University of Groningen Faculty of Economics & Business Department of Business Administration
MASTER THESIS
Master in Business Administration – Change Management
Impact of CSR and institutional pressure on energy efficiency:
The moderating roles of CSR and institutional pressure in the context of
M&A
Oliver Rolle
S3473732
o.rolle@student.rug.nl
First Supervisor: prof. dr. J. Surroca
Co-Assessor: prof. dr. J. D. R. Oehmichen
ABSTRACT
In this master thesis, the effects of M&A on the energy efficiency level of companies is explored. Based on findings of the literature I developed four hypotheses. These hypotheses deal with the moderating roles of the social pillar of CSR and institutional pressure on the experienced energy efficiency improvements after M&A. I assumed that the social pillar of CSR and institutional pressure can work as a predictor for the overall energy efficiency level and that companies with values of institutional pressure and the social pillar of CSR over the sample mean will improve their energy efficiency on average more than companies with values below the sample mean. To test my hypotheses, I measured energy efficiency by applying a DEA analysis to a sample of 267 acquiring companies that are involved in CSR activities and M&A. Data is gathered from multiple databases including ASSET4, Zephyr, and Orbis. Results of this thesis extend current findings of the low-carbon M&A literature by first extending the discovered positive effects of M&A on the energy efficiency for Chinese companies to a sample with companies from Australia, Asia, North America, Africa and Europe. Second, results of this study give support for the moderating roles of the social pillar of CSR and institutional pressure. Companies included in my sample with values of the social pillar of CSR and institutional pressure above the mean experience on average higher improvements in energy efficiency than companies with values below the mean.
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Master Thesis
Impact of CSR and institutional pressure on energy efficiency:
The moderating roles of CSR and institutional pressure in the context of M&A
INTRODUCTION
Mergers and acquisitions (M&A) have aroused the interest of the academic literature since the 1960s (Das and Kapil, 2012). In 2016, the announced volume of the total global M&A market has reached an amount of 3.9 trillion dollars. This is the third highest record in history despite the economic uncertainty in 2016 (Cristerna et al., 2017). Such a market volume is exceeding the GDP of several large countries. Nevertheless, the success rate of M&A did not improve over the last sixty years. Based on self-reports of managers, ranged the success rate of M&A in the year 1974 around approximately 50-54% (Kitching, 1974). Twenty years later, in the year 1994, the success rate was still around 54-55% (Rostand, 1994). More recently studies confirmed a failure rate of more than 50 percent, illustrating that research of more than sixty years did not lead to any significant improvements in the success rate of M&A (Sudarsanam, 2010; Frensch, 2007, Bauch, 2004; Tuch and O´Sullivan, 2007, Weinmann, 2004: Meckl and Theuerkorn, 2015).
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the literature up to date is still missing aspects and variables that have a strong influence on post-acquisition performance.
This study focuses on the second mentioned possibility by expanding the low-carbon M&A literature stream introduced by Tian, Yan, and Peng (2017). Findings of their study suggest that companies experience improvements in ecological efficiency after M&A. This paper will refer to ecological efficiency as energy efficiency due to the reason that this term is the commonly used term in the literature (e.g. Moon and Min, 2017). Several different definitions of energy efficiency exist in the literature, but in general, it either describes the ratio between the input of energy resources and experienced energy service, or it refers to the relationship between energy-related inputs and outputs as performance or products (Moon and Min, 2017). Consequently, firms do have the opportunity to improve their performance by improving their energy efficiency. Two arguments can explain this consequence.
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productivity improvements when either the output increases by the same amount of input or the output stays constant, but the needed input decreases.
One example of productivity improvements while increasing energy efficiency is that investments in specific energy-efficient technologies will not only lead to a reduction in operating cost, it will also enhance the production processes of a company. These experienced benefits are also known as “productivity benefits’’ or ‘’non-energy benefits’’ (Mills and Rosenfeld, 1996). Depending on the acquired new technology the following benefits could be achieved: (1) improvement of the work environment through noise reduction and higher safety standards, (2) reduction of maintenance costs leading to saving in labor time, (3) improved process control, (4) waste reduction or saving of resources, (5) benefits of downsizing equipment or complete elimination of equipment (Worrell et al., 2003). As a further matter, governmental regulations in Europe and other areas of the world were introduced with the primary goal of reducing the use of non-sustainable energy resources. Accordingly, improving energy efficiency can be a strategy for companies to reduce their greenhouse gas emissions and with achieving this, reducing their costs of pollution.
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Leading to the research question: ‘’Impact of CSR and institutional pressure on energy efficiency: The moderating roles of CSR and institutional pressure in the context of M&A.’’ This research contributes to the literature in extending the new introduced low-carbon M&A literature stream (Tian, Yan, and Peng 2017) by first extending the findings from only Chinese companies to a sample with companies located worldwide. Second, two moderator variables influencing the effect of M&A on energy efficiency are introduced. These variables are institutional pressure and the social pillar of CSR. They are important because the social pillar of CSR illustrates the human side of the organization and if treating the workforce well leads to other realized effects. Whereas, institutional pressure emphasizes the degree of regulations companies experience. Governmental regulations towards ecological impact are one of the primary tools in the transition to a more sustainable world. Therefore, this study shows if regulations influence the energy efficiency of companies.
