The Relationship Between Environmental and ...

The Relationship Between Environmental and Financial Performance

By Olayemi Oluwadamilola AMOSUN 4632788

MSc International Accounting and Finance 2016

Amosun Olayemi .O 4632788

ABSTRACT The aim of this research work is to investigate the relationship between environmental performance and financial performance. In this study, a sample of 111 companies that are constituents of FTSE 250 index listed on the London Stock Exchange was used. The environmental data was gotten from ASSET 4 ESG database and the financial data was collated from FAME database. A quantitative method and OLS model were used in analysing the study. The results reflect a negative correlation for financial performance of the companies represented by ROA and ROCE and a positive correlation for financial performance of the companies’ proxy by Tobin Q. The study reflects that environmental performance plays an important role in financial performance which could be as a result of the economic benefits that can be derived from companies investing in environmental incentives, or can be as a result of the potential increase in profitability and market value of firms. Furthermore, this study also supports the Stakeholder Theory, Win win view, Resourced-based view and the Economist’s view which supports that there is a relatioship between environmental performance and financial performance.

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ACKNOWLEDGEMENT First of all, I would like to return all glory and adoration to my father in heaven for giving me the opportunity to a carry out this research work and for giving me the grace to complete my masters. My sincere appreciation goes to my parents Mr O.O. Amosun and Mrs A.O. Amosun for their prayers, love, understanding and financial support towards the completion of my work and throughout my life. Thank you for encouraging me to be the best I can be and for making me believe in myself. I would love to express my gratitude to my supervisors Renata Konadu and Professor Ven Tauringana for their guidance, advice, feedback and support throughout this research work. I would also like to acknowledge my uncle Mr Akinola Amosun. Thank you uncle for your support and understanding all my life. Thank you for encouraging me to push myself further. Lastly, I would like to acknowledge all the staffs of Bournemouth University, my friends and colleagues. I would like to thank them for their support and guidance during my programme.

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Research Project Declaration I agree that, should the University wish to retain it for reference purposes, a copy of my Research Project may be held by Bournemouth University normally for a period of 3 academic years. I understand that once the retention period has expired my Research Project will be destroyed. Confidentiality I confirm that this Research Project does not contain information of a commercial or confidential nature or include personal information other than that which would normally be in the public domain unless the relevant permissions have been obtained. In particular, any information which identifies a particular individual’s religious or political beliefs, information relating to their health, ethnicity, criminal history or sex life has been anonymised unless permission has been granted for its publication from the person to whom it relates. Copyright The copyright for this Research Project remains with me. Requests for Information I agree that this Research Project may be made available as the result of a request for information under the Freedom of Information Act. Signed: Name: Olayemi Oluwadamilola Amosun Date: 23 May 2016 Programme: MSc International Accounting and Finance

Originality Declaration I declare that this Research Project is all my own work and the sources of information and material I have used (including the Internet) have been fully identified and properly acknowledged as required in the guidelines given in the Programme Handbook which I have received. Signed: Name: Olayemi Oluwadamilola Amosun Date: 23 May 2016

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Table of Contents ABSTRACT .............................................................................................................................. ii Research Project Declaration ................................................................................................... iv LIST OF TABLES………………………………………………………………....viii LIST OF FIGURES………………………………………………………........…….ix LIST OF ACRONYMS AND ABBREVATIONS .................................................................. ix CHAPTER ONE: INTRODUCTION ........................................................................................1 1.1 Background of the Study......................................................................................................1 1.2 Justification ..........................................................................................................................5 1.3 Aim and Objectives ..............................................................................................................6 1.3.1 Aim....................................................................................................................................6 1.3.2 Objectives of the Study .....................................................................................................6 1.4 Research Hypothesis ............................................................................................................6 1.5 Research Questions ..............................................................................................................6 1.6 Structure ...............................................................................................................................7 CHAPTER TWO: LITERATURE REVIEW ............................................................................8 2.1 Introduction ..........................................................................................................................8 2.2 Environmental Performance.................................................................................................9 2.3 Environmental Performance Measures ..............................................................................11 2.4 Corporate Financial Performance ......................................................................................12 2.5 Financial Performance Measures and Dependent Variables. .............................................12 2.5.1 Return on Asset [ROA] as a dependent variable ............................................................13 2.5.2 Tobin’s Q as a dependent variable ..................................................................................15 2.5.3 Return on Capital Employed as a dependent variable ....................................................17 2.6 Control Variables. ..............................................................................................................18 Company Size ..........................................................................................................................18 Corporate Governance .............................................................................................................18 Debt to Equity ..........................................................................................................................19 Current Ratio ............................................................................................................................20 2.7 Conclusion .........................................................................................................................20 CHAPTER THREE: RESEARCH METHODOLOGY ..........................................................21 3.1 Introduction ........................................................................................................................21 3.2 Research Purpose ...............................................................................................................21 3.3 Research Design .................................................................................................................21 3.3.1 Data Source and Collection.............................................................................................22 3.3.2.1 Dependent Variables ....................................................................................................22 3.3.2.2 Independent Variables ..................................................................................................24 Explanatory variable ................................................................................................................24 v|Page

Amosun Olayemi .O 4632788 Control variables ......................................................................................................................25 3.4 Sample Size ........................................................................................................................26 3.5 Research Method ................................................................................................................26 3.5.1 Ordinary least square modelling .....................................................................................26 3.5.2 Econometric modelling ...................................................................................................26 CHAPTER FOUR: ANALYSIS AND INTERPRETATION OF DATA ...............................28 4.1 Introduction ........................................................................................................................28 4.2 Data Analysis .....................................................................................................................28 4.2.1 Descriptive Statistics .......................................................................................................28 4.2.2 Correlation results ...........................................................................................................31 4.2.3 Analysis of cross-sectional regression results .................................................................38 4.2.3.1 Model 1: ROA ..............................................................................................................39 4.2.3.2 Model 2: TBQ ..............................................................................................................39 4.2.3.3 Model 3: ROCE ...........................................................................................................40 4.3 Discussion of Findings .......................................................................................................41 4.4 Summary of Findings .........................................................................................................43 CHAPTER FIVE: CONCLUSION AND RECOMMENDATIONS ......................................45 5.1 Introduction ........................................................................................................................45 5.2 Conclusion .........................................................................................................................45 5.3 Limitations .........................................................................................................................46 5.4 Recommendations ..............................................................................................................46 REFERENCES.........................................................................................................................48 APPENDIX A1: REGRESSION OUTPUT: 2013 ................................................................... A MODEL 1: ROA ....................................................................................................................... B MODEL 2: TBQ ....................................................................................................................... C MODEL 3: ROCE ..................................................................................................................... E APPENDIX A2: REGRESSION OUTPUT: 2014 ................................................................... G MODEL 2: TBQ ........................................................................................................................ I MODEL 3: ROCE ...................................................................................................................... J APPENDIX A3: OLS SCATTER GRAPHS FOR 2014…………………………….L APPENDIX B1: SAMPLE OF COMPANIES USED .............................................................. N APPENDIX B2: SAMPLE SIZE .............................................................................................. Q APPENDIX B3: SOURCES OF DATA ................................................................................... R

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LIST OF TABLES Table 4.1 Descriptive Statistics ................................................................................................28 Table 4.2 A Correlation matrix 2013………………..………………………………31 Table 4.2 B Correlation matrix 2014 .......................................................................................35 Table 4.3 Single cross-sectional regression results for 2013 and 2014 ...................................39 Table 5: Correlations……………………………………...………………………….A Table 6 Model Summary........................................................................................................... B Table 7 ANOVAa ...................................................................................................................... B Table 8 Coefficientsa ................................................................................................................. B Table 9 Collinearity Diagnosticsa ............................................................................................. B Table 10 Model Summary........................................................................................................ C Table 11 ANOVAa ................................................................................................................... C Table 12 Coefficientsa .............................................................................................................. C Table 13 Collinearity Diagnosticsa ........................................................................................... D Table 14 Model Summary......................................................................................................... E Table 15 ANOVAa………...…………………………………………………………E Table 16 Coefficientsa……………………….……………………………………….E Table 17 Collinearity Diagnosticsa…………………….....…………………………..F Table 18 Correlations ............................................................................................................... G Table 19 Model Summary………………………...………………………………….H Table 20 ANOVAa………………………..………………………………………….H Table 21 Coefficientsa…….……….……..………………………………………….H Table 22 Collinearity Diagnosticsa……………….………………………………….H Table 23 Model Summary…………………………..………………………………..I Table 24 ANOVAa…………………………….……………………………………...I Table 25 Coefficientsa..………………………..……………………………………..I Table 26 Collinearity Diagnosticsa………………..…………..………………………I Table 27 Model Summary......................................................................................................... J Table 28 ANOVAa .................................................................................................................... J Table 29 Coefficientsa ............................................................................................................... J Table 30 Collinearity Diagnosticsa……………………………..……………………K Table 31..................................................................................................................................... N Table 32..................................................................................................................................... Q Table 33..................................................................................................................................... R

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LIST OF FIGURES Figure 4.1 Relationship between ROA and EP 2013 ...............................................................32 Figure 4.2 The relationship between EP and TBQ 2013 .........................................................33 Figure 4.3 The relationship between EP and ROCE 2013 .......................................................34 Figure 4.4 The relationship between EP and ROA 2014 .......................................................... L Figure 4.5 The relationship between EP and TBQ 2014 .......................................................... L Figure 4.6 The relationship between EP and ROCE 2014 ....................................................... M

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LIST OF ACRONYMS AND ABBREVATIONS ISO

-

International Organisation for Standardization

OLS

- Ordinary Least Square

ROA

- Return on Assets

EPI

- Environmental Performance Index

FPM

-

Financial Performance

EP

-

Environmental Performance

CV

-

Control Variables

TBQ

-

Tobin’s Q ratio

CG

-

Corporate Governance

MIN

-

Minimum

MAX

-

Maximum

STD. DEV - Standard Deviation CR

-

Current Ratio

D2E

-

Debt to Equity

ROCE

-

Return on Capital Employed

ESG

-

Environmental, Social & Governance

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CHAPTER ONE INTRODUCTION 1.1 Background of the Study The Brundtland Commission in 1987 buttressed the significance of sustainable development which was based on the idea of meeting the needs of the present generation without compromising the ability of future generations to meet their own needs (Brundtland 1987). This means adequately managing available and limited resources in order to reduce waste. Government, Companies and all Stakeholders need to ensure that both financial and environmental matters are taken into consideration when making business decisions, as most decisions are taken to improve the financial performance of companies and not the environmental performance of companies. In recent years, organisations in industrialised nations have embraced the idea of improving the environmental performance of their organisations due to the vested interest of their stakeholders and other interested parties. An increasing awareness of environmental problems brought about by economic activity has led to greater political and social demands on firms to reduce their environmental impact (Galdeano-Gómez et al., 2008). Managers, companies and government agencies are confronted with environmental issues and how they affect the environment. Managers, companies and government agencies are confronted with ways to improve environmental impact and performance (López-Gamero et al., 2009). In recent years, many stakeholders such as governments, employees, investors, shareholders and the community are more conscious about environmental issues, corporate environmental management and the impact of companies’ actions on the environment. This directly or indirectly influences the financial performance of firms (Iwata and Okada, 2011). The Paris Climate Change Agreement was drafted in 2015 by the United Nations Framework convention on Climate Change (UNFCCC). The goal of this agreement is to reduce global warming in all countries which is aimed at making finance flows consistent with a pathway towards low green gas emissions and climate-resilient development.

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Amosun Olayemi .O 4632788 Industrialist and green activists are intrigued with the idea that steady improvements in environmental performance will lead to steady improvements in the financial performance of companies. As proven by various studies (McDaniel et al., 2000; Nikolaou et al., 2013; Achim and Borlea, 2014), in most cases no link has been found between the financial performance of a company reflected in the financial statement and the actual performance of the company reflected on the market. However, researchers have emphasised the need for companies to take into account both financial growth targets and other non-financial growth targets such as environmental, social and governance performances in order to maximize the company’s overall performance. Various researches to establish a relationship between environmental performance and financial performance have produced contradictory evidence (Russo and Fouts 1997; Konar and Cohen 2001; Takeda and Tomozawa 2004; Yamaguchi 2008; Jacobs et al., 2010; Mahapatra 1984; Jaggi and Freedman 1992; Takeda and Tomozawa 2004; Aupperle et al., 1985; McWilliams and Siegel 2000; Jayachandran et al., 2013; Primc and Carter 2015). Studies into the relationship between environmental performance and financial performance of companies have been ongoing for some years. However, there have been no conclusive results for various reasons. Early studies have been based on small samples, old data and various theories. Empirical studies have used various environmental and financial performance measures. However, some empirical studies have revealed that there is a positive relationship between environmental performance and financial performance (Russo and Fouts 1997; Yamaguchi 2008; Jacobs et al. 2010), while some studies revealed that there is a negative relationship between environmental performance and financial performance (Mahapatra 1984; Takeda and Tomozawa 2004). Although some research studies revealed that there is a neutral link between environmental performance and financial performance (Aupperle et al., 1985; McWilliams and Siegel, 2000), some studies also revealed that there is no relationship between environmental performance and financial performance (Jayachandran et al., 2013; Primc and Cater, 2015). Various theories postulated have been used to explain the link between environmental performance and financial performance. For example, the Agency

