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CHAPTER ONE

INTRODUCTION

1.1. Introduction

Have you ever stopped to think about where your electricity comes from? Nowadays, it is no longer only a question of ‘how much electricity do I want to use? However, this type of question which is ‘what kind of electricity do I want to use?’ is also count. In the case of Malaysia, most of the electricity is generated by burning coal and gas in power stations. These types of activities eventually will give such a negative impact towards the environment such as the critical global warming and climate change. Hence, a green ‘power’ or electricity that is environmentally friendly is needed in order to prevent the Earth from continually dying.
So what is green electricity? As stated by Green Electricity Marketplace website, ‘green electricity’' is well defined as electricity that is produced from sources which is eco-friendly and do not cause any negative impacts towards the environment. It is undeniable that every type of electricity generation will have some impact upon the environment, but some sources are much environmentally friendly than others. The greenest energy sources are those which utilise the natural energy flows of the Earth and they are usually known as renewable energy sources as they will never run out.
There are various studies were conducted to examined or analysed the green electricity. These kinds of studies, however, almost invariably focus on the conservative variables such as sources, cost and impact in analysing the green electricity while overlooking other variables which is the social perception on it. Different from the prior studies, this study specifies relatively complete on public acceptance or perceptions and focus on how they perceive the green electricity. Moreover, this study tries to fills the research gap through investigating the linkages between the public acceptance and green electricity. We use the following variables to describe public acceptance characteristics which are demographical, attitudinal and socialization characteristics.

1.2. Problem statement

Previously, studies of the correlation between the public acceptance and green electricity have already been studied. However, very little studies were conducted to focus completely on public acceptance characteristic and the effect of it upon the green electricity. Green electricity production becomes even more important as the electricity is not just used to charge up things, but is also used in the production of all manner of products.
However, most of the electricity in the worldwide is currently still produced by burning fossil fuels such as charcoal and the majority of the electricity that is used by most countries has been produced using very “non-green” resources. This statement clearly highlights the importance of eco-friendly electricity production. For instance, it is important to ensure that as much electricity generation as possible is done using renewable, “greenest” methods.
In line with the importance of green electricity, past researchers has rarely considered the influences of the public acceptance characteristics on green electricity. Due to this limitation, investigate is there any relationship between the public acceptance characteristics and the green electricity in Malaysia, precisely on demographical, attitudinal and socialization characteristics.

The research questions of this study are shown below: 1. Is there are any significant relationships between the public acceptance characteristics and the acceptance on green electricity in Malaysia? 2. Do the demographical characteristics bring out any effect to the acceptance on green electricity in Malaysia? 3. Is the attitudinal characteristics brings out any effect to the acceptance on green electricity in Malaysia? 4. Does the socialization a characteristic brings out any effect to the acceptance on green electricity in Malaysia? 5. How to promote the green electricity among society in Malaysia?

1.3. Research objectives

The primary objectives of this study are shown below: 1. To determine if there are any significant relationships between the public acceptance characteristics and the acceptance on green electricity in Malaysia. 2. To identify whether the demographical characteristics brings out any effect to the acceptance on green electricity in Malaysia. 3. To examine whether the attitudinal characteristics brings out any effect to the acceptance on green electricity in Malaysia. 4. To investigate whether the socialization characteristics brings out any effect to the acceptance on green electricity in Malaysia. 5. To promote the green electricity among society in Malaysia.

1.4. Methodology

The current study employs questionnaire survey about public acceptance on green electricity. The sample consists of 150 student respondents from Universiti Tenaga Nasional Kampus Sultan Haji Ahmad Shah, Muadzam Shah. This questionnaire will be divided into three parts. Part one focus on: demographical, part two attitudinal, part three socialization. Upon receipt of the response, all data will be entering into SPSS. Multiple regression procedure will be used to analysis the output.

1.5 Significance of study

This study would be significant to Malaysia because there are very little studies of public acceptance towards green electricity in Malaysia. Most studies that were conducted only reflect on the U.S and Western countries. Hence, all the finding can be useful to many people especially to students, researchers, and investors in evaluating the public acceptance towards green electricity.

1.6. Scope of the study

This empirical study is focusing on the effect of public acceptances characteristics upon the green electricity in Malaysia. The respondents are selected among University Tenaga Nasional (UNITEN) students. This study uses the following variables as the public acceptance characteristics which are demographical, attitudinal and socialization. The effects of public acceptance characteristic on green electricity within those criteria comprised will be examined.

The research is to identify the existence of: 1. Any significant relationships between the public acceptance characteristics upon green electricity in Malaysia. 2. Any effect of the demographical characteristics towards the acceptance on green electricity in Malaysia. 3. The impact of attitudinal characteristics upon the acceptance on green electricity in Malaysia. 4. To what extent the socialization characteristic affect the acceptance on green electricity in Malaysia.

1.7. Gantt chart

| Milestone | Week 1 | Identify the topic | Week 2 | Review of literature | Week 3 | Draft literature review | Week 4 | Design questionnaire | Week 5 | Compile, pilot and review questionnaire | Week 6 | Administer questionnaire | Week 7 | Final collection of questionnaire | Week 8 | Data analysis | Week 9 | Assemble materials | Week 10 | Final writing of research report | Week 11 | Print final copy of research report | Week 12 | Submission of research report |

1.8. Resources

MATERIALS | PRICE | A4 PAPER | 1 x RM25.00 = RM25.00 | PRINTING | 200 x RM0.10 = RM20.00 | BINDING | 1 x RM5 = RM5.00 | BINDING COMB | 1 x RM3.00 = RM3.00 | PLASTIC COVER | 2 x RM0.50 = RM1.00 | STATIONARIES | RM10.00 | MISCELLANEOUS | RM20.00 | TOTAL | RM84.00 |

1.9. Format of project paper

The format of the project paper is organized down into 5 chapters. The first chapter tells introduction of the project paper. It includes the objectives, the significance, the scope, and the format that the project will be governed to.
The second chapter involves mainly of the literature reviews of previous studies. Additional literature review, which link to the study, is also added into this chapter to provide further insights towards this study.
The third chapter in this paper is the data and methodology. This chapter offers a description of the data employed in the analysis as well as discusses the types of methodology that is used in order to obtain the results of the study.
The fourth chapter then presents the empirical results of the study that has been run using the method stated in the methodology. It further elaborates the results that are tabulated.
The fifth and final would be the conclusions and recommendation of the overall results and study, which are highlights the main findings, the significance and implication and recommendations based on the evidence derived from the preceding analysis.

CHAPTER TWO

LITERATURE REVIEW

2.1. Acceptance on green electricity depends on demographic variables

Straughan and Roberts (1999) analyzed that the general belief is that younger individuals are likely to be more sensitive to environmental issues. There are a number of theories offered in support of this belief, but the most common argument is that those who have grown up in a time period in which environmental concerns have been a salient issue at some level, are more likely to be sensitive to these issues.
Jain, S.K. & Kaur, G. (2006) explored that since green consumerism in India is a relatively recent phenomenon, they do not have evidence available to them from the past studies to out rightly subscribe to this reasoning. One can rather argue that the males in India might be having greater concern for the environment and engaging in the environmentally friendly behavior to a greater extent due to their greater exposure to the media and/or interface with the environmental problems owing to their greater outgoing behavior.
Newell and Green (1997) contended that income and education moderate the effect that race plays on shaping environmental concern. Specifically, they found that differences between the perceptions of black and white consumers with respect to environmental issues decrease as both income and education go up.
Fransson N. and T. Grling (1999). The residence hypothesis states that urban residents are more likely to be environmentally concerned than rural residents. A possible explanation for this difference is given in Fransson and Grling (1999) that urban residents are more exposed to the signs of environmental deterioration such as air pollution.