LITERATURE REVIEW
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Efficiency theory in the context of M&A
The efficiency-merger theory sees the motive for the execution of M&A in the creation of synergies between both companies involved in the deal (Trautwein, 2013). Those created synergies are assumed to improve the efficiency of companies and eventually create value through improvements in performance. Consequently, does the efficiency-merger theory entail that both companies combined have a higher performance than each company on its own. Trautwein (2013) differentiates in total between three different kinds of synergies: (1) Financial synergies. (2) operational synergies, and (3) managerial synergies.
Akhavein, Berger and, Humphrey (1997) examined in their study the effects of megamergers in the US bank industry. Their definition of megamergers states that both banks must have at least 1 billion dollars in assets. Findings of their study showed that on average, merged banks improved their profit efficiency by around sixteen percent in comparison to other large banks. Improvements in profit efficiency were exceptionally high when the target banks profit efficiency before the merge belonged to the lowest of their sample. They reasoned their findings with the diversification hypothesis. Merging banks were rewarded from the market for their higher diversification accomplished by a switch from securities to loans. Therefore, profit efficiency improves due to the creation of financial synergies.
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Next to the creation of synergies, efficiency improvements can be explained by the mechanism that M&A improve the productivity of the involved companies. Siegel and Simons (2010) analyzed in their study the effects of M&A on plant productivity. Plants acquired by the M&A gave the opportunity to improve productivity. They concluded, that after the M&A plants were organized and matched more efficiently, the employees made use of the new structure, resulting in improvements in plant productivity. Their findings are supported by other studies (Lichtenberg and Siegel 1990; McGuckin and Nguyen 1998).
Moreover, productivity improvements are especially high for companies entering new markets and participating in international exports (Takechi, 2006). The explanation behind this relationship is again repatriated to the creation of synergies. These synergies are created due to knowledge transfers with international companies. International companies differentiate themselves in most cases from domestic companies because of other government regulations and infrastructure differences.
A theoretical link can be made between improvements in productivity and improvements in energy efficiency as Boyd and Pangs (2000) study shows. Their study analyzed the effect of industrial productivity on energy efficiency in the glass industry. Findings suggest that improvements in productivity by one percent lead to simultaneous improvements in energy efficiency of more than one percent. Nevertheless, these findings were only significant for the production of flat glass and not for container glass. Leading to the assumption that improvements in productivity will contribute to different effects depending on the industry and production process.
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productivity improvements. Only merging banks with different cultures improved their cost-efficiency after M&A (Lin, 2005). Indicating that employees of each bank gained new knowledge from employees of the other bank. Plant productivity was only improved because the employees made use of the new organized plants (Siegel and Simons, 2010). Furthermore, companies entering new markets experienced productivity improvements through knowledge transfers (Takechi, 2006). The influence of the human side in the M&A process is from especially importance in the next section when I introduce the moderating roles of the social pillar of CSR (SOCCSR)1 and institutional pressure.
Lastly, Tian, Yan, and Peng (2017) investigated in their study if low-carbon M&A influence the ecological efficiency of companies. Their sample consisted of companies located in three different regions in China namely Yangtze River Delta, Pearl River Delta and the Circum-Bohai-Sea Region. Moreover, companies of their sample were listed to be low in carbon emission. This fact is especially important for their study because depending on the region in China, different types of ecological civilization education and political involvement exist. Therefore, one of their assumptions was that these low-carbon companies would be acquired by companies with higher emission production, to reduce their emission level in the light of education of ecological civilization. Education of ecological civilization is a program to create awareness for the climate change and its consequences for our planet. The goal of the program is to turn China into a place with almost no emissions; therefore, new regulations were installed for companies to have an incentive to integrate more sustainable technologies in their business activities.
Results of their study show that in total seven out of nine industries improved their average efficiency level after M&A. Nevertheless, as they assumed from the beginning, strong
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differences were identified depending on the region and industry. They identified the highest improvements in ecological efficiency in capital-intensive industries, followed by labor-intensive industry, and the weakest improvements in ecological efficiency were observed in resource-intensive industries.
After reviewing the various researched efficiency improvements experienced after M&A and primarily based on the improvements in energy efficiency already identified by Tian, Yan, and Peng (2017) in their study of low-carbon M&A in China, I conclude the following baseline hypothesis.
Baseline hypothesis: Companies involved in M&A will on average experience
improvements in energy efficiency.
Corporate Social Responsibility
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with economic, social and environmental performance that CSR is not only composed out of one element.
