Janine Chacko - Final Dissertation

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Does Corporate Social Responsibility Enhance
Firms’ Financial Performance?
An Empirical Analysis of the Banking Industry in the United Kingdom.
Abstract
This main objective of this paper is to determine the relationship between the corporate social
responsibility and financial performance for selected banks in the United Kingdom. The results
acquired from this empirical research has ratified that there exists a mixed relationship between
corporate social responsibility and the financial performance of banks in the United Kingdom.
The relationship is found to be negative or of a low positive correlation especially among banks
of a larger asset size leading to a conclusion that the higher amount of resources available is
overused for corporate social responsibility activities in a response to the immense pressure
faced by these banks to appear more socially responsible. Furthermore, the magnitude of
relationship are rather divergent from one bank to another leading to a belief that factors
affecting the financial performance of each bank are unique and occasionally include intangible
factors, such as gender equality and operational transparency, making it an arduous task to
conclude with a unanimous relationship across all banks. Lastly, the results also differ
substantially between the two different measurements of financial performance while the
disparity in measurements corporate social responsibility displayed no considerable difference
for each bank.
Keywords: Corporate social responsibility, financial performance, United Kingdom banks,
empirical research.
Janine Cherrie Vergara Chacko
W1520744
MSc Investment and Risk Finance
University of Westminster
Supervised by: Dr. Wangwei Lin

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Acknowledgements
Deepest appreciation and gratitude are extended to my supervisor, Dr. Wangwei Lin, whose
help provided me with the guidance I needed to produce this piece of research.
I am deeply indebted to my wonderful parents for their immeasurable love and support,
without whom this would not have been possible. Last but not least, I’d like to extend my
sincere thanks for the support of my friends, both in London and back home, for their never-
ending encouragement for my work.

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Table of Contents
1.0 Introduction 7
2.0 Literature Review 11
2.1 Positive Relationship
2.2 No Relationship
2.3 Negative Relationship
2.4 Mixed Relationship
2.5 Measurements of Corporate Social Responsibility
3.0 Research Objectives 16
4.0 Data Collection 18
5.0 Research Design and Methodology 19
5.1 Dependent Variable: FPi and FP1i
5.2 Independent Variable: CSRi and CSR1i
5.3 Independent Variable: SIZEi
5.4 Independent Variable: RISKi
5.5 Independent Variable: PRODi
5.6 Independent Variable: EXPi
5.7 Empirical Tests
6.0 Empirical Results 24
6.1 t-test
6.1.1 Results
6.1.2 T-test Conclusion
6.2 F-test
6.2.1 Results
6.2.2 F-test Conclusion
6.3 R2
and Adjusted R2
6.3.1 Comparison between the R2
Values
6.3.2 Comparison of the Models’ Adjusted R2
6.3.3 R2
and adjusted R2
Conclusion
6.4 Correlation between Financial Performance and CSR
6.4.1 Correlation Matrix Conclusion
7.0 Empirical Analysis 39

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7.1 T-test Results Analysis
7.2 F-test, R2
, and Adjusted R2
Results Analysis
7.2.1 F-test
7.2.2 R2
and Adjusted R2
7.3 Correlation Matrix Results Analysis
8.0 Limitations of Research 46
9.0 Discussion 47
References 50
Appendices 54
Appendix 1: Complete List of Banks Selected
Appendix 2: T-test Values for the Original Model and the Amended Model 1
Appendix 3: T-test Values for the Amended Models 2 and 3
Appendix 4: R2
and Adjusted R2
Values All Models
Appendix 5: Correlation Matrix for Independent Variables

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List of Tables
Table 1: F-test Values and Conclusion for the Original Model 28
Table 2: F-test Values and Conclusion for the Amended Model 1 28
Table 5: Comparison of R2
values 34
Table 6: CSR-FP Correlation for the Original Model 36
Table 7: CSR-FP Correlation for the Amended Model 1 36
Table 10: R2
, Adjusted R2
, and F-test values for the Original Model 41
Table 11: R2
, Adjusted R2
, and F-test values for the Amended Model 1 41
Table 12: R2
, Adjusted R2
Table 13: R2
, Adjusted R2
Table 14: Correlation Matrix for All Models Based on Asset Size 44

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List of Figures
Figure 1: CSR Diagram 8
Figure 2: Carroll’s CSR Model 8
Figure 3: R2
Values Comparison between the Original Model
and the Amended Model 3 32
Figure 4: R2
Values Comparison between the Amended Models 2 and 3 32
Figure 5: R2
Values Comparison between the Original Model
and the Amended Model 2 33
Figure 6: R2
Values Comparison between the Amended Models 1 and 3 34
Figure 7: Correlation Intensity Chart of the Original Model 37
Figure 8: Correlation Intensity Chart of the Amended Model 1 37

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1.0 Introduction
The body of literature surrounding corporate social responsibility (CSR) is a broad and varied
one with research dating back to the 1950’s. Having said that, there has not been a fixed
definition for the field of CSR as it has evolved, and will continue to evolve, along with
advancements in the world of business. (Taneja et al., 2011) With a myriad of terminologies
and theories surrounding the subject, the term CSR holds different meaning from one entity to
another. One of the earliest definitions of CSR was coined by Bowen (1953): “It refers to the
obligations of businessmen to pursue those policies, to make those decisions, or to follow those
lines of action which are desirable in terms of the objectives and value of our society.” (cited
in Carroll, 1999, p270) While the core concept of CSR still holds today, many variations of the
definition has since been put forth. The World Business Council for Sustainable Development
(n.d.) defines CSR as “the continuing commitment by business to contribute to economic
development while improving the quality of life of the workforce and their families as well as
of the community and society at large”. The United Nations Industrial Development
Organization (n.d.) defines CSR as “a management concept whereby companies integrate
social and environmental concerns in their business operations and interactions with their
stakeholders” while the European Commission (2014) states that companies who “take
responsibility for their impact on society” practice corporate social responsibility. It is with this
inconsistency in something as simple as the definition of CSR that mirrors the never-ending
changes in global trends and laws encompassing the subject of firms’ obligations to engage in
CSR. (Taneja et al., 2011)

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Figure 1: CSR Diagram
(Dahlsrud, 2006)
During the 1970’s, when the concept of CSR first came to light, Carroll (1979) established a
conceptual model for CSR where it encompassed four domains: economic, legal, ethical and
discretionary.
Figure 2: Carroll’s CSR Model
(Carroll, 1979)
CSRStakeholder
Environmental
Social
Economic
Voluntariness
• Discretionary
• Ethical
• Legal
• Economic

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Carroll’s (1979) CSR model illustrates the set of responsibilities that an organization has, in
decreasing focus of attention from the bottom up. It establishes that firms’ fundamental
responsibility is to produce economic outputs, followed by abiding by the law, being socially
ethical, and then lastly followed by their own discretionary responsibilities. In other words, the
model represented managerial responsibilities with regards to the firms’ CSR activities. While
this model is still widely adaptable in the business world today, contemporary literature have
questioned the true impact of firms’ CSR activities on the society as a whole. A multitude of
research, both qualitative and quantitative, have emerged throughout the years, analysing the
impacts of CSR on different areas of society, such as consumers and businesses, and have
returned with an even greater number of convoluted results. In recent years, with a general rise
in consumer awareness for ethical responsiveness, an increasing importance for CSR has
emerged among consumers influencing their level of support for firms in terms of their ethical
and social responsibilities. In other words, firms who actively engage in CSR or at least appear
to be more so, are receiving the praise and support of consumers while the less “CSR-savvy”
firms suffer in comparison. (Levy, 1999) Having said that, the top management of small firms
and multinational firms alike are gradually integrating CSR initiatives as part of their business
strategies with hopes to exploit the value-adding benefits that they may bring to enhance
corporate financial performance (FP). However, there has been an extensive debate as to
whether or not CSR initiatives actually do yield positive results in terms of FP. With 30 years
of research revolving around this subject, it is still gradually expanding due to the inconsistency
in results gathered from a diverse range of approaches to research design, research methods
and data analyses. (Malik, 2015) Upon gathering information from numerous research papers
published from as early as 1985 to as recent as 2015, the majority of research claims that CSR
does provide value-enhancing benefits to firms that engage in CSR initiatives than those who
don’t. (Waddock and Graves, 1997; Porter and Kramer, 2002; Saiia et al., 2003; Brammer and
Millington, 2005; Margolis et al., 2007; Giannarakis and Theotokas, 2011)
Nonetheless, there are still inconsistencies in results of research such as one that was conducted
by Soana (2011), which is of similar objective to what this paper is aiming to achieve, where
results show a non-existent relationship between CSR and the FP of Italian banks. The data set
collected by Soana (2011) including both Italian and international banks may have caused the
analysis to overlook certain factors that are country-specific, which could have potentially led
to an inaccurate set of results. Therefore, the main objective, which is to be explained in detail

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in a later section, of this study is to provide a more concentrated group of data set, concentrating
on the banking sector based in the United Kingdom.
The remainder of this proposal will be structured as follows. Section 2 is a literature review
that will provide an analysis of past research and studies on the subject of CSR and its
relationship, or lack thereof, with FP. Section 3 will provide a description of the main
objectives for this research. Section 4 will then outline the data to be collected. Section 5
describes the research design and methodology, with a detailed framework of the models to be
regressed and descriptions of variables included. Section 6 outlines the results acquired from
all tests conducted on the empirical models which are then analysed in detail in Section 7.
Section 8 discloses the limitations of this research. Section 9 concludes.

