MASTER THESIS

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MASTER THESIS

R&D AND PROFITABILITY: A STUDY ON EUROPEAN AND

JAPANESE ENERGY FIRMS

Author : Yudhistira Putra Pradana Student Number : S2419866

Email : y.p.pradana@student.rug.nl Supervisor : Dr. M. Hernandez Tinoco Co-Assessor : Dr. W. Westerman

Msc. International Financial Management, Faculty of Economics and Business University of Groningen

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Abstract

This paper aims to investigate the effect of R&D investments on profitability, particularly in the European and Japanese energy industry. Samples were drawn from Datastream, where 975 firm-year observation were obtained from a total of 112 European and Japanese energy firms. The data were analyzed using the GMM method and the main result of this study have shown that overall, R&D investments (proxied by R&D intensity) has a positive influence towards the firm’s profitability (proxied by return on assets) for both European and Japanese energy firms, which indicates that the improvement in the features of an existing product, the modification or adjustments of the production methods, and an introduction of an entirely unique product will contribute positively to a firm’s profitability. Another interesting result obtained from this study is that the positive effect became more apparent for both regions when the type of company being analyzed are exclusively renewable energy firms. As there are conflicting ideologies among Europe and Japan in regards to the future utilization of clean energy, this study could be a benchmark for policy makers to design an amendment which would encourage a more prevalent development and usage of renewables.

Keywords: R&D, profitability, clean energy, GMM, country studies

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List of Abbreviations

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1 Introduction

The importance for R&D studies have been increasing as constant technological advancements are considered a central support system for a firm’s growth. Arond and Bell (2009), stated that from 1997 until 2007, worldwide R&D spending has increased by 10 times, from US$ 100.2 billion to 1,137.9 billion. Firms through their R&D spending have also demonstrated that innovation is mandatory in order to differentiate themselves from their competitors and to stand out to their consumers. When R&D spending is utilized effectively, this would result in a greater competitive advantage for the firm as new and innovative goods and services will be introduced, and firm will discover a more efficient way of developing them (Seifert and Gonenc, 2011). Apart from that, the never-ending curiosity of human kind to explore the possibilities of where technology could take them, is also one of the driver that encourages firms to undertake risky R&D projects. Tesla, one of the current most talked about company due to their highly ambitious development of electric cars, is worth US$ 30 billion despite them keep reporting net losses over the years. Nonetheless, Tesla keeps on putting their money on developing evolutionary products, which might not sound too attractive for investors in terms of their short-term financial returns, but they are confident that the sales volume will increase to 500,000 units by 2020 as there has been a constant increase in technology and electric cars in particular. (Sparks, 2015). Additionally, Hausman and Johnston (2014), stated that innovation is a driver of economic growth, as it provides more jobs, improves profitability, and ultimately strengthens the economy.

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2 There are some considerations on how the European and Japanese energy industry is considered suitable objects to be observed in this study. However, before going in to the discussion on the regions, it is wise to remind ourselves why energy and R&D has an important connection in present day. Energy is an essential factor both in the development and the production of the end result from the whole R&D activity. Without a sufficient energy source, companies will not be able to run the equipment they require to develop or build a product. The transport and logistics costs are also highly correlated with energy. Therefore, the drops and hikes in energy prices would influence the transport expense being incurred to the firm to distribute their products worldwide (Bointner, 2014). It is then mandatory for energy providers to be able to provide sufficient power supplies to their consumers, as they will bear higher expenses during a shortage in energy. Calabrese et al. (2013), mentioned that energy firms have invested tremendously to gain the rights to new sources in coal, oil, gas, and even renewable energy sources. Firms have also spent excessively to improve their technological competence so that they will be able to gain an advantage over their competitors. Furthermore, the production cycle in this industry has led to considerable opportunities for firms to innovate, and consequently increases their R&D investments (Apergis and Sorros, 2014). Apart from that, energy also holds a key position in the heart of many economies. Looking at the unpredictable fluctuation of oil prices, the changes that took place have constantly influenced various countries’ expenditures. For instance, there was a decrease in the United States’ oil production during the 90s and the 2000s. As their Texas refineries have failed in meeting local demands for energy, they were forced to import oil despite the peaking oil price during that time. As the cost to import is too high, a desire to innovate is required. By the second half of the 2000s, they experienced a relief, as they developed a technology which allowed retrieval of oil and gas from shale sites which were once considered to be too costly to extract from. Despite the high initial cost to develop the extraction tools, self-extracting energy from local sources is still considered cheaper than constantly importing from abroad (Wong et al., 2013).

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3 consumption. The oil crisis of the 1970s and the increasing CO2 concentration in the atmosphere has also raised worldwide concern regarding the security of the supply and its environmental sustainability. These concerns might have influenced the reduction of public energy R&D budget for fossil fuel where it experienced a major decrease during the 2000s, and despite the fluctuation in this budget, the numbers have not been too significant (IEA, 2015). It is thought that an alternative solution for this issue would be the utilization of nuclear energy. However, looking at the 1986 incident in Chernobyl and 2011 incident in Fukushima, some countries have lost interest in developing such projects due to security concerns (Bointner, 2014). This is also evident by the decrease in the total public energy R&D budget for nuclear, where it experienced a constant drop during the last 40 years. In 1974 it stands the highest at 74%, meanwhile it declined to 20% in 2014 (IEA, 2015).

Figure 1.1 Total Public Energy R&D by Segment (Source: IEA, 2015)

The previously discussed issues regarding energy has influenced both firms and countries to develop alternative methods in providing energy. Despite the high cost in R&D, renewable energy sources, which includes wind, solar, hydroelectric, biomass, and geothermal, is believed to provide more benefits for the economy and the environment (Wong et al., 2013). However, some countries are still hesitant in shifting their energy consumption behavior. Europe is still considered dependent on fossil fuel, where they still spent € 545 billion in 2012 to import fossil fuels. This

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4 Energy Sources, in 2020 Europe aims to have 20% of their energy consumption provided by renewables. The target is currently showing progress as there has been renewable companies being established in various parts of Europe, which mostly operated in solar, hydro, and wind energy (Heide, et al., 2010). As renewables are experiencing a rapid growth in popularity, business owners and shareholders who are interested in the field of energy could also participate through developments of new renewable energy sources to support EU’s target. On the other hand, despite Japan being known as a highly technologically advance country and having the largest share of public energy R&D in the world, there has been no initiatives taken to partially shift their energy consumption from fossil fuel to be more dominated by renewables (Yasuda, 2004; Lambrecht, 2014). The use of renewable energy source currently stands at 10% for the total energy consumption in Japan. However, by 2010 Japan still depends on oil imports to satisfy 42% of their energy demands (EIA, 2012). These distinctive ideologies that Europe and Japan have in terms of their energy consumptions would serve as a suitable standpoint for these regions to be compared. Based on the brief discussions above, this study tries to answer the question of “How does R&D investments affect profitability in the European and Japanese energy industry?”

