The sample was comprised of 172 prospective respondents. From 300 selected respondents, the return rate was more than 50%. Hence, 172 samples selected for present study is sufficient to generalize the findings to the population and appropriate for the application of explanatory factor analysis (EFA) (Field, 2013). An urban residential area for population frame in this study was chosen from the respondents who are residing in Klang Valley, Peninsular Malaysia (Kuala Lumpur, Selangor and Negeri Sembilan). This is due to easy accessibility on the population target.
Data Collection
The study utilized a cross-sectional research design and systematic sampling method was adapted as it could be an appropriate technique that produces a random sample. This is because it could eliminate sample selections that create bias. Two steps were conducted in this research.
First, continuous lists containing 1500 emails have been selected from random organizations such as universities, government companies and private companies that located around Klang Valley, Peninsular Malaysia. Secondly, by using the systematic sampling method, the questionnaires systematically sampled to the 300 respondents by distributing the link of the survey form through the email and face to face distribution.
Data Analyses
The factor analysis process is conducted in three stages: pre-analysis checks, extraction and rotation. The purpose of the pre analysis check is to ensure that the data set is appropriate for the application of EFA. In this study, a minimum sample size of 172 (N = 100) might be acceptable (Hair et al., 2010). While, the purpose of extraction is to identify and retain those
factors which are necessary to reproduce adequately the initial correlation matrix. Lastly, the rotation process is conducted to simplify factor structure. For small sample sizes, the rule of ≥ .60 was utilized to determine which items loaded onto the factors. In extraction stage, Eigenvalue23 of 1 and Scree Plot test were used as the factor extraction criteria to determine the number of factors retain. If the factors are assumed to be largely uncorrelated, an orthogonal rotation should be use. Meanwhile, if the factors are assumed to be correlated, an Oblique Rotation should be used. Only factor loading with value above .40 should be retained for further analysis (Field, 2013) and the minimum item for each component should be at least 4 item left (Zainuddin, 2014) to be tested in further data analysis. All analyses were conducted in the Statistical Package for Social Sciences (SPSS), version 20.0.
RESULTS
Demographic Characteristics of the Sample
The study aims to clarify and validate on the dimensionality of the instruments of PPRED using validation study. In order to ensure that the study sample are acquainted with the purpose of study, 1500 respondents from random organizations such as universities, government companies and private companies located around Klang Valley, Peninsular Malaysia are used.
A total of 172 respondents participated in the study. Approximately 76.7% of female had participated in the study. Respondent participating in the study are mostly married and have the average age between 20 to 24 years old with the percentage 52.3% and 33.1% respectively.
In addition, 62.8% of them obtained an education up to Bachelor Degree. Moreover, majority of the respondent have an occupation in education (52%) and lives in a house with the household income between RM2, 000 to RM5, 000 (30.8%).
Exploratory Factor Analysis (EFA)
Firstly, data suitability of the study are checked using two testing i.e. Kaiser-Mayer-Olkin (KMO) and Bartlett Test before EFA being conducted. Results from both tests show that the Kaiser-Mayer-Olkin (KMO) above .60 for all variables and Bartlett Test is significant (with p<.000). Secondly, to test factor extraction, two techniques were employed: Kaiser’s Criterion (eigenvalue >1) and Scree Plot (Rule of thumb – elbow for turning point). Eigen test and Scree Plot produced six components from 33 items that were being tested. Finally, in the last stage, 33 items were rotated using Direct Oblimin, and the result from Pattern Matrix has clarified 33 items under six groups. The items under six groups obtained from pattern matrix table were similar with original instrument adapted from literatures and factor loading for all items under six variables were greater than .30. Those six variables (groups) are as follows, i.e. (1) awareness on RE (2) knowledge on RE, (3) willingness to adopt RE technology, (4) environmental concern, (5) attitude towards RE usage, and (6) willingness to pay. (see Table 1).
Table 1: Factor Structure of Public Participation towards Renewable Energy Development
I strongly support the use of renewable energy resources.
I believe that media has a great responsibility in emphasizing the importance of using renewable energy resources.
I believe that it is necessary to focus and create awareness on the importance of energy resources and energy saving within educational programs.