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Amosun Olayemi .O 4632788 Theory postulated by Jensen and Meckling (1976) suggests that the primary objective of a company is to maximize shareholders’ wealth. Friedman and Friedman, (2002) and Millton, (1970) asserts that the basic social responsibility of a business is to use its resources and engage in activities designed to increase its profits. A company that decides to improve on its environmental performance will incur more costs which will affect the maximization of the shareholders’ wealth. This does not conform to the objective of the theory. The theory therefore proposes that there is a negative relationship between a company’s environmental performance and financial performance. However, the Stakeholder Theory postulated by Freeman (1984) introduced some factors previously ignored in the Agency Theory. This theory suggests that different stakeholders such as public administrators, environment, customers, shareholders, employees and many more should be considered when making business decisions. Environmental issues and the impact of these issues affects all stakeholders. In a research carried out by Ruf et al., (2001) revealed that the findings provided a form of support for a section of the stakeholder theory that asserts that the dominant stakeholder group, shareholders benefit financially when management meets the demands of multiple stakeholders. The research findings showed that long-term financial benefits exist when environmental performance is improved. This theory therefore suggests that there is a positive relationship between environmental performance and financial performance. The Win-win scholars Porter and van der Linde (1995) suggest that properly designed environmental standards can trigger innovations that can lower the cost of production or improve its value which will help increase profit and the financial performance which makes companies competitive. This suggests a positive relationship between environmental performance and financial performance. The Resource-based view suggests that embracing the notion of improving environmental performance also requires a fundamental shift in a firm’s culture and human resources and the organizational capabilities required to manage them which improves a company’s performance. In a research carried out by Russo and Fouts, (1997) posited that environmental performance and financial performance are positively linked and that industry growth moderates the relationship. This view

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Amosun Olayemi .O 4632788 suggests a positive relationship between environmental performance and financial performance. The Economist’s view suggests that when assessing whether enhanced environmental performance will lead to increased profits, a firm must consider the effects of that change on the revenues and costs of the firm. In a research carried out by Erfle and Fratantuono (1992 cited by David Edwards, 1998) concluded that environmental performance was positively correlated to financial performance measured by return on assets, return on equity and return on investment. This view suggests a positive relationship between environmental performance and financial performance. The neoclassical theory argues that environmental regulation imposes additional costs for firms. The theory further argues that improved EP leads to an increase in costs. This view is based on the premise that pollution abatement and environmental improvements have decreased marginal net benefits. This view is in contrast with Win-Win view that believes that environmental initiatives will systematically increase profitability. The idea is seen as unrealistic (Palmer et al., 1995, pp.119-132; Walley and Whitehead, 1994 cited by Horváthová, 2010). This view therefore suggests a negative relationship between environmental performance and financial performance. Companies have recognised the need for sustainability (environmental) reporting and are voluntarily managing and reporting on the impact of their activities on the environment. Although there are various disagreements on the relationship between environmental performance and financial performance, recent theories and research have shown that there is a positive relationship between environmental performance and financial performance. Several studies have analysed the link between environmental performance and financial performance (Horváthová 2010; Iwata and Okada 2011; Horváthová 2012). However, majority of the previous studies were carried out in the US, with little studies conducted in Australia, Japan and Romania. Thus in contrast, this research work examines the relationship between environmental performance, as measured by environmental performance index and financial performance of FTSE 250 companies in the UK during the years 2013 and 2014. 4|Page


1.2 Justification Various scholars and researches have conducted researches on the relationship between environmental performance and financial performance; however, some of the results have been inconclusive. Some researchers have found a positive relationship (Russo and Fouts 1997; Konar and Cohen 2001; Takeda and Tomozawa 2004; Yamaguchi 2008; Jacobs et al. 2010) and there are others that have found a negative relationship (Mahapatra 1984; Jaggi and Freedman 1992; Takeda and Tomozawa 2004). However some researchers found a neutral link between environmental and financial performance (Aupperle et al. 1985; McWilliams and Siegel 2000), whereas others have found no relationship in any way (Jayachandran et al. 2013; Primc and Cater 2015). The results have been different because various environmental performance and financial performance measures have been used. Some of the financial performance measures that have been used in existing literature are Tobin’s Q (Konar and Cohen 2001; Jayachandran et al. 2013), ROA (Ahuja and Hart 1996; Russo and Fouts 1997; Nakao et al. 2007), ROE (Ahuja and Hart 1996; Nakao et al. 2007). There have been various limitations in the existing research as a result this research aims to contribute to existing research and aims to explain the relationship between environmental performance and financial performance as majority of the previous research have been conducted in the US, Australia and very little research has been carried out in the UK. There have been various environmental performance measures and financial performance indicators which have produced different results. The study looked at companies’ environmental performance in relation to the companies’ Tobin Q ratio, return on assets [ROA] and return on capital employed [ROCE]. The study covered periods 2013 and 2014.

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1.3 Aim and Objectives 1.3.1 Aim The aim of this research is to analyse the relationship between environmental performance and financial performance of companies listed in the UK.

1.3.2 Objectives of the Study The overall purpose of this research study is to examine the relationship between environmental and financial performance of companies listed in UK. To determine if there is a link between a company’s environmental performance and financial performance. To draw out relevant conclusions from the research study. The following objectives have been identified for this research study: a) To examine the relationship between environment performance and financial performance measured by Return on Asset [ROA]. b) To examine the link between environmental performance and financial performance of companies measured by Tobin’s Q ratio. c) To examine the relationship between environmental and financial performance measured by Return on Capital Employed [ROCE].

1.4 Research Hypothesis The following null hypothesis will be tested in this study: H0: There is no relationship between a company’s environmental performance and financial performance variables. The alternate hypothesis is that there is a relationship between a company’s environmental performance and financial performance variables.

1.5 Research Questions The following are some of the research questions that lead to the author carrying out the research: a) Does environmental performance have an effect on a business financial performance? b) Does environmental performance have an impact on the market base?

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1.6 Structure This research work is categorized into five chapters with each chapter dealing with different phases of the research work. Chapter one includes the background of the study, justification for the research work, aims and objectives of the study, research questions and the structure of the study. Chapter two reviews existing literature on the study and identifies existing empirical evidence on the relationship between environmental performance and financial performance. Chapter three examines the data description and source, sample size, research purpose and method and the proposed models to be used for analysis. Chapter four presents the findings, results of the study, discussion and summary of findings. Chapter five includes the conclusion, limitations of the study and future research recommendations.

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CHAPTER TWO LITERATURE REVIEW 2.1 Introduction Various researchers, stakeholders, managers and the world at large have been interested in environmental issues such as global warming, pollution, biodiversity, ozone depletion, deforestation, and waste issues for a while. Researchers often ask how environmental performance affects financial performance of companies. Does better environmental performance improve a firm’s financial performance? In seeking to answer the question on the relationship between environmental performance and financial performance and the impact of environmental performance on a company’s financial performance, many studies have been conducted from different perspectives (Iwata and Okada 2011). Various researchers have investigated whether the environmental decisions companies make pays off financially. Researchers have questioned if it pays to be green. In economic literature, environmental problems have traditionally been treated as inconsistencies between social and private benefits and have mainly been left to government intervention to solve them. However, if financial performance is positively related to environmental performance, firms have incentives to reduce their environmental damages. This means that environmental problems may be solved by the market mechanism without government intervention, leading to a preferable environment for both firms and the government (Iwata and Okada 2011). Several researchers have found a positive relationship (Russo and Fouts 1997; Konar and Cohen 2001; Takeda and Tomozawa 2004; Yamaguchi 2008; Jacobs et al. 2010) and there are others that have found a negative relationship (Mahapatra 1984; Jaggi and Freedman 1992; Takeda and Tomozawa 2004). However, some researchers found a neutral link between environmental and financial performance (Aupperle et al. 1985; McWilliams and Siegel 2000), whereas others have found no relationship in any way (Jayachandran et al. 2013; Primc and Cater 2015). Bhat (1990) carried out an analysis to examine the relationship between environmental performances and financial performances of large US companies. The 8|Page

Amosun Olayemi .O 4632788 author used a sample of 230 companies. The analysis indicates that companies generating lower pollution secure higher profit margins and higher stock market values. Investors are willing to bid up prices of companies that generate less pollution because they generate higher profits at a lower risk. Also, Filbeck and Gorman (2004) researched on 24 firms; the study revealed that there is no positive relationship between environmental performance and financial performance. The results differ as there is no positive relationship between holding period returns and an industry-adjusted measure of environmental performance. There is a negative relationship between financial return and a more pro-active measure of environmental performance. Primc and Carter (2015) on the other hand, analysed 27 Australian firms and the result of the analysis implied that environmental proactivity is not always associated with high firm (financial) performance, and that environmental proactivity is not as important as important as the other conditions for high-performing firms in high polluting industries. Vogel

(2005)

conducted

a

study

which

revealed

that

socially

(environmentally) responsible investment funds perform no better than no-socially screened funds and many relatively responsible companies have not been financially successful. This shows that there is no relationship between environmental performance and financial performance. There have been various inconclusive results on the relationship between environmental performance and financial performance. Research into reasons why there have been inconclusive results concluded that various environmental and financial performance measures, time span, sectors, industries, sample size, countries and methodologies were used resulting in various results (Konar and Cohen 2001; Wagner et al. 2001; Filbeck and Gorman 2004; Horváthová 2010).

2.2 Environmental Performance Corporate environmental performance (CEP) has been of fundamental interest in scholarly research during the last few decades. During the last few decades, corporate environmental performance (CEP) has been the subject of considerable interest among organizational research scholars (Trumpp et al., 2013). 9|Page

Amosun Olayemi .O 4632788 There have been limited numbers of articles and books that define environmental performance as articles and books looking into the relationship between environmental performance and financial performance have focused on the links and results. ISO defined environmental performance as environmental results achieved whenever the environmental aspects of activities, processes, products, services, systems, and organisations are managed and controlled. Environmental performance is believed to be improved whenever the environmental aspect mentioned above are managed and controlled and whenever harmful environmental impacts are reduced and beneficial environmental impacts are produced. Waste management (2016) defined environmental performance as consuming less water, less energy, and natural resources while producing green space for wildlife and renewable energy in services for our customers. Emitting less by lowering emissions and having fewer releases to water, land and air and encourage recycling and achieving financial objectives. Environmental

performance

assesses

the

track

record

of

national

governments against record of national governments against specified objectives of specified objectives of environmental quality environmental quality and resource use efficiency (Srebotnjak, 2016). Environmental performance is the minimisation of the negative repercussions on the natural environmental that stems from the productive activities of a company and the social perception of this impact (de Burgos Jiménez and Céspedes Lorente 2001). Lankoski (2000 cited by Salem et al., 2011) defined corporate environmental performance as “the level of harmful environmental impact caused by a firm so that the smaller the harmful environmental impact the better the environmental performance and vice versa”. Furthermore, Wagner (2003 cited by Salem et al., 2011) defined corporate environmental performance as “the results of an organization‟ management on its environmental aspects” EP can basically be conceived as the extent to which companies meet the expectations

of

their

stakeholders

regarding

environmental

responsibility.

Accordingly, EP is considered a multidimensional construct that does not only include environmental outcomes and impacts on the company, its stakeholders and the environment but also principles of environmental responsibility and processes of 10 | Page

Amosun Olayemi .O 4632788 environmental responsiveness which determines future outcomes and impacts (Ruf et al., 1998; Carroll, 2000 cited by Schultze and Trommer, 2011, pp.375-412). EP can be defined as ‘a business organization’s configuration of principles of social responsibility, processes of social responsiveness, and policies, programs, and observable outcomes as they relate to the firm’s societal relationships’ (Wood, 1991 cited by Orlitzky et al., 2003).

2.3 Environmental Performance Measures Measuring environmental performance is a key challenge facing corporate environmental managers. Without accurate measures of performance, it is difficult to set priorities, track progress, analyse problem areas, or create incentives and reward performance (Wells, Calkins and Balikov, 1994). Various environmental performance indexes have been used in explaining the relationship between environmental performance and financial performance by various scholars and researchers. Corporate environmental performance indicators are usually divided into three main categories: 1) environmental impact (toxicity, emissions, energy use, etc.); 2) regulatory compliance (non-compliance status, violation fees, number of audits, etc.); and 3) organizational processes (environmental accounting, audits, reporting, Environmental Management System, etc.) (Ilinitch 1998; Lober 1996; Wood 1991 cited by Delmas and Blass 2010). Some researchers have used firm’s environmental ratings provided by independent organisations, environmental reports issued out by companies, environmental key performance indicators such as waste, greenhouse gas emissions, carbon emissions, energy consumption, paper consumption, water consumption, some studies use only environmental management variables (González-Benito and González-Benito 2005; Wahba 2008), others use only environmental performance variables (Al-Tuwaijri et al., 2004; Wagner 2005 cited by López-Gamero et al., 2009), and a few papers have used both environmental management and environmental performance variables jointly (Judge and Douglas 1998; King and Lenox 2002; Link and Naveh 2006). However, for this research work, the environmental performance measured to be used is the environmental performance score from DataStream. 11 | Page


2.4 Corporate Financial Performance Corporate Financial performance is defined as “financial viability, or the extent to which a company achieves its economic goals (Price and Mueller 1986, quoted by Orlitzky et al., 2003, cited by Fischer and Sawczyn, 2013). Although there have been no adequate definitions and arguments on the definition on FP, there have been disagreements on the best method to measure CFP (Boaventura et al., 2012). Financial performance is commonly used as an indicator of a firm's financial health over a given period of time. The financial performance of a firm can be defined or measured in various different ways. Each of these different measures captures a slightly different aspect of financial performance. Some, such as profitability, gauge return; others, like sales growth and market share growth, gauge the growth of a firm. Some measure profitability (return on investment, return on equity), some liquidity (quick ratio, current ratio), and still others solvency (gearing). Some measures are indicators of commercial success (growth, market share) while others are indicators of financial success (profitability). In this regard it can also be argued that different firms have differing financial goals and therefore one financial performance indicator need not measure the success rate as perceived by the firm itself (Vijfvinkel et al., 2011).

