A Study on Mediating Impact of Eco-Literacy on Green

A STUDY ON MEDIATING IMPACT OF

ECO-LITERACY ON

GREEN CONSUMPTION BEHAVIOR:

AN EMPIRICAL ASSESSMENT OF

MALAYSIA CONSUMER TOWARD SOLAR POWER

RENEWABLE ENERGY

LIM THYE KONG

SCHOOL OF BUSINESS ADMINISTRATION

WAWASAN OPEN UNIVERSITY

PENANG, MALAYSIA

2016

i NAME Lim Thye Kong

DEGREE Commonwealth Executive Master of Business Administration

SUPERVISOR Dr. Quah Hock Soon

TITLE A Study On Mediating Impact of Eco-Literacy On

Green Consumption behavior:

An Empirical Assessment of Malaysia Consumer Toward Solar

Power Renewable Energy.

DATE Nov 2016

INSTITUTION Wawasan Open University (WOU), Penang, Malaysia

Final Project Report submitted in partial fulfillment

of the requirements for the award of

Commonwealth Executive Master of Business Administration (CeMBA)

Wawasan Open University

Penang, Malaysia

ii ACKNOWLEDGEMENTS

The route to pursue the CeMBA certification is not easy journey and become challenging for me as full time working student. I am always busy with my M & E construction works even through on the weekend and public holiday. Hence, time management, dedication and commitment are important factors for me to keep up my spirit to accomplish the final objective completion of my CeMBA program at WOU.

Normally, search the project title and selecting the right title for research study always is headache for every post graduate student and included me as well. I am so lucky to select the talented project supervisor, Dr. Quah Hock Soon, to help me to determine my interest project field and decide for project research title. I would like to express my sincere gratitude to Dr.

Quah for the kindness guidance and advices, when I was facing any doubt and problem throughout the process of the research works. His strong point is in SPSS data analysis will indirectly guide me to perform my collected data analysis more effectively.

I would like to express appreciation to Mr. Chong Fook Suan, course coordinator for final project for helpful assistance on sharing the information via WOU Project LMS. I would like to extent my special thanks to all the M & E Business partner, friend and classmate who had willing to spend their precious time for answering the survey especially my good friend Mr.

Chan Wah Cheong to give his comment on the questionnaires for pilot test. Lastly, I would like to thank my loved wife for her understanding, motivation and encouragement in the course of study. Without her inspiring & moral support, I would not be able to success to attain my dream.

Lim Thye Kong

Nov 2016

iii

TABLE OF CONTENTS

Contents Page

Title Page I Acknowledgements iii Certificate of Originality iv Plagiarism Statement v Table of Contents vi List of Tables ix List of Figures xi Abbreviations xii Abstract xiv

Chapter 1 Introduction to the Study 1 1.1 Introduction 1 1.2 Research Title 4 1.3 Research Background 4 1.4 Problem Statements 9 1.5 Rationale of the Study 11 1.6 Research Objectives 12 1.7 Research Questions 14 1.8 Expected Contributions of the Study 15 1.9 Research Scope 15 1.10 Definitions 16 1.11 Summary of the Introduction 17

Chapter 2 Review of the literature 18 2.1 Introduction 18 2.2 Theory of Purchase & Consumption Behavior Model 18 2.2.1 Theory of Reasoned Action (TRA) 19 2.2.2 Theory of Planned Behavior (TRB) 20 2.3 Principles, Concepts and Indicators of Solar Power 21 2.3.1 Malaysia Climate 22 2.3.2 Solar PV site installation Sites 24 2.3.3 Market Share of Different Types Solar PV cell technologies 24 2.3.4 Solar PV installation capacity around Malaysia 25 2.4 Solar Power Market Outlook in the Global circumstance 27 2.5 Solar Powers Market Outlook in Malaysia 30 2.6 Green Consumption behavior 32 2.7 Green Consumption Behavior Dimensions 33 2.7.1 Environmental Attitude 33 2.7.2 Social responsibility 34 2.7.3 Perceived Self- Image 35 2.7.4 Government incentive 37 2.7.4 Eco-literacy 39 2.8 Demographic Difference on the perception of Green Consumption behavior 40

TABLE OF CONTENTS

Contents Page

2.9 Study Variables and the Sources 41 2.10 Theoretical Framework 44 2.11 Summary of the Literature Review 47

Chapter 3 Research Methodology 48 3.1 Introduction 48 3.2 Research Design 49 3.3 Data Collection Methods 50 3.4 Sampling Method 53 3.5 Questionnaire Design 54 3.6 Pilot Test 56 3.7 Construct Development 57 3.8 Data Analysis 58 3.8.1 Goodness and Correctness of Data 58 3.8.2 Descriptive Analysis 59 3.8.3 Inferential Analysis 59 3.8.3.1 Factor Analysis 60 3.8.3.2 Reliability Analysis 61 3.8.3.3 Pearson Correlation 61 3.8.3.4 Regression Analysis 62 3.8.3.5 Hierarchical Regression 63 3.8.3.6 ANOVA 64 3.9 Assumption 65 3.10 Summary of the Research Methodology 65

Chapter 4 Analysis of Results 66 4.1 Introduction 66 4.2 Demographic Profiles of Respondents 67 4.3 Factor Analysis 89 4.3.1 Environmental Attitude 89 4.3.2 Social responsibility 98 4.3.3 Perceived Self- Image 104 4.3.4 Government incentive 109 4.3.5 Eco-literacy 118 4.3.6 Green Consumption behaviour. 124 4.4 Reliability Analysis 129 4.5 Descriptive Analysis of Variables 132 4.5.1 Normality Test 134 4.6 Correlation among all Variables 138 4.7 Testing of Hypotheses 141 4.7.1 Testing of Hypothesis 1 141

vii TABLE OF CONTENTS

Contents Page 4.7.2 Testing of Hypothesis 2 145 4.7.3 Testing of Hypothesis 3 148 4.7.4 Testing of Hypothesis 4 152 4.7.5 Testing of Hypothesis 5 155 4.7.6 Testing of Hypothesis 6 for Demographics 157 4.7.7 Summary of Hypotheses Testing for Variables 158 4.7.8 Summary of Hypotheses Testing for Demographic Profiles 159 4.8 Hierarchical Multiple Regression Test 160 4.9 Predictive Model for the Research 164 4.10 Summary of the Analysis of Results 166

Chapter 5 Findings, Recommendations and Conclusions 168 5.1 Introduction 168 5.2 Discussion on Findings and Results of Hypotheses Testing 168 5.2.1 Hypothesis 1a (H1a): 168 5.2.2 Hypothesis 1b (H1b): 169 5.2.3 Hypothesis 2a (H2a): 170 5.2.4 Hypothesis 2b (H2b): 170 5.2.5 Hypothesis 3a (H3a): 171 5.2.6 Hypothesis 3b (H3b): 171 5.2.7 Hypothesis 4a (H4a): 172 5.2.8 Hypothesis 4b (H4b): 173 5.2.9 Hypothesis 5 (H5): 174 5.2.10 Hypothesis 6a (H6a): 174 5.2.11 Hypothesis 6b (H6b): 175 5.2.12 Hypothesis 7a (H7a): 176 5.2.13 Hypothesis 7b (H7b): 176 5.2.14 Hypothesis 7c(H7c): 177 5.2.15 Hypothesis 7d (H7d): 177 5.3 Reaffirmation to Research Questions 179 5.4 Reaffirmation to Research Objectives 181 5.5 Implications of the Study 183 5.5.1 Theoretical Implications 184 5.5.2 Practical Implications 184 5.6 Limitations of Research 186 5.6.1 Time Horizon 186 5.6.2 Restriction based on Generalisation 186 5.6.3 Time ad Resource Limitations 187 5.6.4 Scope of Factors/ Dimensions 187 5.6.5 Social Desirability and Cultural Influence 188 5.7 Recommendations for Future Research 188 5.8 Conclusion 190

References 193 Appendices 200 Appendix A Questionnaire Cover Letter 201 Appendix B Survey Questionnaire – Quantitative Study 202

viii LIST OF TABLES

Table No. Description Page

1.1 Timelines of solar power historical evolution 5 2.1 General View of the global Solar PV Manufacturer in Malaysia 30 2.2 Study Variables and Literature Sources 41 2.3 Summary of Dimensions of the Variables 47 3.1 Pearson’s correlation coefficient Strength of associations between two variable 61 4.1 Demographic Profiles of Respondents 68 4.2 KMO and Bartlett’s Test for Environmental Attitude 89 4.3 Correlation Matrix for Environmental Attitude 90 4.4 Communalities for Environmental Attitude 91 4.5 Total Variance Explained for Environmental Attitude 92 4.6 Component Matrix for Environmental Attitude 93 4.7 Rotated Component Matrix for Environmental Attitude 94 4.8 Factor Analysis for Environmental Attitude (2nd Iteration) 95 4.9 Factor Loading for Environmental Attitude 98 4.10 KMO and Bartlett’s Test for Social Responsibility 98 4.11 Correlation Matrix for Social Responsibility 99 4.12 Communalities for Social Responsibility 100 4.13 Total Variance Explained for Social Responsibility 101 4.14 Component Matrix for Social Responsibility 102 4.15 Factor Loading for Social responsibility 103 4.16 KMO and Bartlett’s Test for Perceived Self- Image 104 4.17 Correlation Matrix for Perceived Self- Image 105 4.18 Communalities for Perceived Self- Image 106 4.19 Total Variance Explained for Perceived Self- Image 106 4.20 Component Matrix for Perceived Self- Image 107 4.21 Factor Loading for Perceived Self- Image 108 4.22 KMO and Bartlett’s Test for Government Incentive 109 4.23 Correlation Matrix for Government Incentive 110 4.24 Communalities for Government Incentive 111 4.25 Total Variance Explained for Government Incentive 112 4.26 Component Matrix for Government Incentive 113 4.27 Rotated Component Matrix for Government Incentive 114 4.28 Factor Analysis for Government Incentive (2nd Iteration) 115 4.29 Factor Loading for Government Incentive 118 4.30 KMO and Bartlett’s Test for Eco-literacy 119 4.31 Correlation Matrix for Eco-literacy 120 4.32 Communalities for Eco-literacy 121 4.33 Total Variance Explained for Eco-literacy 121 4.34 Component Matrix for Eco-literacy 122 4.35 Factor Loading for Eco-literacy 123 4.36 KMO and Bartlett’s Test for Green Consumption behaviour. 124 4.37 Correlation Matrix for Green Consumption behaviour. 125 4.38 Communalities for Green Consumption behaviour. 126 4.39 Total Variance Explained for Green Consumption behaviour. 126 4.40 Component Matrix for Green Consumption behaviour. 127 4.41 Factor Loading for Green Consumption behaviour. 128

ix LIST OF TABLES

Table No. Description Page

4.42 Reliability Statistics for Environmental Attitude 129 4.43 Reliability Statistics for Social Responsibility 129 4.44 Reliability Statistics for Perceived Self- Image 130 4.45 Reliability Statistics for Government Incentive 130 4.46 Reliability Statistics for Eco-literacy 130 4.47 Reliability Statistics for Green Consumption behaviour. 131 4.48 Summary of Reliability Analysis Results 131 4.49 Descriptive Statistics for Variables 132 4.50 Correlation of all Variables, Significance Level 139 4.51 Summary of Relationships between Independent and Dependent Variables 140 4.52 Linear Regression between Environmental Attitude and Eco-literacy 142 4.53 Linear Regression between Environmental Attitude and Green Consumption behaviour 143 4.54 Linear Regression between Social Responsibility and Eco-literacy 145 4.55 Linear Regression between Social Responsibility and Green Consumption behaviour 147 4.56 Linear Regression between Perceived Self- Image and Eco-literacy 149 4.57 Linear Regression between Perceived Self- Image and Green Consumption behaviour 150 4.58 Linear Regression between Government Incentive and Eco-literacy 152 4.59 Linear Regression between Government Incentive and Green Consumption behaviour 154 4.60 Linear Regression between Eco-literacy and Green Consumption behaviour 156 4.61 One-Way ANOVA for Solar power product ownership towards Green Consumption Behaviour 158 4.62 One-Way ANOVA for Type of Solar power product ownership towards Green Consumption Behaviour 158 4.63 Summary of Hypotheses Test for Independent Variables 159 4.64 Summary of Hypotheses Test for Demographic Variables 159 4.65 Model Summary of Hierarchical Multiple Regression 160 4.66 Summary of Statistical Value Changes with Addition of Intervening Variable 163 4.67 Model Summary for Hierarchical Multiple Regression with Intervening Variable 164 5.1 Summary of Findings from Hypotheses Testing 178 5.2 Summary of Research Questions and Answer 181 5.3 Conclusion Summary 192

LIST OF FIGURES

Figure No. Description Page

2.1 The theory of planned behaviour (TPB) concept 20 2.2 East and West Malaysia Annual average solar irradiation 23 2.3 Malaysia’s State Cities Annual average solar irradiation 23 2.4 No of Solar PV installation sites in various of Malaysia’s State Cities 24 2.5 Market Share percentage of different types of PV cell technologies in the Malaysia 25 2.6 Total power of Solar PV capacity in various of Malaysia’s State Cities 26 2.7 Total number of Solar PV site installation in various types of building 26 2.8 Total cumulative number of Solar PV site installation from 1998 to 2010 27 2.9 Theoretical Framework 45 4.1 Pie Chart by Gender distribution of respondents 73 4.2 Pie Chart by Age distribution of respondents 74 4.3 Pie Chart by Place of Stay distribution of respondents 75 4.4 Pie Chart by Household Income distribution of respondents 76 4.5 Pie Chart by Race distribution of respondents 77 4.6 Pie Chart by Education Level distribution of respondents 78 4.7 Pie Chart by Residential Area distribution of respondents 79 4.8 Pie Chart by Type of residential distribution of respondents 80 4.9 Pie Chart by Family member living with your distribution of respondents 81 4.10 Pie Chart by own residential or commercial property distribution of respondent 82 4.11 Pie Chart by know about Solar Power Renewable Energy distribution of respondents 83 4.12 Pie Chart by own the Solar-power products distribution of respondents 84 4.13 Pie Chart by type of Solar-power products distribution of respondents 85 4.14 Pie Chart by motivation factor distribution of respondents 86 4.15 Pie Chart by target affordable investment distribution of respondents 87 4.16 Pie Chart by Payback investment distribution of respondents 88 4.17 Scree Plot for Environmental Attitude 93 4.18 Scree Plot for Social Responsibility 102 4.19 Scree Plot for Perceived Self- Image 107 4.20 Scree Plot for Government Incentive 113 4.21 Scree Plot for Eco-literacy 122 4.22 Scree Plot for Green Consumption behaviour 127 4.23 Q-Q Plot for Environmental Attitude 134 4.24 Q-Q Plot for Social Responsibility 135 4.25 Q-Q Plot for Perceived Self- Image 136 4.26 Q-Q Plot for Government Incentive 136 4.27 Q-Q Plot for Eco-literacy 137 4.28 Q-Q Plot for Green Consumption behaviour 138

xi ABBREVIATIONS

Abbreviation Description

AC Alternating Current ANOVA Analysis of Variance ASEAN Association of Southeast Asian Nations a-Si Amorphous silicon BIPV Building Integrated Photovoltaics Cadet Cadmium Telluride (CdTe)( DSSC) Cadmium Telluride Dye Sensitized Solar Cells CeMBA Commonwealth Executive Master of Business Administration CIGS Copper Indium Gallium Selenide CIS Copper Indium Selenide CSP Concentrated Solar Power CSR Corporate Social Responsibility DC Direct Current EPA U.S. Environmental Protection Agency FIT Feed-In-Tariff (FIT) GBI Green Building Index GCPV Grid-Connected Photovoltaic GEF Global Environment Facility GEMS Global Environment Monitoring System (GEMS) GHG Green House Gas HiT Heterojunction with Intrinsic Thin layer ISR Individual Social Responsibility KMO Kaiser-Meyer-Olkin LED Light-Emitting Diode LMS Learning Management System M & E Mechanical & Electrical MBIPV Malaysian Building Integrated Photovoltaics MIDA Malaysia Investment Development Authority MEWC Ministry of Energy, Water and Communications MSA Measure of Sampling Adequacy OPV organic photovoltaic PCA Principal Component Analysis PV Photovoltaics PVSMC Photovoltaic System Monitoring Centre PTM Pusat Tenaga Malaysia (PTM) Q-Q Quantile-Quantile RE Renewable Energy RMSEA Root Mean Square Error of Approximation SEDA Sustainable Energy Development Authority SET Self- Efficacy Theory SGPPL Saint-Gobain Performance Plastics SMS Short Messaging Service SPSS Statistical Package of Social Sciences TNB Tenaga Nasional Berthed TRA Theory of Reasoned Action TPB Theory of Planned Behavior

xii UiTM University Technology MARA UNDP United Nations Development Programmed UV Ultra Violet WOU Wawasan Open University WWW World Wide Web

xiii

NAME Lim Thye Kong

DEGREE Commonwealth Executive Master of Business Administration

SUPERVISOR Dr. Quah Hock Soon

TITLE A Study On Mediating Impact Of Eco-Literacy On Green

Consumption Behavior:

An Empirical Assessment of Malaysia Consumer Toward Solar Power

Renewable Energy.

DATE Nov 2016

ABSTRACT

The fast pace of economic growth has increase consumer consumption of energy globally which cause the overconsumption of our natural resources and subsequently create the environmental deterioration issue. Solar Power Renewable Energy are applied in commercial

& residential industrial recently to minimize the environmental deterioration and fuel resources phase out issue. consumer should change its mind-set to protect and preserve their environment with green consumption behavior. Therefore, the purpose of this research is to investigate between environmental enablers factors and eco-literacy affect successful of green consumption behavior. Four main factors of environmental enablers are Environmental attitude, Social responsibility, Perceived Self- Image, Government incentive. The objective of this study is also to determine whether eco-literacy mediating intensity between environmental enablers factors and green consumption behavior toward solar power industries. This research adopt quantitative research method combine the cross-sectional design as well as primary data collection method was select. An online survey questionnaires were post through on line survey platform to respective respondents. The total of 104 out of

xiv 150 targeted respondents were completed answer the survey. The data was going through

Statistical Package of Social Sciences Software (SPSS) version 21 analysis software.

The single regression analysis finding result reveal that four dimension of environmental enablers (i.e. Environmental Attitude, Social responsibility, Perceived Self- Image,

Government incentive) were significant affects successful of eco-literacy a well as green consumption behavior. Form the one-way ANOVA analysis result, 2 no’s demographic variables (Solar power product ownership & type of Solar power product) were significant demographic profiles have an effect on the consumer consumption behavior in solar power industry. Eco-literacy has fully mediate the impact of Environmental Attitude, partial mediate the impact of Social responsibility and Perceived Self- Image towards green consumption behavior except for government incentive as per result of 2 step Hierarchical Regression.

Based on multi Regression analysis result, three significant predictors variables of predictive model formula are Environmental Attitude, Social responsibility, Perceived Self- Image which were significantly predicts green consumer consumption behavior. This research study has implied that the all the parties concern should work together to cultivate this environment knowledge (eco-literacy) to consumer for change their attitude and mind set towards green consumption behaviors. The marketers will be based on this predictive model to exploit effective marketing strategies and aware the consumers’ behavior for solar powered product offered in Malaysia markets. Research limitations cross sectional design shall be improving with longitudinal designs for similar research in future. In conclusion, the above project research findings are able to find an answer for the research question and subsequently meet the research objective as well. This research definitely helps to create better understanding of the consumer’s behavior toward Solar power green consumption as a whole in the future.

Chapter 1

Introduction to the Study

1.1 Introduction

Over the last decade, fast pace of economic growth has increase consumer consumption of energy globally which cause the overconsumption of our natural resources and subsequently create the environmental deterioration issue. (Chen & Chai, 2010). So, the effect of environmental deterioration is climate change, global warming, ozone depletion, environmental pollution, acid rain and desertification.

Malaysia is one of the developing nation in the world with the population of 30 million in

2016. The rapid economic growth has led to increase the demand and usage of energy. Due to demands of industrialization and urbanization, Malaysian energy consumption & power demand has increase 3 times dramatically from 1990 to 2009. there has been a growing concern about energy consumption and its huge impact on the environment. (Nasrudin et.al n.d.)

Based on study article from the use of energy in Malaysia, Malaysia’s energy utilized a lot on fossil fuels (oil, gas and coal). Furthermore, Malaysia also diversified its energy profile with adopt natural gas and coal as well. At the same times, these rapid increase of conventional energies also bring the adverse environmental impacts such as air pollution release from the power plant which will impact human health. For sustainably feed the rapidly economic growing, increase energy demand, overconsumption of our natural resources & environment impact, Malaysia shall consider to the development of renewable energy completed with green technology to overcome this issue. (Chinhao et al. 2015)

Besides that, this issue had been brought out the concern and attention of local consumer as well as global consumer. The consumer has commenced to understand that his/ her consumption behavior has caused the deterioration to natural environment and ecological system. They should change his/ her mindset to adopt the green consumption behavior to consume and purchase green product. Moreover, Eco-literacy also play an important role in this environmental deterioration issue

Green consumption is one type of consumption to protect of present environment in the green manner for future generation tie in with sustainable development. Whereby green consumption behavior is consumer with environmentally friendly or green behaviors to consume the green product which do not harmful to the environment.

The green product is consisting of energy efficient product (Solar Panel Water Heater, Solar

PV System, Solar Panel Garden Light, Solar -powered Calculator, Solar -powered Watch

, LED light, Hybrid Car), environmental friendly chemical & refrigerant gas, product made by recycle material and Biodegradable product.

Based on Dagnoli and Klein’s study (as cited in Follows and Jobber, 2000) find out that excess of 60% of consumers were relate their purchasing & consumption behavior with the environmental impact. It shown that number of green consumers are gradually increase in the market which start to purchase & consume the green product.

2 Eco-literacy is environmental knowledge whereby consumer knowledge of general information & conceptual design which related to our environment and ecosystems (Nik

Abdul Rashid, 2009; Nabsiah 2011).

Solar power is the clean green electricity that is generate from sunlight. It can be divided into

2 major systems such as photovoltaics (PV) Solar System and concentrated solar power

(CSP) system. Photovoltaics solar system applies solar photovoltaics effect that convert sun light energy directly into electricity by using solar cell inside solar panel. The Photovoltaic

(PV) word is derived from “photo,” meaning light, and “voltaic,” meaning producing electricity. While Concentrated solar power systems (CSP) System that convert sunlight energy directly into heat to create high temperature steam to drive a turbine that generates electrical power by using mirrors to concentrate the sun's light energy.

Renewable Energy (RE) is one type renewable energy whereby energy that is gather from natural resources which are naturally replenished without any limit of phase out time scale.

Based on Rowlands (2002) study, highlight that the RE is one type of environmentally friendly electricity generated by natural resources.

A Solar Power Renewable Energy is one type renewable energy whereby power energy that collect from natural sunlight.

This project report purpose is to investigate between environmental enablers factors and ecoliteracy affect successful of green consumption behavior toward solar power industries in the

Malaysia market.

This study will also determine whether eco-literacy mediating intensity between environmental enablers factors and green consumption behavior toward solar power industries.

3 1.2 Research Title

A Study On Mediating Impact of Eco-Literacy On Green Consumption behavior: An

Empirical Assessment of Malaysia Consumer Toward Solar Power Renewable Energy.

1.3 Research Background

Current worldwide overconsumption of fuel resources such as coal, oil and gas has become hot topic in the current business environment. These fuel resources will be phase out soonest and generate air and water pollution to create an environmental deterioration issue.

Malaysia consumer are always looking for new, cheap and unique product in the market without consider the environmental impact. they are more concern about the features and pricing of the product before really purchase and consume it. Hence, with environmental deterioration issue, it will alert the consumer shall shift his mind set to other green and renewable energy sources like solar, hydro, wind and bio gas in order to save the environment and preserve for next generations.

Hence, Solar Power Renewable Energy are applied in commercial & residential industrial recently to minimize the environmental deterioration and fuel resources phase out issue.

Based on world history about solar power renewable energy, since 7th Century B.C., human already know how to use the magnifying glass or mirrors to focus the sun’s heat to light fires for their religious activities. The below Table 1-1 was shown the timelines of solar power historical evolution:

4 Table 1-1: Timelines of solar power historical evolution

Timelines Event Description 1767 First solar collector or solar oven (US Department of Energy n.d)

1839 Find out photovoltaic effect.

1883 First design of solar cell

1891 First commercial solar water heater. (US Department of Energy n.d)

1905 Photoelectric effect research paper (US Department of Energy n.d)

1932 Find out the new material in cadmium sulfide (CdS). (US Department of Energy n.d) 1954 Major breakthrough the birth of first silicon photovoltaic (PV) cell (US Department of Energy n.d) 1968 First solar powered wristwatch (Wikipedia 2016)

1977 “National Renewable Energy Laboratory” formed

1978 First solar powered calculators created (Wikipedia 2016)

1981 First solar powered aircraft developed.

1982 First solar-powered car (Quiet Achiever) developed (US Department of Energy n.d) 1986 The world’s largest Concentrated solar power systems (CSP) solar thermal facility set up (US Department of Energy n.d)

1999 The New York City tallest skyscraper was retrofit to incorporates building- integrated photovoltaic (BIPV) panels (US Department of Energy n.d) 2000 First Solar world’s largest photovoltaic manufacturing plant. (US Department of Energy n.d) 2000 The largest residential installation in the United States.

2007 New world record of 42.8% in solar cell technology without independent confirmation. 2008 Solar cell efficiency with a photovoltaic device that converts 40.8 percent of the light that hits it into electricity. 2011 The rapid growing of Solar panel factories in China has cause the silicon photovoltaic modules manufacturing costs reduce. (US Department of Energy n.d) (Wikipedia 2016) (Mathias 2013) (Zachary 2012)

In Malaysia, the climate conditions of abundant sunshine throughout the year are suitable to the development of solar energy. The yearly average daily solar radiation is 4.21-5.56

5 kWh/m2. The highest solar radiation is in month of August and November (6.8 kWh/m2), the lowest is solar radiation is in month of December (0.61 kWh/m2).

In Malaysia, solar power is applied on solar thermal applications and Building Integrate photovoltaic (BIPV) technologies. Solar thermal applications are where heat from the solar energy is used for heating purposes (Solar Panel Water Heater), while BIPV technologies are for electricity generation through grid connected. For Concentrated solar power systems

(CSP), Malaysia was not really implemented for this CSP System to generate the electric power due to it need a huge investment cost.

BIPV usually integrating photovoltaics modules into the building roof or façade. BIPV systems able to generate electricity with free of maintenance, no pollution, and no depletion of fuel resources.

Photovoltaics (PV) applications for start apply on 1970s in Malaysia for those house in sub- urban areas without electrical power supply. In the early of 1980, Photovoltaic technology was start to enter into Malaysia market mainly to provide prime electricity to those village, offshore services. In 1998, with the German Rooftop and Japanese Sunshine programs work together with national power utility, Tenaga Nasional Berhad (TNB) started to kick off the grid-connected PV system project. The first BIPV project was installed on the roof of a university in July 1998 with grid-connected PV, until now still working, but only some inverter problems. In August 2000, a 1st family stay at Port Dickson to own a grid connected

PV system come with 3.15 kWp on its roof tiles. Subsequently, with public residences in

Shah Alam (3.24 kWp in November 2000) and Subang Jaya (2.8 kWp in November 2001).

(Daniel Ruoss 2007)

In the government initiative, renewable energy was start to implement first at the national level on the Eighth Malaysia Plan (2001-2005) and Third Outline Perspective Plan 2001-

2010 as well as in Ninth Malaysia Plan (2006-2010) also. Photovoltaic (PV) Solar System is

6 consider as rapid develop renewable energy technologies which contain huge potential and accept by the worldwide market. (Malaysian Building Integrated Photovoltaic Project 2008)

With the cooperation of Malaysia Government, United Nations Development Programmed

(UNDP) and Global Environment Facility (GEF) as well as private sector, these organizations were furnished technical support and funding to assist Malaysia to start up the

Malaysian Building Integrated PV Programmed (MBIPV) in the year of 2005. The objective of MBIPV is to promote PV technology Solar system which integrate with the building designs and envelopes in order to create long-term cost reduction of electrical power and minimize the impact of Green House Gas (GHG) to our environment. This was aligning with it aim to maintain the sustainable solar power market and create the PV Solar Power application extensive. Finally, the MBIPV Project had been successfully called off on 31st

May 2011, with the enactment of the Renewable Energy Act (Act 725). The MBIPV Project had been taken over by Sustainable Energy Development Authority (seda) Malaysia for renewable energy BIPV project until now. (Malaysian Building Integrated Photovoltaic

Project 2008) (Daniel Ruoss 2007)

But PV Solar System are facing the obstacles and challenge when need to penetrate into

Malaysia marker especially high capital investment, lack of awareness and understanding of value add BIPV green technology from consumer, adverse public perception on PV technology, lower subsidized energy tariff given by power provider and long return in investment.

7 The PV Solar System installer shall study on green consumption behavior among the consumer in Malaysia. The consumers’ preferences may change drastically and constantly over the times. Some of the consumers are not really willing to spend more for environmental friendly or green product. Hence, those green product firm shall responsive to this sociodemographic change and understanding on consumer’s green consumption behavior regarding green marketing and green purchasing.

So far, there had been few research studies to examine on green purchasing or consumption behavior on the consumer. (Tanner and Kast, 2003; Lee, 2008; Cheah, 2009).

At the same times, eco-literacy also stresses that the consumer’s knowledge about an environmental issue. It is important to taught to young people pertaining to eco-literacy for the environmental sustainability and reduction of environmental impact since early school days.

There are other related factors such as Environmental Attitude, Social responsibility,

Perceived Self- Image, Government incentive contribute to the Eco-literacy and subsequent

Green Consumption behavior on Solar Power Renewable energy.

In summary, for overconsumption of fuel resources such as coal, oil and gas not only drain out in the fast pace but also produce tons of air and water pollution which create an environmental deterioration issues. Malaysia consumer are always looking for new, cheap and unique product in the market without consider the environmental impact. They are more concern about the features and pricing of the product before really purchase and consume it.

Hence, environmental deterioration issue will directly alert the consumer shall shift his mind set to other green and renewable energy sources like solar, hydro, wind and bio gas for the sake of our future generation. Hence, Solar Power is one type renewable solar energy which adopt in commercial & residential industrial recently to minimize the environmental

8 deterioration and fuel resources phase out issue. solar power is applied on solar thermal applications and BIPV technologies. Solar thermal applications are where heat from the solar energy is used for heating purposes, while BIPV technologies are for electricity generation through grid connected

. But PV Solar System still facing the obstacles and challenge when need to penetrate into

Malaysia Market. PV Solar System installer shall study on green consumption behavior among the consumer in Malaysia. At the same times, Eco-literacy also stress that the consumer’s knowledge about an environmental issue. It is important to taught to young people pertaining to eco-literacy for the environmental sustainability and reduction of environmental impact since early school days. There are other related factors such as

Environmental attitude, Social responsibility, Perceived Self- Image, Government incentive contribute to the Eco-literacy and subsequent Green Consumption behavior on Solar power renewable energy.