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5 Literature Review and Hypothesis Development

R&D Investment and Profitability

A large number of studies have contributed to the opinion that R&D investment does have a positive effect towards a firm’s profitability (Jaffe, 1986; Gerorski, 1995; Connolly and Hirschey, 2005). Thus, the benefit of R&D investment towards the firm’s profitability could be traced all the way back to an early study by Schumpeter (1934), through his Schumpeterian Trilogy of Innovation. The theory divided technological change procedures into invention, innovation, and diffusion, where R&D is considered as an innovation phase. As technology grew more sophisticated and intricate, it is becoming more important for profit seeking companies to innovate. Market participants are driven to strive to explore new ways to expand their technological capabilities, new methods in running their businesses, and to discover other form of advantages that would contribute in the increase of their profitability. This is all done in the hopes of directly affecting the value and quality of the business itself. Innovation is the key to economic dynamics and growth (Hanusch and Pyka, 2007), as well as an important aspect that encourages competitiveness (Porter and Stern. 1999).

Following the previously mentioned theory, the literature has provided an adequate amount of evidence regarding the relationship between R&D investments and profitability. Griliches (1979), pointed out that a firm’s actions in regards to research and development contributes to the enhancement of profitability through the innovation of new products and services, the addition or improvement in the features of an existing product, or through the modifications or adjustments of the production methods. Mata and Woerter (2013), mentioned that a firm’s degree of R&D investment plays a role in the growth of a firm’s profitability. This is in accordance with the statement of Branch (1974), who found out that a high R&D intensity within a firm will elevate the prospects for future growth and profitability.

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6 and Japanese energy industry, both of these regions are active developers of clean energy technologies, and there are plenty of rooms for both regions to discover each other’s potential through knowledge and technological sharing, and foreign direct investments (Lambrecht, 2014). Japan is also currently the country with the highest energy R&D share in their national budget. Some R&D intensive countries such as Finland, Hungary, and France have also reflected the importance of energy R&D to their national development through their annual budgets (IEA, 2015). These facts have shown that these regions do possess the capabilities required to provide more sustainable energy generators as a solution to the current energy crisis, as long as there are policies that would mandate them to expand its application. However, despite the initiatives taken by firms to establish alternative source of energy to prevent upcoming energy shortage, there is no guarantee that it will motivate governmental bodies to change their energy policy, nor it will encourage the society to change their energy consumption behavior. Apart from that, looking at cost that will be incurred by the development of such alternative and the failure to have the proposed product be accepted in the market, will cause a backlash for the firm’s profitability. From 2009 until 2014 the United States has only increased their renewable energy consumption by 1%. During that period, US$ 150 billion in taxpayer money has been spent, and 36 green energy projects have gone bankrupt (Hoft, 2015). Failures would also discourage managers to take risky projects as it might affect their internal stock holding and compensation, due to the short-term goals that need to be fulfilled.

However, internally funded R&D investments could be a positive signal that implies the existence of a surplus of resources that is exploitable in conducting R&D activities. Despite ideological clash the two regions have on their energy dependence, a constant desire to innovate will assist European and Japanese energy firms in providing the best solution to their national energy issues. Thus, will enhance both the firm’s innovative capability and the confidence of the market towards the firm’s performance (Ehie and Olibe, 2010).

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7 they are taking, it will open considerable amount of opportunities to imply fresh and ambitious ideas that will differentiate one firm with their competitors. Superiority among its competitors in terms of product innovation will help the firm, not only in improving profitability, but also their value among customers and investors. Therefore, the first hypothesis was developed:

H1: Energy firms who invest more in R&D is more profitable compared to those who do not Moderating Effect of Firm Age

To further examine the R&D and profitability relationship, a few additional factors were taken into account to examine whether the relationship will be strengthened or weakened by the existence of certain firm characteristics. As time is an important aspect to pay attention to when it gets to analyzing complex relationships, firm age is considered a relevant element to be added to the equation. Research has been performed by the Western countries in order to determine the validity of the law of the proportionate effect since the 1960s. By the 1980s, studies have made it clear that firm age is indeed an important explanatory variable for profitability and growth (Yasuda, 2005). Firm age is defined as the years a firm has been operating from the time it was registered at the Legal Affairs Bureau (Sakai et al., 2010). As firms age, it is expected that they will have a clearer understanding of which activities they do best and how to improve those where they lagged behind (Ericson and Pakes, 1995). Loderer and Waelchli (2009), mentioned that years’ worth of experience will educate firms to specialize, discover new approaches to repair, direct, and accelerate production processes, trim down expenses, develop new and improved products, and ultimately affects financial performance. Teece et al. (1997), stated that the firm’s age also affects the return gained through R&D. The longer the firm has operated, more experiments would be conducted, more lessons would be learned through the flaws in the experimentation, and more improvements would be applied to the final product.

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8 case of Japan for example, despite their rigid organizational structure, older Japanese firms are enjoying the benefit of their resources earned from a wide relationship network (Bonn et al., 2004). Lynn et al. (1996), also mentioned that as firms operate longer, they are more likely to build stronger relationship with research institutions to assist them with their R&D activities. This leads to a wider access to resources and reduce the efforts in new product development.