The following part will focus on CSR's social component and create a connection with environmentally responsible behavior. Nevertheless, CSR activities as implied in this definition are not only limited to the organizational level; they also include actions on the individual and group level (Aguinis and Glavas, 2012). The SOCCSR indicates the degree to which an organization can create trust and loyalty with its employees and also, how external as well as internal stakeholders perceive the reputation of the organization. It consists out of seven category scores as, e.g., employment quality, workforce health and safety, and the workforce development and training category (Thompson Reuters, 2013).
Sarkis, Gonzales, and Adenso-Diaz (2010) investigated in their study the effect of training on the actual execution of environmental practices in the manufacturing industry. They proposed that organizations are responding to pressure from internal as well as external stakeholders. One common pressure companies are facing in our time is pressure towards environmental responsible behavior. Therefore, organizations are adapting to those pressures and introducing new protocols and processes to work more ecologically efficient. Still, only introducing those new mechanism does not automatically imply the execution of those new routines. Their findings show a positive and significant effect of employee training on the adoption of those new practices. Therefore, only with training in place are employees adopting the newly introduced practices.
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overall goals of the organization, (2) the willingness of an individual to give full effort for the organization and, (3) the desire of an individual to be and stay a member of the organization (Daily, Bishop, and Govindarajulu, 2009; Mowday, Porter, & Steers, 1982). Findings of the literature support a positive link between organizational commitment and organization citizenship behavior (OCB) (Bishop, Scott, and Burroughs, 2000; Gregersen, 1993; Shore and Wayne, 1993). Moreover, another strong predictor of organizational commitment is the perceived social performance of the organization by the employees. The perceived perception of the organization's social performance is identified to be a better predictor of organizational commitment than financial performance (Daily, Bishop, and Govindarajulu, 2009). I propose that the perception of the social performance of the organization can be compared to the perceived reputation of the organizations which is displayed by the SOCCSR
Shen and Benson (2016) analyzed in their study the effect of socially responsible human resource management (SRHRM) on the works behaviors of employees. SRHRM describes the components of CSR directed at the workforce. SRHRM is not only providing safe and high-quality working conditions for the workforce, but it also consists of providing employees with training, sufficient payment, and rewarding socially desirable behavior. Their findings suggest that SHRM has a positive effect on task performance and OCB. This effect is mediated by the degree to which an employee identifies with the organization.
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describes the attitudes and behaviors of employees to acquire skills and knowledge necessary to act and use machines in an environmentally responsible way (Boiral, 2009). The SOCCSR measures the degree of an organization to create loyalty and trust with its workforce, and the number of training employees receive. Therefore, I argue that the SOCCSR consists in some degree out of at least three similar components as OCB with organizational loyalty equals workforce loyalty, organizational compliance equals workforce trust towards the organization, and self-development equals the amount of training the workforce receives.
In conclusion, a positive relationship can be made between OCB and eco-friendly behavior. This positive relationship is strengthened through employee training and organizational commitment. All these components are to some extent represented by the SOCCSR. For example, direct values used to create the SOCCSR are the number of training for employees, safety and conditions at the workplace and whether the organization was involved in scandals (Thompson Reuters, 2013). As already mentioned in the beginning, the SOCCSR value indicates the degree to which an employee perceives the reputation of the organization and if he trusts and is loyal towards the organization. Therefore, I suggest that a comparatively high values of SOCCSR stands for the fact that the employees show daily effort for the organization to achieve its environmental goals and adopt the ecological practices proposed by the organization. Leading to the following two hypotheses:
H1: Companies with a SOCCSR value above the sample mean and involved in M&A will
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H2: Companies with a SOCCSR value above the sample mean and involved in M&A will
on average experience higher improvements in energy efficiency than companies with a SOCCSR value below the sample mean.
Institutional Pressure
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environmental impact and therefore, improvements in environmental performance will improve the energy efficiency of companies. This should be the case because greenhouse gas (GHG) emissions are one component of energy efficiency.
Rivera (2004) investigated in his study the effect of institutional pressure on the adoption of voluntary environmental practices of hotels in Costa Rica. Depending on the location are hotels experiencing different kinds of regulations. Therefore, hotels located next to a national park are experiencing higher levels of institutional pressure than hotels located far away from national parks. His findings suggest that hotels experiencing higher degrees of institutional pressure were more likely to adopt a voluntary sustainable tourism certificate. Indicating that companies experiencing higher degrees of institutional pressure are more likely to act in an environmentally responsible way.
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In conclusion, companies facing high levels of institutional pressure are more likely to adopt environmental management practices that reduce the environmental impact of a company, are more likely to adopt environmental practices, and are more likely to have employees that act in an environmentally responsible way than companies facing low levels of institutional pressure. Therefore, after reviewing the before mentioned literature, I propose the following two hypotheses:
H3: Companies experiencing institutional pressure above the sample mean and involved in M&A will have on average a higher energy efficiency level than companies experiencing institutional pressure below the sample mean.
H4: Companies experiencing institutional pressure above the sample mean and involved in M&A will on average experience higher improvements in energy efficiency than companies experiencing institutional pressure below the sample mean.