The expression of renewable energy makes me nervous because I am not used to it.
Using renewable energy resources would remove the negative effects of the greenhouse gasses.
I am not interested in whether the energy resources are renewable or not.
Renewable energy and its resources are subjects that I have no idea about.
.877
I think that I know about hydro electric and the way it is used to produce electricity.
I think that I know about natural gas and the way it is use to produce electricity.
I think that I know about biomass and the way it is used to produce
electricity.
I think that I know about solar power and the way it is used to produce electricity.
I think that I know about fossil fuel and the way it is use to produce energy to accomplish the same tasks.
Solar energy: Using the energy from the sun for heating or electricity production. E.g: Solar Panel.
Energy efficient cars: Producing cars that use less energy to drive the same distance. E.g: Hybrid and solar car.
.853
.835
.781
.771
Carbon sequestration: Using trees to absorb carbon dioxide from the atmosphere.
Wind energy: Producing electricity from the wind, traditionally in a windmill.
Bio-energy/biomass: Producing energy from trees or agricultural wastes such as paddy and palm trees residue.
.718
.716
Environmental Concern If things continue on their current situation, we will soon experience a major environmental disaster. products, I consider how my use of them will affect the environment.
The most effective way to overcome global climate change is reduce energy consumption.
We are consuming energy much more than what we really need.
.631 products such as solar panel is acceptable.
Installation of renewable energy product such as solar panel is added value to the house.
Installation of renewable energy such as solar panel is compatible with modern living style.
To pay 10% more in your monthly electric bill for renewable.
To pay more now in exchange for possibly lower electric rates in the future.
To pay more for your electric bill if you knew the cost paid for
environmentally safe electricity.
To support a fuel adjustment clause in your electric bill to subsidize the cost of developing RE-powered energy.
To support the government’s policy if the government makes a policy to generate 10% of your electricity
Confirmatory Factor Analysis (CFA)
Confirmatory factor analysis (CFA) is a multivariate statistical technique used to measure the factor structure of a set of observed variables. The purpose is to test the hypothesized relationship between observed variables and their underlying latent constructs exists.
The unidimensionality of the six constructs were tested using CFA and all items were examined simultaneously using AMOS 25. The six variables extracted from EFA were tested and each item was designed to load on its corresponding factor. The model fit had been achieved based on certain fitness indexes. All the fit indices imply that the model is consistent with the data. The results of the CFA operation (see Figure 1) on measurement model which consists of six variables shows that all items had factor loadings exceed .60 (Zainuddin, 2014).
Figure 1: CFA Result of Measurement Model on Public Participation towards Renewable Energy Development (PPRED)
Convergence validity
Convergent validity is achieved when all items in a measurement model are statistically significant (Zainuddin, 2014). Convergence validity was tested and confirmed by obtaining Average Variance Extracted (AVE) which is highly correlated between two instruments and statistically significant (Hair et al., 2010). The value of AVE should be 0.5 or higher to achieve
Fitness Indexes 1. ChiSq=161.918 2. df=120
3. GFI=.908 4. CFI=.972 5. TLI=.964 6. IFI=.973 7. RMSEA=.045
the Convergent Validity (Zainuddin, 2014). The value of AVE less than 0.50 indicate that variance explained by construct is smaller than variance explained by measurement error.
Result from Table 2 shows that all variables have AVE value exceeded .50. (see Table 2) Table 2: Summary Result of CFA of Measurement Model on PPRED
Construct Item Factor loading), while, formula for Average Variance Extracted (AVE) = ²/n (Where, is factor loading, n = number of items in a model).
Discriminant validity
Discriminant is achieved when the measurement model is free from redundant items (Hair et al., 2010). AMOS will identify which of the pair item is redundant in the system in terms of high Modification Indices (MI). Discriminant validity can be achieved when Average Variance Extracted (AVE) exceeded the shared variance estimate (square of correlation). The findings of this study shows that all variables have achieved the discriminant validity where the square root of AVE (diagonal value in bold) is higher than the value in the row and column (see Table 3). Thus, those results have shown that all latent variables, i.e. (1) awareness on RE, (2) knowledge on RE, (3) willingness to adopt RE technology, (4) environmental concern, (5) attitude towards RE usage, and (6) willingness to pay in the measurement model are different and can be discriminated with each other.