Deeds and Rothaermel (2003), explained that in the situation of strategic alliances, higher trust is more likely formed with parties who have been operating longer. This will not only assist in new product developments, but also in reducing uncertainty and risks (Ohmae, 1989). In regards to liability of newness, Kogut (1988), found out that newer firms often show good performances during the first five years of intra organizational relationships. However, beyond that timespan, their performance tends to decline, causing alliances to be more exposed to terminations due to the unwillingness of the alliance parties to provide further assistance in resources (Deeds and Rothaermel, 2003). Additionally, Zahra et al. (2005) stated that newer firms might be lacking the expertise in utilizing state-of-the-art technologies, which might reduce their future profitability.

In terms of high-technology firms, rapid new product development is a crucial factor in determining success. The energy industry is also prone to be exposed to a high competition intensity due, not only to the high number of global players, but also to the rapidly changing technologies (Deeds and Rothaermel, 2003). As firms operate longer, it is expected that they would already have the competitive advantages such as relations, reputation, and access to financial resources that younger firms are lacking. Experience will also lessen the effort in identifying weakness and it will ease the assessment of the treatment to the firms’ flaws. Strategic alliances created based on a solid reputation will also assist firms in diminishing risks. Based on the above discussion the second hypothesis was developed:

H2: R&D investments will have a stronger influence on profitability for older firms in the energy industry compared to newer firms.

Moderating Effect of Firm Size

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9 and Frenkel, 2005). Majumdar (1997), stated that large firms have greater advantages over smaller firms, due to their diverse competences and the abilities to exercise economies of scale. This leads to larger production while shrinking costs in terms of R&D, which allows larger firms to perform in a superior manner.

Connolly and Hirschey (2005) also discovered that larger firms conduct their R&D activities more effectively compared to small ones, ultimately affecting the profitability of the firm. Moreover, larger companies are more likely to be exposed to greater technological advances such as high-tech machineries, easier access to the capital markets, and the economies of scale in R&D. This resulted in a better position in the competition scheme between larger firms and smaller firms (Cohen and Klepper, 1996). Additionally, Pagano and Schivardi (2000), in their study on size distribution and growth in European firms, stated that size does nurture productivity growth as firms are able to maximize the extraction of potential benefits associated with R&D.

Hud and Hussinger (2015), in their study regarding the impact of R&D during the financial crisis, mentioned that small enterprises are more vulnerable during an economic downturn compared to large enterprises. This is in line with the result obtained by Haniffa and Hudaib (2006), who stated that size is one of the major determinants of how a company will be exposed to value enhancing opportunities. In the case of financial crisis and transitions, larger companies tend to have greater access to financial resources.

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10 In terms of competition, size does matter. Similar to how long a company operated, the larger a company grows, the better their access to financial resources, have more experience in efficiently utilizing their assets, and ultimately, the larger their opportunities to innovate. Based on the discussion above, the third hypothesis was developed:

H3: R&D investments will have a stronger influence on profitability for larger firms in the energy industry compared to smaller firms.

Fossil Energy vs Renewable Energy

Schumpeter (1942), introduced the term “Creative Destruction”, which implies a form of industrial transformation that reformed an economic structure through replacement of an outdated system. It is a market process where good elements replace the bad, and the better ones replace the good. No firms are expected to last forever, and as capitalism involves the occurrence of financial losses, profit is also the main goal companies need to achieve to be the champion in the market. Therefore, this would force companies to understand the mistakes that occurred during their past performances and innovate to secure their survival.

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11 three Eastern Asian countries the 5th, 8th, and 23rd largest CO2 emitters in the world in 2011,

respectively.

Renewable energy, as an alternative source of energy to fossil fuel, has gained a wide considerable attention. It is believed that a further development of this alternative will not only lessen the negative outcome of excessive fossil fuel usage, but it is also viewed as an initiative to empower national energy security (Chen et al., 2014). This initiative for transformation is driven by the need to innovate. Hauschildt (1997), stated that as one of the major driver of innovation, R&D is the principal element in a company that leads to a potential advancement of technology. As companies strive to increase their profit by means of innovating and utilizing the technology in their disposal, their innovation may end the current product’s regime by shifting the market’s interest and gain the margin in the market. Referring again to Schumpeter (1942), companies who invest in R&D will profit through their action, thus “destructing” the current market. Based on the discussion above, the fourth hypothesis was developed:

H4: Companies operating in the renewable energy sector will profit more through R&D compared to those who are operating in the fossil energy sector.

The Impact of Financial Crisis

During the time of crisis, the outcome for the economy and for the long-term competitiveness and profitability of the firms themselves can be damaging. An alternative taken to alleviate the outcome is by smoothing the investment of R&D by the initiation of an innovation policy (Hud and Hussinger, 2015). The grants of public funds for subsidized firms to replace private investments became more significant. Companies will then need to muddle through the impact of such economic turmoil in the hopes of increasing the crowding out effect. Bloom et al. (2007), stated that firms react inadequately to an initiated policy during an economic uncertainty. As uncertainty raise the value of an investment’s real option, it is more likely that firms will become more vigilant towards their investments in R&D.

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12 gas, and power stations. Consequently, there was a slowdown in the efficiency improvement for energy usage. The halted credit market has also given energy companies some difficulties in to obtain external financial resources to run their current and upcoming operations. Energy demand has also dropped due to the economic slowdown, which has dramatically decreased the need for energy and the urge for suppliers to search for new ventures to invest in. Lastly, consumers are also affected by the economic turmoil, which lead them to be less willing in paying for premium goods due to the decrease of their income. Energy companies then experienced delays in the deployment of their goods and services, ultimately affecting their profitability. The energy price crash since mid-2008 due to low demand, along with low expectation for improvement has also caused R&D investments to be scoring less profit for energy firms.

In one recent study, Paunov (2012) discovered that during the financial crisis of 2008, many companies halted their innovating activity. However, this is mostly restricted to smaller and newer firms. Bigger and more experienced firms are more exposed to public funding, allowing them to only reduce a small proportion of their innovating commotions. Despite the difficulties being faced by the energy industry as a whole in obtaining additional funding, larger and older firms such as Petro China and Shell, were not affected too significantly as there were no immense changes in the R&D investments for these companies (IEA, 2009). Additionally, the innovation trim down is also experienced by those companies who are suppliers for multinationals. For example, during the financial crisis, only one out of three Southern American firms improved their process in production and one out of five firms contributed in new product developments. Hence, due to the disorder or stagnated impact given to innovation, economic events such as the financial crisis are viewed as a disadvantage by some. Yet, it is also viewed as an advantage by others. Some lines of research have also given evidence regarding the competitive advantages of R&D during the crisis, and how to gain opportunities out of these drawbacks. For example, Applegate and Harreld (2009), in their study exposed how IBM’s willingness to innovate succeeded them in gaining opportunities throughout the crisis. Thus, despite the importance in gaining future prospects, in this time of disarray, it is as important to foresee the risk that follows.

