Final Project

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This project will be based on the 2 previous assignment done. Therefore, answers must be based on the 2 assignments.

Assignment 1 (20%)

Combined your 2 assignments from Research Methodology and convert it into final report format (follow the outlines as I sent earlier)

In this submission, you can add on any new sources, materials or writing based on the suggestion I have provided

The assignment 1 should consist of Chapter 1 -3 and questionnaires as appendix.

Chapter 1 Introduction

– Introduction – what are the issues of the topic?  In Malaysia and the rest of the world macro view.

– Problem statement

– Purpose of study

– Research objectives: make sure your RO match with RQ, if you have 5 RO, then match with 5 RQ, eventually develop 5 hypotheses

– Research questions : make sure all RO, RQ, and hypotheses use the same keywords to show consistency, likewise for your IV/DV

– Definition of key variables – standardized definition for your research on the key words, especially those IV/DV, otherwise reader will have their own understanding. For example, hot – weather, hot – spicy, hot – sexy… so which one is you try to mean.

(b) Chapter 2 Literature Review

– Background study – This is related to the industry background, if you research about F&B, then show all the data, graph, tables about F&B in Malaysia then only zoom to particular state or city within Malaysia,

– Related theory/model ( need to find a supporting theory for every research)

– Discussion of recent findings ( these are findings from other researchers on the same topic of your research, what are them? Can quote and support with a few journals, can be counter checked in chapter 5 if your findings same like other?)

– Research framework ( show and explain/define all your IV/DV)

– Hypotheses

 (c) Chapter 3 Research Methodology 

– Variables and measurement

– Population, sample, sampling technique

– Data collection technique

– Techniques of analysis that may be used

– Questionnaire

(d) Bibliography

(e) Appendices

– Variables and measurement

– Population, sample, sampling technique

– Data collection technique

– Techniques of analysis that may be used

– Questionnaire

The research should cover a business phenomenon. You are expected to enhance the content into a researchable form. The Project Proposal contributes 30% to the total marks of the course. 

The Project Proposal should be word-processed and should be 3,000 thousand covering the following suggested topics.

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BLC322/04 Final Year Project Outline

(For Logistics and Supply Chain
Management)

Assignment 1 (Proposal)

Due Date: 11 Feb 2023 11:59:59 PM

Assignment 2 (Presentation)

Due Date: 4 Mar 2023 11:59:59 PM

Assignment 3 (Final Project Report)
Due Date: 27 Mar 2023 11:59:59 PM

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1.0 Introduction

The Final Year Project Course is a capstone course of the BBLC programmes and is
essentially an independent study to be undertaken by a student on an organisational or
management issue or problem either in the business context or improving business
performance

As the Project Course carries 4 credits, a workload of 160 hours is expected to be undertaken
by you for this course. Your project proposal should be derived from the assignment of
BMG318/03 Research Methods

There is NO formal class for this course. Once you have enrolled in the Business Project
Course, you are expected to work closely with the lecturer/supervisor during the period of
your preparation of the Project Report.

Assessment Percentage

Assignment 1 (Proposal) 20%

Assignment 2 (Presentation) 20%

Assignment 3 (Final Report) 60%

2.0 Goal and Objectives of the Project Course

The main goal of the Business Project Course is to provide an opportunity for you either to
achieve a better understanding of an applied ‘research’ problem or to solve/resolve an
organisational problem(s) or improve the business performance in your proposed research.
When you have successfully written the Project Report, you would have achieved the
following two objectives:

(a) Have the ability to synthesize and apply various substantive knowledge from some or

all the courses you have taken to address the ‘research’ problem which is relevant
and interesting to you; and,

(b) Have developed and demonstrated soft skills in the area of communication, analytical
and critical thinking which you have acquired through all the courses you have taken.

You are strongly encouraged to undertake a study in the area of your specialization.

3.0 Requirements for Enrolling in the Project Course

You are allowed to enroll for the Course provided you have studied BMG318/03 Research
Methods.

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4.0 Type of Research for Your Project

Your research project may be from any one of the following major types of study:

• A comprehensive case study (covering problem formulation, analysis and
recommendations in a single organisation/multifunctional area).

• A comparative study aimed at inter-organisational comparison/ validation of theory /
survey of management or developmental practices.

• A survey research (either a descriptive or a pilot study).

You are encouraged to continue from your research project topic from BMG318/03 Research
Methods Your lecturer/supervisor has to agree to your proposal before you can
undertake/continue the study.