1.4 Problem Statements

The Malaysia has experiences rapid industrialization and urbanization; many environmental deterioration problems happen. These environmental issues have been trigger those consumers in the Malaysia market to implement green initiatives with adopt of PV Solar

Power system. At the same times, Malaysian government was implement the Solar Power renewable energy at 8th Malaysia Plan (2001- 2005) and 9th Malaysia Plan (2006-2010). But there are many problem and barriers obstruct the application of Solar Power in the Malaysia market which related to green consumption behaviors and eco-literacy.

The major problem is Solar-Powered Product especially PV Solar Power System initial cost still consider high as most of local consumer are not afford to purchase it. Although the cost reduction of these Product was in progress to reduce the product cost itself and expected going to grid parity in the near future. There were lack of awareness among the local

9 consumer for solar Power PV technologies and value add service to in-cooperate with building energy saving application. (Malaysian Building Integrated Photovoltaic Project

2008)

Besides that, the access of information & resources on Solar Power PV technologies were limited and lack of performance of Solar Power PV technologies to be benchmarks as well.

Hence, most of affordable investors were unwilling to invest in which they consider as high cot and high risk investment. For example, the current investment on Solar PV System is cost about RM 40 to 50k (high cost). The accessories of solar PV power such as inverter only given 5 years’ warranty period and their pay back investment is around 7 years. It is really high risk. (Malaysian Building Integrated Photovoltaic Project 2008)

The another barrier is lack of enforcement of Malaysia energy regulations to in-cooperate better energy conservation measures such as attractive feed in tariff to promote the solar power renewable energy. The limited of quota given by approved government institute to qualify investors cause only few Solar PV System project actual implement in the market.

There was no long term planning from the national renewable energy policy, to implement the counter measure to spur the development of solar power renewable energy. (Malaysian

Building Integrated Photovoltaic Project 2008)

There are many local PV industry installers in the market, but certain unqualified installer is providing low quality of PV panel and accessories to the end user. This subsequently causing the Solar PV system are breakdown frequently and consumer are loss of confident to the overall system. At the same times, there was no appropriate financing support system from

Bank & financiers offer to those potential investors which like to financing Solar Power PV investments. (Malaysian Building Integrated Photovoltaic Project 2008)

The problem statement that leads to this study can be summarized as follow:

10 1. Lack of detailed investigation and/or findings has been carry out on dominating factors that are able to improve and encourage green consumption behavior among consumers with the mediating variable of Eco-literacy.

2. There is lack of empirical pattern has been conduct to examine the related information on demographic with mediating impact variable of Eco-literacy as well as Green

Consumption behavior among the Solar Power

3. There is lack of detail study on the mediating impact of Eco-literacy between

Environmental Enablers and Green Consumption behavior toward Solar Power industry.

4. The predictive model that relates to Eco-literacy and Green Consumption behavior among the Solar Power has not been determine and properly set up.

1.5 Rationale of the Study

The rationale of the study is to determine most suitable dimensions that are act dominant role in promising Malaysia consumer to become Eco-literacy and Green Consumption or form a

“Green Consumerism society in Malaysia”. It also provides a clear observation on whether the factors affecting green consumption behaviors on Solar Power differ by demographic profiles such as gender, age, place of stay, household Income, race, educational level, residential area, type of resident, family members, residential ownership, knowing Solar power renewable energy, Solar power product ownership, type of Solar power product, motivation factor, target affordable investment and Payback investment.

The research result can be use and apply on private organization which would like to become

Solar Power BIPV installer to come out with their strategy action plan and marketing strategies to capture the green consumption behavior in the local Malaysia market. The

11 outcomes of report can be used by local government as a guideline to the implementation of

Solar power BIPV in the Malaysia

Hence, it is to ensure that both parties (Private & Government) have clear picture of the local consumer consumption style and their environmental knowledge (e-literacy) to make the

Solar Power System to be success in the future.

1.6 Research Objectives

These research objective shall align with the aims of this research paper to investigate between environmental enablers factors and eco-literacy affect successful of green consumption behavior toward solar power industries in the Malaysia market. It also determines whether eco-literacy mediating intensity between environmental enablers factors and green consumption behavior toward solar power industries. The detail of research objectives is listed as follows:

The first objective is to determine the dominant factors of environmental enablers that effect successful on eco-literacy and green consumption behavior toward solar power industries.

Each dimension is supposed to be mutually exclusive to influence green consumption behavior on Solar power system. The respective dimensions of environmental enablers are environmental attitude, social responsibility, perceived self- image, government incentive.

The research analysis will determine the degree of each dimension as above.

The Second objective is to identify difference demographic profile effect on the green consumption behavior on solar power renewable energy System. It is good to identify

12 demographic profiles such as gender, age, place of stay, household Income, race, educational level, residential area, type of resident, family members, residential ownership, knowing

Solar power renewable energy, Solar power product ownership, type of Solar power product, motivation factor, target affordable investment and Payback investment have any significant impact on eco-literacy and green consumption behavior on solar power System.

The Third objective is to determine the Eco-Literacy has mediating (intervening) impact between environmental enablers factors and green consumption behavior toward solar power renewable energy in Malaysia.

The Fourth objective is to build a predictive model that could predict the relationship of

Solar Power renewable energy environmental enablers to the eco-literacy and consumer consumption behavior. This model will be useful to help for any future study of Solar Power product implementation in the Malaysia Market for new competitors.

In actual case, green products are normally selling at higher price, consumers have limited income might forego buying them if his/ her buying power is weak.

The above Research Objectives can be summarized as below:

1. To determine the dominant factors of environmental enablers that effect successful on

eco-literacy and green consumption behavior toward solar power industries.

2. To identify difference demographic profile effect on the green consumption behavior

on solar power renewable energy System.

3. To determine the Eco-Literacy has mediating (intervening) impact between

environmental enablers factors and green consumption behavior toward solar power

renewable energy in Malaysia.

4. To build a predictive model that could predict the relationship of Solar Power

renewable energy environmental enablers to the eco-literacy and consumer

consumption behavior

1.7 Research Questions

There are few research questions has been set and required to answer it. With responder answer, it will assist to make clear the dominant factors on environmental enablers that are considered influential towards the eco-literacy and consumer consumption behavior, Eco-

Literacy mediating influence intensity to environmental enablers towards green consumption behavior, the demographic relations among the consumers as well as managerial predictive model that relates to the eco-literacy and green consumption behavior on solar power renewable energy System

There are few questions as below will have the answer at the end of chapter:

1. What are the dominant factors of environmental enablers that affects

successful of eco-literacy and consumer consumption behavior toward the

solar power industry?

2. What are the different demographic profiles have an effect on the consumer

consumption behavior in solar power industry?

3. Does Eco-Literacy has mediate (intervene) impact between environmental

enablers factors and green consumption behavior towards solar power

industry?

4. What is the predictive model that significantly predicts green consumer

consumption behavior on solar power industry?

1.8 Expected Contributions of the Study

This research study is expected to examine and acknowledge environmental enablers factors that will cause success in consumer consumption behavior toward the solar power industry.

The outcomes of research will furnish valuable information to adds contribution to new

14 renewable energy industry in Malaysia as well as academic scholars, the government, private sectors.

Academic scholars may use this research finding to further their research. The local authority may like to use it to forecast the changes of the environmental issues and improve on the renewable energy policies. The Solar power system installer can use it to evaluate the trend of green consumption behavior among working consumers and come out with proper strategy to tackle the green consumer. The most important in this study can determined that working consumers have eco-literacy to understand of environmental issues and willing to protect and safe their environment through purchase Solar- powered Product in order to improve their current and future quality of life.

1.9 Research Scope

This research study propose is to find out the dominant environmental enablers factors that effects successful consumer consumption behavior with the mediating variable of Eco- literacy on solar power renewable energy System.

Chapter 2, describe about literature review which required to search & collect supportive facts from article and journal. There are few hypotheses formulated to be test on chapter 4.

Chapter 3, describe about Research Methodology and Design planning for this project, it also covers data analysis plan and statistical approaches.

Chapter 4 carry out the processing of quantitative data which collected by SPSS statistical analysis software as it is important to proof the formulated Hypotheses have relationship with each other. It will furnish the relevant research findings on the study.

Finally, Chapter 5 covers corresponding findings, result, research limitations, as well as future research recommendations. Lastly, a final conclusion to conclude the overall findings of this study.

1.10 Definitions

There are necessary to define for the independent variable, Mediating Variable and

Dependent Variable meaning are as below:

Environmental Attitude is define as beliefs of people and society in relate to nature, ecology and environment issue .

Social responsibility is defining as individual or company shall behave & develop business with consideration of society benefit and responsibilities. It is not solely on own benefit and maximizing business profits margin.

Perceived self- image is defining as human consider himself or herself as person demonstrate environmental friendly which related to personal view, or mental picture,

Government incentive is defining as an incentive given by government to motivate or promote for green consumption.

Eco-literacy is defining as an individual shall aware environmental knowledge for environmental issue. The environmental knowledge that is contained in various forms (e.g. books, articles, education and etc.)

Green Consumption Behaviors is defining as an individual with environmental friendly behavior for green consumption.

1.11 Summary

This research summary is to determine the dominant dimension of environmental Enablers that affects successful consumer consumption behavior with the mediating variable of Eco- literacy on Solar Power Renewable Energy System.

It covers research title, background, problem statement, rationale and the research objectives.

There are few questions had been set seeking an answer for research study.

16 The following chapter will provide more detail on the study which included literatures review, Research Methodology, Data Analysis, Findings, Recommendations and Conclusions

17 Chapter 2

Review of the Literature

2.1 Introduction

Basically Chapter 2 is review of literature on green consumption behavior with the mediating variable of Eco-literacy on solar Power Renewable Energy System. The chapter start with a topic of theory of purchase or consumption behavior model. Then describe about Principles,

Concepts and Indicators of Solar Power, Landscape and Solar Power Market Outlook in the

Global circumstance as well as Solar Power Market Outlook in Malaysia and in Penang.

Discussion also focus on Principles, Concepts and Key Indicators on Green Consumption

Behavior, Green Consumption Behavior Dimensions, Demographic Difference on the perception green consumption behavior, following these sections are variables and the literature sources study, proposed the theoretical framework. Lastly, conclude with literature review summary.

2.2 Theory of Purchase or Consumption Behavior Models

Theory of purchase or consumption behavior models are widely adopted to describe or predict pro-environmental behaviors purchase of the consumer. These models including

Theory of Reasoned Action (TRA) and Theory of Planned Behavior (TPB). For detail & application for the both theory to be elaborate on next sections:

18 2.2.1 Theory of Reasoned Action (TRA)

Martin Fischbein and Icek Ajzen had initiated the theory of reasoned action (TRA) from his old research study of attitude theory in the year of 1967. The purpose of this theory is to elaborate the relationship between attitudes and behaviors within human action. TRA is widely use to predict how peoples will behave as per their pre-existing attitudes and behavioral intentions. (Wikipedia TRA 2016)

The TRA theory suggest that the main critical of this behavior is human intention to liaise with this behavior. In other word, it means that human attitudes to a special behavior by adopt his/her behavior impact through intentions. Intention is a human mental describe about a person commitment or motivation to conduct the conscious plan or decision to make the behavior. (Witchuda Posri 2014) Intentions and behavior is having strong positive relationship which related to the action, target, context and time frame measurement.

(Fishbein & Ajzen, 1991), It is crucial to ensure that intentions still maintain when time frame given is short for decision making. (Randall &Wolff, 1994).

Nevertheless, this TRA still furnish a good platform to determine the impact of attitudes, subjective norm on intention, and intention to behavior. (Witchuda Posri 2014)

The TRA still adopt by many marketing researches to study purchasing intention or behaviors of consumers (Mostafa, 2007; Cheah & Phau, 2011; Mei, Ling, & Piew, 2012). Hence, this theory has been proof with statistical significance in predicting behavioral intentions on numerous times empirically tested. (Witchuda Posri 2014)

19 2.2.2 Theory of Planned Behavior (TPB)

The theory of planned behavior (TPB) is another human behavior study which correlate beliefs and behavior. (Wikipedia TPB 2016) It was initiated by Icek Ajzen in 1985 via his research article "From intentions to actions: A theory of planned behavior." This theory was originally arising from the previous theory of reasoned action by Martin Fishbein and Icek

Ajzen in 1980. (Wikipedia TPB 2016) The aim is to enhance of the theory of reasoned action predictive power by add in perceived behavioral control. It has been widely applied in health care, advertising and public relation business environment in order to determine of their relationship among beliefs, attitudes, behavioral intentions and behaviors. (Wikipedia TPB

2016) The concept of perceived behavioral control was originally defined from social cognitive theory called for self- efficacy theory (SET) which initialed by Bandura in 1977.

(Wikipedia TPB 2016) The TPB examine that human attitude, subjective norms, and perceived behavioral control, establish human behavioral intentions and subsequently behaviors. (Wikipedia TPB 2016) Detail refer to below figure 2.1

Figure 2.1 The theory of planned behavior (TPB) concept

20 There are few variables of theory of planned behavior (TPB) concepts as below:

 Attitude toward behavior: human positive or negative assessment of own

performance of the certain behavior. (Wikipedia TPB 2016)

 Subjective norm: human perception about the certain behavior, which is effect by the

other evaluation factors such as parents, spouse, friends, teachers. (Wikipedia TPB

2016)

 Perceived behavioral control: human perceived ease or difficulty of carry out the

certain behavior. (Wikipedia TPB 2016)

 Behavioral intention: act as sign of human readiness for carry out given behavior. It

always assumed to be an immediate antecedent of behavior. (Wikipedia TPB 2016)

 Behavior: human observable response in a given condition with related to a given

target. (Wikipedia TPB 2016)

2.3 Principles, Concepts and Indicators of Solar Power

The Solar Power is absorbing sunlight and transform it into electric power by using solar cell.

It is applying the principle of photovoltaic (PV) effect. The electric power created from PV is direct current (DC). Hence DC can be used to power up that electrical equipment’s.

Sometimes, the PV generated electric power can be kept in (DC) batteries, or transform to alternating current (AC) electricity by the inverter for utility grid connection or other application. (Energy Design Resources 2004)

As a solar cell only can produce about 1-2 Watts of power, we need to combine them into solar power PV panels and integrate into roof and Solar farm in order to generate more power.

The basic design concept of Solar Power technology are solar cells are gather together to form a PV module, then modules are join together with wiring to establish a field matrix

21 arrangement. There are balance-of-system components consist of the support instruments need to transform, applied, and kept the electricity such as an inverter, electrical switches, control devices, power meters, mounting structure, and storage instrument. (Energy Design

Resources 2004)

The Solar Power technology design concept also involved into two supplement factors in building and architecture aspects. The 1st is the functional integration of the solar component which included weather protection (rain, snow, wind, hail, UV radiation); mechanical rigidity and structural integrity as well as thermal and solar protection (shading/daylighting). (BIPV

2013)

The 2nd is the aesthetic integration that is the architectural quality of integration such as size, dimension, position and color shall be matching & related with the architectural elements of the entire building. (BIPV 2013) It is crucial for modern architects to design Solar PV system which require to take consideration of functional technical side and aesthetic on architectural as well during initial design stage. (BIPV 2013)

The Malaysia Building Integrated Photovoltaics Project (MBIPV) was assign Photovoltaic

System Monitoring Centre (PVSMC) act as the control body that monitors the performance of PV and BIPV installations in Malaysia. There are many key performance indicators was assign into as below:

2.3.1. Malaysia Climate

Malaysia was located on the equatorial zoning with tropical hot and humid climate. The whole year average temperature was around 33°C during the day time and 23°C during the night time. The heavy rainfalls in yearly were from 2000mm to 2500mm. The Malaysia weather conditions of abundant sunshine throughout the year with annual average solar irradiation of approximately 1,643 kWh/m2/yr and have around 6 hours of sunlight per day.

22 The highest of annual average solar irradiation city is Kota Kinabaru with approximately

1,850 kWh/m2/yr and follow with Georgetown (1,800 kWh/m2/yr) & lowest of annual average solar irradiation city is Kuching with approximately 1,450 kWh/m2/yr.

The annual average solar irradiation on East and West Malaysia as well as respective state cities as illustrate Figure 2.2 and 2.3 (Hussin and Yaacob 2012)

Figure 2.2. East and West Malaysia Annual average solar irradiation

Figure 2.3. Malaysia’s

State Cities Annual average solar irradiation

23 2.3.2. Solar PV installation sites

Since from July 2005 until the end of December 2010, there are totally 113 Solar PV installation sites was record and monitor under Photovoltaic System Monitoring Centre

(PVSMC). The total solar PV capacity of Grid-Connected Photovoltaic (GCPV) install completed with testing & commissioning is 1008.73 kWp. As per below figure 2.4, Selangor had been recorded as highest ranking with 51 no’s PV installation sites, follow by Kuala

Lumpur (29 no’s) and lowest ranking is Negeri Sembilan (1 no). (Hussin and Yaacob 2012)

Figure 2.4. No of Solar PV installation sites in various of Malaysia’s

State Cities

2.3.3. Market Share of Different Types Solar PV cell technologies

There are totally 7 types of Solar PV cell technologies has been install over Malaysia. Almost half of the Solar PV technologies were adopt type of monocrystalline module (53% of market share). While, polycrystalline modules were contribute 37% of market share under

24 MBIPV project. For other thin-film modules a-Si, CdTe and CIS were recorded 6 %, 1 % and 1 % market share respectively. The last 2 type of Solar PV cell technologies

Heterojunction with Intrinsic Thin-layer (HiT) and Tandem (a-Si/μc-Si) modules, both were contributing 1 % of market share as per data record by PVSMC, UiTM. (Hussin and Yaacob

2012)

Market Share percentage of different types of PV cell technologies in the Malaysia was present on Figure 2.5 below

Figure 2 5. Market Share percentage of different types of PV cell technologies in the

Malaysia

2.3.4. Solar PV installation capacity around Malaysia

The total solar PV capacity of Grid-Connected Photovoltaic (GCPV) had been install completed with testing & commissioning around Malaysia is 1008.73 kWp by the end of

December 2010 which recorded under MBIPV project. As per below figure 2.6, Selangor had been recorded as highest ranking with 406.49 kWp, follow by Kuala Lumpur (200 kWp) and lowest ranking is Negeri Sembilan (10.08 kWp). (Hussin and Yaacob 2012)

Figure 2.6. Total power of Solar PV capacity in various of Malaysia’s

State Cities

Solar PV system was applied into four types of building design such as residential, commercial, school, and industrial building as well. Residential type application was recorded as highest ranking with 62 no’s of Solar PV site. follow by Commercial type (40 no’s site) and lowest ranking is industrial type (1 no site) as illustrate Figure 2.7. (Hussin and

Yaacob 2012)

Figure 2.7. Total number of Solar PV site installation in various types of building.

During baseline of GCPV installation from 1998 until 2004, there were total Solar PV capacity 462.78 kWp being install. Then start of MBIPV project in July 2005, the total PV capacity was increase every year until achieved 1600 kWp in Dec 2010.

Figure 2. 8 below show the total cumulative increase in Solar PV capacity from 1998 to 2010

(Hussin and Yaacob 2012)

Figure 2.8. Total cumulative number of Solar PV site installation from 1998 to 2010

2.4 Solar Power Market Outlook in the Global circumstance

Solar Power Renewable Energy market outlook is foreseen to have great development in the global circumstance, owing to increase of environmental concerns and follow with market demand for exploit for other alternative energy sources such as solar power. Solar Power PV

System such as BIPV are photovoltaic materials used to replace conventional building materials especially in exposed places such as the roofs, facades or skylights. It can be

27 integrating during construction of new building or being retro-fitted into old buildings to facilitate ancillary power sources. Solar Power PV System demand has exponentially increased on account of subsidies offered by governments to integrate BIPV into buildings.

France has witnessed highest subsidy offered by its government in the form of rate paid for electricity generated by BIPV. (Grand View Research 2016)

Various flexible thin-film solar modules have been developed which are expected to boost the demand for Solar Power PV System in aesthetically pleasing and energy-producing buildings designed by top architects. The U.S. Environmental Protection Agency (EPA) had implement and increase federal regulation to raise the market demand for clean or green energy against carbon emissions at the same times, also Global Environment Monitoring

System (GEMS) had been help to boosted up market growth of Solar Power PV System as well. (Grand View Research 2016)

Solar Power PV System market has been divided its product into flat roof, pitched roof, shading, cladding, wall-integrated, glass, façade and glazing panels. Flat roof BIPV panels have witnessed the highest growth rate in recent years. Another classification can be done on the basis of end-user applications including industrial, residential and commercial buildings.

Highest application so far has been in the commercial and industrial building segments; wide- scale commercialization has not yet been done to facilitate application in residential complexes. On technological basis, Solar Power PV System BIPVs can be segmented into two types of technologies; namely crystalline silicon and thin film technologies. The crystalline silicon technology is further segmented into multi crystalline and monocrystalline silicon. Thin film technology is further categorized into cadmium telluride dye sensitized

28 solar cells (DSSCs), (CdTe), copper indium gallium selenide (CIGS), amorphous silicon and organic photovoltaic (OPV). (Grand View Research 2016)

Several obstacles such as seasonal fluctuations, short life-span, adherence to standards and building codes, as well as performance and cost-competitive prices are likely to hamper the

Solar Power PV System market growth over the forecast period. Other barriers such as inconsistent units of energy measurement and consumer perception issues are also likely to obstruct the Solar Power PV System or BIPV market growth. (Grand View Research 2016)

North American market is characterized by intense regulatory framework by EPA and increasing consumer preference for off-grid ancillary power sources. Major demand is expected from U.S. and Canada where consumer awareness regarding solar power is high.

Europe market is expected to be driven by increasing BIPV installations in UK, Italy,

Germany, France and Spain. Heavy investments in Australia and Japan along with increasing demand for off-grid power sources in rural areas of China and India are expected to be major factors driving Asia Pacific BIPV market growth. (Grand View Research 2016)

There are few global Solar Power PV System market player which include Ascent Solar

Technologies, Dow Solar, Centrosolar AG, Schott Solar AG, Sharp Corp., Suntech Power

Holdings Co. Ltd., Kyocera Corp., Ertex Solar GmbH, Konarka Technologies Inc. and First

Solar. (Grand View Research 2016)

29 2.5 Solar Powers Market Outlook in Malaysia

Solar Power Renewable Energy market outlook in Malaysia will gradually increase become crucial part in the worldwide Solar Power products manufacturer. There are 4 major Solar PV manufacturer were confirming to set up its local manufacturing of polysilicon, solar cells or thin film modules plant in Malaysia on the second quarter of 2009. (Pusat Tenaga Malaysia

2008)

The local Government of Malaysia such as Malaysian Investment Development Authority

(MIDA) was offer attractive incentive package like pioneer status, full income tax exemption,

Export incentives, import duty exemption and etc. to these four major solar PV manufacturer for set up local manufacturing plant. At the same times, Malaysia has equipped with very good manufacturing facilities such as fully trained workforce, well establish financial and banking services, Export credit refinancing, reasonable electricity and water supply rate, attractive land cost, Advancement of telecommunications network and services and proper infrastructure highway, railway, seaports and airports. (Pusat Tenaga Malaysia 2008)

Append below Table 2.2 show on general view of the Global Solar PV business in Malaysia.

Table 2.1. General View of the global Solar PV Manufacturer in Malaysia

Besides that, the setting up of these four solar PV manufacturers plant in Malaysia definitely generate for other good business opportunities for the local industry as well as oversea suppliers. This has created another business environment along with this supply chain of

30 solar PV System. There were few local companies become its supplier of raw material of solar PV business locally such as solar cables and other accessories. One of US Supplier

Saint-Gobain is expanding its business activities in the solar power renewable energies to set up their new plant next to first solar Kulim plant. Its product Saint-Gobain Performance

Plastics (SGPPL) furnish full range of polymeric materials for Solar PV module manufacturing engineered solutions. (Pusat Tenaga Malaysia 2008)

There were 22 approved PV service providers (PVSP) has been certified by Malaysia

Authority up to date in order they can providing excellent work quality to the green consumption user. These PV Service provider (PVSP) required to went through an intensive training exercise which under ISP accreditation and sit for PTM exam, they need to be strong financial background and obey PV service providers code of conduct. (Pusat Tenaga

Malaysia 2008)

In order Malaysia need to become a “leading global solar hub”, it involve an overall parties planning & strategy to grow respective capabilities and to train local player to become world- class players with full range of service. There are few program shall be promoting which included human workforce, supply chain, locations, product and technology roadmap, research and development, and partnerships. (Pusat Tenaga Malaysia 2008)

An energy policy such as national RE policy shall be launch to furnish a platform for the local player to be able to penetrate into international business arena and become a world-class industry player. This will develop the local industry and open the door for Malaysia to anew blooming high-tech industry. (Pusat Tenaga Malaysia 2008)

This is good times for Malaysia to develop the Solar Power Renewable Energy at this moment even Malaysia still far behind other Europe countries and at least it was more suitable compare to other ASEAN Region. Malaysia SEDA was proposed FIT scheme in

2011 with the target of 27MWp annual grid connected PV installation capacity in 2015 and

31 follow by target of 125MWp annual grid connected PV installation capacity in 2020. (Pusat

Tenaga Malaysia 2008)

2.6 Green Consumption Behavior

Green Consumption Behavior is the dependent variable of this research study. It is one type of human consumption behavior that showing their concern and consideration about environment matters as per Chan (2001) research study. Moreover, green consumption behavior from (Mostafa, 2007) study meaning that the consumer consume these products are able to recycle or conserve the energy, benefit to our natural environment, and aware to ecological interest (Mostafa, 2007).

Apart from that, the distinction in term of respective country’s cultural background and socio-economic situation might result different determinate factors to be selected for test on consumers’ green consumption behavior. The independent and dependent variables adopt in

Sinnappan et al. (2011) and Lee (2008) studies were “environmental attitude”, “government initiative”, “peer pressure”, “green purchase intention” and et cetera. They applied the Theory of Reasoned Actions model to examine the Malaysian consumers’ toward green purchasing behavior

Ajzen (2005) stated that an individual’s intention consists of behavioral character, when an individual have decide to engage in certain behavior will convert his or her intention into action in an appropriate opportunity and right time.

However, the study of Ohtomo and Hirose (2007) revealed that green consumers will not necessary behave in green manner or supporting and buying green products even though they are care and aware to the environmental issue. It meant that consumers did not transform into actual performance although they have the intention to do so.

32 This project research study is to investigate dimension of Environmental enablers

(Environmental Attitude, Social responsibility, Perceived Self- Image, Government incentive) with mediating variable of Eco-literacy toward Green Consumption behavior that will aid to facilitate expansion & development on Solar Power Renewable Energy System in the Malaysia Market. It will furnish the perception of the green consumption behavior and become good reference for those companies to formulate and implement their green strategies effectively.

2.7 Green Consumption Behavior Dimensions

There are five (5) Green Consumption Behavior dimensions are selected to evaluate the

Green Consumption Behavior Toward Solar Power industry. They are listed below:

 Environmental Attitude

 Social Responsivities

 Perceived Self - Image

 Government Incentive

 Eco-Literacy

2.7.1 Environmental Attitude

Attitude also can be defined as desirable or undesirable evaluations (Ajzen, 1991) and continuous positive or negative feelings (Newhouse (1991) that a particular people have towards objects, issues or other people.

According to Laroche et al. (2001), attitudes is the most significant dimension in forecast consumers’ willingness to pay more for environmental friendly products. This means that price is not an obstacle for consumers who are voluntary participate in pro-environment activities or to buy green products. Environment degradation will decrease if consumer

33 possess a positive attitude towards environmental protection will eventually transfer it into a real practice by being a green consumerism (Tanner and Kast, 2003). However, many people feel that government have the fundamental and essential responsibility in preserving the environment although people have high conscious and concern on their environment.

In additions, Abdul Wahid et al. (2002) discovered that young Malaysian willing to behave in more green manner to improve the quality of their environment.

According to Abdul Wahid, Abustan and Karwi (2000) revealed that individual, industries, government and finance have equal important roles in build up a positive attitude of individual towards environmental protection. Therefore, many companies recently started to emphasize their responsibility towards environment by produced environmental friendly products and keep track with the changes of environment.

The awareness of the changes in Solar-power product for the Environmental Attitude and this will lead to below hypothesis:

H1: Environmental Attitude positively influence Eco-Literacy and Green Consumption Behavior

2.7.2 Social Responsibilities

Social responsibility defined as individuals and companies shall have a responsibility to protect environments and entire society. For businesses arena, it always calls for Corporate

Social Responsibility, or CSR. For individual, it names as Individual Social Responsibility, or ISR. (Social Responsibility nd)

Corporate Social responsibility is a concept where by Organization or Corporation should voluntarily develop & operate their businesses activities with responsible to the overall society and not only solely focused on maximizing profits. This kind of social conscience

34 attitude will be apt to increase corporation morale & reputation and subsequently encourage consumer to purchase on its green products. (Social Responsibility nd)

Based on Milton Friedman (nd) statement "social responsibili•ties of business are notable for their analytical looseness and lack of rigor." Friedman believed that only consumer could have social responsibilities. But Businesses cannot have social responsibilities by naturally.

However, social responsibility is contra to the fundamental of business and economics by many research. (Social Responsibility nd)

Individual Social Responsibility (ISR) is consider as a new concept which related with CSP.