From these studies we can state a hypothesis: Willingness to pay premium for green electricity depends on: live in a household with a larger income, live in a household in which someone has more formal education, are younger, are female, have greater knowledge about energy issues in their community.

2.2. Acceptance on green electricity depends on attitudinal characteristics

James A. Roberts (1997) argued that democrats and liberals are more concerned about environmental quality than are their Republican and conservative counterparts (citing Van Liere and Dunlap, 1980). The rise of widespread public support for environmental reform in the late 1960s and 1970s led to argument that environmental concern transcended political affiliations. Recent studies, however, have questioned the consensus quality of environmental politics. Support for environmental reform varies by political groupings (citing Samdahl and Robertson, 1989).
Dunlap (1975) noted three reasons to expect a split along traditional ideological and partisan lines: (1) environmental reforms generally are opposed by business and industry because of cost involved; (2) environmental reforms entail extending government activities and regulations; and (3) environmental reforms often require innovative action.
Mickel Laroche, Jasmin Bergeron and Guido Barbaro-Forleo (2001) founded that the two most studied attitudes, with respect to environmentally friendly behavior, are importance and inconvenience. Importance is simply whether consumers view environmentally compatible behaviors as important to themselves or society as a whole. Inconvenience refers to how inconvenient it is perceived for the individual to behave in an ecologically favorable fashion. For example, a person may feel that recycling is important for the long-rung good of society, but he or she may also feel that it is personally inconvenient.
According to Banerjee and Mc Keage (1994), green consumers sternly believe that current environmental conditions are deteriorating and represent serious problems facing the security of the world. Conversely, consumers who do not engage in environmentally friendly behavior perceive that ecological will “resolve themselves”. Therefore, an individual’s perception about the severity of ecological problems might influence his/her willingness to pay more for ecologically compatible products.
Ogunjinmi A. and Onadeko S (2012) stated that environmental attitudes are related to environmental problems. Environmental attitudes have been defined as “the collection of beliefs, affect, and behavioral intentions a person holds regarding environmentally relates activities or issues” (citing Schultz et al.2004). As definition of environmental attitudes indicates, two types of environmental attitudes have been used on previous literature: “(1) attitudes toward the environment, and (2) attitudes toward ecological behavior” (citing Kaiser et al.1999).
Webster (1975) founded that socially conscious customer feels strongly that he/she can do something about pollution and tries to consider the social impact of his/her buying behavior. According to Wiener and Sukhdial (1990), one of the main reasons that stops individuals from engaging in ecologically favorable actions is their perceived level of self-involvement toward the protection of the environment. As the authors point out, many individuals may have high ecological concern, but feel that the preservation of the environment is the responsibility of the government and/or big corporations.

From these studies we can state a hypothesis: Willingness to pay premium for a green electricity directly depends on whether respondents are liberals or not, whether they perceive the severity of ecological problems or not.

2.3. Acceptance on green electricity depends on socialization characteristics

Frederick E. Webster (1975) stated that the socially conscious consumer will be more involved in community affairs. Some respondents were asked to list all community organizations to which they belonged or in which they participated or volunteered services. This variable, Community Activities was measured by summing the organizations listed. While this simple counting is only a crude measure, there was no reasonable alternative that would not devote excessive attention to this item.
L.J Shrum, John A. McCarty and Tina (1995) have found that in spite of differences across the green buying variables, the commonalities may provide a guide to advertisers interested in speaking to the green consumer. The results show that the green consumer has an interest in new products, is an information seeker, and talks with others about products. Additionally, green consumers consider themselves opinion leaders, and hence may provide word-of-mouth information that other consumers respect. The green consumer is also a careful shopper, not prone to impulse buying, and pays attention to price, so advertisers must consider those issues as well.
Ian H. Rowlands, Daniel Scott and Paul Parker (2000) argued that the level of stated willingness to pay premium for a green electricity is hypothesized to increase for respondents who believe more firmly that members of their own social network are trying to improve the environment.
From these studies we can state a hypothesis: Willingness to pay premium for a green electricity directly depends on whether respondents are involved in community affairs or not, whether they are interested in new products and talk about it with others or not.

CHAPTER THREE

RESEARCH AND METHODOLOGY 3.0 Introduction

This chapter will explain about the data and methodology that is being used in this study specifically the data that using for process of collecting data, research framework, method measurement, methodology used including model specification, hypothesis and expected income from that research.

4.1 Sources and Sample of Data

This research is based on primary data (questionnaires). This questionnaire will be divided into three parts. Part one focus on: demographical, part two attitudinal, part three socialization. Upon receipt of the response, all data will be entering into SPSS. Multiple regression procedure will be used to analysis the output. The sample of this study will comprise of 150 respondents from University of Tenaga Nasional, Sultan Haji Ahmad Shah’s Campus.

4.2 Variables and Measurement

This study comprises of three independent variables which are demographical, attitudinal and socialization characteristics. Whilst as for the dependent variables, public acceptance towards the green electricity will be use.

Variables | Description of variables | Prior studies | Dependent | Public acceptance on green electricity | Rowlands, et. Al (2002) | Independent | Demographic (demo) | | | Attitudinal (attitude) | | | Socialization (social) | |

4.3.1 Demo

So-called ‘demographic characteristics’ are often used in efforts to characterize or profile potential purchasers of green products. This is because such characteristics are easy to assess and therefore have the potential to be extremely valuable in market segmentation (Balderjahn, 1988). Generally, much of the literature has led ‘marketers to adopt an upscale profile of the ecologically conscious consumer: high income, more education, and prestigious occupation’ (Roberts, 1996). The author argues that there have been ‘inconsistent results’ with such studies. However, it is widely accepted that demographic characteristics still merit investigation (Laroche et al., 2001).

4.3.2 Attitude

Other than demographic characteristic, attitudinal characteristics also have been identified as key elements of the first ‘stream of research’ in ‘research on marketing and the environment’ (Kilbourne and Beckmann, 1998).

4.3.3 Social

According to Webster, the author developed the so-called social involvement model, which ‘suggests that the socially conscious consumer will be more involved in community affairs’ (Webster, 1975).

4.3 Research Framework INDEPENDENT VARIABLES | DEMOATTITUDESOCIAL | DEPENDENT VARIABLES | PUBLIC ACCEPTANCE ON GREEN ELECTRICITY |

Yit = αi + βXit + εit

With the subscript i denoting the cross-sectional dimension and t representing the time-series dimension. The left-hand variable Yit, represents the dependent variable in the model, which is the public acceptance on green electricity. Xit contains the set of explanatory variables in the estimation model, αi is taken to be constant overtime t and specific to the individual cross-sectional unit i. The model for this study also follows the one used by Abor (2007) with some modifications. This takes the following form:

ACCEPTANCE = β0 + β1 DEMO + β2 ATTITUDE +β3 SOCIAL + ε

4.4 Hypotheses

4.5.4 Relationship between public acceptance characteristics and the acceptance on green electricity

Ho1: There is no significant relationship between the demographic characteristics and the acceptance on green electricity in Malaysia.
Ha1: There is a significant relationship between the demographic characteristics and the acceptance on green electricity in Malaysia.

Ho2: There is no significant relationship between the attitudinal characteristics and the acceptance on green electricity in Malaysia.
Ha2: There is a significant relationship between the attitudinal characteristics and the acceptance on green electricity in Malaysia.

