20160629 Final version Master Thesis Kees van Rengs 939173
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THE EFFECT OF CORPORATE SOCIAL PERFORMANCE ON AN ORGANIZATION’S FINANCIAL PERFORMANCE A geographical approach AUTHOR KEES VAN RENGS SUPERVISOR JAMES SMALL DATE 29/06/2016
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Transcript of 20160629 Final version Master Thesis Kees van Rengs 939173
THE EFFECT OF CORPORATE SOCIAL
PERFORMANCE ON AN ORGANIZATION’S
FINANCIAL PERFORMANCE
A geographical approach
AUTHOR KEES VAN RENGS
SUPERVISOR JAMES SMALL
DATE 29/06/2016
1
DOES A HIGHER CORPORATE SOCIAL
PERFORMANCE LEAD TO A BETTER
FINANCIAL PERFORMANCE?
A geographical approach
DOES A HIGHER
Master Thesis Strategic Management
Tilburg University
Tilburg School of Economics and Management
Department of Organization and Strategy
A study on sustainability
Author Kees van Rengs
ANR 939173
Supervisor Dr. J.S. Small
Second reader M.J. Iwanczuk-Prost
Date 29/06/2016 Word count 13982
2
Management Summary
Corporate Social Responsibility (CSR) has gained attention in academic literature as well as in
corporate life. In order to measure an organization’s CSR outcome, literature defined Corporate Social
Performance (CSP). The CSP arises from an organization’s environmental performance, social
performance and corporate governance performance. In addition, considerable research on the
relationship between CSP and Financial Performance (FP) has been carried out. However, results on
this relationship are scattered. While some studies found a reverse or U-shaped relationship, more
studies have shown a positive effect of CSP on an organization’s FP. Moreover, research argues
longitudinal data is needed since organizations which have a high level of CSP outperform their
counterparts in the long run. Besides, most studies did not use multiple measures for financial
performance. Due to the conflicting results, this research revisits the problem and tests the following
main hypothesis:
“The level of CSP of an organization will have a positive effect on its financial performance.”
Other studies show significant variation in the level of CSP across organizations, industries and
countries. Results indicate differences in the environmental dimension across European countries. This
study tests a possible moderating effect of the European region on the CSP-FP link. In addition, two
other possible moderators in this research are the business industry in which the organization is active:
B2B vs. B2C and governance structure in the form of share ownership.
A total of four hypotheses are formulated, the main hypothesis and three including the different
moderators. The hypotheses are tested using OLS regression analysis and Panel Data analysis. This
empirical research uses quantitative data from the ASSET4 dataset. Finally, the dataset comprises 730
organizations from 16 different European countries. Both analyses methods use the following variables:
dependent variable (financial performance) measured by Return On Assets (ROA), Operating Profit
Margin (OPM) and Net Sales. Independent variables (level of CSP) measured by overall CSP score,
environmental performance, social performance and corporate governance performance. The three
dummy variables with a possible moderating effect and the control variables (firm size) measured by
Total Assets and Total Employees.
The results argue the effects of CSP on an organization’s FP are positive. However, these results
seem highly dependent on which financial performance measure is used. Still, the results show northern
and central European countries have a positive moderating effect on the CSP-FP. In addition, a negative
moderating effect is found for southern European countries. For the business types B2B vs. B2C no
consistent significant results are found. On the other hand, the governance structure in the form of share
ownership resulted in a positive moderating effect on the CSP-FP link.
3
Different tools for measuring an organization’s financial performance lead to contrasting results.
This research provides novel information for future research and suggests to look for a deeper
understanding of how different financial performance measures are influenced.
Central and northern European countries have a positive effect on the CSP-FP link, while
southern European countries have a negative effect. This contributes to literature and suggests research
should look for further, country level, explanations. In addition, this study uses a dataset which is
sufficient to research the effect for each individual European country and to study the differences in
comparison to other continents. However, due to time limits and the scope of the thesis this is left out.
When management wants to expand in Europe or other European countries, they should first
consider central or northern European countries. This study also suggests they should focus on the B2C
industry and issues shares with ownership, because this benefits their CSP-FP link.
4
Preface
Research has been done to investigate the link between CSP and FP and whether this link is
influenced by the different European regions, business industry and share ownership structure. I have
chosen these subjects, because I believe in order to change to world for the better we need to become
sustainable in every way possible. Some organizations act sustainable, because they really want to make
a change and believe in the benefits for our people and planet. Others are only in it for the profits. I am
very interested in the effects of investing in sustainability and CSR initiatives and wanted to research
the effects on financial performance, because if it has a positive effect on the FP there is no reason not
to invest in CSP. I have chosen to specify this study for European countries, because I believe we have
the resources to lead by example and set a new, more vital world, in motion. I wanted to research which
regions performed best so we can learn from those region or countries and become better as a whole.
After I graduated for my Bachelor degree in Business Administration at Tilburg University, I
started studying Strategic Management, also at Tilburg University. Since, I had chosen the minor
Entrepreneurship during my bachelor study, I consciously chose the Entrepreneurship Track for my
Master year. At the moment, I am starting my own business together with, fellow student, Niek Franken.
Thus, I wanted to develop a more profound knowledge about Entrepreneurship. One of the courses was
sustainable entrepreneurship, given by James Small, whom I already knew from one of the minor
courses during my bachelor. I immediately realized I wanted to write my Master thesis on a subject
which matched on of his courses. My interests for sustainability and Entrepreneurship lead me to this
research.
Since I already had an interesting part-time job and I am working on my own business I did not
want to do a qualitative study at a company. In addition, I like to work with programs like SPSS and
Stata so performing a quantitative study was clearly the best option. Afterwards it appeared to be quite
a challenge to get all the needed data from Stata and interpret it the right way. I have learned a lot during
the process.
This thesis would not have been a success without the help of others. A big word of appreciation
goes to my girlfriend for being there for me and let me spend hours and hours in my ‘thesis dome’. I
want to thank Niek Franken, my friend and fellow student during my four years at Tilburg University.
He was a valuable sparring partner, mental coach and especially companion who knew all the ups and
downs during the process. I also want to thank Rick van den Bergh who helped me with Stata.
A last word of appreciation is for my supervisor James Small. I am very thankful he was my
mentor, coach and guided me during my thesis. With a lot of gratefulness I look back at each session in
which he provided valuable comments, recommendations, grammar advice and time. Gratitude goes out
to the second reader, M.J. Iwanczuk-Prost, as well.
Kees van Rengs Tilburg, 30 June 2016
5
Table of Contents
Management Summary ............................................................................................................................ 2
Preface ..................................................................................................................................................... 4
Table of Contents .................................................................................................................................... 5
Chapter 1 Introduction ............................................................................................................................. 7
1.1 Problem Indication ........................................................................................................................ 7
1.2 Problem Statement ........................................................................................................................ 9
1.3 Research Questions ....................................................................................................................... 9
1.4 Research Structure ........................................................................................................................ 9
Chapter 2 The CSP-FP Link .................................................................................................................. 10
2.1 Corporate Social Responsibility .................................................................................................. 10
2.2 Corporate Social Performance .................................................................................................... 12
2.3 Relationship between CSP and Financial Performance .............................................................. 13
2.4 National Influences and Business Types ..................................................................................... 17
Chapter 3 Research methodology .......................................................................................................... 21
3.1 Background ................................................................................................................................. 21
3.2 Research Design .......................................................................................................................... 21
3.3 Data Collection ........................................................................................................................... 21
3.3.1 Sampling Strategy ................................................................................................................ 22
3.3.2 Dependent Variable ............................................................................................................. 24
3.3.3 Independent Variable ........................................................................................................... 24
3.3.4 Control Variables ................................................................................................................. 24
3.3.5 Moderators ........................................................................................................................... 25
3.4 Data Analysis .............................................................................................................................. 25
3.4.1 Dealing with Outliers ........................................................................................................... 26
3.4.2 Validity ................................................................................................................................ 26
3.4.3 Reliability ............................................................................................................................ 27
Chapter 4 Findings and Results ............................................................................................................. 28
4.1 The CSP-FP link ......................................................................................................................... 29
6
4.2 A geographical approach ............................................................................................................ 31
4.3 Business-to-Business vs. Business-to-Consumers ...................................................................... 32
4.4 Governance Structure in the Form of Share Ownership ............................................................. 33
4.5 R-Squared ................................................................................................................................... 34
4.3 Reverse Causality Analysis ......................................................................................................... 39
4.4 Overview ..................................................................................................................................... 39
Chapter 5 Conclusion and Discussion ................................................................................................... 41
5.1 Conclusion .................................................................................................................................. 41
5.2 Discussion ................................................................................................................................... 41
5.3 Contribution ................................................................................................................................ 43
5.3 Limitations .................................................................................................................................. 44
References ............................................................................................................................................. 46
Appendices ............................................................................................................................................ 53
Appendix 1: European Regions ........................................................................................................ 53
Appendix 2: ASSET4 Dataset ........................................................................................................... 54
Appendix 3: Skewness test ............................................................................................................... 56
Appendix 4: Hausman Tests ............................................................................................................. 56
Appendix 5: Abbreviations ............................................................................................................... 57
7
Chapter 1 Introduction
1.1 Problem Indication Recently, Corporate Social Responsibility (CSR) is gaining attention in academic literature as
well as in the business world (Ioannou & Serafeim, 2012). Ioannou and Serafeim (2012) stated an
Accenture CEO study showed ninety-three percent (93%) of 766 participant CEOs worldwide
considered sustainability as an important factor for their organizations’ success. Eighty-one percent
(81%) of these organizations stated sustainability is entirely embedded in their organizations’ strategy
and operations. Also the weight organizations have put on their CSR activities fundamentally shifted.
Organizations adapted their view on firm relationships, institutional environment in which they operate,
and their various stakeholders (Ioannou & Serafeim, 2012).
Another widely used term, in the research field on CSR, is Corporate Social Performance (CSP).
Wood (1991) has defined CSP as the outcome of social performance of an organization involved in CSR
activities. Many recent studies have used CSP ratings and rakings in order to measure an organizations’
CSR (Sen & Bhattacharya, 2001; Ioannou & Serafeim, 2012; Rock, 2003). Ioannou & Serafeim (2012)
note CSP rakings and ratings show a significant variation in CSP across organizations and industries
and also throughout countries (Ioannou & Serafeim, 2012).
Considerable research on the relationship between CSP and financial performance has been
carried out (Brammer & Millington, 2008). However, results on this relationship are scattered (Brammer
& Millington, 2008). According to Eccles, Ioannou and Serafeim (2014) the social performance of an
organization is synergistic to economic performance. Hull and Rothenberg (2008) state the relationship
of CSP on financial performance is everything but straight forward. Moreover, they found CSP most
strongly affects performance in low-innovation firms and in industries with little differentiation.
Besides, Brammer and Millington (2008) showed a U-shaped relationship between CSP and financial
performance. They stated firms with a low CSP rate have a higher financial performance short-term
while firms with a high CSP rate perform financially better long-term. In addition, Brammer and
Millington (2008) mentioned the link between CSP and financial performance should be considered as
being contingent on several contextual factors: an organization’s size, its industry environment, and the
proximity between these, in relation to the social initiatives set in motion. Margolis and Walsh (2003)
suggest CSP has been difficult to measure, and the right variables, which can be used in an accessible
model, prove to be a major challenge. At the same time, Desender and Epure (2015) argued incentives
and pressure to engage in CSR are likely to be context dependent, which may help to explain the
variation across firms with different governance mechanisms and also across the institutional designs in
which firms are embedded.
Kang (2013) stated CSP serves as a complementary measure of firm performance, especially as
a predictor of viability and long-term performance. Further, Brammer and Millington (2008) conclude
8
benefits to the financial performance accrue only over the long-run. According to their study, multiple
researchers found evidence suggesting significant longitudinal aspects to the relationship between CSP
and corporate financial performance. This could have caused inconclusive results in current cross-
sectional studies or a further decrease into the relationship. In order to verify their conclusion
longitudinal data is needed. Longitudinal studies have the ability to show patterns of a variable over
time. This way one can research the cause-and-effect relationship of CSP on financial performance.
Further, Eccles, Ioannou and Serafeim (2014) suggest high sustainability companies significantly
outperform their counterparts over the long-term, both in terms of stock market and accounting
performance. The theory leaves room for further research on the longitudinal relationship between CSP
and financial performance.
Maignan and Ralston (2002) showed businesses in France, The Netherlands, the U.K. and the
U.S. did not display the same eagerness to appear as socially responsible and employ diverse means to
convey social responsibility images. This is supported by Ioannou and Serafeim (2012) who stated CSP
ratings have revealed significant variation in CSP across firms, industries and countries. Therefore, the
geographical region could be an important explanatory factor in the relationship between CSP and
financial performance. Quéré, Nouyrigat and Baker (2015) also made the distinction between countries,
they conducted research in the European context. Their research made no distinction between Northern
and Southern European countries. Because the institutional designs of those countries differ (Desender
& Epure, 2015) and the Vigeo database is used (Quéré et al., 2015), novel research in this field can be
conducted.
Brammer and Millington (2008) suggested the industry life cycle as a possible moderator.
According to their research it may play a contingency role in the link between CSP and financial
performance. Therefore, a firm’s age could also moderate the relationship between CSP and financial
performance.