In generally, ISR is origin from CSR, this is due to a corporate consists of individual’s personnel which define & create the social responsibility culture. Hence, Individuals are voluntary social responsible toward Corporations and Corporations are more socially responsible to accomplish consumer requirement. (Understanding ISR-Individual Social

Responsibility 2008)

The CSR and ISR shall be practice in proactive way when facing a problem instead of reactive way to a problem. It can avoid of greedy, irresponsible or unethical behavior that may happen in the society. (Understanding ISR-Individual Social Responsibility 2008)

According to the Harris Poll (2007), there are three types of people involve in individual social responsibility as below: (Understanding ISR-Individual Social Responsibility 2008)

1st type peoples which have “Good Intentions” to show individual volunteering toward social responsibility, but they do not spend big amounts money or minimize time spent. Two-thirds of U.S. adults having this social responsibility. (Understanding ISR-Individual Social

Responsibility 2008)

2nd type peoples which have “Practice What They Preach” to show social responsibility is extremely important for individual, as well as corporate. 8 percent of U.S. adults having this social responsibility. (Understanding ISR-Individual Social Responsibility 2008)

35 3rd type peoples which have “To Thine Own Self Be True” to show social responsibility has little consequence in their lives and take care its own self will be true. One-quarter of U.S. adults having this social responsibility. (Understanding ISR-Individual Social Responsibility

2008)

This leads to the following hypothesis:

H2: Social responsibility positively influence Eco-Literacy and the Green Consumption Behavior

2.7.3 Perceived Self – Image

Perceived Self-image can be elaborate as the degree of how much an individual perceived himself or herself (Goldsmith, 1999). In this research study, Perceived self-image can be explaining as the degree of consumer perceived him or her to become environmental- friendly. There are many of research study which investigate the impact of self-image on green consumption behavior (Wahid et al., 2011; Irawan & Darmayanti, 2012). (Charlie and

Ng 2014)

Based on research study of Oliver and Lee (2010) finding, US consumer and Korean consumer had positive relationship between perceived self-image conformity toward hybrid car’s purchase intention. But, US consumers had great influence toward hybrid car’s purchase intention contrast to Korean consumers. Hence, car manufacturer should aware & determine the consumer culture which have relationship to hybrid car’s purchase intention and consumers concern of perceived self-image suggest the parts of social works in younger consumer’s green consumption behavior as proposed by researcher. Lee (2008)

36 But, there are few researchers had come out some difference finding compare to above study.

(Charlie and Ng 2014)

Based on the research study of Wahid et al. (2011), Penang Green Volunteers concern about perceived self-image did not tie up with green consumption behaviors. Respondents are adult with the aged between 21 to 40 years who unable to accept perceived self-image as the determinant factors in green consumption. (Charlie and Ng 2014)

Consumers too hold a wide array of enduring images about themselves, which are somewhat associated with their inherent personalities and their consumption patterns related to self- image (Yusof, Musa, & Rahman, 2012). (Zia & Muhammad 2013)

Most of the times, consumers try to preserve, save, show care and try to improve the environment by altering their self- images through buying of green products and services & they also stick to the brands which in their eyes are eco-friendly and so in consistency with that individual’s self-image and in the same way they avoid those brands and companies, who are not environmentally friendly in their eyes (Schiffman & Kanuk, 1997). (Zia &

Muhammad 2013)

Thus, based on above studies, this leads to hence the third hypothesis:

H3: Perceived Self- Image positively influence Eco-Literacy and the Green Consumption Behavior

2.7.4 Government Incentive

In order to promote the Solar power renewable energy for power generation, the initial investment cost of generating the solar green electricity is huge and return of investment is longer Most of commercial entities and consumers were unable to afford to go for it. Hence local government shall take initiative action to overcome it. (K. Raja Kumar et al 2014)

It is concurrent as (Yoo & Kwak, 2009) research study, the government shall take initiative action to reimburse the huge amount of investment for solar green electricity power

37 generation. The government initial action is to provide financial incentive scheme to & attract consumer and commercial entities. Subsequently encourage them to consume solar power product such Solar PV System for private use for water heating propose and commercial use for sell back solar green electricity to Malaysia Power provider TNB through national grid.

Other than financial incentive, government shall provide the technical support to all the consumer and commercial entities for any technical enquiry via government portal and toll free line. (K. Raja Kumar et al 2014)

Hence, Sustainable Energy Development Authority of Malaysia (SEDA) was established under government statutory body Sustainable Energy Development Authority Act 2011

[Act726]. The SEDA function role is to provide advice on the all type of renewable energy matter for government ministries and service provider. SEDA was formulate a feed-in-tariff

(FIT) incentive to consumer and commercial entities who adopt the Solar PV system.

This FIT allow Solar PV System owner to sell their green solar electricity to the national power grid. As such, Power provider (TNB) will reimburse to owner with monthly passive income with the contract of 21 years. The above financial incentive support by research study of (Coad, deHaan, & Woersdorfer, 2009) with consumer are more motivate with financial incentive such as tax exemption, tax rebate or subsidies. (K. Raja Kumar et al 2014)

There are few government incentive was providing to promote green technology and product

1st incentive is implementation of Green Building Index (GBI) for new commercial & residential building. GBI indicate as a green rating index on buildings. GBI allow building owners to have 100% tax exemption on additional capital expenditure spend on getting GBI certificate (“Budget 2010”, 2009). (K. Raja Kumar et al 2014)

2nd incentive is tax rebates for building owner who buying 5 stars rating on energy efficient appliances such as high efficient chiller, pump Air-Cond units, LED lighting, Solar PV

System. (Pusat Tenaga Malaysia 2008)

38 3rd incentive is import duty and sales tax exemption on solar PV system equipment which imported from oversea. (Pusat Tenaga Malaysia 2008)

4th incentive is Sales tax exemption on the purchase of solar heating system equipment from local manufacturers. (Pusat Tenaga Malaysia 2008)

The fourth hypothesis follows:

H4: Government Incentive positively influence Eco-Literacy and the Green Consumption Behavior

2.7.5 Eco-Literacy

Eco-literacy can be defined as environmental knowledge whereby consumers shall aware about environmental knowledge which related to environmental issue. The environmental knowledge that is contained in various forms (e.g. books, articles, education and etc.).

This is aligning with research study of (Nik Abdul Rashid, 2009; Nabsiah 2011) that highlight consumer knowledge of general information & conceptual design which related to our environment and ecosystems. (Mudiarasan and Behrooz 2015)

An Eco-literacy had been quoted by few researcher as the fundamental function of education in future that consumer is more knowledgeable and state-of-the-art. There is more pressure for society to form and cultivate a sustainable community based on (Ramayah et al., 2010) study.

As per study of Cheah and Phau (2011) proof that Eco-literacy making consumer attitudes and intentions through the behavior system. (Mudiarasan and Behrooz 2015)

However, there was contradictory of empirical study of Eco-literacy from Maloney and Ward

(1973) research study, it reveals that Eco-literacy has no significant impact on compatible behavior. (Mudiarasan and Behrooz 2015)

39 This observation therefore requires further studies on the relationship between Eco-literacy towards Green Consumption Behavior. We also need to determine the Eco-Literacy has mediating (intervening) impact between environmental enablers factors (Environmental

Attitude, Social Responsibilities, Perceived Self- Image, and Government Incentive) and green consumption behavior toward solar power renewable energy in Malaysia thereby leading to the following hypotheses:

H5: Eco-Literacy positively influence Green Consumption Behavior H7: Eco-Literacy has mediate (intervene) impact of Environmental Enablers factors towards Green Consumption behavior

2.8 Demographic Difference on the perception green consumption behavior

Demographic profiles such as gender, age, place of stay, household Income, race, educational level, residential area, type of resident, family members, residential ownership, knowing

Solar power renewable energy, Solar power product ownership, type of Solar power product, motivation factor, target affordable investment and Payback investment are expected to have significant correlations toward green consumption behavior

It is crucial for researchers, service provider and government to explore & aware the consumer demographic variable which emphasize green consumption behavior. The service provider will come out with their business strategy to attract consumer and government will implement new renewable energy policy for making the Solar power renewable energy more effectively.

H 6a: There is significant difference between Solar power product ownership towards Green Consumption Behavior.

H6b: There is significant difference between type of solar power product towards Green Consumption Behavior

40 2.9 Study Variables and the Literature Sources

Append below are those study respective variables (Independent variable, mediating variable and Dependent variable) and the relevant literature record, research contribution and Gaps

Table 2.2: Study respective Variables, Literature Record, research contribution and Gaps

No. Independent Literature Research Contributions Gaps

Variable Record

1 Environmental 1. Laroche et 1. significant dimension in 1. Align with al. (2001) forecast consumers’ research study. Attitude willingness to pay more for environmental friendly products

2. Tanner 2. consumer possess a 2. Align with and Kast, positive attitude towards research study (2003) environmental protection will eventually transfer it into a real practice by being a green consumerism)

3. Abdul 3. Young Malaysian willing 3. Align with Wahid et al. to behave in more green research study (2002) manner to improve the quality of their environment

4. Abdul 4. Individual, industries, 4. Align with Wahid, government and finance have research study Abustan and equal important roles in build Karwi (2000) up a positive attitude of individual towards environmental protection

2 Social 1. Milton 1. Consumer could have 1. Not emphasize Friedman social responsibilities. But cooperation Responsibilities (nd) Businesses cannot have social social responsibilities by naturally responsibilities

41 2. Harris Poll 2. Elaborate on individual 2. Not emphasize (2007), social responsibility. cooperation social responsibilities 3 Perceived Self- 1. Oliver and 1. consumer had positive 1.Align with Lee (2010) relationship between research study Image perceived self-image conformity toward hybrid car’s purchase intention

2. Wahid et 2. Perceived self-image did 2. Different age al. (2011) not tie up with green of respondent consumption behaviors group.

3. Yusof, 3. Consumers hold inherent 3. Align with Musa, & personalities and their research study Rahman, consumption patterns related 2012 to self-image

4. Schiffman 4. Consumers try to improve 4. Align with & Kanuk, the environment by altering research study 1997 their self- images through buying of green products and services

4 Government 1. Yoo & 1.Government needs to take 1. Align with Kwak, visible action to compensate research study Incentive (2009). for the high cost of production on green electricity

2. Banerjee 2. governments’ support is 2. Align with & Solomon, crucial in determining the research study 2003 programs’ credibility, financial stability and long term viability

No. Mediating Literature Contributions Gaps

Variable Sources

1 Eco-Literacy 1. Nik Abdul 1. consumer knowledge of 1. Align with Rashid, 2009 general information & research study. conceptual design which

42 related to our environment and ecosystems

2. Ramayah 2 Understanding of how et al., (2010) environmental knowledge 2. Align with works, through education, the research study most basic principles which government and school taught was to minimize the inputs and maximize the recycling output

3. Cheah and 3. Eco-literacy helps to shape Phau (2011) the attitudes and intentions 3. Align with through the behavior system research study

4. Maloney 4. No significant linkage and Ward between environmental 4) Not emphasize (1973) knowledge and compatible Eco-Literacy. behavior

No. Dependent Literature Contributions Gaps

Variable Sources

1 Green 1. Mostafa, 1. consumer consume these 1. Align with (2007) products are able to recycle or research study Consumption conserve the energy, benefit to our natural environment, behavior and aware to ecological

2. Sinnappan 2.Theory of Reasoned 2.Align with et al. (2011) Actions model to examine the research study Malaysian consumers’ toward green purchasing behavior

3. Ajzen 3.Individual’s intention 3.Align with (2005) consists of behavioral research study character, when an individual have decide to engage in certain behavior will convert his or her intention into action in an appropriate opportunity and right time.

43 4.green consumers will not 4. Ohtomo necessary behave in green 4.Not emphasize and Hirose manner or supporting and green purchasing (2007) buying green products even behavior though they are care and aware to the environmental issue

2.10 Theoretical Framework

This research study of theoretical framework model was taken from Tan Shwu Shyan (2010) research study. But, there are only three independent variables had been selected for the current research study.

The research framework stated below consists of four (4) independent variables and one (1) mediating variable which have significant impact on the dependent variable. These four (4) independent variables are group under Environmental Enablers.

The detail of research theoretical framework is appending below in Figure 2.8.

44 Figure 2.9: Theoretical Framework

Independent Variables Mediating Variable Dependent Variable

H1 Environmental Attitude

H2 Social Responsibilities Green Eco - Literacy Mediate H5 Consumption (Environmental H3 Knowledge) Behaviour Perceived Self – Image

H4 Government Incentive

Environmental Enablers

With refer to the above framework, this model able to inculcate consumer with right environmental attitude toward environmental issue in the Malaysian society. With this influence, consumer will be aware about environmental impact and start to change its mind sets & attitude to consume Solar power product.

This model also cultivates the Social responsibility in term of individual & cooperate level.

the consumer and cooperation will take immediate action to take part in environmental awareness and Go – Green program. All the parties involved such as consumer, cooperation entities, Service provider, Government and Non- profit organization shall be work together to save and preserve our environment on mother earth for the sake of future generation

45 In term of perceived self-image, consumer always try to change their self- image to save the environmental problem via consume green product and go for eco-friendly brand.

At the same times, consumer able to share their self-Image concept of green consumption to their family and friends. Finally, this model able to make consumer perceived himself/herself as person who act environmental friendly.

For government incentive, this model emphasize the government incentive play the important role to drive the development of Solar power industries and encourage the consumer go for green product consumption. This included green research and development (R & D), environmental rules and regulation enforcement, new renewable energy policies,

Feed in tariff and green campaigns to promote solar power renewable energy system.

From the above framework model, Eco-Literacy able to act as mediating variable to influence between environmental enablers factors (Environmental Attitude, Social Responsibilities,

Perceived Self- Image, and Government Incentive) and green consumption behavior toward solar power renewable energy in Malaysia. Eco -Literacy is crucial to inculcate our new generation with environmental knowledge on solar power renewable energy system in our education system since early school days.

These variables in the research framework will be pass through several hypotheses test as below Table 2.3

46 Table 2.3: Summary of Dimensions of the Variables

Hypothesis Description 1a Higher level of Environmental Attitude positively influences Eco-Literacy. Higher level of Environmental Attitude positively influences Green 1b Consumption behaviour 2a Higher level of Social responsibility positively influences Eco-Literacy. Higher level of Social responsibility positively influences Green 2b Consumption behaviour 3a Higher level of Perceived Self- Image positively influences Eco-Literacy. Higher level of Perceived Self- Image positively influences Green 3b Consumption behaviour 4a Higher level of Government incentive positively influences Eco-Literacy. Higher level of Government incentive positively influences Green 4b Consumption behaviour Higher level of Eco-Literacy positively influences Green Consumption 5 behaviour There is significant demographic difference between Solar power product 6a ownership towards Green Consumption Behaviour. There is significant demographic difference between type of solar power 6b product towards Green Consumption Behaviour. Eco-Literacy has mediate (intervene) impact of Environmental Attitude 7a towards Green Consumption behaviour Eco-Literacy has mediate (intervene) impact of Social responsibility towards 7b Green Consumption behaviour Eco-Literacy has mediate (intervene) impact of Perceived Self- Image 7c towards Green Consumption behaviour Eco-Literacy has mediate (intervene) impact of Government incentive 7d towards Green Consumption behaviour

2.11 Summary of the Literature Review

In overall, the chapter 2 has summarize the literature review which sources from other journals and research paper from various studies. But, the determinants that influence green consumption in Malaysia will be difference with western countries. This could be due to

Malaysia has a distinct multi-racial society with difference social background or cultural compare to other countries. Then theoretical framework model was preparing to develop the hypotheses the research study by end of chapter. The next chapter 3 will elaborate the research methodology used for this study.

Chapter 3

Research Methodology

3.1 Introduction

The project research objective was to carry out in depth study on the Malaysia consumer pertaining for green consumption behavior toward solar power renewable energy. The main purpose is to investigate between environmental enablers factors and eco-literacy affect successful of green consumption behavior toward solar power industries in the Malaysia market. It is important to determine whether eco-literacy mediating intensity between environmental enablers factors and green consumption behavior toward solar power industries.

Hence, the consumers in Malaysia have become unit of analysis of this research study as they have been person to utilize and consume the solar powered product in this country. So, this green consumption behavior has become a main topic to analyzed in order to obtain better research finding & result to help future marketers to organize and strategize business planning.

The detail of research methodology to be discuss this chapter which cover research design, data collection methods, sampling methods, design of the questionnaire and research instruments as well as the types of data analysis to be conducted. It is crucial to abstract the intended results which covers the all the consumer consumption behavioral elements.

3.2 Research Design

A research design composes the master plan for the collection, measurement and analysis of data as per Cooper and Schindler (2014). It is play an important role on the research study and assists the researcher to systematically formulating the structure and plan to minimize the potential of deviation of research scope and objectives.

For this research study, I am using the quantitative research method combine the cross- sectional design. The detail of these methods are as below:

 Quantitative Method:

Quantitative research method is defining as systematic empirical investigation of observable phenomena via statistical, mathematical or computational techniques. The objective of quantitative research is to adopt mathematical models, theories and/or hypotheses regarding to event. (Wikipedia 2016) Quantitative data always presented in numerical form, and applies of statistics to analyzed through. It used to describe and to test relationships between events/numbers and determine the cause-and-effect of relationships.

 Cross-Sectional Design:

In my research study, the cross -sectional design is appropriate research design adopted over here. The cross -sectional design research design involves gather of data at a single point in time (e.g. within a week, month, etc.) or over a short period on different categories of consumer as support. The cross -sectional design allow the researcher to examine the relationships between the variables to determine dominating factors which influence to green consumption behavior among Malaysians. This cross-sectional design further proof by

Bryman and Bell (2007) to elaborate the alliance pattern between variables at the same times and no discrepancies.

49 In this respect, this research design was assign over here due to Malaysia has a multi-ethnic, multicultural, and multilingual society with diversified in terms of the demographic profile such as race, religion, culture, household income, education background and etc. The finding from Bryman and Bell (2007) has shown that cross-sectional design is good design to identify the variations diversification which exists among Malaysian community.

Deductive approach had been employ to develop hypotheses as per existing theories and further testing on pre-determined hypotheses.

However, the cross-sectional research design has one limitation with inaccurate information on cause-and-effect relationships replace with its offer a single period of time and it never consider before & after the snapshot is taken (Institute for Work & Health 2009).

3.3 Data Collection Methods

There are two types of data which researcher collected for their research study as below based on Chisnall (2005):

 Primary Data where the first time raw data that have been collected by either one of

these methods observation, experimentation or questionnaires. It is more focus on

intended study and able to use statistical method to test the hypothesis and interpret

the research result.

 Secondary Data where previous or existing data that originated from existing

journals, articles and case studies.

In this research study, the primary data method to be selected. It starts to data collection from my Mechanical & Electrical (M & E) construction industries business & associate partner and other friends as well as university friend located several states in our country. The reasons of this method data collection as these peoples having difference trait in following items: place of stay, age, gender, race, educational background, income level & Solar power

50 product knowledge as well as payback investment. The primary data collection taken directly through questionnaire.

There are total 150 questionnaires will be distributed out to the all the respondents as above.

All the respondents were requested to answer the online survey questionnaire through on line survey platform via computer email system and smart phone social media WhatsApp. The online questionnaire was generated by online survey platform call for Google Forms

(https://www.google.com/forms/about/), There is few advantages of adopting this Google form as below:

 It is best survey forms set up platform for researcher to prepare on line questionnaire

with free of charges & easily by their standard icon provided compare other on line

survey like survey monkey need to pay some subscribe money.

 The researchers are requiring less time for questionnaire preparation as few types of

questions formats are available and compare to other computer software tools

Microsoft Word or Microsoft Excel which need some time to set up the survey

format.

 The researchers are avoiding to manual printing out the questionnaires to distribute to

other. This is good practice to be paperless and promote green environmental concept.

 The on line questionnaires can link through electronically by email, social media such

as WhatsApp, Facebook massager and etc.

 The on line questionnaire could be set to let respondent to mandatory answer all the

questions to improve the accuracy of the survey result before you can go to next

section.

 The on line questionnaire let respondents could response based on his/ her convenient

time, everywhere through computer, Smartphone and laptop.

51  All the respondents survey answers are automatically save into the Google forms

database. These data can be extracted out from data base & covert into other format

like Microsoft Excel and electronically copy to SPSS easily.

Although the google form can provide some benefit in term of convenient, less time consuming and cost savings, However, the limitation is to ensure that the potential respondents are aware about my survey request, this can be done by sending friendly reminders through email or other social media method such as short messaging service

(SMS) and WhatsApp applications that manage to reaches these respondents.

This online survey questionnaire form is divided into 2 main sections;

1st Section (Part A): Collection of respondents’ demographic or general information which expected the respondents to fill in the demographic information such as gender, age, place of stay, household Income, race, educational level, residential area, type of resident, family members, residential ownership, knowing Solar power renewable energy, Solar power product ownership, type of Solar power product, motivation factor, target affordable investment and Payback investment.

2nd Section (Part B): To collect respondent’s opinion and data for determine the influences of the independent variables and Mediating variable towards dependent variables.

Finally, all the primary data gather will be go through the SPSS software Version 21 data analysis. I will be able to tested hypotheses which have been developed adopt the Statistical tools on the following sub-chapter.

52 3.4 Sampling Methods

Regarding to project title, the main objective is to investigate and determine environmental enablers factors and eco-literacy that influence successful of green consumption behavior toward solar power industries in the Malaysia market Hence, the appointed respondents for this sampling method is consumers age shall be more than 18-year-old which stay in

Malaysia. Base on Lee (2008) opinion, the respondents age more than 18-year-old are mature to make their decision for purchasing.

In this research, the non-probability sampling method will be select for seeking the impact of few factors toward Green consumption behavior of Malaysian consumer for purchase solar powered products.

In this sampling method, samples are collected in the method not all the individuals being selected are given an equal chance as probability of each individual being chosen is unknown. Besides, it much depends on the personal judgment to select the unit of the sample.

Convenience sampling, judgmental sampling, quota sampling and snowball sampling are four types of non-probability sampling. (Zikmund, 2003).

This study is applied a convenience sampling technique that is simplicity of sampling and the ease of research. Convenience sampling technique also prove to be effective when conduct pilot test during exploration stage of the research area, this sampling technique is to achieve and records the relevant information from the model or the unit of the study that are easily available (Zikmund, 2003). Convenience sampling can use to facilitated data collection in short duration of time. This type of sample is very low cost and extensively used for social media like E-mail, Facebook pool and WhatsApp pool. This is also aligning to this research study to use for Email and WhatsApp to collect primary data.

53 3.5 Questionnaires Design

Questionnaire design is the most crucial stages in the research survey activities as it will impact the response rate and the data collection reliability and validity. Researcher should use simple, understandable, clear words in designing the questionnaire’s questions (Zikmund,

2003). It is important that the questionnaire design need to be in appropriate language

(English), attractive and clear in order to encourage respondents to fill up completely and submit to researcher.

This complete set of questionnaire comprise of cover letter, demographic or general information, factors influence and green consumption behavior, the layout of the questionnaire was started from cover letter which is used to explain the purpose of the survey, thanks the respondents for their participation and appreciates them for spending time in completing the questionnaire. Lastly, researcher’s contact method such as name and email address was providing to respondents for further clarification for their queries. These will further enhance the participation of the targeted respondents. According to Dillman (2000), explaining why researcher want the respondents to complete the survey helps to achieve as high a response rate as possible, and this should be done on the first page of the questionnaire

This questionnaire comprises of two section which is section A (demographic or general information), B (factors influencing) that require answering by the respondents.

The 1st section is part A with the general information (demographics profile) questions such as:

1) Gender (Male and Female)

2) Age (start 18 years to 50 and above)

3) State (Penang, Kuala Lumpur, Ipoh and others)

4) Household income per month (ranging from below RM3000 to RM10,000 and above)

54 5) Race (Malay, Chinese, Indians, Local Native and others)

6) Education Level (Up to SPM, STPM / Diploma, Degree, Post Graduate)

7) Residential Area (Urban, Sub-urban)

8) Type of residential you stay (Bungalow, Semi- Detached, Terrace House, Flat/

Apartment, Condominium)

9) How many Family member living with you (Up to 2 to More than 9)

10) Do you own residential or commercial property (Rented, personally owned)

11) Do you know about Solar Power Renewable Energy (Yes, No)

12) Do you own the Solar-power products (Yes, No)

13) Which type of Solar-power products are your own (Solar Panel Water Heater, Solar

PV System, Solar Panel Garden Light, Solar -powered Calculator, Solar -powered

Watch, Other)

14) What is motivation factor influence you going for Solar-powered product (Energy

Saving, Support on green energy, Government Incentive, Social respected,

Passive income, other)

15) What is your target affordable investment for Solar -powered Product (Below RM

10 K to Above RM 100K)

16) What is Payback investment are you prefer (Below 2 Year to above 6 Years)

The above demographic or general information collected will be help to conduct descriptive statistics for the analysis of secondary hypotheses in Chapter 4. The respondents were not required to provide their particular information and the information provided will be kept strictly as private & confidential and solely for academic purpose.

The 2nd section is Part B which cover five (5) dimensions of variables as below and green consumption behavior need to be examine for their influence toward solar powered product:

1. Environmental Attitude

55 2. Social Responsibility

3. Perceived Self – Image

4. Government Incentive

5. Eco-Literacy

Each of the five dimensions as above will have few questionnaires need to test for any correlation the impact of that respective dimension towards the green consumption behavior among Malaysia Consumer. Respondents are compulsory need to answer all the questionnaires for related dimension. He or she shall choose a suitable Likert rating scale that indicate the best answer for respective questionnaires. For this research study, we only adopt five points Likert rating scale (1 = Strongly Disagree; 2 = Disagree; 3 = Neither Agree nor

Disagree; 4 = Agree; 5 = Strongly Agree)

3.6 Pilot Test

Pilot Test is the important test to be carry out during research study to examine the effectiveness of the questionnaire. It is can identify any weak point in the design and instrumentation as strong support by Cooper and Schindler (2014).

Before proceed to actual survey, it is necessary to carry out the pilot test with 40 sets of questionnaire were distributed via email. These sets of questionnaire were distributed to my good friends and schoolmate. But only 33 sets of completed questionnaire received on time.

There was some minor comment received from 33 sets of respondents pertaining to structure of sentence, wording of questions, better interpretation and appropriateness of the questions to be asking. These initial response is crucial to test for questionnaire’s validity and suitability. Further fine tune of questions was requested to ensure questionnaire is appropriate and understood before the go ahead with mass process of data collection.

56 3.7 Construct Development

Prior to proceed to statistical analysis, we have to plan to measure our variables first. There are four most commonly used measurement scales in the market call for the nominal, ordinal, interval, and Ratio scales. The researcher shall select the most appropriate type of measurement scale and easily converted into numerical values for his/ her variable.

However, for this research study, only the nominal and interval scales were applied.

On this survey, questionnaire was designed into two sections (Part A & B),

Part A “demographic profile / general Information” generally use nominal scale for measurement.

A nominal scale always organized data from responses into mutually exclusive categories, with no order or structure. It solely used for labelling variables and without true value as well as no order for ranking. These demographic profile include gender, age, place of stay, household income, race, educational level, residential area, type of resident, family members, residential ownership, knowing Solar power renewable energy, Solar power product ownership, type of Solar power product, motivation factor, target affordable investment and

Payback investment

In Part B, each of the five dimensions as above (i.e. Determinant Affecting Solar Power

Renewable Energy Adoption in Green Consumption Behavior) uses a five-point Likert scale

(one of interval scale) to measure the perception of respondents.

The detail of five-point Likert scale as below:

1=Strongly Disagree 2=Disagree 3=Neither Agree nor Disagree 4=Agree 5= Strongly Agree

57 3.8 Data Analysis

After primary raw data had been gather from the on line survey (Google form), we need to convert these data into tabulated from, the next step data analysis shall be proceeding. But, data analysis is important to find an answers for survey doubt and help for decision-making process from interpretation of these data.

As per Sunanda and Sharmila (2006) study, this analysis need to furnish clear outcomes to respective scope.

The data analysis strategy selection is based on following criteria:

 Target objectives

 Level of analysis needed

 Statistical techniques characteristics consideration

 Researcher comfortable level with these Statistical techniques

The Statistical Package for Social Science (SPSS) software version 21 window base will be use to statistically analyze the primary data collected. The aim of SPSS analysis is to furnish elaboration on the relationship between variable, testing the verifying formulate hypotheses.

Hence, in this research, the hypotheses will be go through the descriptive analysis and inferential analysis.

3.8.1 Goodness and Correctness of Data

For ensuring the goodness and correctness of the data, descriptive analysis verification will be carry out to test for the mean, maximum figure and standard deviation result on respective questionnaire.

58 3.8.2 Descriptive Analysis

Descriptive analysis is an initial stage of data analysis where the primary raw data is transform into an interpretable form. Hence, the researcher able to categorized the variable in proper manner for easy to tracking and reference. It is always present in tables and pie charts

& histogram format for below parameters:

 Frequencies

 Central tendency measurement (mean, median and mode)

 Dispersion (range, variance, standard deviation and skewness)

The survey questionnaire respondents demographic profile usually adopting descriptive analysis to group their variable in the presentable format to further study the respondents’ detail & background. So, the demographic profile plays crucial role for generalization need to conduct.

Hence, the survey questionnaire on demographic profiles (Part A), frequency analysis is conduct to show gender, age, place of stay, household income, race, educational level, residential area, type of resident, family members, residential ownership, knowing Solar power renewable energy, Solar power product ownership, type of Solar power product, motivation factor, target affordable investment and Payback investment. Other parameter such as mean, median, mode, standard deviation, and skewness distribution also present in the table form for above demographic variable.

3.8.3 Inferential Analysis

Inferential analysis will be adopting as a statistic analysis method to infer data samples to indicate the whole target population. It infers of the whole population which includes testing hypotheses and deriving estimates. It is important to test validity and reliability of respective variables. There are few methods of inferential analysis to be elaborate as below:

59 4.8.3.1 Factor Analysis

Factor analysis is one of inferential analysis tool with data reduction method to cut down on huge number of variables to a smaller set of underlying factors It always used to test for construct validity of the dependent, mediate (intervene) and independent variables.

Normally the factor analysis correlation table will be tabulated to demonstrate for any linkage or association between one variable with the other variable. There are two scenario of correlation as below:

 High correlations - + or -.60 or greater (proof that two variables are associated and

may be categorized together with factor analysis as per (Leech et al. 2008) study.)

 Low correlations - nearer to zero. (proof that chance computational Issue by the

factor analysis, following a warning message issue by SPSS or not able to processing

the factor analysis (UCLA: Statistical Consulting Group 2015)

There are 4 steps of performing factor analysis in this research study:

1st step is Kaiser-Mayer-Olkin (KMO) test is adopted to measure the sampling adequacy which furnish an KMO value index from 0 to 1. KMO value are consider good if the value is approximate to 1. The minimum value of KMO is 0.6.

2nd step is Bartlett's Test of Sphericity is used to examine the null hypothesis that the correlation matrix is an identity matrix. Bartlett test is considering significant if significance value of less than 0.50 which proof that the variables are highly correlated suitable for factor analysis.

3rd Step is Varimax rotation is normally adopted to rotate the loading data components to verify is that significant factors are more than one.

4th Step is scree plot is one type of graph which indicate components factor no (Axis X) versus eigenvalue (Axis Y). It elaborates which components factor have variability in the data by checking on the eigenvalues.

60 4.8.3.2 Reliability Analysis

Reliability is one of important testing condition whereby measurement is without any mistake, finally manage to obtain stable and consistent output measurement. (Nonis, et al.

2014). Normally it is applied to check for any measurement of component factors in respective questionnaire are closely related or not. This tests are relatively important, to find out related troubles at the initial time. This is to make sure all the measurement results are complying the reliability situation (Eng, et al. 2012).

For this research study, Cronbach’s alpha coefficient is adopted to measure internal consistency and reliability of the respondents’ answering the questionnaires. The Cronbach’s alpha coefficient values range is between 0 and 1. The coefficient value below 0.6 is consider poor internal consistency and reliability. The coefficient value above 0.7 is consider good internal consistency and reliability

4.8.3.3 Pearson Correlation In the earlier stage of research study, Pearson’s correlation coefficient analysis is usually applied to examine the linear relationship among variables as well as strength of association between two variable. Pearson’s correlation coefficient (“Pearson’s r”) values range is between -1 to +1. Detail of Pearson’s correlation coefficient will be explaining as below:

Table 3.1: Pearson’s correlation coefficient Strength of associations between two variable

Pearson’s Strength of Association r Perfect positive correlation (If one variable increase, the other variable will follow to (+1) increased also) Perfect independence relationship (0) (If one variable increase, the other variable will maintain without changes) Perfect negative correlation (If one variable increase, the other variable will follow to (-1) decrease also)

In summary, the coefficient value is closer to -1 or +1, it means that the correlation is stronger (Calkins 2005).