Ho3: There is no significant relationship between the socialization characteristics and the acceptance on green electricity in Malaysia.
Ha3: There is a significant relationship between the socialization characteristics and the acceptance on green electricity in Malaysia.

4.5.5 Effect of public acceptance characteristics towards the acceptance on green electricity

Ho1: There is no significant effect between between the demographic characteristics and the acceptance on green electricity in Malaysia.
Ha1: There is a significant effect between the demographic characteristics and the acceptance on green electricity in Malaysia.

Ho2: There is no significant effect between the attitudinal characteristics and the acceptance on green electricity in Malaysia.
Ha2: There is a significant effect between the attitudinal characteristics and the acceptance on green electricity in Malaysia.

Ho3: There is no significant effect between the socialization characteristics and the acceptance on green electricity in Malaysia.
Ha3: There is a significant effect between the socialization characteristics and the acceptance on green electricity in Malaysia.

4.5 Research Methods

A proxy for all variables is developed for computer analysis of data. The software that is going to be use for the data analysis is SPSS. This study will use the following methods to analyse all the data.

3.5.1 Correlation

The correlation analysis is done in analysis to shows the relationship of the variables. The extreme coefficients lie between -1 perfectly negative and + 1 perfectly positive correlated. It can be expressed as (-1≤rp ≤+1). The idea here is to determine whether there is any correlation between the dependent variable and the independent variables. This determine by the association between dependent and independent variables (Sig < 1% or 5% = reject or null hypothesis).

3.5.2 Regression

The regression is done in analysis to express the linear relationship between two or more variables. The dependent variables and the independent variables have to be identified and these usually based on a theoretical basis.

4.6 Expected Outcomes

This particular study posits several expected outcomes which are:
1. There is a significant relationship and effect between the demographic characteristics and the acceptance on green electricity in Malaysia.
2. There is a significant relationship and effect between the attitudinal characteristics and the acceptance on green electricity in Malaysia.
3. There is a significant relationship and effect between the socialization characteristics and the acceptance on green electricity in Malaysia.

CHAPTER FOUR

FINDINGS OF THE STUDY

4.0 Introduction

This chapter presents a complete account of data analysis and results of study by using two methods. The first method which is to measure the relationship between dependent and independent variables is correlation. Second method is regression which to express the linear relationship between two or more variables.

4.1 Findings

4.1.1 Bivariate Pearson Product-Moment Correlation

Relationship between public acceptance characteristics and the acceptance on green electricity

Correlations | | GENDER | RACE | AGE | PROGRAM | INCOME | EDULEVEL | KNOWLEDGE | ACCEPT 1 | ACCEPT 2 | ACCEPT 3 | GENDER | Pearson Correlation | 1 | .041 | -.105 | .113 | -.126 | -.052 | -.014 | -.015 | .057 | -.044 | | Sig. (1-tailed) | | .310 | .101 | .084 | .062 | .262 | .432 | .426 | .243 | .296 | | N | 150 | 150 | 150 | 150 | 150 | 150 | 150 | 150 | 150 | 150 | RACE | Pearson Correlation | .041 | 1 | .266 | -.118 | -.007 | -.171 | .072 | .057 | -.011 | -.026 | | Sig. (1-tailed) | .310 | | .001 | .075 | .466 | .018 | .192 | .243 | .448 | .377 | | N | 150 | 150 | 150 | 150 | 150 | 150 | 150 | 150 | 150 | 150 | AGE | Pearson Correlation | -.105 | .266 | 1 | -.177 | .144 | .133 | .040 | .065 | .035 | .102 | | Sig. (1-tailed) | .101 | .001 | | .015 | .039 | .052 | .315 | .214 | .336 | .107 | | N | 150 | 150 | 150 | 150 | 150 | 150 | 150 | 150 | 150 | 150 | PROGRAM | Pearson Correlation | .113 | -.118 | -.177 | 1 | .002 | -.202 | .094 | -.121 | -.199 | -.173 | | Sig. (1-tailed) | .084 | .075 | .015 | | .488 | .007 | .126 | .071 | .007 | .017 | | N | 150 | 150 | 150 | 150 | 150 | 150 | 150 | 150 | 150 | 150 | INCOME | Pearson Correlation | -.126 | -.007 | .144 | .002 | 1 | .310 | -.021 | -.091 | -.008 | .081 | | Sig. (1-tailed) | .062 | .466 | .039 | .488 | | .000 | .399 | .135 | .459 | .163 | | N | 150 | 150 | 150 | 150 | 150 | 150 | 150 | 150 | 150 | 150 | EDULEVEL | Pearson Correlation | -.052 | -.171 | .133 | -.202 | .310 | 1 | -.045 | .059 | .018 | .039 | | Sig. (1-tailed) | .262 | .018 | .052 | .007 | .000 | | .291 | .236 | .415 | .316 | | N | 150 | 150 | 150 | 150 | 150 | 150 | 150 | 150 | 150 | 150 | KNOWLEDGE | Pearson Correlation | -.014 | .072 | .040 | .094 | -.021 | -.045 | 1 | -.081 | -.079 | -.031 | | Sig. (1-tailed) | .432 | .192 | .315 | .126 | .399 | .291 | | .162 | .168 | .354 | | N | 150 | 150 | 150 | 150 | 150 | 150 | 150 | 150 | 150 | 150 | ACCEPT | Pearson Correlation | -.015 | .057 | .065 | -.121 | -.091 | .059 | -.081 | 1 | .772 | .643 | | Sig. (1-tailed) | .426 | .243 | .214 | .071 | .135 | .236 | .162 | | .000 | .000 | | N | 150 | 150 | 150 | 150 | 150 | 150 | 150 | 150 | 150 | 150 | ACCEPT2 | Pearson Correlation | .057 | -.011 | .035 | -.199 | -.008 | .018 | -.079 | .772 | 1 | .801 | | Sig. (1-tailed) | .243 | .448 | .336 | .007 | .459 | .415 | .168 | .000 | | .000 | | N | 150 | 150 | 150 | 150 | 150 | 150 | 150 | 150 | 150 | 150 | ACCEPT3 | Pearson Correlation | -.044 | -.026 | .102 | -.173 | .081 | .039 | -.031 | .643 | .801 | 1 | | Sig. (1-tailed) | .296 | .377 | .107 | .017 | .163 | .316 | .354 | .000 | .000 | | | N | 150 | 150 | 150 | 150 | 150 | 150 | 150 | 150 | 150 | 150 |