2.5 Financial Performance Measures and Dependent Variables. There are various methods and ways a company’s financial performance can be measured. However, the most commonly used methods of measuring financial performance by scholars and researchers alike are the accounting-based measures and the market-based measures. The accounting-based measures and market-based measures will be used in this research work. The accounting-based measures commonly used are ROA, ROCE, Return on sales, Return on Equity, Growth ratio, Reputation. However, the accounting-based measures to be used in this work are Return on Asset [ROA] and Return on Capital Employed [ROCE]. The market-based measures commonly used are Market Capitalization, Price to book ratio, dividend yield, Tobin’s Q. However, the market-based measure to be used in this work is Tobin’s Q ratio. 12 | Page


2.5.1 Return on Asset [ROA] as a dependent variable. Various researchers have used ROA as an accounting-based measure of financial performance (Ahuja and Hart 1996; Russo and Fouts 1997; Iwata and Okada 2011; Moneva and Ortas 2010; Nakamura 2011; Goll and Rasheed 2013; Achim and Borlea 2014; Ong et al., 2014; Muhammad et al., 2015). However, some have found a positive correlation while some have found a negative correlation. Achim and Borlea (2014) conducted a study on 76 Romanian companies to evaluate the link between environmental performance and financial performance. The authors’ results revealed a negative correlation for the ROA and a positive correlation for Tobin’s Q. The study attested that environmental investments are not immediately reflected in an increase in the companies’ internal performance but rather in an increase in investment costs and a decrease in assets’ efficiency. For investors, these investments in environmental activities are appreciated as “good news” and as a factor that will ensure long-term sustainability of the company. The authors hypothesised that an increase in a company’s environmental performance contributes to an increase in a company’s financial performance. However, the hypothesis was rejected as the study attests a negative relationship between EP and ROA, which shows that the adoption of CSR activities is not directly reflected in an increase of the company’s ROA. Russo and Fouts (1997) conducted a study on 243 firms using ROA as a measure of financial performance. The results revealed that although correlations were generally low, the correlation for the relationship between frim growth rate and ROA, and the industry growth were strong and positive. The results indicated that "it pays to be green" and that this relationship strengthens with industry growth. Ong et al (2014) carried out a research on 78 Malaysian companies to evaluate the impact of environmental improvements on financial performance. Ong et al (2014) hypothesized that there is a significant relationship between environmental performance (impact) and ROA. The results reflect a positive relationship for ROA. The results of the study suggest that it does indeed benefit a company to be green. This research is consistent with the research of Russo and Fouts (1997), which stated that policies on materials reuse is positively related to profit development. 13 | Page

Amosun Olayemi .O 4632788 Ahuja and Hart (1996) conducted a research on a sample of S&P 500 firms in USA on the relationship between emission reduction and firm performance and used ROA as a measure of financial performance. The results revealed that it does pay to be green. Although the relationship between emission and ROA became highly significant as a result of an increase in emissions, however, as emissions dwindled ROA increased. This shows that emissions reduction in any time period enhances operating performance of any company. In a research carried out by Iwata and Okada (2011) on Japanese manufacturing firms explaining how environmental performance affects financial performance revealed that the responses of financial performance are different depending on each environmental issue, the results of which are attributed to varying stakeholder preferences. The findings revealed that greenhouse gas reduction increases most long-run financial performance (ROA, ROE etc.); however, it does not have a significant effect on ROS. Goll and Rasheed (2013 cited by Achim and Borlea, 2014) emphasize that the environmental and financial performance can be connected to each other only in a specific context in which managerial choices matter more in certain types of environments than in others. In their study, they have identified a variety of factors that influence the relation between environmental and financial performance reflected by ROA (return on assets) and ROS (Return on sales) and highlight the major role that the munificent environments have on the intensity of this correlation. They found a significant positive correlation between financial performance (measured by ROA and ROS) and CSR in highly munificent and not in low munificent environments. Nakamura (2011) conducted a study on 3,237 Japanese companies to investigate the effect of environmental investment on firm performance. The authors’ result revealed that environmental investment / performance increases a firms (financial) performance. The positive effect of environmental efforts on economic performance ROA is insignificant at first but becomes significant later. Moneva and Ortas (2010) analysed the environmental and financial performance of 230 European companies between 2004 and 2007. Under the stakeholder approach, the commitment to environmental performances is analysed and linked with the firms’ financial improvement. The results supported the idea that 14 | Page

Amosun Olayemi .O 4632788 enterprises which obtained higher rates of environmental performance show better financial performance levels in the future. They found a positive and significant correlation between the two indicators only on the mid / long-term. Thus, firms that included corporate environmental performance issues in their strategic management policies would obtain a competitive advantage in the mid/long-term, significantly increasing their financial performance reflected by ROA, Profit margin, Cash flow, Operating profit. In support of explaining the negative correlation found between environmental performance and the company’s profitability, any invocation of high costs of CSR reporting, in addition to actual costs of investment in CSR, costs that lead to a lower operational efficiency of the organization during the investment period, is not negligible. As various researchers have found a positive relationship between environmental performance and financial performance (ROA), the following hypothesis is proposed:  H1a: An increase in a company’s environmental performance will necessitate an increase in financial performance proxy by ROA.

2.5.2 Tobin’s Q as a dependent variable. Various researchers have used Tobin-Q ratio as a market-based measure of financial performance (Dowell et al., (2000); King and Lenox 2001; Konar and Cohen 2001; Jayachandran et al., 2013; Achim and Borlea 2014; Muhammad et al., 2015). However, some have found a positive correlation while some have found a negative correlation. King and Lenox (2001 cited by Brouwers et al., 2014) differentiated between pollution performance and divestiture of operations in dirtier industries by splitting environmental performance into two constructs: relative performance within one’s industries and the average performance of the industries in which one chooses to operate. For an unbalanced sample of 652 firms constituting 4483 firm-year observations for the years 1987 to 1996, they find an evidence of a positive association between pollution reduction and financial gains, as peroxided by Tobin’s Q.

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Amosun Olayemi .O 4632788 Konar and Cohen (2001 cited by Brouwers et al., 2014) related the market value of 321 S&P 500 firms to environmental performance, as proxied by TRI emissions and environmental lawsuits. After controlling for variables traditionally thought to explain firm-level financial performance, they find that poor environmental performance is associated with lower Tobin's Q values. This shows that there is a relationship between EP and TBQ. Jayachandran et al. (2013) carried out a research on S&P 500 firms and Donuni 400 index firms on Corporate Social Performance (CSP) - firm performance. In analysing and explain the relationship the author used Product Social Performance (PSP) and Environmental Social Performance (ESP) to represent CSP. The results revealed that PSP had a significant positive effect on Tobin-Q while ESP does not have a significant impact on Tobin-Q. Muhammad et al. (2015) investigated the nature of the relationship between environmental performance and financial performance of publicly listed companies in Australia. The authors investigated the relationship of CEP using Tobin-Q (TBQ), the results revealed that CEP had a positive impact on TBQ during the pre-crisis period (2001-2007) and had no significant impact on TBQ during the crisis period (2008-2010). Wagner (2010) analysed the link between sustainability management and economic performance using a set of firms in Standard & Poor's 500 index. The study revealed that the effect of CSP on TBQ is high. Dowell et al. (2000) used a sample of firms drawn from the U.S. Standard and Poor’s 500 list of corporations. The authors concluded that there is a reliable positive and significant relationship between the use of a single global environmental standard and a firm’s Tobin’s q; there is a significant and positive relationship between the market value of a company (as measured by Tobin’s q) and the level of environmental standard it uses. This study points to the fact that there is a relationship between environmental performance and economic (financial) performance using TBQ. Achim and Borlea (2014) in their study observed that there is a positive correlation for the market performance represented by TBQ and EP. Their study illustrates that environmental investments are not immediately reflected in a growth in the companies’ financial accounting (or domestic) performance but rather in a 16 | Page

Amosun Olayemi .O 4632788 performance’s decrease based on the increase of environmental investment costs that leads to a decrease of assets’ efficiency for the analysed period. As various researchers have found a positive relationship between environmental performance and financial performance (Tobin-Q), the following hypothesis is proposed:  H1b: An increase in a company’s environmental performance will necessitate an increase in financial performance proxy by TBQ.

2.5.3 Return on Capital Employed as a dependent variable Few researchers have used ROCE as an accounting-based measure of financial performance (Edwards 1998; Vinayagamoorthi et al., 2015). However, some have found a positive correlation while some have found a negative correlation. Vinayagamoorthi et al., (2015) carried out a research on Indian firms and the results revealed that the profitability variables like ROA, ROE, and ROS create the positive impact on energy intensity (proxy of environmental performance) of the sample firms. At the same time, one profitability variable such as ROCE recorded negative impact on EI. The results overall concluded that profitability variables of the firm (ROA, ROE, ROCE, and ROS) registered significant impact on environmental performance. Also, Wagner (2005) carried out a research on the European paper industry and the results indicated that the linear term of the environmental performance index has a positive, but insignificant effect on ROS whilst the squared term of the index has a significant and negative effect, which is also relevant in economic terms. ROCE had a negative effect on environmental performance. The results revealed an inversely U-shaped relationship between environmental and economic performance. Edwards (2015) researched on the link between company’s environmental performance and financial performance using ROCE as a measure of financial performance. The result revealed that there is a positive link between EP and ROCE. The following hypothesis is being proposed:  H1c: An increase in a company’s environmental performance will lead to an increase in financial performance proxy by ROCE. 17 | Page


2.6 Control Variables. Various authors have used various variables such as company size, corporate governance, debt to equity, risk, current ratio, sales, growth, R&D, debt dependence and many more to explain financial performance. However, for this research work, the following variables have been chosen:

Company Size López-Gamero et al. (2009) in their research used size as a control variable. The results showed that size only had a significant influence on the early investment time and intensity in environmental issues, size is not considered a determinant for the degree of proactivity in the development of environmental management. Arago´n-Correa et al. (2008) also indicate in their study that size is a relevant but not a determining condition for developing the most proactive environmental management. Also, Russo and Fouts (1997) used firm size as a control variable for profitability. It was observed that firm size variable is significant and positive and size is a significant predictor of profitability. Waddock and Graves (1997) carried out a research on the corporate social performance – financial performance link and used size as a control measure of ROA. It was revealed there is a significant relationship between ROA and CSP when size is measured by sales, when size is measured by total assets; and when size is measured by number of employees. Therefore, it is expected that company size will have a positive influence on financial performance. It is hypothesized that:  H2: There is a significant relationship between company size and financial performance.

Corporate Governance Achim and Borlea (2014) revealed that various studies have identified a strong correlation between the adoption of good corporate governance practices and companies’ performances. The authors argued that there is a positive relation between good corporate practices and financial performances. The coefficient for corporate governance (CG) indicated a weak but still positive correlation for 18 | Page

Amosun Olayemi .O 4632788 Romanian companies. In other words, the companies with good values for return on shareholders’ equity ratio, debt ratio, financial flexibility ratio, with strong corporate governance are expected to generate an increase in profitability’s companies (expressed by ROA). Furthermore, Bauer et al., (2007) examined the impact of corporate governance on firm valuation as measured by Tobin’s q and analysed the effect of corporate governance on firm performance. Gompers et al., 2003 (cited by Bauer et al 2007) in their research revealed that superior governance standards positively impact firm performance as measured by Net-Profit-Margin and Return-on-Equity in the U.S. the results showed that good-governance firms outperform poor-governance firms. However, various researchers have arrived at various results, it is expected that corporate governance will have a positive relationship with financial performance. Therefore, it is hypothesized that:  H3: There is a significant relationship between corporate governance and financial performance.

Debt to Equity Jayachandran (2014) used financial leverage (debt to equity) measured as the ratio of long-term debt to total assets. This is expected to have a negative effect on Tobin’s Q. The results revealed that financial leverage and firm size are negatively related to Tobin’s Q. Achim and Borlea (2014) observed that debt to equity (leverage) was statistically significant to TBQ and ROA. Abuja and Hart (1996) observed that leverage was only significant in relation to ROE, and was a positive factor in one the observed years (1989) which became a negative factor in a later year (1990) as a result of recession. This observation reveals that leverage has a negative effect on ROA. For this research work, it is expected that Debt to equity will have a negative impact on financial performance. It is hypothesized that:  H4: A decrease in Debt to equity will lead to an increase in financial performance.

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Current Ratio Muhammad et al. (2015) investigated the nature of the relationship between environmental performance and financial performance of publicly listed companies in Australia. The authors observed that current ratio had a positive impact on ROA during the pre-crisis period and had no statistical significant impact on ROA during the crisis period. However, there results also showed that current ratio had a positive impact on TBQ during the pre-crisis period. Therefore, it is expected that current ratio will have a positive influence on financial performance. It is hypothesized that:  H5: There is a significant relationship between current ratio and financial performance.

2.7 Conclusion In conclusion, although the results from the literature review communicate an inconclusive result in the study of the relationship between environmental performance and financial performance as a result of differences in environmental performance measures and financial performance measures, sample size, countries and industries. This research work will contribute more to existing literatures on the relationship between environmental performance and financial performance of FTSE 250 listed companies in the UK. The research work will use one of the least used financial performance measure ROCE as one of the financial performance measure. The study will be looking to answer the question most researchers ask on how environmental performance affects financial performance of companies. Does better environmental performance improve a firm’s financial performance?

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CHAPTER THREE RESEARCH METHODOLOGY 3.1 Introduction The main aim of this study is for the researcher to investigate the research questions and to investigate theories supporting the hypothesis mentioned in the literature review chapter. It is necessary that the methodological scientific procedures be followed in order to validate the entire preconceived hypotheses. In this chapter, the methodology, research design, research questions, hypotheses, sources of data, ordinary least square, sample size, sample description, data collection instruments, limitation of the methodology, procedures for processing the data collected are all discussed.

3.2 Research Purpose The main aim of this study is to investigate the relationship between environmental performance and financial performance of companies in the UK, there is a need for a clear, specific and measurable descriptive analysis with thorough explanation. This study therefore adapts a descripto-explanatory research will analyse the descriptive data in order to draw conclusions which will help explain the relationship between environmental performance and financial performance (Zikmund, 2003). Descriptive studies are studies that aim to portray an accurate profile of persons, events or situations. This research intends to give a clearer picture on the phenomena on the data collected. Explanatory studies on the other hand are studies that establish causal relationships between variables. This study will also examine the set hypotheses in order to explain the relationship between variables. Combining both descriptive and explanatory studies result in a descripto-explanatory where, usually, description is the precursor to explanation (Saunders et al., 2012).