A possible gap is mentioned by Servaes and Tamayo (2013) who found CSR and firm value are
positively related for firms with high customer awareness. On the other hand, Alshammari (2015)
suggests firms could best benefit from CSR activities when they have a decent reputation among major
stakeholders. A business-to-business (B2B) environment has a more complex stakeholder environment
which could mean it is more difficult to obtain a good reputation (Han & Childs, 2016). This suggests a
less positive effect of CSP in the B2B market. Therefore, it is interesting to research if B2B vs. business-
to-consumer (B2C) businesses have a moderating effect on the relationship between CSP and financial
performance.
Alshammari (2015) also suggested a moderating effect of the corporate ownership structure on
the influence of CSR activities on firm performance, leaving room for additional research on the
moderating effect of ownership structure on the relationship. Thus, ownership structure is another
moderator in this research.
9
This thesis explains the relationship between CSP and financial performance. In addition, the
moderating effect of geographical region, B2B vs B2C and ownership structure on this relationship is
studied. As a result, novel theory is originated.
1.2 Problem Statement “What is the influence of an organizations’ Corporate Social Performance on their financial
performance?”
Moderated by: geographical region, business type, and share ownership structure.
1.3 Research Questions • What is Corporate Social Performance?
• What is the relationship between CSP and financial performance, as explained in the
extended literature?
• How are geographical region, B2B vs. B2C, ownership structure and a firms’ age linked
with this relationship?
• What is the relationship between CSP and financial performance in my dataset?
• What moderating effect do geographical region, B2B vs. B2C and ownership structure have
on the relationship between CSP and financial performance?
1.4 Research Structure This research has the following structure. Chapter 2 forms the theoretical framework and hypotheses
development of this study. Subsequently, Chapter 3 describes the methodology and explains the
database which has been used. Next, Chapter 4 presents the results and gives an overview at the end of
the chapter. Thereafter, in Chapter 5 the conclusions are defined based on the major findings after which
a discussion argues the possible mechanisms behind the results.
Financial performance
(FP)
Corporate Social
Performance (CSP)
Share Ownership structure
Geograpical region
Business type
10
Chapter 2 The CSP-FP Link
2.1 Corporate Social Responsibility According to Aksak, Ferguson and Duman (2016) there are many different ways to approach
Corporate Social Responsibility, from here on CSR, and it largely depends on the context, era and
culture. Snider, Hill and Martin (2003) argue CSR implies a moral obligation which organizations have
to our civilization. CSR is the extent to which organizations act ethically outside the limits of legal
requirements and obligations to stakeholders (Snider et al. 2003). In the study of Lichtenstein,
Drumwright and Braig (2004) CSR is recognized as an important tool for organizations to determine the
part they want to play in society and add ethical and social principles to their businesses.
According to Lindgreen and Swaen (2010) the focus of research on CSR has shifted from
ethical-oriented arguments to performance-oriented managerial studies. Research by Flammer (2013)
states CSR was formerly about “social” responsibility like treating employees fairly and ethically. A
current development is the increased importance of “environmental” responsibility, like for example the
reduction of CO2 emissions (Flammer, 2013). She concludes organizations need to focus on a triple
bottom line: People, Planet, and Profit. This way shareholder value is much higher than when a single
bottom line is pursued, for example only profit. This is also what Lindgreen and Swaen (2010)
concluded. This “environmental” responsibility is measured by a company’s environmental or carbon
footprint (Matsumara, Prakash & Vera-Munoz, 2014) which affects the shareholder value.
Flammer (2013) distinguishes two forms of environmental CSR, external norms and internal
levels. When an organization is externally driven to set up environmental CSR it means there is external
pressure to becoming ‘green’. This pressure has increased over the past decade. Some examples are
environmental regulations, customer awareness of environmental concerns and media attention
(Flammer, 2013).
Assuming becoming green is standardized, CSR can have a negative influence on an
organization’s value when they do not meet this norm. Moreover, Flammer (2013) concludes positive
effects of becoming green on stock-market reaction have become less positive, while the negative effects
of being an environmental unfriendly organization have become more negative. Secondly, internal
levels of environmental CSR are a resource which result in a decrease in marginal returns. This implies,
pursuing in-house CSR results in minimal profits. Flammer (2013) concludes organizations who
perform well on environmental CSR encounter a smaller stock-price increase when green initiatives are
announced and a smaller decrease when damaging behavior is issued.
Carroll (1991) argues CSR includes expectations society has of an organization. Carroll (1991)
suggests CSR can be divided into four dimension: economic, legal, ethical and philanthropic social
responsibilities, as shown in Figure 1:
11
Figure 1: The Pyramid of Corporate Social Responsibility (Carroll, 1991)
This is largely supported by Garriga and Melé (2004) who divide CSR into four theories; as follows:
1. Instrumental Theories – achieving economic results
2. Political Theories – a responsible use, in a political area, of the power of corporations in
society
3. Integrative Theories – satisfying social demands
4. Ethical Theories – based on ethical responsibilities of corporations to society.
According to Garriga and Melé (2004) each of these theories offer four dimensions linked with profits,
political performance, social demands and ethical values. In contrast, to Carroll (1991) they name
“philanthropic responsibility,” “social demands”.
Dahlsrud (2006) argues the existing definitions of CSR are largely harmonious. The challenge
is how CSR is socially constructed. Therefore, Dahlsrud (2006) suggests a slightly different approach.
Dahlsrud (2006) defines five dimensions as shown in Table 1. Dahlsrud (2006) combines the ethical and
legal dimension and calls it the voluntariness dimension. Furthermore, he adds the stakeholder and
environmental dimension. As stated earlier, environmental responsibility has gained importance and is
now seen as a vital measurement tool for CSR, (Flammer, 2013; Matsumara, Prakash & Vera-Munoz,
2014) making the environmental dimension essential for understanding CSR.
PHILANTHROPIC Responsibilities
Be a good corporate citizen. Contribute resources to the
community, improve quality of life.
ETHICAL Responsibilities
Be ethicalObligation to do what is right, just,
and fair. Avoid harm.
LEGAL Responsibilities
Obey the lawLaw is society's codification of right and wrong. Play by the rules of the
game.
ECONOMIC Responsibilities
Be profitable.The foundation upon which all others rest.
12
Dimensions The definition is coded to the
dimension if it refers to
Example phrases
The environmental
dimension
The natural environment ‘a cleaner environment’
‘environmental stewardship’
The social dimension The relationship between business and
society
‘contribute to a better society’
‘integrate social concerns in their business operations’
‘consider the full scope of their impact on communities’
The economic
dimension
Socio-economic or financial aspects,
including describing CSR in terms of a
business operation
‘contribute to economic development’
‘persevering the profitability’
‘business operations’
The stakeholder
dimension
Stakeholder or stakeholder groups ‘interaction with their stakeholders’
‘how organizations interact with their employees,
suppliers, customers and communities’
‘treating the stakeholders of the firm’
The voluntariness
dimension
Actions not prescribed by law ‘based on ethical values’
‘beyond legal obligations’
‘voluntary’
Table 1: The five dimensions, how the coding scheme was applied and example phrases (Dahlsrud, 2006)
CSR was formally about “social” and “ethical” responsibilities, but recently shifted to a more
“environmental” responsible approach. Some researchers argue current studies on CSR are more
focused on performance outcomes. The triple bottom line of Flammer (2013) gives a more
comprehensive approach. He argues CSR is about the balance between People, Planet and Profit. Doing
business in a “green” way is becoming the standard. Companies who not pursue CSR activities can face
negative consequences. The environmental dimension, Planet, is seen as the most vital tool for CSR and
essential for understanding CSR.
2.2 Corporate Social Performance Carrol, Prima and Richter (2016) argue corporate social responsible activities differ from
business to business, so measurement of CSR should focus on social outcomes (Carroll, 1991; Garriga
& Melé, 2004; Dahlsrud, 2006). Early data-driven research on CSR had huge measurement problems.
Few good measures for CSR have been defined and many researchers used a single item as a valuation
of CSR performance (Surroca, Tribó & Waddock, 2010). Following these challenges Frederick (1994)
proposes a narrower and more technical definition called corporate social performance, from here on
CSP. However, 20 years later the literature has not yet reached a general agreement on the definition of
CSP.
Based on a comprehensive study of work on CSP (Hillman & Keim, 2001; Waddock & Graves,
1997; Waldman, Siegel & Javidan, 2006) it can be defined as a multidimensional concept which captures
“a business organization’s configuration of principles of social responsibility, processes of social
responsiveness, policies, programs, and observable outcomes as they relate to the firm’s social
relationships” (Wood, 1991: 693). According to Hong and Kacperczyk (2009) observing an
organizations’ CSP captures their reaction to the demands of their stakeholders (Dahlsrud, 2006) and
the social problems the organization has to deal with.
13
Thus, CSP can be seen as measuring the level of organizations engagement in a number of
social, ethical, and legal issues and is a multidimensional concept covering organization response to a
wide range of stakeholder demands related to the organization’s operations (Carroll, 1991; Rowley &
Berman, 2000).
For example, an organization’s CSP is measured by collectively considering the organization’s
performance across a broad field of areas such as environmental protection, humanitarian contributions,
governance transparency, labor policy, employee relations, workforce diversity, and product-related
responsibility (Brammer et al., 2006; Griffin & Mahon, 1997). CSP is used by several researchers
(Ioannou & Serafeim, 2012; Stanwick & Stanwick, 1998; Carol et al., 2016) to define how organizations
score on their CSR behavior. In fact, CSP is a tool to measure CSR based upon several different methods
and designs (Petcu, Gherhes & Suciu, 2010). If you compare CSP and CSR, the dimensions largely
converge such that social, environmental and corporate governance performance reflect the dimensions
given in Table 1.
In order to measure the CSP it needs to capture the stakeholder outcome, social outcome and
environmental outcome. Therefore, measuring CSP is a challenging task because CSP represents a broad
range of economic, social, and environmental impacts generated by business operations and thus
requires multiple metrics to fully cover its scope (Gond & Crane 2010; Rowley & Berman, 2000). Extant
research uses data from databases covering the areas of environmental performance, social performance
and corporate governance (Chen & Delmas, 2010; Ioannou & Serafeim, 2012; Carrol et. al., 2016). The
social and environmental dimension are explicitly named and corporate governance is a combination of
the stakeholder and voluntariness dimension (Petcu, Cherhes & Suciu, 2010).
Margolis, Elfenbein and Walsh (2007) used 9 indicators to measure an organization’s CSP. The
first five represented the different dimensions of CSP: charitable contributions, corporate policies,
environmental performance, revealed misdeeds and transparency. The last four represented different
approaches for capturing CSP namely: self-reported social performance, observers’ perceptions, third-
party audits and screened mutual funds. Table 2 shows how the weighted average CSP score arises.
Dimension Measure CSP score
Environmental Cleaner environment, environmental stewardship X (value)
Social Contribute to a better society X (value)
Corporate Governance Stakeholders/ethical values X (value)
X (Weighted average)
Table 2: CSP measurement dimensions
2.3 Relationship between CSP and Financial Performance The effect of CSP on an organizations’ financial performance is not clear cut (Margolis and
Walsh, 2003; Vogel, 2006). Currently, empirical studies on the relationship between CSP and financial
performance are distinguished by an enormous variety of methods. Brammer and Millington (2008)
14
point out a lot of research is conducted on the relationship between CSP and profitability or, on CSP as
a market based measure like greenhouse gas (GHG) emissions, and in some cases both.
When studying the research of Porter (1991), it becomes clear profitability and pollution reduction might
not be mutually exclusive goals. In Porter’s view a higher CSP leads to less waste of resources (e.g.,
energy, commodities). In addition, the attempt to decrease an organizations’ quantity of pollution has
complementary advantages. It reduces the environmental footprint and strengthens an organizations
competitive position.
Brammer and Millington (2008) take a closer look at the CSP-financial performance relationship
and conclude organizations, which have an unusually high or low CSP, generate a higher financial
performance than other organizations. Organizations with an unusually low level of CSP perform better
in the short-run while organizations with an unusually high level of CSP perform better in the long-run
(Brammer & Millington, 2008). Brammer and Millington (2008) consider two issues related to the link
between CSP and financial performance. Firstly, the shape of this relationship, which can be linear or
U-shaped and secondly the time horizon over which they are related. In Figure 2, four different
descriptive models of the link between CSP and financial performance are shown. According to
Brammer and Millington (2008) there are three fundamental questions to ask:
1: Does good CSP have a positive financial payoff?
2: Are these payoffs a result from the pure level of CSP or only in comparison to its peers?
3: Do these payoffs have to deal with decreasing returns?
Model (I) shows a linear relationship between CSP and financial performance which is positive. In this
model the answer to question 1 is yes and question 3 is no. Much research predicts a positive and linear
relationship between CSP and financial performance.
In contrast, model (II) shows a linear relationship between CSP and financial performance but it is
negative. Therefore, the answer to question 1 is no, suggesting that there is no financial payoff to a good
CSP. Some researchers argue social unresponsive firms have lower direct costs and, reap higher profits
than social responsive organizations.
Model (III) and (IV) show a non-linear relationship between CSP and financial performance. Model
(III) suggests a positive financial payoff to good CSP, but the returns are diminishing and eventually
decreasing. When CSP is properly aligned with stakeholder management it increases financial
performance, if not it decreases financial performance (Brammer & Millington, 2008).
In Model (IV) the financial performance is the highest at the extremes of CSP. A low level of CSP
suggests a high financial performance as well as a high level of CSP.