Correlations | | ACCEPT | ACCEPT2 | ACCEPT3 | ATT | ATT2 | ATT3 | ATT4 | ACCEPT | Pearson Correlation | 1 | .772 | .643 | .317 | .308 | .513 | .435 | | Sig. (1-tailed) | | .000 | .000 | .000 | .000 | .000 | .000 | | N | 150 | 150 | 150 | 150 | 150 | 150 | 150 | ACCEPT2 | Pearson Correlation | .772 | 1 | .801 | .174 | .368 | .614 | .475 | | Sig. (1-tailed) | .000 | | .000 | .017 | .000 | .000 | .000 | | N | 150 | 150 | 150 | 150 | 150 | 150 | 150 | ACCEPT3 | Pearson Correlation | .643 | .801 | 1 | .152 | .294 | .497 | .352 | | Sig. (1-tailed) | .000 | .000 | | .032 | .000 | .000 | .000 | | N | 150 | 150 | 150 | 150 | 150 | 150 | 150 | ATT | Pearson Correlation | .317 | .174 | .152 | 1 | .258 | .142 | .043 | | Sig. (1-tailed) | .000 | .017 | .032 | | .001 | .042 | .300 | | N | 150 | 150 | 150 | 150 | 150 | 150 | 150 | ATT2 | Pearson Correlation | .308 | .368 | .294 | .258 | 1 | .511 | .388 | | Sig. (1-tailed) | .000 | .000 | .000 | .001 | | .000 | .000 | | N | 150 | 150 | 150 | 150 | 150 | 150 | 150 | ATT3 | Pearson Correlation | .513 | .614 | .497 | .142 | .511 | 1 | .571 | | Sig. (1-tailed) | .000 | .000 | .000 | .042 | .000 | | .000 | | N | 150 | 150 | 150 | 150 | 150 | 150 | 150 | ATT4 | Pearson Correlation | .435 | .475 | .352 | .043 | .388 | .571 | 1 | | Sig. (1-tailed) | .000 | .000 | .000 | .300 | .000 | .000 | | | N | 150 | 150 | 150 | 150 | 150 | 150 | 150 |

Correlations | | ACCEPT | ACCEPT2 | ACCEPT3 | SOCIAL | SOCIAL2 | SOCIAL3 | ACCEPT | Pearson Correlation | 1 | .772 | .643 | .231 | .161 | .246 | | Sig. (1-tailed) | | .000 | .000 | .002 | .025 | .001 | | N | 150 | 150 | 150 | 150 | 150 | 150 | ACCEPT2 | Pearson Correlation | .772 | 1 | .801 | .183 | .097 | .181 | | Sig. (1-tailed) | .000 | | .000 | .013 | .118 | .014 | | N | 150 | 150 | 150 | 150 | 150 | 150 | ACCEPT3 | Pearson Correlation | .643 | .801 | 1 | .145 | .024 | .104 | | Sig. (1-tailed) | .000 | .000 | | .038 | .386 | .104 | | N | 150 | 150 | 150 | 150 | 150 | 150 | SOCIAL | Pearson Correlation | .231 | .183 | .145 | 1 | .712 | .558 | | Sig. (1-tailed) | .002 | .013 | .038 | | .000 | .000 | | N | 150 | 150 | 150 | 150 | 150 | 150 | SOCIAL2 | Pearson Correlation | .161 | .097 | .024 | .712 | 1 | .714 | | Sig. (1-tailed) | .025 | .118 | .386 | .000 | | .000 | | N | 150 | 150 | 150 | 150 | 150 | 150 | SOCIAL3 | Pearson Correlation | .246 | .181 | .104 | .558 | .714 | 1 | | Sig. (1-tailed) | .001 | .014 | .104 | .000 | .000 | | | N | 150 | 150 | 150 | 150 | 150 | 150 | 4.1.2 Regression

Demographic characteristics vs. Public acceptance (I am aware of Green Electricity)

REGRESSION MODEL:

Y = β0 + β1X1 + β2X2 + β3X3 + β4X4 ……. + ε

Where: Y = Public acceptance (I am aware of Green Electricity) β = Regression coefficient X = Question 1 – Question 7

Model Summaryb | Model | R | R Square | Adjusted R Square | Std. Error of the Estimate | 1 | .196a | .039 | -.009 | .930 | a. Predictors: (Constant), KNOWLEDGE, GENDER, EDULEVEL, AGE, PROGRAM, INCOME, RACEb. Dependent Variable: ACCEPT1 |
R2 0.39
Means variance is 39% change in dependent variable is due to change demographic characteristics

ANOVAb | Model | Sum of Squares | df | Mean Square | F | Sig. | 1 | Regression | 4.924 | 7 | .703 | .813 | .578a | | Residual | 122.816 | 142 | .865 | | | | Total | 127.740 | 149 | | | | a. Predictors: (Constant), KNOWLEDGE, GENDER, EDULEVEL, AGE, PROGRAM, INCOME, RACEb. Dependent Variable: ACCEPT1 |
Ho: Model is not fit for prediction
0.578 > 0.05
Fail to reject Ho, because significant is not fit for prediction

Coefficientsa | Model | Unstandardized Coefficients | Standardized Coefficients | t | Sig. | | B | Std. Error | Beta | | | 1 | (Constant) | 3.922 | .492 | | 7.964 | .000 | | GENDER | -.032 | .164 | -.016 | -.196 | .845 | | RACE | .063 | .101 | .055 | .623 | .534 | | AGE | .071 | .139 | .045 | .508 | .612 | | PROGRAM | -.046 | .049 | -.080 | -.922 | .358 | | INCOME | -.097 | .068 | -.125 | -1.423 | .157 | | EDULEVEL | .059 | .067 | .081 | .887 | .377 | | KNOWLEDGE | -.108 | .114 | -.079 | -.946 | .346 | a. Dependent Variable: ACCEPT1 |
Y = β0 + β1X1 + β2X2 + β3X3 + β4X4 …. + ε

Y = 3.922 – 0.032X1 + 0.063X2 + 0.071X3 - 0.046X4 - 0.097X5 + 0.059X6 – 0.108X7
X1 increases in 1 unit, so will decrease in 0.032 in Y
So, there is negative relationship

Ho2: There is no significant effect between the demographic characteristics and the acceptance on green electricity in Malaysia.

Ho2: There is no significant relationship between X1 and Y
Sig. <0.05 0.845 < 0.05
So, fail to reject Ho2, there is no relationship between X1 and Y

In this situation, none of the independent variables have significant relationship with Y (Public acceptance) because the Sig. value is more than 0.05.
Demographic characteristics vs. Public acceptance (I am willing to use Green Electricity)

REGRESSION MODEL:

Y = β0 + β1X1 + β2X2 + β3X3 + β4X4 …… + ε

Where: Y = Public acceptance (I am willing to use Green Electricity) β = Regression coefficient X = Question 1 – Question 7

Model Summaryb | Model | R | R Square | Adjusted R Square | Std. Error of the Estimate | 1 | .229a | .052 | .006 | .90344 | a. Predictors: (Constant), KNOWLEDGE, GENDER, EDULEVEL, AGE, PROGRAM, INCOME, RACEb. Dependent Variable: ACCEPT2 |

R2 0.052
Means variance is 5.2% change in dependent variable is due to change demographic characteristics

ANOVAb | Model | Sum of Squares | df | Mean Square | F | Sig. | 1 | Regression | 6.393 | 7 | .913 | 1.119 | .354a | | Residual | 115.900 | 142 | .816 | | | | Total | 122.293 | 149 | | | | a. Predictors: (Constant), KNOWLEDGE, GENDER, EDULEVEL, AGE, PROGRAM, INCOME, RACEb. Dependent Variable: ACCEPT2 |
Ho: Model is not fit for prediction
0.354 > 0.05
Fail to reject Ho, because significant is not fit for prediction

Coefficientsa | Model | Unstandardized Coefficients | Standardized Coefficients | t | Sig. | | B | Std. Error | Beta | | | 1 | (Constant) | 3.978 | .478 | | 8.317 | .000 | | GENDER | .162 | .160 | .084 | 1.012 | .313 | | RACE | -.054 | .098 | -.048 | -.548 | .585 | | AGE | .039 | .135 | .025 | .286 | .776 | | PROGRAM | -.118 | .048 | -.212 | -2.457 | .015 | | INCOME | .007 | .066 | .009 | .105 | .916 | | EDULEVEL | -.027 | .065 | -.038 | -.415 | .678 | | KNOWLEDGE | -.077 | .111 | -.057 | -.692 | .490 | a. Dependent Variable: ACCEPT2 |
Y = β0 + β1X1 + β2X2 + β3X3 + β4X4 …. + ε

Y= 3.978 + 0.162X1 – 0.054X2 + 0.039X3 - 0.118X4 + 0.007X5 – 0.027X6 – 0.077X7
X1 increases in 1 unit, so will increase in 0.162 in Y
So, there is positive relationship

Ho2: There is no significant effect between the demographic characteristics and the acceptance on green electricity in Malaysia.