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2.0 Literature Review
2.1 Positive Relationship
In a research by Beurden and Gossling (2008), a collection of previous studies concluded that
68% of the research showed a positive relationship between CSR and FP. However, it is
claimed that the methodologies used in past research are inconsistent, which poses as a
hindrance for a more generalized set of results. It is also mentioned that consumers make
investment decisions based on companies’ sustainability scores, as access to these scores is
readily available. In other words, firms perceived as being socially responsible encourages an
increased level of support from customers and stakeholders as opposed to those who appear
less socially responsible, inevitably enhancing the firm’s financial performance. (McGuire et
al., 1988; Verschoor, 1998) Having said that, companies in controversial industries, such as
tobacco, casinos and alcohol use this to their advantage as research results found by Cai et al.
(2012) showed. They also found that the value of firms in these industries are found to be
positively related to the level of CSR initiatives undertaken as the top management make extra
effort to ensure that the firms are managed in an ethical manner given that their products pose
potential harm to the society, the environment and to the health of its consumers. Having said
that, firms utilize their CSR engagement as a tool to boost corporate reputation and competitive
advantage against competitors. Furthermore, Balabanis et al. (1998) state that CSR engagement
would provide for a better relationship between the firms and government officials or investors
contributing to a boost in FP. Another research exhibiting the benefits of CSR engagement is
one by Giannarakis and Theotokas (2011) where CSR is said to be exploited to aid in a
company’s survival by building or sustaining their brand name before and during the financial
crises of 2008 and 2011.
Firms these days are involved in multi-dimensional CSR policies with initiatives targeting the
environment, the community, social welfare etc. Crifo et al. (2015) emphasizes on the
importance of taking each dimension into account as failure to do so may yield erroneous
results regarding the true relationship between CSR and FP. Their research, using a multitude
of empirical approaches with different data sets, concluded that CSR showed a positive impact
when measured by the profits of a firm. Using a more thorough analysis including at least two
dimensions of CSR, results further affirm the previous findings that CSR and FP have a positive
relationship. After conducting multiple analyses on the subject and attaining the same

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conclusion, they established that the relationship between CSR and FP is positive with a varied
intensity according to the different forms of CSR initiatives that a firm may undertake. A
seemingly important dimension is corporate philanthropy, as tested by Brammer and
Millington (2005) as higher levels of charitable expenditures positively enhanced firm FP.
Margolis (2007) found that the relationship between CSR and FP is weaker, albeit still positive,
for certain dimensions such as corporate policies, transparency, third-party audits and screened
mutual funds after sorting the CSR initiatives into nine different categories. In relation to
Beurden and Gossling’s (2008) research, which ratified the fact that the relationship between
CSR and FP is solely a positive one, the results acquired by Crifo et al. (2015) further confirms
this statement after having carried out different approaches to prove otherwise.
Extending the CSR-FP relationship further, Waddock and Graves (1997) have established that
a causal relationship between CSR and FP exists. This means that while the effects of CSR on
FP is positive, firms with a strong FP would evidently invest in superior CSR initiatives, which
in turn, would result in enhanced FP in the long-run. In a separate study, Nelling and Webb
(2009) used a “fixed effects Granger causality approach” instead of the usual ordinary least
squares (OLS) regression method to analyse the data set and determined a positive, albeit weak,
CSR-FP relationship. Similarly, Margolis (2007) has found that while firms do reap beneficial
effects from engaging in CSR initiatives, it is tenuous due to the weak positive relationship
between CSR and CFP.
2.2 No Relationship
In Aupperle et al.’s (1985) research, no correlation was found between companies’ corporate
social responsibility initiatives and their financial performance. With an extensive empirical
research using both short-term and long-term Return on Assets (ROA), no significant evidence
have been found to support the claim that there exist a relationship, let alone a positive one,
between CSR and FP. It is further declared that firms’ social initiatives were “neither beneficial
nor harmful” for the corporate financial performance. A similar research conducted by Griffin
and Mahon (1997) on companies from a single industry, in this case chemical, established that
there is no link between CSR and FP. They have also stressed that inconsistencies in results
may be due to a broad focus when testing for correlation, as going across multiple industries
may cause certain industry-specific factors to be neglected during analyses. Similarly,
McWilliams and Siegel (2000) stressed that an inclusion of firms’ investment in R&D in the

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empirical research would prove to be a more accurate regression as previous research have
misspecified equations in which this important variable was mistakenly omitted leading to an
overestimation of the relationship between CSR & FP. Results from the inclusion of R&D
showed that the effects of CSR on FP is neutral in comparison to an identical regression as
performed by Waddock and Graves (1997) which demonstrated a positive relationship.
Furthermore, the alternative method used by Nelling and Webb (2009), as mentioned
previously, established that the causal CSR-FP relationship previously determined by
Waddock and Graves (1997) is non-existent.
2.3 Negative Relationship
An acclaimed study in the field of CSR is one by Friedman (1970) as he strongly affirms that
firms’ investment in CSR initiatives generate unnecessary high costs, greatly reducing profits
and negatively affecting FP. In terms of stock market value, Vance (1975) conducted an
empirical analysis whereby the ratings on firms’ CSR given by both businessmen and students
were used as the independent variable in a regression against the firms’ stock price. The results
concluded that the higher the CSR, the lower the stock market value, which evidently indicates
poor FP. On similar grounds, Brammer et al. (2006) have found rather intriguing results
showing firms with high CSR scores performing significantly worse in terms of average stock
returns than those that have zero scores, which remarkably outperformed the market.
2.4 Mixed Relationship
Given the multifaceted nature of CSR initiatives, many scholars have tested the CSR-FP
relationship with respect to each individual CSR dimension. One study by Balabanis et al.
(1998) demonstrated that certain CSR initiatives are more costly than others when they tested
each firm’s CSR activity against their financial ratios. They determined that environmental
activities are costly and negatively affected firms’ profitability while a focus on ethnic
minorities, which required less monetary resources, positively enhanced firms’ FP. The time
period has also proven to be an important research factor as McGuire et al. (1990) have found
that when data from two different time periods were regressed and compared, the effects of
CSR on FP differ significantly from a positive to a negative relationship.

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2.5 Measurements of Corporate Social Responsibility
Despite the copious amounts of research devoted to the field of CSR, there is yet to be a
homogenous approach to the fundamental issue of CSR measurement. It may be due to this
reason that the findings acquired regarding the CSR-FP link are inconsistent. Furthermore, the
empirical studies are especially at risk as the lack of uniformity in CSR measurement may lead
to erroneous equations, yielding inaccurate results. (Waddock and Graves, 1997) Given that
CSR comprises of multiple dimensions and differs from one firm to another, it poses a
challenge for scholars to accurately measure CSR both quantitatively and qualitatively. A
significant body of research (Chatterji et al., 2007; Taneja et al., 2011; Turker, 2009) has been
actively expanding in an effort to develop an accurate and consistent method of measuring
CSR. Although a fixed CSR measurement tool has yet to be developed, there are several
approaches that have been utilized frequently in previous studies.
The most widely adopted approaches to measuring CSR in past research (Griffin and Mahon,
1997; Waddock and Graves, 1997; McWilliams and Siegel, 2000; Nelling and Webb, 2009;
Mishra and Suar, 2010) are reputation indices and databases such as the Kinder, Lydenberg
and Domini (KLD) Database, the Fortune Index, the FTSE4Good Global Index and the
Canadian Social Investment Database (CSID), just to name a few. (Malik, 2015) These indices
provide a social rating in terms of the company’s reputation in ethical responsibilities and social
performance. While these databases hold a vast number of company information, they are
typically limited to a specific country. (Turker, 2009)
An alternative method that is increasing in popularity among scholars (Verschoor, 1998) is
content analysis of companies’ social reports where the annual reports are analysed for the
declaration of CSR efforts. (Turker, 2009) However, this method has received numerous
criticisms due to the possibility that information reported may not be necessarily practiced by
the company and may merely be façade for social responsibility. (McGuire et al., 1988)
Surveys are another approach to CSR measurement (Aupperle, 1985; McGuire et al., 1988;
Preston and O’Bannon, 1997; Saiia et al., 2003; Rettab et al., 2009; Torugsa and O’Donohue,
2012) whereby managers and directors are asked to complete a questionnaire with regards to
the company’s social performance. However, this method has proven to be problematic, as the

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results may be biased based on the manager’s personal perception of what the firm’s CSR
should be as opposed to what it is in reality. (Soana, 2011)
Measurement via single or multiple dimension indicators has also been utilized by scholars in
previous research (Scholtens, 2009; Jo et al., 2014) but has proven to have certain limitations
given its unilateral approach to assessing firms’ CSR engagement levels. (Turker, 2009)
Moreover, ethical rating scores from rating agencies have also been utilized in empirical
analyses (Vance, 1975; Balabanis et al., 1998; Simpson and Kohers, 2002; Giannarakis and
Theotokas, 2011; Kiessling et al., 2015) over the years put forth by rating agencies such as the
Dow Jones Sustainability Index, the Carbon Disclosure Project, the FTSE4Good Index etc.
However, each rating agency adopts their very own specialized method of quantifying the
aggregate CSR scores of firms making it a tall order for a comparison between agencies.
Potentially, one firm may yield different rating scores from different agencies, which could
bring about confusion regarding the firms’ true CSR performance.
While it may be assumed that there could be a “better” or “superior” approach to CSR
measurements, Soana (2011) attempted to find a pattern in terms of results by categorizing
previous studies by the CSR measurement method employed. However, while the majority of
results showed a positive relationship, every method seemed to still generate a mixed set of
results showing that it is inevitable that there exists a limitation for each measurement method.