This is in line with Porter (1980) who suggested that firms which are stuck in the middle are
outperformed by low-cost or differentiation strategies. In contrast, improved CSP could also lower costs
by wage reduction, improving productivity, increasing qualified labor (Turban & Greening, 1996), or
by waste reduction in the process (Porter & van der Linde, 1995).
15
Figure 2: Alternative models of the relationship between corporate financial
performance and corporate social performance
Stanwick and Stanwick (1998) show CSP is a multi-faceted construct which is impacted by
various organizational variables. According to Stanwick and Stanwick (1998) organizational size,
financial performance, and environmental performance have a positive impact on an organization’s CSP.
They suggest a reverse effect of FP on CSP. This could suggest there might be an endogeneity problem.
Waddock and Graves (1997) suggest the causation of the CSP-FP link runs in both directions. According
to them a better financial performance may lead to improved CSP and the other way around, ceteris
paribus (Waddock & Graves, 1997). They also argue investing in CSP is beneficial if the investment
fosters key stakeholder relations. Moreover, strategic managers must focus on the concerns of all
stakeholders, including the environment.
Other researchers argue that the financial capabilities of an organization are considerable
beneficiaries from CSP ratings (Alshammari, 2015). For example, El Ghoul, Guedhami, Kwok and
Mishra (2011) state an organization’s cost of equity is cheaper for organizations which have a high CSP.
This notion was supported by Lin and Wu (2014). Russo and Fouts (1997) studied 243 organizations in
a period of 2 years. They found engaging in social and environmental responsible behavior lead to a
better financial performance. Hillman and Keim (2001) also found a positive impact of an organizations
CSP on its financial performance. Ekatah, Samy, Bampton and Halabi (2011) argue regardless of the
causal effect, CSR is found to be positively related to an organization’s financial performance. Almsafir
(2014) suggests CSR leads to improved profitability and the financial performance is better off when
organizations are highly rated in their CSP indexes in comparison to other organizations. In order to
maximize the CSP advantage on financial performance, consistency in their CSP is salient to create
synergies.
Several other variables have been identified and tested. Research by McWilliams and Siegel
(2000) identifies innovation as a significant driver of firm performance. Their results show when
16
innovation is included among the independent variables the significance of CSP on financial
performance disappears. Therefore, innovation should be used as a moderator in other theoretically
robust models which have received mixed or ambiguous empirical support (Hull and Rothenberg, 2008).
Orlitzky, Schmidt, and Rynes (2003) state reputation may be an important mediating variable of the
relationship between corporate social responsibility and financial performance.
For measuring an organizations’ financial performance several metrics are used. Waddock and
Graves (1997) use Return On Assests (ROA), Return On Equity (ROE) and Return On Sales (ROS).
Margolis et al. (2007) suggest the most used measures for financial returns in their meta-analysis are
accounting-based measures e.g. ROA and ROE against market-based measures e.g. Stock returns and
market/book value ratio. In their study Margolis et al. (2007) find a mildly positive relationship between
CSP and financial performance.
When the literature on CSR is reviewed, these are the major findings. The environmental
dimension, our planet, is gaining importance. In order to measure CSR researchers defined it by CSP.
CSP measures the level of engagement in environmental, social, ethical and legal issues. In addition,
CSP covers an organizations’ reaction to a wide scope of stakeholder demands. The dimensions of both
CSR and CSP largely converge. Therefore, CSP is measured by an organizations’ social, environmental
and governance performance, where governance performance represents the stakeholder and
voluntariness dimension.
When analyzing the relationship between CSP and financial performance many different results
are found. For example, Brammer and Millington (2008) argue organizations with an unusual low CSP
have a higher FP in the short-run, while firms with an unusual high CSP perform better long-term. This
suggests an inverted U-shaped relationship. This result is backed by previous research by Porter (1980).
On the other hand, Stanwick and Stanwick (1998) state FP affects CSP in a positive way, which suggest
a reverse causal effect. Research by Waddock and Graves (1997) even suggested a relationship which
goes both ways. Nevertheless, most research agrees CSP affects an organization’s financial performance
and not the other way around (Alshammari, 2015; El Ghoul et al., 2011; Russo & Fouts, 1997; Hillman
& Keim (2001); Ekatah et al., 2011; Almsafir, 2014). Thus, although a central assumption of this thesis
is that the relationship between CSP and financial performance is not straightforward, this research
predicts the overall relationship is positive. Leading to the following hypothesis.
Hypothesis 1: The level of CSP of an organization will have a positive effect on its financial
performance.
This research is aware of the different views on the CSP-FP relationship and reverse causality
is possible. In order to exclude reverse causality the relationship is researched in both directions.
17
Meaning the CSP-FP link as well as the FP-CSP link, because when there is reverse causality the results
only show correlations and not causalities.
There are many moderators known which could influence the relationship between CPS and
financial performance: e.g. innovation and reputation. The next section takes a closer look at the
moderators.
2.4 National Influences and Business Types Several studies conducted research on CSP incentives across a small number of countries
(Maignan & Ralston, 2002; Ramasamy & Ting, 2004; Welford, 2004). For example, Maignan and
Ralston (2002) studied the extent to which organizations in France, the Netherlands, the United
Kingdom, and the United States had publicly committed to socially responsible behavior through
postings on their corporate websites. Using a sample of 100 organizations in each country, they found
systematic and significant differences in managerial incentives and stakeholder pressure on the
organization to act in socially responsible ways. In another study, Chapple and Moon (2005) analyze
website reporting of CSR activities by 50 organizations in seven Asian countries (India, Indonesia,
Malaysia, the Philippines, South Korea, Singapore and Thailand) and found cross-country CSP variation
cannot be explained by the stage of the country’s economic development as they hypothesized; rather,
Chapple and Moon (2005) suggest “national factors” could explain such variation although they do not
formally investigate such factors. They also found that multinational corporations adjust their CSR
activities according to the specific national contexts in which they operate. Ioannou and Serafeim (2012)
argue CSP ratings and rakings have revealed not only a significant variation in CSP across organizations
and industries, but also across countries. Ioannou & Serafeim (2012) suggest nation-level institutional
variation significantly impacts the observed variation in CSP across corporations. They classify
institutions into four systems:
(1) Political system;
(2) Education and labor system;
(3) Financial system; and
(4) Cultural system
They conclude the political and educational/labor system have the highest impact on CSP. Suggesting a
variation in CSP scores across countries.
In a related study, Maignan (2001) investigates consumers’ understanding of CSP in France,
Germany, and the US and finds their perceptions are significantly influenced by nation-level institutions.
In particular, she finds in France and Germany consumers are more likely to support socially responsible
corporations. Paying less attention to the corporations’ economic responsibilities, whereas the opposite
applies for consumers in the US. Maignan (2001) argues her findings are based on differences in
“national ideologies”: individualistic (US) vs. communitarian (France, Germany). Her study therefore
18
suggests nation-level institutions may affect the corporations’ undertaking of CSR activities and
resulting CSP via their impact on consumer perceptions. Figure 3 summaries these findings.
Figure 3: What countries find important
More recently, Jackson and Apostolakou (2010) empirically investigate the influence of the
institutional environment on CSR activities of European organizations and find organizations based in
the more liberal market economies (LME) of the Anglo-Saxon countries achieve higher levels of CSP,
compared to organizations based in the more coordinated market economies (CMEs) of Continental
Europe. They argue these findings support the hypothesis voluntary CSR activities in liberal economies
act as a substitute for institutionalized forms of stakeholder participation whereas in CMEs CSP often
takes on more implicit forms. In other words, the Jackson and Apostolakou (2010) study provides
evidence of the influence of institutions on CSP across two broadly-defined national business systems
(NBS). The results of Jackson and Apostolakou (2010) show countries differ along the environmental
dimension. The Anglo-Saxon countries (UK and Ireland) score significantly higher than the Nordic
(Denmark, Finland, Norway and Sweden) or Latin (France, Greece, Italy, Portugal and Spain) countries.
The different groups are ranked in the following order across all three dimensions of CSR:
economic, social and environment. The Anglo-Saxon countries score the highest level of CSR followed
by the Central European countries, the Latin countries and lastly the Nordic countries. The above
findings argue an influence of geographical region on the CSP of an organization. This thesis researches
if the geographical region also acts as a moderator on the CSP-FP link. Therefore, our second hypothesis
can be stated as follows:
Hypothesis 2: In the relationship of CSP and FP the geographical region has a moderating effect such
that it is positive for central European countries.
In general, the literature on CSR tends to focus mostly on CSR in relation to B2C companies.
Homburg, Stierl and Bornemann (2013) argue business practice CSR improves customers' trust, while
philanthropic CSR strengthens customer-company recognition. Homburg et al. (2013) state their
France
Support Social Responsible
Corporations
Communitarism
Germany
Support Social Responsible
Corporations
Communitarism
US
Support Economic
Responsible Corportations
Individualism
19
research is the first to empirically show CSR commitment provides positive customer outcome. In
general, they state the literature on CSR tends to focus mostly on CSR in relation to B2C companies.
Another important suggestion they make is both parties in the B2B relationship expect CSR to become
more salient in the next five years (Homburg et al. 2013). Yoo, Choudhary and Mukhopadhyay (2007)
argue the sales volume of business is clearly the highest for B2B marketplace, pursued by B2C and C2C.
This suggests higher incentives for B2B businesses to invest in CSR. This is an example of the
instrumental stakeholder theory, which discusses stakeholder-oriented activities should provide certain
benefits to generate successful relationships. While the social exchange theory, explains which benefits
in particular may be relevant in exchange relationships (Jones, 1999). Han and Childs (2016) state there
is little empirical evidence upholding the advantage to engage in CSR programs for B2B organizations.
Kubenka and Myskova (2009) suggest the success of global organizations is influenced or dependent
on popularity and sympathy of the public. That is why many organizations engage in CSR activities.
The biggest pressure is on organizations who sell to end customers (B2C), because they are well known
publically. Kubenka and Myskova (2009) argue customers in more developed countries tend to choose
CSR products more often. To this they add, buyers who are higher educated have a clearer buyer
preference. These findings suggest a higher intention to engage in CSR activities in B2C organizations
in developed countries. In contrast, the highest opportunity lies in the B2B environment. When they
adopt higher CSR activities, they influence their suppliers, which lead to global changes through the
supply chain (Kubenka & Myskova, 2009). According to Leppelt, Foerstl and Hartmann (2013) effective
marketing of CSR capabilities, in a B2B environment, enhances a supplier’s reputation fostering long-
term comparative advantages. It is vital the organization sends consistent signals through the cross-
functional integration of CSR-related purchasing and marketing practices. This is supported by Sharma,
Iyer, Mehrotra and Krishnan (2010) who argue the marketing role in environmental sustainability is
vital in achieving competitive and financial advantages in a B2B market. These findings suggest a
positive influence of B2B business engaging in CSR activities, leading to the third hypothesis.
Hypothesis 3: In the relationship of CSP and FP the business environment has a moderating effect such
that it is positive for B2B organizations.
Adams, Licht and Sagiv (2011) find directors’ personal values and roles play a salient factor in
their decisions. According to Adams et al. (2011) CEOs and directors who endorse entrepreneurial
values are more pro-shareholders. Values of higher achievement, power and self-direction are more
important than universalism values, suggesting lower intention to CSR initiatives. Brammer and Pavelin
(2006) argue the need for a ‘fit’ among the types of corporate social performance undertaken and an
organization’s stakeholder environment. An organization with a strong environmental performance can
either damage or enhance the corporate reputation depending on the ‘fit’ between the performed
activities and the environmental interest of the stakeholder. According to Li and Zhang (2010) the
20
ownership structure of an organization affects their CSR intentions. For non-state-owned organizations,
in developing markets, the corporate ownership dispersion is positively associated to CSR (Li & Zhang,
2010). This finding is supported by Ullmann (1985) who argues a heightened pressure for managers to
engage in CSR activities when shareholders are concerned with corporate social activities. Thus, leading
to a higher CSR intention. In addition, Li and Zhang (2010) suggest a reverse effect for state-owned
organizations. Johnson and Greening (1999) study the effects of corporate governance and institutional
ownership type on CSP, their research indicated a correlation between ownership structure and CSP.
Waddock and Graves (1997) state if an owner has more social concerns he is more likely to invest in
CSP. Muller and Kolk (2010) suggest the management commitment to ethics as a dominant driver of
CSP among domestic and foreign organizations. In addition, Muller and Kolk (2010) find management
commitment to ethics also has a positive influence on the trade-related pressure in raising CSP levels.
Besides, they make the distinction between extrinsic and intrinsic drivers of CSP. Lukas, Tan and Hult
(2001) state there must be a ‘fit’ between environmental pressure and an organization’s internal
characteristics. Oh and Chang (2011) describe different types of shareholders all have others motivations
towards CSR. Oh and Chang (2011) break down ownership into three different groups of shareholders:
institutional, managerial and foreign ownership. Their results show a positive relationship between
institutional and foreign ownership and CSR ratings in developing countries. Managerial ownership is
negatively associated with CSR ratings (Oh & Chang, 2011). These results are supported by Soliman,
El Din and Sakr (2012) although they focus on the developed countries. Thus, the literature shows the
relationship between an organization’s ownership structure and the incentive to engage in CSR activities
is not clear cut, but researchers agree upon the fact there is a relationship. According to Kubenka and
Myskova (2009) multinational organizations have a higher engagement in CSR activities than national
organizations. Alshammari (2015) argues the effect of institutional ownership is expected to positively
moderate the relationship between CSP and financial performance. Thus, the following hypothesis can
be constructed.