Ho2: There is no significant relationship between X1 and Y
Sig. <0.05 0.313 < 0.05
So, fail to reject Ho2, there is no relationship between X1 and Y

In this situation, only X4 (Program) have significant relationship with Y (Public acceptance) because the Sig. value less than 0.05 which is 0.015.

Demographic characteristics vs. Public acceptance (I am willing to spend on electric bill that generated from Green Electricity)

REGRESSION MODEL:

Y = β0 + β1X1 + β2X2 + β3X3 + β4X4 …… + ε

Where: Y= Public acceptance (I am willing to spend on electric bill that generated from Green Electricity) β = Regression coefficient X = Question 1 – Question 7 Model Summaryb | Model | R | R Square | Adjusted R Square | Std. Error of the Estimate | 1 | .215a | .046 | -.001 | .85690 | a. Predictors: (Constant), KNOWLEDGE, GENDER, EDULEVEL, AGE, PROGRAM, INCOME, RACEb. Dependent Variable: ACCEPT3 |
R2 0.046
Means variance is 4.6% change in dependent variable is due to change demographic characteristics

ANOVAb | Model | Sum of Squares | df | Mean Square | F | Sig. | 1 | Regression | 5.065 | 7 | .724 | .985 | .444a | | Residual | 104.268 | 142 | .734 | | | | Total | 109.333 | 149 | | | | a. Predictors: (Constant), KNOWLEDGE, GENDER, EDULEVEL, AGE, PROGRAM, INCOME, RACEb. Dependent Variable: ACCEPT3 |
Ho: Model is not fit for prediction
0.444 > 0.05
Fail to reject Ho, because significant is not fit for prediction Coefficientsa | Model | Unstandardized Coefficients | Standardized Coefficients | t | Sig. | | B | Std. Error | Beta | | | 1 | (Constant) | 3.804 | .454 | | 8.383 | .000 | | GENDER | -.008 | .152 | -.005 | -.055 | .956 | | RACE | -.080 | .093 | -.075 | -.855 | .394 | | AGE | .125 | .128 | .086 | .975 | .331 | | PROGRAM | -.092 | .046 | -.175 | -2.016 | .046 | | INCOME | .058 | .063 | .082 | .931 | .353 | | EDULEVEL | -.031 | .062 | -.046 | -.510 | .611 | | KNOWLEDGE | -.016 | .105 | -.013 | -.157 | .876 | a. Dependent Variable: ACCEPT3 |
Y = β0 + β1X1 + β2X2 + β3X3 + β4X4 …. + ε

Y= 3.804 – 0.008X1 – 0.080X2 + 0.125X3 - 0.092X4 + 0.058X5 – 0.031X6 – 0.016X7
X1 increases in 1 unit, so will decrease in 0.080 in Y
So, there is negative relationship

Ho2: There is no significant effect between the demographic characteristics and the acceptance on green electricity in Malaysia.

Ho2: There is no significant relationship between X1 and Y
Sig. <0.05 0.956 < 0.05
So, fail to reject Ho2, there is no relationship between X1 and Y

In this situation, only X4 (Program) have significant relationship with Y (Public acceptance) because the Sig. value less than 0.05 which is 0.046.
Attitudinal characteristics vs. Public acceptance (I am aware of Green Electricity)

REGRESSION MODEL:

Y = β0 + β1X1 + β2X2 + β3X3 + β4X4 + ε

Where: Y = Public acceptance (I am aware of Green Electricity) β = Regression coefficient X1 = Question 11 X2 = Question 12 X3 = Question 13 X4 = Question 14

Model Summaryb | Model | R | R Square | Adjusted R Square | Std. Error of the Estimate | 1 | .599a | .359 | .341 | .751 | a. Predictors: (Constant), X4, X1, X2, X3b. Dependent Variable: Y |
R2 0.359
Means variance is 35.9% change in dependent variable is due to change X1, X2, X3 and X4

ANOVAb | Model | Sum of Squares | df | Mean Square | F | Sig. | 1 | Regression | 45.869 | 4 | 11.467 | 20.310 | .000a | | Residual | 81.871 | 145 | .565 | | | | Total | 127.740 | 149 | | | | a. Predictors: (Constant), X4, X1, X2, X3b. Dependent Variable: Y |
Ho: Model is not fit for prediction
0.000 < 0.05
Reject Ho, because significant is fit for prediction

Coefficientsa | Model | Unstandardized Coefficients | Standardized Coefficients | t | Sig. | | B | Std. Error | Beta | | | 1 | (Constant) | .809 | .351 | | 2.304 | .023 | | X1 | .249 | .065 | .265 | 3.832 | .000 | | X2 | -.035 | .080 | -.035 | -.433 | .666 | | X3 | .371 | .090 | .362 | 4.129 | .000 | | X4 | .245 | .087 | .230 | 2.806 | .006 | a. Dependent Variable: Y |

Y = β0 + β1X1 + β2X2 + β3X3 + β4X4 + ε

Y= 0.809 + 0.249X1 – 0.035X2 + 0.371X3 + 0.245X4
X1 increases in 1 unit, so will increase in 0.249 in Y
So, there is positive relationship

Ho2: There is no significant effect between the attitudinal characteristics and the acceptance on green electricity in Malaysia.

Ho2: There is no significant relationship between X1 and Y
Sig. <0.05 0.00 < 0.05
So, reject Ho2, there is relationship between X1 and Y

Ho2: There is no significant relationship between X2 and Y
0. 666 > 0.05
So, fail to reject Ho2, There is no relationship between X2 and Y

Ho2: There is no significant relationship between X3 and Y
0. 00 < 0.05
So, reject Ho2, There is relationship between X3 and Y

Ho2: There is no significant relationship between X4 and Y
0. 00 < 0.05
So, reject Ho2, There is relationship between X4 and Y

Attitudinal characteristics vs. Public acceptance (I am willing to use Green Electricity)

REGRESSION MODEL:

Y = β0 + β1X1 + β2X2 + β3X3 + β4X4 + ε

Where: Y = Public acceptance (I am willing to use Green Electricity) β = Regression coefficient X1 = Question 11 X2 = Question 12 X3 = Question 13 X4 = Question 14

Model Summaryb | Model | R | R Square | Adjusted R Square | Std. Error of the Estimate | 1 | .640a | .409 | .393 | .70586 | a. Predictors: (Constant), X4, X1, X2, X3b. Dependent Variable: Y |
R2 0.409
Means variance is 40.9% change in dependent variable is due to change X1, X2, X3 and X4

ANOVAb | Model | Sum of Squares | df | Mean Square | F | Sig. | 1 | Regression | 50.048 | 4 | 12.512 | 25.112 | .000a | | Residual | 72.245 | 145 | .498 | | | | Total | 122.293 | 149 | | | | a. Predictors: (Constant), X4, X1, X2, X3b. Dependent Variable: Y |
Ho: Model is not fit for prediction
0.000 < 0.05
Reject Ho, because significant is fit for prediction

Coefficientsa | Model | Unstandardized Coefficients | Standardized Coefficients | t | Sig. | | B | Std. Error | Beta | | | 1 | (Constant) | .852 | .330 | | 2.583 | .011 | | X1 | .084 | .061 | .091 | 1.371 | .172 | | X2 | .026 | .076 | .027 | .348 | .728 | | X3 | .482 | .085 | .480 | 5.703 | .000 | | X4 | .195 | .082 | .187 | 2.374 | .019 | a. Dependent Variable: Y |

Y = β0 + β1X1 + β2X2 + β3X3 + β4X4 + ε

Y= 0.852 + 0.084X1 + 0.026X2 + 0.482X3 + 0.195X4
X1 increases in 1 unit, so will increase in 0.084 in Y
So, there is positive relationship

Ho2: There is no significant effect between the attitudinal characteristics and the acceptance on green electricity in Malaysia.