5.0 Project Topics

The scope and depth of the business project is not expected to be extensive given the limited
time you are given to complete the research project. Acceptable project titles that will meet
the course objective cover a wide range of topics. The chosen topic should be relevant to
theyour specialization or to the Bachelor of Business’s programme.

Please conduct your research in the area of your specialization:

Research in the area of Logistics and Supply Chain Management:

1. Research on Digital Transformation:

Digitization of the supply chain, encompassing all efforts to integrate corporate systems into a
unified whole as well as implementing new digital technologies, will continue to be a priority.
Transforming old concepts with technological disruption, there are new trends to look out for
logistics and supply chain management

Under this area, at least following aspects are covered:

i. Digital Transformation Key Attributes; Challenges; enablers & Success Factors
ii. Smart Government Initiatives: How Governments are Driving Digital Change
iii. Digital Leadership linking to Virtual Teams or Self Organised Teams
iv. COVID 19 impacted the implementation of Digital Transformation
v. Cross-functional collaboration in the decision-making process
vi. The value of data and interdependencies in decision-making
vii. Machine learning techniques in supply chain management

2. Research on Industry 4.0 and Big Data:

The digitization of the supply chain, the growth in IoT, and the greater availability of customer
data. Companies today have access to enormous amounts of data and are using this to generate
business intelligence ranging from understanding past performances to predicting future trends.

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By using Big Data, it’s possible to determine customer preferences and market trends, as well
as redefine the supply chain.

Various aspects covered under this head may be listed as below:

i. Big data and the impact in logistics and supply chain management
ii. Evaluation of technology use in modern supply chain management.
iii. The extension of supply chain resilience through Industry 4.0
iv. The Impact of Industry 4.0 on supply chain management.
v. Implementation of E-logistics in Supply Chain Operations

3. Research on Operations and Supply Chain Management:

The supply chain systems of today are more likely to see massive changes technologically in
the coming years. As companies respond to the conflicting demands of supply chains, especially
with regard to the need for flexibility and agility, many are turning to robotics to speed up labor-
intensive tasks.

This branch covers:

i. Risk Evaluation and Management involved in a supply chain
ii. Partnerships Perspective in Supply Chain Management
iii. Assessing Supply Chain Risk Management Capabilities
iv. Implementation of Green Supply Chain Management Practices
v. Supply Chain Management Practices and Supply Chain Performance Effectiveness
vi. The Impact of Supply Chain Management Practices on the Overall Performance of the

org
vii. The Influence of Environmental Management Practices and Supply Chain Integration on

Technological Innovation Performance
viii. The Relationship between Total Quality Management Practices and their Effects on

Firm Performance
ix. Level of Commitment to Top Management regarding the TQM Implementation
x. Impact of Mobility Solutions (transportation / latest technologies) on logistics.
xi. Study on the roles of supply chain management in corporate outsourcing.
xii. Evaluating strategies for cost reduction in SCM relating to exports and imports

However, you should consider the following factors before you make the final choice of your
topic:

• The extent of your interest in and familiarity with the topic

• Availability and accessibility of adequate information or data on the topic

• Limited time frame (11 weeks) to conduct your project successfully

• Resource (e.g. financial, expertise, etc.) requirements to undertake the research
project

You may need the assistance of the lecturer/supervisor to help you in the choice of a suitable
topic.

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6.0 Project Administration

The School will appoint a lecturers / supervisor who will be overseeing the organisation and
management of the Business Project course, as well as providing support for academic
related matters for the Course.

Throughout the duration of the Business Project Course, students are encouraged to meet
the supervisor at least 5 times or more. Additional online support will be provided via
Wawasan-iWawasan-i. It is important that you check Wawasn-i regularly for any
updates/information about the course during the semester.

7.0 Timeline or Schedule of Project Work

All students registered for the Project course are required to complete their research work
and submit their Project Report within the period of one semester period (normally 11 weeks).
There will be no extension given (under normal circumstances) to complete the
Project Report beyond the stipulated submission deadline. Deadlines are indicated on the front
page of this outline.

8.0 Project Proposal (Assignment 1) [20%]

The Project Proposal comprises chapter 1 to chapter 3 of your project report. You should
have the document from your previous BMG318/03 course. The research should cover a
business phenomenon. You are expected to enhance the content into a researchable form.
The Project Proposal contributes 30% to the total marks of the course.

The Project Proposal should be word-processed and should be 3,000 thousand covering
the following suggested topics.