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14 Methodology and Data

Methodology

In estimating the formulated hypotheses previously discussed, panel data methodology will be used. Specifically, the Generalized Methods of Moments (GMM) estimator (Arellano and Bond, 1991) with the assistance of the StataSE 14 software, using the xtabond2 command. GMM is a general estimator designed for situations of a panel data with “small T and large N”, implying a dataset with few time periods, yet large samples (Mileva, 2007).

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15 As potential problems are identified, the models will then be checked for potential misspecifications following the main statistical regression. The AR (1) and AR (2) tests developed by Arrelano and Bond (1991) will be conducted to test for the first-order and second-order serial correlation in the first-difference residuals. The result of these tests will determine the presence of autocorrelation in the models being utilized in this study. The Sargan test of over identified restrictions will be conducted to test the validity of the models through examining the correlation between the instruments and the error term (Pindado et al., 2010). Lastly the presence of endogeneity will be tested through the difference of Sargan test of exogeneity (Pindado et al., 2010).

Dependent Variable

In measuring the firm’s profitability, return on assets (ROA) will be utilized. Sher and Yang (2003), mentioned that ROA is a good indicator of profitability. Not only because it denotes the effectiveness in the utilization of assets to ultimately score profit, but also due to its representation of how innovative capabilities able to influence performance. ROA is also an indicator of financial sustainability, which is closely affected by gross margins, which is also influenced by R&D (Dave et al. 2013). Following Kouser et al. (2011) and Apergis and Soros (2014), ROA is calculated as net income divided by total assets.

Independent Variables

As discussed in the previous section of this paper, there are five hypotheses that will be tested separately to reach a conclusion for this study. The first hypothesis that will be the base line model of this study, solely examines the relationship between R&D expenses towards profitability. In line with several authors who have conducted research on this field (Grilliches 1981; Jaffe, 1986; Connolly and Hirschey, 2005) the independent variable for the first hypothesis will be the firm’s R&D intensity, which is calculated by dividing R&D expenses by total assets.

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16 total assets (Shefer and Frenkel, 2005; Haniffa and Hudaib, 2006; Kouser et al., 2011). Following Pindado et al. (2010), in applying the interaction dummies, samples for firm age and firm size will be given the value of 1 when the firm is older and larger than the sample mean, respectively. Otherwise, the samples will be given the value of 0.

In order to test for the fourth hypothesis, which proposed that R&D will have stronger impact on profitability in renewable energy firms compared to fossil energy firms, the model will include a dummy variable. For this case, renewable energy firms will be given the value of 1. Meanwhile fossil energy firms will be given the value of 0. If the dummy variable is positively correlated, significant, and greater for renewable energy firms, the fourth hypothesis is supported. For the fifth hypothesis, which examines the impact of the occurrence of a financial crisis towards R&D and profitability, the dummy variable for financial crisis will also be introduced. As the time period for this study takes places between 2006 until 2015, all the samples that were extracted during the 2008-2009 crisis will be given the value of 1. The remainder will be given the value of 0. If the new variable is positively correlated, significant, and greater for renewable energy firms, the fifth hypothesis is supported.

Control Variables

Three control variables were assigned to further clarify the relationship between the R&D and profitability. The first variable is the degree of internationalization (DOI), measured by the percentage of foreign assets to total assets. DOI is one of the most essential element contributing to profitability through R&D projects as multinational companies tend to be more exposed to location-specific benefits, diversification, and ultimately, a better competitive advantage (Bae and Noh, 2001). A dummy will be used to control whether a company is domestic or multinational, where 25% foreign assets will be given a value of 1, otherwise 0.

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17 The last control variable is the country’s GDP. This variable is considered to be included in the model as GDP determines the country’s ability for consumption, which also affects the energy usage in that area (Soytas and Sari, 2003; Lee, 2005). Since GDP is found to be a significant factor in influencing the firm’s performance, this country dummy is included in the estimation model.

Regression Models

Through the previous discussions regarding the hypotheses and variables, five models were developed to give a clearer explanation of how the regression processes are going to be conducted. The models are presented in the following list:

1. ROA𝑖𝑡 = 𝛽0 + 𝛽1 * 𝑅&𝐷𝑖𝑡−1+ 𝛽2 * DOI𝑖𝑡−1+ 𝛽3 * FINANCE𝑖𝑡−1 + 𝛽4 * ECONOMY𝑖𝑡−1 +

e

it

2. ROA𝑖𝑡 = 𝛽0 + 𝛽1*𝑅&𝐷𝑖𝑡−1 + (𝛽2 + α1 * AGE 𝑖𝑡−1)*(𝑅&𝐷𝑖𝑡−1) + 𝛽3 * DOI𝑖 +

𝛽4*FINANCE 𝑖𝑡−1 + 𝛽5 * ECONOMY𝑖𝑡−1 +

e

it

3. ROA𝑖𝑡 = 𝛽0 + 𝛽1*𝑅&𝐷𝑖𝑡−1 + (𝛽2 + α1 * SIZE 𝑖𝑡−1)*(𝑅&𝐷𝑖𝑡−1) + 𝛽3 * DOI𝑖 +

𝛽4*FINANCE𝑖𝑡−1 + 𝛽5 * ECONOMY𝑖𝑡−1 +

e

it

4. ROA𝑖𝑡 = 𝛽0 + 𝛽1*𝑅&𝐷𝑖𝑡−1 + (𝛽2 + α1 * RENEWABLE i)*(𝑅&𝐷𝑖𝑡−1) + 𝛽3 * DOI𝑖 +

e

it

5. ROA𝑖𝑡 = 𝛽0 + 𝛽1*𝑅&𝐷𝑖𝑡−1 + (𝛽2 + α1 * CRISISi) *(𝑅&𝐷𝑖𝑡−1) + 𝛽3 * DOI𝑖 +

e

it

Where:

