finding supports the literature suggesting that the impact of the R&D spending on the revenue rises with three-year time. A similar significant lagged effect is found in net profits regression.
3.4.
Structural Break Analysis
If model coefficients is not fixed and change over time, that case is called “structural break”. Structural break topic is important especially in the regression models which include time series. Chow Test is used to detect structural breaks and it is called to the name of the developer “Gregory Chow”. In Chow Test, the consistency relationship between two periods analyzes and it is searched that whether link between dependent and independent variables are same or not in two periods.
When we examine structural stability, the sample can be divided in two periods; before and after 2000. In this way, we make three different regressions. Chow Test requires variances equality among two periods and in here there are two assumptions: Assumption 1: error terms of models are normally distributed with equal and constant variances, ε
1t
~N(0,σ 2 ) and ε
2t
~N(0,σ 2 ). Assumption 2: ε
1t
and ε
2t
distributed independently. In Chow Test, it is assumed that period of structural break point is known. Chow Test examines whether two regression are different each other or not but does not explain reason of difference (Yalta, 2011).
The last Global Financial Crisis which was occurred in United States and spread to other countries impacted also our economy and industry. Therefore, it will explained that if there is a structural break in 2008. For this purpose data were separated two periods. Data group of before 2008 cover 2000, 2001, 2002, 2003 and 2004, data group of after 2008 include 2008, 2009, 2010, 2011, 2012 and 2013. Null and alternative hypothesis are there is no structural break and there is a structural break respectively. Structural break test results are given in Table 6 . Under 5% significance level R&D Expenditures Active Profitability, R&D Expenditures Equity Capital Profitability, R&D
Expenditures Net Profit relationships’ probability values are all greater than 0.05, and therefore we have not rejected the null hypothesis of “no structural break”, that is, there is no structural break in 2008. When the relationship between R&D consumptions and selling revenues is analyzed, null hypothesis can be rejected since 0.0016 is lower

Author name / Procedia - Social and Behavioral Sciences 00 (2015) 000–000 than 0.05 which means in 2008, there is a structural break among R&D consumptions and selling revenues. The interaction between R&D consumptions and selling revenues is different for before 2008 and after 2008. Therefore two different periods are shown with two different regressions. When both period’s regressions are considered, there is an unexpected result. Both models intercept and coefficient of independent variables dramatically increased after the crisis. In the relationship of R&D Sales Revenue, the positive effect of a 1 TL worth of R&D expenditures on the Sales Revenue seems to be increased after the 2008 global financial crisis. In Table 6 another case of break for the year 2008 is observed between R&D expenditures and the ratio of sales cost net sales. But models are not significant statistically before and after period of structural break. Therefore even if 2008 is seen as a structural break period for these relationship, we cannot conclude statistically significant regression models.
Table 6.
Chow Test for Structural Break
Relationship
R&D-Sales Revenue
R&D – Active Profitability
R&D – Equity Capital Profitability
R&D - Net Profit
R&D – Sale Cost Net Sales Ratio
F
6.584185
0.451493
0.078365
1.746975
3.239823 p-value
0.0016
0.6372
0.9246
0.1763
0.0407
4.
Conclusion
According to the panel data regression analysis, there is a positive relationship between R&D activities and sales revenue. Each TL 1 increases the R&D investment by TL 78. Our model is significant for every group of sector at
5% significance level. Highest correlation between R&D and sales exists for the group of home appliance. Besides, explanatory power of sales revenue by the R&D expenditures is observed also for the home appliance. Food sector makes the highest contribution to sales revenue for each unit of R&D expenditures. Relationship between R&D activities and net profit is positive. Each TL of R&D investment increases the annual net profit by TL 3.8. Our model is significant at 5% significance level. This relationship is statistically significant for the sectors of home appliance, food, machine manufacturing and automotive, however insignificant for the sectors of informationtechnology, petrochemical. Highest correlation between R&D and revenue is detected in the machine manufacturing sector among the sectors where there is significant relationship. The contribution of R&D spending made in Food
Industry to the net profit took the highest value among the groups. Active profitability ratio is a ratio indicating whether all the assets owned by the firm is used efficiently or not. This ratio is calculated with the purpose of detecting to what extend the actives, that is to say the investments are used efficiently in the business. He model established between R&D activities and active profitability is accepted as significant at the level of 5% of significance level. When it is evaluated specifically in all sector groups, no significant result was detected for any of the groups.
Equity capital profitability ratio is calculated with the aim of detecting to what extend the values allocated for the business by the shareholders are used efficiently and effectively. It indicates the equity capital profitability of the business. The model established between R&D activities and equity capital profitability was not accepted as significant at 5% of significance level. However, spreading financial data to long years and due to having high variability, it has been seen in some researches that hypothesis tests were conducted at 10% of significance level. If we evaluate this model at 10% of significance level, it can be interpreted that R&D expenses are effective in equity capital profitability. When it is evaluated specifically in all sector groups, no significant result was detected for any of the groups. The model established between R&D expenses and the ratio of sales costs to net sales was accepted as significant at 5% of significance level. Accordingly, considering the possibility values specifically in all of the groups, no significant result was detected for the groups except for the group of information technologies. Only in the group of information technologies, a significant but negative effect was detected between research-development expenses and the ratio of the sales costs to net sales.

Author name / Procedia - Social and Behavioral Sciences 00 (2015) 000–000
In the cases that innovation activities require a certain maturity time in literature, it was revealed that its reflection on financial performance would be retarded. Generally in order to be seen of R&D investments on financial performance, it is suggested that approximately 3-year period must pass. Within the scope of the implementation, the effect of the lag for respectively 1, 2 and 3 years for the same sampling on financial performance has been investigated. The lag effect on active profitability, equity capital profitability and the ratio of sales costs to net sales was detected not to change the result. Despite this, especially 3-year lag effect on sales incomes and net profit was observed to increase the effect level of financial performance in positive way. In this way, without lag effect, while it was affecting in the direction of 7,8 TL increase for the sales income of R&D sales income as much as 1 TL, in 3-year lag effect model it affected in the direction of 101,2 TL increase. Without lag effect, while the effect of a unit of R&D effect was affecting net profit in the direction of 3,78 TL increase, in 3year lag effect model it affected in the direction of 4,56 TL increase. Results indicate that R&D investment matured on the basis of sales revenue and net profit increase the financial performance.
As a result of Chow test conducted to investigate whether there is a structural break in the years we have chosen within the scope of the application or not, the ratio of sales revenue and the sales costs to net sales in 2008 indicate that there is a structural break in financial performance indicators. Despite the impacts of the crisis after the year of
2008, the impact of R&D spending to increase their sales revenue has been strengthened. This situation can be resulted from the growth rate attained in Turkey in recent years and that production and growth index displaced to the countries like Turkey, China, India, Russia, Japan, and South Korea since the US-based economic crisis impacts were felt severely in European countries and the United States.
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