3.3 Research Design Research Design is a plan or blueprint which specifies how data relating to a given problem should be collected and analysed. It is a strategy a researcher employs 21 | Page

Amosun Olayemi .O 4632788 in investigating the relationship that exist among variables of the study so as to enable him/her collect data which will be used for the study (Adebayo, 2006). Research Design means the structuring of investigation aimed at identifying variables and their relationships to one another. It is an outline that serves as a useful guide to the researcher in his efforts to generate data for his/her study (Adebayo, 2006).

3.3.1 Data Source and Collection For this research work, the main source of data used is the secondary data. Secondary Data are primary data and other data collected earlier by other researchers and made available for reuse (Hox and Boeije, 2005). Secondary data comprises of textbooks, journals, e-book and databases. All financial data used was collected form the Financial Accounting Made Easy [FAME] database (2016) and all Environmental, Social and Governance data was collected from Thompson Reuters DataStream (2016). This study is cross sectional and the data was thus collected over a time period of 2 years.

3.3.2 Data Description 3.3.2.1 Dependent Variables Researchers and Scholars have all disagreed on the best measurement for measuring financial performance, accounting-based measures or the market-based measures(Achim and Borlea 2014). Previous researchers utilized accounting-based measures (Russo and Fouts 1997) and market-based measures (King and Lenox 2001). The accounting-based measure reflects companies’ internal efficiency and it meets the informational needs of managers, while the market-based measure meets the informational needs of the investors as the market value better reflects shareholders wealth than the accounting value (Achim and Borlea 2014). Both measures will be used in this research. The variables used were ROA, ROCE and TBQ. Return on Asset [ROA] and Return on Capital Employed were used as the accounting-based measure, while Tobin-Q was used as the market-based measure. ROA is the ratio of net profit after taxes to total assets. ROA reflects the current profitability of companies (Jayachandran et al., 2013). ROA indicates the efficient use of the firm’s total assets and also an indicator of the amount of profit a 22 | Page

Amosun Olayemi .O 4632788 firm generates for each unit of investment in assets (Palepu et al., 2010). There have been mixed results using this proxy in a number of studies as some researchers found a positive correlation and some found a negative correlation (Ahuja and Hart 1996; Russo and Fouts 1997).

Net profit (after taxes) *100

Return on Total Asset (ROA) =

Total Asset

TBQ (Tobin’s Q) ratio was used as the market-based measure. Tobin’s q is the firm market valuation over replacement value of assets (Dowell et al. 2000). It measures the market valuation of a firm relative to the replacement costs of tangible assets (Lindenberg and Ross 1981). This variable, is used to gauge how the market assesses the current ongoing value of the firm’s assets. A Tobin’s Q value greater than one indicates that investors assess the current value of assets as being higher than the replacement costs of the same assets (Lee and Tompkins 1999). Tobin Q (TBQ) =

Total Market Value of Firm Total Asset Value of Firm

ROCE was used as an accounting-based measure. This is included as a measure of the efficiency of the capital employed in producing income (Edwards 2015). This ratio measures how much a company has earned on invested long-term funds. It measures a company’s profitability and the efficiency in which capital is employed (Aerts and Walton, 2015). ROCE =

Profit before interest and tax *100 Capital employed (Total Asset - Current Liabilities)

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3.3.2.2 Independent Variables Explanatory variable The explanatory variable used was environmental performance. An environmental score card from Thomson Reuters ASSET4 was used as a proxy for environmental performance. This has been used in a number of literature (Russo and Fouts 1997; Nakao et al. 2007). Environmental Performance score measures a company’s impact on living and non-living natural systems, including the air, land and water, as well as complete ecosystems. It reflects how well a company uses best management practices to avoid environmental risks and capitalise on environmental opportunities in order to generate long-term shareholder value. The environmental performance score covers emission reduction, resource reduction and product innovation (Thomson Reuters 2016). Emission reduction comprises of: emissions reduction policy, CO2 equivalents emission total, CO2 equivalents emission direct, CO2 equivalents emission indirect, CO2 equivalent indirect emissions, scope three, commercial risks and/or opportunities due to climate change, CO2 reduction, ozone-depleting substances reduction, NOx and SOx emissions reduction, NOx emissions, SOx emissions, VOC emissions reduction, VOC emissions, waste total, non-hazardous waste, hazardous waste, waste recycling ratio, water pollutant emissions, waste recycling ratio, water pollutant emissions, waste reduction initiatives, environmental management systems certified percent, sustainable transportation and environmental expenditures. While resource reduction includes: energy efficiency policy, toxic chemicals or substance reduction, energy use total, direct energy purchased, direct energy produced, coal energy purchased, coal energy produced, natural gas energy purchased, natural gas energy produced, oil energy purchased, oil energy produced, electricity purchased, electricity produced, renewable energy use, green buildings, water efficiency policy, water use total, water recycled and environmental supply chain management. Product innovation covers:

energy footprint reduction,

environmental R&D expenditures, renewable/clean energy products, water technologies and product innovation/product impact minimization (Thomson Reuters 2016). 24 | Page


Control variables The control variables used in this research are company size, corporate governance, debt to equity and current ratio (King and Lenox 2001; Nakao et al. 2007). These control variables were selected as a result of the significant influences they have on environmental performance. Studies by (Russo and Fouts 1997; Iwata, H. and Okada, K. 2011; Hatakeda et al. 2012; Muhammad et al. 2015) have confirmed that each control variable chosen have a significant relationship with financial performance of companies. Corporate Governance score measures a company’s systems and process, which ensures that its board members and executives act in the best interests of its long-term shareholders. It reflects a company’s capacity, through its use of best management practices, to direct and control its rights and responsibilities through the creation of incentives, as well as checks and balances in order to generate long-term shareholder value. The corporate governance score covers board structure, board function, compensation policy, shareholder rights and vision & strategy (Thompson Reuters 2016). Company Size is measured as the logarithm of the firm asset as suggested by Wanger (2010); Russo and Fouts (1997); Iwata, H. and Okada, K. (2011). Research has shown that company size has an impact on a firm’s environmental performance and the response of firms in terms of improving their environmental issues (Iwata, H. and Okada, K., 2011; Hatakeda et al., 2012). Company Sze =

log 𝑇𝑜𝑡𝑎𝑙 𝐴𝑠𝑠𝑒𝑡

Debt to Equity is measured as the ratio of total liabilities to stockholders’ equity. Research using debt to equity evince that debt to equity influences a firm’s financial performance (Iwata and Okada 2011; Muhammad et al. 2015). Debt to Equity (D2E) =

Total Liabilities *100 Stockholders’ Equity

Current Ratio is calculated as current assets over current liabilities is used to measure the liquidity of a firm and is expected to influence a firm’s financial performance (Muhammad et al. 2015; Iwata and Okada 2011). 25 | Page


Current Ratio =

Current Assets Current Liabilities

3.4 Sample Size The research sample used consist of 111 companies listed on the FTSE 250 index on the London Stock Exchange in order to ensure comparable results, companies with data missing from FAME and DataStream were excluded from the sample as well as all companies from the financials industry (Chithambo and Tauringana 2014). This is as a result of the difference in the disclosure requirements for financials. The years of comparative study were 2013 and 2014.

3.5 Research Method A quantitative method was applied to this research work as numerical data and statistical analysis were applied to examine the relationship between environmental performance and financial performance (Saunders et al. 2012).

3.5.1 Ordinary least square modelling The relationship between environmental performance and financial performance was analysed using Ordinary Least Square method [OLS] regression as used by (Hayes and Cai 2007; Field 2013). The OLS model is as stated follows: Model 1: Multiple linear regression 𝑌 = 𝛽0 + 𝛽1 𝑋1 + ⋯ + 𝛽𝑘 𝑋𝑘 + 𝜀 Where Y is the dependent variable, 𝛽0 is the constant that is the intercept with Yaxis, X1,…,k, is the independent variable and β1…k, is the predicted value of the independent variables. ε is the error term of the model which represents all other factors affecting Y other than X1,…,k,. This means that the β provides information about the relationship between the independent and dependent variables (Hill).

3.5.2 Econometric modelling The above model can be replicated as follows in order to incorporate the variables of interest in this research: 26 | Page

Amosun Olayemi .O 4632788 Model 2: General model specification 𝐹𝑃𝑀 = 𝛽0 + 𝛽1 𝐸𝑃1 + ⋯ + 𝛽𝑘 𝐶𝑉𝑘 + 𝜀 Where: FPM represents Financial Performance proxy by ROA and ROCE EP represents Environmental Performance proxy by EPI CV represents Control Variables which are Corporate governance, Company size [total asset], Gearing level [debt to equity], and liquidity [current ratio]. Model 3: ROA 𝑅𝑂𝐴 = 𝛽0 + 𝛽1 𝐸𝑃1 + 𝛽2 𝐶𝐺 + 𝛽3 𝑆𝐼𝑍𝐸 + 𝛽4 𝐺𝐸𝐴𝑅𝐼𝑁𝐺 + 𝛽5 𝐿𝐼𝑄𝑈𝐼𝐷𝐼𝑇𝑌 + 𝜀

Model 4: Tobin’s Q 𝑇𝐵𝑄 = 𝛽0 + 𝛽1 𝐸𝑃 + 𝛽2 𝐶𝐺 + 𝛽3 𝑆𝐼𝑍𝐸 + 𝛽4 𝐺𝐸𝐴𝑅𝐼𝑁𝐺 + 𝛽5 𝐿𝐼𝑄𝑈𝐼𝐷𝐼𝑇𝑌 + 𝜀

Model 5: ROCE 𝑅𝑂𝐶𝐸 = 𝛽0 + 𝛽1 𝐸𝑃1 + 𝛽2 𝐶𝐺 + 𝛽3 𝑆𝐼𝑍𝐸 + 𝛽4 𝐺𝐸𝐴𝑅𝐼𝑁𝐺 + 𝛽5 𝐿𝐼𝑄𝑈𝐼𝐷𝐼𝑇𝑌 + 𝜀 Where: The dependent variables are ROA [return on assets], TBQ [Tobin’s Q] and ROCE [return on capital employed]. The explanatory variables are EP [environmental performance], CG [corporate governance], SIZE [company size], GEARING [debt to equity], and LIQUIDITY [current ratio]. 𝛽0 Is the intercept and ε is the error term.

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CHAPTER FOUR ANALYSIS AND INTERPRETATION OF DATA 4.1 Introduction This chapter covers the interpretation, examination, discussion and summary of the findings of the study. The descriptive statistics covering the minimum, maximum, mean, standard deviation, correlation between the dependent, independent and control variables and the regression results are elucidated. The econometric model for each variable are proffered and interpreted and finally, the discussion of the findings concludes the chapter.

4.2 Data Analysis 4.2.1 Descriptive Statistics The descriptive statistics of both 2013 and 2014 for dependent variables ROA, ROCE and TBQ, the independent variables EP (explanatory variable) and CG, SIZE, CR and D2E (control variables) are shown in table 4.1 below. Table 4.1 Descriptive Statistics 2013 N

Max

Mean

Std. Dev N

251.7

10.06

25.04

111 -65.1

282.82 10.31

28.38

ROCE 111 -324.14 141.41 10.91

37.36

111 -236.95 404.14 14.38

49.36

TBQ

111 0

132.77 3.98

15.49

111 0.087

225.09 4.85

22.8

EP

111 11%

94%

60.41% 24.46%

111 11%

94%

62.17%

24.38%

CG

111 33%

95%

76.20% 14.22%

111 25%

96%

81.50%

12.41%

CR

111 0

5.8

1.6

1.06

111 0

5.57

1.55

0.98

SIZE

111 0

4.03

2.97

0.62

111 0.76

3.87

3.03

0.48

D2E

111 -30.08

111.55 -1.11

11.95

111 -34.7

38.23

-1.18

7.02

ROA

Min

2014

111 -38.19

Min

Max

Mean Std. Dev

Source: Authors output through SPSS 22 using Fame and Asset4 ESG data (2016). The total number of observation (N) for both 2013 and 2014 is 112. In 2013 and 2014 the minimum ROA was -38.19% and -65.10%, while the maximum ROA was 251.70% and 282.82% in both 2013 and 2014. The mean for ROA in 2013 and 28 | Page

Amosun Olayemi .O 4632788 2014 was 10.06% and 10.31%, this suggests that the managers of the companies sampled utilised the assets in an effective and efficient manner (Muhammad et al., 2015). The standard deviation was 25.04% and 28.38% in 2013 and 2014 which shows that the values of the data set were widely spread around the mean. The mean, maximum, minimum and standard deviation values of this research indicates that the number of observation is of a larger scale than the previous research of Achim and Borlea, (2014); Ong et al., (2014) and Muhammad et al., (2015). In 2013 and 2014 the minimum ROCE was -324.14% and -236.95% while the maximum ROCE was141.41% and 404.14% respectively. The mean for ROCE in 2013 and 2014 was 10.91% and 14.38% which suggests that managers of the sampled companies proficiently used the equity and debt to generate more returns for the companies. Standard deviation for 2013 and 2014 was 37.36% and 49.36% correspondingly, which is higher than the standard deviation value for the research work of Ruchika, (2013) and Vinayagamoorthi et al., (2015). Although the mean, minimum, maximum and standard deviation values were higher than previous research works, the total number of observation was lower than the previous investigations. The minimum TBQ values for 2013 and 2014 was 0.00 and 0.087 while the maximum TBQ values were 132.77 and 225.09 in 2013 and 2014 correspondingly. TBQ shows a standard deviation value of 15.49 and 22.80 which shows a very good and large spread in the observation and a mean value of 3.98 and 4.85 in 2013 and 2014. In applying the needed interpretation, the mean of TBQ established in this study suggests that on average the FTSE 250 UK firms have a high market value. The mean from this observation is similar to the results from Dowell et al., (2013); King and Lenox, (2001); Konar and Cohen, (2001); Jayachandran et al., (2013) and Muhammad et al., (2015). As it can be seen in the table above, the explanatory variable EP in 2013 and 2014 had a minimum value of 11% and a maximum value of 94% in 2013 and 2014 respectively. EP shows a standard deviation of 24.46% (2013) and 24.38% (2014) which reveals that the observations were broadly spread round the mean. While the mean was 60.41% and 62.17% respectively which suggests that companies are involved in environmental matters that affect the companies and the community. 29 | Page

Amosun Olayemi .O 4632788 With regards to CG as a control variable, the minimum values were 33% and 25%, while the maximum values were 95% and 96% in 2013 and 2014 correspondingly. CG shows a standard deviation of 14.22% and 12.41% which shows a high spread of the observation, while the mean value was 76.20% and 81.50%. In interpreting, the mean suggests that companies used in the study are highly involved in having a good corporate governance system. Control variables CR and SIZE had the lowest minimum values of 0.00 in 2013 and 0.00, 0.76 in 2014, and the lowest maximum values for CR 5.80, 5.57and SIZE 4.03, 3.87 in 2013 and 2014 respectively. CR and Size also had the lowest standard deviation values of 1.06; 0.62 in 2013 and 0.98; 0.48 in 2014 which reveals that the observations had a very low and uneven spread. The mean for CR 1.60 and 1.55 and the mean for SIZE 2.97 and 3.03 in 2013 and 2014 correspondingly. The minimum values for D2E were -30.08 and -34.70, while the maximum values were 111.55 and 38.23 in 2013 and 2014 respectively. The standard deviation values were 11.95 and 7.02 which shows that the observation was largely set round the mean in 2013 and 2014. The mean values were -1.11 and -1.18 in 2013 and 2014. From the table above, it can be observed that the variables increased in 2014 in comparison to 2013 which shows more companies increased their assets, capital employed, liabilities, size and market value.