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3.0 Research Objectives
As competition among firms in the corporate world gradually increases along with
contemporary developments in management and marketing, information on whether or not
CSR truly enhances FP is crucial information for firms’ stakeholders. While the focus of
research surrounding the subject varies from a generalized study across multiple industries to
industry-specific studies and region-specific to country specific, the amount of research
conducted on the banking industry or the financial services sector is relatively limited.
This study aims to focus on a rather fundamental industry as banks have a significant impact
on society and play a vital role in economic development. From a recent research conducted
by Jo et al. (2014), the banking industry is found to be the most “eco-friendly” sector making
it the prime industry to further investigate in this current study. In a similar study conducted by
Soana (2011), it is explained that the banking sector is one whereby reputation is of utmost
importance and that banks are quick to implement CSR activities with an objective to enhance
and protect their reputation. With an emphasis on the United Kingdom alone, a smaller data set
of 10 banks selected from a list published by the Bank of England (2015) is anticipated to
reduce the possibility of erroneous regression analyses due to misspecificed equations. This
comprehensive study of the true CSR-FP link in a specific industry is anticipated to produce a
more conclusive set of results. The objectives of this study is as follows:
1. To determine the possible link between the CSR initiatives of banks in the United
Kingdom and their potential effects on FP
2. To compare the results from one bank to another to analyse the difference in how CSR
affects their FP
3. To ascertain whether or not the difference in proxies for CSR and/or financial
performance measurement would affect results
The potential relationship between CSR and FP will be analysed via econometric estimations
based on the banks’ information available on the annual reports and through the use of
Bloomberg terminals.
With sufficient theoretical and methodological research, it is anticipated that this study would
contribute to an ever-expanding body of literature with hopes of providing valuable

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information to individuals at the top-level management of banks in the United Kingdom and
worldwide.
For the complete list of banks please refer to Appendix 1.

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4.0 Data Collection
As previously mentioned, the lack of a standardized CSR measurement has been the main cause
of contradicting research results over the years. Having said that, it is decided that two sources
of ethical rating scores for the banks are to be used in this empirical research with the objective
of understanding whether the proxy for CSR measurement would cause a significant difference
in results. The ethical rating scores to be utilized in this study will be the company’s ESG
Disclosure Scores and those provided by the Carbon Disclosure Project (CDP). For the proxies
of financial performance, the return on assets (ROA) and the net operating profit after tax
(NOPAT) of the banks will be used, just as many past empirical research has conducted to aid
in comparison. For ease of analyses, each of the banks’ data is computed as an average
annualised value for the year 2005 to 2014. The data for this study will be collected via the
banks’ annual reports and the Bloomberg terminal.

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5.0 Research Design and Methodology
A data set comprised of 10 banks incorporated in the United Kingdom will be utilized in an
empirical research by regression analyses, as conducted by the majority of past research in an
effort to obtain a more generalized and consistent set of results for ease of comparison. This
empirical analysis will be conducted by running multiple linear regression models using the
EViews statistical software package. The firm financial performance will act as the dependent
variable and the independent variables will include CSR measurement scores, the size, risk
management efforts, the labour productivity and expense management efforts.
Four different models with combinations of financial performance and CSR measurements will
be conducted in order to analyse the difference in results and to gain a more wholesome set of
results on how CSR truly affects banks’ financial performance. By alternating the combinations
of measurements of both the financial performance and CSR, the consistency, or lack thereof,
will be apparent allowing for a potentially stronger vantage point for the banks’ CSR-FP
relationship. The four models are as follows:
Original Model
𝐹𝑃𝑖 = 𝛽1 + 𝛽2 𝐶𝑆𝑅𝑖 + 𝛽3 𝑆𝐼𝑍𝐸𝑖 + 𝛽4 𝑅𝐼𝑆𝐾𝑖 + 𝛽5 𝑃𝑅𝑂𝐷𝑖 + 𝛽6 𝐸𝑋𝑃𝑖 + 𝑢𝑖
Amended Model 1
𝐹𝑃𝑖 = 𝛽1 + 𝛽2 𝐶𝑆𝑅1𝑖 + 𝛽3 𝑆𝐼𝑍𝐸𝑖 + 𝛽4 𝑅𝐼𝑆𝐾𝑖 + 𝛽5 𝑃𝑅𝑂𝐷𝑖 + 𝛽6 𝐸𝑋𝑃𝑖 + 𝑢𝑖
Amended Model 2
𝐹𝑃1𝑖 = 𝛽1 + 𝛽2 𝐶𝑆𝑅𝑖 + 𝛽3 𝑆𝐼𝑍𝐸𝑖 + 𝛽4 𝑅𝐼𝑆𝐾𝑖 + 𝛽5 𝑃𝑅𝑂𝐷𝑖 + 𝛽6 𝐸𝑋𝑃𝑖 + 𝑢𝑖
Amended Model 3
𝐹𝑃1𝑖 = 𝛽1 + 𝛽2 𝐶𝑆𝑅1𝑖 + 𝛽3 𝑆𝐼𝑍𝐸𝑖 + 𝛽4 𝑅𝐼𝑆𝐾𝑖 + 𝛽5 𝑃𝑅𝑂𝐷𝑖 + 𝛽6 𝐸𝑋𝑃𝑖 + 𝑢𝑖
Where:
𝐹𝑃𝑖 = Return on assets (ROA)
𝐹𝑃1𝑖 = Net operating profits after tax (NOPAT)
𝐶𝑆𝑅𝑖 = Environmental, Social, and Governance (ESG) Disclosure Scores

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𝐶𝑆𝑅1𝑖 = Carbon Disclosure Project (CDP) scores
𝑆𝐼𝑍𝐸𝑖 = Total assets of the bank
𝑅𝐼𝑆𝐾𝑖 = Equity-to-assets Ratio
𝑃𝑅𝑂𝐷𝑖 = Labour productivity measured by
𝑇𝑜𝑡𝑎𝑙 𝑅𝑒𝑣𝑒𝑛𝑢𝑒
𝑁𝑜. 𝑜𝑓 𝐸𝑚𝑝𝑙𝑜𝑦𝑒𝑒𝑠
𝐸𝑋𝑃𝑖 = Expense management measured by
𝑂𝑝𝑒𝑟𝑎𝑡𝑖𝑛𝑔 𝐸𝑥𝑝𝑒𝑛𝑠𝑒𝑠
𝑇𝑜𝑡𝑎𝑙 𝐴𝑠𝑠𝑒𝑡𝑠
𝑢𝑖 = Error term
𝛽1 = Market beta
The regression equations above is similar to multiple empirical studies conducted in the past
(McWilliams and Siegel, 2000; Soana, 2011; Kiessling et al., 2015; Crifo et al., 2016) with
some variables altered to tailor specifically to the UK banking sector.
5.1 Dependent Variable: 𝐅𝐏𝐢 and 𝐅𝐏𝟏𝐢
The measurement for firm performance in past empirical research (Aupperle et al., 1985;
Waddock and Graves, 1997; McWilliams and Siegel, 2000; Pasiouras and Kosmidou, 2007;
Kiessling et al., 2015) have predominantly been ROA, influencing the decision to utilize this
profitability ratio as the first proxy, 𝐹𝑃𝑖 for firm performance. The ROA has served well as a
measurement for financial performance as it measures firms’ profitability with respect to the
total number of assets. Moreover, in a recent study conducted by Crifo et al. (2015), it is found
that profits of a firm serve as an appropriate proxy as well when regressed against its CSR
initiatives. This influenced the decision to employ the NOPAT as the second proxy, 𝐹𝑃1𝑖, for
financial performance. The administration of two proxies provides this research with a larger
set of results for comparison purposes.
5.2 Independent Variable: 𝐂𝐒𝐑 𝐢 and 𝐂𝐒𝐑𝟏𝐢
As for the measurement of the banks’ CSR, it was initially intended for the proxies to be the
FTSE4Good Index and the Dow Jones Sustainability Index (DJSI). Unfortunately, due to

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security reasons and the private nature of the rating agencies, it proved to be a tall order to
acquire the scores of the banks. Instead, environmental, social and governance (ESG) scores
and the Carbon Disclosure Project (CDP) scores, which are publicly available, have been
applied to the regression models. Contradictory to what has been reported by Brammer et al.
(2006), the scores for large banks are not significantly better than those of the smaller banks in
any way. For example, Marks and Spencer Financial Services managed to score a 44.50 in the
ESG disclosure score, surpassing Barclays Bank Plc that scored a meagre 32.02 in the same
year. Similarly for the CDP scores, Sainsbury’s Bank Plc scored a 95 while The Royal Bank
of Scotland failed in comparison with a score of 88. Having said that, both proxies prove to be
a standardized forms of measurement for CSR across the banking sector, making it ideal for
this study.
5.3 Independent Variable: 𝐒𝐈𝐙𝐄𝐢
Congruent to past empirical studies (Pasiouras and Kosmidou, 2007), the banks’ total assets is
appointed as the proxy for the independent variable 𝑆𝐼𝑍𝐸𝑖. Given that this research only aims
to study a small set of ten banks incorporated in the United Kingdom, it is important to
acknowledge that the banks vary in terms of size, which evidently influences the amount of
resources invested in CSR activities. (Waddock and Graves, 1997) Within the small sample
size of ten banks, a wide range of bank sizes, in terms of total assets, have been included. From
one of the biggest banks in the world, HSBC Bank Plc to the lesser known Marks and Spencer
Financial Services, it is with this range that the effects of CSR on financial performance can
truly be tested. This eliminates any potential bias for those of large banks that seemingly have
a larger amount of resources dedicated to the implementation of CSR activities.
5.4 Independent Variable: 𝐑𝐈𝐒𝐊 𝐢
The proxy used for the variable 𝑅𝐼𝑆𝐾𝑖 is the equity-to-assets ratio, as conducted by various
empirical studies (McWilliams and Siegel, 2000; Pasiouras and Kosmidou, 2007) in the past.
The equity-to-assets ratio is a measurement of the banks’ financial competence and its ability
to fulfil its debt obligations in the event of liquidation. Banks with a high equity-to-assets ratio
indicates low leverage resulting in higher profitability and, in turn, lower risk. Including this