Hypothesis 4: In the relationship of CSP and FP governance structure in the form of share ownership
the moderating effect is positive for shares with ownership.
21
Chapter 3 Research methodology
3.1 Background This thesis investigates the impact of the level CSP on a firm’s financial performance,
moderated by national characteristics, whether the organization operates in a B2B or B2C industry or
the governance structure in the form of share ownership of the organization. Already in the 1990’s
research on CSP measures has been carried out (Aupperle, 1991; Clarkson, 1991), Brammer and
Millington (2008) suggest the benefits of CSP on financial performance only accrue in the long-run,
thus longitudinal data is needed. More recently, Ioannou and Sarafeim (2012) showed significant
variations in an organizations CSP throughout countries using the longitudinal ASSET4 dataset. Quéré
et al. (2015) indicates as of 2012 there were 37 CSR rating agencies worldwide. Other CSP databases
used are e.g. the KLD (Carrol et al., 2016) and Vigeo database (Quéré et al., 2015).
3.2 Research Design This study conducts an empirical research by using quantitative data from the ASSET4 dataset.
This thesis is of exploratory nature, relying on secondary data. Further, this research defines the problem
of the effect of CSP on financial performance and what moderates this relationship by means of three
hypotheses. Thus, this research tests hypotheses to explain the variance in the dependent variable, in this
case financial performance, through CSP and three moderators. The used data is longitudinal and has
the time span of 5 years from 2010 – 2014.
3.3 Data Collection This study conducts an empirical research using quantitative data from the ASSET4 database
which provides transparent, objective, and auditable extra-financial information and thus offers an
extensive platform in order to build criterion for the judgment of corporate performance (Schäfer et al.,
2006). The following timeline shows the establishment of the ASSET4 database.
Figure 4: Timeline Thomson Reuters ASSET4 database
Establishment ASSET4
• 2003
Taken over by Thomson Reuters
• 2009
3000+ public global countries
• 2010
Data for fiscal years (2003-2015)
• 2016
Currently 5000+ public global countries
• 30/06/2016
22
The ASSET4 was founded in 2003 and taken over by Thomson Reuters in 2009. According to
Ribando and Bonne (2010) the ASSET4 is the leading provider of environmental, social and corporate
governance data, or ESG. In 2010 the ASSET4 consisted of more than 3000 public global companies.
It gathers quantitative and qualitative ESG data and scores the data on four categories: Environmental,
Social, Corporate Governance, and Economic (Ribando & Bonne, 2010). Ribando and Bonne (2010)
suggest organizations which have a high ESG score, or level of CSP, are focused on creating long-term
shareholder value. The ASSET4 dataset is created by specialized and trained research analysts who
collected 900 evaluation points per organization. In addition, all primary data used is objective and
publically available. According to G. Sgambati (personal communication, June 17, 2016) the Thomson
Reuters ASSET4 dataset checks every data point question multiple times in order to generate a high
level of accuracy, timeliness and quality. Currently, it contains more than 5000 public global
organizations (G. Sgambati, personal communication, June 17, 2016). The ASSET4 dataset is available
within the Data lab of Tilburg University.
Through DataStream one can access the ASSET4 dataset. It includes not only the ESG ratings,
but also the ROA, Total Assets, Net Sales, Industry, Operating Profit Margin (OPM), Total Employees,
Governance Structure through Share Ownership, and the country in which the organization is located
for the years 2003-2015. Therefore, all the data for this research has been acquired through the Thomson
Reuters ASSET4 dataset (See Appendix 2).
3.3.1 Sampling Strategy
The sample used for this study consisted of financial and company data that have been collected
from the ASSET4 dataset filings of the selected companies over the five year period of 2010-2014. This
thesis has used a purposive sampling strategy which is a non-probability sampling technique (Trochim,
2006). It implies an organization’s needs to meet certain criteria in order to be included. The specific
criteria set for the sample is listed below. The rest of this section is used to describe how the various
criteria for inclusion in the sample, were computed.
Criteria:
The company has to be in the ASSET4 dataset
The company has to be European
Per European country at least 10 companies must have available data
Data of the years 2010 – 2014 must be available
ROA, Total Assets, Net Sales, Industry, OPM, Total Employees, and Governance Structure
through Share Ownership must be available for the years 2010 – 2014
The sample of companies was constructed using the following steps.
23
Firstly, this thesis researches the differences in the CSP-FP performance link between European
countries. Therefore, the data of all available European countries have been used. A minimum of 10
observations per independent variable is necessary in order to conduct a regression (Parasuraman,
Grewal & Krishnan, 2007). Thus, the sample only comprises of organizations from: Austria, Belgium,
Denmark, Finland, France, Germany, Greece, Iceland, Ireland, Italy, Netherlands, Norway, Poland,
Portugal, Spain, Sweden, Switzerland and United Kingdom. Cyprus, Czech Republic, Hungary and
Luxembourg are left out. After excluding missing variables in the dataset also Portugal is left out of the
analysis, 730 organizations remain. This thesis researches if there is a distinction in the CSP-FP link
between different European regions. Previous research suggested central European organizations score
higher on CSP than northern or southern European organizations (Jackson & Apostolakou, 2010).
Therefore, the following dummy variables have been created:
Dummy Countries
DNortheurope Denmark, Finland, Norway, Sweden
DSoutheurope Greece, Italy, Spain,
DCenteurope Austria, Belgium, France, Germany, Ireland, Netherlands, Poland, Switzerland, United Kingdom
Table 3: Country dummies
First, this research divided the country dummies based on the European region as provided on
Nationsonline (2016). Since this research does not make the distinction between Western and Eastern
Europe, Poland is included in the DCenteurope dummy. Also see appendix 1.
Secondly, data of the following years must be available 2010 – 2014. As stated before, the ASSET4
dataset includes data from 2004 – 2015. This thesis researches the CSP-FP link for the years 2010 –
2014, because for fiscal year 2015 a lot of data was missing. The financial data are also obtained from
the ASSET4, so organizations of which no CSP and/or financial data are available are left out of the
sample.
Thirdly, in order to make the distinction between B2B vs. B2C industries the TRBC industry group
needs to be available. When this data are not available the company is left out of the sample.
The fourth and final step is to select companies which include data for measuring ownership structure.
This thesis uses data of governance structure in the form of share ownership (See Appendix 2, Table 5).
This Table shows if shares include ownership. When this data is not available the company is excluded
from the sample.
These steps resulted in a sample of 730 companies, 53 different industries and 16 countries. This
thesis uses 3 different variables as dependent variable, 1 independent variable, 2 different variables as
control variable and 3 moderators. All variables contain data for the years 2010 – 2014.
24
3.3.2 Dependent Variable
This thesis uses ROA, OPM and Net Sales as the three dependent variables to operationalize
firm performance. ROA has been used frequently as a profitability ratio in strategic and organizational
studies (Hax & Majluf, 1984). ROA also reflects a return which is most directly influenced by corporate
management and is therefore a suitable tool for quantitative research (Bettis & Hall, 1982). It is
calculated by dividing a companies’ net income by its total assets. The second ratio used to measure
profitability is the OPM, also known as Return On Sales (ROS) (Berk & DeMarzo, 2010). The OPM is
a widely used profitability ratio and is calculated by dividing operating income by net sales (Waddock
& Graves, 1997). According to Wiley (1991) another measure for an organization’s financial
performance is Net Sales. This is a third measure for the dependent variable used in this thesis. Data for
all profitability measures are acquired from DataStream in Tilburg University’s Data lab.
3.3.3 Independent Variable
The independent variable used is Corporate Social Performance. In prior literature, constructing
a truly representative measure for CSP has been rather challenging. Ioannou and Serafeim (2012) and
Eccles, Ioannou and Serafeim (2014) use a global ESG dataset from Thomson Reuters ASSET4. This
dataset contains an environmental performance score, a social performance score and a corporate
Governance score. This thesis has used the CSP rakings and scores, of the European countries defined
under sampling strategy, from the ASSET4 dataset. The overall CSP score used as independent variable
has been generated by the weighted average of the environmental, social and corporate governance score
as shown in Appendix 2, Table 2, 3 and 4. However, the literature to date has not identified a
theoretically derived ranking of importance for each category. Accordingly, this thesis follows the
covenant established by Waddock and Graves (1997), Hillman and Keim (2001) and Waldman et al.
(2006) who constructed a combined CSP score by assigning equal importance (and thus equal weights)
to each of the three categories. In this particular example the overall CSP score of BP (Appendix 2,
Table 2, 3 and 4) for 2010 would be: 84 + 82 + 85 / 3 = 83.6667. The overall CSP score is calculated
for all years 2010 – 2014 this way. Companies are scored on each dimension separately based on a scale
of 0 (poor) to 100 (excellent) (Jackson & Apostolakou, 2010).
3.3.4 Control Variables
According to Waddock and Gaves (1997) an organizations size, risk and industry in which it is
active are factors that affect CSP as well as financial performance. An organization’s size is used as a
control variable. It is relevant because there is evidence showing smaller organizations do not engage in
as many social responsible behaviors as larger firms do (Waddock & Graves, 1997). Baumann-Pauly,
Wickert, Spence and Scherer (2013) argue large organizations tend to focus on communicating CSR
symbolically but do less to implement it into their core structures and procedures, while small
organizations pay less attention to communication and put more emphasis on implementation. In order
25
to measure a firm’s size the natural logarithm of the number of employees and the natural logarithm of
the total assets have been used (Hendricks, Singhal, & Zhang, 2009; Yang, Hong & Modi, 2011). This
thesis uses the natural logarithm, because it is a way to deal with potential outliers (Ray, 2016). This
thesis uses both measures because total assets is able to provide further information about the capital
intensity of the firm.
3.3.5 Moderators
This thesis investigates three potential moderators on the CSP-FP relationship. Firstly, the
geographical region has been used, focusing on three European regions, as stated in the sampling
strategy and individual European countries. Dummy variables are created accordingly. Secondly,
governance structure in the form of share ownership has been used as a moderator, see Appendix 2 Table
5. For the years 2010 – 2014 the data shows if shares include ownership or not. A dummy variable is
created, where 1 means “shares with ownership” and 0 “shares without ownership”. In addition, a
dummy variable is created for an organization’s business type, 1 meaning “an organization is active in
a B2B industry” and 0 “an organization is active in a B2C industry”. All 53 industries are converted into
the dummy variable using the SIC Code list (Laiderman, 2005).
3.4 Data Analysis This thesis has used two analysis methods to determine the relationship between CSP and
financial performance. Ordinary Least Squares (OLS) analysis and Panel Data analysis. Several previous
studies used OLS regressions to analyze this relationship (Jackson & Apostolakou, 2010; Ioannou &
Serafeim, 2012). In this way only results from comparing organizations are obtained. However, this
study also uses the analyses method of Panel Data.
Research by Finkel (1995) suggests there are two ways of analyzing Panel Data. One is by fixed-
effects models and the other one is by random-effects models. In order to determine which model to use
a Hausman test is performed. The main difference between fixed-effects models and random-effects
models is that fixed-effect models excluded time-invariant variables while random-effect models
include them (Finkel, 1995).
STATA was used to perform the data analyses. The OLS regressions have been performed five
times for each of the three dependent variables: ROA, OPM, and Net Sales. First the OLS regression
has been performed with CSP, total employees and Northern Europe. Secondly, with Southern Europe.
Thirdly, with Middle Europe. Fourthly, with Ownership and finally with business type. This is repeated
with the control variable total assets. In total 30 OLS regressions have been generated.
Following the OLS regressions a Panel Data analysis using random or fixed-effects models has
been performed for each of the three dependent variables. The Hausman test has been conducted, in
each case, to determine whether fixed- or random-effects models where applicable.
26
In order to address endogeneity, a reverse causality analysis has been performed for each of the
measures for firm performance. Six Panel Data analyses using random effects models have been
conducted with overall CSP as the dependent variable and the firm performance measures: ROA, OPM
and Net Sales as the independent variable. The reverse causality analysis has also been executed for six
OLS regressions, using the same models.
Table 4 gives an overview of the variables used for the OLS regressions and Panel Data analysis.
Table 4: Variables for OLS Regressions and Panel Data analysis
3.4.1 Dealing with Outliers
When computing statistics, the results can often be heavily influenced by extreme values
(Heckert, 2012). To address the concern of outliers, this thesis winsorizes two dependent variables: the
Return on Assets and the OPM, because of negative variables it is not possible to use natural logarithm
as with the control variables. Both variables are winsorized at the 5th and 95th percentiles. When this is
not sufficient to deal with the skewness value, the 10th and 90th percentiles are used (Jose & Winkler,
2008). Bulmer (1979) stated the rule of thumb: skewness less than -1 or higher than 1 means the data is
highly skewed. Between -1 and -0.5 and 0.5 and 1 is moderately skewed and between -0.5 and 0.5 is
approximately symmetric.
3.4.2 Validity
Construct validity measures how well the key constructs of a study are operationalized (Schijven
& Jakimowicz, 2003). Because dependent, independent and control variables are used that have proven
to be successful measures in previous research, the threat to the construct validity is relatively small.
External validity is about the generalizability of this study to other settings (Gigliotti, 2007). In addition
to European firms, this study can also be copied to other continents or countries. The internal validity in
Variable Description
CSP Overall level of CSP
Environmental Environmental dimension score
Social Social dimension score
Corporate Governance Corporate Governance dimension score
OPM Firm performance measure.