61 In this research, the Pearson correlation coefficient is adopting here to test the strength of correlation in term of positive, negative or no relationship among these variables (i.e.

Environmental Attitude, Social responsibility, Perceived Self- Image, Government incentive.), Mediating variable (Eco-Literacy) and dependent variable (Green Consumption behaviour).

4.8.3.4 Regression analysis Regression analysis is another famous relationship statistical assessment tool by adopting one or more independent (explanatory) variables to forecast a dependent (outcome) variable.

It is important to test the strength relationship between quantifiable dependent variable and one or more quantifiable independent variables (Mark, Philip and Adrian 2007).

There are two type of regression analysis as below:

 Simple Linear Regression which is used to forecast an outcome (scale) variable

from one explanatory (scale) variable (University of Fribourg 2015).

 Multiple regression, is used to form the regression equation which show the

relationship between two or more independent variables versus one dependent

variable, (Nonis, et al. 2014). This is to forecast the key significance of respective

independent variables’ which contribute to the single dependent variable.

Two main measurement always used in this kind of regression analysis are the R Square and p-value (Sig.).

R Square is Coefficient of Determination (“R”), it means that square of the correlation between the dependent and independent variables for single linear regression and pairwise correlations among all the variables for multiple regression model. (Nau 2015). R Square value is between 0 and 1. If the R Square value is higher & close to 1, thus means the relationship between the independent variables and the dependent variable become stronger and it manage to fits the data in the proper way for analysis. (GraphPad Software, Inc. 2015).

At the same times, the p-value (or “Sig.” for “significance”) proof the variable having statistical significance relationship between the independent variable and dependent variable.

P-values must be less than 0.05. it means that the result of probability by chance is less than

0.0005.” (University of Colorado Denver 2015).

In this study, testing of individual hypotheses for respective variable will used the simple linear regression method, but multiple regression method will have used to find out the main predictor of green consumption behavior among Environmental Attitude, Social responsibility, Perceived Self- Image, Government incentive, and Eco-Literacy.

4.8.3.5 Hierarchical Regression

Hierarchical regression is considering as high level of statistical assessment tool used to determine the relationship between a group of independent variables and the dependent variable. In this case, a group of independent variables may consist of few variable to become individual bock. The arrangement of this regression with enter with a set of control variables as first block while another set of predictor variables enter as second block.

Before proceed this regression, multiple regressions have to test first with assign independent variables to confirm that these independent variables have significant impact to dependent variable. After that, those significant impact independent variable will be entering as first block while the mediating variable is entered as second block. We will test with Hierarchical regression for seeing any of independent variables from first block that have changed from significant to non-significant to dependent variable. It shows that second block of mediating

63 variable has mediating impact to any of independent variables from first block toward dependent variable.

In this study, the hierarchical regression has been adopted to investigate the mediating impact of Eco-literacy between the environmental enabler and green consumption behaviour. The first block consists of those environmental enabler which are Environmental Attitude, Social responsibility, Perceived Self- Image and Government incentive. The Second block is Eco-

Literacy.

4.8.3.6 ANOVA ANOVA is short form for Analysis of Variance. It is used to proof all the hypotheses of independent or mediate variable have any significant influence toward dependent variable.

The one-way analysis of variance (ANOVA) is consider as univariate method of variance analysis. It is to ascertain for any significant differences exist between the mean of two or more independent variables toward dependent variable. The minimum requirements measurement scale for this test are dependent variable in interval scale and independent variable in nominal or ordinal (Fahed 1998).

The result of the ANOVA normally will mark with the “F” statistic whereby a p-value <0.05 shows the particular dimension has a significant impact on the dependent variable, and p- value>0.05 show particular dimension has a non-significance impact on the dependent variable

In this research, the one-way ANOVA will be used:

• To test the significance of the difference in the various demographic profiles of respondents towards their green consumption behaviors of solar powered product selection.

64 • To generate additional hypotheses based on the significance of specific demographic profiles of respondents towards their green consumption behaviors of solar powered product selection.

3.9 Assumptions

An assumption had been made that consumers’ green consumption behavior towards Solar powered product was significantly influenced by environmental attitude, Social responsibility, Perceived Self- Image and Government incentive as well as Eco-literacy. For overall, consumers will be more alert and aware about environmental problem happen in the mother earth and take initiative to purchase and consume Solar powered product.

The other assumption is to ensure the previous research results are reliable and valid up to date and the target respondents were giving their appropriate and truthful answer.

3.10 Summary of Research Methodology

The research methodology used in this research study had been explain in this Chapter 3. So, this chapter was start with the explanation on the research design, follow by data collection methods and the design of questionnaires. Besides that, SPSS version 21 was adopt to study the relationships between all the independent variables, mediating variable and the dependent variable. The factor analysis, reliability analysis, descriptive analysis and inferential analysis. will be further elaborated in Chapter 4,

65 CHAPTER 4

Analysis of Results

4.1 Introduction

Chapter 4 served how to analyse the primary data that were gather from the on line survey questionnaires on previous chapter. Data analysis is important to editing & reduce huge of data into manageable size. Then apply statistical methods to examine for any relationships between all independent variables, mediate variable and dependent variable. The finding result of this analysis able to let researcher to make decision to accept or reject initial formulated hypotheses. The Statistical Package for the Social Sciences (SPSS) software version 21 are used to analysed collected primary raw data and convert to more describable data and numbers for study purpose.

A pilot test was carry out through a sample of 33 complete respondents out of the total number of 40 e-mailed to good friend and schoolmate. The results of this pilot test was minor comment received pertaining to structure of sentence, wording of questions, better interpretation and appropriateness of the questions to be asking. Further fine tune of questions was requested to ensure questionnaire is appropriate and understood before the go ahead with mass process of data collection.

150 sets of questionnaires were distributed out to my Mechanical & Electrical (M & E) construction industries business & associate partner and other friends as well as university friend located several states in our country. Out of 150 sets of questionnaires, 104 sets of questionnaires were successful complete returned with the record of 69.3% good respond rate. The google forms primary data was convert into excel format first. Then data in excel format will be transfer to SPSS software (v.21) tools for on- going data analysis.

66 The data analysis commences with using the descriptive analysis tools such as frequencies table, pie charts and histogram to studies and present on the respondents’ demographic profile nominal data collect from Part A of questionnaire. The respondents’ demographic profile information included gender, age, place of stay, household income, race, educational level, residential area, type of resident, family members, residential ownership, knowing

Solar power renewable energy, Solar power product ownership, type of Solar power product, motivation factor, target affordable investment and Payback investment.

After that, various of statistical tools such as factor analysis, reliability test, descriptive analysis, Pearson’s Correlation analysis, regression and multiple regression and ANOVA are used to perform data analysis on the interval data collected in part B of questionnaire. The outcomes of above analysis will be used to obtain an answer for the research question and subsequently meet the research objective as well.

4.2 Demographic Profiles of Respondents

In this study, sixteen forms of demographics profile of respondent have been tested on; gender, age, place of stay, household income, race, educational level, residential area, type of resident, family members, residential ownership, knowing Solar power renewable energy,

Solar power product ownership, type of Solar power product, motivation factor, target affordable investment and Payback investment. A descriptive analysis was used to test and condense the respondent’s demographic profiles. This is shown in Table 4.1 below.

67 Table 4.1: Demographic Profiles of Respondent Statistics

G A Re Ho R Ed Resi Type Famil Res KnowSol OwnSol TypeSol Motiv Targe Pay e g sid use a uc dent Resi yMe idC arPwerE arPowe arPowe ationF tAffrd back n e enc hol c ati Area dent mber om nergy rProd rProd actor Invst Invst d e d e on s m er Inc Le om vel e V 1 1 10 10 1 10 104 104 104 104 104 104 104 104 104 104 ali 0 0 4 4 0 4 d 4 4 4 NMi 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ss in g

Gender

Frequency Percent Valid Percent Cumulative Percent Male 81 77.9 77.9 77.9 Valid Female 23 22.1 22.1 100.0 Total 104 100.0 100.0

Age

Frequency Percent Valid Percent Cumulative Percent 18 to 29 8 7.7 7.7 7.7 30 to 39 31 29.8 29.8 37.5 Valid 40 to 49 38 36.5 36.5 74.0 ? 49 27 26.0 26.0 100.0 Total 104 100.0 100.0

Residence

Frequency Percent Valid Percent Cumulative Percent Penang Only 75 72.1 72.1 72.1 KL only 21 20.2 20.2 92.3 Valid Ipoh 1 1.0 1.0 93.3 Others 7 6.7 6.7 100.0 Total 104 100.0 100.0

HouseholdIncome

Frequency Percent Valid Percent Cumulative Percent Below RM 3,000 4 3.8 3.8 3.8 RM 3,000 to RM 6,999 26 25.0 25.0 28.8 Valid RM 7,000 to RM9,999 28 26.9 26.9 55.8 Over RM 10,000 46 44.2 44.2 100.0

Total 104 100.0 100.0

Race

Frequency Percent Valid Percent Cumulative Percent Malay 4 3.8 3.8 3.8 Chinese 96 92.3 92.3 96.2 Valid Indian 3 2.9 2.9 99.0 Other Race 1 1.0 1.0 100.0 Total 104 100.0 100.0

EducationLevel

Frequency Percent Valid Percent Cumulative Percent Up to SPM 4 3.8 3.8 3.8 STPM or Diploma 26 25.0 25.0 28.8

Valid Degree 55 52.9 52.9 81.7 Post Graduate 19 18.3 18.3 100.0 Total 104 100.0 100.0

ResidentArea

Frequency Percent Valid Percent Cumulative Percent Urban 81 77.9 77.9 77.9 Valid Sub-urban or Rural only 23 22.1 22.1 100.0 Total 104 100.0 100.0

TypeResident

Frequency Percent Valid Percent Cumulative Percent Bungalow Only 6 5.8 5.8 5.8 Semi-Detached 18 17.3 17.3 23.1 Terrace House 43 41.3 41.3 64.4 Valid Flat / Apartment 16 15.4 15.4 79.8

Condominium 19 18.3 18.3 98.1 Other 2 1.9 1.9 100.0 Total 104 100.0 100.0

FamilyMembers

Frequency Percent Valid Percent Cumulative Percent Up to 2 22 21.2 21.2 21.2 3 to 6 73 70.2 70.2 91.3 Valid 7 to 9 8 7.7 7.7 99.0 More Than 9 1 1.0 1.0 100.0 Total 104 100.0 100.0

ResidComm

Frequency Percent Valid Percent Cumulative Percent Rented Only 2 1.9 1.9 1.9 Valid Personally Owned 102 98.1 98.1 100.0 Total 104 100.0 100.0

KnowSolarPwerEnergy

Frequency Percent Valid Percent Cumulative Percent Yes 95 91.3 91.3 91.3 Valid No 9 8.7 8.7 100.0 Total 104 100.0 100.0

OwnSolarPowerProd

Frequency Percent Valid Percent Cumulative Percent yes 61 58.7 58.7 58.7 Valid No 43 41.3 41.3 100.0 Total 104 100.0 100.0

TypeSolarPowerProd

Frequency Percent Valid Percent Cumulative Percent Solar Panel Water Heater 8 7.7 7.7 7.7 Solar PV System 2 1.9 1.9 9.6 Solar Panel Garden Light 7 6.7 6.7 16.3 Valid Solar Power Calculator 43 41.3 41.3 57.7 Solar Power Watch 1 1.0 1.0 58.7 Other 43 41.3 41.3 100.0 Total 104 100.0 100.0

MotivationFactor

Frequency Percent Valid Percent Cumulative Percent Energy Saving 65 62.5 62.5 62.5

Support On Green Energy 31 29.8 29.8 92.3 Government Icentive 3 2.9 2.9 95.2 Valid Social Respect 2 1.9 1.9 97.1 Passive Income 3 2.9 2.9 100.0 Total 104 100.0 100.0

TargetAffrdInvst

Frequency Percent Valid Percent Cumulative Percent Below RM 10,000 68 65.4 65.4 65.4 RM 10,000 to 50,000 28 26.9 26.9 92.3 Rm 50 ,001 to 99,999 4 3.8 3.8 96.2 Valid Above 100,000 1 1.0 1.0 97.1 Other 3 2.9 2.9 100.0 Total 104 100.0 100.0

PaybackInvst

Frequency Percent Valid Percent Cumulative Percent Below 2 Years 58 55.8 55.8 55.8 2 to 4 Years 38 36.5 36.5 92.3 Valid 4 to 6 years 7 6.7 6.7 99.0

Above 6 Years 1 1.0 1.0 100.0 Total 104 100.0 100.0

72 The demographic profile was also express in pie chart for easier understanding, as refer to the following:

(a) Gender

Figure 4.1: Pie Chart by Gender distribution of respondents

Out of total 104 respondents (100%), 81 respondents were male which contribute 77.8 % and

22.12 % of the respondents being female (23 respondents). This surveys look like were ascribe to more male respondents compare to female respondents.

73 (b) Age

Figure 4.2: Pie chart for age distribution of respondents

From the above research pie chart, the respondents age starts from 18 to 50 years and above.

The highest percentage group is 36.54% with the respondents in the group of 40-49 years old.

It was Mechanical & Electrical business partner and Schoolmate. For the respondents in the group of 30-39 years old which contribute 29.81 % were either the University post – graduate friends. The other group which is above 50 years of age which contribute 25.96 % is either

Management staffs from Mechanical & Electrical business partner. As for the category of 18 to 29 years and above, is 7.69 %. They are mostly the University students’ who study for degree courses.

74 (c) Place of Stay (State)

Figure 4.3: Pie Chart for Place of Stay (State) distribution of respondents

Bases on various states in our country as above, Penang state (72%) was record with highest respondent’s percentage. The main reason is most of Mechanical & Electrical Business partner and University Postgraduate friends are situated in Penang. Second highest respondents were from KL State (20.19%) as most of them Mechanical & Electrical Business partner are from KL as well. Ipoh state (0.96 %) and other states (6.73%) such as Kedah &

Johor, had some contribution because there were university students as well as their schoolmate and the university friend

75 (d) Household Income

Figure 4.4: Pie Chart by Household Income distribution of respondents

As per Household Income Pie chart above, 44.23 % of the respondent were under category of earning over RM 10 ,000. This is due to most respondent are top management personnel at

Mechanical & Electrical Company. Following by 26.92 % of old schoolmate and Mechanical

& Electrical Company Manager with earning from RM 7,000 to RM 9,999 were also grouped under this category. As for 25 % of respondent is those earning above RM 3000 and

RM6,999 are mostly come from University post – graduate friends, for 3.85% of respondent is those earning below RM 3000 are mostly come from University student which take part time degree courses.

76 (e) Race

Figure 4.5: Pie Chart by Race distribution of respondents

Bases on multi-racial society in our country, Chinese community was record as largest group of respondents with 92.31%, Malays community at 3.8% of respondents, Indian community at 2.9 % of respondents and other race at 1% of respondents. This surveys look like were ascribe to more Chinese respondents compare to other race respondents.

77 (f) Education Level

Figure 4.6: Pie Chart for Education Level distribution of respondents

As per Education level on the above pie chart, the highest percentage of respondents are

Degree holders with 52.88% and the least percentage of respondents are Up to SPM holders with 3.85%. The main reason of highest percentage degree holder is come from Mechanical

& Electrical Executive staffs and old schoolmate. For Up to SPM holders are come from university student which study for diploma courses. As for the STPM and Diploma holder who are mostly university student which study for degree courses consist of 25%.

Mechanical & Electrical top management staffs, they will be categorized under the Post

Graduate.

78 (g) Residential Area

Figure 4.7: Pie Chart by Residential Area distribution of respondents

The above Residential Area pie chart show that 77.88% of respondents are stay at urban area compare to balance of 22 .12 % respondents are stay at Sub-urban. This is due to the reason that the survey is conduct on the big city like Penang, KL and Ipoh.

79 (h) Type of Residential you stay

Figure 4.8: Pie Chart by Type of Residential distribution of respondents

The above survey pie chart indicates six (6) categories type of residential respondents, 41.3% of respondents are stay in the terrace house category which is highest percentage record. The

Condominium category, Semi- Detached category and Flat/ Apartment category are contributing between 15 to 19% distribution of respondents. The Bungalow category and other residential category, which constitutes only 5.77% and 1.92 % from the total number of respondents.

80 (i) How Many Family member living with you?

Figure 4.9: Pie Chart by Family member living distribution of respondents

This above survey pie chart indicates approximately two third (70.19 %) of the respondents have family member of 3 to 6. The second group which have family member of up to 2 is around 21.15%. The third group of respondents who have family member of 7 to 9 are around 7.89% and around 0.96 % of the respondents only have family member more than 9.

81 (j) Do you Own Residential or Commercial property?

Figure 4.10: Pie Chart by Own Residential or Commercial property distribution of respondents

This above survey pie chart indicates that most of respondents have their own Residential or

Commercial property which indicates that 98.08% compare to balance of 1.92 % respondents are Rent Residential or Commercial property. Most of respondents are prefer to own

Residential or Commercial property rather than rented from other peoples.

82 (k) Do you know about Solar Power Renewable Energy?

Figure 4.11: Pie Chart by Know About Solar Power Renewable Energy distribution of respondents

The above survey pie chart indicates that most of respondents know about the Solar Power

Renewable Energy which indicates that 91.35 % compare to balance of 8.65 % respondents are don’t know about the Solar Power Renewable Energy. Thus mean most of respondents having knowledge of Solar Power Renewable Energy compare to small group of respondents.

83 (l) Do you own Solar Power Product?

Figure 4.12: Pie Chart by Own Solar Power Product distribution of respondents

The above survey pie chart indicates approximately more than half (58.65 %) of the respondents have own Solar Power Product compare to balance of 41.35 % respondents are not own Solar Power Product. Thus mean most of respondents are not really use the Solar

Power Product in their daily activities.

84 (m)Which Type of Solar-Power product are you own Group

Figure 4.13: Pie Chart by Type of Solar-Power product distribution of respondents

The above survey pie chart indicates that six (6) categories type of solar power product respondents own that were stated in the survey. Under these six categories, Own Solar Power

Calculator category and other category are having the same 41.35 % of respondents. The other categories of Solar Power Water Heater, Solar Power Garden Light and Solar PV

System contribute (between 2% to 8 % each), the Solar Watch, which constitutes only 0.96 % from the total number of respondents.

85 (n) What is motivation factors influence you going for solar -powered product

Figure 4.14: Pie Chart by is motivation factors distribution of respondents

This study finds that It is more than half (62.50 %) of the respondents select the energy saving as motivation factor to go for solar power product. The second rank have select support of green energy is around 29.81%. The third rank have select Government incentive and Passive Income respectively are around 2.88% of respondents who have select

Government incentive and Passive Income respectively. The last rank is around 1.9 % of the respondents only select the social respect.

86 (o) What is your target affordable investment for solar -powered product Group?

Figure 4.15: Pie Chart by target affordable investment distribution of respondents

For target affordable investment on solar-powered product, most of the respondent are willing to invest on the category of below RM 10 ,000 solar – powered product which comprises of 65.38 %. Follow by 26.92 % of the respondent are willing to invest on the category of RM 10,000 to RM 50,000 solar – powered product. As for 3.88 % of respondent is those respondents are willing to invest on the category of RM 50,001 to RM 99,999 solar – powered product. For 2.9 % of respondent are willing to invest on the category of other amount on solar – powered product. For 1.0 % of respondent are willing to invest on the category of RM 100,000 on solar – powered product.

87 (p) What is payback investment being you prefer?

Figure 4.16: Pie Chart by payback investment distribution of respondents

This study finds that it is more than half (55.77%) of the respondents only can accept payback investment below 2 years. The second rank of the respondents only can accept payback investment from 2 to 4 year is around 36.54 % which is equivalent to 38 respondents. There are around 6.73% of respondents only can accept payback investment from 4 to 6 year. The last rank is around 0.96 % (1 respondents) of the respondents only can accept payback investment above 6 years.

88 4.3 Factor Analysis

During the data collection process, there is high possibilities that various variables data are inter-related. Factor analysis is adopting data reduction method to cut down on huge number of variables to a manageable set of factors. It always used to test for construct validity of the dependent, mediate (intervene) and independent variables.

Normally the factor analysis correlation table will be tabulated to demonstrate for any linkage or association between one variable with the other variable. If the two variables are strongly correlated. The correlation table shown high correlations values. For detail, refer back to chapter 3. The KMO Measure of Sampling Adequacy and Bartlett's Test of Sphericity. correlation matrix, communalities, component matrix (using varimax rotation), and scree plot are few tests will be carry out under this factor analysis.

4.3.1 Environmental Attitude

The factor analysis was carry out to measure all the components under Environmental

Attitude independent variable as below:

Analysis 1: KMO and Bartlett’s Test

The first step of analysis was the (Kaiser-Meyer-Olkin) KMO Test and Bartlett’s Test for

Environmental Attitude. The detail of above tests is display in Table 4.2 below:

Table 4.2: KMO and Bartlett’s Test for Environmental Attitude

KMO and Bartlett's Test

Kaiser-Meyer-Olkin Measure of Sampling Adequacy. .730 Approx. Chi-Square 275.199 Bartlett's Test of Sphericity df 21 Sig. .000

89 Uunder the independent variable of Environmental Attitude, the KMO Measure of Sampling

Adequacy (MSA) is 0.730 which is higher than the minimum of 0.60 (KMO=0.730>0.6) This means that there is sufficient of inter-correlation for the present research study.

The Bartlett’s Test of Sphericity result indicate a significant level of 0.000, which is below the significant level of p<0.005. The analysis output will support the seven components under

“Environmental Attitude” independent variable for on-going factor analysis.

Analysis 2: Measurement Correlation

The 2nd step is to examine the model of relationship within the components under the

Environmental Attitude independent variable. The Anti-Image Matrix is adopted as per below Table 4.3

Table 4.3: Correlation Matrix for Environmental Attitude

Anti-image Matrices

EnvAttd1 EnvAttd2 EnvAttd3 EnvAttd4 EnvAttd5 EnvAttd6 EnvAttd7 EnvAttd1 .369 -.265 .050 -.074 .035 -.173 .016 EnvAttd2 -.265 .494 -.114 -.004 -.055 .114 -.039 EnvAttd3 .050 -.114 .504 -.155 .131 -.124 -.128 Anti-image Covariance EnvAttd4 -.074 -.004 -.155 .638 -.230 -.028 -.026 EnvAttd5 .035 -.055 .131 -.230 .871 -.062 .032 EnvAttd6 -.173 .114 -.124 -.028 -.062 .373 -.186 EnvAttd7 .016 -.039 -.128 -.026 .032 -.186 .500 EnvAttd1 .681a -.621 .115 -.153 .063 -.466 .037 EnvAttd2 -.621 .641a -.229 -.008 -.084 .267 -.078 EnvAttd3 .115 -.229 .791a -.274 .197 -.286 -.255 Anti-image Correlation EnvAttd4 -.153 -.008 -.274 .824a -.309 -.058 -.047

EnvAttd5 .063 -.084 .197 -.309 .418a -.109 .049 EnvAttd6 -.466 .267 -.286 -.058 -.109 .719a -.432 EnvAttd7 .037 -.078 -.255 -.047 .049 -.432 .821a a. Measures of Sampling Adequacy(MSA)

There are two sections of anti-image matrix shown on above Table 4.3. We will ignore 7 components data of Anti-Image Covariance section at top portion. We only taking Anti-

Image Correlation section 7 components data for reference. Principal Component Analysis

(PCA) need to have respective components of variable Environmental Attitude MSA data more than 0.50. Out of 7 components data, six components MSA data which are BOLD style are more than 0.50 Hence, these data will be hold for further analysis.

Analysis 3: Principal Component Analysis and the Varimax Rotated Component

This third step of analysis with the Principal Component Analysis (PCA) first and then, the

Varimax PCA analysis. The PCA used to reduce of the number of factors and concurrently retain the variability of the data. For those factors exceed 1.0 determine the extraction number of factors and the remaining number of factors for following analysis. The Varimax

PCA analysis is computerized to determine the significant factors of instruments. Hence, those higher value factor will have a higher chance of being extracted.

Under the PCA analysis, communalities table indicate the original variables proportion that are consider by factor solution. At least 50% of the variance of each original variable are explain by factor solution, thus means the respective components communality value should be equal to or higher than 0.50

Table 4.4: Communalities for Environmental Attitude

Communalities

Initial Extraction EnvAttd1 1.000 .634 EnvAttd2 1.000 .447 EnvAttd3 1.000 .653 EnvAttd4 1.000 .580 EnvAttd5 1.000 .862 EnvAttd6 1.000 .688 EnvAttd7 1.000 .636 Extraction Method: Principal Component Analysis.

From Table 4.4, the communality value for EnvAttd2 is 0.447 which is below the 0.50 threshold. EnvAttd2 may likely to be extract in the iteration of the next PCA.

Table 4.5: Total Variance Explained for Environmental Attitude

Total Variance Explained

Component Initial Eigenvalues Extraction Sums of Squared Rotation Sums of Squared Loadings Loadings Total % of Cumulative Total % of Cumulative Total % of Cumulative Variance % Variance % Variance % 1 3.392 48.462 48.462 3.392 48.462 48.462 3.265 46.639 46.639 2 1.109 15.846 64.307 1.109 15.846 64.307 1.237 17.668 64.307 3 .903 12.899 77.206 4 .586 8.368 85.574 5 .456 6.512 92.086 6 .359 5.128 97.214 7 .195 2.786 100.000

Extraction Method: Principal Component Analysis.

There are two eigenvalues (3.392 & 1.109) that are more than 1.0 as per table 4.5. The latent root criterion for the number of factors to obtain, it means that there will be extraction of two components for this Environmental Attitude variable. In addition, the criteria of variance cumulative proportion able to accomplish with two components to meet the criterion of explaining 64.307% of the total variance.

Figure 4.17: Scree Plot for Environmental Attitude

Based on Scree Plot for Environmental Attitude on Figure 4.8, it has showing a curve is flatter with eigenvalues of less than 1.0. There are two components had been taper off by factors manager as indicates in this Scree Plot. This will further elaborate why only extraction of two components.

Table 4.6: Component Matrix for Environmental Attitude

Component Matrixa

Component 1 2 EnvAttd6 .821 -.119 EnvAttd1 .795 .044 EnvAttd3 .765 -.262 EnvAttd7 .755 -.256 EnvAttd4 .678 .347 EnvAttd2 .663 .088 EnvAttd5 .178 .911 Extraction Method: Principal Component Analysis. a. 2 components extracted.

Table 4.7: Rotate Component Matrix for Environmental Attitude

Rotated Component Matrixa

Component 1 2 EnvAttd6 .826 .078 EnvAttd3 .805 -.074 EnvAttd7 .794 -.070 EnvAttd1 .762 .231 EnvAttd2 .623 .243 EnvAttd4 .577 .498 EnvAttd5 -.042 .928 Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization.a a. Rotation converged in 3 iterations.

Tables 4.6 and 4.7 display the Component Matrix before and after rotation respectively. Based on Table 4.7, EnvAttd6 having a loading factor of 0.826, thus means it has highest relation with the variable Environmental Attitude. Table 4.7 display that component 1 consists of

EnvAttd6, EnvAttd3, EnvAttd7, EnvAttd1, EnvAttd2, EnvAttd4 and EnvAttd5 whilst

Component 2 consists of EnvAttd6, EnvAttd3, EnvAttd7, EnvAttd1, EnvAttd2, EnvAttd4 and

EnvAttd5.

In this scenario, we had to extract the component of EnvAttd4 and EnvAttd5 from table 4.7 and run for another round of iteration. The output of the iteration is concluding in Table 4.8 below.

Table 4.8: Factor Analysis for Environmental Attitude (2nd Iteration)

94 KMO and Bartlett's Test

Kaiser-Meyer-Olkin Measure of Sampling Adequacy. .703 Approx. Chi-Square 228.070

Bartlett's Test of Sphericity df 10 Sig. .000

Anti-image Matrices

EnvAttd1 EnvAttd2 EnvAttd3 EnvAttd6 EnvAttd7 EnvAttd1 .378 -.274 .035 -.183 .013 EnvAttd2 -.274 .498 -.122 .112 -.038 Anti-image Covariance EnvAttd3 .035 -.122 .553 -.139 -.150 EnvAttd6 -.183 .112 -.139 .381 -.190 EnvAttd7 .013 -.038 -.150 -.190 .502 EnvAttd1 .650a -.631 .075 -.483 .030 EnvAttd2 -.631 .608a -.231 .258 -.076

Anti-image Correlation EnvAttd3 .075 -.231 .814a -.303 -.286 EnvAttd6 -.483 .258 -.303 .683a -.435 EnvAttd7 .030 -.076 -.286 -.435 .794a a. Measures of Sampling Adequacy(MSA)

Communalities

Initial Extraction EnvAttd1 1.000 .652 EnvAttd2 1.000 .461 EnvAttd3 1.000 .596 EnvAttd6 1.000 .694 EnvAttd7 1.000 .608 Extraction Method: Principal Component Analysis.

Total Variance Explained

Component Initial Eigenvalues Extraction Sums of Squared Loadings Total % of Variance Cumulative % Total % of Variance Cumulative % 1 3.011 60.224 60.224 3.011 60.224 60.224 2 .914 18.279 78.503 3 .499 9.975 88.478 4 .376 7.511 95.989 5 .201 4.011 100.000

Extraction Method: Principal Component Analysis.

Component Matrixa

Component 1 EnvAttd6 .833 EnvAttd1 .807 EnvAttd7 .780 EnvAttd3 .772 EnvAttd2 .679 Extraction Method: Principal Component Analysis. a. 1 components extracted.

Table 4.8 is display the output of the factor analysis for variable Environmental Attitude after the 2nd Iteration. This is based on the further analysis of five remaining items under variable

Environmental Attitude, the final results are as below:

 the KMO MSA reading is 0.703 which is higher than the minimum of 0.60

96 (KMO=0.703>0.6) This means that there is sufficient of inter-correlation for the present research study. The Bartlett’s Test of Sphericity result indicate a significant level of 0.000, which is lower than the level of p<0.005. The output supports the five items under the variable “Environmental Attitude” for further factor analysis.

 All the five components of Environmental Attitude which MSA data in BOLD style are

more than 0.50 were shown on Anti-Image Correlation Matrix as above.

 All the four components of Environmental Attitude communality value are more than 0.50 and except one components (EnvAttd2) communality value is slightly below 0.5 (0.461)

 Total Variance Explained for Environmental Attitude table and Scree Plot explain that there

is only one eigenvalue that is more than 1.0 (3.011)

 The Component Matrix table indicate all five components of Environmental Attitude are more than 0.50.

The above result ascertains that the five remaining components for Environmental Attitude are fit to use in the next step of analysis.