22
variable in the study provides for a larger spectrum of risk-return characteristics in the sample
of banks chosen.
5.5 Independent Variable: 𝐏𝐑𝐎𝐃𝐢
As per the research conducted by Athanasoglou et al. (2008), the quality of labour productivity,
as measured by
𝑇𝑜𝑡𝑎𝑙 𝑅𝑒𝑣𝑒𝑛𝑢𝑒
𝑁𝑜. 𝑜𝑓 𝐸𝑚𝑝𝑙𝑜𝑦𝑒𝑒𝑠
, is said to pose a substantial impact on banks’ financial
performance. Following their study, it is decided that the variable for labour productivity will
be included in the regression model. The inclusion of this variable is also further acclaimed by
Huselid (1995) and Gimenez et al. (2012) as the quality of human resources have been proven
to have an effect on financial performance, both positively and negatively.
5.6 Independent Variable: 𝐄𝐗𝐏𝐢
The final independent variable included is a proxy for banks’ efficiency in managing expenses.
This variable has been included in a limited number of past empirical studies (Pasiouras and
Kosmidou, 2007) but has received considerable attention in recent studies (Vigano and Nicolai,
2009) as an important determining factor in banks’ financial performance. This variable
encompasses critical information about the operational costs of a bank that has previously
shown to have an adverse effect on financial performance, thereby proving to be an important
variable to be included in this study.
5.7 Empirical Tests
Prior to running the regressions, it is imperative to test for the presence of perfect
multicollinearity among the independent variables to avoid erroneous equations. However, the
presence of imperfect multicollinearity, whereby the correlation is less than perfect (Asteriou
and Hall, 2016) does not indicate a misspecified model as the model will still remain unbiased
given that the variance of the error term is small. In addition, imperfect multicollinearity among
variables produce estimates that are the best, also known as BLUE which stands for Best Linear
Unbiased Estimators in econometric terms. (Asteriou and Hall, 2016) A monster correlation
matrix (Appendix 4) was first formed to check for correlation among the independent variables.

23
Given that a small number of correlation was present, an inspection of the variation inflation
factors (VIF), with a maximum threshold of 10 (O’Brien, 2007), was administered to test for
multicollinearity. While a small number of variables with VIF values exceeding the threshold
were eliminated from the models, most of the variables’ VIF values were well below 10,
implying imperfect multicollinearity among the independent variables. Upon ensuring that
perfect multicollinearity is not present in any one of the models, tests may be performed
accurately and erroneous results may be avoided.
A 5% significance level will be utilized for all tests in this study encompassing t-tests for
individual significance, an F-test for the joint significance of the model and an analysis of the
changes in the R2
and adjusted R2
as adjustments in the model are made. These tests on the
regression models will first determine if there exists a relationship between CSR and FP.
Upon determining that a relationship does exist between CSR and FP in the regression model,
it is with the correlation matrix that we determine if that relationship is positive or negative.
Both the regression analyses and the correlation matrix will be conducted on the average
annualized data from the years 2005 to 2014.

24
6.0 Empirical Results
Individual regression models were created for each of the ten banks to obtain the data set
needed. Amendments for the measurements for financial performance and CSR were made in
each model to demonstrate the potential difference in results produced. In some models,
variables were excluded due to the presence of multicollinearity.
6.1 t-test
The first test conducted across all models is the t-test. With the t-test, the significance and
relevance of each individual variable to the independent variable is tested. Prior to conducting
the t-tests, it is hypothesized:
𝑯 𝟎: 𝜷 𝟏 = 𝟎; 𝑻𝒉𝒆 𝒊𝒏𝒅𝒊𝒗𝒊𝒅𝒖𝒂𝒍 𝒗𝒂𝒓𝒊𝒂𝒃𝒍𝒆 𝒊𝒔 𝒏𝒐𝒕 𝒔𝒊𝒈𝒏𝒊𝒇𝒊𝒄𝒂𝒏𝒕
𝑯 𝟏: 𝜷 𝟏 ≠ 𝟎; 𝑻𝒉𝒆 𝒊𝒏𝒅𝒊𝒗𝒊𝒅𝒖𝒂𝒍 𝒗𝒂𝒓𝒊𝒂𝒃𝒍𝒆 𝒊𝒔 𝒔𝒊𝒈𝒏𝒊𝒇𝒊𝒄𝒂𝒏𝒕
The t-test formula employed is as follows:
𝑡∗
=
𝛽̂ − 𝛽
𝜎̂(𝛽̂)
~𝑡 𝛼
2
,𝑛−𝑘
𝛼 = 0.05
𝑘 = 6 (5 𝑖𝑛 𝑠𝑜𝑚𝑒 𝑚𝑜𝑑𝑒𝑙𝑠 𝑑𝑢𝑒 𝑡𝑜 𝑜𝑚𝑖𝑠𝑠𝑖𝑜𝑛 𝑓𝑜𝑟 𝑚𝑢𝑙𝑡𝑖𝑐𝑜𝑙𝑙𝑖𝑛𝑒𝑎𝑟𝑖𝑡𝑦)
𝑛 = 10
Setting the significance level at 5% (𝛼 = 0.05), a p-value smaller than 0.05 would lead us to
accept 𝐻1 and conclude that the variable is significant to the model. Otherwise, 𝐻0 is accepted,
portraying the variable’s non-significance. The acceptance region for the t-test is determined
by the equation of 𝐴. 𝑅. = [−𝑡 𝛼
2
,𝑛−𝑘, 𝑡 𝛼
2
,𝑛−𝑘], which is [-2.776, 2.776] for 𝑘 = 6 or [-2.571,
2.571] for 𝑘 = 5 as per the distribution table. The difference in acceptance region is due to the
one less variable excluded from the model to eliminate the presence of multicollinearity. If the

25
value of the t-test falls within the A.R., the 𝐻0 will be accepted concluding that the variable is
not significant and vice versa. In other words, a t-test value within the A.R. indicates that it
does not hold individual explanatory power towards the independent variable.
6.1.1 Results
Please refer to Appendix 2 and 3 for all t-test results.
Original Model
The original model employs the ROA as 𝐹𝑃𝑖 and ESG scores as 𝐶𝑆𝑅𝑖. As per the results found,
it is noted that out of the 10 banks and 42 independent variables in total, only 9 independent
variables were proven to be individually significant to the banks’ financial performance. The
bank with the most number of individual statistically significant variables is Marks and Spencer
Financial Services with three out of four variables holding individual explanatory power
towards its financial performance. Surprisingly, the only variable not significant was the 𝐶𝑆𝑅𝑖
variable. Moreover, only Lloyds Bank Plc have returned with its 𝐶𝑆𝑅𝑖, the ESG Disclosure
Score, being of individual significance to its financial performance. Others such as Barclays
Bank Plc, Citibank International Limited, Sainsbury’s Bank Plc, Standard Chartered Bank and
Westpac Europe have returned with no individually significant independent variables.
Amended Model 1
The amended model 1 with the Carbon Disclosure Project (CDP) scores as the 𝐶𝑆𝑅1𝑖 and ROA
as 𝐹𝑃𝑖 returned with less significant independent variables. Only 5 out of the 44 variables have
been proven to be significant. Only Barclays Bank Plc have returned with an individually
significant 𝐶𝑆𝑅1𝑖 variable. Similar to the original model, Tesco Personal Finance Plc had all
but one significant variable, the 𝐶𝑆𝑅1𝑖. On the other hand, this model holds the least presence
of multicollinearity among independent variables.

26
Amended Model 2
With net operating profits after taxes (NOPAT) as 𝐹𝑃1𝑖 and ESG scores as 𝐶𝑆𝑅𝑖, the amended
model 2 exhibited a more convincing set of results, with 12 out of the 42 variables being
statistically significant making it the model with the most number of individually significant
variables. On two occasions, for Sainsbury’s Bank Plc and Marks and Spencer Financial
Services, the 𝐶𝑆𝑅𝑖 proved to be individually significant.
Amended Model 3
The amended model 3 using NOPAT and the CDP scores have returned with 8 out of 44
variables being statistically significant. The model on Tesco Personal Finance Plc exhibited
satisfactory results as all its independent variables have been proven to be individually
significant towards its financial performance. None of the models for the other banks showed
any individual significance of the 𝐶𝑆𝑅1𝑖 towards the 𝐹𝑃1𝑖.
6.1.2 T-test Conclusion
The t-tests conducted across all four models have portrayed a rather pessimistic set of results.
While the t-tests displaying the dependent variables’ explanatory power towards the banks’
financial performance are relatively weak, it does not necessarily indicate that the models are
misspecified. Especially the empirical models for Citibank International Limited, whereby the
results have shown individual non-significance across all four amended models, further tests
are conducted to evaluate the independent variables’ joint significance.