ROA Firm performance measure. Return On Assets
Net Sales Firm performance measure.
Dummy Ownership A dummy variable indicating whether the shares have right of ownership “YES” or “NO”
(1= “YES”, 0= “NO”).
Dummy Mideurope A dummy variable indicating whether the company is located in Central/Mid Europe
“YES” or “NO” (1= “YES”, 0= “NO”).
Dummy Southeurope
A dummy variable indicating whether the company is located in Southern Europe “YES”
or “NO” (1= “YES”, 0= “NO”).
Dummy Northeurope
A dummy variable indicating whether the company is located in Northern Europe “YES”
or “NO” (1= “YES”, 0= “NO”).
Total Assets A control variable used to measure a firm’s size.
Total employees A control variable used to measure a firm’s size
B2B A dummy variable indicating whether the company is active in a B2B or B2C industry
“YES active in a B2B industry” or “NO active in a B2C industry” (1= “YES”, 0= “NO”).
27
this thesis is of major importance. It relates to establishing the causal relationship of the CSP-FP link.
As stated by Stanwick and Stanwick (1998) the possibility of endogeneity may exist. In addition,
Waddock and Graves (1997) suggest the causation works both ways. In order to tackle this a large
dataset is used with longitudinal data over five years and regressions for reverse causality has been
performed. Moreover, the external validity will hold when studying other firms in European countries.
It is important then to define what those countries characterize and if the law and legislations are the
same.
3.4.3 Reliability
Reliability can be described best as how suitable a measure is for replication in future research.
It is about the consistency and repeatability of a construct (Trochim, 2006). As this research is based on
archival data gathered from ASSET4, it can be replicated by anyone who has access to it. In addition,
the study is conducted in a similar manner as previous research. Therefore, the reliability is high because
it is easily repeatable.
28
Chapter 4 Findings and Results
This thesis investigates the effect of an organization’s level of CSP on its FP. In addition, it uses
three different moderators to see how they influence the CSP-FP link. The moderators used in this thesis
are: the geographical region Europe, business type in the form of B2B vs. B2C and governance structure
in the form of share ownership. The results on the relationship between CSP and financial performance
are scattered (Brammer & Millington, 2008). Eccles, Ioannou and Serafeim (2014) argue when looking
at an organization’s stock market and financial performance, high sustainability organizations
outperform their counterparts in the long-run. To research whether CSP influences an organizations
financial performance, the relationship is conversely or even works both ways, additional study is
needed. Through hypothesis testing this thesis provides insights in this relationship and tests how the
lationship develops over a period of 4 years. Moreover, it tests the CSP-FP link in combination with
each separate moderator.
Based on a skewness test (Appendix 3) the following variables are transformed into a natural
log variable: Net Sales, Employees and Totalassets. Because of the negative data in the ROA and OPM
variable, natural logarithm caused many missing variables. Therefore, these variables where winsorised.
Tables 6, 7, 8, 9 and 10 present the results of the OLS regressions. Table 6 uses ROA as the
dependent variable, Table 7 uses OPM and Table 8 uses Net Sales as its dependant variable. Table 9
shows the results when the moderators are excluded and Table 10 uses the overall CSP score instead of
the 3 dimensions. Table 6, 7 and 8 consist of 5 tests. For each of these tests a different sample is used.
The first test in each table uses the dummy of central European countries (total of 543 companies). The
second test uses the dummy Northern European companies (total of 103 companies). The third test uses
Southern European companies (total of 129 companies). The fourth test uses the dummy Ownership.
Lastly, model five uses the dummy B2B.
Subsequently, a panel data analysis has been performed. There are two ways: through random
effects or fixed effects (Torres-Reyna, 2007). Kohler, Ulrich and Kreuter (2009) describe the following
method:
“The fixed-effects model controls for all time-invariant differences between the individuals, so
the estimated coefficients of the fixed-effects models cannot be biased because of omitted time-
invariant characteristics like e.g. culture, religion, gender, race. One side effect of the features
of fixed-effects models is that they cannot be used to investigate time-invariant causes of the
dependent variables. Technically, time-invariant characteristics of the individuals are perfectly
collinear with the person dummies. A time-invariant characteristic cannot cause such a change,
because it is constant for each person.” (pp. 245)
Based on this method all models which include dummy variables use a random effect approach. Each
model is tested for random effects by Breusch-Pagan Lagrange multiplier (LM) (Torres-Reyna, 2007).
For the models which exclude time-invariant variable, in the case of this research dummies, are first
29
tested through a Hausman test (Green, 2008) after which the strategy is determined. All results of the
LM and Hausman test are presented in the appendices. Through the results of Panel Data Analysis the
hypotheses are again tested.
Tables 11, 12, 13, 14 and 15 present the results of the Panel Data Analysis. Table 11 uses
ROA as dependent variable, Table 12 uses OPM and Table 13 uses Net Sales as its dependant variable.
Table 14 shows the results when the moderators are excluded and Table 15 uses the overall CSP score
instead of the 3 dimensions. The rest is equal to the description of the OLS regression method.
4.1 The CSP-FP link The results in Table 10 and 15 show the main effect of the overall level of CSP on FP. The
results of the OLS regression analysis in Table 7 show CSP has a positive effect on an organization
based on OPM (OPM) (p<0.05), this applies for model 3 and 4. When using Net Sales as dependent
variable CSP has a significant negative effect (p<0.001). The other observations did not provide
significant effects. The results of the Panel Data Analysis in Table 15 show no significant effect of the
overall level of CSP variable on an organization’s financial performance, taking all three dependent
variables into account. No model in Table 15 provides significant results for the overall CSP level on
FP. This research concluded to dig deeper. Thus, this research generated models based on the three
dimensions: Environmental, Social and Corporate Governance, on which the overall CSP is based, for
both OLS regression and Panel Data.
Hypothesis 1: The level of CSP of an organization will be positively related to its financial
performance.
Many researchers argued an equal importance of each dimension (Waddock & Graves, 1997;
Hill & Keim, 2001; Waldman, Siegel & Javidan, 2006). Looking at the individual dimensions, of the
OLS regression analysis, in Table 9 shows a high level of significance (p<0.001) for all three
independent variables. For ROA and OPM the environment dimension has a negative effect, social and
corporate governance both have a positive effect. When using Net Sales as dependent variable the effect
of the dimensions seem to invert. Environment now has a positive effect on FP and Corporate
Governance a negative, while social stays the same. The social dimension is positive significantly
(p<.001) related in 6/9 models. When combining the dimensions with the moderators, Table 6, 7 and 8
show many significant results. The environment is negative and highly significant (p<.001) related with
FP when using ROA or OPM. Reverse results are again found when using Net Sales, resulting in a
positive significant (p<.001) relationship. The coefficients in Table 8, Net Sales, are much lower than
those in Table 6 and 7 suggesting a weaker relationship. The corporate governance dimension shows
opposite results compared to the environmental dimension. In Table 6 and 7 it has a positive significant
(p<.001) influence on FP, while in Table 8 the results are negative and significant (p<.001). The social
30
dimension has a positive significant (p<.001) impact on the FP in Table 6, 7 and 8, in 22/30 models.
Thus, these results show a straightforward impact.
For Panel Data analysis the individual dimensions in Table 14 show a significant positive effect
for the Corporate Governance dimension in all models with Net Sales as dependent variable. It is
noticeable that 4/9 models, as a whole, where not significant. Based on these results hypothesis 1 cannot
be accepted. Thus, this research analyses the results in Table 11, 12 and 13 to see how the three
dimensions react in the dummy models. In the results of Table 9 only a positive effect of the Social and
Corporate Governance dimensions on ROA are found. However, both with the lowest level of significant
accepted in research (Nieuwenhuis, 2009) (p<.10) and this result is found in only 2/10 models. Table 10
shows significant positive effect for Corporate Governance in 4/5 models (p<.10 and p<.05). Because
all models with Total Assets as control variable are insignificant no conclusion based on these results
can be made. Lastly, Table 11 generated significant results for the Environment and Social dimension.
6/10 models are significant and positive for Environment varying between a significance level of p<.10
and <p.01. For the social dimension 9/10 models shows significant positive results with 8/9 with a p-
value of <.05.
An interesting finding is the environmental dimension is negatively related with an
organizations FP, measuring with ROA and OPM. This partly contradicts, Russo and Fouts (1997) who
found engaging in social and environmental responsible behavior lead to a better financial performance.
The results support the social part of Russo and Fouts (1997), since the social dimension results in only
positive impacts. Based on all results this research concludes all individual dimensions can have a
positive effect on FP, but not simultaneously in the same model with the same dependent variable. These
results suggest the effect of CSP and the individual dimensions on a FP depends on the financial
performance indicator used. This supports both model I and II as given by Brammer and Millington
(2008). In addition, the overall CSP shows no significant effects on FP, adding thereto it results in
negative effects. Based on these findings the following conclusions are made. The social dimension has
a positive influence on the FP while both the environmental and corporate governance dimension
provide mixed results. However, all dimensions show a positive impact depending on the dependent
variable used. This explains why the overall level of CSP shows no positive relationship with FP,
because there is no model where all individual dimension show a positive effect. The coefficient of all
models are low, suggesting a weak effect of the dimensions. Although, all dimensions found a positive
effect in one of the models, the overall level of CSP did not lead to a higher FP. Thus, hypothesis 1
cannot be accepted.
31
4.2 A geographical approach
Hypothesis 2: In the relationship of CSP and FP the geographical region has a moderating
effect such that it is positive for central European countries.
The OLS regression results presented in Table 6, model 6, show using ROA as dependent
variable, the dummy variable for central European countries shows a significant (p<0.001) positive
moderating effect on the CSP-FP link, model 6. In addition, the dummy variable for northern European
countries also shows a significant (p<0.01) positive moderating effect in model 3 and 8. In contrast, the
dummy variable for southern European countries results in a significant (p<0.001) negative moderating
effect. When using total assets as control variable, only the results for southern and northern European
countries (model 7 and 8) are significant (p<0.001) and show the same effect. The second dependent
variable OPM, presented in Table 7, also shows a significant (p<0.001) positive moderating effect for
central European countries and significant (p<0.001) negative effect for southern European countries.
No significant results for northern European countries were found. The third dependent variable Net
Sales generated slightly contrasting results. It does deliver a significant (p<0.001) positive moderating
effect for northern European countries, for both control variables. It also shows a significant (p<0.001)
negative moderating effect for southern European countries, supporting the results of the other
dependent variables. However, the moderating effect of central European countries is significantly
negative for both employees (p<0.001) and total assets (p<0.01). This contradicts the findings when
using ROA or OPM as dependent variable for measuring an organization’s financial performance.
The Panel Data results presented in Table 9 found that model 1, with the dummy variable for
central European countries, has a significant (p<.05) positive moderating effect on the CSP-FP link.
Further, model 8 indicates northern European countries also have a significant (p<.001) positive
moderating effect. In contrast, the findings in model 2 and 7 generated highly significant (p<.001)
negative moderating results for southern European countries. Table 12 largely supports these findings,
through the positive result in model 1 and negative result in model 2. Only model 3 does not generate
significant results. Models 6 till 10 were not significant, therefore no conclusion are made about these
results. Table 13 presents an important contrasting result, in model 1 central European countries have a
negative (p<.001) moderating effect. However, the effect of model 3, 7 and 8 support earlier findings.
As noted in the results of the OLS regressions the results found different variables for measuring an
organization’s financial performance, deliver contrasting findings for the moderating effect of European
regions on the CSP-FP link. An interesting finding is shown by model 1 in Table 13, this model contains
reverse results for the environmental dimensions and the central European dummy. This in line with the
results of Table 8 of OLS regression. Based on the findings of this research Hypothesis 2 is supported
when using ROA and OPM. The coefficients for the central European dummy is between 1.004 and
1.618, which is much higher than the coefficients found for the 3 dimension in the results of hypothesis
32
1, suggesting a strong moderating effect of central European countries on the CSP-FP link. This applies
for both the OLS and Panel Data analyses. When using Net Sales hypothesis 2 is rejected. As for the
moderating effect of southern European countries all models show the same significant negative
moderating effect. Leading to a new hypothesis: In the relationship of CSP and FP the geographical
region has a moderating effect such that it is negative for southern European countries. Based on the
findings of this research, this hypothesis would be supported.
Research of Apostolakou (2010) argued Anglo-Saxon and central European countries score
significant higher on the three CSP dimensions than southern and northern European countries. The
results of this research support these findings, suggesting central European countries, in this case
including Anglo-Saxon countries, scoring higher on CSP. However, the results show that different
variables for measuring an organization’s financial performance, deliver contrasting findings for the
moderating effect of European regions on the CSP-FP link. The results for ROA and OPM support the
research of Apostolakou (2010) for Anglo-Saxon and central European countries, while all 3 dependent
variables support their finding southern European countries score lower. However, they do not mention
it to be a negative influence.
4.3 Business-to-Business vs. Business-to-Consumers
Hypothesis 3: In the relationship of CSP and FP the business environment has a moderating
effect such that it is positive for B2B organizations.