As per table 4.8 as above, the component Matrix for Environmental Attitude has shown the factor loading are in a descending order from 0.833, 0.807, 0.780, 0.772 and 0.679.

Table 4.9 as below are shows the summary of factor loading for respective measurement components for Environmental Attitude, the measurement components (EnvAttd6) - “I believe solar power renewable energy is meaningful to our society” has the highest relationship to the variable Environmental Attitude with a factor loading of 0.833. While the measurement components EnvAttd2 is “I believe that solar power renewable energy is a form of green energy” has lowest factor loading of 0.679 which indicated that it has the weakest relationship

97 to the variable Environmental Attitude. All the five components had been rotated as a single component with significant loadings > 0.5 after the Varimax rotation.

Table 4.9: Factor Loading for Environmental Attitude

Measurement Component ENVINRONMENTAL ATTITUDE Factor Component The awareness of conserving energy and keeping environmental clean Loading

I believe solar power renewable energy is meaningful to our society EnvAttd6 0.833 I believe it’s necessary to promote solar power renewable energy in EnvAttd1 0.807 our country I believe that the Environmental Attitude is related to Eco-literacy which promote green consumption behaviours on solar power EnvAttd7 0.780 renewable energy system. I believe that environmental awareness can help in the adoption of EnvAttd3 0.772 solar power renewable energy I believe that solar power renewable energy is a form of green energy EnvAttd2 0.679

4.3.2 Social Responsibility

The factor analysis was carry out to measure all the components under Social responsibility independent variable as below:

Analysis 1: KMO and Bartlett’s Test

The first step of analysis was the (Kaiser-Meyer-Olkin) KMO Test and Bartlett’s Test for

Social Responsibility. The detail of above tests is display in Table 4.10 below:

Table 4.10: KMO and Bartlett’s Test for Social Responsibility

KMO and Bartlett's Test

Kaiser-Meyer-Olkin Measure of Sampling Adequacy. .853 Approx. Chi-Square 393.510 Bartlett's Test of Sphericity df 21 Sig. .000

98 Under the independent variable of Social Responsibility, the KMO Measure of Sampling

Adequacy (MSA) is 0.853 which is higher than the minimum of 0.60 (KMO=0.853>0.6) This means that there is sufficient of inter-correlation for the present research study.

The Bartlett’s Test of Sphericity result indicate a significant level of 0.000, which is below the significant level of p<0.005. The analysis output will support the seven components under

“Social Responsibility” independent variable for on-going factor analysis.

Analysis 2: Correlation Measurement

The 2nd step is to examine the model of relationship within the components under the Social

Responsibility independent variable. The Anti-Image Matrix is adopted as per below Table

4.11. The Anti-Image Correlation results as per below Table 4.11 shown that seven components MSA data for Social Responsibility which are BOLD style are more than 0.50.

Hence, these data will be hold for further analysis.

Table 4.11: Correlation Matrix for Social Responsibility

Anti-image Matrices

SocialRes SocialRes SocialRes SocialRes SocialRes SocialRes SocialRes p1 p2 p3 p4 p5 p6 p7 SocialRes .470 -.200 -.070 .015 -.192 .000 .044 p1 SocialRes -.200 .477 -.092 -.020 .059 -.028 -.071 p2

Anti- SocialRes -.070 -.092 .398 -.175 -.033 -.067 -.017 image p3 Covarian SocialRes .015 -.020 -.175 .501 -.042 -.033 -.060 ce p4 SocialRes -.192 .059 -.033 -.042 .463 .004 -.136 p5 SocialRes .000 -.028 -.067 -.033 .004 .393 -.187 p6

99 SocialRes .044 -.071 -.017 -.060 -.136 -.187 .318 p7 SocialRes .803a -.421 -.161 .030 -.411 -.001 .114 p1 SocialRes -.421 .862a -.212 -.040 .125 -.065 -.183 p2 SocialRes -.161 -.212 .890a -.393 -.076 -.169 -.048 Anti- p3 image SocialRes .030 -.040 -.393 .899a -.088 -.075 -.150 Correlatio p4 n SocialRes -.411 .125 -.076 -.088 .844a .008 -.354 p5 SocialRes -.001 -.065 -.169 -.075 .008 .858a -.529 p6 SocialRes .114 -.183 -.048 -.150 -.354 -.529 .820a p7 a. Measures of Sampling Adequacy(MSA)

Analysis 3: Principal Component Analysis (PCA) and Varimax Rotated Component

From Table 4.12, the communality value of all the components on Social Responsibility independent variable which is above the 0.50 threshold. These data were hold for on- going analysis in the next PCA cycle.

Table 4.12: Communalities for Social Responsibility

Communalities

Initial Extraction SocialResp1 1.000 .525 SocialResp2 1.000 .582 SocialResp3 1.000 .695 SocialResp4 1.000 .577 SocialResp5 1.000 .588 SocialResp6 1.000 .633 SocialResp7 1.000 .704 Extraction Method: Principal Component Analysis.

Table 4.13: Total Variance Explained for Social Responsibility

100

Total Variance Explained

Component Initial Eigenvalues Extraction Sums of Squared Loadings

Total % of Variance Cumulative % Total % of Variance Cumulative % 1 4.303 61.472 61.472 4.303 61.472 61.472 2 .774 11.062 72.534 3 .587 8.381 80.915 4 .521 7.441 88.356 5 .319 4.558 92.914 6 .290 4.139 97.053 7 .206 2.947 100.000 Extraction Method: Principal Component Analysis.

There are one eigenvalues (4.303) that are more than 1.0 as per table 4.13. it means that there will be extraction of one components for this Social Responsibility independent variable. In table 4.13, the total variance cumulative proportion of 61.47% elaborate that the seven components measurement manage to explain 61% of the total variance for the Social

Responsibility independent variable.

Based on Scree Plot for Social Responsibility on Figure 4.18, it has showing a curve is flatter with eigenvalues of less than 1.0. There is one component had been taper off by factors manager as indicates in this Scree Plot.

Figure 4.18: Scree Plot for Social Responsibility

101

Table 4.14: Component Matrix for Social Responsibility

Component Matrixa

Component 1 SocialResp7 .839 SocialResp3 .833 SocialResp6 .796 SocialResp5 .767 SocialResp2 .763 SocialResp4 .760 SocialResp1 .725 Extraction Method: Principal Component Analysis. a. 1 components extracted.

As per table 4.14 as above, the component Matrix for Social Responsibility has shown the factor loading are in a descending order from 0.839 ,0.833, 0.796, 0.767, 0.763, 0.760 and

0.725.

102 Table 4.15 as below are shows the summary of factor loading for respective measurement components for Social Responsibility, the measurement components (SocialResp7) - “I believe that the social responsibilities are related to Eco-literacy which promote green consumption behaviours on solar power renewable energy system” has the highest relationship to the variable Social Responsibility with a factor loading of 0.839. While the measurement components SocialResp1 is “As an individual, if affordable, I am willing to invest in solar -powered product” has lowest factor loading of 0.725 which indicated that it has the weakest relationship to the variable Social Responsibility.

The analysis results reveal that none of any components to be extracted from this Social

Responsibility variable. All the seven components had been rotated as a single component with significant loadings > 0.5 after the Varimax rotation.

Table 4.15: Factor Loading for Social Responsibility Measurement Components SOCIAL RESPONSIBILITIES Factor Components The level of individual and corporate social responsibility towards Loading environment I believe that the social responsibilities are related to Eco-literacy which promote green consumption behaviours on solar power SocialResp7 0.839 renewable energy system Besides me, the government must be socially responsible for SocialResp3 0.833 implementation of solar power renewable energy system For the sake of our future generation, promoting the use solar power SocialResp6 0.796 renewable energy system is most encouraging. We have been taught to protect the environment since early school SocialResp5 0.767 days, thus I support solar power renewable energy system. If I am a business owner, I am willing to consider the solar -powered SocialResp2 0.763 product as it’s part of Corporate Social Responsibilities (CSR) Besides our Government, Environmental organizations (NGOs) should play their roles to implement solar power renewable energy SocialResp4 0.760 system. As an individual, if affordable, I am willing to invest in solar - SocialResp1 0.725 powered product.

103 4.3.3 Perceived Self- Image

The factor analysis was carry out to measure all the components under Perceived Self- Image independent variable as below:

Analysis 1: KMO and Bartlett’s Test

The first step of analysis was the (Kaiser-Meyer-Olkin) KMO Test and Bartlett’s Test for

Perceived Self- Image. The detail of above tests is display in Table 4.16 below:

Table 4.16: KMO and Bartlett’s Test for Perceived Self- Image

KMO and Bartlett's Test

Kaiser-Meyer-Olkin Measure of Sampling Adequacy. .861 Approx. Chi-Square 412.120 Bartlett's Test of Sphericity df 21 Sig. .000

Under the independent variable of Perceived Self- Image, the KMO Measure of Sampling

Adequacy (MSA) is 0.861 which is higher than the minimum of 0.60 (KMO=0.861>0.6) This means that there is sufficient of inter-correlation for the present research study.

The Bartlett’s Test of Sphericity result indicate a significant level of 0.000, which is below the significant level of p<0.005. The analysis output will support the seven components under

“Perceived Self- Image” independent variable for on-going factor analysis.

Analysis 2: Correlation Measurement

The 2nd step is to examine the model of relationship within the components under the

Perceived Self- Image independent variable. The Anti-Image Matrix is adopted as per below

Table 4.17. The Anti-Image Correlation results as per below Table 4.17 shown that seven components MSA data for Perceived Self- Image which are BOLD style are more than 0.50.

Hence, these data will be hold for further analysis.

104 Table 4.17: Correlation Matrix for Perceived Self- Image

Anti-image Matrices

PervImag PervImag PervImag PervImag PervImag PervImag PervImag e1 e2 e3 e4 e5 e6 e7 PervImag .430 -.157 -.020 -.070 -.117 -.045 .037 e1 PervImag -.157 .437 -.167 -.021 .025 .028 -.090 e2 PervImag -.020 -.167 .437 .068 -.063 -.093 -.068 Anti- e3 image PervImag -.070 -.021 .068 .457 -.191 -.002 -.070 Covarianc e4 e PervImag -.117 .025 -.063 -.191 .346 -.060 -.026 e5 PervImag -.045 .028 -.093 -.002 -.060 .407 -.181 e6 PervImag .037 -.090 -.068 -.070 -.026 -.181 .386 e7 PervImag .879a -.362 -.045 -.158 -.303 -.108 .090 e1 PervImag -.362 .846a -.383 -.047 .064 .067 -.219 e2 PervImag -.045 -.383 .873a .153 -.162 -.220 -.167 Anti- e3 image PervImag -.158 -.047 .153 .842a -.482 -.004 -.166 Correlatio e4 n PervImag -.303 .064 -.162 -.482 .850a -.161 -.072 e5 PervImag -.108 .067 -.220 -.004 -.161 .870a -.455 e6 PervImag .090 -.219 -.167 -.166 -.072 -.455 .865a e7 a. Measures of Sampling Adequacy(MSA)

105 Analysis 3: Principal Component Analysis (PCA) and Varimax Rotated Component

From Table 4.18, the communality value of all the components on Perceived Self- Image independent variable which is above the 0.50 threshold. These data were hold for on- going analysis in the next PCA cycle.

Table 4.18: Communalities for Perceived Self- Image

Communalities

Initial Extraction PervImage1 1.000 .633 PervImage2 1.000 .600 PervImage3 1.000 .607 PervImage4 1.000 .536 PervImage5 1.000 .691 PervImage6 1.000 .645 PervImage7 1.000 .667 Extraction Method: Principal Component Analysis.

Table 4.19: Total Variance Explained for Perceived Self- Image

Total Variance Explained

Component Initial Eigenvalues Extraction Sums of Squared Loadings Total % of Variance Cumulative % Total % of Variance Cumulative % 1 4.380 62.570 62.570 4.380 62.570 62.570 2 .803 11.464 74.034 3 .615 8.786 82.820 4 .377 5.382 88.202 5 .350 5.005 93.207 6 .245 3.500 96.707 7 .230 3.293 100.000 Extraction Method: Principal Component Analysis.

There are one eigenvalues (4.380) that are more than 1.0 as per table 4.19. it means that there will be extraction of one components for this Perceived Self- Image independent variable. In

106 table 4.19, the total variance cumulative proportion of 62.57% elaborate that the seven components measurement manage to explain 62% of the total variance for the Perceived Self-

Image independent variable.

Based on Scree Plot for Perceived Self- Image on Figure 4., it has showing a curve is flatter with eigenvalues of less than 1.0. There is one component had been taper off by factors manager as indicates in this Scree Plot.

Figure 4.19: Scree Plot for Perceived Self- Image

Table 4.20: Component Matrix for Perceived Self- Image

Component Matrixa

Component 1 PervImage5 .831 PervImage7 .817 PervImage6 .803 PervImage1 .796 PervImage3 .779 PervImage2 .775 PervImage4 .732

107 Extraction Method: Principal Component Analysis. a. 1 components extracted.

As per table 4.20 as above, the component Matrix for Perceived Self- Image has shown the factor loading are in a descending order from 0.831 ,0.817, 0.803, 0.796, 0.779, 0.775 and

0.732.

Table 4.21 as below are shows the summary of factor loading for respective measurement components for Perceived Self- Image, the measurement components (PervImage5) - “I am willing to making the wise decision to invest in solar -powered product” has the highest relationship to the variable Perceived Self- Image with a factor loading of 0.831. While the measurement components PervImage4S is “I support green initiatives by adopting solar power renewable energy” has lowest factor loading of 0.732 which indicated that it has the weakest relationship to the variable Perceived Self- Image.

The analysis results reveal that none of any components to be extracted from this Perceived

Self- Image variable. All the seven components had been rotated as a single component with significant loadings > 0.5 after the Varimax rotation.

Table 4.21: Factor Loading for Perceived Self- Image

Measurement Components PERCEIVED SELF – IMAGE Factor Component An individual perceived him/her as person who act environmental Loading friendly. I am willing to making the wise decision to invest in solar -powered PervImage5 0.831 product I understand that the Perceived self-Image is related to Eco-literacy which promote green consumption behaviours on solar power PervImage7 0.817 renewable energy I feel great if I can convince my friends to purchase solar -powered PervImage6 0.803 product I am willing to be the first to invest in solar -powered product if I am PervImage1 0.796 affordable I will be gain respect from society through adopting solar power PervImage3 0.779 renewable energy. I am willing to enhance my family image to use solar -powered PervImage2 0.775 product in my house. I support green initiatives by adopting solar power renewable energy. PervImage4 0.732

108

4.3.4 Government Incentive

The factor analysis was carry out to measure all the components under Government Incentive independent variable as below:

Analysis 1: KMO and Bartlett’s Test

The first step of analysis was the (Kaiser-Meyer-Olkin) KMO Test and Bartlett’s Test for

Government Incentive. The detail of above tests is display in Table 4.22 below:

Table 4.22: KMO and Bartlett’s Test for Government Incentive

KMO and Bartlett's Test

Kaiser-Meyer-Olkin Measure of Sampling Adequacy. .845 Approx. Chi-Square 342.396

Bartlett's Test of Sphericity df 21 Sig. .000

Under the independent variable of Government Incentive, the KMO Measure of Sampling

Adequacy (MSA) is 0.845 which is higher than the minimum of 0.60 (KMO=0.845>0.6) This means that there is sufficient of inter-correlation for the present research study.

The Bartlett’s Test of Sphericity result indicate a significant level of 0.000, which is below the significant level of p<0.005. The analysis output will support the seven components under

“Government Incentive” independent variable for on-going factor analysis.

Analysis 2: Measurement Correlation

The 2nd step is to examine the model of relationship within the components under the

Government Incentive independent variable. The Anti-Image Matrix is adopted as per below

Table 4.23. The Anti-Image Correlation results as per below Table 4.23 shown that seven components MSA data for Government Incentive which are BOLD style are more than 0.50.

Hence, these data will be hold for further analysis.

109

Table 4.23: Correlation Matrix for Government Incentive

Anti-image Matrices

GovIncenti GovIncenti GovIncenti GovIncenti GovIncenti GovIncenti GovIncenti ve1 ve2 ve3 ve4 ve5 ve6 ve7 GovIncenti .680 -.125 -.001 .006 -.045 .143 -.251 ve1 GovIncenti -.125 .378 -.067 -.143 -.098 -.030 .008 ve2 GovIncenti -.001 -.067 .480 -.055 -.168 -.002 .003 Anti- ve3 image GovIncenti .006 -.143 -.055 .410 -.011 -.158 -.048 Covaria ve4 nce GovIncenti -.045 -.098 -.168 -.011 .347 -.117 -.057 ve5 GovIncenti .143 -.030 -.002 -.158 -.117 .466 -.082 ve6 GovIncenti -.251 .008 .003 -.048 -.057 -.082 .654 ve7 GovIncenti .692a -.246 -.001 .012 -.092 .254 -.376 ve1 GovIncenti -.246 .868a -.158 -.363 -.270 -.072 .015 ve2 GovIncenti -.001 -.158 .882a -.125 -.413 -.005 .006 Anti- ve3 image GovIncenti .012 -.363 -.125 .862a -.029 -.362 -.094 Correlati ve4 on GovIncenti -.092 -.270 -.413 -.029 .852a -.290 -.120 ve5 GovIncenti .254 -.072 -.005 -.362 -.290 .829a -.149 ve6 GovIncenti -.376 .015 .006 -.094 -.120 -.149 .843a ve7 a. Measures of Sampling Adequacy(MSA)

Table 4.23 displayed above has two sections of anti-image matrix. We will ignore the

110 Anti-image Covariance and consider the data in the Anti-Image Correlation section.

Principal Component Analysis (PCA) requires that the KMO MSA be higher than 0.50 for each individual item of the variable Government Incentive. In the case of this variable, all the seven items which are highlighted in BOLD are higher than 0.50 and therefore will be retained for further analysis.

Analysis 3: Principal Component Analysis and the Varimax Rotated Component

This third step of analysis with the Principal Component Analysis (PCA) first and then, the

PCA analysis is computerized to determine the significant factors of instruments. Hence, those higher value factor will have a higher chance of being extracted.

Table 4.24: Communalities for Government Incentive

Communalities

Initial Extraction GovIncentive1 1.000 .847 GovIncentive2 1.000 .722 GovIncentive3 1.000 .626 GovIncentive4 1.000 .717 GovIncentive5 1.000 .756 GovIncentive6 1.000 .713 GovIncentive7 1.000 .615 Extraction Method: Principal Component Analysis.

111 From Table 4.24, the communality value of all the components on Government Incentive independent variable which is above the 0.50 threshold. These data were hold for on- going analysis in the next PCA cycle

Table 4.25: Total Variance Explained for Government Incentive

Total Variance Explained

Component Initial Eigenvalues Extraction Sums of Squared Rotation Sums of Squared Loadings Loadings Total % of Cumulative Total % of Cumulative Total % of Cumulative Variance % Variance % Variance % 1 3.925 56.072 56.072 3.925 56.072 56.072 3.331 47.589 47.589 2 1.069 15.278 71.350 1.069 15.278 71.350 1.663 23.761 71.350 3 .629 8.983 80.333 4 .483 6.901 87.234 5 .353 5.048 92.283 6 .303 4.325 96.608 7 .237 3.392 100.000 Extraction Method: Principal Component Analysis.

There are two eigenvalues (3.925 & 1.069) that are more than 1.0 as per table 4.25. The latent root criterion for the number of factors to obtain, it means that there will be extraction of two components for this Government Incentive variable. In addition, the criteria of variance cumulative proportion able to accomplish with two components to meet the criterion of explaining 71.35% of the total variance

Figure 4.20: Scree Plot for Government Incentive

112

Based on Scree Plot for Government Incentive on Figure 4.20, it has showing a curve is flatter with eigenvalues of less than 1.0. There are two components had been taper off by factors manager as indicates in this Scree Plot. This will further elaborate why only extraction of two components.

Table 4.26: Component Matrix for Government Incentive

Component Matrixa

Component 1 2 GovIncentive5 .864 -.095 GovIncentive2 .850 -.011 GovIncentive4 .820 -.209 GovIncentive3 .778 -.145 GovIncentive6 .748 -.392 GovIncentive7 .628 .469 GovIncentive1 .474 .788

Extraction Method: Principal Component Analysis. a. 2 components extracted.

113

Table 4.27: Rotate Component Matrix for Government Incentive

Rotated Component Matrixa

Component 1 2 GovIncentive6 .844 -.008 GovIncentive4 .825 .188 GovIncentive5 .812 .309 GovIncentive2 .761 .378 GovIncentive3 .759 .226 GovIncentive1 .063 .918 GovIncentive7 .345 .704 Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization.a a. Rotation converged in 3 iterations.

Tables 4.26 and 4.27 display the Component Matrix before and after rotation respectively.

Based on Table 4.27, GovIncentive6 has the highest relation with the variable Government

Incentive, having a factor loading of 0.844. Table 4.7 shows that Component 1 consists of

GovIncentive6, GovIncentive4, GovIncentive5, GovIncentive2, GovIncentive3,

GovIncentive1, and GovIncentive7 whilst Component 2 consists of GovIncentive6,

GovIncentive4, GovIncentive5, GovIncentive2, GovIncentive3, GovIncentive1, and

GovIncentive7.

In this scenario, we had to extract the component of GovIncentive1 and GovIncentive7 from table 4.27 and run for another round of iteration. The output of the iteration is concluding in

Table 4.28 below.

114 Table 4.28: Factor Analysis for Government Incentive (2nd Iteration)

KMO and Bartlett's Test

Kaiser-Meyer-Olkin Measure of Sampling Adequacy. .844 Approx. Chi-Square 278.611 Bartlett's Test of Sphericity df 10 Sig. .000

Anti-image Matrices

GovIncentive2 GovIncentive3 GovIncentive4 GovIncentive5 GovIncentive6 GovIncentive2 .405 -.072 -.157 -.124 -.007 GovIncentive3 -.072 .480 -.056 -.174 -.002 Anti-image GovIncentive4 -.157 -.056 .414 -.017 -.175 Covariance GovIncentive5 -.124 -.174 -.017 .360 -.124 GovIncentive6 -.007 -.002 -.175 -.124 .500 GovIncentive2 .852a -.164 -.383 -.323 -.016

GovIncentive3 -.164 .864a -.125 -.420 -.004 Anti-image GovIncentive4 -.383 -.125 .834a -.045 -.386 Correlation GovIncentive5 -.323 -.420 -.045 .823a -.292 GovIncentive6 -.016 -.004 -.386 -.292 .851a a. Measures of Sampling Adequacy(MSA)

Communalities

Initial Extraction GovIncentive2 1.000 .724 GovIncentive3 1.000 .643 GovIncentive4 1.000 .710 GovIncentive5 1.000 .760 GovIncentive6 1.000 .624 Extraction Method: Principal Component Analysis.

Total Variance Explained

115 Component Initial Eigenvalues Extraction Sums of Squared Loadings Total % of Variance Cumulative % Total % of Variance Cumulative % 1 3.461 69.210 69.210 3.461 69.210 69.210 2 .558 11.167 80.378 3 .420 8.394 88.771 4 .322 6.440 95.211 5 .239 4.789 100.000 Extraction Method: Principal Component Analysis.

Component Matrixa

Component 1 GovIncentive5 .872 GovIncentive2 .851 GovIncentive4 .842 GovIncentive3 .802 GovIncentive6 .790

Extraction Method: Principal Component Analysis. a. 1 components extracted.

116

Table 4.28 is display the output of the factor analysis for variable Government Incentive after the 2nd Iteration. This is based on the further analysis of five remaining items under variable

Government Incentive, the final results are as below:

 the KMO MSA reading is 0.844 which is higher than the minimum of 0.60

(KMO=0.844>0.6) This means that there is sufficient of inter-correlation for the present research study. The Bartlett’s Test of Sphericity result indicate a significant level of 0.000, which is lower than the level of p<0.005. The output supports the five items under the variable “Government Incentive” for further factor analysis.

 All the five components of Government Incentive which MSA data in BOLD style are

more than 0.50 were shown on Anti-Image Correlation Matrix as above.

 All the five components of Government Incentive communality value are more than 0.50.

 Total Variance Explained for Government Incentive table and Scree Plot explain that there

is only one eigenvalue that is more than 1.0 (3.461)

 The Component Matrix table indicate all five components of Government Incentive are more than 0.50.

The above result ascertains that the five remaining components for Government Incentive are fit to use in the next step of analysis.

As per table 4.28 as above, the component Matrix for Government Incentive Environmental

Attitude has shown the factor loading are in a descending order from 0.872, 0.851, 0.842, 0.802 and 0.790.

117 Table 4.29 as below are shows the summary of factor loading for respective measurement components for Government Incentive, the measurement components (GovIncentive5) - “The government should improve feed-in tariff for promote solar power renewable energy system” has the highest relationship to the variable Government Incentive with a factor loading of

0.872. While the measurement components GovIncentive6 is “The government should organize more campaigns to promote solar power renewable energy system” has lowest factor loading of 0.790 which indicated that it has the weakest relationship to the variable

Government Incentive. All the five components had been rotated as a single component with significant loadings > 0.5 after the Varimax rotation.

Table 4.29: Factor Loading for Government Incentive Measurement Item Government Incentive Factor Item An incentive given by government to promote for green Loading consumption The government should improve feed-in tariff for promote solar GovIncentive5 0.872 power renewable energy system. The government should continue providing the incentive on green GovIncentive2 0.851 research on solar power renewable energy system. The government should strategize its policies to promote solar GovIncentive4 0.842 power renewable energy system. The government should enforce environmental rules and GovIncentive3 0.802 regulations on solar power renewable energy industries The government should organize more campaigns to promote solar GovIncentive6 0.790 power renewable energy system

4.3.5 Eco-Literacy

The factor analysis was carry out to measure all the components under Eco-literacy mediating variable as below:

Analysis 1: KMO and Bartlett’s Test

The first step of analysis was the (Kaiser-Meyer-Olkin) KMO Test and Bartlett’s Test for

Eco-literacy. The detail of above tests is display in Table 4.30 below:

118 Table 4.30: KMO and Bartlett’s Test for Eco-literacy

KMO and Bartlett's Test

Kaiser-Meyer-Olkin Measure of Sampling Adequacy. .833 Approx. Chi-Square 373.452 Bartlett's Test of Sphericity df 15 Sig. .000

Under the mediating variable of Eco-literacy, the KMO Measure of Sampling Adequacy

(MSA) is 0.833 which is higher than the minimum of 0.60 (KMO=0.833>0.6) This means that there is sufficient of inter-correlation for the present research study.

The Bartlett’s Test of Sphericity result indicate a significant level of 0.000, which is below the significant level of p<0.005. The analysis output will support the seven components under

“Eco-literacy” mediating variable for on-going factor analysis.

Analysis 2: Correlation Measurement

The 2nd step is to examine the model of relationship within the components under the Eco- literacy mediating variable. The Anti-Image Matrix is adopted as per below Table 4.31. The

Anti-Image Correlation results as per below Table 4.31 shown that seven components MSA data for Eco-literacy which are BOLD style are more than 0.50. Hence, these data will be hold for further analysis.

Table 4.31: Correlation Matrix for Eco-literacy

Anti-image Matrices

EcoLiteracy EcoLiteracy EcoLiteracy EcoLiteracy EcoLiteracy EcoLiteracy 1 2 3 4 5 6

EcoLiteracy .545 -.135 -.022 -.158 -.078 .047 1 Anti-image EcoLiteracy -.135 .653 -.151 .040 .060 -.077 Covarianc 2 e EcoLiteracy -.022 -.151 .302 -.019 -.129 -.017 3

119 EcoLiteracy -.158 .040 -.019 .485 -.033 -.108 4 EcoLiteracy -.078 .060 -.129 -.033 .211 -.132 5 EcoLiteracy .047 -.077 -.017 -.108 -.132 .299 6 EcoLiteracy .868a -.226 -.054 -.308 -.229 .118 1 EcoLiteracy -.226 .807a -.341 .070 .163 -.174 2 EcoLiteracy -.054 -.341 .842a -.049 -.510 -.057 Anti-image 3 Correlatio EcoLiteracy -.308 .070 -.049 .891a -.103 -.284 n 4 EcoLiteracy -.229 .163 -.510 -.103 .782a -.525 5 EcoLiteracy .118 -.174 -.057 -.284 -.525 .833a 6 a. Measures of Sampling Adequacy(MSA)

Analysis 3: Principal Component Analysis (PCA) and Varimax Rotated Component

The Communality of all the variables shown in Table 4.32 below were retained for analysis in the next PCA phase as all except one of them exceed the threshold of 0.50. EcoLiteracy2 which is at 0.39 is also retained for the next analysis.

From Table 4.32, the communality value of five components on Eco-literacy mediating variable which is above the 0.50 threshold except one component below the threshold of

0.50. EcoLiteracy2 which is at 0.39. These data were hold for on- going analysis in the next

PCA cycle.

120 Table 4.32: Communalities for Eco-literacy

Communalities

Initial Extraction EcoLiteracy1 1.000 .560 EcoLiteracy2 1.000 .393 EcoLiteracy3 1.000 .762 EcoLiteracy4 1.000 .614 EcoLiteracy5 1.000 .816 EcoLiteracy6 1.000 .746 Extraction Method: Principal Component Analysis.

Table 4.33: Total Variance Explained for Eco-literacy

Total Variance Explained

Component Initial Eigenvalues Extraction Sums of Squared Loadings Total % of Variance Cumulative % Total % of Variance Cumulative %

1 3.892 64.874 64.874 3.892 64.874 64.874 2 .736 12.270 77.144 3 .569 9.491 86.635 4 .396 6.594 93.229 5 .267 4.445 97.674 6 .140 2.326 100.000 Extraction Method: Principal Component Analysis.

There are one eigenvalues (3.892) that are more than 1.0 as per table 4.33. it means that there will be extraction of one components for this Eco-literacy mediating variable. In table 4.33, the total variance cumulative proportion of 64.87% elaborate that the six components measurement manage to explain 64% of the total variance for the Eco-literacy mediating variable.

Based on Scree Plot for Eco-literacy on Figure 4.21, it has showing a curve is flatter with eigenvalues of less than 1.0. There is one component had been taper off by factors manager

121 as indicates in this Scree Plot.

Figure 4.21: Scree Plot for Eco-literacy

Table 4.34: Component Matrix for Eco-literacy

Component Matrixa

Component 1 EcoLiteracy5 .903 EcoLiteracy3 .873 EcoLiteracy6 .864 EcoLiteracy4 .784 EcoLiteracy1 .748 EcoLiteracy2 .627 Extraction Method: Principal Component Analysis. a. 1 components extracted.

As per table 4.34 as above, the component Matrix for Eco-literacy has shown the factor loading are in a descending order from 0.903 ,0.873, 0.864, 0.784, 0.748, and 0.627.