27
6.2 F-test
Given that most of the variables seem to have exhibited inadequate results, the F-test is
conducted to test if all of the independent variables jointly explain the dependent variable. The
hypothesis 𝐻0 depicts a situation when not one of the variables is significant while 𝐻1 is when
at least one of the variables is significant.
𝑯 𝟎: 𝜷 𝟏 = 𝜷 𝟐 = 𝜷 𝟑 = 𝜷 𝟒 = 𝜷 𝟓 = 𝜷 𝟔 = 𝟎; 𝑽𝒂𝒓𝒊𝒂𝒃𝒍𝒆𝒔 𝒂𝒓𝒆 𝒏𝒐𝒕 𝒋𝒐𝒊𝒏𝒕𝒍𝒚 𝒔𝒊𝒈𝒏𝒊𝒇𝒊𝒄𝒂𝒏𝒕
𝑯 𝟏: 𝑯 𝟎 𝑭𝒂𝒍𝒔𝒆; 𝑽𝒂𝒓𝒊𝒂𝒃𝒍𝒆𝒔 𝒂𝒓𝒆 𝒋𝒐𝒊𝒏𝒕𝒍𝒚 𝒔𝒊𝒈𝒏𝒊𝒇𝒊𝒄𝒂𝒏𝒕
The F-test formula to be used is as follows:
𝐹∗
=
𝑅2
/(𝑘 − 1)
(1 − 𝑅2)/(𝑇 − 𝑘)
∼ 𝐹1−𝛼,𝑘−1,𝑇−𝑘
Where:
𝛼 = 0.05; 𝑘 = 6 𝑜𝑟 5; 𝑇 = 10
From the F-test distribution table, it is determined that the critical F-value = [0, 6.2561] for k
= 6 and [0, 5.1922] for k = 5. An F-test value that is larger than the critical F-value concludes
that the variables are jointly significant in explaining the dependent variable.
6.2.1 Results
Original Model

28
Table 1: F-test Values and Conclusion for the Original Model
The results from the F-test show that half of the banks’ independent variables have joint
significance in explaining the 𝐹𝑃𝑖 . This suggests that for Marks and Spencer Financial
Services, Barclays Bank Plc, Citibank International Limited, Standard Chartered Bank, and
Westpac Europe Limited, the CSR proxy of ESG Disclosure Scores along with the other
independent variables do not jointly affect their ROA.
Amended Model 1
Table 2: F-test Values and Conclusion for the Amended Model 1
Results from the amended model 2 have returned with less number of banks holding jointly
significant variables. In the original model, Barclays Bank Plc had an F-test value of 3.73923,
which has now increased to 6.34562, whereas The Royal Bank of Scotland and Lloyds Bank
F-test Conclusion
The Royal Bank of Scotland Plc 13.28941 Not within F-crit.; Jointly significant
HSBC Bank Plc 12.78880 Not within F-crit.; Jointly significant
Lloyds Bank Plc 10.91495 Not within F-crit.; Jointly significant
Tesco Personal Finance Plc 8.97370 Not within F-crit.; Jointly significant
Sainsbury's Bank Plc 6.97569 Not within F-crit.; Jointly significant
Marks and Spencer Financial Services 3.81056 Within F-crit.; Not jointly significant
Barclays Bank Plc 3.73923 Within F-crit.; Not jointly significant
Citibank International Limited 3.52955 Within F-crit.; Not jointly significant
Standard Chartered Bank 1.91890 Within F-crit.; Not jointly significant
Westpac Europe Ltd 0.66021 Within F-crit.; Not jointly significant
F-test Conclusion
HSBC Bank Plc 6.54754 Not within F-crit.; Jointly significant
Barclays Bank Plc 6.34562 Not within F-crit.; Jointly significant
Citibank International Limited 3.58022 Within F-crit.; Not jointly significant
The Royal Bank of Scotland Plc 3.38835 Within F-crit.; Not jointly significant
Lloyds Bank Plc 1.82459 Within F-crit.; Not jointly significant
Standard Chartered Bank 1.78646 Within F-crit.; Not jointly significant
Westpac Europe Ltd 0.35682 Within F-crit.; Not jointly significant

29
Plc experienced dramatic drops from 13.28941 to 3.38835 and from 10.91495 to 1.82459
respectively. This suggests that using the CDP scores as a proxy for CSR does not determine
the ROA of The Royal Bank of Scotland and Lloyds Bank Plc as well as the ESG Disclosure
scores, and vice versa for Barclays Bank Plc. Moreover, results from this model suggests that
the CDP scores of Citibank International Limited, The Royal Bank of Scotland, Lloyds Bank
Plc, Standard Chartered Bank, Marks and Spencer Financial Services and Westpac Europe,
along with the other independent variables do not play a role in determining the banks’ ROA.
Amended Model 2
Compared with the original model and the amended model 1, the amended model 2 has
performed significantly better with seven out of ten banks showing jointly significant
independent variables. It is also noted that in the amended model 2, after the NOPAT was
utilised as a proxy for financial performance, the overall results have changed substantially,
with Westpac Europe Ltd now having the highest F-test value while it exhibited the lowest
values in the previous models. Moreover, HSBC Bank Plc and Lloyds Bank Plc showed an
unusually low F-test value after being at the top of the list in the previous models. The findings
in this model suggest that for those banks with diminished F-test value, the measurement of
CSR using the ESG scores is not as strong as a determinant for NOPAT than it was for ROA
and vice versa for those with a higher F-test value.
F-test Conclusion
Westpac Europe Ltd 41.36742 Not within F-crit.; Jointly significant
Standard Chartered Bank 12.41425 Not within F-crit.; Jointly significant
Marks and Spencer Financial Services 11.09465 Not within F-crit.; Jointly significant
Citibank International Limited 9.03408 Not within F-crit.; Jointly significant
HSBC Bank Plc 0.64775 Within F-crit.; Not jointly significant

30
Amended Model 3
Finally, in the amended model 3, the results for the F-test showed that six out of ten banks have
jointly significant independent variables. The amended model 3 had results similar to the
amended model 2 in the sense that Westpac Europe Ltd is still the best performer while HSBC
Bank Plc and Lloyds Bank Plc are still at the bottom of the list. With similarities in results, the
model suggests that the proxy for CSR being CDP or ESG does not have a disparate effect on
the banks’ financial performance when measured with the NOPAT proxy. With the exception
of Marks and Spencer Financial Services, whereby the F-test value fell from 11.09465 in the
amended model 2 to 1.88750 in this model, the ESG scores undoubtedly outweighs the CDP
scores in determining its NOPAT. On the positive side, Tesco Personal Finance, albeit being
jointly significant in the amended model 2, had substantial increments in F-test values in this
model. This implies that the use of CDP scores proves to be a stronger determinant of its
NOPAT than that of the ESG scores.
6.2.2 F-test Conclusion
Across all four models, the overall F-test values have proven that the independent variables
included within the models are jointly significant, for all of the banks in at least one of the
models. The banks that have consistently had jointly significant independent variables across
all models are Sainsbury’s Bank Plc and Tesco Personal Finance Plc. It is also found that the
results are similar between the original model and the amended model 1 and between the
F-test Conclusion
Westpac Europe Ltd 29.93014 Not within F-crit.; Jointly significant
Standard Chartered Bank 9.78807 Not within F-crit.; Jointly significant
Citibank International Limited 8.85777 Not within F-crit.; Jointly significant
HSBC Bank Plc 0.54964 Within F-crit.; Not jointly significant

31
amended model 2 and 3. While it is not as apparent between the original model and the
amended model 1 (with ROA as 𝐹𝑃𝑖), the results of the amended models 2 and 3 (with NOPAT
as 𝐹𝑃1𝑖) are similar in terms of order of significance. This shows that the true difference lies
in the differing proxies of financial performance and not in those of CSR.
6.3 R2 and Adjusted R2
The R2
measures the percentage of variance of the dependent variable explained by the
regression. (Asteriou and Hall, 2016) In simpler terms, the R2
measures the model’s goodness-
of-fit in that the higher the R2
, the higher the model’s explanatory power towards the dependent
variable. For example, an R2
value of 0.85 would mean the independent variables explain 85%
of the variation in the banks’ financial performance. On the other hand, the adjusted R2
accounts for changes in the R2
in that it measures the significance of the particular amendment
made in the model. The adjusted R2
will increase only if changes in the model is statistically
significant to the model and decrease if changes are not significant. (Asteriou and Hall, 2016)
Having said that, the adjusted R2
is historically used as a determining tool as to whether or not
the adding or subtracting of a variable should be done. (Brooks, 2014)
In the next section, the models will be compared against each other to analyse the changes in
the R2
as the models undergo amendments. For all R2
values, please refer to Appendix 4.