METHODS
In this section methods of this study will be presented. It starts by elaborating on the procedure used to collect the data. Next, steps to create the sample will be elaborated followed by introducing the dependend and indepentend variables of this study. This section ends by explaining the selected analyses. All methods used are selected from previous research.
Data Collection
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than 6000 different companies during the period 2002-2018. ASSET4 works with a framework that compares and rates firms against more than 750 publicly available data points and is globally known to be one of the primary sources to provide information about CSR scores. The used CSR score in ASSET4 is based on more than 250 key performance indicators which are classified into the four main pillars environmental, governance, corporate and social. As already described in the literature review above, only the SOCCSR score will be considered in this paper. The SOCCSR consists in total out of six different categories namely (1) society and community, (2) customer and product responsibility, workforce- (3) diversity and opportunity, (4) employment quality, (5) health and safety, and (6) training and development. Calculation of a pillar score is done by the equal weight and z-score of all data points. The z-score is used to make the pillar score comparable with the average score of all companies included in the database (Thompson Reuters, 2013). The second database used in this study is Zephyr. Zephyr is a database consisting out of M&A deals and rumors worldwide. All deal information listed in Zephyr are provided in English and updated on an hourly basis.
Lastly, the ORBIS database provided by Bureau van Dijk was used to collect values for the variables operational cost, operational revenue, profit and total assets. Orbis is a commercial database providing data for over 130 million companies worldwide. These companies are located in more than 100 different countries, and data is provided starting by the year 2005. Financial data in Orbis is gathered from business registers by different Chambers of commerce (Kalemli-Ozcan, Sorensen, Villegas-Sanchez, Volosovych and Yesiltas, 2015).
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without complete CSR scores in this period, the sample size consisted of 3978 international companies. Second, by using the ISIN obtained from ASSET4 a list was created and uploaded to Zephyr. This process led to a sample of 2519 companies. By first collecting company information from ASSET4 and then matching them with Zephyr, the whole sample consists of companies involved in M&A and performing CSR activities. After filtering for acquiring companies and M&A deals with a known completed date, the sample consisted of 2074 companies.
Third, again ASSET4 was viewed to search for companies with SOCCSR scores in the period of 2010-2015. Out of the 2074 matched companies, 738 companies were left over with continuous SOCCSR scores in this period. Next, data was collected from ASSET4 for the variables total energy usage, total C02 emissions and the total number of employees. Due to unavailability of all necessary variables to measure energy efficiency in ASSET4 operating cost, operating revenue, total assets and profit were collected from Orbis. This step reduced the sample size to 313 companies. The last step of creating the final sample was to control for the percentage stake of the acquisition. Only M&As with at least a majority share of 50,1% were considered, to see significant changes after the M&A process. This last step reduced the sample size to 267 companies. Therefore, the final sample of this study consists out of 267 companies from 26 different countries which were later categorized into eleven industries.
Dependent Variable
Energy efficiency
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function. Instead, it develops a best practice cost frontier which is based on a convex combination of different firms in an industry (Charnes et al., 1994) and was first established by Farrel (1957). DEA measures the relative efficiency of so-called decision-making units (DMUs), in our case the organizations under investigation (Zhang and Kim, 2014). In general, the relative efficiency score is given as an interval (0,1). Whereas, a value of one represents the most efficient frontier and zero the most inefficient combination. Traditional measurements of energy efficiency are physical- and economic-thermodynamic as well as economic indicators (Moon and Min, 2014). These measurements represent a firm’s performance in comparing input values the amount of output created. One benefit of using DEA lies in the possibility to select multiple inputs and outputs. Due to the rising concerns of multiple institutional characters regarding the environmental impacts of industries, DEA also offers the opportunity to take undesirable outputs as GHG emissions into the analysis of energy efficiency. Since economic production processes are a combined multi-factor process, only using energy-related input factors would not display a realistic scenario. Therefore, energy-related input factors and non-energy related input factors as, e.g. labor and capital need to be considered (Zhang and Kim, 2014). In DEA, standard inputs chosen to measure energy efficiency are the total use of energy, capital, labor and emissions emerging from the production processes. Typical outputs chosen by many studies in the DEA analysis are on the macro level GDP and the micro level revenue or profit (Moon and Min, 2017).
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total number of employees all gathered from Asset4. Due to the availability of data operational cost2 was selected as the input of capital. As a replacement for the not availability of operational cost in the banking industry total capital was chosen as capital input in the DEA analysis for the banking industry. Lastly, to make comparisons between thirteen industries possible operating revenue was chosen as the output variable.
Independent Variables
SOCCSR
As already elaborated in the literature review, this study will focus on the SOCCSR provided in the ASSET4 database. Choosing the SOCCSR instead of the overall CSR score is reasoned by two arguments. Firstly, one component of the overall CSR score is SOCCSR. The SOCCSR consists of variables which are in our case used to measure energy efficiency. Therefore, high energy efficiency scores can be displayed to some degree into a high overall CSR value. Secondly, the theoretical link presented in the literature review is represented more precisely by the SOCCSR instead of the overall CSR score. The SOCCSR states CSR activities towards the workforce which in the end are responsible for acting and behaving in an environmentally responsible manner. The SOCCSR score is computed and provided annually (Thompson Reuters, 2013).