122 Table 4.35 as below are shows the summary of factor loading for respective measurement components for Eco-literacy, the measurement components (EcoLiteracy5) - “I understand that our new generation shall be cultivating with environmental knowledge on solar power renewable energy system” has the highest relationship to the variable Eco-literacy with a factor loading of 0.903. While the measurement components EcoLiteracy2 is “I understand that solar -powered product does not pose any hazards to the environment” has lowest factor loading of 0.627 which indicated that it has the weakest relationship to the variable Eco- literacy.

The analysis results reveal that none of any components to be extracted from this Eco-literacy variable. All the six components had been rotated as a single component with significant loadings > 0.5 after the Varimax rotation.

Table 4.35: Factor Loading for Eco-literacy Measurement Component Eco-Literacy An individual shall aware environmental knowledge for Factor Component environmental issue Loading The environmental knowledge that is contained in various forms (e.g. books, articles, education and etc.) I understand that our new generation shall be cultivating with EcoLiteracy5 0.903 environmental knowledge on solar power renewable energy system I understand that our education system shall in co-operated the EcoLiteracy3 0.873 environmental knowledge on solar power renewable energy system I understand that the Eco-literacy is important to promote green EcoLiteracy6 0.864 consumption behaviours on solar power renewable energy system I understand that the benefits of solar -powered product before I can EcoLiteracy4 0.784 share to other. I understand that the details the of the solar -powered product before purchase it. EcoLiteracy1 0.748

I understand that solar -powered product does not pose any hazards to the environment EcoLiteracy2 0.627

123 4.3.6 Green Consumption behaviour

The factor analysis was carry out to measure all the components under Green Consumption behaviour dependent variable as below:

Analysis 1: KMO and Bartlett’s Test

The first step of analysis was the (Kaiser-Meyer-Olkin) KMO Test and Bartlett’s Test for

Green Consumption behaviour. The detail of above tests is display in Table 4.36 below:

Table 4.36: KMO and Bartlett’s Test for Green Consumption behaviour

KMO and Bartlett's Test

Kaiser-Meyer-Olkin Measure of Sampling Adequacy. .864 Approx. Chi-Square 288.119 Bartlett's Test of Sphericity df 15 Sig. .000

Under the dependent variable of Green Consumption Behaviour, the KMO Measure of

Sampling Adequacy (MSA) is 0.864 which is higher than the minimum of 0.60

(KMO=0.864>0.6) This means that there is sufficient of inter-correlation for the present research study.

The Bartlett’s Test of Sphericity result indicate a significant level of 0.000, which is below the significant level of p<0.005. The analysis output will support the seven components under

“Green Consumption Behaviour” dependent variable for on-going factor analysis.

Analysis 2: Correlation Measurement

The 2nd step is to examine the model of relationship within the components under the Green

Consumption Behaviour dependent variable. The Anti-Image Matrix is adopted as per below

Table 4.37. The Anti-Image Correlation results as per below Table 4.37 shown that six

124 components MSA data for Green Consumption Behaviour which are BOLD style are more than 0.50. Hence, these data will be hold for further analysis.

Table 4.37: Correlation Matrix for Green Consumption behaviour

Anti-image Matrices

GreenConsu GreenConsu GreenConsu GreenConsu GreenConsu GreenConsu mpBehv1 mpBehv2 mpBehv3 mpBehv4 mpBehv5 mpBehv6 GreenConsu .507 -.166 -.128 .022 .095 -.116 mpBehv1 GreenConsu -.166 .482 -.045 -.094 -.060 -.064 mpBehv2

Anti- GreenConsu -.128 -.045 .406 -.143 -.094 -.065 image mpBehv3 Covari GreenConsu .022 -.094 -.143 .402 -.095 -.135 ance mpBehv4 GreenConsu .095 -.060 -.094 -.095 .753 -.045 mpBehv5

GreenConsu -.116 -.064 -.065 -.135 -.045 .443 mpBehv6 GreenConsu .827a -.335 -.281 .050 .155 -.245 mpBehv1 GreenConsu -.335 .887a -.102 -.214 -.100 -.139 mpBehv2

Anti- GreenConsu -.281 -.102 .867a -.355 -.170 -.152 image mpBehv3 Correl GreenConsu .050 -.214 -.355 .848a -.173 -.321 ation mpBehv4 GreenConsu .155 -.100 -.170 -.173 .872a -.078 mpBehv5 GreenConsu -.245 -.139 -.152 -.321 -.078 .887a mpBehv6 a. Measures of Sampling Adequacy(MSA)

125 Analysis 3: Principal Component Analysis (PCA) and Varimax Rotated Component

From Table 4.38, the communality value of five components on Green Consumption

Behaviour dependent variable which is above the 0.50 threshold except one component below the threshold of 0.50. GreenConsumpBehv5 (0.308). These data were hold for on- going analysis in the next PCA cycle.

Table 4.38: Communalities for Green Consumption behaviour

Communalities

Initial Extraction GreenConsumpBehv1 1.000 .563 GreenConsumpBehv2 1.000 .655 GreenConsumpBehv3 1.000 .724 GreenConsumpBehv4 1.000 .710 GreenConsumpBehv5 1.000 .308 GreenConsumpBehv6 1.000 .694

Extraction Method: Principal Component Analysis.

Table 4.39: Total Variance Explained for Green Consumption behaviour

Total Variance Explained

Component Initial Eigenvalues Extraction Sums of Squared Loadings Total % of Variance Cumulative % Total % of Variance Cumulative % 1 3.654 60.905 60.905 3.654 60.905 60.905 2 .846 14.096 75.001 3 .469 7.810 82.811 4 .397 6.615 89.426 5 .368 6.141 95.567 6 .266 4.433 100.000 Extraction Method: Principal Component Analysis.

There are one eigenvalues (3.654) that are more than 1.0 as per table 4.39. it means that there will be extraction of one components for this Green Consumption Behaviour dependent variable. In table 4.39, the total variance cumulative proportion of 60.905% elaborate that the

126 seven components measurement manage to explain 61% of the total variance for the Green

Consumption Behaviour dependent variable.

Based on Scree Plot for Green Consumption Behaviour on Figure 4.22, it has showing a curve is flatter with eigenvalues of less than 1.0. There is one component had been taper off by factors manager as indicates in this Scree Plot.

Figure 4.22: Scree Plot for Green Consumption behaviour

Table 4.40: Component Matrix for Green Consumption behaviour

Component Matrixa

Component 1 GreenConsumpBehv3 .851 GreenConsumpBehv4 .843 GreenConsumpBehv6 .833 GreenConsumpBehv2 .809 GreenConsumpBehv1 .751 GreenConsumpBehv5 .555 Extraction Method: Principal Component Analysis. a. 1 components extracted.

127 As per table 4.40 as above, the component Matrix for Green Consumption Behaviour has shown the factor loading are in a descending order from 0.851 ,0.843, 0.833, 0.809, 0.751, and 0.555.

Table 4.41 as below are shows the summary of factor loading for respective measurement components for Green Consumption Behaviour, the measurement components

(GreenConsumpBehv3) - “I would like to repeatedly purchase solar -powered product for next purchase” has the highest relationship to the variable Green Consumption Behaviour with a factor loading of 0.851. While the measurement components GreenConsumpBehv5 is

“I would like to purchase solar -powered product even the price is higher” has lowest factor loading of 0.555 which indicated that it has the weakest relationship to the variable Green

Consumption Behaviour.

The analysis results reveal that none of any components to be extracted from this Green

Consumption Behaviour variable. All the six components had been rotated as a single component with significant loadings > 0.5 after the Varimax rotation.

Table 4.41: Factor Loading for Green Consumption behaviour Measurement Component Green Consumption Behaviours Factor An individual with environmental friendly behaviour for Component green consumption Loading

I would like to repeatedly purchase solar -powered product GreenConsumpBehv3 0.851 for next purchase. I would like to purchase the new product come with solar power renewable energy system compare to other normal GreenConsumpBehv4 0.843 product I would like to support the solar power renewable energy GreenConsumpBehv6 0.833 development in our country. I would like to recommend solar -powered product to my friends and family members if it is beneficial to GreenConsumpBehv2 0.809 communities. I would like to use solar -powered product if I can affordable. GreenConsumpBehv1 0.751

I would like to purchase solar -powered product even the price is higher. GreenConsumpBehv5 0.555

128 4.4 Reliability Analysis

Reliability Test is an analysis process which used to examine the consistency and reliability of the primary data measurement from the questionnaire which close relationship to one another. It is crucial to detect the possible problem on questionnaire in the beginning time.

Normally Cronbach’s alpha will be used to. measure the scale reliability on the questionnaires that use Likert scale. When survey questionnaire was considered to be good and reliable when Cronbach Alpha result is 0.70 or higher. If the Cronbach Alpha result is

0.60 or below, the measurement indicates internal inconsistency and unreliability.

Append below are reliability test results table for the respective variable.

Table 4.42: Reliability Statistics for Environmental Attitude

Reliability Statistics

Cronbach's N of Items Alpha

.772 7

The above Table 4.42 indicate a Cronbach Alpha score of Environmental Attitude is 0.772. which is higher than reliability level of 0.70, this Environmental Attitude variable is consider reliable for on-going analysis.

Table 4.43: Reliability Statistics for Social Responsibility Responsiveness Reliability Statistics

Cronbach's N of Items Alpha .893 7

The above Table 4.43 indicate a Cronbach Alpha score of Social Responsibility is 0.893. which is higher than reliability level of 0.70, this Social Responsibility variable is consider reliable for on-going analysis.

129 Table 4.44: Reliability Statistics for Perceived Self- Image

Reliability Statistics

Cronbach's N of Items Alpha .897 7

The above Table 4.44 indicate a Cronbach Alpha score of Perceived Self- Image

Environmental Attitude is 0.897. which is higher than reliability level of 0.70, this Perceived

Self- Image variable is consider reliable for on-going analysis.

Table 4.45: Reliability Statistics for Government Incentive

Reliability Statistics

Cronbach's N of Items Alpha .847 7

The above Table 4.45 indicate a Cronbach Alpha score of Government Incentive is 0.847. which is higher than reliability level of 0.70, this Government Incentive variable is consider reliable for on-going analysis.

Table 4.46: Reliability Statistics for Eco-literacy

Reliability Statistics

Cronbach's N of Items Alpha .876 6

The above Table 4.46 indicate a Cronbach Alpha score of Eco-Literacy Environmental

Attitude is 0.876. which is higher than reliability level of 0.70, this Eco-Literacy

Environmental Attitude variable is consider reliable for on-going analysis.

Table 4.47: Reliability Statistics for Green Consumption behaviour.

130

Reliability Statistics

Cronbach's N of Items Alpha .855 6

The above Table 4.47 indicate a Cronbach Alpha score of Green Consumption behaviour is

0.772. which is higher than reliability level of 0.70, this Green Consumption behaviour variable is consider reliable for on-going analysis.

Summary of the reliability analysis results are listed on the below Table 4.48:

Table 4.48: Summary of Reliability Analysis Results Variable Used No. of items Cronbach’s Alpha Environmental Attitude 7 0.772 Social Responsibility 7 0.893 Perceived Self- Image 7 0.897 Government Incentive 7 0.847 Eco-literacy 6 0.876 Green Consumption behaviour 6 0.855

The above summary reveal that Perceived Self- Image obtain highest reliability measure on

Cronbach Alpha score of 0.897, while Environmental Attitude obtain the lowest reliability measure on Cronbach Alpha score of 0.772. The results conclude that the survey questionnaires used is good, consistent and reliable manner to measure all the six variables.

131 4.5 Descriptive Analysis of Variables

Descriptive statistics are act as a data cleaning tool to tidy up all the data during the data analysis process. It furnishes an overall summary for processing huge quantities of numerical raw data (Boeree 2005).

The table 4.49 listed below are the result of descriptive analysis for all the dependent, mediating and independent variables. The descriptive analysis parameter involve are number of respondents (N), lowest figure (Minimum), highest figure (Maximum), average figure

(Mean), Standard Deviation and Skewness.

Table 4.49: Descriptive Statistics for Variables

Descriptive Statistics

N Minimum Maximum Mean Std. Deviation Skewness

Statistic Statistic Statistic Statistic Statistic Statistic Std. Error Environmental Attitude 104 2.40 5.00 4.3327 .55920 -.828 .237 Social Responsibility 104 2.17 5.00 4.2484 .61050 -1.016 .237 Perceived Self- Image 104 1.29 5.00 3.7569 .69471 -.498 .237 Government Incentive 104 2.20 5.00 4.3942 .60449 -1.213 .237 Eco Literacy 104 2.00 5.00 4.0385 .62198 -.672 .237 Green Consumption 104 2.33 5.00 3.8974 .61984 -.403 .237 Valid N (listwise) 104

The first column is shown N Statistic is 104. Thus mean 104 respondents have answer all the respective variable survey questionnaire. Append below are explanation of descriptive statistics for respective variable.

The Environmental Attitude is describing with mean 4.33 value; it elaborates that most of the respondents consider that Environmental Attitude is crucial factors for Green Consumption behaviour. The questionnaire responses rate is near to the mean with the standard deviation of 0.55 value. The overall data are approximate to normal data distribution with skewness of -

0.82 value.

132 The Social Responsibility is describing with mean 4.24 value; it elaborates that most of the respondents consider that Social Responsibility is crucial factors for Green Consumption behaviour. The questionnaire responses rate is near to the mean with the standard deviation of

0.61 value. The overall data are approximate to normal data distribution with skewness of -

0.1.02 value.

The Perceived Self- Image Environmental Attitude is describing with mean 3.75 value; it elaborates that most of the respondents consider that Perceived Self- Image Environmental

Attitude is crucial factors for Green Consumption behaviour. The questionnaire responses rate is near to the mean with the standard deviation of 0.69 value. The overall data are approximate to normal data distribution with skewness of -0.5 value.

The Government Incentive is describing with mean 4.39 value; it elaborates that most of the respondents consider that Government Incentive is most crucial factors for Green

Consumption behaviour. The questionnaire responses rate is near to the mean with the standard deviation of 0.60 value. The overall data are approximate to normal data distribution with skewness of -1.213 value.

The Eco Literacy is describing with mean 4.03 value; it elaborates that most of the respondents consider that Eco Literacy is crucial factors for Green Consumption behaviour.

The questionnaire responses rate is near to the mean with the standard deviation of 0.62 value. The overall data are approximate to normal data distribution with skewness of -0.67 value.

The Green Consumption behaviour is describing with mean 3.89 value; it elaborates that five factors having significant impact on the Green Consumption behaviour. The questionnaire responses rate is deviate or vary from the mean with the high standard deviation of 0.62 value. The overall data are approximate to normal data distribution with skewness of -0.403 value.

133 4.5.1 Normality Test

It is necessary to carry out normality check of the respective variables in the research study as most of statistical study model are accordingly to assumptions. If statistical model is lack of normality definitely caused data interpretations become unreliable Normality Test can be check through visualised graphically or tested numerically.

The Quantile-Quantile (Q-Q) Plot has been used to perform Normality Test on this research study. It furnishes a visualised graphically method to study distribution of variables. The Q-

Q Plot compares variable ordered values with quantiles of a normal distribution. The points on the plot will establish a linear style that passes through the origin and has a unit slope

(Park 2004).

The Q-Q Plots for respective variables are display as followings:

Figure 4.23: Q-Q Plot for Environmental Attitude

The Q-Q Plot for Environmental Attitude display that the above data points not diverge far from the diagonal straight line. This consistently of data point distribution show that this

134 variable has a normal distribution. Hence, it is not required to perform data transformation.

Figure 4.24: Q-Q Plot for Social Responsibility

The Q-Q Plot for Social Responsibility display that the above data points not diverge far from the diagonal straight line. This consistently of data point distribution show that this variable has a normal distribution even there is an outlier among the data points. Hence, it is not required to perform data transformation

Figure 4.25: Q-Q Plot for Perceived Self- Image

135

The Q-Q Plot for Perceived Self- Image display is almost similar as Q-Q Plot for Social

Responsibility. The plot show that the above data points not diverge far from the diagonal straight line. This consistently of data point distribution show that this variable has a normal distribution even there is an outlier among the data points. Hence, it is not required to perform data transformation

Figure 4.26: Q-Q Plot for Government Incentive

136 The Q-Q Plot for Government Incentive display that the above data points not diverge far from the diagonal straight line. This consistently of data point distribution show that this variable has a normal distribution. Hence, it is not required to perform data transformation

Figure 4.27: Q-Q Plot for Eco Literacy

The Q-Q Plot for Eco Literacy display that the above data points not diverge far from the diagonal straight line. This consistently of data point distribution show that this variable has a normal distribution. Hence, it is not required to perform data transformation

Figure 4.28: Q-Q Plot for Green Consumption behaviour

137

The Q-Q Plot for Green Consumption behaviour display that the above data points are closely located along the diagonal straight line. This consistently of data point distribution show that this variable has a normal distribution. Hence, it is not required to perform data transformation

For summary the above the Q-Q Plot, we may conclude with assumption that all the variables are normally distributed.

4.6 Correlation among all Variables

The correlation between the independent variables, mediating variable and dependent variable can be measure by Pearson Correlation Analysis.

Table 4.50 below show the correlation analysis summary results:

138 Table 4.50: Correlation of All Variables, Significance Level

Correlations

EnvAttd Social PervImage GovtIncentives EcoLiteracy GreenConsumption Respv Pearson 1 .719** .581** .574** .551** .616** Correlation EnvAttd Sig. (2-tailed) .000 .000 .000 .000 .000 N 104 104 104 104 104 104 Pearson .719** 1 .726** .653** .600** .685** Correlation SocialRespv Sig. (2-tailed) .000 .000 .000 .000 .000 N 104 104 104 104 104 104 Pearson .581** .726** 1 .509** .521** .648** Correlation PervImage Sig. (2-tailed) .000 .000 .000 .000 .000 N 104 104 104 104 104 104 Pearson .574** .653** .509** 1 .457** .455** Correlation GovtIncentives Sig. (2-tailed) .000 .000 .000 .000 .000 N 104 104 104 104 104 104 Pearson .551** .600** .521** .457** 1 .655** Correlation EcoLiteracy Sig. (2-tailed) .000 .000 .000 .000 .000 N 104 104 104 104 104 104 Pearson .616** .685** .648** .455** .655** 1 GreenConsumpti Correlation on Sig. (2-tailed) .000 .000 .000 .000 .000 N 104 104 104 104 104 104 **. Correlation is significant at the 0.01 level (2-tailed). EnvAttd = Environmental Attitude SocialRespv = Social responsibility PervImage= Perceived Self- Image GovtIncentives = Government Incentive EcoLiteracy = Eco-literacy GreenConsumption= Green Consumption behaviour

139 Refer to table 4.50, it is obvious that all the independent variables (Environmental Attitude,

Social responsibility, Perceived Self- Image, Government incentive) have correlation relationship with dependent variable (Green Consumption behaviour). As for mediating variable (Eco-literacy), it also has significant correlation with independent variables

(Environmental Attitude, Social responsibility, Perceived Self- Image, Government incentive) and dependent variable (Green Consumption behaviour).

Table 4.51: Summary of Relationships between Independent and Dependent Variables Hypothesis Relationship Coefficient Correlation Environmental Attitude positively influence 0.551 Positive H1a Eco-Literacy relationship Environmental Attitude positively influence 0.616 Positive H1b Green Consumption behaviour relationship Social responsibility positively influences 0.600 Positive H2a Eco-Literacy relationship Social responsibility positively influences 0.685 Positive H2b Green Consumption behaviour relationship Perceived Self- Image positively influence 0.521 Positive H3a Eco-Literacy relationship Perceived Self- Image positively influence 0.648 Positive H3b Green Consumption behaviour relationship Government incentive positively influence 0.457 Positive H4a Eco-Literacy relationship Government incentive positively influence 0.455 Positive H4b Green Consumption behaviour relationship Eco-Literacy positively influence on Green 0.655 Positive H5 Consumption behaviour relationship

Summary:

As per above Table 4.51 shown Pearson Correlation Matrix summary report, all of the independent variables were positive correlated relationship with the dependent variable and mediating variable was positive correlated relationship with all the independent variables as well as dependent variable as proposed by the hypotheses in Chapter 2.

140 4.7 Testing of Hypotheses

The chapter 2 proposed hypotheses will be test through with Regression Analysis in SPSS software. Regression Analysis is to examine the strength of the relationship between the independent variables, mediating variable and the dependent variable

4.7.1 Testing of Hypothesis

H1: Environmental Attitude positively influence Eco-Literacy and Green

Consumption behaviour.

H1 suggest that Environmental Attitude positively influence Eco-Literacy and Green

Consumption behaviour. Since H1 has positively influence to mediating & dependent variables, we can divide this H1 into two subsection of hypotheses name it as H1a and H1b, as append below:

H1a: Environmental Attitude positively influence Eco-Literacy.

H1b: Environmental Attitude positively influence Green Consumption behaviour

A simple linear regression was carry out to test on Environmental Attitude against Eco-

Literacy, and then against Green Consumption behaviour. This is to prove that is that the above hypotheses would be accepted or rejected after linear regression process.

Table 4.52 below show the regression result for Environmental Attitude versus Eco-Literacy

Table 4.52: Linear regression between Environmental Attitude and Eco-Literacy.

Model Summary

141 Model R R Square Adjusted R Std. Error of the Square Estimate 1 .551a .303 .297 .52162 a. Predictors: (Constant), mEnvAttd

ANOVAa

Model Sum of Squares df Mean Square F Sig. Regression 12.093 1 12.093 44.446 .000b 1 Residual 27.753 102 .272 Total 39.846 103 a. Dependent Variable: mEcoLiteracy b. Predictors: (Constant), mEnvAttd

Coefficientsa

Model Unstandardized Coefficients Standardized t Sig. Coefficients B Std. Error Beta (Constant) 1.384 .401 3.446 .001 1 mEnvAttd .613 .092 .551 6.667 .000 a. Dependent Variable: mEcoLiteracy

Based on the above regression model summary come with the coefficient of correlation (R) and determination (R2). Environmental Attitude versus Eco-Literacy show Strong relationship with the 0.551 value. Environmental Attitude provides a moderate adequacy

( 푅2 value is 0.303) toward Eco-Literacy and 30.3 % of variations of effectiveness in Eco-

Literacy.

Based on the above regression ANOVA table, the significance level at P -value is 0.000 which is less than 0.05, this hypotheses model proof to be statistically significant. Thus mean

Environmental Attitude is statistically significant influence Eco-Literacy.

Based on the above Coefficients table, Standardised Coefficients (Beta) is 0.551 mean that

142 Environmental Attitude positive relationship toward Eco-Literacy. Hence, the hypothesis

(H1a) is proven.

H1a: Environmental Attitude proven positively influence Eco-Literacy

Next, Environmental Attitude is subject to regressed toward Green Consumption behaviour, and Table 4.53 below show the regression result for Environmental Attitude versus Green

Consumption behaviour.

Table 4.53: Linear regression between Environmental Attitude and Green Consumption behavior

Model Summary

Model R R Square Adjusted R Std. Error of the Square Estimate

1 .616a .379 .373 .49080 a. Predictors: (Constant), mEnvAttd

ANOVAa

Model Sum of Squares df Mean Square F Sig. Regression 15.002 1 15.002 62.281 .000b 1 Residual 24.570 102 .241 Total 39.573 103 a. Dependent Variable: mGreenConsumption b. Predictors: (Constant), mEnvAttd

ANOVAa

Model Sum of Squares df Mean Square F Sig. Regression 15.002 1 15.002 62.281 .000b

1 Residual 24.570 102 .241 Total 39.573 103 a. Dependent Variable: mGreenConsumption b. Predictors: (Constant), mEnvAttd

143 Based on the above regression model summary come with the coefficient of correlation (R) and determination (R2). Environmental Attitude versus Green Consumption behaviour show

Strong relationship with the 0.616 value. Environmental Attitude provides a moderate adequacy ( 푅2 value is 0.379) toward Green Consumption behaviour and 37.9 % of variations of effectiveness in Green Consumption Behaviour.

Based on the above regression ANOVA table, the significance level at P -value is 0.000 which is less than 0.05, this hypotheses model proof to be statistically significant. Thus mean

Environmental Attitude is statistically significant influence Green Consumption Behaviour.

Based on the above Coefficients table, Standardised Coefficients (Beta) is 0.616 mean that

Environmental Attitude positive relationship toward Green Consumption Behaviour. Hence, the hypothesis (H1b) is proven.

H1b: Environmental Attitude proven positively influence Green Consumption

behaviour

144 4.7.2 Testing of Hypothesis 2 H2: Social responsibility positively influences Eco-Literacy and Green

Consumption behaviour.

H2 suggest that Social responsibility positively influence Eco-Literacy and Green

Consumption behaviour. Since H2 has positively influence to mediating & dependent variables, we can divide this H2 into two subsection of hypotheses name it as H2a and H2b, as append below:

H2a: Social responsibility positively influences Eco-Literacy.

H2b: Social responsibility positively influences Green Consumption behaviour

A simple linear regression was carry out to test on Social responsibility against Eco-Literacy, and then against Green Consumption behaviour. This is to prove that is that the above hypotheses would be accepted or rejected after linear regression process.

Table 4.54 below show the regression result for Social responsibility versus Eco-Literacy

Table 4.54: Linear regression between Social responsibility and Eco-Literacy.

Model Summary

Model R R Square Adjusted R Std. Error of the Square Estimate 1 .600a .360 .353 .50020 a. Predictors: (Constant), mSocialRespv

ANOVAa

Model Sum of Squares df Mean Square F Sig.

Regression 14.326 1 14.326 57.257 .000b 1 Residual 25.520 102 .250 Total 39.846 103

145 a. Dependent Variable: mEcoLiteracy b. Predictors: (Constant), mSocialRespv

Coefficientsa

Model Unstandardized Coefficients Standardized t Sig. Coefficients B Std. Error Beta (Constant) 1.443 .346 4.165 .000 1 mSocialRespv .611 .081 .600 7.567 .000 a. Dependent Variable: mEcoLiteracy

Based on the above regression model summary come with the coefficient of correlation (R) and determination (R2). Social responsibility versus Eco-Literacy show Strong relationship with the 0.600 value. Social responsibility provides a moderate adequacy ( 푅2 value is 0.360) toward Eco-Literacy and 36 % of variations of effectiveness in Eco-Literacy.

Based on the above regression ANOVA table, the significance level at P -value is 0.000 which is less than 0.05, this hypotheses model proof to be statistically significant. Thus mean

Social responsibility is statistically significant influence Eco-Literacy.

Based on the above Coefficients table, Standardised Coefficients (Beta) is 0.600 mean that

Social responsibility positive relationship toward Eco-Literacy. Hence, the hypothesis (H2a) is proven

H2a: Social responsibility proven positively influence Eco-Literacy

Next, Social responsibility is subject to regressed toward Green Consumption behaviour, and

Table 4.55 below show the regression result for Social responsibility versus Green

Consumption behaviour.

146

Table 4.55: Linear regression between Social responsibility and Green Consumption behavior

Model Summary

Model R R Square Adjusted R Std. Error of the Square Estimate 1 .685a .469 .464 .45369 a. Predictors: (Constant), mSocialRespv

ANOVAa

Model Sum of Squares df Mean Square F Sig. Regression 18.577 1 18.577 90.254 .000b

1 Residual 20.995 102 .206 Total 39.573 103 a. Dependent Variable: mGreenConsumption b. Predictors: (Constant), mSocialRespv

Coefficientsa

Model Unstandardized Coefficients Standardized t Sig. Coefficients B Std. Error Beta (Constant) .942 .314 2.998 .003 1 mSocialRespv .696 .073 .685 9.500 .000 a. Dependent Variable: mGreenConsumption

Based on the above regression model summary come with the coefficient of correlation (R) and determination (R2). Social responsibility versus Green Consumption behaviour show

Strong relationship with the 0.685 value. Social responsibility provides a moderate adequacy ( 푅2 value is 0.469) toward Green Consumption behaviour and 46.9 % of variations of effectiveness in Green Consumption behaviour.

147

Based on the above regression ANOVA table, the significance level at P -value is 0.000 which is less than 0.05, this hypotheses model proof to be statistically significant. Thus mean

Social responsibility is statistically significant influence Green Consumption behaviour.

Based on the above Coefficients table, Standardised Coefficients (Beta) is 0.685 mean that

Social responsibility positive relationship toward Green Consumption behaviour. Hence, the hypothesis (H2b) is proven.

H2b: Social responsibility proven positively influence Green Consumption

behaviour

4.7.3 Testing of Hypothesis 3

H3: Perceived Self- positively influence Eco-Literacy and Green Consumption

behaviour.

H3 suggest that Perceived Self- Image positively influence Eco-Literacy and Green

Consumption behaviour. Since H3 has positively influence to mediating & dependent variables, we can divide this H3 into two subsection of hypotheses name it as H3a and H3b, as append below:

H3a: Perceived Self- Image positively influence Eco-Literacy.

H3b: Perceived Self- Image positively influence Green Consumption behaviour

A simple linear regression was carry out to test on Perceived Self- Image against Eco-

148 Literacy, and then against Green Consumption behaviour. This is to prove that is that the above hypotheses would be accepted or rejected after linear regression process.

Table 4.56 below show the regression result for Perceived Self- Image versus Eco-Literacy

Table 4.56: Linear regression between Perceived Self- Image and Eco-Literacy.

Model Summary

Model R R Square Adjusted R Std. Error of the Square Estimate 1 .521a .271 .264 .53349 a. Predictors: (Constant), mPervImage

ANOVAa

Model Sum of Squares df Mean Square F Sig. Regression 10.815 1 10.815 38.000 .000b 1 Residual 29.031 102 .285 Total 39.846 103 a. Dependent Variable: mEcoLiteracy b. Predictors: (Constant), mPervImage

Coefficientsa

Model Unstandardized Coefficients Standardized t Sig. Coefficients B Std. Error Beta (Constant) 2.286 .289 7.909 .000 1 mPervImage .466 .076 .521 6.164 .000 a. Dependent Variable: mEcoLiteracy

Based on the above regression model summary come with the coefficient of correlation (R) and determination (R2). Perceived Self- Image versus Eco-Literacy show Strong

149 relationship with the 0.521 value. Perceived Self- Image provides a moderate adequacy ( 푅2 value is 0.271) toward Eco-Literacy and 27.1 % of variations of effectiveness in Eco-

Literacy.

Based on the above regression ANOVA table, the significance level at P -value is 0.000 which is less than 0.05, this hypotheses model proof to be statistically significant. Thus mean

Perceived Self- Image is statistically significant influence Eco-Literacy.

Based on the above Coefficients table, Standardised Coefficients (Beta) is 0.521 mean that

Perceived Self- Image positive relationship toward Eco-Literacy. Hence, the hypothesis

(H3a) is proven.

H3a: Perceived Self- Image proven positively influence Eco-Literacy

Next, Perceived Self- Image is subject to regressed toward Green Consumption behaviour.

Table 4.57 below show the regression result for Perceived Self- Image versus Green

Consumption behaviour.