Ho2: There is no significant relationship between X1 and Y
Sig. <0.05 0.172 > 0.05
So, fail to reject Ho2, there is no relationship between X1 and Y

Ho2: There is no significant relationship between X2 and Y
0. 728 > 0.05
So, fail to reject Ho2, There is no relationship between X2 and Y

Ho2: There is no significant relationship between X3 and Y
0. 00 < 0.05
So, reject Ho2, There is relationship between X3 and Y

Ho2: There is no significant relationship between X4 and Y
0. 019 < 0.05
So, reject Ho2, There is relationship between X4 and Y

Attitudinal characteristics vs. Public acceptance (I am willing to spend on electric bill that generated from Green Electricity)

REGRESSION MODEL:

Y = β0 + β1X1 + β2X2 + β3X3 + β4X4 + ε

Where: Y= Public acceptance (I am willing to spend on electric bill that generated from Green Electricity) β = Regression coefficient X1 = Question 11 X2 = Question 12 X3 = Question 13 X4 = Question 14

Model Summaryb | Model | R | R Square | Adjusted R Square | Std. Error of the Estimate | 1 | .512a | .262 | .242 | .74601 | a. Predictors: (Constant), X4, X1, X2, X3b. Dependent Variable: Y |
R2 0.262
Means variance is 26.2% change in dependent variable is due to change X1, X2, X3 and X4

ANOVAb | Model | Sum of Squares | df | Mean Square | F | Sig. | 1 | Regression | 28.635 | 4 | 7.159 | 12.863 | .000a | | Residual | 80.698 | 145 | .557 | | | | Total | 109.333 | 149 | | | | a. Predictors: (Constant), X4, X1, X2, X3b. Dependent Variable: Y |
Ho: Model is not fit for prediction
0.000 < 0.05
Reject Ho, because significant is fit for prediction

Coefficientsa | Model | Unstandardized Coefficients | Standardized Coefficients | t | Sig. | | B | Std. Error | Beta | | | 1 | (Constant) | 1.484 | .349 | | 4.257 | .000 | | X1 | .072 | .065 | .083 | 1.117 | .266 | | X2 | .019 | .080 | .020 | .232 | .817 | | X3 | .396 | .089 | .417 | 4.432 | .000 | | X4 | .101 | .087 | .102 | 1.162 | .247 | a. Dependent Variable: Y |
Y = β0 + β1X1 + β2X2 + β3X3 + β4X4 + ε

Y= 1.484 + 0.072X1 + 0.019X2 + 0.396X3 + 0.101X4
X1 increases in 1 unit, so will increase in 0.072 in Y
So, there is positive relationship

Ho2: There is no significant effect between the attitudinal characteristics and the acceptance on green electricity in Malaysia.

Ho2: There is no significant relationship between X1 and Y
Sig. <0.05 0.266 > 0.05
So, fail to reject Ho2, there is no relationship between X1 and Y

Ho2: There is no significant relationship between X2 and Y
0. 817 > 0.05
So, fail to reject Ho2, There is no relationship between X2 and Y

Ho2: There is no significant relationship between X3 and Y
0. 00 < 0.05
So, reject Ho2, There is relationship between X3 and Y

Ho2: There is no significant relationship between X4 and Y
0. 247 < 0.05
So, fail to reject Ho2, There is no relationship between X4 and Y

Socialization characteristics vs. Public acceptance (I am aware of Green Electricity)

REGRESSION MODEL:

Y = β0 + β1X1 + β2X2 + β3X3 + ε

Where: Y = Public acceptance (I am aware of Green Electricity) β = Regression coefficient X1 = Question 15 X2 = Question 16 X3 = Question 17

Model Summaryb | Model | R | R Square | Adjusted R Square | Std. Error of the Estimate | 1 | .288a | .083 | .064 | .896 | a. Predictors: (Constant), X3, X1, X2b. Dependent Variable: Y |
R2 0.083
Means variance is 8.3% change in dependent variable is due to change X1, X2 and X3

ANOVAb | Model | Sum of Squares | df | Mean Square | F | Sig. | 1 | Regression | 10.568 | 3 | 3.523 | 4.390 | .005a | | Residual | 117.172 | 146 | .803 | | | | Total | 127.740 | 149 | | | | a. Predictors: (Constant), X3, X1, X2b. Dependent Variable: YHo: Model is not fit for prediction0.005 < 0.05Reject Ho, because significant is fit for prediction |

Coefficientsa | Model | Unstandardized Coefficients | Standardized Coefficients | t | Sig. | | B | Std. Error | Beta | | | 1 | (Constant) | 2.979 | .284 | | 10.475 | .000 | | X1 | .206 | .110 | .212 | 1.870 | .064 | | X2 | -.175 | .142 | -.165 | -1.230 | .221 | | X3 | .220 | .102 | .245 | 2.158 | .033 | a. Dependent Variable: Y |
Y = β0 + β1X1 + β2X2 + β3X3 + ε

Y= 2.979+ 0.206X1 - 0.175X2 + 0.220X3
X1 increases in 1 unit, so will increase in 0.206 in Y
So, there is positive relationship

Ho3: There is no significant effect between the socialization characteristics and the acceptance on green electricity in Malaysia.
Ho3: There is no significant relationship between X1 and Y
Sig. <0.05 0.064 > 0.05
So, fail to reject Ho3, there is no relationship between X1 and Y

Ho3: There is no significant relationship between X2 and Y
0. 221 > 0.05
So, fail to reject Ho3, There is no relationship between X2 and Y

Ho3: There is no significant relationship between X3 and Y
0. 033 < 0.05
So, reject Ho3, There is relationship between X3 and Y
Socialization characteristics vs. Public acceptance (I am willing to use Green Electricity)

REGRESSION MODEL:

Y = β0 + β1X1 + β2X2 + β3X3 + ε

Where: Y = Public acceptance (I am willing to use Green Electricity) β = Regression coefficient X1 = Question 15 X2 = Question 16 X3 = Question 17

Model Summaryb | Model | R | R Square | Adjusted R Square | Std. Error of the Estimate | 1 | .237a | .056 | .037 | .88915 | a. Predictors: (Constant), X3, X1, X2b. Dependent Variable: Y |
R2 0.056
Means variance is 5.6% change in dependent variable is due to change X1, X2 and X3 ANOVAb | Model | Sum of Squares | df | Mean Square | F | Sig. | 1 | Regression | 6.867 | 3 | 2.289 | 2.895 | .037a | | Residual | 115.426 | 146 | .791 | | | | Total | 122.293 | 149 | | | | a. Predictors: (Constant), X3, X1, X2b. Dependent Variable: Y |
Ho: Model is not fit for prediction
0.037 < 0.05
Reject Ho, because significant is fit for prediction

Coefficientsa | Model | Unstandardized Coefficients | Standardized Coefficients | t | Sig. | | B | Std. Error | Beta | | | 1 | (Constant) | 3.213 | .282 | | 11.383 | .000 | | X1 | .200 | .109 | .210 | 1.825 | .070 | | X2 | -.206 | .141 | -.199 | -1.458 | .147 | | X3 | .180 | .101 | .205 | 1.780 | .077 | a. Dependent Variable: Y |
Y = β0 + β1X1 + β2X2 + β3X3 + ε

Y= 3.213 + 0.200X1 – 0.206X2 + 0.180X3
X1 increases in 1 unit, so will increase in 0.200 in Y
So, there is positive relationship

Ho3: There is no significant effect between the socialization characteristics and the acceptance on green electricity in Malaysia.