ROA𝑖𝑡 = Return on assets for year “t” of company “i”

𝑅&𝐷𝑖𝑡−1 = R&D intensity for the year “t-1”1 of company “i”

AGEi𝑡−1 = Dummy variable that takes the value of 1 for older firms, and 0 for newer firms

SIZE 𝑖𝑡−1 = Dummy variable that takes the value of 1 for larger firms, and 0 for smaller

firms

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18 RENEWABLEi = Dummy variable that takes the value of 1 for renewable energy firms, and 0 for

fossil energy firms

CRISIS = Dummy variable that takes the value of 1 for the years of financial crisis (2008 and 2009), and 0 for other years

DOIi = Dummy variable that takes the value of 1 for multinationals, and 0 for domestic

companies

FINANCE𝑖𝑡−1 = Working capital of the company for the year “t-1” of company “i”

ECONOMY𝑖𝑡−1 = GDP for the firm’s country of operation for the year “t-1” of company “i”

e

it = Error terms

Data Availability

The empirical study conducted for this paper relies on annual firm level data obtained from the Datastream database during the period of 2006-2015. The primary criteria to decide which companies to include in the dataset are active companies which are publicly listed in the region of Continental Europe and Japan. The selection of these countries is considered a deliberate choice as most prior studies in this field were conducted with United States companies as samples (Morbey and Reithner, 1987; Apergis and Soros, 2013). Yasuda (2004), even stated that investigations which confirmed the applicability of the conclusions of R&D related studies being aimed at Western companies in the case of Japan, are also quite rare. The following table presents the sample structure by country.

Table 3.1 Sample Structure by Country

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19 The industry where the company is operating was also taken in consideration in picking up samples, as this study focuses primarily on the energy industry. Data were then also extracted from companies selected based on their industry codes. Included in the energy industry, as referred to PWC (2015), are companies working in the fields of petroleum (including oil companies, refineries, and distributors), gas (including gas extraction, manufacturer, and distributors), electrical power (including electricity generators and distributors), coal and mining, nuclear, and renewable energy (including solar, wind, biomass, hydro and geothermal). The following table presents the sample structure by the firm’s industry.

Table 3.2 Sample Structure by Industry

In order to examine the variables, data on the firm’s return on assets (ROA), R&D expenses, years of operation, total assets, percentage of foreign assets, working capital, and GDP are required. The majority of these data were obtained from Datastream. Meanwhile, data on country GDP were extracted from the Main Economic Indicators published by the Organization for Economic Cooperation and Development (OECD). Moreover, the observations that were missing data on ROA and R&D were deleted as they were the primary concerns of this study. The final dataset of this study then consists of 112 firms and 975 firm-year observation, within the time period of 2006-2015.

Fossil Energy 85 75.89%

Oil and Gas 30 26.79%

Electrical Power 16 14.29% Coal and Mining 39 34.82%

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20 Results

Descriptive Statistics

Table 4.1 Descriptive Statistics

Observations Mean Std. Dev. Min Max

ROA Complete Dataset 975 0.054 1.259 -16.498 8.750

Europe 631 0.068 1.564 -16.498 8.750

Japan 344 0.029 0.049 -0.452 0.191

R&D Complete Dataset 975 0.054 0.264 0.000 5.559

Europe 631 0.078 0.326 0.000 5.559

Japan 344 0.011 0.23 0.000 0.230

R&D*Age Complete Dataset 975 0.007 0.047 0.000 1.112

Europe 631 0.005 0.552 0.000 1.112

Japan 344 0.010 0.023 0.000 0.230

R&D*Size Complete Dataset 975 0.004 0.016 0.000 0.230

Europe 631 0.002 0.106 0.000 0.209

Japan 344 0.008 0.228 0.000 0.230

R&D*Renewable Complete Dataset 975 0.005 0.031 0.000 0.619

Europe 631 0.007 0.039 0.000 0.619

Japan 344 0.001 0.006 0.000 0.045

R&D*Crisis Complete Dataset 975 0.166 0.203 0.000 5.559

Europe 631 0.025 0.253 0.000 5.559

Japan 344 0.002 0.009 0.000 0.114

DOI Complete Dataset 975 0.343 0.568 0.010 5.730

Europe 631 0.474 0.664 0.010 5.730

Japan 344 0.101 0.125 0.000 0.930

Finance Complete Dataset 975 1,049,161.00 4,310,703.00 -16,500,000.00 36,700,000.00

Europe 631 1,567,808.00 5,033,449.00 -16,500,000.00 36,700,000.00

Japan 344 97,806.76 2,197,606.00 -13,200,000.00 8,642,227.00

Economy Complete Dataset 975 3,129.41 1,119.38 216.55 4,376.04

Europe 631 2,505.85 910.38 216.55 3,868.29

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21 The descriptive statistics of the variables being used in this study is presented in table 4.1, which includes the dependent variable; return on assets, independent variable; R&D, and the control variables; DOI, financial capabilities, and economy. As observed, return on assets for Europe and Japan seems to significantly differ in terms of their maximum and minimum value. This could be explained through their standard deviation, where the score for Japan is much lower compared to Europe, impacting in less variability in the values for Japan compared to Europe. The differences in the number of observations for both countries might also give an impact to this issue, as the number of observations for Europe is nearly twice the size of Japan, differences in sub-samples are more likely to be greater for that region. Additionally, the average in ROA for Europe and Japan also implies that ROA for European energy firms are higher compared to those in Japan. The analysis on R&D intensity have also given some insights about the R&D investing activities in both regions. Europe in this case, has a higher mean of 0.078 compared to Japan’s 0.011 when it gets to their R&D intensity. Implying that energy firms in Europe invest more in R&D compared to Japanese firms.

Analysis of the control variables also provide some interesting information regarding the dataset being used in the study. The degree of internationalization seems to significantly differ between the two regions, implying that there are more MNCs in Europe in terms of energy firms for this dataset. Meanwhile, Japanese firms might prefer to keep operating on a local level. The level of financial capabilities and economy for Japan and Europe also greatly differ. This might be the impact of the differences of the number of the observations for companies from those regions, the differences in exchange rate between Yen and Euros, or due to the differences of the standard of living for both regions, causing a highly different standard deviation for GDP. The inconsistencies of the economic level of various EU countries might also contribute in explaining this issue.