Table 4.57: Linear regression between Perceived Self- Image and Green Consumption behavior

Model Summary

Model R R Square Adjusted R Std. Error of the Square Estimate 1 .648a .420 .414 .47441 a. Predictors: (Constant), mPervImage

ANOVAa

150 Model Sum of Squares df Mean Square F Sig. Regression 16.616 1 16.616 73.829 .000b 1 Residual 22.956 102 .225

Total 39.573 103 a. Dependent Variable: mGreenConsumption b. Predictors: (Constant), mPervImage

Coefficientsa

Model Unstandardized Coefficients Standardized t Sig. Coefficients B Std. Error Beta (Constant) 1.725 .257 6.713 .000 1 mPervImage .578 .067 .648 8.592 .000 a. Dependent Variable: mGreenConsumption

Based on the above regression model summary come with the coefficient of correlation (R) and determination (R2). Perceived Self- Image versus Green Consumption behaviour show

Strong relationship with the 0.648 value. Perceived Self- Image provides a moderate adequacy ( 푅2 value is 0.420) toward Green Consumption behaviour and 42.0 % of variations of effectiveness in Green Consumption behaviour.

Based on the above regression ANOVA table, the significance level at P -value is 0.000 which is less than 0.05, this hypotheses model proof to be statistically significant. Thus mean

Perceived Self- Image is statistically significant influence Green Consumption behaviour.

Based on the above Coefficients table, Standardised Coefficients (Beta) is 0.648 mean that

Perceived Self- Image positive relationship toward Green Consumption behaviour. Hence, the hypothesis (H3b) is proven.

H3b: Perceived Self- Image proven positively influence Green Consumption

behaviour

151 4.7.4 Testing of Hypothesis 4

H4: Government incentive positively influence Eco-Literacy and Green

Consumption behaviour.

H4 suggest that Government incentive positively influence Eco-Literacy and Green

Consumption behaviour. Since H4 has positively influence to mediating & dependent variables, we can divide this H4 into two subsection of hypotheses name it as H4a and H4b, as append below:

H4a: Government incentive positively influence Eco-Literacy.

H4b: Government incentive positively influence Green Consumption behaviour

A simple linear regression was carry out to test on Government incentive against Eco-

Literacy, and then against Green Consumption behaviour. This is to prove that is that the above hypotheses would be accepted or rejected after linear regression process.

Table 4.58 below show the regression result for Government incentive versus Eco-Literacy

Table 4.58: Linear regression between Government incentive and Eco-Literacy.

Model Summary

Model R R Square Adjusted R Std. Error of the Square Estimate

1 .457a .209 .201 .55600 a. Predictors: (Constant), mGovtIncentives

ANOVAa

152 Model Sum of Squares df Mean Square F Sig. Regression 8.314 1 8.314 26.896 .000b 1 Residual 31.532 102 .309

Total 39.846 103 a. Dependent Variable: mEcoLiteracy b. Predictors: (Constant), mGovtIncentives

Coefficientsa

Model Unstandardized Coefficients Standardized t Sig. Coefficients B Std. Error Beta (Constant) 1.973 .402 4.909 .000 1 mGovtIncentives .470 .091 .457 5.186 .000 a. Dependent Variable: mEcoLiteracy

Based on the above regression model summary come with the coefficient of correlation (R) and determination (R2). Government incentive versus Eco-Literacy show Strong relationship with the 0.457 value. Government incentive provides a moderate adequacy ( 푅2 value is 0.209) toward Eco-Literacy and 20.9 % of variations of effectiveness in Eco-

Literacy.

Based on the above regression ANOVA table, the significance level at P -value is 0.000 which is less than 0.05, this hypotheses model proof to be statistically significant. Thus mean

Government incentive is statistically significant influence Eco-Literacy.

Based on the above Coefficients table, Standardised Coefficients (Beta) is 0.457 mean that

Government incentive positive relationship toward Eco-Literacy. Hence, the hypothesis

(H4a) is proven

153 H4a: Government incentive proven positively influence Eco-Literacy

Next, Government incentive is subject to regressed toward Green Consumption behaviour,

Table 4.59 below show the regression result for Government incentive versus Green

Consumption behaviour.

Table 4.59: Linear regression between Government incentive and Green Consumption behavior

Model Summary

Model R R Square Adjusted R Std. Error of the Square Estimate 1 .455a .207 .200 .55456 a. Predictors: (Constant), mGovtIncentives

ANOVAa

Model Sum of Squares df Mean Square F Sig. Regression 8.204 1 8.204 26.676 .000b 1 Residual 31.369 102 .308 Total 39.573 103 a. Dependent Variable: mGreenConsumption b. Predictors: (Constant), mGovtIncentives

Coefficientsa

Model Unstandardized Coefficients Standardized t Sig. Coefficients B Std. Error Beta (Constant) 1.846 .401 4.604 .000 1 mGovtIncentives .467 .090 .455 5.165 .000 a. Dependent Variable: mGreenConsumption

Based on the above regression model summary come with the coefficient of correlation (R) and determination (R2). Government incentive versus Green Consumption behaviour show

Strong relationship with the 0.455 value. Government incentive provides a moderate

154 adequacy ( 푅2 value is 0.207) toward Green Consumption behaviour and 20.7 % of variations of effectiveness in Green Consumption behaviour.

Based on the above regression ANOVA table, the significance level at P -value is 0.000 which is less than 0.05, this hypotheses model proof to be statistically significant. Thus mean

Government incentive is statistically significant influence Green Consumption behaviour.

Based on the above Coefficients table, Standardised Coefficients (Beta) is 0.455 mean that

Government incentive positive relationship toward Green Consumption behaviour. Hence, the hypothesis (H4b) is proven.

H4b: Government incentive proven positively influence Green Consumption

behaviour

4.7.5 Testing of Hypothesis 5

H5: Eco-Literacy positively influence Green Consumption behaviour.

H5 suggest that Eco-Literacy positively influence Green Consumption behaviour.

A simple linear regression was carry out to test on Eco-Literacy against Green Consumption behaviour. This is to prove that is that the above hypotheses would be accepted or rejected after linear regression process.

Table 4.60 below show the regression result for Eco-Literacy versus Green Consumption behaviour

Table 4.60: Linear regression between Eco-Literacy and Green Consumption behavior

155

Model Summary

Model R R Square Adjusted R Std. Error of the Square Estimate 1 .655a .429 .424 .47050 a. Predictors: (Constant), mEcoLiteracy

ANOVAa

Model Sum of Squares df Mean Square F Sig. Regression 16.993 1 16.993 76.764 .000b 1 Residual 22.580 102 .221 Total 39.573 103 a. Dependent Variable: mGreenConsumption b. Predictors: (Constant), mEcoLiteracy

Coefficientsa

Model Unstandardized Coefficients Standardized t Sig. Coefficients B Std. Error Beta (Constant) 1.260 .305 4.138 .000 1 mEcoLiteracy .653 .075 .655 8.762 .000 a. Dependent Variable: mGreenConsumption

Based on the above regression model summary come with the coefficient of correlation (R) and determination (R2). Eco-Literacy versus Green Consumption behaviour show Strong relationship with the 0.655 value. Eco-Literacy provides a moderate adequacy ( 푅2 value is

0.429) toward Green Consumption behaviour and 42.9 % of variations of effectiveness in

Green Consumption behaviour.

Based on the above regression ANOVA table, the significance level at P -value is 0.000

156 which is less than 0.05, this hypotheses model proof to be statistically significant. Thus mean

Eco-Literacy is statistically significant influence Green Consumption behaviour.

Based on the above Coefficients table, Standardised Coefficients (Beta) is 0.655 mean that

Eco-Literacy positive relationship toward Green Consumption behaviour. Hence, the hypothesis (H5) is proven

H5: Eco-Literacy proven positively influence Green Consumption behaviour

4.7.6 Testing of Hypothesis 6 (H6) for Demographic profile

H6 suggest that there is the impact of different demographic profiles towards green consumption behaviour for Solar Power Product as follows:

H 6a: There is significant difference between Solar power product ownership towards Green Consumption Behavior.

H6b: There is significant difference between type of solar power product towards Green Consumption Behavior

A one-way ANOVA (Analysis of Variance) was carry out to test on the above hypothesis.

This is to prove that is that the above hypotheses would be accepted or rejected after

ANOVA process.

Table 4.61 & 4.62 below show the ANOVA result for the above hypothesis.

Table 4.61: One-Way ANOVA for Solar power product ownership towards Green Consumption Behaviour

157

ANOVA mGreenConsumption Sum of Squares df Mean Square F Sig. Between Groups 3.397 1 3.397 9.579 .003 Within Groups 36.175 102 .355 Total 39.573 103

Table 4.61 above shows ANOVA result proven that Solar power product ownership have significant (0.003) differences towards Green Consumption Behaviour.

Table 4.62: One-Way ANOVA for type of solar power product towards Green Consumption Behaviour

ANOVA mGreenConsumption Sum of Squares df Mean Square F Sig. Between Groups 5.799 5 1.160 3.365 .008 Within Groups 33.773 98 .345 Total 39.573 103

Table 4.61 above shows ANOVA result proven that Type of Solar power product ownership have significant (0.008) differences towards Green Consumption Behaviour.

4.7.7 Summary of Hypotheses Testing for Independent Variables

Append below are the Table 4.63 which display the overall summary for the 5 hypotheses test between independent, mediating and dependent variables by using simple regression analysis.

Table 4.63: Summary of Hypotheses Test for Independent & Mediating Variables

158 푹ퟐ Sig (P- Hypothesis Description Outcome Value) Environmental Attitude proven positively 1a 0.303 0.000 Accepted influence Eco-Literacy. Environmental Attitude proven positively 1b 0.379 0.000 Accepted influence Green Consumption behaviour Social responsibility proven positively 2a 0.360 0.000 Accepted influence Eco-Literacy. Social responsibility proven positively 2b 0.469 0.000 Accepted influence Green Consumption behaviour Perceived Self- Image proven positively 3a 0.271 0.000 Accepted influence Eco-Literacy. Perceived Self- Image proven positively 3b 0.420 0.000 Accepted influence Green Consumption behaviour Government incentive proven positively 4a 0.209 0.000 Accepted influence Eco-Literacy. Government incentive proven positively 4b 0.207 0.000 Accepted influence Green Consumption behaviour Eco-Literacy proven positively influence 5 0.429 0.000 Accepted Green Consumption behaviour

Based on above summary, all the five hypotheses were accepted with the assessment was made on 푅2 values and the P-values (<0.50).

4.7.8 Summary of Hypotheses Testing for Demographic Profiles

Append below are the Table 4.64 which display the overall summary for the 2 hypotheses test between demographic Profiles and dependent variables by using one-way ANOVA test.

Table 4.64: Summary of Hypotheses Test for Demographic Variables.

Hypothesis H6a H6b There is significant difference There is significant difference between Solar power product between type of solar power product Description ownership towards Green towards Green Consumption Consumption Behaviour. Behaviour. Sig (P - 0.008 0.003 value) Outcome Accepted Accepted Based on above summary, all the two hypotheses were accepted with the assessment was made on significant level P-values (<0.50).

159 4.8 Hierarchical Multiple Regression Test for Hypothesis 7 (H7)

Two-steps of hierarchical multiple regression analysis was applied in this research study to determine the presence of mediating (Intervening) variable which influence the relationship between independent variables and dependent variable.

H7 suggest that there is Eco-Literacy has a mediating impact of different dimension environmental enablers (Environmental Attitude, Social responsibility, Perceived Self-

Image, Government incentive) towards green consumption behavior for Solar Power Product as below:

H7a Eco-Literacy has mediating (intervening) impact on the influence of Environmental

Attitude towards Green Consumption behaviour

H7b: Eco-Literacy has mediating (intervening) impact on the influence of Social

responsibility towards Green Consumption behaviour

H7c: Eco -Literacy has mediating (intervening) impact on the influence of Perceived Self-

Image towards Green Consumption behaviour

H7d: Eco-Literacy has mediating (intervening) impact on the influence of Government

incentive towards Green Consumption behaviour

Table 4.65 below show the two-steps of hierarchical multiple regression analysis result:

Table 4.65 Model Summary of Hierarchical Multiple Regression

Model Summaryc

160 a. Predictors: (Constant), mGovtIncentives, mPervImage, mEnvAttd, mSocialRespv b. Predictors: (Constant), mGovtIncentives, mPervImage, mEnvAttd, mSocialRespv, mEcoLiteracy c. Dependent Variable: mGreenConsumption

ANOVAa

Model Sum of Squares df Mean Square F Sig. Regression 21.428 4 5.357 29.229 .000b 1 Residual 18.144 99 .183 Total 39.573 103 Regression 23.942 5 4.788 30.023 .000c 2 Residual 15.630 98 .159 Total 39.573 103 a. Dependent Variable: mGreenConsumption b. Predictors: (Constant), mGovtIncentives, mPervImage, mEnvAttd, mSocialRespv c. Predictors: (Constant), mGovtIncentives, mPervImage, mEnvAttd, mSocialRespv, mEcoLiteracy

Coefficientsa

Model Unstandardized Coefficients Standardized t Sig. Coefficients B Std. Error Beta (Constant) .565 .361 1.564 .121 mEnvAttd .254 .111 .229 2.279 .025 1 mPervImage .262 .089 .293 2.937 .004 mSocialRespv .343 .126 .338 2.715 .008 mGovtIncentives -.047 .094 -.046 -.499 .619 (Constant) .262 .345 .759 .450 mEnvAttd .176 .106 .159 1.664 .099 mPervImage .218 .084 .244 2.602 .011 2 mSocialRespv .246 .120 .242 2.042 .044 mGovtIncentives -.069 .088 -.067 -.782 .436 mEcoLiteracy .325 .082 .326 3.970 .000 a. Dependent Variable: mGreenConsumption

161 From the above table 4.65, two-steps of hierarchical multiple regression analysis result, 2 formula models had been work out in this research study. The 1st formula model is independent variable has significant influence toward dependent variable without mediating variables. The 2nd formula model is independent variable has significant influence toward dependent variable with mediating variables. The above result can observe the significance level (p-value) changes from significant to non- significant or vice versa when add in mediating variable to the above model.

As for Environmental Attitude, when add in mediating variable (Eco-literacy), the above 4.65 table show that Environmental Attitude significance level (p-value) was changed from 0.025

(significant) to 0.099 (non- significant). This has proven that the impact of mediating variable was reduced the influence of Environmental Attitude Toward Green Consumption Behavior.

This is considering as Full Mediation impact.

As for Social responsibility, when add in mediating variable (Eco-literacy), the above 4.65 table show that Social responsibility significance level (p-value) was changed from 0.008

(significant) to 0.044 (significant). This has proven that the impact of mediating variable was slightly reduced the influence of Social responsibility toward Green Consumption Behavior.

This is considering as Partial Mediation impact.

As for Perceived Self- Image, when add in mediating variable (Eco-literacy), the above 4.65 table show that Perceived Self- Image significance level (p-value) was changed from 0.004

(significant) to 0.011 (significant). This has proven that the impact of mediating variable was slightly reduced the influence of Perceived Self- Image Toward Green Consumption

Behavior. This is considering as Partial Mediation impact.

As for Government incentive, when add in mediating variable (Eco-literacy), the above 4.65 table show that Government incentive significance level (p-value) was changed from 0.619

(non-significant) to 0.436 (non-significant). This has proven that the impact of mediating

162 variable was further reduced the influence of Government incentive toward Green

Consumption Behavior. This is considering as no mediation impact.

Table 4.66 has display the summary of changes of the significance level with add in mediating variable (Eco-literacy).

Table 4.66 Summary of changes of the significance level with add in mediating variable

(Eco-literacy).

Without Mediating With Mediating Variable Predictors Variable (Eco- Impact (Eco-Literacy) Literacy) Beta p-value Beta p-value

Environmental 0.025 0.099 (Not Full Attitude 0.254 (Significant) 0.176 Significant) Mediation Social 0.008 Partial responsibility 0.343 (Significant) 0.246 0.044(Significant) Mediation Perceived Self- 0.004 Partial Image 0.262 (Significant) 0.218 0.011 (Significant) Mediation Government 0.619(Not - 0.436 (Not No incentive -0.047 Significant) 0.069 Significant) Mediation

Eco-Literacy NA NA 0.325 0.000(Significant) NA Dependent Variable: Green Consumption Behavior

As such, the final result of Hypothesis 7 as below:

H7a Eco-Literacy has a full mediating impact on the influence of

Environmental Attitude towards Green Consumption behaviour

H7b: Eco-Literacy has a partial mediating impact on the influence of Social

responsibility towards Green Consumption behaviour

H7c: Eco -Literacy has a partial mediating impact on the influence of Perceived

Self- Image towards Green Consumption behaviour

H7d: Eco-Literacy has no mediating impact on the influence of Government

incentive towards Green Consumption behaviour

163 4.9 Predictive Model for Research

Table 4.67 Model Summary for Hierarchical Multiple Regression with Intervening Variable.

Model Summaryc

Model R R Adjusted R Std. Error Change Statistics Square Square of the R Square F df1 df2 Sig. F Estimate Change Change Change 1 .736a .541 .523 .42811 .541 29.229 4 99 .000 2 .778b .605 .585 .39936 .064 15.764 1 98 .000 a. Predictors: (Constant), mGovtIncentives, mPervImage, mEnvAttd, mSocialRespv b. Predictors: (Constant), mGovtIncentives, mPervImage, mEnvAttd, mSocialRespv, mEcoLiteracy c. Dependent Variable: mGreenConsumption

Coefficientsa

Model Unstandardized Coefficients Standardized t Sig. Coefficients B Std. Error Beta

(Constant) .565 .361 1.564 .121 mEnvAttd .254 .111 .229 2.279 .025 1 mPervImage .262 .089 .293 2.937 .004 mSocialRespv .343 .126 .338 2.715 .008 mGovtIncentives -.047 .094 -.046 -.499 .619 (Constant) .262 .345 .759 .450 mEnvAttd .176 .106 .159 1.664 .099 mPervImage .218 .084 .244 2.602 .011 2 mSocialRespv .246 .120 .242 2.042 .044 mGovtIncentives -.069 .088 -.067 -.782 .436

mEcoLiteracy .325 .082 .326 3.970 .000 a. Dependent Variable: mGreenConsumption

In order to obtain the predictive model for this research, the SPSS hierarchical multiple regression test shall be carry out to take out those non-significance independent variables toward dependent variable from the table.

164 As per highlighted in the above table, model 1 Environmental Attitude (p=0.025), Social responsibility (p= 0.008), Perceived Self- Image p=0.004) are consider as significance independents variable which is lower than 0.05 significance level (p<0.005) and remove non- significance independent variables Government incentive (p=0.619) which is more than 0.05 significance level. It proven that the above three independent variables have significant influence to the dependent variable. Hence, these independent variables such as

Environmental Attitude, Social responsibility, Perceived Self- Image have respective Beta value (0.254, 0.343, 0.2620).

The predictive model formula is derived from the below formula (Green and Salkind 2014):

Y = β1X1 + β 2X2 + β 3X3 + …..+ a

Where:

Y- dependent variable

X1 , X2, … etc - independent variables.

“a” - constant value of the intercept point at Y when all independent variables are zero

β 1, β 2 … etc - partial regression coefficient for respective independent variables (rate of changes in dependent variable)

The predictive model of this research study can be determine from table 4.67 Hierarchical

Multiple Regression result and formulate as below:

Green Consumption Behavior (GCB) = 0.254(EA)+0.343(SR)+ 0.262 PI+0.565

Where EA = Environmental Attitude, SR = Social responsibility. And PI = Perceived Self-

Image

165 Based on the above predictive model of Green Consumption Behavior, while Social responsibility & Perceived Self- Image are maintaining unchanged, there is an increment of

0.254 units in Green Consumption Behavior if rise of one (1) unit in Environmental Attitude.

While Environmental Attitude & Perceived Self- Image are maintaining unchanged, there is an increment of 0.343 units in Green Consumption Behavior if rise of one (1) unit in Social responsibility.

While Environmental Attitude & Social responsibility are maintaining unchanged, there is an increment of 0.262 units in Green Consumption Behavior if rise of one (1) unit in Perceived

Self- Image.

While Environmental Attitude, Social responsibility & Perceived Self- Image are zero, there is an increment of 0.565 units in Green Consumption Behavior for constant value.

As per above predictive model of Green Consumption Behavior, Social Responsibility surmise to be intense influence on Malaysia Green Consumption Behavior compare to

Environmental Attitude or Perceived Self- Image.

4.10 Summary of Analysis of Results

All data collection from survey questionnaires were went through descriptive analysis and inferential analysis, it will definitely filter & summarize these data to be easy to understand and present to the public. In this research study, the demographic profiles of respondents related with the dependent variables (Green Consumption behaviors) is analyze through descriptive analysis. The factor analysis, reliability test and normality Quantile-Quantile (Q-

Q) Plot had been carry out to test for validity, reliability and normality of survey data. Then these data were convert into another form of statistic data, subsequently proceed further with

Pearson Correlation and regression analysis.

166 The Pearson correlation analysis is adopting to identify the correlation between the independent variables, mediating variable and dependent variable. A simple linear regression was carry out to test on chapter 2 proposed hypotheses would be accepted or rejected.

Besides that, the one-way ANOVA was carry out to test the impact of different demographic profiles towards green consumption behavior for Solar Power Product. Two-steps of hierarchical multiple regression analysis was applied to determine the mediating impact of potential mediating (Intervening) variable which influence the relationship between independent variables and dependent variable Finally, the SPSS hierarchical multiple regression test shall be carry out to determine significance independent variables toward dependent variable and take out those non-significance independent variables in order to formulate the predictive model for the research study .

The following chapter is elaborate the details explanation on the data analysis result and finding obtain from this chapter. The conclusion and summary shall be finalizing at the end of next chapter.

167

CHAPTER 5

Findings, Recommendations and Conclusions

5.1 Introduction

This chapter is the last chapter of this research study. It was covered all the findings and outcomes from the data analysis and hypotheses testing which carry out in chapter 4. These included factor analysis, ANOVA, descriptive analysis, correlation analysis and regression analysis. The result from the data analysis will be used back to identify the problem statements, research objectives and research questions as stated in Chapter 1.

After that, the implication and limitation of this research will be discuss and good recommendations to be share among future research. Lastly, the final conclusion of overall research study to be summarized in order to furnish the proper ending of this research study.

5.2 Discussion on Findings and Results of Hypotheses Testing

All the variables had been gone through the quantitative approach using the SPSS software on the previous chapter 4.

5.2.1 Hypothesis 1a (H1a):

Environmental Attitude positively influence Eco-Literacy

The above hypothesis findings result in this research show that Environmental Attitude positively influence Eco-Literacy. For proof the hypothesis findings result is significant, the significant p value must be less than 0.05 (p<0.05).

In this case, the hypothesis findings result significant p value is 0.00 (p<0.05). The relationship is significant. This implies that Environmental Attitude is one of key determinant for Eco-Literacy. Then the result has implication and inference to Malaysia consumer on

168 Solar Power Product. Therefore, H1a is supported.

This result is supported by the findings of Haron et al. (2005) which study on Malaysian’s environmental knowledge was correlates positively with environmental attitude and behaviour as well as participation. Chan (1999) and Vining and Ebreo (1990) in which they did find a positive correlation of environmental knowledge with environmental attitude, behaviour.

(Tan Shwu Shyan 2010)

5.2.2 Hypothesis 1b (H1b):

Environmental Attitude positively influence Green Consumption behaviour.

The above hypothesis findings result in this research show that Environmental Attitude positively influence Green Consumption behaviour. For proof the hypothesis findings result is significant, the significant p value must be less than 0.05 (p<0.05).

In this case, the hypothesis findings result significant p value is 0.00 (p<0.05). The relationship is significant. This proof that Environmental Attitude is one of key determinant for Green Consumption behaviour. Then the result has implication and inference to Malaysia consumer on Solar Power Product. Therefore, H1b is supported.

This result is supported by the findings of Sinnappan and Azmawani (2011) where proof that environmental attitude was the best contributor of Green consumption behaviour.

Besides that, Mostafa (2007) research study on the Egyptian consumers on environmental attitude act as main predictor to aware Green consumption behaviour. It was also in line with

Chan (2013) research study which highlight there was positive correlation between environmental attitude and green consumption on few countries cultures such as Asia, United

States and European nations.

169 5.2.3 Hypothesis 2a (H2a):

Social responsibility positively influences Eco-Literacy

The above hypothesis findings result in this research show that Social responsibility positively influence Eco-Literacy. For proof the hypothesis findings result is significant, the significant p value must be less than 0.05 (p<0.05).

In this case, the hypothesis findings result significant p value is 0.00 (p<0.05). The relationship is significant. This proof that Social responsibility is one of key determinant for

Eco-Literacy. Then the result has implication and inference to Malaysia consumer on Solar

Power Product. Therefore, H2a is supported

This result was not supported by the findings from the former researcher.

5.2.4 Hypothesis 2b (H2b):

Social responsibility positively influences Green Consumption behaviour.

The above hypothesis findings result in this research show that Social responsibility positively influence Green Consumption behaviour. For proof the hypothesis findings result is significant, the significant p value must be less than 0.05 (p<0.05).

In this case, the hypothesis findings result significant p value is 0.00 (p<0.05). The relationship is significant. This proof that Social responsibility is one of key determinant for

Green Consumption behaviour. Then the result has implication and inference to Malaysia consumer on Solar Power Product. Therefore, H2b is supported

We had conduct the Pearson Correlation Analysis and regression analysis between Social responsibility and Green Consumption behaviour which indicate in the highest correlation and R Square values compare to other independent variables. This proof that Social responsibility is one of the strongest influence on Green Consumption behaviour among

170 Malaysian consumer on Solar Power Product.

This finding is in line with Yahya, Hashim, Mohamad, and Zuraidah (2013) research study, the society concern about environmental issues increased quickly due to globalization, most of the companies should look for green initiative and focus on individual social responsibility to preserve for public interest.

This is further enhanced by findings of research of Benabout and Tirolet (2010) stated that some of the people are likely to spend in socially responsible funds, consuming green products and so on. (Ang et.al 2014)

5.2.5 Hypothesis 3a (H3a):

Perceived Self- Image positively influence Eco-Literacy.

The above hypothesis findings result in this research show that Perceived Self- Image positively influence Eco-Literacy. For proof the hypothesis findings result is significant, the significant p value must be less than 0.05 (p<0.05).

In this case, the hypothesis findings result significant p value is 0.00 (p<0.05). The relationship is significant. This proof that Perceived Self- Image is one of key determinant for

Eco-Literacy. Then the result has implication and inference to Malaysia consumer on Solar

Power Product. Therefore, H3a is supported

This result was not supported by the findings from the former researcher.

5.2.6 Hypothesis 3b (H3b):

Perceived Self- Image positively influence Green Consumption behaviour.

The above hypothesis findings result in this research show that Perceived Self- Image positively influence Green Consumption behaviour. For proof the hypothesis findings result is significant, the significant p value must be less than 0.05 (p<0.05).

171 In this case, the hypothesis findings result significant p value is 0.00 (p<0.05). The relationship is significant. This proof that Perceived Self- Image is one of key determinant for

Green Consumption behaviour. Then the result has implication and inference to Malaysia consumer on Solar Power Product. Therefore, H3b is supported

The findings of this study agrees with Baker and Ozaki (2008) research study found pro environmental self -images was influencing green behaviours. This also in line with Lee

(2008)’s finding on concern for self-image as the third predictor of her study on green young consumers’ purchasing behaviour in Hong Kong.

(Tan Shwu Shyan 2010)

5.2.7 Hypothesis 4a (H4a):

Government incentive positively influence Eco-Literacy.

The above hypothesis findings result in this research show that Government incentive positively influence Eco-Literacy. For proof the hypothesis findings result is significant, the significant p value must be less than 0.05 (p<0.05).

In this case, the hypothesis findings result significant p value is 0.00 (p<0.05). The relationship is significant. This proof that Government incentive is one of key determinant for

Eco-Literacy. Then the result has implication and inference to Malaysia consumer on Solar

Power Product. Therefore, H4a is supported

This result was not supported by the findings from the former researcher.

172 5.2.8 Hypothesis 4b (H4b):

Government incentive positively influence Green Consumption behaviour.

The above hypothesis findings result in this research show that Government incentive positively influence Green Consumption behaviour. For proof the hypothesis findings result is significant, the significant p value must be less than 0.05 (p<0.05).

In this case, the hypothesis findings result significant p value is 0.00 (p<0.05). The relationship is significant. This proof that Government incentive is one of key determinant for

Green Consumption behaviour. Then the result has implication and inference to Malaysia consumer on Solar Power Product. Therefore, H4b is supported

This finding is similar to Tsen, Phang, Hasan and Buncha (2006) show that government act as crucial model towards consumer’s consumption behaviour on green products.

Further, Tan S.S. (2010) indicated that Malaysian government had set up strategies & policies for environmental sustainable consumption and development. These included encouraging car-pooling, giving incentives to green product producers and end user, and encourage consumers to behave in green manner or purchase green products that in order to preserve environmental sustainability.

(Tan Sook Mun 2014)

173 5.2.9 Hypothesis 5 (H5):

Eco-Literacy positively influence Green Consumption behaviour.

The above hypothesis findings result in this research show that Eco-Literacy positively influence Green Consumption behaviour. For proof the hypothesis findings result is significant, the significant p value must be less than 0.05 (p<0.05).

In this case, the hypothesis findings result significant p value is 0.00 (p<0.05). The relationship is significant. This proof that Eco-Literacy is one of key determinant for Green

Consumption behaviour. Then the result has implication and inference to Malaysia consumer on Solar Power Product. Therefore, H5 is supported.

In line with this finding, based on Getzner and Grabner-Krauter (2004) research study, ecoliteracy has a positively influence toward environmentally friendly behaviour. It also had been empirically supported in other research studies (Vining and Ebreo, 1990; Chan, 1999).

Chan and Lau (2000) research study shown Chinese people come with eco-literacy had a stronger intention to consume green product (Chan and Lau, 2000)

(Tan Shwu Shyan 2010)

5.2.10 Hypothesis 6a (H6a):

There is significant demographic difference between Solar power product

ownership towards Green Consumption Behaviour.

The one-way Analysis of Variance (ANOVA) test was conduct for this hypothesis. It proposed that out of the sixteen demographic profiles, only 2 demographic profiles have a significant influence towards the Green Consumption Behaviour due to it p-values for the relationship are all less than 0.05 (p<0.05). The above hypothesis findings result significant p value is 0.003 (p<0.05). The relationship is significant demographic difference between

174 Solar power product ownership towards Green Consumption Behaviour. Then the result has implication and inference to Malaysia consumer on Solar Power Product. Therefore, H6a is supported.

Actually there were no researcher have been concluded on the demographic factors affecting

Solar power product ownership towards Green consumption behaviour. However,

Soonthonsmai noted that consumer’s green purchase intention has positive correlation with different age and income group but education was found does not influence the intention to purchase green products.

5.2.11 Hypothesis 6b (H6b):

There is significant demographic difference between type of solar power product towards Green Consumption Behaviour.

The above hypothesis findings result significant p value is 0.008 (p<0.05). The relationship is significant demographic difference between type of Solar power product towards Green

Consumption Behaviour. Then the result has implication and inference to Malaysia consumer on Solar Power Product. Therefore, H6b is supported.