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4.2.2 Correlation results Table 4.2 A Correlation matrix 2013 Variable ROA

ROA

ROCE

TBQ

EP

CG

CR

SIZE

D2E

1

ROCE

0.125

1

TBQ

0.099

-0.044

1

EP

-0.154

-0.101

0.075

1

CG

-0.117

-0.178

0.047

0.045**

CR

-0.091

-0.026

-0.09

-0.077

-0.052

1

SIZE

-0.283**

-0.106

-0.466**

0.102

0.109

0.099

D2E

0.006

-0.046

-0.046

0.016

0.034

-0.02

1

1 0.061

Note: ** indicates the significance level at 1%. Source: Authors output through SPSS 22 using Fame and Asset4 ESG data (2016). Table 4.2A shows the level of correlation between the dependent and independent variables for 2013. From the above table, it can be denoted that the correlation between CG and EP is a weak but positive relationship and significant. The relationship between SIZE, ROA and TBQ is weak and negative but is significant. However, the correlation between EP and ROA -0.154 suggests a weak and negative the relationship between EP and ROA which does not support hypothesis H1a. The correlation is not significant. The relationship between EP and ROA is shown in the scatterplot below.

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1

Amosun Olayemi .O 4632788 Figure 4.1 Relationship between ROA and EP 2013 300 250 200

ROA

150 100 50 0 -50 0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

EP

Source: Authors output from the regression through E-Views (2016) Figure 4.1 shows that although companies with high ROA have high EP, most of the observation reveals that an increase in EP does not mean an increase in ROA. This can be as a result of the use of various industries in this study, as each industry has various rules and requirements for EP as well as a result of an increase or decrease in the assets of companies. The correlation matrix shows that there is no relationship, the trend line is negative in 2013. Therefore, the null hypothesis applies to this variable which states that an increase in a company’s environmental performance will not necessitate an increase in financial performance proxy by ROA. The correlation matrix reveals that there is a weak but positive relationship between EP and TBQ with a coefficient of 0.075, which is in agreement with hypothesis H1b. the relationship is shown in the scatter plot and interpreted below.

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Amosun Olayemi .O 4632788 Figure 4.2 Relationship between EP and TBQ 2013 140 120 100

TBQ

80 60 40 20 0 0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

EP

Source: Authors output from the regression through E-Views (2016) In Figure 4.2 shows that an increase in the observations EP lead to a slight increase in TBQ which shows that an increase in EP may lead to an increase in the market capitalisation and asset of a company. This means the companies were not undervalued or overvalued. However, the correlation is not significant. The correlation between EP and ROCE suggests a negative relationship with a negative co efficient of 0.101, this relationship does not conform to the expectations from hypothesis H1c. The correlation is not significant and the relationship is shown in figure 4.3 below.

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Amosun Olayemi .O 4632788 Figure 4.3 Relationship between EP and ROCE 2013 200

100

ROCE

0

-100

-200

-300

-400 0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

EP

The above figure divulges that an increase in EP does not lead to an increase ROCE which can be attributable to a decline in profitability and the efficiency in which companies generated cash. This means companies did not efficiently use their resources to generate more profit which can be interpreted as a decline in the rate of return earned by the companies. SIZE is the only control variable that is significantly correlated with ROA and TBQ. All control variables had negative relationships with the dependent variables excluding CG which had a positive relationship with TBQ and D2E which had a positive relationship with ROA. The highest correlation coefficient recorded in Table 4.2 A is 0.125.

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Amosun Olayemi .O 4632788 Table 4.2 B Correlation matrix 2014 Variable ROA

ROCE

TBQ

EP

CG

CR

SIZE

ROA

1

ROCE

0.166

1

TBQ

0.076

-0.03

1

EP

-0.087

-0.031

0.119

1

CG

-0.046

-0.14

0.107

0.408**

CR

0.008

-0.041

-0.102

0.004

-0.041

1

SIZE

-0.396**

-0.209**

-0.607**

0.211*

0.106

-0.034

D2E

-0.079

0.256**

-0.031

-0.04

-0.027

-0.042

D2E

1

1 0.008

Note: ** and * indicates the significance level at 1% and 5%. Source: Authors output through SPSS 22 using Fame and Asset4 ESG data (2016). Table 4.2 B reports the correlation coefficients between the dependent and independent variables for 2014. The correlation coefficient of EP and ROA is -0.087, this negative linear relationship suggests that there is no relationship between EP and ROA which does not support hypothesis H1a. There is a positive correlation relationship between EP and CG which can be interpreted as an increase in corporate governance standards will lead to an increase in environmental performance of companies. The correlation is significant at 1%. The relationship between ROA and EP is shown in Figure 4.4 (see appendix A3). In comparison with 2013, the scatterplot shows a greater decline in ROA. This can be further interpreted as an increase in EP leads to a decline in ROA. It can be propounded that as companies improve on emission reduction, resource reduction and product innovation, return on asset declines as most of the companies may be less asset heavy which can be as a result in variations in the industries used in the sample. The correlation coefficient between EP and TBQ is 0.119, this represents a weak but positive relationship which corresponds with hypothesis H1b. In comparison with 2013, the correlation coefficient increased by 0.044 which can be deduced as a yearly increase in TBQ will lead to a yearly increase in EP. The scatterplot for this relationship is shown in figure 4.5 (see appendix A3).

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1

Amosun Olayemi .O 4632788 Figure 4.5 reveals that an increase in EP lead to the increase in market capitalisation and asset of few companies. Although an improvement in EP lead to a slight increase in the market value of the companies sampled when compared to 2013, the increase was low. It is therefore proposed that as companies improve further on their environmental impacts, the market value of the companies will increase. The correlation relationship between EP and ROCE is weak, negative and insignificant. The coefficient is -0.031. This suggests that there is no relationship between EP and ROCE which does not support hypothesis H1c. The relationship is shown in figure 4.6 (see appendix A3). From Figure 4.6, it can be seen that companies with a high ROCE also have high EP scores. Most of the companies have high EP and ROCE between 0 and 100. However, from the correlation the trend line explaining the relationship is negative as the scatterplot reveals that a high number of the companies from the observation with negative ROCE have high EP which will have impacted the coefficient. It can be propounded that a decrease in a company’s emission production, resource reduction and an increase in product innovation does not necessarily lead to an increase in the company’s ROCE. SIZE also is the only control variable that is significantly correlated with all the dependent variable and independent variable (EP). All control variables had negative relationships with the dependent variables excluding CG which had a positive relationship with TBQ, CR which had a positive relationship with ROA and D2E had a weak, positive and significant relationship with ROCE. The correlation coefficient relationship is in correspondence to hypothesis H2 and H4. The highest correlation coefficient recorded in Table 4.2 B is 0.166. According to Gujarati and Porter (2009), multicollinearity will occur if the correlation coefficient among variables exceeds the rule of thumb level i.e. in excess of 0.8. The observation was tested for multicollinearity. The highest correlation recorded among the independent variables in 2013 was 0.109 and 0.408 in 2014. The correlation coefficient between the independent variables for both years did not exceed the rule of thumb. There were no obvious concerns and irregularities in the data. In additional analysis, a Variance Inflation Factors (VIFs) test was carried out 36 | Page

Amosun Olayemi .O 4632788 to diagnose multicollinearity among the variables used in this study. The results show that the highest VIF value in 2013 is 1.264 and the lowest is 1.005, while the highest VIF value in 2014 is 1.244 and the lowest is 1.004 suggesting that multicollinearity is not an issue as O’Brien (2007) states that the VIFs should be less than 10 (rule of thumb) in order to avoid multicollinearity. Auto correlation was not tested for as this research work is across sectional regression and not a panel regression.

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4.2.3 Analysis of cross-sectional regression results Table 4.3 below reports the results of a single cross-sectional regression for 2013 and 2014 for the dependent variables ROA, ROCE and TBQ. Table 4.3 Single cross-sectional regression results for 2013 and 2014 2013

2014

Dependent

Model 1:

Model 2:

Model 3:

Model 1:

Model 2:

Model 3:

Variables

ROA

TBQ

ROCE

ROA

TBQ

ROCE

Constant

56.714

31.633

60.764

82.849

81.138

(3.506)**

(3.423)**

(2.435)**

(3.616)**

(5.46)**

(3.068)**

-0.117

0.062

-0.036

-0.006

0.212

0.171

(-1.107)

(-1.025)

(-0.218)

(-0.05)

(2.837)**

-0.835

-0.074

0.058

-0.416

-0.011

0.146

-0.584

(-0.405)

(-0.56)

(-1.483)

(-0.05)

-1.01

(-1.476)

-1.728

-0.478

-1.013

-0.264

-2.853

-2.234

(-0.812)

(-0.382)

(-0.299)

(-0.102)

(-1.703)

(-0.488)

-10.472

-11.829

-4.89

-23.486

-31.998

-22.331

(-2.795)**

(-5.528)**

(-0.846)

(-4.325)**

(-9.085)**

(-2.319)*

0.05

-0.027

-0.113

-0.311

-0.065

1.793

-0.26

(-0.244)

(-0.378)

(-0.863)

(-0.277)

(2.808)**

R- squared

0.103

0.236

0.042

0.162

0.454

0.13

Adjusted R

0.061

0.2

-0.004

0.123

0.428

0.089

F-statistics

2.447

6.538

0.92

4.111

17.635

3.166

Observation

111

111

111

111

111

111

124.612

Regressors EP

CG

CR

SIZE

D2E

Notes: The coefficients for the independent variables are in the table above with the tstatistics in parenthesis; ** and * indicates the significance level of 1% and 5% respectively. See Appendix A2 for full details.

Source: Authors output through SPSS 22 using Fame and Asset4 ESG data (2016).

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4.2.3.1 Model 1: ROA Table 4.3 shows the results of the test carried out using ROA as a dependent variable for 2013 and 2014. The results for 2013 are recorded in column 2 and for 2014 in column 4. Model 1 is estimated using EP and the financial control variables. The result indicates that EP has a negative impact on ROA in 2013 (β=-0.117, tstatistics = -1.107) and 2014 (β=-0.006, t-statistics = -0.05). The R square and Adjusted R square for this model is 0.103 and 0.061 in 2013 is very low and the R square and Adjusted R square for 2014 is 0.162 and 0.123 is low which suggests that the model explains none of the variability of the response data around the mean. Therefore, it is expected that other countless factors affecting ROA were not included in the model. The results confirm that an increase in environmental performance leads to a decrease in return on assets. The model predicts an increase in EP leads to a decline in ROA by 0.117 and 0.006 in 2013 and 2014 respectively. The result is not significant and does not confirm hypothesis H1a: An increase in a company’s environmental performance will necessitate an increase in financial performance proxy by ROA. Therefore, the hypothesis is rejected. In 2013 and 2014 Size had a negative but significant relationship with ROA which supports hypothesis H2: There is a significant relationship between company size and financial performance. CG and CR had a negative and insignificant relationship with ROA which does not support hypothesis H3: There is a significant relationship between corporate governance and financial performance and H5: There is a significant relationship between current ratio and financial performance. Thus, both hypotheses are rejected. D2E had a weak but positive relationship with ROA in 2013 which supports hypothesis H4: A decrease in debt to equity will lead to an increase in financial performance. The hypothesis is accepted. However, D2E had a weak and negative relationship with ROA in 2014 which does not support hypothesis H4, thus, the hypothesis is rejected and the null hypothesis is accepted.