Table 3: The Discriminant Validity Index Summary of Public Participation towards Renewable Energy Development (PPRED)
Construct
Awareness on RE Knowledge on RE Willingness to Adopt RE Technology EnvironmentalConcern Attitude towards RE Usage Willingness to Pay
Awareness on RE 0.80
Knowledge on RE 0.40 0.80
Willingness to Adopt
RE Technology 0.69 0.36 0.82
Environmental
Concern 0.68 0.31 0.63 0.77
Attitude towards
RE Usage .23 .08 .22 .12
0.75
Willingness to Pay .17 .08 .12 .04 .09 .84
Note: Diagonal value represents the square root of the AVE, while the off-diagonals represent the correlations among the variables.
DISCUSSION
The aim of this study is to clarify and validate the factor structure and dimensionality of the instrument of PPRE. Hence, the development of instrument has follow standard procedures including extensive literature review of existing questionnaires, input from experts and using available statistical tool. As far as the authors’ knowledge, this is the first study of its kind which specifically trying to gauge PPRED in Malaysia. The six components obtained from EFA, were equivalent to those of several adapted literatures, i.e. (1) awareness on RE (item- ARE4, ARE2 and ARE8), (2) knowledge on RE (item-KRE10, KRE8, and KRE9), (3) willingness to adopt RE technology (item-WTA2, WTA1, and WTA3), (4) environmental concern (item-EC10, EC2, and EC1), (5) attitude towards RE usage (item-AURE6, AURE7, and AURE5), and (6) willingness to pay (item- WTP6, WTP5, and WTP3). Then, using CFA the result shows that the underlying factor structure of PPRED is equivalent to the original instrument. All factor loadings for each item are above .60 and appropriate level of fitness indexes were achieved (see Figure 1). The other purpose of this study is to determine the discriminant, convergent validity and internal consistency (Cronbach’s Alpha) of the factor structure of questionnaire. Result shows that six constructs has greater AVE value than 0.50, which indicates a good convergent validity. Meanwhile, for discriminant validity, results from this study show square root of AVE for all six constructs of (1) awareness on RE, (2) knowledge on RE, (3) willingness to adopt RE technology, (4) environmental concern, (5) attitude towards RE usage, and (6) willingness to pay, AVE value were greater than shared variance (square of correlation) between any two constructs. This result shows that there is a significant different between each latent variable in the model. Finally, the study checked on the internal consistency of four variables. The result shows that all variables have acceptable value which is above .70. In summary, this information provides preliminary evidence that the public
participation towards RE instruments is a reliable and valid measure to obtain the data regarding RE development in Malaysia.
However, in terms of sample size, future study can be conducted in the larger sample size to sufficiently represent the population.
CONCLUSION
The RE policy in Malaysia was initiated in 2001 and still in its infancy stage. Although several initiatives and action plan have been developed to improve RE development, in general they are not directly linked with the public per se. Hence, it is the purpose of this paper to clarify and validate on the dimensionality of the instruments on PPRED using validation study. The results show validated instruments of PPRED that are willingness to pay on energy generated from RE, public awareness on RE, degree of knowledge on RE, willingness to adopt RE technology, environmental concern and attitude towards RE usage are reliable and valid measurements among Malaysian respondents. The awareness and knowledge of Malaysians on RE can further be improved through public education formulation and well-targeted campaign that focuses on educating the young generation who seem to be receptive towards RE development. Other mass media channels, such as newspaper, television and radio can be useful to disseminate information on RE. The other factors that will be a great influence on rapid improvement in RE development are appropriate legislations and incentives. These include incentives for purchasing of RE products such as exemption of tax on hybrid cars which has been adopted by government of Malaysia.
ACKNOWLEDGMENTS
This work was supported in part by Ministry of Higher Education, Malaysia (KPT) under Grant Nos. FRGS/1/2015/SS08/USIM/02/2 and RAGS/1/2015/SS0/USIM/02/1.
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