Institutional Pressure
This paper follows the protocol used by Surroca, Tribó, and Zahra (2013) to measure institutional pressure experienced by companies. In their paper, they referred to institutional pressure as institutional control. Institutional control refers to the regulatory system executed in
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the host country of the company and is measured by using in total three different indexes. The first index used to measure institutional pressure is the Economic Freedom index provided by the Fraser Institute (Gwartney, Hall, & Lawson, 2010). This index illustrates to what degree the labor market of the host country is protected by law. A scale ranging from 0 to 10 represents the degree of labor market regulation. As in Surroca, Tribó, and Zahra (2013) study, I reversed the scores on the scale so that low regulations equal 0 and high regulations equals a score of 10. The index of the World Bank’s Rule of Law is the second index used to measure institutional pressure. This index indicates the degree to which private property is protected by the laws of the county. A scale between 2.5 and -2.5 indicates the degree of protection; the former score equals high protection and the later poor protection. Finally, Government involvement in environmental problems is the last index to compute institutional pressure. As in the studies of Delmas and Montes-Sancho (2011) and Surroca, Tribó, and Zahra (2013) the World Resources Institute (2011) is used to collect data of the thirteen main international environmental treaties. After collecting the data, institutional pressure was composed through a principle component analysis. Stata was used to carry out the principle component analysis. Values of institutional pressure ranged between -2.19562 and 0.753829, in which former indicates low regulations and the later indicates high regulations experienced by companies.
Control variables Industry
The companies in the sample will be divided into different industries. Research suggests
that the type of industry can be one explanation for differences in environmental performance
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Code obtained from Zephyr will be used. This paper follows the classification proposed by
Waddock and Graves (1997) who categorized in his study companies into thirteen different
industries. Table 1 shows the classification of the industries.
Size
In this study results of the DEA analysis will be controlled for size by splitting the sample
into companies with employees above and below the sample mean and median. The same
procedure was done for the variable total assets. To control results of a DEA analysis for size is
in line with prior research measuring energy efficiency (Moon and Min, 2017). Typical variables
used to measure for size are total assets and number of employees (Hackston and Milne, 1996).
Analysis
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After finishing the DEA analysis and calculating the average energy efficiency scores for each industry at the three different timepoints, a non-parametric test was carried out to test for statistical significance of the energy efficiency values. This procedure is in line with prior research (Liu et al., 2015). The sign test was chosen to determine median differences between the two observations pre and after M&A. We chose to run a sign test instead of the Wilcoxon signed-rank test because one of the three assumptions necessary to carry out the Wilcoxon signed-rank test was violated. To run a Wilcoxon signed-rank test, it is necessary to see a symmetrical shape in the distribution between the differences in both observations (Altman, 1999). This assumption was not fulfilled for all the industries in our sample. To have consistent results a sign test was carried out for all thirteen industries and the control groups. SPSS was chosen to run the sign tests.
RESULTS
In this section results of the study are presented. This section starts by presenting the results of the DEA analysis. Next, results of the sign test are shown to test for the proposed hypotheses. In the end, I test my results for robustness.
DEA Analysis
Table 1 represents the average energy efficiency scores for each of the thirteen constructed
industries (Waddock and Graves, 1997) and the difference between post and pre-merger
efficiency scores. The difference in energy efficiency scores is calculated by subtracting the post
efficiency scores by the pre-efficiency scores. In the following, I will only name the first listed
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containers, computers, wholesale, hotel and hospital industries improved their energy efficiency
level after M&A. Whereas, the food, refining, transportation, telephone and bank industry
experienced decreasing level of energy efficiency after the M&A. In total, eight of the thirteen
industries improved their energy efficiency level after M&A. The most significant improvements
in the energy efficiency level can be seen in in the chemicals industry followed by the wholesale
and mining industry. The most energy efficient industry is the transportation industry with an
overall efficiency level over 0,9. With a score below 0,2 is the container group the least efficient
industry.