Table 6, 7 and 8 show the moderating effect of being in a B2B industry for OLS regression
analysis. The results present mixed findings. With ROA as dependent variable the moderating effect is
negative and significant (p<0.001) in both models 5 and 10. The coefficient is -2.142 in model 5 and -
1.211 in model 10. The moderating effect on OPM is positive and significant (p<0.001) only in
combination with total assets (Table 7, model 10), with a coefficient of 1.505. The most interesting
findings are shown by Table 8. Model 5 results in a positive significant (p<0.001) moderating effect of
B2B, while model 10 with total assets results in a negative significant (p<0.001) moderating effect of
B2B. Again different dependent and control variables results in different moderating effects.
The Panel Data results presented in Table 11, 12 and 13 again present mixed results for the
effect of being in a B2B industry. Table 11, with ROA as dependent variable the moderating effect is
negative and significant (p<0.001) in combination with both control variables. Again the highest
coefficient is found in Model 5, with total employees as control variable. No moderating effect on OPM
is found in Table 12. The findings in Table 13 are in line with those found in Table 8. Model 5 with total
employees results in a positive significant (p<0.001) moderating effect of B2B, while Model 10 with
total assets results in a negative significant (p<0.001) moderating effect of B2B. Again different
dependent and control variables results in different moderating effects.
33
Based on the results of Table 6 and 11 the hypothesis is rejected, so when using ROA as
dependent variable. The results of Table 5 support the hypothesis, using OPM, but no significant results
were found in Panel Data analysis using OPM. Finally Table 8 and 13 generate mixed results and cannot
accept or reject the hypothesis. Yoo, Choudhary and Mukhopadhyay (2007) argued the sales volume of
business is clearly the highest for B2B which means also the net Sales are higher. This could potentially
influence the relationship of B2B and the CSP-FP link and lead to these mix findings. Kubenka and
Myskova (2009) suggest higher intentions to engage in CSR in B2C market, in developed countries.
This thesis only researches developed countries, so this could lead to the negative effect in Table 6 and
11.
4.4 Governance Structure in the Form of Share Ownership
Hypothesis 4: In the relationship of CSP and FP governance structure in the form of share
ownership the moderating effect is positive for shares with ownership.
Table 6, 7 and 8 show the moderating effect of Ownership for OLS regression. The results
indicate identical moderating effects in all Tables. In Table 6 Ownership has a significant (p<0.01)
positive moderating effect for model 4 and (p<0.001) for model 9 on the CSP-FP link. In Table 7 the
results are highly significant (p<0.001) for model 4 with a coefficient of 2.941 and model 9 with a
coefficient of 2.566. These coefficients are more than 2 times as high as in Table 6. In Table 8 the
moderating effect is highly significant (p<0.001) and positive in model 4 and less significant (p<0.05)
and positive in model 9. In addition, the coefficients are very low, 0.332 and 0.089.
For Panel Data analysis the results in Table 11 and 12 provide a positive (p<.10) moderating
effect of Ownership, in Table 11 using Total Assets as control variable while in Table 12 using Total
Employees. A side note is the level of significance, in both models, is low (p<.10). The results do match
the results of the OLS regressions only showing positive effects of Ownership. The moderating effect is
highly significant for most models and positive in all cases. The Panel Data analysis, apart from the fact
the level of significance is low, also support hypothesis 4. Thus, hypothesis 4 is accepted, supporting
research of Alshammari (2015).
34
4.5 R-Squared The R-Squared indicates what amount of the dependent variable is explained by the model
(Cameron & Windmeijer, 1997). The results show a large variance across the models. Table 9, which
shows the main relationship of CSP-FP, presents a span from 1% – 74%. Model 3 implies the control
variable Total Assets explains a large amount of ROA, since the R-Squared increases with 8% in
comparison to model 1. Which is a logic finding since ROA is calculated by dividing net income by its
total assets. The coefficient is negative because when total assets increase, ceteris paribus, the ROA
decreases. For OPM model 5 shows the most interesting results, with an R-Squared of 14.6%. OPM is
calculated by dividing operating income by Net Sales. Net Sales is positively influenced by Total
Employees (model 8) so this explains why model 5 results in the highest R-Squared and the coefficient
of total employees in negative. Model 7, 8 and 9 of Table 9 result in the highest R-Squared, relatively
to the other models in Table 9. Meaning the environmental dimensions explain more of Net Sales than
they do of ROA or OPM. Model 7 stands out compared to model 1 and 4 where no control variables are
included. Almost 30% of Net Sales is explained by the CSP dimensions. This could suggest the costs of
implementing CSP is reflected in the costs of the goods. Model 8 and 9 have an R-Squared of 62.5%
and 73.7%. This suggests a firm size largely explains an organization’s Net Sales. According to Gaur et
al. (2008) when the firm size increases, the sales increase.
However, the results of the Panel Data analysis, Table 14, do not fully support these findings.
Since model 1 till 6 all have an R-Squared <1%. In addition, model 1, 4, 5 and 6 are not significant so
those results are not considered. However, a higher R-Squared is found in model 8 and 9 again
suggesting a firm’s size explains a large amount of its Net Sales.
The R-Squared analysis presents some unobservable effects from the viewpoint of this research.
Two types of problems are occurring. First, the several potential technical problems e.g. linear (OLS)
vs. non-linear (Panel Data). Second, the sample and other mechanisms. Thus, it could be interesting to
further research what could be behind these findings.
35
***p<.001, **p<.01, *p<.05 a. standard errors in parentheses. b. All models Prob > F = 0.000 c. Number of observations = 3650
d. Adjusted R-squared differs max 0.002 from R-squared
Table 7: OLS Analysis Using Winsorized Operating Profit Margin Variable
Models /
Control Variable
Model 1
(DCenteurope)
Model 2
(DSoutheurope)
Model 3
(DNortheurope)
Model 4
(Ownership)
Model 5
(B2B)
Environment -0.048***
(0.008)
-0.051***
(0.008)
-0.048***
(0.008)
-0.051***
(0.008)
-0.499***
(0.008)
Social 0.075***
(0.009)
0.078***
(0.009)
0.070***
(0.009)
0.064***
(0.009)
0.070***
(0.009)
Corporate
Governance
0.030***
(0.006)
0.029***
(0.006)
0.035***
(0.006)
0.054***
(0.006)
0.037***
(0.006)
lnemployees -2.068***
(0.095)
-2.047***
(0.095)
-2.025***
(0.095)
-2.039***
(0.095)
-1.997***
(0.095)
Model dummy 1.618***
(0.338)
-2.533***
(0.461)
-0.394
(0.418)
2.941***
(0.412)
0.326
(0.305)
R-squared 0.151 0.153 0.146 0.157 0.146
Model 6
(DCenteurope)
Model 7
(DSoutheurope)
Model 8
(DNortheurope)
Model 9
(Ownership)
Model 10
(B2B)
Environment -0.069***
(0.009)
-0.071***
(0.009)
-0.069***
(0.009)
-0.069***
(0.009)
-0.071***
(0.009)
Social 0.001
(0.009)
0.005
(0.009)
-0.000
(0.009)
-0.005
(0.009)
0.005
(0.009)
Corporate
Governance
0.538***
(0.007)
0.052***
(0.007)
0.057***
(0.007)
0.072***
(0.007)
0.059***
(0.007)
lntotalassets 0.273**
(0.089)
0.234**
(0.089)
0.211*
(0.089)
0.167
(0.089)
0.126
(0.089)
Model dummy 1.004**
(0.366)
-1.996***
(0.491)
0.164
(0.455)
2.566***
(0.441)
1.505***
(0.325)
R-squared 0.038 0.040 0.036 0.045 0.042 ***p<.001, **p<.01, *p<.05 a. standard errors in parentheses. b. All models Prob > F = 0.000 c. Number of observations = 3650
d. Adjusted R-squared differs max 0.002 from R-squared
Table 6: OLS Analysis Using Winsorized ROA Variable Models /
Control Variable
Model 1
(DCenteurope)
Model 2
(DSoutheurope)
Model 3
(DNortheurope)
Model 4
(Ownership)
Model 5
(B2B)
Environment -0.017***
(0.005)
-0.021***
(0.005)
-0.019***
(0.005)
-0.018***
(0.005)
-0.012*
(0.002)
Social 0.020***
(0.006)
0.026***
(0.006)
0.017**
(0.006)
0.015**
(0.006)
0.017**
(0.006)
Corporate
Governance
0.013***
(0.004)
0.009*
(0.004)
0.018***
(0.004)
0.022***
(0.004)
0.011**
(0.004)
lnemployees -0.328***
(0.059)
-0.033***
(0.059)
-0.279***
(0.059)
-0.299***
(0.059)
-0.425***
(0.059)
Model dummy 1.105***
(0.213)
-2.941***
(0.288)
0.716**
(0.263)
0.816**
(0.262)
-2.142***
(0.189)
R-squared 0.024 0.044 0.018 0.019 0.049
Model 6
(DCenteurope)
Model 7
(DSoutheurope)
Model 8
(DNortheurope)
Model 9
(Ownership)
Model 10
(B2B)
Environment -0.007
(0.005)
-0.010*
(0.005)
-0.009
(0.005)
-0.008
(0.005)
-0.005
(0.005)
Social 0.027***
(0.005)
0.034***
(0.005)
0.030***
(0.005)
0.025***
(0.005)
0.023***
(0.005)
Corporate
Governance
0.007
(0.004)
0.001
(0.004)
0.008*
(0.004)
0.145***
(0.004)
0.005
(0.004)
lntotalassets -0.908***
(0.052)
-0.897***
(0.052)
-1.010***
(0.052)
-0.942***
(0.052)
-0.843***
(0.052)
Model dummy 0.172
(0.209)
-2.630***
(0.277)
1.984***
(0.026)
1.231***
(0.252)
-1.211***
(0.185)
R-squared 0.091 0.113 0.105 0.096 0.101
36
Table 8: OLS Analysis Using Natural Log Net Sales Variable Models /
Control Variable
Model 1
(DCenteurope)
Model 2
(DSoutheurope)
Model 3
(DNortheurope)
Model 4
(Ownership)
Model 5
(B2B)
Environment 0.007***
(0.001)
0.008***
(0.001)
0.005***
(0.001)
0.009***
(0.001)
0.007***
(0.001)
Social 0.004***
(0.001)
0.008***
(0.001)
0.007***
(0.001)
0.007***
(0.001)
0.007***
(0.001)
Corporate
Governance
-0.005***
(0.001)
-0.009***
(0.001)
-0.007***
(0.001)
-0.007***
(0.001)
-0.008***
(0.001)
lnemployees 0.697***
(0.010)
0.665***
(0.010)
0.697***
(0.010)
0.663***
(0.010)
0.691***
(0.010)
Model dummy -1.007***
(0.040)
-0.088
(0.059)
1.601***
(0.046)
0.332***
(0.053)
0.409***
(0.038)
R-squared 0.681 0.626 0.719 0.629 0.637
Model 6
(DCenteurope)
Model 7
(DSoutheurope)
Model 8
(DNortheurope)
Model 9
(Ownership)
Model 10
(B2B)
Environment 0.004***
(0.001)
0.003***
(0.001)
0.003***
(0.001)
0.004***
(0.001)
0.004***
(0.001)
Social 0.014***
(0.001)
0.015***
(0.001)
0.015***
(0.001)
0.014***
(0.001)
0.012***
0.001)
Corporate
Governance
-0.005***
(0.001)
-0.006***
(0.001)
-0.005***
(0.001)
-0.005***
(0.001)
-0.006***
(0.001)
lntotalassets 0.693***
(0.010)
0.702***
(0.010)
0.672***
(0.010)
0.697***
(0.010)
0.736***
(0.010)
Model dummy -0.111**
(0.037)
-0.450***
(0.049)
0.555***
(0.045)
0.089*
(0.443)
-0.609***
(0.031)
R-squared 0.738 0.743 0.748 0.738 0.762 ***p<.001, **p<.01, *p<.05 a. standard errors in parentheses. b. All models Prob > F = 0.000 c. Number of observations = 3650
d. Adjusted R-squared differs max 0.002 from R-squared
Table 9: OLS Analysis Without moderators Independent /
Dependent
Environment Social Corporate
Governance
Lnemployees Lntotalassets R-squared
(1) ROA -0.020*** 0.007 0.020*** 0.010
(2) ROA -0.018*** 0.017** 0.017*** -0.294*** 0.016
(3) ROA -0.007 0.027*** 0.007 -0.918*** 0.091
(4) OPM -0.065*** 0.004 0.053*** 0.034
(5) OPM -0.049*** 0.070*** 0.036*** -2.017*** 0.146
(6) OPM -0.069*** -0.000 0.056*** 0.218* 0.036
(7) Net Sales 0.013*** 0.029*** -0.015*** 0.297
(8) Net Sales 0.008*** 0.007*** -0.009*** 0.666*** 0.625
(9) Net Sales 0.004*** 0.014*** -0.005*** 0.699*** 0.737 ***p<.001, **p<.01, *p<.05 a. All models Prob > F = 0.000 b. Number of observations = 3650
d. Adjusted R-squared differs max 0.002 from R-squared
Table 10: OLS Analysis Without moderators Variables /
Models
CSP Lnemployees Lntotalassets R-squared
(1) ROA -0.003 -0.263*** 0.008
(2) ROA 0.000 -0.081*** 0.078
(3) OPM 0.017* -1.774*** 0.121
(4) OPM 0.018* -0.148 0.003
(5) Net Sales 0.000 0.760*** 0.594
(6) Net Sales -0.003*** 0.792*** 0.696 ***p<.001, **p<.01, *p<.05 a. All models Prob > F = 0.000 b. Number of observations = 3650
d. Adjusted R-squared differs max 0.002 from R-squared
37
Table 11: Panel data Analysis Using Winsorized ROA Variable Models /
Control Variable
Model 1