32
6.3.1 Comparison between the R2 Values
Figure 3: R2
Values Comparison between the Original Model and the Amended Model 3
It is found that the values from the original model and the amended model 1 are relatively
similar. Most of the banks held rather consistent R2
values despite the change in the proxy of
CSR measurement from 𝐶𝑆𝑅𝑖 (ESG Scores) to 𝐶𝑆𝑅1𝑖 (CDP Scores).
Figure 4: R2
Values Comparison between the Amended Models 2 and 3
0.000000
0.100000
0.200000
0.300000
0.400000
0.500000
0.600000
0.700000
0.800000
0.900000
1.000000
Original Model and Amended Model 1
Original Model Amended Model 1
0.000000
0.200000
0.400000
0.600000
0.800000
1.000000
1.200000
Amended Model 2 and 3
Amended Model 2 Amended Model 3

33
Similar to the comparison of the original model and the amended model 1, the amended model
2 and 3 have R2
values that are almost identical. The movements in both charts are similar
between the compared models potentially signifying the consistency in explanatory power of
the models when the dependent variable employed is constant. Within the original model and
the amended model 1, the FP proxy was ROA while the NOPAT is utilized for the amended
models 2 and 3. On the other hand, the amendments made for the proxies of CSR between all
models does not have a sizable effect on the explanatory power of the models on the dependent
variable. At this point, it can be speculated that the change in CSR proxy does not have a
considerable effect on the models. However, a further comparison shall be covered in the next
section to evaluate this speculation.
Figure 5: R2
Values Comparison between the Original Model and the Amended Model 2
Chart 3 illustrates a significant difference between the R2
values of the original model and the
amended model 2. The disparity between both models is the proxy used for financial
performance while the measurement for CSR is held constant with the ESG disclosure scores.
0.000000
0.200000
0.400000
0.600000
0.800000
1.000000
1.200000
Original Model and Amended Model 2
Original Model Amended Model 2

34
Figure 6: R2
Values Comparison between the Amended Models 1 and 3
Similar to Chart 3, Chart 4 depicts a conflicting set of R2
values between the amended models
1 and 3 whereby the CSR proxies are held constant with the CDP scores and regressed against
the ROA in the amended model 1 and NOPAT in the amended model 3.
6.3.2 Comparison of the Models’ Adjusted R2
Table 5: Comparison of R2
values
Figure x compiled the adjusted R2
values of all the models. The columns in light grey show the
lowest value for each bank while the columns in dark grey show the highest value. In other
0.000000
0.200000
0.400000
0.600000
0.800000
1.000000
1.200000
Amended Models 1 and 3
Amended Model 1 Amended Model 3
Original Model Amended Model 1 Amended Model 2 Amended Model 3
Barclays Bank Plc 0.549029 0.748097 0.517349 0.636867
Citibank International Limited 0.584253 0.589061 0.816963 0.813621
HSBC Bank Plc 0.867537 0.755020 -0.243311 -0.333685
Lloyds Bank Plc 0.815042 0.314178 -0.382436 -0.843927
Marks and Spencer Financial Services 0.555385 0.124898 0.817735 0.282869
Sainsbury's Bank Plc 0.726467 0.726849 0.937808 0.827943
Standard Chartered Bank 0.289975 0.259005 0.835337 0.796160
Tesco Personal Finance Plc 0.779923 0.860070 0.706294 0.890253
The Royal Bank of Scotland Plc 0.845248 0.514914 0.771293 0.768307
Westpac Europe Ltd -0.177884 -0.400279 0.947205 0.927839

35
words, the dark grey columns represent the model that experienced the most statistically
significant change for the particular bank while the ones in light grey show the model in which
the changes were of least statistical significance for the bank.
At a glance, the amended model 2, whereby NOPAT is the proxy for financial performance
and ESG for CSR, seems to have to most statistically significant amendment for most banks.
At this point, it is speculated that the amended model 2 holds variables that are most statistically
significant, in comparison to the other models, to explain the variance in financial performance.
6.3.3 R2 and adjusted R2 Conclusion
Most of the values for all models are above 0.60, signifying the independent variables’
explanatory power towards the dependent variable. Comparing the values using line charts
provided for a clear visualization of the movements in R2
values as changes are made in the
models. It is concluded from the comparisons that the disparity in results are caused by the
change in proxies for financial performance. The change from ROA to NOPAT caused a
substantial difference in results, while the change in CSR measurement from ESG to CDP did
not have as considerable an impact on the R2
values.
As for the adjusted R2
, the amended model 2 yielded the most number of high values implying
that the change in the model proved to be of high statistical significance in explaining the
variance in financial performance. The combination of NOPAT as the proxy for financial
performance and ESG for CSR proves to be of most dynamic in explaining the banks’ CSR-
FP relationship.
6.4 Correlation between Financial Performance and CSR
Now that it is determined that there exists a relationship between banks’ financial performance
and their CSR activities, it is with the correlation between the proxies that we ascertain if that
relationship is positive or negative. Tables 6-9 illustrate the correlation of the proxies in each
model.

36
Table 6: CSR-FP Correlation for the Original Model
Table 7: CSR-FP Correlation for the Amended Model 1
Original Model CSR
FP Barclays Citibank HSBC Lloyds M&S RBS Sainsbury's Std Chartered Tesco Westpac
Barclays -0.79171 - - - - - - - - -
Citibank - -0.38443 - - - - - - - -
HSBC - - -0.29857 - - - - - - -
Lloyds - - - -0.902702 - - - - - -
M&S - - - - -0.34734 - - - - -
RBS - - - - - -0.584772 - - - -
Sainsbury's - - - - - - 0.797441 - - -
Std Chartered - - - - - - - -0.772612 - -
Tesco - - - - - - - - -0.50001 -
Westpac - - - - - - - - - -0.456953
Amended Model 1 CSR1
FP Barclays Citibank HSBC Lloyds M&S RBS Sainsbury's Std Chartered Tesco Westpac
Barclays -0.56259 - - - - - - - - -
Citibank - 0.18372 - - - - - - - -
HSBC - - -0.02967 - - - - - - -
Lloyds - - - 0.154813 - - - - - -
M&S - - - - -0.273013 - - - - -
RBS - - - - - 0.078101 - - - -
Sainsbury's - - - - - - 0.449771 - - -
Std Chartered - - - - - - - -0.305865 - -
Tesco - - - - - - - - -0.49996 -
Westpac - - - - - - - - - 0.249598
Amended Model 2 CSR
FP1 Barclays Citibank HSBC Lloyds M&S RBS Sainsbury's Std Chartered Tesco Westpac
Barclays -0.50981 - - - - - - - - -
Citibank - -0.077369 - - - - - - - -
HSBC - - -0.277258 - - - - - - -
Lloyds - - - 0.067793 - - - - - -
M&S - - - - 0.395681 - - - - -
RBS - - - - - -0.739699 - - - -
Sainsbury's - - - - - - 0.802809 - - -
Std Chartered - - - - - - - 0.83168 - -
Tesco - - - - - - - - 0.657358 -
Westpac - - - - - - - - - 0.784258
Amended Model 3 CSR1
FP1 Barclays Citibank HSBC Lloyds M&S RBS Sainsbury's Std Chartered Tesco Westpac
Barclays -0.61065 - - - - - - - - -
Citibank - -0.17311 - - - - - - - -
HSBC - - -0.263987 - - - - - - -
Lloyds - - - 0.141756 - - - - - -
M&S - - - - -0.30479 - - - - -
RBS - - - - - -0.382709 - - - -
Sainsbury's - - - - - - 0.614438 - - -
Std Chartered - - - - - - - 0.312734 - -
Tesco - - - - - - - - 0.292288 -
Westpac - - - - - - - - - 0.448408

37
6.4.1 Correlation Matrix Conclusion
For ease of interpretation, Charts 5-8 portray the strength of correlations between each bank’s
financial performance and CSR initiatives.
Figure 7: Correlation Intensity Chart of the Original Model
Figure 8: Correlation Intensity Chart of the Amended Model 1
-1
-0.8
-0.6
-0.4
-0.2
0
0.2
0.4
0.6
0.8
1
Original Model
-0.8
-0.6
-0.4
-0.2
0
0.2
0.4
0.6
Amended Model 1

38
Referring to the figures above, it is apparent that the amended model 2 holds the most number
of positively correlated combinations of financial performance and CSR. Moreover, the bar
chart makes it apparent that the magnitude of its positive correlation is the highest among the
four models. The correlation for the other banks in other models have been erratic and differ
greatly in terms of magnitude, showing a lack of consistency. The only bank that has managed
to maintain a positive correlation across all four models is Sainsbury’s Bank Plc, with the
highest correlation at 0.802809 and the lowest holding at 0.449771. The banks that maintained
a negative correlation, on the other hand, are Barclays Bank Plc and HSBC Bank Plc.
-1
-0.8
-0.6
-0.4
-0.2
0
0.2
0.4
0.6
0.8
1
Amended Model 2
-0.8
-0.6
-0.4
-0.2
0
0.2
0.4
0.6
0.8
Amended Model 3

39
7.0 Empirical Analysis
The results acquired from the models’ t-tests, F-tests, R2
and adjusted R2
values were gathered
and compiled in the earlier section. This section aims to provide a critical analysis of what the
results represent and how they influence the potential relationship of banks’ CSR and FP.
7.1 T-test Results Analysis
After conducting t-tests across the four models, it is found that the t-test values for most of the
independent variables have been within the acceptance region, explaining that the independent
variable is not significant in explaining the dependent variable. However, while the variable is
not significant, it does not necessarily mean that it is insignificant. The non-significance of a
variable may imply that while it does not affect the dependent variable in a significant way,
there may be a meagre effect of the independent variable on the dependent variable. For
example, in the amended model 1, the independent variable for bank size in the Barclays Bank
Plc model exhibited that the t-test value is -2.402784, with a p-value of 0.0741. The acceptance
region for the models is [-2.776, 2.776] while the α = 0.05. Comparing this value with that of
the t-test value for its independent variable for risk, which is -0.555656 and a p-value of 0.6081,
the degree of non-significance for the risk variable is stronger than that of the size variable.
While they are both classified as individually not significant, there is a higher element of
significance, albeit not significant enough, in the independent variable for bank size than for
risk.
In terms of the CSR variable for each bank, regardless of using either the ESG or the CDP
scores, have not shown to be individually significant in explaining the banks’ financial
performance across all models. Only in three occasions were the CSR variables individually
significant to the banks’ financial performance. Having said that, while it can be concluded that
the banks’ CSR is not individually significant as a determinant in their financial performance,
it does not convey that there is no relationship between CSR and FP. It simply implies that the
level of CSR employed does not affect the banks’ financial performance in a direct manner.