Table 1 DEA average energy efficiency scores for each industry
Industry N T-1 T1 T1 - T-1 Median
difference p-value Z-value negative positive tie Mining, Construction 31 0.4177 0.493 0.0753 0.09753 0.001 3.213*** 5 23 3 Food, Textiles. Apparel 19 0.658 0.6153 -0.0427 -0.02021 0.035 -2.066** 12 3 4 Forest Products, Paper,
Publishing 10 0.6508 0.6629 0.0121 0.00153 0.727 0.354 3 5 4 Chemicals. Pharmaceuticals 30 0.5979 0.6819 0.084 0.09718 0.004 2.8*** 5 20 5 Refining, Rubber, Plastic 7 0.6696 0.6675 -0.0021 0 1 0 2 3 2 Containers, Steel, Heavy
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Non-Parametric Sign Test to test Hypothesis
Table 2 represents the energy efficiency scores for the SOCCSR and institutional pressure groups in combination with the results of the sign test. In the right columns, the number of companies per measurement is given which either increase, remain the same or decrease in
energy efficiency. In addition to the already presented industries in Table1, I constructed more
groups to test the hypothesis. First, the all-industry group was created including all companies of
our sample with their pre and post efficiency scores. From there we constructed new groups
comparing the efficiency scores at T-1 and T1 for companies with SOCCSR scores and
institutional pressure above the median and below. Moreover, I also did the same for companies
above and below the mean, to be able to make precise statements later in the discussion and
conclusion section. Worth noticing, the institutional sample group is the only group in which
calculating the mean and the median and splitting the companies into two groups did not make
any changes in the final group. Therefore, I only ran the sign test for below and after the mean
which equals the above and below the median group. This study carried out the sign test with a
significance level of α = 0,1; therefore, the null hypothesis is rejected if p-value ≤ 0.1.
In total, 267 acquiring companies were analyzed to understand the effects of M&A on the
energy efficiency level of companies. Of the 267 companies in our sample, a total of 154
experienced improvements in energy efficiency after the M&A. Although, 63 companies
experienced decreasing level of efficiency in comparison to the efficiency level before M&A and
50 companies did not experience any changes in their energy efficiency level. To compare for
differences in T1 and T-1 a sign test was carried out. Companies after M&A experienced a
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7.365, p = 0.005. These findings give support for the baseline hypothesis that on average
companies experience improvements in energy efficiency level after M&A.
To test the moderating role of the SOCCSR on the energy efficiency level, the whole
sample was divided into companies with CSR above the mean and below the median. In
comparison to the low SOCCSR group did the high SOCCSR group experience a higher significant change in median 0.01953 ≥ 0.00717 and in mean 0.0326 ≥ 0.0303. Moreover, in both groups, a total of 77 companies improved their energy efficiency level. Even more striking
is the differences between the SOCCSR above the mean and below the mean group. The above
SOCCSR mean group experiences a significantly higher change in median difference than the
below the mean group with 0.01734 ≥ 0.0307 and a change in mean of 0.0372 ≥ 0.017. Showing
evidence for the hypothesis two that companies with SOCCSR scores over the mean experience
higher improvements in energy efficiency than companies below the mean.
To test for hypothesis three and four, the sample was divided into companies with
institutional pressure above the mean and below the mean. Companies experiencing institutional
pressure above the mean have in comparison to the institutional pressure group below the mean a
significant median difference of 0.0132 ≥ 0.0123 and a difference in mean of 0.0341 ≥ 0.0291. In
both groups the amount of companies which decline in energy efficiency is almost equal, only
the amount of improving companies and ties differs slightly. These results show support for the
third hypothesis. Companies experiencing institutional pressure above the mean experienced
higher improvements in their energy efficiency level than companies with institutional pressure
below the mean. Moreover, no support is found for hypothesis one and three. Institutional
pressure, as well as the SOCCSR value, are not working as an indicator of the energy efficiency
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energy efficiency scores before the M&A and after the M&A than the above the median and
mean groups. Therefore, hypothesis one and three are rejected.
Table 2 Descriptive statistics
Industry N T-1 T1 T1 - T-1 Median
difference p-value Z-value negative positive tie All Industries 267 0.551 0.5826 0.0316 0.0132 0.0005 7.365*** 63 154 50 SOCCSR below Median 134 0.5398 0.5701 0.0303 0.00717 0.0005 4.565*** 29 77 28 SOCCSR above Median 134 0.5656 0.5982 0.0326 0.01953 0.0005 3.986*** 34 77 23 SOCCSR below average 75 0.5789 0.5959 0.017 0.00307 0.005 2.806*** 17 39 19 SOCCSR above average 192 0.5401 0.5773 0.0372 0.01734 0.0005 5.359*** 46 115 31 Below institutional pressure 134 0.5771 0.6062 0.0291 0.01229 0.0005 4.099*** 31 74 29 Above institutional pressure 133 0.5247 0.5588 0.0341 0.0132 0.0005 4.441*** 32 80 21 Note: ***, **, * represents 1%, 5%, 10% significance level.
Robustness Test
To check the robustness of my results, I test the moderating variables for size and profitability. Therefore, I divided each constructed group from Table 2 again into groups with size and profitability above and below the median, resulting in the groups presented in Table 3. Table 3 represents the results of the robustness check. First, independent of the combination are all constructed groups experiencing on average improvements in energy efficiency after the M&A process. Although, the groups small in size and low in profitability with a SOCCSR score below the average show insignificant changes in median difference.
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for companies with profit below the median and profit above the median. For instance, companies with low institutional pressure and low profitability are experiencing almost the same significant improvement in mean difference as the group with high institutional pressure and high profitability 0.0420 ≤ 0.0422. Still, the high profitability group experiences higher improvements in median difference than the low profitability group 0.0139 ≤ 0.0218. These results indicate a positive influence of size for the energy improvements experienced after M&A. A similar influence of profit is not supported.