4.2.3.2 Model 2: TBQ Table 4.3 reports TBQ results for both periods 2013 and 2014. Model 2 is estimated using EP and financial control variables. The result indicates that in 2013 EP has a positive impact on TBQ (β=0.062, t-statistics = -1.025) and in 2014 (β=39 | Page

Amosun Olayemi .O 4632788 0.212, t-statistics = 2.837). The R square and Adjusted R square for this model is 0.236 and 0.2 in 2013 is low and the R square and Adjusted R square for 2014 is 0.454 and 0.428 is low which suggests that the model explains none of the variability of the response data around the mean. Therefore, it is expected that other factors affecting TBQ were not included in the model. The results show that EP has a positive relationship TBQ in 2013. However, the relationship is not significant, which implies that although an increase in EP necessitated an increase in financial performance proxy by TBQ, hypothesis H1c will be rejected. In 2014 EP had a significant and positive relationship with TBQ. This model suggests that an increase in environmental performance will lead to a 0.212 increase in TBQ which supports hypothesis H1c: An increase in a company’s environmental performance will lead to an increase in financial performance proxy by TBQ. Size in 2013 and 2014 had a negative and significant relationship with TBQ which supports hypothesis H2: There is a significant relationship between company size and financial performance. D2E and CR for both years had a negative and insignificant relationship with TBQ which does not support hypothesis H4: A decrease in debt will lead to an increase in financial performance and hypothesis H5: There is a significant relationship between current ratio and financial performance. CG has a weak and positive relationship for both years, however, the relationship was not significant and is not in line with hypothesis H3: There is a significant relationship between corporate governance and financial performance. The hypotheses for D2E, CR and CG is therefore rejected.

4.2.3.3 Model 3: ROCE Table 4.3 reports ROCE results for both periods 2013 and 2014. Model 3 is estimated using EP and financial control variables. The result indicates that in 2013 EP has a negative impact on ROCE (β= -0.036, t-statistics = -0.218) and in 2014 has a positive impact on ROCE (β=0.171, t-statistics = -0.835). The R square and Adjusted R square for this model is 0.042 and -0.004 in 2013 is low and the R square and Adjusted R square for 2014 is 0.13 and 0.089 is low which suggests that the model explains none of the variability of the response data around the mean.

40 | Page

Amosun Olayemi .O 4632788 Therefore, it is expected that other factors affecting ROCE were not included in the model. The regression results in 2013 show that EP has a weak and negative relationship with ROCE and in 2014 has a weak and positive relationship with ROCE. The relationship is insignificant and does not conform to hypothesis H1c: An increase in a company’s environmental performance will lead to an increase in financial performance proxy by ROCE. Since the relationship is insignificant relationship, hypothesis H1c is rejected and hypothesis H0 is accepted. The relationship between CG, CR and ROCE in 2013 and 2014 is negative and insignificant, which is not in correlation with hypothesis H3: There is a significant relationship between corporate governance and financial performance and H5: There is a significant relationship between current ratio and financial performance. Size in 2013 has a negative relationship with ROCE which does not support hypothesis H2, while in 2014 has a negative but significant relationship with ROCCE which is in correspondence with hypothesis H2: There is a significant relationship between company size and financial performance. Furthermore, the relationship between D2E and ROCE in 2013 was negative and insignificant and does not correlate with hypothesis H4, while in 2014 the relationship is positive and significant and is in line with hypothesis H4: A decrease in debt to equity will lead to an increase in financial performance.

4.3 Discussion of Findings This study has attempted to address what has become an important question Does better environmental performance improve a firm’s financial performance? In undertaking the research, this study is exploring whether or not any significant link exists between EP and FP measures using data from the FTSE 250 UK market. Several researchers have noted that it is best not to make a general conclusion that EP and FP have no relationship or have a negative relationship (Cordeiro and Sarkis, 1997; Gilley, K., 2000). Various studies (Mahapatra, 1984; Aupperle et al., 1985; Jaggi and Freedman, 1992; Russo and Fouts, 1997; McWilliams and Siegel, 2000; Konar and Cohen, 2001; Takeda and Tomozawa, 2004; Takeda and Tomozawa, 2004; Yamaguchi, 2008; Jacobs et al., 2010; Jayachandran et al. 2013; Primc and Cater 2015) have identified the relevance of environmental information to investors. 41 | Page

Amosun Olayemi .O 4632788 Thus, any positive or negative environmental performance information will have an immediate impact on a company’s market value and financial performance. The results of a negative and insignificant relationship between environmental performance and financial performance (ROA) are consistent with prior studies (Connelly and Limpaphayom, 2004; Achim and Borlea, 2014). The negative effect on ROA can be explained by the fact that good environmental performance of a company does not necessarily increase the financial performance of a company, but has a significant or insignificant effect on it. However, it was observed that the impact of environmental performance on ROA was better off in 2014 compared to 2013, which means as a company improves on environmental performance there is a likelihood that there will be an increase in ROA. A company’s improvement on environmental matters such as environmental R&D expenditures, reduction in ozone-depleting substances, waste recycling, renewable energy products, product innovation impact minimization and so on may lead to an increase in cost which will lead to a decrease in income which will lead to a decrease in the total asset of companies. It is often argued that social and political pressures are what motivates or obligates companies to be clean and green corporate citizens. The results of a positive and insignificant relationship between EP and TBQ in 2013 and a positive and significant relationship between EP and TBQ in 2014 are consistent with previous studies (Dowell et al., 2000; King and Lenox, 2001; Konar and Cohen, 2001; Wanger, 2010; Achim and Borlea, 2014; Muhammad et al., 2015). The positive relationship between environmental performance and TBQ as a marketbased measure of financial performance implies that companies that engage in corporate social responsibilities in order to achieve high environmental performance have a higher competitive advantage, as there will be an increase in market value which signifies an increase in market performance. A negative and insignificant relationship between EP and ROCE in 2013 is consistent with prior research (Wagner, 2005; Vinayagamoorthi et al., 2015). This can be interpreted as an improvement in the environmental performance (product innovation, environmental expenditures, footprint reduction, green buildings and water technology) of a company leads to a decrease in the income of a company which in turn leads to a decrease in ROCE. However, it was observed in 2014 that as 42 | Page

Amosun Olayemi .O 4632788 companies improved more on product innovation, energy efficiency, waste recycling, water use, sustainable transportation and much more, reduces costs and increases the return the company gets. The result is not significant and thus, does not conform to the previous study that found a positive relationship (Edwards, 2015). It was expected that company size will have a significant relationship with financial performance. It was observed that size had a negative and significant relationship with financial performance. This result is in line with (Waddock and Graves, 1997; Lopez-Gamero et al., 2009, Jayachandran et al., 2013), and contradicts the work of Russo and Fotus, (1997). Corporate governance was expected to have a significant relationship with financial performance. However, this study found out that corporate governance had a negative and insignificant relationship with ROA and ROCE, but had a positive and insignificant relationship with TBQ which does not support the findings of Bauer et al., (2007) and Achim and Borlea (2014). It was observed that current ratio had a negative and insignificant relationship with the three financial variables used. The findings are not in line with the previous studies of Muhammad et al., (2015) who found out that current ratio had a positive impact on ROA and TBQ during the pre-crisis period but had no statistical significant impact on ROA during the crisis period. Debt to equity was expected to have an impact on environmental performance. The findings showed that debt to equity has a significant impact on ROCE and ROA and not on TBQ. This is in line with the findings of Abuja and Hart, (1996) and Achim and Borlea, (2014) and contradicts the work of Jayachandran, (2014) and Muhammad et al., (2015).

4.4 Summary of Findings In summary, the empirics provided in this study shows that an increase in environmental performance by companies in the form of energy efficiency, emission reduction policy, reduction of commercial risks and/or opportunities due to climate change, reduction in waste, increase in recycling, increase in the use of renewable energy, production in renewable/clean energy products and product innovation will lead to an increase in TBQ. Although the impact on ROA and ROCE was not 43 | Page

Amosun Olayemi .O 4632788 significant, the results reveal that a yearly improvement in environmental performance will lead to an improvement in financial performance. It is therefore proved that an increase in environmental performance will have an impact on the financial performance and success of a company. This finding therefore supports the research works of Russo and Fouts, (1997), Konar and Cohen, (2001), Takeda and Tomozawa, (2004), Yamaguchi, (2008), Jacobs et al., (2010) and Muhammad et al., (2015). Furthermore, this study also supports the Stakeholder Theory, Win win view, Resourced-based view and the Economist’s view which supports that there is a relatioship between environmental performance and financial performance.

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CHAPTER FIVE CONCLUSION AND RECOMMENDATIONS 5.1 Introduction This chapter presents the conclusions of the main results and gives limitations and recommendations for further studies.

5.2 Conclusion This study examines the relationship between environmental and financial performance using the data of FTSE 250 firms in the UK from 2013 and 2014. Environmental issues have been diverse in recent years and various stakeholders have various expectations and preferences for various environmental issues. In order to examine the relationship, this study employed three financial performance indices to further explain the relationship with environmental performance. A large number of studies have observed examined the relationship between environmental performance and financial performance, however, there have been no conclusive results about the relationship. Environmental performance plays an important role in financial performance which could be as a result of the economic benefits that can be derived from companies investing in environmental incentives, or can be as a result of the potential increase in profitability and market value of firms. In testing the hypotheses, the regression results revealed that only one of the financial performance measure had a positive and significant relationship with environmental performance. It was observed that in 2014, the correlation between environmental performance and ROA and ROCE increased. This suggests that companies endeavour to minimize the impact of their environmental issues. However, as the regression shows different results on the three financial measures used, it is advisable that general conclusions should not be made. Overall, the results provide support that environmental issues are important and firms need to have incentives to reduce their environmental damages as financial performance is positively related to environmental performance. This means that environmental problems may be solved by the market mechanism without 45 | Page

Amosun Olayemi .O 4632788 government intervention, leading to a preferable environment for both firms and the government. However, companies, governments and the society at large still need to invest more into ensuring that environmental matters are taken care of effectively.

5.3 Limitations It is expected that any research work will encounter limitations. Among the limitations of the research work, the sample size was relatively small as financial companies and companies without data were excluded. The study used a limited analysis period of 2 years (2013 and 2014) which prohibited an industry and sectorial analysis. The study was limited to FTSE 250 UK companies.

5.4 Recommendations In light of the findings of this research work, some recommendations have been made for future contributions to the investigation on the relationship between environmental performance and financial performance. In order to account for an increase in the correlation and significance in the relationship between environmental performance and financial performance, other financial variables such as Return on equity, net profit margin, gross profit margin, stock return volatility and return on sales can be included in future studies. Other control variables such as growth ratio, flexibility ratio, fixed asset, advertisement and capital intensity should be used. It is advisable that other environmental performance variables such as GHG emissions, waste, carbon emissions, emissions to air, biodiversity, resource efficiency and water should be used in further research works. Also, it is recommended that a panel regression should be used and a larger analysis period be used in investigating the relationship between environmental performance and financial performance. For future research, it is recommended that a larger sample size, sample study and geographical location be used as this will provide more insight to the relationship between environmental performance and financial performance. Lastly, recommended for further research is the relationship between environmental performance and financial performance on an industrial and sectorial level. The relationship between corporate governance and environmental performance. The relationship between corporate governance and financial 46 | Page

Amosun Olayemi .O 4632788 performance. The link between debt to equity and financial performance. The link between firm size and financial performance. The correlation between current ratio and financial performance. The impact of environmental accounting on business organisations: costs and implication. The impact of environmental management on financial firms. Environmental disclosure in annual reports: a case study of UK firms.

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REFERENCES Achim, M. V. and Borlea, S. N., 2014. Environmental performances - Way to boost up financial performances of companies. Environmental Engineering and Management Journal, 13 (4), 991-1004. Ahuja, G. and Hart, S. L., 1996. Does it pay to be green? An emiprical examinnation of the relationship between emission reduction and firm perfromance. Business Strategy & the Environment (John Wiley & Sons, Inc), 5 (1), 30-37. Aupperle, K. E., Carroll, A. B. and Hatfield, J. D., 1985. An empirical examination of the relationship between corporate social responsibility and profitability. Academy of Management Journal, 28 (2), 446-463. Britton, J. and Gray, R., 2001. Environmental performance (?), profit, size and industry in UK companies: A brief exploration. Social and Environmental Accountability Journal, 21 (2), 4-7. Brundtland, G. H., 1987. Our common future [Non-fiction]. Oxford : Oxford University Press, 1987. Chithambo, L. and Tauringana, V., 2014. Company specific determinants of greenhouse gases disclosures. Journal of Applied Accounting Research, 15 (3), 323-338. Connelly, J. T. and Limpaphayom, P., 2004. Environmental Reporting and Firm Performance. Journal of Corporate Citizenship, (13), 137. Cordeiro, J. and Sarkis, J., 1997. Environmental proactivism and firm performance: evidence from security analyst earnings forecasts. Business Strategy and the Environment,

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Available

from:

http://onlinelibrary.wiley.com/doi/10.1002/(SICI)10990836(199705)6:2%3C104::AI D-BSE102%3E3.0.CO;2-T/epdf [Accessed 10 May 2016]. Dănilă, A. and Horga, M., 2014. The Importance of Environmental Resposibility in Firm Financial Performance. Ovidius University Annals, Series Economic Sciences. Vol. 14 (2014 Special Issue,), p11-15. Dowell, G., Hart, S. and Yeung, B., 2000. Do Corporate Global Environmental Standards Create or Destroy Market Value?, 1059. Field, A., Miles, J. and Field, Z., 2012. Discovering statistics using R. London: Sage.

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Amosun Olayemi .O 4632788 Field, A. P., 2013. Discovering statistics using IBM SPSS statistics : and sex and drugs and rock 'n' roll [Non-fiction]. Los Angeles : SAGE, 2013. 4th edition. Filbeck, G. and Gorman, R. F., 2004. The Relationship between the Environmental and Financial Performance of Public Utilities. Environmental & Resource Economics, 29 (2), 137-157. Galdeano-Gómez, E., Céspedes-Lorente, J. and Martínez-del-Río, J., 2008. Environmental performance and spillover effects on productivity: Evidence from horticultural firms. Journal of Environmental Management, 88, 15521561. Gilley, K., 2000. Corporate Environmental Initiatives and Anticipated Firm Performance: The Differential Effects of Process-Driven Versus Product-Driven Greening Initiatives. Journal of Management, 26 (6), 1199-1216. Gujarati, D. and Porter, D., 2009. Basic econometrics. 5th ed. New York: McGrawHill. Hayes, A. F. and Cai, L., 2007. Using heteroskedasticity-consistent standard error estimators in OLS regression: An introduction and software implementation. Behavior Research Methods, 39 (4), 709-722. Hill, R. C., Principles of econometrics [Non-fiction]. Hoboken, NJ : Wiley, 2012. 4th ed. Horváthová, E., 2010. Analysis: Does environmental performance affect financial performance? A meta-analysis. Ecological Economics, 70, 52-59. Horváthová, E., 2012. Analysis: The impact of environmental performance on firm performance:

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costs

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long-term

benefits?