Dummy Variables

Zeroes % Zeroes Ones % Ones

AGE Complete dataset 546 0.56 429 0.44

Europe 506 0.52 125 0.13

Japan 40 0.04 304 0.31

SIZE Complete dataset 475 0.49 500 0.51

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22 Table 4.2 Dummy Variables

Some interesting facts can also be taken from dummy variables that are being used to explain certain phenomenon that are the interest of this study. Looking again at the mean scores for the individual regions, the number of larger and older firms are higher in Japan compared to the ones included in the European side of the dataset. This might imply that in terms of their operation in the energy industry, Japanese firms are expected to be more experienced as they have more companies in their disposal that have been running for a considerable amount of time, and managed to grow in a superior manner without any sign of bankruptcy. In the dataset, some Japanese energy companies managed to run for more than 100 years, meanwhile no European firms have reached that point of age yet. On the other hand, Europe leads in the establishments of renewable energy firms compared to Japan, mostly in the area of wind and solar energy. As Heide et al. (2010) pointed, the EU has a target of being able to provide 20% of their energy consumption through clean energy by 2020. The growth in the popularity of renewable energy these past couple of years could be an inspiration for business owners and shareholders to take initiative in further establishing and developing renewable energy providers to support EU’s plans.

Preliminary Statistical Analysis Results

Table 4.3 provided the regression results on the R&D and profitability relationship using the complete dataset. Model 1 is the basic model which examines the relationship of R&D and profitability without any moderating effects. Model 2 examines the moderating effect of firm age, meanwhile model 3 examines the moderating effect of firm size. Model 4 examines whether R&D investment would impact more positively towards profitability for renewable energy firms, and model 5 examines to examine whether the R&D investment would have a more positive effect towards profitability during the 2008/2009 financial crisis.

Japan 82 0.08 262 0.27

RENEWABLE Complete dataset 800 0.82 175 0.18

Europe 469 0.48 162 0.17

Japan 331 0.34 13 0.01

CRISIS Complete dataset 767 0.79 208 0.21

Europe 491 0.50 140 0.14

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23 Table 4.3 Regression Results for the Complete Dataset

Basic Model

The observation in Model 1 have shown that there is a significant positive effect of R&D investment towards profitability for the complete dataset, as shown by the coefficient of 0.15 and that the variable is statistical significant at the 1% level. Additionally, it can also be interpreted that in every 1-point increase in the firm’s R&D intensity, it will increase the firm’s ROA by

0.15-Model 1 Model 2 Model 3 Model 4 Model 5

Coefficients Coefficients Coefficients Coefficients Coefficients

R&D 0.15 *** 0.17 *** 0.15 *** 0.16 *** (-0.01) *** (12.58) (12.10) (12.51) (12.10) (-8.28) R&D*Age 0.21 *** (7.97) R&D*Size 2.41 ** (2.74) R&D*Renewable 0.58 *** (6.64) R&D*Crisis (-1.31) ** (-1.99) DOI 0.78 ** 0.78 ** 0.79 ** 0.79 ** 0.61 ** (2.34) (2.02) (1.85) (1.99) (2.12) Finance 0.23 ** 0.17 ** 0.44 ** 0.16 ** 0.46 ** (2.88) (2.00) (2.73) (2.97) (2.12) Economy -0.01 *** -0.01 *** -0.01 *** -0.01 *** 0.09 *** (-9.31) (-9.28) (-9.13) (-9.04) (9.11) Observations 700 700 700 700 700 AR(1) 0.000 0.000 0.000 0.000 0.000 AR(2) 0.848 0.788 0.916 0.835 0.012

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24 point. This indicates that hypothesis 1, which stated that “Energy firms who invest more in R&D is more profitable compared to those who do not” is supported. In terms of the control variables, the coefficients and significance of degree of internationalization and financial capabilities have also shown that these two variables have significant positive effect towards the R&D and profitability relationship. This information might imply that R&D investments might have a better impact on profitability when firms are more courageous in investing abroad; for example, establishing foreign direct investments in foreign countries, and also when they are able to exercise money management decisions through their working capital. On the other hand, the presence of the country’s economic level reacts negatively towards the aforementioned relationship. Despite there being a positive impact from R&D investments towards profitability for energy firms, the ability for consumption of the country’s population might contribute inversely to the formula as there might be some parties in the community that are not able to provide themselves with proper energy, such as those that are suffering from poverty, or people who are living in rural areas with very limited access to energy.

Moderating Effect of Firm Age

In Model 2, hypothesis 2 which stated “R&D investments will have a stronger influence on profitability for older firms in the energy industry compared to newer firms” was examined. Therefore, the dummy variable AGE was used. The variable takes the value of one for firms whose years of operation surpassed the sample mean. Otherwise the dummy variable takes the value of zero. Model 2shows that the R&D coefficient for older firms (ß2 + a1= 0.17 + 0.21 = 0.38) is greater than the coefficient for younger firms (ß2 = 0.17). The variables are also statistically significant on a 1% significance level. Thus, the evidence for the second hypothesis was found. Moderating Effect of Firm Size

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25 Fossil Energy vs Renewable Energy

In Model 4, hypothesis 4 which stated “companies operating in renewable energy sector will profit more through R&D compared to those who are operating in the fossil energy sector” was examined. Therefore, the dummy variable RENEWABLE was used. The variable takes the value of one for renewable energy firms. Otherwise the dummy variable takes the value of zero. Model 4shows that the R&D coefficient for renewable energy firms (ß2 + a1= 0.16 + 0.58 = 0.74) is greater than the coefficient for fossil energy firms (ß2 = 0.16). The variables are also statistically significant on a 1% significance level. Thus, the evidence for the fourth hypothesis was found. R&D and Profitability During the Financial Crisis

In Model 5, hypothesis 5 which stated “higher R&D investment will lead to a higher profitability in the situation of a financial crisis, compared to when a financial crisis does not take place” was examined. Therefore, the dummy variable CRISIS was used. The variable takes the value of one for data taken from the years 2008 and 2009, where the financial crisis took place. Otherwise the dummy variable takes the value of zero. Model 2shows that the R&D coefficient for the time of crisis (ß2 + a1= -0.01 + (-1.31) = -1.32) is lower than the coefficient for the time of non-crisis (ß2 = -0.01). Despite the variables being significant at the level of 1% for the time of non-crisis and 5% for the time of crisis, this does not give evidence towards the proposed hypothesis.