Table 3 Testing robustness for size and profit
Industry N T-1 T1 T1 - T-1 Median
difference p- value Z-value negative positive tie SOCCSR LOW – Size low 38 0.6264 0.6366 0.0102 0.0029 0.137 1.486 10 18 9 SOCCSR LOW – Size high 38 0.5201 0.5443 0.0243 0.0038 0.014 2.457** 7 21 9 SOCCSR HIGH – Size low 96 0.5223 0.5559 0.0336 0.0149 0.003 2.946*** 28 56 12 SOCCSR HIGH – Size high 96 0.5579 0.5988 0.0409 0.0195 0.0005 4.558*** 18 59 19 IP LOW – Size low 67 0.5776 0.5969 0.0193 0.0086 0.013 2.472** 17 36 14 IP LOW – Size high 67 0.5767 0.6155 0.0388 0.0167 0.001 3.190*** 14 38 15 IP HIGH – Size low 67 0.5571 0.5862 0.0291 0.0062 0.031 2.157*** 19 36 12 IP HIGH – Size high 67 0.4965 0.5359 0.0393 0.0244 0.0005 3.875*** 13 43 10 SOCCSR LOW – Profit low 31 0.5627 0.6248 0.0621 0.0339 0.015 2.400** 6 19 6 SOCCSR LOW – Profit high 31 0.6014 0.6123 0.0108 0 0.115 1.565 6 14 11 SOCCSR HIGH – Profit low 87 0.5443 0.5717 0.0275 0.0139 0.004 2.906*** 24 50 13 SOCCSR HIGH – Profit high 87 0.5667 0.6174 0.0508 0.0179 0.0005 3.705*** 19 51 17 IP LOW – Profit low 59 0.5898 0.6318 0.0420 0.0139 0.005 2.801*** 13 33 13 IP LOW – Profit high 59 0.5813 0.6221 0.0408 0.0003 0.025 2.261** 14 30 15 IP HIGH – Profit low 59 0.5132 0.5446 0.0313 0.0112 0.007 2.687*** 15 35 9 IP HIGH – Profit high 59 0.5831 0.6254 0.0422 0.0218 0.002 3.031*** 13 35 11 Note: ***, **, * represents 1%, 5%, 10% significance level.
DISCUSSION
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The introduction of low-carbon M&A is therefore still brightly unknown and leaves a lot of opportunities open for academia and business. On the one hand, offers it scholars opportunities to discover yet unknown relationships. On the other hand, creates it new opportunities for business, to view M&A as a strategic tool to decrease GHG emission and to develop synergies. This paper aimed to extend the low-carbon M&A literature stream in identifying moderating roles influencing the effect of M&A on the energy efficiency level of companies. Therefore, the impacts of the SOCCSR and institutional pressure on the energy efficiency level of companies after M&A were analyzed. To identify the influence of the SOCCSR and institutional pressure I carried out a DEA analysis with a sample of 267 companies executing CSR activities. To test for the hypothesis a non-parametric sign test was run to identify changes in medians and means between the two observed periods before M&A and after M&A. In the end, I tested my results for robustness.
28
analysis. Therefore, it is more likely that even more than the 154 companies experienced energy improvements after M&A.
One question that remains is why are there 63 company with decreasing level of energy efficiency? An explanation for this outcome may be that highly efficient companies merge with non-efficient companies. In such a case, the energy efficiency level of the target company can lead to a reduction of the efficiency level of the acquiring company. Furthermore, the process of improving the efficiency level of the acquired target firm could take more time than just one year after the completion of the M&A. By finding support for the baseline hypothesis, the results of Tian, Yan, and Peng (2017) are validated.
Second, results of the sign test give support for the second and fourth hypothesis. Companies possessing SOCCSR scores over the mean experienced higher improvements in energy efficiency than companies with low values of SOCCSR. This finding indicates how important the roles of employees are for a successful M&A process and for the transition of companies to be more sustainable. Moreover, companies experiencing institutional pressure over the mean improved their energy efficiency level more than companies experiencing low levels of institutional pressure. Indicating that high regulations in the host country are influencing the transition to a more sustainable business environment.
Third, results of this thesis give no support for hypothesis one and three. Neither institutional pressure nor SOCCSR provide indications about the overall energy efficiency level. Therefore, the overall efficiency is influenced by other factors than the SOCCSR or institutional pressure.
29
efficiency more than small companies. This is the case for companies with values of SOCCSR and institutional pressure above and below the mean. This finding is in line with prior research in the field of energy efficiency (Moon and Min, 2017). Results of Moon and Min’s study showed that larger firms are more flexible to manage capital and labor than smaller firms and have, therefore, more capabilities to improve their energy efficiency. Nevertheless, future research needs to examine this relationship more closely. An influence of profitability on the development of energy efficiency after M&A could not be found. Profitable firms, as well as firms low in profit, improved their energy efficiency. Furthermore, size and profitability do not work as a predictor for the overall energy efficiency level.