Ho3: There is no significant relationship between X1 and Y
Sig. <0.05 0.070 > 0.05
So, fail to reject Ho3, there is no relationship between X1 and Y

Ho3: There is no significant relationship between X2 and Y
0. 147 > 0.05
So, fail to reject Ho3, There is no relationship between X2 and Y

Ho3: There is no significant relationship between X3 and Y
0. 077 > 0.05
So, fail to reject Ho3, There is no relationship between X3 and Y
Socialization characteristics vs. Public acceptance (I am willing to spend on electric bill that generated from Green Electricity)
REGRESSION MODEL:

Y = β0 + β1X1 + β2X2 + β3X3 + ε

Where: Y = Public acceptance (I am willing to spend on electric bill that generate from Green Electricity) β = Regression coefficient X1 = Question 15 X2 = Question 16 X3 = Question 17

Model Summaryb | Model | R | R Square | Adjusted R Square | Std. Error of the Estimate | 1 | .212a | .045 | .025 | .84563 | a. Predictors: (Constant), X3, X1, X2b. Dependent Variable: Y |

R2 0.045
Means variance is 4.5% change in dependent variable is due to change X1, X2 and X3

ANOVAb | Model | Sum of Squares | df | Mean Square | F | Sig. | 1 | Regression | 4.930 | 3 | 1.643 | 2.298 | .080a | | Residual | 104.403 | 146 | .715 | | | | Total | 109.333 | 149 | | | | a. Predictors: (Constant), X3, X1, X2b. Dependent Variable: Y |

Ho: Model is not fit for prediction
0.080 < 0.05
Fail to reject Ho, because significant is not fit for prediction

Coefficientsa | Model | Unstandardized Coefficients | Standardized Coefficients | t | Sig. | | B | Std. Error | Beta | | | 1 | (Constant) | 3.359 | .268 | | 12.512 | .000 | | X1 | .220 | .104 | .245 | 2.116 | .036 | | X2 | -.253 | .134 | -.259 | -1.886 | .061 | | X3 | .125 | .096 | .152 | 1.306 | .194 | a. Dependent Variable: Y |
Y = β0 + β1X1 + β2X2 + β3X3 + ε

Y= 3.359 + 0.220X1 – 0.253X2 + 0.125X3
X1 increases in 1 unit, so will increase in 0.220 in Y
So, there is positive relationship

Ho3: There is no significant effect between the socialization characteristics and the acceptance on green electricity in Malaysia.
Ho3: There is no significant relationship between X1 and Y
Sig. <0.05 0.036 > 0.05
So, reject Ho3, there is relationship between X1 and Y

Ho3: There is no significant relationship between X2 and Y
0. 061 > 0.05
So, fail to reject Ho3, There is no relationship between X2 and Y

Ho3: There is no significant relationship between X3 and Y
0. 194 > 0.05
So, fail to reject Ho3, There is no relationship between X3 and Y

CHAPTER FIVE

CONCLUSION AND RECOMMENDATIONS

This research aimed to identify what factors influence on public acceptance on green electricity. As for the analysis the method of collecting information was questionnaire. It was divided into three parts representing each characteristic.
To study the relationship between dependent and independent variables it is necessary to conduct correlation analysis. Furthermore in this study there were used the regression which is to express the linear relationship between two or more variables.

The results were shown that there is no relationship between demographic and socialization characteristics and public acceptance on green electricity, while there is a significant relationship between attitudinal characteristics and the acceptance on green electricity in Malaysia.

So from three tested characteristics we founded that only attitudinal characteristics has impact on acceptance on green electricity in Malaysia.

We can conclude that the finding were significant based on the variables that has been studied in Malaysia. The results showed that attitude of people on green electricity influences on their acceptance. Willingness to pay premium for green electricity directly depends on whether respondents are liberals or not, whether they perceive the severity of ecological problems or not.
As for recommendation we can say that there should be more deep research on this topic. The whole picture of acceptance of people on green electricity cannot be seen by survey of 150 persons. This research was conducted among students only. There is a possibility that there will be different results among different age and activity categories of people.

REFERENCES (Chapter 1)

A.H.G.M. Spithoven, (2005),"Distribution Of Income And The Structure Of Economy And Society", International Journal Of Social Economics, Vol. 32 Issue: 1, Pp. 133 – 154.
Bird, L. And Swezey, B. (2005), “Green Power Marketing In The United States: A Status Report”, 7th Edition, Technical Report Nrel/Tp-620-36823, National Renewable Energy Laboratory, Golden, Co.
Chris Eves, Stephan Kippes, (2010),"Public Awareness of "Green" And "Energy Efficient" Residential Property: An Empirical Survey Based On Data from New Zealand", Property Management, Vol. 28 Issue: 3, Pp. 193 – 208.
Christa Liedtke, Maria Jolanta Welfens, Holger Rohn, Julia Nordmann, (2012),"Living Lab: User-Driven Innovation for Sustainability", International Journal of Sustainability in Higher Education, Vol. 13 Issue: 2, Pp. 106 – 118.
Daniel Castro-Lacouture And Kathy O. Roper, (2009), “Renewable Energy In Us Federal Buildings”, Building Construction Program, Georgia Institute Of Technology, Atlanta, Georgia, USA, Vol. 27 No. 5, Pp. 173-186.
Ian H. Rowlands, Paul Parker, Daniel Scott, (2002),"Consumer Perceptions of "Green Power"", Journal of Consumer Marketing, Vol.19 Issue: 2, Pp. 112 - 129
Johan Jansson, Agneta Marell, Annika Nordlund, (2010),"Green Consumer Behaviour: Determinants of Curtailment and Eco-Innovation Adoption", Journal of Consumer Marketing, Vol. 27 Issue: 4, Pp. 358 – 370.
John Grant, (2008),"Green Marketing", Strategic Direction, Vol. 24 Issue: 6, Pp. 25 – 27.
Josephine Pickett-Baker, Ritsuko Ozaki, (2008),"Pro-Environmental Products: Marketing Influence on Consumer Purchase Decision", Journal of Consumer Marketing, Vol. 25 Issue: 5, Pp. 281 – 293.
Matthew James, Karen Card, (2012), "Factors Contributing To Institutions Achieving Environmental Sustainability", International Journal Of Sustainability In Higher Education, Vol. 13 Issue: 2, Pp. 166 – 176.
Peter L. Daniels, (2005),"Technology Revolutions and Social Development: Prospects For A Green Techno Economic Paradigm in Lower Income Countries", International Journal of Social Economics, Vol. 32 Issue: 5, Pp. 454 – 482.
Pieter Keizer, (2005),"A Socio-Economic Framework Of Interpretation And Analysis", International Journal Of Social Economics, Vol. 32 Issue: 1, Pp. 155 – 173.
Robert D. Straughan, James A. Roberts, (1999),"Environmental Segmentation Alternatives: A Look at Green Consumer Behaviour in the New Millennium", Journal of Consumer Marketing, Vol. 16 Issue: 6, Pp. 558 – 575.
Rowlands, Ian H, Scott, Daniel, Parker, Paul (2003), “Consumers and Green Electricity: Profiling Potential Purchasers”, Vol. 12 No.1, Pg. 36.
Wilco W. Chan, (2005),"Predicting And Saving The Consumption Of Electricity In Sub-Tropical Hotels", International Journal Of Contemporary Hospitality Management, Vol. 17 Issue: 3, Pp. 228 – 237.