(DCenteurope)
Model 2
(DSoutheurope)
Model 3
(DNortheurope)
Model 4
(Ownership)
Model 5
(B2B)
Environment -0.005
(0.007)
-0.007
(0.007)
-0.006
(0.007)
-0.006
(0.007)
-0.003
(0.007)
Social 0.006
(0.007)
0.008
(0.007)
0.004
(0.007)
0.004
(0.007)
0.004
(0.007)
Corporate
Governance
0.006
(0.005)
0.005
(0.005)
0.008
(0.005)
0.009+
(0.005)
0.006
(0.005)
lnemployees -0.386***
(0.095)
-0.376***
(0.095)
-0.361***
(0.095)
-0.372***
(0.095)
-0.467***
(0.095)
Model dummy 1.142**
(0.401)
-2.893***
(0.540)
0.632
(0.501)
0.279
(0.361)
-2.234***
(0.356)
Overall R-squared 0.020 0.040 0.014 0.013 0.047
Model 6
(DCenteurope)
Model 7
(DSoutheurope)
Model 8
(DNortheurope)
Model 9
(Ownership)
Model 10
(B2B)
Environment -0.000
(0.007)
-0.001
(0.007)
-0.001
(0.007)
-0.000
(0.007)
0.001
(0.007)
Social 0.010
(0.007)
0.013+
(0.007)
0.011
(0.007)
0.009
(0.007)
0.008
(0.007)
Corporate
Governance
0.004
(0.005)
0.002
(0.005)
0.005
(0.005)
0.007
(0.005)
0.004
(0.005)
lntotalassets -0.864***
(0.092)
-0.846***
(0.092)
-0.955***
(0.092)
-0.889***
(0.092)
-0.802***
(0.092)
Model dummy 0.182
(0.391)
-2.523***
(0.513)
1.906***
(0.481)
0.599+
(0.352)
-1.230***
(0.345)
Overall R-squared 0.087 0.108 0.101 0.091 0.098 ***p<.001, **p<.01, *p<.05, +p<.10 a. standard errors in parentheses. b. All models Prob > chi2 = 0.000 (red cells are not significant)
c. All models use random effects, based on Breusch and Pagan Lagrangian multiplier test for random effects
d. Number of observations = 3650
Table 12: Panel data analysis Using Winsorized Operating Profit Margin Variable Models /
Control Variable
Model 1
(DCenteurope)
Model 2
(DSoutheurope)
Model 3
(DNortheurope)
Model 4
(Ownership)
Model 5
(B2B)
Environment 0.001
(0.009)
0.000
(0.009)
0.001
(0.009)
0.000
(0.009)
0.000
(0.009)
Social 0.014
(0.009)
0.014
(0.009)
0.013
(0.009)
0.013
(0.009)
0.013
(0.009)
Corporate
Governance
0.011+
(0.007)
0.011
(0.007)
0.012+
(0.007)
0.015*
(0.007)
0.013+
(0.007)
lnemployees -1.441***
(0.144)
-1.429***
(0.144)
-1.427***
(0.144)
-1.440***
(0.144)
-1.409***
(0.144)
Model dummy 1.565*
(0.692)
-2.405*
(0.945)
-0.433
(0.868)
0.882+
(0.482)
0.404
(0.628)
Overall R-squared 0.138 0.139 0.134 0.141 0.133
Model 6
(DCenteurope)
Model 7
(DSoutheurope)
Model 8
(DNortheurope)
Model 9
(Ownership)
Model 10
(B2B)
Environment -0.008
(0.009)
-0.008
(0.009)
-0.008
(0.009)
-0.008
(0.009)
-0.008
(0.009)
Social -0.004
(0.009)
-0.003
(0.009)
-0.005
(0.009)
-0.004
(0.009)
-0.003
(0.009)
Corporate
Governance
0.009
(0.007)
0.009
(0.007)
0.010
(0.007)
0.012+
(0.007)
0.011
(0.007)
lntotalassets 0.129
(0.163)
0.089
(0.163)
0.065
(0.163)
0.045
(0.163)
0.002
(0.163)
Model dummy 1.396+
(0.761)
-2.404*
(1.016)
-0.080
(0.950)
0.643
(0.492)
1.291+
(0.675)
Overall R-squared 0.016 0.018 0.033 0.040 0.020 ***p<.001, **p<.01, *p<.05, +p<.10 a. standard errors in parentheses. b. All models Prob > chi2 = 0.000 (red cells are not significant)
c. All models use random effects, based on Breusch and Pagan Lagrangian multiplier test for random effects
d. Number of observations = 3650
38
Table 13: Panel data Analysis Using Natural Log Net Sales Variable Models /
Control Variable
Model 1
(DCenteurope)
Model 2
(DSoutheurope)
Model 3
(DNortheurope)
Model 4
(Ownership)
Model 5
(B2B)
Environment 0.002**
(0.001)
0.002**
(0.001)
0.002**
(0.001)
0.002**
(0.001)
0.002*
(0.001)
Social 0.001*
(0.001)
0.001*
(0.001)
0.002*
(0.001)
0.001*
(0.001)
0.002*
(0.001)
Corporate
Governance
0.000
(0.001)
0.000
(0.001)
-0.000
(0.001)
0.000
(0.001)
0.000
(0.001)
lnemployees 0.504***
(0.013)
0.486***
(0.013)
0.519***
(0.013)
0.487***
(0.013)
0.494***
(0.013)
Model dummy -1.056***
(0.088)
0.065
(0.130)
1.606***
(0.103)
0.037
(0.038)
0.305***
(0.085)
Overall R-squared 0.663 0.606 0.696 0.608 0.622
Model 6
(DCenteurope)
Model 7
(DSoutheurope)
Model 8
(DNortheurope)
Model 9
(Ownership)
Model 10
(B2B)
Environment 0.001
(0.001)
0.001
(0.001)
0.001
(0.001)
0.001
(0.001)
0.001+
(0.001)
Social 0.001*
(0.001)
0.001*
(0.001)
0.001*
(0.001)
0.001*
(0.001)
0.001+
(0.001)
Corporate
Governance
0.001
(0.000)
0.001
(0.000)
0.001
(0.000)
0.001
(0.000)
0.000
(0.000)
lntotalassets 0.792***
(0.014)
0.799***
(0.014)
0.783***
(0.014)
0.797***
(0.014)
0.817***
(0.014)
Model dummy -0.114
(0.079)
-0.337***
(0.106)
0.469***
(0.097)
-0.022
(0.032)
-0.666***
(0.067)
Overall R-squared 0.705 0.708 0.713 0.704 0.735
***p<.001, **p<.01, *p<.05, +p<.10 a. standard errors in parentheses. b. All models Prob > chi2 = 0.000 (red cells are not significant)
c. All models use random effects, based on Breusch and Pagan Lagrangian multiplier test for random effects
d. Number of observations = 3650
Table 14: Panel data Analysis Without moderators
Variables /
Models
Environment Social Corporate
Governance
Lnemployees Lntotalassets R-squared
(1) ROA -0.002 -0.011 -0.001 0.001
(2) ROA -0.000 -0.009 0.002 -0.872*** 0.007
(3) ROA -0.001 -0.009 0.001 -0.944** 0.004
(4) OPM 0.017 -0.000 -0.002 0.001
(5) OPM 0.018+ 0.001 -0.001 -0.362 0.002
(6) OPM 0.016 -0.001 -0.003 0.678+ 0.002
(7) Net Sales 0.001 0.001 0.002*** 0.010
(8) Net Sales 0.000 0.000 0.001* 0.353*** 0.150
(9) Net Sales 0.000 -0.000 0.001+ 0.828*** 0.350 ***p<.001, **p<.01, *p<.05, +p<.10 a. standard errors in parentheses. b. All fixed effects models Prob > F = significant* (red cells are
not significant) c. All random effects models Prob > chi2 = significant* (red cells are not significant) d. Number of observations =
3650
Table 15: Panel data analysis Without moderators Independent/
Model
CSP Lnemployees Lntotalassets R-squared
(1) ROA -0.015 -0.888*** 0.007
(2) ROA -0.005 -0.820*** 0.078
(3) OPM 0.006 -0.323 0.001
(4) OPM 0.010 0.010 0.001
(5) Net Sales -0.001 0.810*** 0.695
(6) Net Sales -0.000 0.359*** 0.149 ***p<.001, **p<.01, *p<.05, +p<.10 a. standard errors in parentheses. b. All fixed effects models Prob > F = significant* (red cells are not
significant) c. All random effects models Prob > chi2 = significant* (red cells are not significant) d. Number of observations = 3650
39
4.3 Reverse Causality Analysis Chapter 2.3 and 2.4 theorized the possible reverse causal effect CSP on FP. It stated the effect
could work both directions. To research if a reverse causal effect exists, a causality analysis has been
performed. Since the data were not sufficient to go back far enough to perform a Granger test, this
research performed six OLS regression analyses and six Panel Data analyses using random effects (See
Appendix 4, Hausman tests) models where CSP was the dependent variable and each of the measures
for firm performance were the independent variable.
Table 16 presents the results of reverse casual effects. The OPM for firm performance shows
significant (p<.05) positive results, meaning there is a reverse causal effect. A higher OPM leads to a
higher CSP. When using Net Sales as independent variable for firm performance, it results in a
significant negative effect on CSP. Highly significant (p<.001) in model 1 and a low significance in
model 2 (p<.10) both in combination with Total Assets as control variable. These findings can be due
to resource slack, where larger companies have an abundance of resources leading to higher total assets
but not a higher financial results (Barney, 1991). Based on the theory these results were not expected. If
an organization wants to invest in CSP it needs financial resources, so higher financial resources could
lead to a higher CSP, which could also explain these findings.
The results might influence the internal validity since this relates to the establishment of the
causal effect of the CSP-FP link. This thesis used a large dataset with longitudinal data over a five year
period. This is only used in the Panel Data analysis. In model 2 only 1 reverse causal effect is found
with the lowest significance. Statistically reverse causality exists. However, what this means in reality
is not in the scope of this research and therefore additional research is needed to explain these findings.
Table 16: Reverse Causality Independent/
Model
ROA OPM Net Sales
Employees TotalAssets Employees TotalAssets Employees TotalAssets
(1) OLS CSP -0.040 0.001 0.099* 0.092* 0.061 -1.212***
(2) Panel CSP -0.048 -0.043 0.024 0.023 -0.010 -0.747+ ***p<.001, **p<.01, *p<.05, +p<.10 a. For Panel CSP only random effects models are used (see appendices)
b. All random effects models Prob > chi2 = significant* (red cells are not significant)
4.4 Overview This research used the OLS method to compare organizations and Panel Data Analysis to study
the longitudinal effects of the level of CSP on FP. An overview of the results lead to the following
conclusions.
First, the results of both the OLS regressions and Panel Data Analysis provided mixed results
for the relationship of CSP with FP. While the results seem dependent of which FP measure is used, the
Social dimension has a significant and positive effect on CSP in each OLS model. This suggests
organizations should invest in the Social aspect of CSP to boost FP. However, if you want to increase
ROA or OPM also the Corporate Governance dimension is worth the investment, while for Net Sales
40
the Environmental dimension results in a positive effect. These results are supported in the Panel Data
Analysis. The social dimension shows consistent positive effect on FP in all models, this only applies
for Corporate Governance in the ROA and OPM models. In contrast, Net Sales are positively influenced
by the Environmental dimension.
Second, the dummy for central European countries results in a positive influence on the CSP-
FP link, when using ROA or OPM as FP indicator. This confirms findings of Jackson and Apostolakou
(2010). However, this relationship is negative when using Net Sales, which is in contrast with Jackson
and Apostolakou (2010). These findings are the same for OLS and Panel Data Analysis. A consistent
result is the negative effect of southern European countries on the CSP-FP link. All models show a
negative effect, this is in line with Jackson and Apostolakou (2010). However, they do not mention it to
be a negative influence. Northern European countries have a positive effect in each model where the
relationship is significant. Thus, central and northern European countries score better on CSP-FP link.
Third, the B2B dummy shows both positive and negative effects. Most results suggest rejection
of hypothesis 3. Based on the results of this research it is difficult to draw one conclusion. Again the
dependent variable used in the model is of great influence on the moderating effect.
Last, the moderating effect of the Ownership dummy shows unified results in all models.
Therefore, hypothesis 4 is accepted. Supporting research of Alshammari (2015).
41
Chapter 5 Conclusion and Discussion
5.1 Conclusion The main goal of this research was to study the relationship between CSP and an organization’s
FP. As a start several research questions have been formulated. CSP is defined by three dimensions:
Environmental, Social and Corporate Governance. For FP three different measures are used: ROA, OPM
and Net Sales. After describing and arguing the theory four hypotheses have been developed. In addition,
the European regions, B2B vs. B2C industries and governance structure in the form of share ownership
were defined as possible moderators on the CSP-FP link. According to some researchers CSP has a
positive linear effect, whereas other found a negative or U-shaped relationship. The results are consistent
for the Social dimension, showing a positive effect on FP in all models. However, the effect of the
Environmental and Corporate Governance dimensions on FP are dependent of which financial
performance measure is used. Still, the results show central European countries have a positive
moderating effect on the CSP-FP link. In addition, a negative moderating effect is found for southern
European countries. For the business types B2B vs. B2C no conclusive results were found. On the other
hand, the governance structure in the form of share ownership resulted in a positive moderating effect
on the CSP-FP link.