Additional Analysis

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26 investigation on the moderating effect of financial crisis towards the R&D-profitability relationship should be examined to obtain more insights regarding this matter.

The Sargan test of overridentified restriction was done in order to test the validity of the variables being used in the models, where the null hypothesis is all instruments are valid. Having valid instruments in the model is crucial as it indicates that the model have eliminated instruments correctly and instruments are uncorrelated with the error term and (Mileva, 2007). As the results for models 1, 2, 3, and 4 are not statistically significant. Therefore, the null hypothesis is accepted, indicating that all the instruments are valid. Lastly, the difference in Sargan test of exogeneity was done to test whether the instruments being used in the equation are exogenous. The result has shown that the variables in models 1, 2, 3, and 4 are all not statistically significant. Therefore, the null hypothesis cannot be rejected, implying the instruments are exogenous and that the firm’s past performance did not have any significant influence on the flow of the R&D-profitability relationship. However, model 5 have shown statistical significance for both Sargan tests. Similar to the autocorrelation test, the Sargan test result for model 5, did not fulfill the expectation of this study.

Additionally, the observations that are being used in the regression are only 700 out of the total 975 of the total samples in the dataset. This is due to the lag being applied to some variables and the unbalanced condition of the panel, which allows the statistical software to eliminate blank and/or unneeded samples. This filtration however, despite it affecting the amount of samples taken to be included in the statistical analysis, does not have any impact on the initial dataset.

Europe vs Japan Comparison

As suggested by the title of this paper, this study mainly tries to compare the R&D and profitability relationship between European and Japanese energy firms. Table 4.4 presented the regression results for this analysis, which used the same equation models as the preliminary tests.

Model 1 Model 2 Model 3 Model 4 Model 5

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27 Table 4.4 Regression Results for European Sub-Samples

R&D*Size 70.93 ** (2.01) R&D*Renewable 0.54 *** (8.67) R&D*Crisis (-1.31)*** (-7.51) DOI 0.95 *** 0.94 *** 1.16 *** 0.95 *** 0.74*** (8.17) (8.16) (8.96) (9.17) (8.93) Finance -0.57 ** -0.62 ** 0.49 ** -0.63 ** -0.23** (-2.03) (-2.03) (-2.19) (-2.03) (-2.01) Economy -0.01 *** -0.01 *** -0.08 *** -0.01 *** -0.08*** (-9.74) (-9.73) (-9.28) (-9.65) (-9.04) Observations 432 432 432 432 432 AR(1) 0.000 0.000 0.000 0.000 0.000 AR(2) 0.848 0.788 0.916 0.835 0.012 Sargantestofoverrid.restrictions 0.260 0.312 0.587 0.385 0.000 Diff-in-Sargantestofexogeneity 0.117 0.115 0.351 0.148 0.000 The regression equation result is shown in the table above. To recap, profitability is the dependent variable, which is proxied by Return on Assets (ROA). R&D is the independent variable, proxied by R&D intensity. The moderating variables included in the models include Firm Age, Firm Size, Renewable, and Crisis. The control variables included in the models include DOI (Degree of Internationalization) which is proxied by the % of foreign assets, financial capabilities which is proxied by the working capital, and economic condition proxied by country GDP. Figures with parentheses under coefficients represent the t-values obtained from the equation. Meanwhile, symbols *, **, and *** indicates statistical significance at the level of 10%, 5%, and 1%. AR (1) and AR (2) are tests for first-order and second-order serial correlation in the first-differenced residuals, under the null of no serial correlation. The Sargan test of overrid. restrictions is under the null that all instruments are valid. The Diff-in-Sargan tests of exogeneity is under the null that instruments used for the equations in levels are exogenous.

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28 Table 4.5 Regression Results for Japanese Sub-Samples

Basic Model

Both the European and Japanese samples have shown that there is a significant positive effect of R&D investment towards profitability. This has given support for hypothesis 1, which stated that “Energy firms who invest more in R&D is more profitable compared to those who do not” for both regions. However, there are conflicting outputs in terms of their control variables. Despite both regions showing positive effects from the degree of internationalization towards the R&D and profitability relationship, financial capabilities and economic level are only giving similar effects in Europe and not in Japan. This might indicate that despite both regions are bold in investing and doing business abroad, and being effective in utilizing their investments, Europe might lack the asset management skill that Japan possesses, which ultimately gave some drawbacks in their profitability. Apart from that, the diverse economic and education level of various European countries might contribute inversely to the formula as there might be some parties in the community that are not able to provide themselves with proper energy, such as those that are suffering from poverty. Japan on the other hand, assisted by their technological

DOI 0.02 ** 0.02 ** 0.02 ** 0.02 ** 0.02 ** (2.39) (2.39) (2.38) (2.43) (2.43) Finance 0.13 ** 0.15 ** 0.13 ** 0.13 ** 0.11 ** (2.40) (2.42) (2.39) (2.39) (2.43) Economy 0.09 *** 0.16 *** 0.14 *** 0.08 *** 0.03 *** (9.28) (9.40) (9.29) (9.24) (9.82) Observations 268 268 268 268 268 AR(1) 0 0 0 0 0 AR(2) 0.222 0.132 0.113 0.162 0.003

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29 competence, might have been able to distribute their energy more equally and more cost efficient across the region.

Moderating Effect of Firm Age

There were conflicting results for Europe and Japan when the moderating effect of firm age towards the R&D and profitability relationship was examined. There was a positive effect given by firm age towards R&D and profitability in Europe, meanwhile it inversely affected the Japanese firms. Looking at how most Japanese firms are operated on a hierarchical system, it is assumed that most firms would follow traditional business models that are considered effective by the top level management. This would result in less space to innovate and discovering new methods in running the business. This could also be a supporting reason to why Japan is still highly dependent on energy imports despite their ability to develop in house energy sources. These results might also imply that older European energy firms are prone to constantly innovate as change is an essential element in scoring high profits and to impress stakeholders. This is in line with the result provided by Teece et al. (1997), where it was stated that the longer a firm manage to run, they would conduct more experiments, giving opportunities to understand the flaws in their products, and will ultimately use the experienced they have gained through the learning process to develop innovations in terms of their products, process, and strategy. Therefore, hypothesis 2 which stated “R&D investments will have a stronger influence on profitability for older firms in the energy industry compared to newer firms” was supported for the case of European energy firms, and it was rejected for Japanese energy firms.