Findings of this study have both practical and theoretical implications; I will elaborate on these implications in the following sections. The next section starts with the theoretical and practical implications of this study, followed by the limitations and suggestions for future research. I will end this thesis by drawing a conclusion.
Theoretical Implications
Findings of this study have theoretical implications for the low-carbon literature stream. First,
this study transferred the efficiency improvements of companies in China engaging in M&A
discovered by Tian, Yan, and Peng (2017) to a sample of companies from sixteen different
countries located in Australia, Europe, North America, Asia, and Africa. They argued that
companies in China engage in M&A to decrease their GHG emissions due to the newly emerged
ecological civilization education with the goal to make China greener. Since global warming is a
30
improvements after M&A is not only an effect only experienced by Chinese companies instead it
is an effect experienced by companies worldwide.
Second, this study extended the CSR literature in first decomposing CSR into the social
pillar provided by Thomson Reuters (2013) and then applying it to the low-carbon M&A
literature. To my knowledge is this the first study connecting the social dimension of CSR to
environmentally responsible behavior of employees. I argued that the SOCCSR represents the
degree to which employees identify and commit to the organization and how they perceive the
reputation of the organization. Moreover, the SOCCSR also represents the number of training
employees perceive. Training for employees is connected to the actual implementation of
environmentally responsible practices (Sarkis, Gonzales and Adenso-Diaz, 2010). By using the
decomposition of OCB proposed by Organ et al. (2005) I further argued that the SOCCSR is at
least related to three of the six pillars of OCB. Since, OCB, the amount of training employees
receives and commitment to the organization is theoretical linked with environmental friendly
behavior (Boiral, 2009; Sarkis, Gonzales and Adenso-Diaz 2010), I came to the hypothesis that
high values of the SOCCSR should lead to environmental responsibility behavior and therefore,
to higher improvements in energy efficiency after M&A. Findings of this thesis give support for
this assumption. Companies with a SOCCSR score over the median as well as a SOCCSR score
over the average are experiencing higher improvements in energy efficiency than companies
with low SOCCSR scores. Especially companies with SOCCSR score over the average
experienced significant higher differences in median and mean than the low SOCCSR group.
These findings give support for the positive relationship between the SOCCSR and
31
Third, I connected institutional theory to the low-carbon M&A literature. I argued that
high regulations in the host country lead to the adoption of environmental management practices
by organizations (Aragón-Correa, 1998; Christmann, 2000; Dean and Brown, 1995; Delmas,
2003; Hart, 1995; Nehrt, 1996; Nehrt, 1998; Russo and Fouts, 1997; Sharma and Vredenburg,
1998). These high regulations should be displayed in high values of institutional pressure. To
measure institutional pressure, we adopted the procedure used by Surroca, Tribó, and Zahra
(2013). Results of the DEA analysis and sign test support this assumption. Companies
experiencing high values of institutional pressure, experienced on average higher improvements
in energy efficiency than companies experiencing low values of institutional pressure.
Practical Implications
Findings of this study have important implications for practitioners. Managers involved
in M&As can use insights of this study to make more precise decision in choosing target firms
for the M&A process. First, M&A can work as a strategic tool for companies to reduce GHG
emissions while gaining other synergetic effects due to the M&A. In selecting companies with
high SOCCSR values and companies experiencing high degrees of institutional pressure, can
managers increase the chances of energy efficiency improvements after M&A. In addition to
that, findings of this study can act as a new incentive for companies to execute CSR initiatives,
especially those directed at the workforce reflected in the SOCCSR. This study showed that CSR
activities towards the workforce are connected to employees acting in an environmentally
responsible way. Therefore, companies can experience more positive effects than just increasing
32
involved in the M&A process play a major role to experience efficiency improvements. In the
end are employees the ones that carry out the daily tasks of the organization.
Limitations and Future Research
First, for the used database ASSET4 in our study, no complete access was available. Therefore, many companies have dropped out in the production of the sample with only having access to the overall scores and not the required micro variables to measure energy efficiency. Another limitation lies in the creation of industries by using the industry classification by Waddock and Graves (1997). A DEA analysis is most effective when companies with identical production processes are analyzed against each other. This comparison is, of course, most precisely given if only companies with the identical four-digit SIC number are analyzed. This limitation is also a chance for later studies. By creating samples of companies with almost identical production processes, the effects of M&A on improvements in energy efficiency and the moderating roles of SOCCSR and intuitional pressure can be researched more in detail.
Another possibility for later research and at the same time a limitation of this study is to discover the relationship between the SOCCSR, institutional pressure, profit and size measured by total assets. By clarifying this relationship, more precise decisions can be made by managers to determine the correct target company and to accomplish the goal of experiencing efficiency improvements. Moreover, clarifying these relationships will help validating the findings of this thesis.
33
scale a BCC model can be used that allows variable returns to scale. A comparison of the results between both models would be of interest. Future research has to examine this comparison.
34
CONCLUSION
35
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