5.2 Discussion The CSP-FP link has been the topic of numerous studies, most of them with mixed results. To
provide additional insights into this subject, the relationship is studied based on a sample of European
organizations. In addition, the moderated effect of European regions, business types and governance
structure in the form of share ownership were generated. No previous research was focused on making
the distinction between northern, southern and central European countries. Therefore, this research
provides novel insight in the CSP-FP link, but makes it difficult to compare the results with previous
research. Yet, the findings seem in line with the main results on the CSP-FP link. Eccles, Ioannou and
Serafeim (2014) argued an organization’s social performance is synergistic to economic performance,
which is supported by findings of this research.
However, results also validate the study of Hull and Rothenberg (2008) and Brammer and
Millington (2008) who stated results are scattered. Positive and negative results were found confirming
model I and II (Figure 2), overall CSP has a positive effect on OPM and a negative on Net Sales. In
addition, the environmental dimension and corporate governance dimension confirm a possible negative
effect in some models. Research (Flammer, 2013; Matsumara, Prakash & Vera-Munoz, 2014) stated the
environmental dimension has gained importance, therefore one should expect a positive influence of the
environmental dimension. Since different models result into different influences the three dimensions
are reviewed more closely. The environmental dimension has a negative effect on ROA and OPM. The
42
negative effect on ROA could be explained by the cost of investment which affect the total assets (Short,
Libby & Libby, 2011). In addition, Net Sales does not included a cost component which could influence
the CSP-FP link.
Lannelongue, Gonzalez-Benito and Gonzalez-Benito (2015) argue three important aspects of
environmental management should be considered for environmental investments to positively impact
FP. Namely, environmental inputs, outputs and environmental management productivity (EMP). These
findings are more recent and could therefore explain the negative effects. Another possible underlying
mechanism which may influences the results are the backchannel effects of the indirect investment
mechanism. However, this is an unobservable effect from the point of view of this dataset, as such
factors are not included nor investigated within the scope of this research.
For the social dimensions only positive impacts were found. This partly supports the findings
of Barnett and Salomon (2012) who suggested a U-shaped influence, where this research only generated
high social performance leading to high FP. Their data ran from 1998 – 2006. Currently, stakeholders
place more value on social performance than during the time-span of Barnett and Salomon (2012) which
could explain why a low CSP does not lead to a higher FP in this research. One of the datatypes of social
performance is the Six Sigma, also known as the Lean Six Sigma, which gained major importance recent
years (Reis, 2011). This might be an underlying factor why a low level of the social dimension does not
result in a higher FP as stated by (Barnett & Salomon, 2012). According to Motwani (2012)
communicating CSR in more challenging than paying CSR. Properly communicating CSR minimizes
stakeholder skepticism and generates intrinsic motives in the CSR activities of an organization. This
could lead to a better FP. In addition, Saeidi et al. (2015) argued CSR, as a business strategy, pursues
an excellent spot in the competitive environment. According to Saeidi et al. (2105) organizations with a
major competitive advantage were able to obtain exceptional customer value. This leads to a higher level
of customer satisfaction creating large profits (Saeidi et al., 2015). Thus, this suggest if the social
dimensions leads to higher customer satisfaction, next more sales and therefore it could generate a higher
FP.
However, future research should take a closer look at the individual datatypes of the
environmental and corporate governance dimension, available in the ASSET4 dataset, to conclude if
certain datatypes can explain the mixed results found for these dimensions.
Early research of Waddock and Graves (1997) argued the CSP-FP link works both ways.
OPM and Net Sales showed signs of a reverse causal effect. These findings could be due to resource
slack, where larger companies have an abundance of resources leading to higher total assets, but not to
higher financial results (Barney, 1991). If an organization wants to invest in CSP it needs financial
resources, so higher financial resources could lead to a higher CSP, which could also explain these
findings. Nevertheless, the main findings of this research support Margolis and Walsh (2001) who
argued CSP predicts FP and not the other way around.
43
This research suggests a positive moderating influence of both northern and central European
countries, while southern countries show a consistent negative moderating effect. This supports the
findings of Jackson and Apostolakou (2010) who argued Anglo Saxon and central European countries,
in this research combined, score best in the CSP-FP link. However, the results of northern European
countries, found in this research, contradict their research. In addition, Jackson and Apostolakou (2010)
do not speak of a positive or negative influence of CSP on FP, but a higher CSP overall. Hartman, Rubin
and Dhanda (2007) argued U.S.-based companies are far more likely to rely on financial terms than are
EU firms when investing in CSR. This could explain why certain dimensions have a negative influence
on FP, because the focus lies not solely on economic benefits. Chapple and Moon (2005) found
multinational corporations adjust their CSR activities according to the specific national contexts in
which they operate. Thus, further research on the national context could give a more comprehensive
explanation for our findings. In central European countries the costs of implementing CSP could be
more directly reflected in the costs of goods sold, which could clarify the negative impact in the Net
Sales model. Another interesting finding is the consistent negative effect of southern European countries
on the CSP-FP. According to Idowu, Schmidpeter and Fifka (2015) organizations in southern European
countries perceive CSR as less of a management strategy, but rather a marketing tool. This could explain
the possible negative effect since using CSR solely as a marketing tool could backfire in the long run.
Research on business industries suggests a positive moderating effect for B2B organizations.
However, this research shows no results which confirm these studies. The main findings is being active
in a B2B environment has a negative moderating effect. This could be explained by Kubenkan and
Myskova (2009). They state higher CSR intention for B2C industries. This could be defined by the
different industries, impacting the CPS-FP link. Meaning not every industry, active in a B2B
environment, can be compared in the same sample. Further research on the intentions of B2B vs. B2C
industries could provide additional insight in this relation.
The positive moderating influence of ownership could be supported by Li & Zhang (2010) and
Ullmann (1985) who argued a heightened pressure for managers to engage in CSR activities when
shareholders are concerned with corporate social activities. When shareholders also have ownership
rights they are more likely concerned with the activities of an organization.
5.3 Contribution This research provides many theoretical and managerial implications and recommendations.
First, it uses several FP measures as dependent variable, providing mixed results. Therefore, contributing
which FP measure to use when examining the CSP-FP link. Research of Jaruzelski, Dehoff and Bordia
(2005) supports using multiple measures for firm performance. Since Net Sales is the dependent variable
resulting in contrasting findings compared to ROA and OPM. Future research should use all possible
performance measure as dependent variable to give a more comprehendible conclusion. In addition, this
could explain the positive vs. negative and mixed causal effects found in previous literature (Brammer
44
& Millington, 2008; Stanwick & Stanwick, 1998; Waddock & raves, 1997; Alshammari, 2015). Second,
this research shows the influence of the different European regions on the CSP-FP link. The database
used provides the bases for other regions and also countries moderating effects on an individual level.
It is interesting to research the effects of Europe compared to e.g. Asia, Africa or South Amerika. These
results can act as fundamentals for previous mentioned research. Third, the governance structure of share
ownership positively effects the CSP-FP link. This puts the research of Conelly, Hoksisson, Tihanyi and
Certo (2010) in a different perspective. They stated heightened managerial awareness of heterogeneous
owner interests increase owner’s influence on firm-level outcomes. This suggests they also have an
increased influence on an organizations CSP intentions.
Central and northern European countries have a positive influence on the CSP-FP relationship
while southern countries have a negative impact on the CSP-FP link. When management wants to
expand business to another European country, and they value CSP, this research provides a bases for
them to choose the right region. As previously mentioned the database of this research also has the
potential to see which individual country performs best. Due to the timespan and scope of this research,
this was excluded.
The influence of being in a B2B on the CSP-FP link is negative, managers can take this into
account when starting a new social responsive or green business and they are wondering which business
industry best fits their idea.
Ownership has a positive influence, suggesting organizations to issue shares with ownership
benefiting the CSP-FP link. This way shareholders are more involved with the organization and value
CSR more.
5.3 Limitations Apart from the fact the scores accurately reflect and summarize all the information which is
available of the focal organization’s CSP. It is likely the CSP scores, retrieved from the Thomson
Reuters ASSET4 dataset, might not reflect the full social impact of an organization’s CSR activities
(Ioannou & Serafeim, 2012). Therefore, future research could establish a tool which directly measures
the real social impact of CSR activities, instead of merely based on public admissions.
A better measurement would have been the Vigeo database as mention in the research of (Quéré
et al., 2015). Unfortunately this database was only accessible for selected researchers and subscribing
investment firms, this thesis could therefore not use this database.
The findings of Lannelongue et al. (2015) are not considered in the ASSET4. Which could have
an explanatory factor. This research uses the research of Brammer and Millington (2008) in many
aspects. However, their research only looks at the relationship of philanthropic or social outcome and
the influence on financial performance. Making conclusion for the environmental and corporate
governance dimension ungrounded.
45
The data is obtained from the year’s right after the crisis, this can affect the results. Brammer
and Millington (2008) conclude benefits to the financial performance accrue only long-term. This
research uses a timespan of five years, which could be insufficient. However, a timespan which is longer,
for example 20 years, could be highly biased by the fast changes the world goes through. This is not
accounted for in this research.
In terms of the moderators the following limitations have to be taking into account. The
geographical moderator uses 9 countries for the central European dummy, 3 for the southern European
dummy and 4 for the northern European dummy, which could influence the findings. In addition, more
data points and organizations were available for the central European countries. Moreover, Poland was
added to the central European countries, while according to Nationsonline (2016) it is part of Eastern
Europe. For the B2B dummy all organizations are coded based on the SIC Code list. Some organizations
are active in a B2B and B2C environment which could bias the results. Future research should focus on
the industries which captures this limitation. The Ownership dummy is based on one key indicators and
stays the same over the years, while a switch would be interesting to research.
Lastly, the reverse causal effects could possibly influence the internal validity. However, what
these findings mean in reality is not in the scope of this research and therefore additional research is
needed to explain these results.
46
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Appendices
Appendix 1: European Regions
Table 1: European regions http://www.nationsonline.org/oneworld/europe.htm
European regions
Northern Europe Southern Europe Middle Europe
Denmark Albania Belarus
Estonia Andorra Bulgaria
Faroe Islands Bosnia & Herzegovina Czech Republic
Finland Croatia (Hrvatska) Hungary
Greenland (DK) Cyprus Moldova
Iceland Gibraltar (UK) Poland
Ireland Greece Romania
Latvia Vatican City State Russia
Lithuania Italy Slovakia
Northern Ireland(UK) Macedonia, Rep. of Ukraine
Norway Malta Austria
Scotland (UK) Montenegro Belgium
Sweden Portugal France
United Kingdom San Marino Germany
Wales (UK) Serbia Liechtenstein
Slovenia Luxembourg
Spain Monaco
Turkey Netherlands
Switzerland
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Appendix 2: ASSET4 Dataset
Table 1: ASSET4 dataset print screen Companies, Industries and Countries
Table 2: ASSET4 dataset print screen Corporate Governance Score (Example BP)
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Table 3: ASSET4 dataset print screen Social Score (Example BP)
Table 4: ASSET4 dataset print screen Environmental (Example BP)
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Table 5: ASSET4 dataset print screen Shareholder Rights Ownership (Example BP)
Appendix 3: Skewness test
Netsales ROA operatingprofitmargin Employees Totalassets
Skewness (before) 7.630895 7.574436 -49.01877 4.697629 14.64934
Lnnetsales Wroa Woperatingprofitmargin Nlemployees nltotalassets
Skewness (after) -0.2481447 0.6251066 0.6959838 -0.5287238 0.3362445 Table 6: Summarize detail, Skewness test before and after natural logarithm and winsorising.
*ROA is winsorized at the 5th and 95th percentile
*Operatingprofitmargin is winzorized at the 10th and 90th percentile
Appendix 4: Hausman Tests Hausman Tests for Table 14 + 15
Independent /
Dependent
models
CSP Environment Social Corporate
Governance
Lnemployees Lntotalassets Hausman
test
(1) ROA x x x 0.042
(2) ROA x x x x 0.003
(3) ROA x x x x 0.011
(4) OPM x x x 0.000
(5) OPM x x x x 0.000
(6) OPM x x x x 0.000
(7) Net Sales x x x 0.000
(8) Net Sales x x x x 0.000
(9) Net Sales x x x x 0.000
(10) ROA x x 0.014
(11) ROA x x 0.437
(12) OPM x x 0.000
(13) OPM x x 0.078
(14) Net Sales x x 0.000
(15) Net Sales x x 0.205
Table 7: Results Hausman Tests for Table 14 and 15 in the results chapter
*Prob>chi2 = result column Hausman test
*When Prob>chi2 ≤ 0.05 than Fixed-Effects models are used. If Prob>chi2 ≥ 0.05 Random-Effects model are used
*For model 7 Hausman fixed random, sigmamore is used to solve (V_b-V_B is not positive definite)
57
Appendix 5: Abbreviations
Abbreviation Meaning
CSP Corporate Social Performance
CSR Corporate Social Responsibility
FP Financial Performance
CSP-FP link Corporate Social Performance – Financial Performance link
ROA Return On Assets
OPM Operating Profit Margin
ROS Return On Sales
EMP Environmental Management Productivity
ESG Environmental Social and Governance
B2B Business-To-Business
B2C Business-To-Consumer
C2C Consumer-To-Consumer