(a) Abstract, Chapter 1 Introduction

– Problem statement
– Purpose of study
– Research objectives
– Research questions
– Definition of key variables

(b) Chapter 2 Literature Review

– Background study
– Related theory/model
– Discussion of recent findings
– Research framework
– Hypotheses

(c) Chapter 3 Research Methodology

– Variables and measurement
– Population, sample, sampling technique

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– Data collection technique
– Techniques of analysis that may be used
– Questionnaire

(d) Bibliography
(e) Appendices

The format should be as follows:

• Times New Roman, 12pt, un-Justify, double spacing

• Cover page, title page: (As shown in the Appendix)

• Content page with correct page number listed
• APA referencing style is expected

Your lecturer/supervisor is expected to provide guidance and the clarifications of research
objectives and content related matters, and on how to improve the writing style and other
presentational aspects (such as acknowledgement of sources and display of summary data).
He/she is also expected to provide assistance to data analysis whenever possible.

The Project proposal should be submitted as per the date in the course outline. The feedback
that you receive from your assignment 1 is in addition to other feedbacks that you may receive
from your lecturer during the face-to-face meetings and forum discussions.
The marking rubric for project proposal is shown in Appendix K.

Project Proposal:

i) Abstract and Chapter 1: Introduction to the Study (30%)
ii) Chapter 2: Review of the Literature (30%)
iii) Chapter 3: Research Methodology (30%)
iv) Format & Overall Impression (10%)

9.0 Final Project Report (Assignment 2) [60%]

Your Project Proposal will provide a focus for conducting the rest of the study. The project
report should contain Abstract, Chapter 1 to Chapter 5, Bibliography and Appendices. The
length of the report should be to a maximum of 10,000 words (excluding abstract,
appendices and exhibits).

9.1 Writing the Project Report
Each Project Report must adequately describe the research problem and objectives, review
the relevant literature, justify the research approach and methods adopted, explain the
research findings, indicate what has been learnt or propose relevant recommendations and
suggest how you would improve the research in future efforts.

Your lecturer/supervisor is expected to provide guidance towards the clarifications of research
objectives and content related matters, and on how to improve the writing style and other
presentational aspects (such as acknowledgement of sources and display of summary data).
He/she is also expected to provide assistance to data analysis whenever possible.

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9.2 Submission of Project Report

You are to submit your project report as per the deadline indicated in the course outline.
This is according to the schedule given by the University. You have to ensure that your
report has been submitted to Turnitin and the percentage of similarity is within acceptable
range.

The Project Report should be word-processed and should to a maximum of 10,000
(excluding abstract, appendices and exhibits) words covering the following suggested
topics.

(a) Cover page, Acknowledgement, Table of Content, Abstract, List of Tables and List of

Figures.
(b) Chapter 1 Introduction

– Problem statement
– Purpose of study
– Research objectives
– Research questions
– Definition of key variables

(c) Chapter 2 Literature Review
– Background study
– Related theory/model
– Discussion of recent findings
– Research framework
– Hypotheses

(d) Chapter 3 Research Methodology

– Variables and measurement
– Population, sample, sampling technique
– Data collection technique
– Techniques of analysis that may be used
– Questionnaire

(e) Chapter 4 Analysis of Results

– Data Analysis
– Tables, summary statistics
– Result of hypothesis testing, meeting research objectives and questions

(f) Chapter 5 Findings, Conclusions and Recommendations

– Comment on the results
– Managerial implications
– Limitation of the research
– Future research opportunities

(g) Bibliography
(h) Appendices

– Survey questionnaire
– Statistical data

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9.3 Project Report Examination

The examination of the Project Report shall be carried out by the lecturer/supervisor and
moderated by another lecturer in the SBA school.

9.4 Indicative Marking Scheme for Project Report

The marks for the Project Report are based on the indicative marking rubric shown below. The
quality of the research work or originality of your work will play an important factor in the mark
of the Project Report.

Abstract and Chapter 1: Introduction to the Study (15%)
Chapter 2: Review of the Literature (20%)
Chapter 3: Research Methodology (20%)
Chapter 4: Analysis of Results (20%)
Chapter 5: Findings, Conclusions & Recommendations (15%)
Format, References and Style of Presentation: (10%)

Marking rubric and marksheet for BMG322/04 Business Project report are shown in
Appendix L.

10.0 Presentation (Assignment 3) [20%]

The student’s presentation will be assessed by at least two lecturers from the School and it
is normally will be held one (1) week after the due date of the Project report submission.

The tentative date of the presentation will be published on Wawasn-i. Each student will be
given a period of ten (10) to fifteen (15) minutes for the presentation and ten (10) minutes for
questions and answers (Q&A). Assessment of the student’s presentation will be mainly
based on the contents, style of the presentation and also the ability to answer questions.

11.0 Presentation of Written Project Report

All Project Reports need to comply with the following report writing requirements.