40
7.2 F-test, R2, and Adjusted R2 Results Analysis
7.2.1 F-test
From the F-test results across all four models, it is clear that the results in the amended models
2 and 3 are relatively similar in order of significance. While it is not as apparent in the original
model and the amended model 1, there are signs that the order is of similar nature. The
consistencies between the original model and the amended model 1 and between the amended
models 2 and 3 are the proxies for financial performance, providing grounds to conclude that
the true factor for the disparity in results lies in the measurement banks’ financial performance.
7.2.2 R2 and Adjusted R2
The R2
values obtained across all four models provides for a relatively optimistic outlook for
the CSR-FP relationship for most banks. There is some consistency between the original model
and the amended model 1, and between amended model 2 and 3, similar to what is found in the
F-test results.
For the original model and the amended model 1, the R2
values for most banks are of similar
numerical range, as portrayed in Charts 5 and 6. This portrays the consistency of results in
utilising the ROA as the measurement for financial performance and either the ESG or CDP
scores as the measurement for CSR. While there are changes in values, they are relatively not
significant enough to determine that there is a considerable difference in CSR measurements
between the ESG or CDP scores when regressed against the ROA.
On the other hand, as the measurement for financial performance changes to NOPAT, the R2
values change dramatically. Having said that, each bank still showed consistent values between
the amended models 2 and 3 despite the substantial change from when the FP proxy was ROA.
Similar to the comparison between the original model and the amended model 1, the difference
in CSR proxy did not have a sizable difference between the amended models 2 and 3. The
values remained at similar levels despite the change from ESG scores to CDP scores.
The Tables 10-13 summarizes the R2
, Adjusted R2
, and F-test values for ease of comparison.

41
Table 10: R2
, Adjusted R2
, and F-test values for the Original Model
Table 11: R2
, Adjusted R2
, and F-test values for the Amended Model 1
Table 12: R2
, Adjusted R2
R-squared Adjusted R-squared F-test Conclusion
HSBC Bank Plc 0.941128 0.867537 13.28941 Not within F-crit.; Jointly significant
The Royal Bank of Scotland Plc 0.914027 0.845248 12.78880 Not within F-crit.; Jointly significant
Lloyds Bank Plc 0.897246 0.815042 10.91495 Not within F-crit.; Jointly significant
Tesco Personal Finance Plc 0.877735 0.779923 8.97370 Not within F-crit.; Jointly significant
Sainsbury's Bank Plc 0.848037 0.726467 6.97569 Not within F-crit.; Jointly significant
Citibank International Limited 0.815223 0.584253 3.81056 Within F-crit.; Not jointly significant
Marks and Spencer Financial Services 0.752992 0.555385 3.73923 Within F-crit.; Not jointly significant
Barclays Bank Plc 0.749460 0.549029 3.52955 Within F-crit.; Not jointly significant
Standard Chartered Bank 0.605542 0.289975 1.91890 Within F-crit.; Not jointly significant
Westpac Europe Ltd 0.345620 -0.177884 0.66021 Within F-crit.; Not jointly significant
Original Model
HSBC Bank Plc 0.891120 0.755020 6.98719 Not within F-crit.; Jointly significant
Barclays Bank Plc 0.888043 0.748097 6.54754 Not within F-crit.; Jointly significant
Citibank International Limited 0.817361 0.589061 3.58022 Within F-crit.; Not jointly significant
The Royal Bank of Scotland Plc 0.730508 0.514914 3.38835 Within F-crit.; Not jointly significant
Lloyds Bank Plc 0.695190 0.314178 1.82459 Within F-crit.; Not jointly significant
Standard Chartered Bank 0.588336 0.259005 1.78646 Within F-crit.; Not jointly significant
Westpac Europe Ltd 0.222067 -0.400279 0.35682 Within F-crit.; Not jointly significant
Amended Model 1
Westpac Europe Ltd 0.970669 0.947205 41.36742 Not within F-crit.; Jointly significant
Citibank International Limited 0.918650 0.816963 12.41425 Not within F-crit.; Jointly significant
Standard Chartered Bank 0.908520 0.835337 11.09465 Not within F-crit.; Jointly significant
Marks and Spencer Financial Services 0.898742 0.817735 9.03408 Not within F-crit.; Jointly significant
HSBC Bank Plc 0.447417 -0.243311 0.64775 Within F-crit.; Not jointly significant
Lloyds Bank Plc 0.231980 -0.382436 0.37756 Within F-crit.; Not jointly significant
Amended Model 2

42
Table 13: R2
, Adjusted R2
Across all four models, for the banks that have not returned with jointly significant variables
are lacking independent variables that could potentially increase the R2
values and F-test values
and evidently show a jointly significant model to explain the variance in financial performance.
In other words, there are more factors that may influence the financial performance of the bank
along with the 𝐶𝑆𝑅𝑖, especially for the amended model 1, whereby only four of the banks have
shown to have jointly significant variables explaining the variance in the banks’ financial
performance. In reference to a previous study conducted by Kiessling et al. (2015) variables
that have been included in the regression are customer orientation, customer interaction and
market orientation. McWilliams and Siegel (2000) included a variable to account for the
research and development intensity of firms while Soana (2011) included market returns as a
variable along with systematic and non-systematic risk when measuring the financial
performance with stock returns. Given that a large majority of past empirical research has been
conducted on other business areas other than the banking sector, the aforementioned variables
are not applicable to the banking sector and were not included in the study. A study conducted
by Vigano and Nicolai (2009) on the European banking sector showed the inclusion of banks’
capabilities in countering bribery and promoting gender equality as variables. Certain
intangible variables such as operational transparency, business ethics, and trust have also
shown to have significant impacts on banks’ financial performance. (Perez and del Bosque,
2012) However, implementing these variables as a part of the regression models in this study
is difficult due to the lack of standardized numerical representations available.
While the impact of the addition of these two variables are unclear, it is a reasonable
assumption at this point of the research that the relationship between CSR and FP from the t-
test results are put to rest as the F-test, R2
and adjusted R2
values evidently prove that not only
Westpac Europe Ltd 0.959910 0.927839 29.93014 Not within F-crit.; Jointly significant
Citibank International Limited 0.917165 0.813621 11.82708 Not within F-crit.; Jointly significant
Standard Chartered Bank 0.886756 0.796160 8.85777 Not within F-crit.; Jointly significant
HSBC Bank Plc 0.407251 -0.333685 0.54964 Within F-crit.; Not jointly significant
Lloyds Bank Plc 0.180477 -0.843927 0.17618 Within F-crit.; Not jointly significant
Amended Model 3

43
does a relationship exist between banks’ CSR and FP, but the relationship is proven to be a
substantial one.
7.3 Correlation Matrix Results Analysis
In numerous past empirical analyses (Beurden and Gossling, 2008; Crifo et al., 2015), the
correlation between firms’ financial performance and their CSR initiatives have predominantly
held a positive position. The findings in this study have proven otherwise. With the exception
of the amended models 2 and 3 showing a strong positive correlation, whereby the NOPAT
was used for the measurement of financial performance, the correlation between FP and CSR
have not been consistent. This further proves Crifo et al. (2015) findings that the relationship
between FP and CSR is predominantly positive when the profits are used as a form of
measurement.
For the amended model 1, the results showed erratic results from one bank to another, with no
sign of consistency. The intensity of correlation is relatively similar for both the positive and
negative values. For example, in the amended model 1, the highest negative correlation is -
0.49996 while the positive correlation is at 0.449771, both of which were banks of a smaller
asset size, Sainsbury’s Bank Plc and Tesco Personal Finance Plc.
In the original model, nine out of ten of the banks showed a strong negative correlation between
the two variables. These findings are in line with those of Friedman (1970) as the banks’
involvement in CSR seems to have a direct negative effect on its financial performance. Given
that the original model utilizes the banks’ ROA as a measurement for financial performance,
the costs incurred for banks’ CSR activities are potentially exceeding the point as to where it
will be profitable and, in turn, cause a negative effect on its financial performance. Moreover,
the two models using the ROA as a proxy for financial performance showed more
inconsistency in results as opposed to the ones using NOPAT.
Upon closer inspection, it is noted that banks of a larger asset size were the ones performing
poorly. Table 14 sorts the banks in order of decreasing size based on total assets. Columns in
grey are negative correlations.