Ecological

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Amosun Olayemi .O 4632788 Jaggi, B. and Freedman, M., 1992. An examination of the impact of pollution perfromance on economic and market performance: pulp and paper firms. Journal of Business Finance & Accounting, 19 (5), 697-713. Jayachandran, S., Kalaignanam, K. and Eilert, M., 2013. Product and environmental social performance: Varying effect on firm performance. Strategic Management Journal, 34 (10), 1255-1264. King, A. A. and Lenox, M. J., 2001. Does It Really Pay to Be Green?: An Empirical Study of Firm Environmental and Financial Performance. Journal of Industrial Ecology, 5 (1), 105-116. Kim, K., 2015. Revisiting The Relationship Between Financial And Environmental Performance: Does Granger Causality Matter?. JABR, 31 (5), 1861. Konar, S. and Cohen, M. A., 2001. Does the Market Value Environmental Performance?, 281. Lee, D. E. and Tompkins, J. G., 1999. A Modified Version of the Lewellen and Badrinath Measure of Tobin's Q, 20. Lindenberg, E. B. and Ross, S. A., 1981. Tobin's q Ratio and Industrial Organization. Journal of Business, 54 (1), 1-32. López-Gamero, M. D., Molina-Azorín, J. F. and Claver-Cortés, E., 2009. The whole relationship between environmental variables and firm performance: Competitive advantage and firm resources as mediator variables. Journal of Environmental Management, 90, 3110-3121. Mahapatra, S., 1984. Investor reaction to a corporate social accounting. Journal of Business Finance & Accounting, 11 (1), 29-40. McWilliams, A. and Siegel, D., 2000. Corporate social responsibility and financial performance: Correlation or misspecification? Strategic Management Journal, 21 (5), 603. Muhammad, N., Scrimgeour, F., Reddy, K. and Abidin, S., 2015. The relationship between environmental performance and financial performance in periods of growth and contraction: evidence from Australian publicly listed companies. Journal of Cleaner Production, 102, 324-332. Nakao, Y., Amano, A., Matsumura, K., Genba, K. and Nakano, M., 2007. Relationship between environmental performance and financial performance: 50 | Page

Amosun Olayemi .O 4632788 an empirical analysis of japanese corporations. Business Strategy & the Environment (John Wiley & Sons, Inc), 16 (2), 106-118. O’Brien, R., 2007. A Caution Regarding Rules of Thumb for Variance Inflation Factors. Quality & Quantity, 41 (5), 673-690. Primc, K. and Cater, T., 2015. Environmental proactivity and firm performance: a fuzzy-set analysis. Management Decision, 53 (3), 648-667. Russo, M. V. and Fouts, P. A., 1997. A Resource-Based Perspective on Corporate Environmental Performance and Profitability, 534. Saunders, M., Lewis, P. and Thornhill, A., 2012. Research methods for business students [Non-fiction]. Harlow : Pearson, 2012. 6th ed. Takeda, F. and Tomozawa, T., 2004. An empirical study on stock price responses to the release of the environmental management ranking in Japan. Economics Bulletin, 13 (1). U S Environmental Protection Agency, 2000. 'Green Dividend? The relationship between environmental performance and financial performance'. [Online] Available from: http://nepis.epa.gov/ [Accessed 12 May 2016]. Wagner, M., Schaltegger, S. and Wehrmeyer, W., 2001. The Relationship between the Environmental and Economic Performance of Firms. Greener Management International, (34), 95. Yamaguchi, K., 2008. ANALYSIS: Reexamination of stock price reaction to environmental performance: A GARCH application. Ecological Economics, 68, 345-352. Zikmund, W., 2003. Business research methods. Mason, OH: Thomson/SouthWestern.

51 | Page


APPENDIX A1: REGRESSION OUTPUT 2013

Table 5:

Correlations ROA

ROCE

TBQ

ROA Pearson 1 .125 .099 Correlation Sig. (2-tailed) .190 .297 N 111 111 111 ROCE Pearson .125 1 -.044 Correlation Sig. (2-tailed) .190 .644 N 111 111 111 TBQ Pearson .099 -.044 1 Correlation Sig. (2-tailed) .297 .644 N 111 111 111 EP Pearson -.154 -.101 .075 Correlation Sig. (2-tailed) .106 .288 .429 N 111 111 111 CG Pearson -.117 -.178 .047 Correlation Sig. (2-tailed) .219 .061 .626 N 111 111 111 CR Pearson -.091 -.026 -.090 Correlation Sig. (2-tailed) .341 .785 .347 N 111 111 111 SIZE Pearson -.283** -.106 -.466** Correlation Sig. (2-tailed) .002 .264 .000 N 111 111 111 D2E Pearson .006 -.046 -.046 Correlation Sig. (2-tailed) .948 .628 .630 N 111 111 111 **. Correlation is significant at the 0.01 level (2-tailed).

EP

CG

CR

SIZE

D2E

-.154

-.117

-.091 -.283**

.106 111

.219 111

.341 111

-.101

-.178

-.026

.288 111

.061 111

.785 111

.075

.047

.429 111

.626 111

.347 111

.000 111

.630 111

1

.450**

-.077

.102

.016

111

.000 111

.421 111

.285 111

.867 111

.450**

1

-.052

.109

.034

.000 111

111

.586 111

.253 111

.725 111

-.077

-.052

1

.421 111

.586 111

.102

.002 111

.006 .948 111

-.106 -.046 .264 111

.628 111

-.090 -.466** -.046

.099 -.020

111

.300 111

.835 111

.109

.099

1

.061

.285 111

.253 111

.300 111

111

.521 111

.016

.034

-.020

.061

1

.867 111

.725 111

.835 111

.521 111

111

A1 | P a g e


MODEL 1: ROA a. Dependent Variable: ROA b. Predictors: (Constant), D2E, EP, CR, SIZE, CG Table 6

Model Summary

Adjusted R Model R R Square Square Std. Error of the Estimate a 1 .322 .103 .061 24.25931 a. Predictors: (Constant), D2E, EP, CR, SIZE, CG ANOVAa

Table 7 Model 1

Sum of Squares Regression Residual Total

7199.220 62382.516 69581.736

Unstandardized Coefficients B

(Constan 56.714 t) EP -0.117 CG -0.074 CR -1.782 SIZE -10.472 D2E 0.05 a. Dependent Variable: ROA 1

Std. Error

Dimension

5 106 111

Standardized Coefficients

F

1439.844 588.514

Sig.

2.447

Eigenvalue

1 4.598 2 0.99 3 0.279 1 4 0.09 5 0.03 6 0.013 a. Dependent Variable: ROA

0.106 0.182 2.195 3.746 0.193

t

Sig.

Beta

16.178

Table 9 Mode l

Mean Square

.039b

Coefficientsa

Table 8

Model

df

-0.115 -0.042 -0.075 -0.261 0.024

3.506

0.001

-1.107 -0.405 -0.812 -2.795 0.26

0.271 0.687 0.419 0.006 0.796

Collinearity Statistics Toleranc VIF e

0.791 0.792 0.981 0.969 0.995

Collinearity Diagnosticsa Variance Proportions Condition Index (Constant) EP CG CR SIZE 1 0 0 0 0.01 0 2.155 0 0 0 0 0 4.062 0 0.06 0 0.85 0 7.147 0.03 0.76 0.01 0.13 0.09 12.285 0.03 0.13 0.36 0 0.66 18.794 0.94 0.05 0.63 0.01 0.24

B1 | P a g e

1.264 1.262 1.019 1.032 1.005

D2E 0 0.99 0 0 0 0


MODEL 2: TBQ a. Dependent Variable: TBQ b. Predictors: (Constant), D2E, EP, CR, SIZE, CG Table 10

Model Summary

R Adjusted R Std. Error of the Model R Square Square Estimate a 1 .486 .236 .200 13.856998230 a. Predictors: (Constant), D2E, EP, CR, SIZE, CG ANOVAa

Table 11

Regression Residual Total

26630.965

Model 1

Sum of Squares 6277.226 20353.738

Table 12 Unstandardized Coefficients Model Std. B Error 1 (Constant) 31.633 9.241 EP 0.062 0.06 CG 0.058 0.104 CR -0.478 1.254 SIZE -11.83 2.14 D2E -0.027 0.11 a. Dependent Variable: TBQ

df

Mean Square 5 1255.445 106 192.016

F 6.538

Sig. .000b

111

Coefficientsa Standardized Coefficients

t

Sig.

3.423 1.025 0.56 -0.382 -5.528 -0.244

0.001 0.308 0.577 0.703 0 0.808

Beta 0.098 0.053 -0.033 -0.477 -0.021

Collinearity Statistics Toleranc VIF e 0.791 0.792 0.981 0.969 0.995

C1 | P a g e

1.264 1.262 1.019 1.032 1.005


Table 13

Collinearity Diagnosticsa Variance Proportions

Eigenvalu Conditio Model Dimension e n Index (Constant) 1 1 4.598 1.000 .00 2 .990 2.155 .00 3 .279 4.062 .00 4 .090 7.147 .03 5 .030 12.285 .03 6 .013 18.794 .94

EP CG CR .00 .00 .01 .00 .00 .00 .06 .00 .85 .76 .01 .13 .13 .36 .00 .05 .63 .01

SIZ E D2E .00 .00 .00 .99 .00 .00 .09 .00 .66 .00 .24 .00

a. Dependent Variable: TBQ

D1 | P a g e


MODEL 3: ROCE a. Dependent Variable: ROCE b. Predictors: (Constant), D2E, EP, CR, SIZE, CG Table 14 Model

R .204a

1

Model Summary R Adjusted Std. Error of the Square R Square Estimate 0.042 -0.004 37.42321639

a. Predictors: (Constant), D2E, EP, CR, SIZE, CG

ANOVAa

Table 15 Sum of Squares 6442.66 148453 154895

Model Regression 1 Residual Total

Table 16 Model


(Constant) 60.764 EP -0.036 CG -0.416 1 CR -1.013 SIZE -4.89 D2E -0.113 a. Dependent Variable: ROCE

Std. Error 24.956 0.163 0.281 3.385 5.779 0.298

df

Mean Square

5 106 111

1288.53 1400.5

Coefficientsa Standardize d Coefficients

t

F

.471b

0.92

Collinearity Statistics

Sig.

Beta -0.023 -0.158 -0.029 -0.082 -0.036

Sig.

Tolerance 2.435 -0.22 -1.48 -0.3 -0.85 -0.38

0.017 0.828 0.141 0.765 0.399 0.706

E1 | P a g e

0.791 0.792 0.981 0.969 0.995

VIF 1.264 1.262 1.019 1.032 1.005

Amosun Olayemi .O 4632788 Table 17 Model

Collinearity Diagnosticsa

Condition Dimension Eigenvalue Index (Constant)

1 4.598 2 0.99 3 0.279 1 4 0.09 5 0.03 6 0.013 a. Dependent Variable: ROCE

1 2.155 4.062 7.147 12.285 18.794

0 0 0 0.03 0.03 0.94

Variance Proportions EP

CG

CR

0 0 0.06 0.76 0.13 0.05

0 0 0 0.01 0.36 0.63

0.01 0 0.85 0.13 0 0.01

F1 | P a g e

SIZE

D2E

0 0 0 0.09 0.66 0.24

0 0.99 0 0 0 0


APPENDIX A2: REGRESSION OUTPUT 2014

Table 18

Correlations ROA

ROA

Pearson Correlation

ROCE

TBQ

EP

CG

CR

SIZE

.008 -.396**

D2E

1

.166

.076

-.087

-.046

111

.080 111

.428 111

.362 111

.629 111

.166

1

-.030

-.031

-.140

.080 111

111

.757 111

.744 111

.141 111

.076

-.030

1

.119

.107

.428 111

.757 111

111

.211 111

.262 111

.286 111

-.087

-.031

.119

1 .408**

.004

.362 111

.744 111

.211 111

111

.000 111

.968 111

.026 111

.676 111

-.046

-.140

.107 .408**

1

-.041

.106

-.027

.629 111

.141 111

.262 111

.000 111

111

.665 111

.266 111

.780 111

.008

-.041

-.102

.004

-.041

1

-.034

-.042

.937 111

.664 111

.286 111

.968 111

.665 111

111

.725 111

.658 111

-.209* -.607**

.211*

.106

-.034

1

.008

Sig. (2-tailed) .000 .027 .000 .026 N 111 111 111 111 D2E Pearson -.079 .256** -.031 -.040 Correlation Sig. (2-tailed) .405 .007 .742 .676 N 111 111 111 111 **. Correlation is significant at the 0.01 level (2-tailed). *. Correlation is significant at the 0.05 level (2-tailed).