Actually there were no researcher have been concluded on the demographic factors affecting type of Solar power product towards Green consumption behaviour. However, Chen and Chai

(2010) who found there is no significant differences among males and females in environmental attitude or green purchasing behaviour. They have also indicated that demographic variables have less explanatory power compare to psychographic variables.

175 5.2.12 Hypothesis 7a (H7a):

Eco-Literacy has mediating impact on the influence of

Environmental Attitude towards Green Consumption behaviour

The above hypothesis findings result in this research show that Eco-Literacy has mediating impact on the influence of Environmental Attitude towards Green Consumption behaviour

In this case, when add in mediating variable (Eco-literacy), Environmental Attitude significance level (p-value) was changed from 0.025 (significant) to 0.099 (non- significant).

Then the result has implication and inference to Malaysia consumer on Solar Power Product.

Therefore, H7a is support with full mediation impact.

So far, no reported findings had been recorded on this hypothesis

5.2.13 Hypothesis 7b (H7b):

Eco-Literacy has mediating impact on the influence of

Social responsibility towards Green Consumption behaviour

The above hypothesis findings result in this research show that Eco-Literacy has mediating impact on the influence of Social responsibility towards Green Consumption behaviour

In this case, when add in mediating variable (Eco-literacy), Social responsibility significance level (p-value) was changed from 0.008 (significant) to 0.044 (significant). Then the result has implication and inference to Malaysia consumer on Solar Power Product. Therefore, H7b is supported with partial mediation impact.

So far, no reported findings had been recorded on this hypothesis

176 5.2.14 Hypothesis 7c (H7c):

Eco-Literacy has mediating impact on the influence of

Perceived Self- Image towards Green Consumption behaviour

The above hypothesis findings result in this research show that Eco-Literacy has mediating impact on the influence of Perceived Self- Image towards Green Consumption behaviour

In this case, when add in mediating variable (Eco-literacy), Perceived Self- Image significance level (p-value) was changed from 0.004 (significant) to 0.011 (significant). Then the result has implication and inference to Malaysia consumer on Solar Power Product.

Therefore, H7c is supported with partial mediation impact.

So far, no reported findings had been recorded on this hypothesis

5.2.15 Hypothesis 7d (H7d):

Eco-Literacy mediating impact on the influence of

Government incentive towards Green Consumption behaviour

The above hypothesis findings result in this research show that Eco-Literacy has mediating impact on the influence of Government incentive towards Green Consumption behaviour

In this case, when add in mediating variable (Eco-literacy), Government incentive significance level (p-value) was changed from 0.619 (non-significant) to 0.436 (non- significant). Then the result has no implication and inference to Malaysia consumer on Solar

Power Product. Therefore, H7d is not supported with no mediation impact.

So far, no reported findings had been recorded on this hypothesis

177

The result of above hypotheses will be listed down as provided in the below Table 5.1.

Table 5.1: Summary of Findings from Hypotheses Testing

Result Hypothesis Description Supported (P) Environmental Attitude positively influences 1a 0.000 Yes Eco-Literacy. Environmental Attitude positively influences 1b 0.000 Yes Green Consumption behaviour Social responsibility positively influences 2a 0.000 Yes Eco-Literacy. Social responsibility positively influences 2b 0.000 Yes Green Consumption behaviour Perceived Self- Image positively influences 3a 0.000 Yes Eco-Literacy. Perceived Self- Image positively influences 3b 0.000 Yes Green Consumption behaviour Government incentive positively influences 4a 0.000 Yes Eco-Literacy. Government incentive positively influences 4b 0.000 Yes Green Consumption behaviour Eco-Literacy positively influences Green 5 0.000 Yes Consumption behaviour There is significant demographic difference 6a between Solar power product ownership 0.003 Yes towards Green Consumption Behaviour. There is significant demographic difference 6b between type of solar power product towards 0.008 Yes Green Consumption Behaviour. Eco-Literacy has mediate (intervene) impact Yes (full 0.025 to 7a of Environmental Attitude towards Green mediation 0.099 Consumption behaviour impact) Eco-Literacy has mediate (intervene) impact Yes (partial 0.008 to 7b of Social responsibility towards Green mediation 0,044 Consumption behaviour impact) Eco-Literacy has mediate (intervene) impact Yes (partial 0.004 to 7c of Perceived Self- Image towards Green mediation 0.011 Consumption behaviour impact) Eco-Literacy has mediate (intervene) impact No (No 0.619 to 7d of Government incentive towards Green mediation 0.436 Consumption behaviour impact)

178 5.3 Reaffirmation to Research Questions.

There are four research questions which listed on chapter 1 were answer through the findings from previous chapter 4. Append below are research questions which are recapitulated:

1. What are the dominant factors of environmental enablers that affects successful of

eco-literacy and consumer consumption behavior toward the solar power industry?

2. What are the different demographic profiles have an effect on the consumer

consumption behavior in solar power industry?

3. Does Eco-Literacy has mediate or intervene the effect of environmental enablers

towards green consumption behavior on solar power industry?

4. What is the predictive model that significantly predicts green consumer consumption

behavior on solar power industry?

The 1st question answer indicated that these 4 nos of environmental enablers factors such as

Environmental Attitude, Social responsibility, Perceived Self- Image, Government incentive were significant affects successful of eco-literacy at 5% of significant level. At the same times, the answer also shows that these 4 nos of environmental enablers factors such as

Environmental Attitude, Social responsibility, Perceived Self- Image, Government incentive were significant affects successful of green consumption behaviour at 5% of significant level.

The 2nd question answer indicated that out of 16 demographic variables, we had identified only 2 demographic variables (Solar power product ownership & type of Solar power product) were significant demographic profiles have an effect on the consumer consumption behaviour in solar power industry.

179 The 3rd question answer indicated that eco-literacy has fully mediate (intervene) the impact of Environmental Attitude towards green consumption behaviour on solar power industry.

Eco-literacy has partial mediate (intervene) the impact of Social responsibility and Perceived

Self- Image towards green consumption behaviour on solar power industry. Eco-literacy has no mediate (intervene) the impact of Government incentive towards green consumption behaviour on solar power industry

The last question no 4 answer indicated that this predictive model involved three dimensions of independents variables, such as Environmental Attitude, Social responsibility, Perceived

Self- Image with respective Beta value (0.254, 0.343, 0.2620), which were significantly predicts green consumer consumption behaviour on solar power industry.

Hence, this model expected that the raise of one (1) unit in Environmental Attitude will lead to 0.254 units increase of Malaysia Green Consumption Behaviour when Social responsibility & Perceived Self- Image remain constant. While the raise of one (1) unit of

Social Responsibility e will lead to 0.343-unit increase of Malaysia Green Consumption

Behaviour as well as raise of one (1) unit of Perceived Self- Image will lead to 0.262- unit increase of Malaysia Green Consumption Behaviour

180 The research questions and answers are summarize as per below tabled 5.2:

Table 5.2: Summary of Research Questions and Answer. Number Research Question Answer What are the dominant factors The strongest dominant factor is Social of environmental enablers that responsibility. 2nd ranking is Eco-Literacy. affects successful of eco-literacy follow by Perceived Self- Image, 1 and consumer consumption Environmental Attitude and Government behaviour toward the solar incentive power industry? What are the different Only 2 demographic variables (Solar power demographic profiles have an product ownership & type of Solar power 2 effect on the consumer product) have a significant relationship with consumption behaviour in solar consumer consumption behaviours power industry? Does Eco-Literacy has mediate Eco-literacy has mediating or intervening or intervene the effect of impact on the influence of one of the environmental enablers towards environmental enablers variable 3 green consumption behaviour on (Environmental Attitude) towards green solar power industry? consumption behaviour on solar power industry. What is the predictive model Green Consumption Behaviour (GCB) = that significantly predicts green 0.254(EA)+0.343(SR)+ 0.262 PI+0.565 4 consumer consumption Where EA = Environmental Attitude, SR = behaviour on solar power Social responsibility. And PI = Perceived industry? Self- Image.

6.4 Reaffirmation to Research Objectives.

There are four research objectives on this study as follows:

1. To determine the dominant factors of environmental enablers that effect successful on

eco-literacy and green consumption behaviour toward solar power industries.

2. To identify difference demographic profile effect on the green consumption behaviour

on solar power renewable energy System.

3. To determine the Eco-Literacy has mediating (intervening) impact between

environmental enablers factors and green consumption behaviour toward solar power

renewable energy in Malaysia.

4. To build a predictive model that could predict the relationship of Solar Power

renewable energy environmental enablers to the eco-literacy and consumer

181

The first objective as stated above had been fulfilled as there are dominant factors of environmental enablers that effect successful on eco-literacy and green consumption behaviour toward solar power industries. This can be found from finding and result from the previous chapter analysis, it shown that all the fours dimension of environmental enablers

(i.e. Environmental Attitude, Social responsibility, Perceived Self- Image, Government incentive) were significant affects successful of eco-literacy at 5% of significant level. As well as these 4 no’s of environmental enablers factors such as Environmental Attitude, Social responsibility, Perceived Self- Image, Government incentive were significant affects successful of green consumption behaviour at 5% of significant level.

The second objective had been attained as there are significant demographic profile effect on the green consumption behaviour on solar power renewable energy. Among these 16 demographic variables, we had identified only 2 demographic variables (Solar power product ownership & type of Solar power product) have a significant relationship with green consumption behaviour in solar power industry.

The third objective as stated above had been achieved through two-step hierarchical regression analysis from the previous chapter analysis. It concluded that the eco-literacy has fully mediate (intervene) the impact of Environmental Attitude towards green consumption behaviour on solar power industry. Eco-literacy has partial mediate (intervene) the impact of

Social responsibility and Perceived Self- Image towards green consumption behaviour on solar power industry. Eco-literacy has no mediate (intervene) the impact of Government incentive towards green consumption behaviour on solar power industry

182 The fourth objective as stated above had been accomplished through SPSS hierarchical multiple regression tools from the previous chapter analysis. It concluded that this model involved three dimensions of independents variables such as Environmental Attitude, Social responsibility, Perceived Self- Image with respective Beta value (0.254, 0.343, 0.2620) which were significantly predicts green consumer consumption behaviour on solar power industry.

As for overall summary, all the above objectives are concluded that manageable to achieved successfully via SPSS Statistical tools in this research study.

6.5 Implications of the Study.

Due to demands of industrialization and urbanization, it which cause the overconsumption of our natural resources and subsequently create the environmental deterioration issue. The consumers are realizing this issue and start to change his/ her mind-set to adopt the green consumption behaviour to consume and purchase solar powered product in order to protect the environment. Hence, this research study tends to is to identify any dimension of

Environmental enablers (Environmental Attitude, Social responsibility, Perceived Self-

Image, Government incentive), which will impact on f Eco-literacy toward Green

Consumption behaviour for facilitate expansion & development on Solar Power Renewable

Energy System in the Malaysia Market. This study will also ascertain the Eco-Literacy variable whether mediating or intervening influence intensity between environmental enablers and green consumption behaviour toward solar power renewable energy in

Malaysia. The outcomes of this research study also furnish valuable implications for those people’s study on field on green consumption. and beneficial to those solar investors which would interest to invest in the Malaysia. There are two division of implications such as theoretical and practical implications to be elaborate on the following section

183 5.5.1 Theoretical Implications

In principle, this research paper model was applying few researches literature to drawn out the theoretical framework which included environmental enablers (i.e. Environmental

Attitude, Social responsibility, Perceived Self- Image, Government incentive) & eco-literacy as well as Green Consumption behaviour. The TRA and TPB theories were adopt over here.

Social responsibility, Eco-literacy, Perceived Self- Image and Environmental Attitude were proof to be strong predictors towards Green Consumption behaviour. At the same times, this paper is also intending to close up the lack of section in such research to further improve environmental awareness among Malaysia consumer. Besides that, this research will also furnish an insight which involved on government initiative. It is also planned to make sure that the local government will play their crucial roles for implement the green policy and initiative as well as environment law enforcement to ensure successful of green consumption behaviours.

The results and findings of this study will be useful for Solar provider to reconsider their capabilities towards improving their business and to serve as background for future research studies. Thus, future researchers can have a depth investigate on how the factors of personal’s ecological beliefs can significantly explained on consumers’ green purchasing behaviour.

6.5.2 Practical Implications.

This research study confirms that in order for green consumption behaviours toward Solar power renewable energy to be successful, the main dominant factor Social responsibility shall be tackle first. Social responsibility provides the meaning that consumers shall have its natural tendency on their social responsibility either cultivate via education system since childhood time or environmental awareness programmes to remind the communities about his / her duty in environmental protection and preservation. Government should be first

184 mover to kick off environmental awareness on education programmes in our school syllabus since young age time for our new generation and must reserve some budgets for environmental awareness programmes for communities. Malaysian Government should encourage and emphasize in production of environmental-friendly products by exempting duties and taxes of imported green products to attract young consumers as well as general public to buy environment-friendly products

Furthermore, based on this research study, Eco-literacy are 2nd significantly influencing the green consumption behaviour toward Solar Power Renewable Energy. As a result, government shall put in effort to initiate these environmental slogan and advertisements via various of mass media. This environmental knowledge shall be organized in the more proper manner to consumer in order to promote the green consumption behaviour.

We do believe that if the consumer is equipping with well environmental knowledge and good awareness, it will definitely aid to enhance their environment concern and subsequently cause them to be more motivate and willing to buy solar power product.

On top of that, this research paper finding also proof that self-image has a significant influence to green consumption behaviours toward Solar power renewable energy.

The Malaysia consumer might have the sense of “feeling glad & proud “about themselves when consume green product and introduce to his family and friend. Thus, it is also crucial for Solar provider to integrate proper image on its solar product in term of package design, eco-labelling and excellent quality shall be in -line with environmental-friendly or eco concepts.

In additional, Environmental attitude had been proof that has a significant influence to green consumption behaviours toward Solar power renewable energy in this research paper finding.

Consumer Environmental attitude can help to improve the green consumption behaviour through our daily recycle activities on their waste materials. Hence, the government, Non-

185 Government organization and marketer shall work together to cultivate this knowledge to consumer for change their attitude and mind set towards green consumption behaviours.

For example, implementation of solar power renewable energy is NOT a waste of time, money and resources, aesthetic look of the solar -powered product is not that important compared to environmental values and solar power renewable energy is meaningful to our society.

5.6 Limitations of Research.

Normally, this kind of research study will be facing many of limitations such as time horizon, restriction based on generalisation, time and resource limitations as well as social desirability and cultural influence, but these limitations can be mitigating with various of remedial method like pilot test, factory analysis for validity, reliability test, and etc. Detail of respective limitations to be discuss as below sub -topic:

5.6.1 Time Horizon.

The above study was adopting the cross sectional design for data collection. The limitation of such design only can determine the impact of predictor variables for a special dimension of variable at single point of time and does not furnish detail elaboration impact on future period. Hence, this research is restricting to only depict the consumer behaviours on green consumption at a single point of time.

5.6.2 Restriction based on Generalisation.

The 2nd limitation is there is selection of samples boundary in this study. Sample only involve on the consumer from Mechanical & Electrical (M & E) construction industries business & associate partner (industry restriction) and other friends as well as university friend situated

186 several state in West Malaysia (geographical restriction). Therefore, the outcomes of this may not be a right indication of the overall Malaysian Population in East & West Malaysia. The primary sampling data insufficient to make generalisation across all the Malaysia consumer who consume, active and prefer in green consumption products.

5.6.3 Time and Resource Limitations.

For better and effective way for data collection, time and resources are play the crucial roles in the research study. Longer time frame and sufficient human resources are required. For this research study, due to time and human resources constraints, only one person to manage the data collection process without any assistance, subsequently email out 40 no’s pilot test on line survey questionnaires to my friends and only able to collect 30nos of respondent reply on questionnaires in 4 days’ times. After that, I had email out 150 nos on line survey questionnaires to all respondent. Finally, only able to collect 104 no’s of respondent reply on questionnaires within 10 days’ times. It is very stressful to perform the data collection in short time frame and carry out alone for one person.

5.6.4 Scope of Factors/ Dimensions.

There is another limitation is this research study only investigates 5 key dimensions of influence green consumption behaviour on Solar power industries such as Social responsibility, Eco-literacy, Perceived Self- Image, Environmental Attitude and Government incentive. There are still have other dimensions of influence which does not take into consideration e.g. social influence, environmental concern, perceived seriousness of environmental problems, perceived environmental responsibility and perceived effectiveness of environmental behaviour, these other dimension will become our limitations that may impact the outcomes of this study.

187 5.6.5 Social Desirability and Cultural Influence.

Finally, respondent’s social desirability and cultural influence may become another limitation in this research. This is due to suspicion of respondent’s rightness toward survey questionnaire may impact by social or cultural situation especially Malaysia has a distinct multi-racial society with difference social background or cultural compare to other countries in Asian. So that, the respondent’s answer is accordingly to personal social and culture perception instead actual perception.

5.7 Recommendations for Future Research.

Even though there are 5 limitations on previous section, but, we still can propose with few recommendations to further improve overall research limitation for the benefit on future research study.

1st recommendation, the cross sectional design shall be replacing with longitudinal designs.

The main reason is cross sectional design only study on particular human behaviour towards green consumption at single point of time and insufficient time for study on changes of human behaviour towards green consumption. whereas longitudinal designs able to grab the changes of consumers’ perception and trace on the trends of consumers’ consumption behaviour pattern at more than one point of time. Moreover, the finding on the research my not valid for future research due to dynamic changes on consumers’ consumption behaviour.

So, researcher proposed that longitudinal study is more suitable for future research.

2nd recommendation, researchers shall recommend to widen the research coverage through whole business industries of Malaysia to eliminate the industry restriction. Researchers shall distribute survey questionnaire to East Malaysia (Sabah & Sarawak) to eliminate the geographical restriction This is importation to obtain the final accurate data on consumers on

188 green consumption behaviour toward Solar power industries in Malaysia

3rd recommendation, researchers shall recommend to have longer times for data collection period to gather more data for analysis. As compare to short time, the research was solely depending on online survey questionaries’ distribution approach instead of other way manual hardcopy distribution approach. If longer timeframe was allowable; then ample timeframe for the researchers to collect data from a larger number of respondents with difference distribution approach. the outcome of this research would be more accurate and reliable. At the same times, researchers shall recommend to have sharing the human resources for data collection process. This is to cut down researcher workload and time of researcher. It can be arranging with a group of best friend and hire third parties to collect the data at various of state in East and West Malaysia.

4th recommendation, researchers shall recommend and look into other potential dimensions or factors such as social influence, environmental concern, perceived seriousness of environmental problems, perceived environmental responsibility and perceived effectiveness of environmental behaviour. It is important for researchers to capture a proper perception and point of view on the key factor of influence on green consumption behaviour on Solar power industries.

5th recommendation, researchers shall recommend to improve social desirability and cultural influence toward green consumption behaviours on Solar power industries for future research as Malaysia is a multi-racial society with different social activities and cultural dimension, it would be good to have cultural dimensions into researching consumer attitude towards protection to the environment.

189 5.8 Conclusion.

This research study aim is to investigate between environmental enablers factors and ecoliteracy affect successful of green consumption behaviour toward solar power industries in the Malaysia market. This study will also determine whether eco-literacy mediating intensity between environmental enablers factors and green consumption behaviour toward solar power industries. Based on the analysis results, the study reveal that all environmental enablers factors have positive significant influence to eco-literacy and green consumption behaviour toward to Solar power industries via hypothesis test conduct on chapter 4.

In conclusion, the above project research findings are able to find an answer for the research question and subsequently meet the research objective as well.

At the same times, the above research study also proven that eco-literacy has full mediation effect between Environmental Attitude and green consumption behaviour. Thus, eco-literacy able to mediate the consumer environmental attitude in order to improve the green consumption behaviour toward to Solar power industries. The government, Non- Government organization and marketer shall work together to cultivate this environment knowledge (ecoliteracy) to consumer for change their attitude and mind set towards green consumption behaviours toward on Solar power industries.

Apart from that, the above research study also drawn out the predictive model formula. From the predictive model, we can measure the impact level of the Environmental Attitude, Social responsibility, Perceived Self- Image towards the green consumption behaviour on Solar power industries. The marketers will be based on this predictive model to exploit effective marketing strategies and aware the consumers’ behaviour for solar powered product offered

190 in Malaysia markets.

The above research study outcomes also support the Theory of Reasoned Action (TRA) which introduced by Fischbein & Ajzen (1975). The TRA suggests that consumers’ attitudes and subjective norms towards the environment issue which have a strong impact on their behaviour and action towards green consumption on Solar power industries. In this case, consumer attitudes (environmental attitude) and subjective norms (Social responsibility,

Perceived Self- Image, eco-literacy) leads to green consumption behaviours on Solar power industries. Therefore, the above research findings support the above theory that our green consumption behaviour on Solar power industries is influenced by our consumers’ attitudes and subjective norms except for government incentive.

Last but not least, this study also covers theoretical & practical Implications as well as limitations. But, the above recommendations had been overcome these limitations to further improve the green consumption behaviour toward to Solar power industries. This research definitely helps to create better understanding of the consumer’s behaviour toward Solar power green consumption as a whole in the future.

191 Table 5.3 below shown the chapter 5 Conclusion Summary

Table 5.3: Conclusion Summary Summary of Findings in Relation to Research Objectives and Research Questions Research Objectives Research Questions Findings 1 To determine the 1. What are the dominant Result indicated fours dominant factors of factors of dimensions of environmental environmental enablers environmental enablers enablers (i.e. Environmental that effect successful on that affects successful Attitude, Social responsibility, eco-literacy and green of eco-literacy and Perceived Self- Image, consumption behaviour consumer consumption Government incentive) were toward solar power behaviour toward the significant affects successful of industries. solar power industry? eco-literacy a well as green consumption behaviour at 5% of significant level.

2. To identify difference 2. What are the different Result indicated 2 no’s demographic profile demographic profiles demographic variables (Solar effect on the green have an effect on the power product ownership & consumption behaviour consumer consumption type of Solar power product) on solar power renewable behaviour in solar were significant demographic energy System. power industry? profiles have an effect on the consumer consumption behaviour in solar power industry. 3. To determine the Eco- 3. Does Eco-Literacy has Result indicated eco-literacy Literacy has mediating mediate or intervene has fully mediate (intervene) (intervening) impact the effect of the impact of Environmental between environmental environmental enablers Attitude enablers factors and towards green And partial mediate (intervene) green consumption consumption behaviour the impact of Social behaviour toward solar on solar power responsibility and Perceived power renewable energy industry? Self- Image towards green in Malaysia. consumption behaviour. Eco- literacy has no mediate (intervene) the impact of Government incentive towards green consumption behaviour

4. To build a predictive 4. What is the predictive Result indicated three model that could predict model that significantly dimensions of independents the relationship of Solar predicts green variables such as Power renewable energy consumer consumption Environmental Attitude, Social environmental enablers behaviour on solar responsibility, Perceived Self- to the eco-literacy and power industry? Image with respective Beta consumer value (0.254, 0.343, 0.2620) which were significantly predicts green consumer consumption behaviour on solar power industry.

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200 Appendix A Questionnaire Cover Letter

Dear Respondents, Re: Research A Study On Mediating Impact of Eco-Literacy On Green Consumption behaviour: An Empirical Assessment of Malaysia Consumer Toward Solar Power Renewable Energy

My name is Lim Thye Kong; I am a student from Wawasan Open University (WOU) in Penang which doing my final year CEMBA project research paper. As part of CEMBA research requirement, I am required to collect some valuable data or information from you Hence, your participation and kind co-operation for filling up this survey form as per attachment are much appreciated. My project research paper title is A Study On Mediating Impact of Eco-Literacy On Green Consumption behaviour: An Empirical Assessment of Malaysia Consumer Toward Solar Power Renewable Energy. The survey questionnaire purpose is to examine the dimension of Environmental enablers (Environmental Attitude, Social responsibility, Perceived Self- Image, Government incentive) with mediating variable of Eco-literacy toward Green Consumption behaviour that will aid to facilitate expansion & development on Solar Power Renewable Energy System in the Malaysia Market. The survey is intended to understand the consumer rationale to adopt Solar Power Renewable Energy in our country. Solar Power Renewable Energy is one type renewable energy whereby energy that is gather from natural resources such as sunlight which are naturally replenished without any limit of phase out time scale. Solar power is the conversion of sunlight directly into electricity, either directly using photovoltaics (PV), or indirectly using concentrated solar power (CSP). Photovoltaics applies solar photovoltaics effect that convert sun light energy directly into electricity by using solar cell inside solar panel. Concentrated solar power systems (CSP) use mirrors to concentrate (focus) the sun's light energy and convert it into heat to create steam to drive a turbine that generates electrical power. There are many types of solar -powered product in the Malaysia market as below:  Solar Panel Water Heater  Solar PV System  Solar Panel Garden Light  Solar -powered Calculator  Solar -powered Watch The variable factors under study covers the Environmental Attitude, Social responsibility, Perceived Self- Image, Government incentive which impact on Eco-literacy and Green Consumption behaviour. However, the information provided will be kept strictly as private & confidential and solely for academic purpose. Lastly, thank you for your time and participation for assisting in filling up this questionnaire.

Yours truly, Lim Thye Kong

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Appendix B

Survey Questionnaire – Quantitative Study

PART A: General Information

Please tick (√ ) the appropriate box (□) provided .

1. Gender Male Female

2. Age (years old) 18 to 29 30 to 39 40 to 49 ≥ 49

3. Place of Stay (State) Penang only KL only Ipoh If other, please specify ______

4. Household Income Below RM 3,000 RM 3,000 to RM 6,999 (RM) per month RM 7,000 to RM9,999 Over RM 10,000

5. Race Malay Chinese Indian Local Native Other Race: ______

6. Education Level Up to SPM STPM or Diploma Degree Post Graduate

7. Residential Area Urban Sub-urban or rural only

8. Type of residential you stay Bungalow only Semi-Detached Terrace House Flat / Apartment Condominium If other, please specify ______

9. How many Family member living with you? Up to 2 3 to 6

202 7 to 9 More than 9

10. Do you own residential or commercial property? Rented only Personally owned

11. Do you know about Solar Power Renewable Energy? Yes No

12. Do you own the Solar-power products? Yes No

13. Which type of Solar-power products are your own? Solar Panel Water Heater Solar PV System Solar Panel Garden Light Solar -powered Calculator Solar -powered Watch If other, please specify ______

14. What is motivation factor influence you going for Energy Saving Solar-powered product? Support on green energy Government Incentive Social respected Passive income If other, please specify ______

15. What is your target affordable investment for Below RM 10,000 Solar -powered Product? RM 10,000 to RM 50,000 RM 51,000 to RM 99,000 Above 100,000 If other, please specify ______

16. What is Payback investment are you prefer? Below 2 Years 2 to 4 Years 4 to 6 Years Above 6 Years If other, please specify ______

203 PART B: DETERMINANTS AFFECTING SOLAR POWER RENEWABLE ENERGY ADOPTION IN GREEN CONSUMPTION BEHAVIOUR

The survey is intended to understand the consumer rationale to adopt solar power renewable energy in our country.

Please select the option which best represents your response to each statement, using the scale provided below:

1 = Strongly Disagree; 2 = Disagree; 3 = Neither Agree nor Disagree; 4 = Agree; 5 = Strongly Agree

A. ENVINRONMENTAL ATTITUDE: The awareness 1 2 3 4 5 of conserving energy and keeping environmental clean 1. I believe it’s necessary to promote solar power renewable energy in our country 2. I believe that solar power renewable energy is a form of green energy. 3. I believe that environmental awareness can help in the adoption of solar power renewable energy. 4. I believe that implementation of solar power renewable energy is NOT a waste of time, money and resources 5. I believe that aesthetic look of the solar -powered product is not that important compared to environmental values. 6. I believe solar power renewable energy is meaningful to our society 7. I believe that the Environmental Attitude is related to Eco-literacy which promote green consumption behaviours on solar power renewable energy system.

B. SOCIAL RESPONSIBILITIES: The level of 1 2 3 4 5 individual and corporate social responsibility towards environment 1. As an individual, if affordable, I am willing to invest in solar -powered product. 2. If I am a business owner, I am willing to consider the solar -powered product as it’s part of Corporate Social Responsibilities (CSR) 3. Besides me, the government must be socially responsible for implementation of solar power renewable energy system. 4. Besides our Government, Environmental organizations (NGOs) should play their roles to implement solar power renewable energy system.

204 5. We have been taught to protect the environment since early school days, thus I support solar power renewable energy system. 6. For the sake of our future generation, promoting the use solar power renewable energy system is most encouraging. 7. I believe that the social responsibilities are related to Eco-literacy which promote green consumption behaviours on solar power renewable energy system.

C. PERCEIVED SELF – IMAGE: An individual 1 2 3 4 5 perceived him/her as person who act environmental friendly.

1. I am willing to be the first to invest in solar -powered product if I am affordable 2. I am willing to enhance my family image to use solar - powered product in my house. 3. I will be gain respect from society through adopting solar power renewable energy. 4. I support green initiatives by adopting solar power renewable energy. 5. I am willing to making the wise decision to invest in solar -powered product 6. I feel great if I can convince my friends to purchase solar -powered product. 7. I understand that the Perceived self-Image is related to Eco-literacy which promote green consumption behaviours on solar power renewable energy

D. Government Incentive: An incentive given by 1 2 3 4 5 government to promote for green consumption 1. The government incentive has driven the development of solar power renewable energy industries. 2. The government should continue providing the incentive on green research on solar power renewable energy system. 3. The government should enforce environmental rules and regulations on solar power renewable energy industries. 4. The government should strategize its policies to promote solar power renewable energy system. 5. The government should improve feed-in tariff for promote solar power renewable energy system.

205 6. The government should organize more campaigns to promote solar power renewable energy system 7. I understand that the Government Incentive is related to Eco-literacy which promote green consumption behaviours on solar power renewable energy system

E. Eco-Literacy: An individual shall aware 1 2 3 4 5 environmental knowledge for environmental issue The environmental knowledge that is contained in various forms (e.g. books, articles, education and etc.)

1. I understand that the details the of the solar -powered product before purchase it. 2. I understand that solar -powered product does not pose any hazards to the environment. 3. I understand that our education system shall in co- operated the environmental knowledge on solar power renewable energy system 4. I understand that the benefits of solar -powered product before I can share to other. 5. I understand that our new generation shall be cultivating with environmental knowledge on solar power renewable energy system 6. I understand that the Eco-literacy is important to promote green consumption behaviors on solar power renewable energy system

F. Green Consumption Behaviours: An individual with 1 2 3 4 5 environmental friendly behaviour for green consumption 1. I would like to use solar -powered product if I can affordable. 2. I would like to recommend solar -powered product to my friends and family members if it is beneficial to communities. 3. I would like to repeatedly purchase solar -powered product for next purchase. 4. I would like to purchase the new product come with solar power renewable energy system compare to other normal product. 5. I would like to purchase solar -powered product even the price is higher. 6. I would like to support the solar power renewable energy development in our country.

206