44
Table 14: Correlation Matrix for All Models Based on Asset Size
The top four banks have returned with a negative correlation across all four models with the
exception of the amended model 1 in which Citibank International Limited and The Royal
Bank of Scotland Plc exhibited a positive, albeit weak, correlation between its CSR initiatives
and financial performance. A research conducted by Soana (2011) have explained that banks
pay special attention to improving and maintaining a good reputation and would go to great
lengths to ‘appear’ socially responsible. Some examples of the CSR activities of these banks
include:
1. The Prince’s Trust Fairbridge Programme launched by HSBC Bank Plc UK in 2012
donates £5 million over five years to aid young adults who have been or are potentially
in danger of not receiving adequate education (HSBC, 2016)
2. Citibank’s £100 billion investment towards a five-year sustainability strategy involving
renewable energy, financing green bonds and green real estate to contribute to global
environmental solutions (Citigroup, 2014)
3. The Barclays Social Innovation Facility saw a £25 million investment in the innovation
of commercial solutions for businesses across seven countries (Barclays, 2013)
4. The £46.5 million investment in community support by The Royal Bank of Scotland to
provide funding for skill development and career advancement for individuals
embarking on their careers (The Royal Bank of Scotland, 2014)
Given that the aforementioned banks have a long-standing history, a strong reputation, and a
large amount of resources to invest in CSR initiatives, the invested amounts in some of the
initiatives are of a substantial amount. Whether or not these investments truly provide a positive
return in financial performance does not seem to be the case.
On the other hand, banks of a smaller total asset size had a positive CSR-FP correlation on
more occasions, especially in the amended models 2 and 3. The financial performance of
Original Model Amended Model 1 Amended Model 2 Amended Model 3
HSBC Bank Plc -0.298565 -0.029669 -0.277258 -0.263987
Citibank International Limited -0.384434 0.183716 -0.077369 -0.17311
Barclays Bank Plc -0.791712 -0.562586 -0.509809 -0.610647
The Royal Bank of Scotland Plc -0.584772 0.078101 -0.739699 -0.382709
Lloyds Bank Plc -0.902702 0.154813 0.067793 0.141756
Westpac Europe Ltd -0.456953 0.249598 0.784258 0.448408
Standard Chartered Bank -0.772612 -0.305865 0.83168 0.312734
Tesco Personal Finance Plc -0.500009 -0.499955 0.657358 0.292288
Sainsbury's Bank Plc 0.797441 0.449771 0.802809 0.614438
Marks and Spencer Financial Services -0.34734 -0.273013 0.395681 -0.30479

45
Lloyds Bank Plc, Westpac Europe Limited, Standard Chartered Bank, Tesco Personal Finance
Plc, Sainsbury’s Bank Plc and Marks and Spencer Financial Services have exhibited a positive
CSR-FP relationship. Provided that these banks are of a smaller asset size than the top four of
the list, the resources available to be invested in CSR are less. This, in turn, causes significantly
smaller CSR investments that effectively reduces negative effects on the firms’ financial
performance. To clarify, the CSR activities of these banks have a positive effect on the NOPAT
but have an ambiguous effect on ROA as the results from the original model and the amended
model 1 are mixed. Having said that, our results from the correlation matrix further endorses
the results established by Crifo et al. (2015) that the CSR-FP relationship is positive when the
financial performance is measured using the profits of the firm.

46
8.0 Limitations of Research
One of the major issues faced in this study was the difficulty in obtaining CSR scores for all of
the UK banks. Some of the scores, such as the FTSE4Good and Dow Jones Sustainability Index
are limited and are not readily available to the general public. The original intention was to
utilize the scores from these two indices so as to analyse the differing methods in assigning
scores to each bank. Comparing these scores with that of others will aid researches in studies
over time.
Another limitation of this empirical research is the time period employed. The data of each
bank is annualized and averaged to a single number for ease of application and comparison
with results from past studies. Given that the most recent financial crises occurred within the
time frame of 2005-2014, the average data may not measure each variable with exact precision.
Future research could perhaps compare between the pre- and post-financial crises data to obtain
information about the true effects of CSR on banks’ financial performance before and after
financial crises.

47
9.0 Discussion
In recent years, the rise in ethical sensitivity has influenced individuals to be more socially
aware regarding the environment, society, and the economy as whole. In turn, businesses and
organizations alike have implemented CSR initiatives as a part of their business strategy in an
effort to appeal to consumers, so much so that it is a rarity that to find a business that does not
partake in CSR activities. Having said that, it is found that firms implement CSR activities in
their business strategies as a means to gain consumer support due to the rising pressure of being
involved to some extent in these activities, as opposed to being sincerely socially responsible.
(Sen and Bhattacharya, 2001; Perez and Bosque, 2012) This influenced a copious amount of
research investigating the potential effects of CSR on firm financial performance. On the one
hand, a vast majority have argued that CSR does have a positive influence. On the other hand,
there are also significant number of papers have that argued otherwise, asserting that the
relationship is mixed, negative and non-existent. The contradicting array of results have lead
scholars to pinpoint the source of the dispute as the differing forms of CSR measurement. This
empirical study aims to take this issue into account with an intent to actualize more precise
results regarding the effects of UK banks’ implementation of CSR on their financial
performance. In order to test for the potential contrast in CSR measurement, four econometric
models incorporating two different measurements of CSR, namely ESG and Carbon Disclosure
Project scores, were regressed against each of the banks’ financial performance. Furthermore,
two accounting measures are set as the proxies for the measurement of the banks’ financial
performance, as conducted by a large number of empirical research. With four sets of
alternative combinations between CSR and financial performance measurements, this study
aims to close a gap in previous literature that was unsuccessful in illustrating the difference in
both measures, as the focus has predominantly been on differing measurements of CSR,
neglecting the potential difference in the proxies for financial performance.
Research Objective 1: To determine the possible link between the CSR initiatives of banks in
the United Kingdom and their potential effects on FP
Analysing the data results as a whole, it is found that the findings on the CSR-FP relationship
are mixed. The relationship between CSR and banks’ financial performance is not as
unanimous or straightforward as most past empirical research has found. Theoretically, banks

48
with a high CSR score should have a better financial performance. Results from this study
show that the relationship is not uniform across all banks and that it differs from one bank to
another. The t-tests, F-tests, R2
and R2
values have determined that there exists a relationship
between banks’ CSR activities and their financial performance. Through the analysis of the
correlations of CSR and FP proxies, the relationship proves to be a mix of both positive and
negative, depending on the proxy for financial performance. The argument for the negative
CSR-FP relationship believes that the costs for implementing CSR activities significantly
outweighs the returns realized from these investments. (Friedman, 1970) Moreover, some
studies have also explained that the pressures for companies to partake in CSR activities have
caused managers to neglect other aspects of the business, potentially affecting profitability.
(Wood, 2010) The results acquired, especially apparent in the correlation matrix, illustrate that
the banks of larger asset size tend to have either a negative or low positive association between
the CSR and FP proxies. This finding demonstrates the possible high costs that exceed the point
of profitability for the banks. Furthermore, banks of a larger asset size face more pressure to
be socially responsive that influences the implementation of more significant CSR initiatives,
likely reduces their profitability.
Research Objective 2: To compare the results from one bank to another to analyse the
difference in how CSR affects their FP
As mentioned previously, the results acquired for all banks have been disparate and have not
shown distinctive consistency from one test to another. In past empirical research, the unified
positive conclusion across all organizations influenced an initial positivist outlook for this
study, which was soon curtailed as results from the tests portrayed the relationship in a
pessimistic manner. Running separate regressions for each bank granted individualized sets of
results for ease of analytical comparison demonstrated the distinctive variation in effects of
CSR on FP. The acquired results are realistic because factors affecting each banks’ financial
performance are unique and may differ substantially from one bank to another. A number of
past research have acknowledged the inclusion of variables to represent research and
development intensity (McWilliams and Siegel, 2000), systematic and non-systematic risk
Soana (2011) and even intangible responsibilities such as gender equality, banks’ capabilities
in hindering the occurrence of bribery (Vigano and Nicolai, 2009), operational transparency,
business ethics and even trust. (Perez and del Bosque, 2012) While the inclusion of these

49
variables could potentially affect the end results of this study, the variables utilized in the
models are conventional and are part of the basic structure of banks. Moreover, some of the
aforementioned variables are imperceptible and are difficult to measure with numerical
representations making it a tall order to be regressed. Having said that, it is decided that the
variables are sufficient and provide factual representations of the CSR-FP relationship of each
bank.
Research Objective 3: To ascertain whether or not the difference in proxies for CSR and/or
financial performance measurement would affect results
While the measurements of CSR are multitudinous and possibly misleading to some extent, the
results from this study has proven that there are consistencies in the ethical rating scores
awarded to organizations. Both the scores obtained for each of the bank are dissimilar and, at
first glance, seem to paint a contrasting picture regarding the social responsibility of each bank.
Upon running the regressions and analysing the results, the models with both the ESG and CDP
scores illustrated similar results, while those of different measurements financial performance
are the ones that have inconsistent and erratic results. A formidable stream of research have
historically been directed at the inconsistencies in CSR measurement, neglecting those
potentially present in the measurement of financial performance. While a myriad of prior
research used ROA and NOPAT as proxies for financial performance, there are an abundant
number of approaches to measuring financial performance, which ultimately could differ from
one industry to another. The use of return on capital employed, operating margin, and gross
profit margin are just some examples of potential measures of financial performance.
The results accomplished in this study is in line with those of Friedman (1970), Vance (1975),
McGuire et al. (1990), Balabanis et al. (1998), Brammer et al. (2006), and a multitude of others
as it is concluded that the UK banks’ CSR-FP relationship is mixed and that each bank should
be wary of the perceived benefits of CSR on their financial performance. While partaking in
CSR may gain the support of clients and enhance corporate image, it may not yield positive
benefits financially.

50
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Appendices
Appendix 1: Complete List of Banks Selected
1 Barclays Bank Plc
2 Citibank International Limited
3 HSBC Bank Plc
4 Lloyds Bank Plc
5 Marks and Spencer Financial Services
6 Sainsbury's Bank Plc
7 Standard Chartered Bank
8 Tesco Personal Finance Plc
9 The Royal Bank of Scotland Plc
10 Westpac Europe Ltd
Banks Selected for Research

Janine Chacko - Final Dissertation

Janine Chacko - Final Dissertation

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