Moderating Effect of Firm Size

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30 activities and managed to spawn various new services over the years that has proven their management capabilities in scoring profit for the companies. Referring to the study by Connolly and Hirschey (2005), as larger firms are more effective in conducting R&D activities due to their access to technological advances, financial services, and economies of scale, it would position them in a more superior rank in the competition scheme compared to smaller firms. Thus, resulting in a more positive impact towards their overall profitability. As the result in the case of Europe have supported this idea, the result from Japan, might give a new perspective on the moderating effect of firm size towards the examined relationship. Therefore, hypothesis 3 which stated “R&D investments will have a stronger influence on profitability for larger firms in the energy industry compared to smaller firms” was supported for the case of European energy firms, and it was rejected for Japanese energy firms.

Fossil Energy vs Renewable Energy

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31 R&D and Profitability During the Financial Crisis

There were negative effects given during the 2008-2009 financial crisis towards the R&D investments and profitability relationship in both European and Japanese energy firms. It was previously discussed that there were restrictions on financial supports given to the energy industry as energy was a very risky business to invest in during the crisis, energy trade was not considered appealing during that time, and the demand of energy is considerably low. This have contributed to a lower opportunity to innovate and to go further with current projects, which therefore halted some production processes, reduced the chance for a company to introduce new goods and services to the market during the crisis, and ultimately diminished profit taking. Therefore, hypothesis 5 which stated “higher R&D investment will lead to a higher profitability in the situation of a financial crisis, compared to when a financial crisis does not take place” was rejected for both European and Japanese energy firms.

Additional Analysis

Both sub-samples were also tested for model misspecification using the same method applied in the preliminary analysis. The AR (1) test for all models in both Europe and Japan have shown statistical significance, resulting in the rejection of the null hypothesis. At this stage, there is indeed a presence of autocorrelation in all models. However, this is not a principal matter. The AR (2) test results for both European and Japan on the other hand, have been proven not statistically significant for model 1, 2, 3, and 4. As the null hypothesis is rejected for this test, there is no evidence of serial autocorrelation to the aforementioned models. Similar to the preliminary analysis however, the AR (2) test results for model 5 in both regions have shown statistical significance, indicating the presence of autocorrelation.

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32 Robustness Check

Table 4.6 Robustness Check Results

A robustness test was conducted to provide some insights on whether the use of a different proxy in the equation will alter the profitability outcome of R&D investments. The statistical analysis uses the complete dataset, and return on equity was used as the proxy for profitability (Han et al., 1998; Qian and Li, 2003; Ballester et al., 2003). This test presented similar results to

RD 0.12 *** 0.09 *** 0.11 *** 0.14 *** (-2.24) *** (7.49) (7.12) (6.99) (7.23) (-8.31) RD*Age 0.05 *** (5.67) RD*Size 2.28 ** (6.79) RD*Renewable 0.03 *** (10.60) RD*Crisis (-1.73) *** (-10.70) DOI 0.58 ** 0.56 ** 0.45 ** 0.39 ** 0.45 ** (2.18) (2.16) (2.19) (2.20) (2.21) Finance 0.11 ** 0.14 ** 0.13 ** 0.13 ** 0.12 ** (2.36) (2.32) (2.35) (2.30) (2.28) Economy 0.01 ** 0.07 ** 0.1 ** 0.09 ** 0.02 ** (2.12) (2.19) (2.21) (2.15) (2.16) Observations 700 700 700 700 700 AR(1) 0.000 0.000 0.000 0.000 0.000 AR(2) 0.335 0.532 0.413 0.562 0.025

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34 Conclusion

The main aim of this study is to explore the relationship between R&D expenses and firm’s profitability in the European and Japanese energy industry. Taking into account the high interest given to R&D research in the field of energy, valuation models were designed to examine the previously mentioned relationship, where the firm’s profitability, which is proxied by return on assets, depends on to which extent a firm is willing to invest in R&D, proxied by R&D intensity. Several firm characteristics were then added to the formula to further explore their roles in the profitability outcome of R&D investments.

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35 older firms might prefer operating on a non-changing business formula, giving little opportunity for these types of firms to innovate. Smaller and younger firms however, are more forward looking and tend to give a fresher vision on how a company should run.

There were also effects given to profitability in regards to whether the firm that is conducting the R&D activities is a renewable or a fossil fuel energy. Referring to the statistical results, higher positive effects of R&D investments towards the firm’s profitability were projected for renewable energy firms in the case of both Europe and Japan. As the climate change and energy shortage has been a global concern, companies and countries around the world should take part in innovating in the field of clean energy. As R&D is highly correlated with technological advancements and looking at the high interest being taken in this field of business, the asset utilization of renewable energy firms is not only expected to score huge profits, but it could also be the solution to the energy crisis being faced worldwide (Chen et al., 2014).

Lastly, as the financial crisis was also examined as a moderating effect to the R&D and profitability relationship, it was proven that an economic disturbance does give negative effects towards the profitability outcome of an R&D investment. This supported the idea that energy industry is a risky investment during the financial crisis. Firms are then restricted access to financial resources and halted some of their operations. Ultimately affecting their R&D activities and profitability in the long-run. A further investigation in this issue has also shown that during the crisis, the demand for energy has dropped significantly and the restriction to financial resources has halted plenty of innovating activities (IEA, 2009).

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36 their innovating activities. The benefit given by renewables are considerable for both firms and the environment. Therefore, the result provided by this study is hoped to be considered by policy makers around the globe to inspire them in designing a policy which would encourage business owners and the government to establish more renewable energy sources.

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MASTER THESIS

MASTER THESIS

R&D AND PROFITABILITY: A STUDY ON EUROPEAN AND

JAPANESE ENERGY FIRMS

Table of Contents

List of Abbreviations

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