REFERENCES (Chapter 2)

Straughan RD, Roberts JA. 1999. Environmental segmentation alternatives: a look at green consumer behavior in the new millennium. Journal of Consumer Marketing 16(6): 558–575.
Jain, S.K. & Kaur, G. (2006). Role of Socio-demographics in Segmenting and Profiling Green Consumers: An Exploratory Study of Consumers in India. Journal of International Consumer Marketing, 18 (3), 107-142
Newell, S.J. and Green, C.L. (1997), “Racial differences in consumer environmental concern”, The Journal of Consumer Affairs, Vol. 31 No. 1, pp. 53-69.
Fransson, N. and T. Grling. 1999. Environmental concern: Conceptual definitions, measurement methods, and research findings. Journal of Environmental Psychology, 19: 369-382.
Roberts JA, Bacon DR. 1997. Exploring the subtle relationships between environmental concern and ecologically conscious consumer behavior. Journal of Business Research 40: 79–89.
Van Liere KD, Dunlap RE. 1980. The social bases of environmental concern: a review of hypotheses, explanations, and empirical evidence. Public Opinion Quarterly 44: 181–197.
Dunlap RE. 1975. The impact of political orientation on environmental attitudes and actions. Environment and Behavior 7(4): 428–454.
Laroche M, Bergeron J, Barbaro-Forleo G. 2001. Targeting consumers who are willing to pay more for environmentally friendly products. Journal of Consumer Marketing 18(6): 503–520.
Webster FE Jr. 1975. Determining the characteristics of the socially conscious consumer. Journal of Consumer Research 2(3): 188–196.
Shrum LJ, McCarty JA, Lowrey TM. 1995. Buyer characteristics of the green consumer and their implications for advertising strategy. Journal of Advertising 24(2): 71–82.
Rowlands IH, Parker P, Scott D. 2000. Ready to go green? The prospects for premium-priced green electricity in Waterloo Region, Ontario. Environments 28(3): 97–119.
Ogunjinmi, A.A., Onadeko, S.A., 2012. An Empirical Study of the Effects of Personal Factors on Environmental Attitudes of Local Communities around Nigeria’s Protected Areas.

WEBSITE REFERENCES http://www.greenelectricity.org/ APPENDIX
PUBLIC ACCEPTANCE ON GREEN ELECTRICITY
Dear respondents,
We are the QNTB 313 (Research Method) students. This questionnaires survey is conducted as part of our course requirement. You’re been selected as one of the respondent. Please help us to complete this questionnaire survey by circle the most appropriate response. All the responses to this survey will be kept strictly confidential. Only consolidated results will be reported.
Thank you for your time and participation in this survey.

Regards: BF 089098 | BF 085241 | BF 086968 | BF 085897 | BF 085269 | BF 089096 | BF 089099 |
Muratova Yelnur
Mu’ain Affendi bin Fazli
Nik Nadia binti Nik Abdul Mutalib
Norzulaika binti Zulkifli
Nurfahana binti Mat Jasmi
Sadenov Amir
Smagulova Gaukhar

SECTION A: DEMOGRAPHIC CHARACTERISTIC

1) Gender Male Female 2) Race Malay Chinese Indian Others 3) Age 18 – 21 years 22– 25 years 26 – 29 years Above 30 years 4) Program Bachelor of Accounting Bachelor of Finance Bachelor of Human Resources Bachelor of Marketing Bachelor of Entrepreneurial Development Bachelor of International Business 5) Family Income Under RM1999 RM2000 – RM2999 RM3000 – RM3999 More than RM4000

6) Highest level achieved by someone in the household: * SPM * STPM * Diploma * Degree * Master * PhD

7) Traditionally, Malaysia’s energy sources for electricity are based on a “four-fuel mix” strategy which are: * Gas, oil, hydro, and coal * Gas, nuclear, biomass, and solar * Oil, biomass and thermal * Wind, hydro and solar

SECTION B
Please circle one answer in box based on 1-5 below:
1-Strongly Disagree 2-Disagree 3-Unsure 4-Agree 5-Strongly Agree

No | Item | Strongly disagree | Disagree | Unsure | Agree | Strongly Agree | | Public Acceptance | | | | | | 8 | I am aware of Green Electricity | 1 | 2 | 3 | 4 | 5 | 9 | I am willing to use Green Electricity | 1 | 2 | 3 | 4 | 5 | 10 | I am willing to spend on electric bill that generated from Green Electricity | 1 | 2 | 3 | 4 | 5 | | Attitudinal Characteristic | | | | | | 11 | Even if everyone tried to conserve energy at home, it wouldn’t make a big impact on energy use in Malaysia | 1 | 2 | 3 | 4 | 5 | 12 | Government should let industry decide how best to supply energy and conserve energy | 1 | 2 | 3 | 4 | 5 | 13 | I am very concerned about how climate change will affect future generations of Malaysians | 1 | 2 | 3 | 4 | 5 | 14 | The seriousness of environmental problems is exaggerated (revealed) by environmentalists | 1 | 2 | 3 | 4 | 5 | | Socialization Characteristic | | | | | | 15 | I am involve in Green environment activities | 1 | 2 | 3 | 4 | 5 | 16 | I talk about Green community services | 1 | 2 | 3 | 4 | 5 | 17 | I discussed about energy conservation with my family members | 1 | 2 | 3 | 4 | 5 |…...

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...HELL ! what the Bible says about it… Dr. John R. Rice “And ye shall know the truth, and the truth shall make you free” (Jn. 8:32) ©1997 by Trumpet Publications P. O. Box 1969, 0700 PIETERSBURG, South Africa. All rights reserved. Published with the permission of Sword of the Lord Publishers, Tennessee, USA. This version was edited by Prof. Johan Malan. This booklet may, however, be duplicated and distributed among interested persons without gain. Charges are only to cover the cost of duplication and distribution. No changes may be introduced to the text. For translation, or commercial publishing, please write to the above address. Scripture quotations are from the Authorised King James Version. The titles in this series on Internet are: Who is Jesus? From darkness to the light The judgement seat of Christ The Antichrist Israel The rapture Revival Hell - what the Bible says about it Spiritual warfare 2 1. How can we know about Hell? “There was a certain rich man, who was clothed in purple and fine linen, and fared sumptuously every day: And there was a certain beggar named Lazarus, who was laid at his gate, full of sores, and desiring to be fed with the crumbs which fell from the rich man’s table: moreover the dogs came and licked his sores. And it came to pass, that the beggar died, and was carried by the angels into Abraham’s bosom: the rich man also died, and was buried; and in hell he lift up his eyes, being in torments, and seeth Abraham afar off, and......

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