.266 111

.725 111

111

.931 111

-.027

-.042

.008

1

.780 111

.658 111

.931 111

111

ROCE

TBQ

EP

CG

CR

SIZE

Sig. (2-tailed) N Pearson Correlation Sig. (2-tailed) N Pearson Correlation Sig. (2-tailed) N Pearson Correlation Sig. (2-tailed) N Pearson Correlation Sig. (2-tailed) N Pearson Correlation Sig. (2-tailed) N Pearson Correlation

-.396**

.937 111

.000 111

-.079 .405 111

-.041 -.209* .256** .664 111

.027 111

.007 111

-.102 -.607**

-.031

G1 | P a g e

.000 111

.742 111

.211* -.040


MODEL 1: ROA a. Dependent Variable: ROA b. Predictors: (Constant), D2E, EP, CR, SIZE, CG Table 19 Model Summary Adjusted R Model R R Square Square a 1 .403 0.162 0.123 a. Predictors: (Constant), D2E, SIZE, CR, CG, EP ANOVAa

Table 20 Sum of Squares

Model Regression 1 Residual Total

F

14517

5

2903.39

74856.6 89373.6

106 111

706.194

Unstandardized Coefficients Model Std. B Error (Constant) 82.849 22.909 EP -0.006 0.115 CG -0.011 0.223 1 CR -0.264 2.583 SIZE -23.486 5.43 D2E -0.311 0.36 a. Dependent Variable: ROA Table 22 Dimension

Mean Square

df

Table 21

Model

Std. Error of the Estimate 26.5743

Eigenvalue

1 4.67 2 0.963 3 0.253 1 4 0.087 5 0.02 6 0.008 a. Dependent Variable: ROA


t

Sig. .002b

4.111

Sig.

Beta

Collinearity Statistics Tolerance

-0.005 -0.005 -0.009 -0.394 -0.077

3.616 -0.05 -0.05 -0.102 -4.325 -0.863

0 0.96 0.96 0.919 0 0.39

0.804 0.831 0.995 0.954 0.996

Collinearity Diagnosticsa Variance Proportions Condition Index (Constant) EP CG CR SIZE 1 2.202 4.299 7.335 15.244 24.483

0 0 0 0.02 0.01 0.98

0 0 0.04 0.85 0.03 0.07

0 0 0 0.01 0.45 0.54

0.01 0 0.91 0.05 0 0.03

H1 | P a g e

0 0 0 0.03 0.57 0.4

VIF 1.244 1.203 1.005 1.049 1.004

D2E 0 1 0 0 0 0


MODEL 2: TBQ a. Dependent Variable: TBQ b. Predictors: (Constant), D2E, EP, CR, SIZE, CG Table 23 Model Summary Std. Error of the Estimate 47.11765

Model R R Square Adjusted R Square 1 .360a .130 .089 a. Predictors: (Constant), D2E, SIZE, CR, CG, EP ANOVAa

Table 24 Model

Sum of Squares Regression 1 Residual Total

5

235327.699 270471.252

106 111


(Constant) 81.138 EP 0.212 CG 0.146 1 CR -2.853 SIZE -31.998 D2E -0.065 a. Dependent Variable: TBQ Table 26

Mean Square

35143.552

Table 25

Model

df

Std. Error 14.859 0.075 0.145 1.675 3.522 0.234

Coefficientsa Standardi zed Coefficie t nts

Sig. .011b

7028.71 3.166 2220.073


Sig.

Beta 0.227 0.079 -0.123 -0.668 -0.02

F

Tolerance 5.46 2.837 1.01 -1.703 -9.085 -0.277

0 0.005 0.315 0.091 0 0.782

VIF

0.804 0.831 0.995 0.954 0.996

1.244 1.203 1.005 1.049 1.004


Condition Model Dimension Eigenvalue Index (Constant) 1 4.67 2 0.963 3 0.253 1 4 0.087 5 0.02 6 0.008 a. Dependent Variable: TBQ

1 2.202 4.299 7.335 15.244 24.483

0 0 0 0.02 0.01 0.98


CG

CR

0 0 0.01 0 0 0 0.04 0 0.91 0.85 0.01 0.05 0.03 0.45 0 0.07 0.54 0.03

I1 | P a g e

SIZE D2E 0 0 0 0.03 0.57 0.4

0 1 0 0 0 0


MODEL 3: ROCE a. Dependent Variable: ROCE b. Predictors: (Constant), D2E, EP, CR, SIZE, CG Table 27

Model Summary

Adjusted R Std. Error of Model R R Square Square the Estimate a 1 .674 .454 .428 17.236883234 a. Predictors: (Constant), D2E, SIZE, CR, CG, EP ANOVAa

Table 28 Model 1

Sum of Squares Regression Residual Total

26197.757 31493.675 57691.433

Table 29 Unstandardized Coefficients Std. B Error

Model (Constant)

124.612

EP 0.171 -0.584 1 CG CR -2.234 SIZE -22.331 D2E 1.793 a. Dependent Variable: ROCE

df

Mean Square 5 106 111


5239.551 297.110

t

0.084 -0.147 -0.044 -0.215 0.255

Sig. .000b

17.635


Sig.

Beta

40.619 0.205 0.395 4.579 9.628 0.639

F

Tolerance 3.068

0.003

0.835 -1.476 -0.488 -2.319 2.808

0.406 0.143 0.627 0.022 0.006

J1 | P a g e

0.804 0.831 0.995 0.954 0.996

VIF

1.244 1.203 1.005 1.049 1.004

Amosun Olayemi .O 4632788 Table 30


Condition Model Dimension Eigenvalue Index (Constant) 1 4.67 2 0.963 3 0.253 1 4 0.087 5 0.02 6 0.008 a. Dependent Variable: ROCE

1 2.202 4.299 7.335 15.244 24.483

0 0 0 0.02 0.01 0.98


CG

CR

0 0 0.01 0 0 0 0.04 0 0.91 0.85 0.01 0.05 0.03 0.45 0 0.07 0.54 0.03

K1 | P a g e

SIZE D2E 0 0 0 0.03 0.57 0.4

0 1 0 0 0 0


APPENDIX A3: OLS SCATTER GRAPHS FOR 2014 Figure 4.4 The relationship between EP and ROA 2014 300 250 200

ROA

150 100 50 0 -50 -100 0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

EP

Figure 4.5 The relationship between EP and TBQ 2014 240

200

TBQ

160

120

80

40

0 0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

EP

L1 | P a g e

Amosun Olayemi .O 4632788 Figure 4.6 The relationship between EP and ROCE 2014 500 400 300

ROCE

200 100 0 -100 -200 -300 0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

EP

M1 | P a g e


APPENDIX B1: SAMPLE OF COMPANIES USED Table 31 CODE ACA AGK AMFW ATK AVV BBY BBA BWY BRSN BOY BOK BVS BVIC BTG CWC CNE CLLN COB CCC CWK CRDA DCG DEB DPH DTY DPLM DOM DRX DNLM ECM ELM ESNT ERM EVR FDSA FGP GFS GFRD GNS GOG GNK GRG HFD

NAME ACACIA MINING PLC AGGREKO PLC AMEC FOSTER WHEELER PLC ATKINS (WS) PLC AVEVA GROUP PLC BALFOUR BEATTY PLC BBA AVIATION PLC BELLWAY PLC BERENDSEN PLC BODYCOTE PLC BOOKER GROUP PLC BOVIS HOMES GROUP PLC BRITVIC PLC BTG PLC CABLE & WIRELESS COMMUNICATIONS CAIRN ENERGY PLC CARILLION PLC COBHAM PLC COMPUTACENTER PLC CRANSWICK PLC CRODA INTERNATIONAL PLC DAIRY CREST GROUP PLC DEBENHAMS PLC DECHRA PHARMACEUTICALS PLC DIGNITY PLC DIPLOMA PLC DOMINO’S PIZZA GROUP PLC DRAX GROUP PLC DUNELM GROUP PLC ELECTROCOMPONENTS PLC ELEMENTIS PLC ESSNTRA PLC EUROMONEY INSTITUTIONAL INVESTMENT PLC EVRAZ PLC FIDESSA GROUP PLC FIRST GROUP PLC G4S PLC GALLIFORD TRY PLC GENUS PLC GO-AHEAD GROUP (THE) PLC GREENE KING PLC GREGGS PLC HALFORDS GROUP PLC

INDUSTRY/ SECTOR BASIC MATERIALS INDUSTRIAL OIL AND GAS INDUSTRIAL TECHNOLOGY INDUSTRIAL INDUSTRIAL CONSUMER GOODS INDUSTRIAL INDUSTRIAL CONSUMER SERVICES CONSUMER GOODS CONSUMER GOODS HEALTHCARE TECHNOLOGY OIL AND GAS INDUSTRIAL INDUSTRIAL TECHNOLOGY CONSUMER GOODS BASIC MATERIALS CONSUMERS GOODS CONSUMER SERVICES HEALTHCARE CONSUMER SERVICES INDUSTRIAL CONSUMER SERVICES UTILITIES CONSUMER SERVICES INDUSTRIAL BASIC MATERIALS INDUSTRIAL CONSUMER SERVICES BASIC MATERIALS TECHNOLOGY CONSUMER SERVICES INDUSTRIAL CONSUMER GOODS HEALTHCARE CONSUMER SERVICES CONSUMER SERVICES CONSUMER SERVICES CONSUMER SERVICES N1 | P a g e

Amosun Olayemi .O 4632788 HALMA PLC HAYS PLC HIKMA PHARMACEUTICALS PLC HOME RETAIL GROUP PLC HOMESERVE PLC HOWDEN JIONERY GROUP PLC IMI PLC INCHCAPE PLC INTERSERVE PLC JD SPORTS FASHION PLC KAZ MINERALS PLC KELLER GROUP PLC KIER GROUP PLC LADBROOKERS PLC LAIRD PLC MARSHALLS PLC MARSTON’S PLC MEGGITT PLC MICHEAL PAGE INTERNATIONAL PLC MICRO FOCUS INTERNATIONAL PLC MILENNIUM & COPTHORNE HOTELS PLC MITCHELLS & BUTLERS PLC MAB MITIE GROUP PLC MTO MONY MONEYSUPERMARKET.COM GROUP PLC MGAM MORGAN ADVANCED MATERIALS PLC NATIONAL EXPRESS GROUP PLC NEX NORTHGATE PLC NTG OCDO OCADO GROUP PLC OPHR OPHIR ENERGY PLC PAYPOINT PLC PAY PENDRAGON PLC PDG PENNON GROUP PLC PNN PETROFAC LTD PFC POLYMETAL INTERNATIONAL PLC POLY PZ CUSSONS PLC PZC QINETIQ GROUP PLC QQ. RANK GROUP (THE) PLC RNK REDROW PLC RDW REGUS PLC RGU RENISHAW PLC RSW RENTOKIL INITIAL PLC RTO RESTURANT GROUP (THE) PLC RTN RIGHTMOVE PLC RMV ROTORK PLC ROR RPC GROUP PLC RPC SENIOR PLC SNR HLMA HAS HIK HOME HSV HWDN IMI INCH IRV JD. KAZ KLR KIE LAD LRD MSLH MARS MGGT MPI MCRO MLC

INDUSTRIAL INDUSTRIAL HEALTHCARE CONSUMER SERVICES INDUSTRIAL INDUSTRIAL INDUSTRIAL CONSUMER SERVICES INDUSTRIAL CONSUMER SERVICES BASIC MATERIALS INDUSTRIAL INDUSTRIAL CONSUMER SERVICES TECHNOLOGY INDUSTRIAL CONSUMER SERVICES INDUSTRIAL INDUSTRIAL TECHNOLOGY CONSUMER SERVICES CONSUMER SERVICES INDUSTRIAL CONSUMER SERVICES INDUSTRIAL CONSUMER SERVICES INDUSTRIAL CONSUMER SERVICES OIL AND GAS INDUSTRIAL CONSUMER SERVICES UTILITIES OIL AND GAS BASIC MATERIALS CONSUMER GOODS INDUSTRIAL CONSUMER SERVICES CONSUMER GOODS INDUSTRIAL INDUSTRIAL INDUSTRIAL CONSUMER SERVICES CONSUMER SERVICES INDUSTRIAL INDUSTRIAL INDUSTRIAL O1 | P a g e

Amosun Olayemi .O 4632788 SRP SHI SMDS SMIN SXS SPX SPD SGC SGP SYNT TALK TATE TEP TCG TLW UBM ULE VED VCT WEIR JDW SMWH WMH

SERCO GROUP PLC SIG PLC SMITH (DS) PLC SMITHS GROUP PLC SPECTRIS PLC SPIRAX-SACRO ENGINEERING PLC SPORTS DIRECT INTERNATIONAL PLC STAGECOACH GROUP PLC SUPERGROUP PLC SYNTHOMER PLC TALK TALK TELECOM GROUP PLC TATE & LYLE PLC TELECOM PLUS PLC THOMAS COOK GROUP PLC TULLETT OIL PLC UBM PLC ULTRA ELECTRONICS HOLDINGS PLC VEDANTA RESOURCES PLC VICTREX PLC WEIR GROUP PLC WETHERSPOON (JD) PLC WH SMITH PLC WILLIAM HILL PLC

INDUSTRIAL INDUSTRIAL INDUSTRIAL INDUSTRIAL INDUSTRIAL INDUSTRIAL CONSUMER SERVICES CONSUMER SERVICES CONSUMER GOODS BASIC MATERIALS TELECOMMUNICATION CONSUMER GOODS TELECOMMUNICATION CONSUMER SERVICES OIL AND GAS CONSUMER SERVICES INDUSTRIAL BASIC MATERIALS BASIC MATERIALS INDUSTRIAL CONSUMER SEERVICES CONSUMER SERVICES CONSUMER SERVICES

P1 | P a g e


APPENDIX B2: SAMPLE SIZE Table 32 INDUSTRY Oil and Gas Basic Materials Industrials Consumer Goods Healthcare Consumer Services Telecommunication Utilities Technology Total sample

5 8 42 10 4 32 2 2 6 111

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APPENDIX B3: SOURCES OF DATA Table 33 DATA ROA ROCE EP SIZE CR D2E CG TBQ

SOURCE FAME (2016) FAME (2016) ASSET4 DATASTREAM 5.1. THOMSON REUTERS (2016) FAME (2016) FAME (2016) FAME (2016) ASSET4 DATASTREAM 5.1. THOMSON REUTERS (2016) FAME (2016)

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