Length
• A maximum of 10,000words (excluding abstract, appendices and exhibits).
• Approximately 25 pages.

Page set up
• The standard margins for the general text, tables and diagrams are as follows:

Top: 2.5 cm
Right: 2.5 cm
Left: 4.0 cm

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Bottom: 2.5 cm

• The right margin should be unjustified (i.e. leave right margin ragged) for readability.
• Headers and footers (apart from page numbers) should not be used.
• All text, figures and tables must be within this area.
• The paragraphs should not be indented.

Paper size and printing
• Use only good quality plain white paper (80 g/m²) of A4 size (210 × 297 mm) for printing.
• The report should only be printed only one side of the paper using a ‘laser printer’.

Font type
• The body of the report should be in font size 12, Times New Roman.

Line spacing
• Double spacing between lines.

Format for headings
• The chapter headings must be centered, regular type and bold (Example: Chapter 1)
• The sections and sub-section in the report must follow the numbering format below:

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Chapter 1 Introduction

1.0 Main heading
1.1 Second level heading

1.1.1 Third level heading

Page number
• The page number should be at the bottom of the page, right hand corner.

Tables and figures
• These should be numbered in sequence by chapter, e.g. Table 3.1 is the first table in
• Chapter 3; Figure 4.1 is the first figure in Chapter 4.
• Each table and figure should be properly referenced in the text and accompanied by a

descriptive title which clearly explains the contents of the table or figure.
• All the figures and diagrams must be properly inserted at the relevant sections of the

report.
• The title of the Table/Figure must be on top of the Table/Figure, in italic and justified.
• The Table/Figure and Table/Figure number must not be in italic.
Example:

Figure 3.1 Annual Sales of ABC Company Table 4.1 Sales Details of ABC Company

Decimal
• Keep the results to 2 decimal points.

• Example: Mean, =0.86, Standard Deviation, =0.32

Symbols
• All algebraic/statistical symbols should be written in italics except Greek symbols (e.g.:

α and β).

References

• The source of information or references should be placed at the end of the report in

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numbered order.
• References should be cited using the Wawasan Open University Citation Guide (refer to

the Student Handbook, Wawasan-i, and the E-library resources).

Sections of the report
• Every report comprises four major parts: Introduction, Chapter 1-5, Reference and

Appendix.
• Every part has sections that have to be organised in a specific order.

Part 1: Introduction
• The Introduction is made up of a number of sections in the following order:

Title Page (as shown in Appendix B)
Acknowledgment (as shown in Appendix C)
Certificate of Originality (as shown in Appendix D)
Plagiarism Statement (as shown in Appendix E)
Table of Contents (as shown in Appendix F)
List of Tables (if any)
List of Figures (if any)
List of Symbols (if any)
Abbreviations (if any)
Abstract (as shown in Appendix G)

• All pages in the Introduction are numbered using lower case Roman numerals (i,

ii, iii, etc).
• The Title Page of the report is considered as page i, but the number is not printed on

the page.
• The Abstract is a summary of the entire Project Report and should provide a brief

exposition of the research problems and aims, approaches taken to solve the problems
and a summary of findings in the context of the whole area of study.

• Subsequent research proposals may be incorporated.
• The length of The Abstract should not exceed 400 words.
• The Abstract should be placed immediately before the Chapter 1 of the Project Report.

Part 2: Chapter 1-5
• The Text is made up of five chapters.
• Chapter 1: Introduction to the Study
• Chapter 2: Review of the Literature
• Chapter 3: Research Methodology
• Chapter 4: Analysis of Results
• Chapter 5: Findings, Conclusions and Recommendations
• The length of the text (Chapters 1 ~ 5) should be to a maximum of 10,000 words.

Part 3: Reference
• The Reference is the section after the Text that begins on a fresh page bearing the

heading in capital letters, centralised without any punctuation marks.
• The list of references are double-spaced between entries but single-spaced within each

entry.

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• References must be presented according to the Wawasan Open University Citation
Guide.

Part 4: Appendix
• The Appendix is a section that is separated from the preceding material by a cover

sheet bearing the heading APPENDICES in capital letters (or, if there is only one,
APPENDIX), centralised without any punctuation marks.

• This sheet is not numbered and also not included in the total number of pages.
• Appendices present materials that are referred to in the Text. It contains supplementary

illustrative material, notes on the interview/questionnaires, data or quotations too long
for inclusion in the text or long explanations about a particular method/experiment.

• Appendices may be divided into Appendix A, Appendix B, etc., such divisions being
treated as first order subdivisions. Each appendix with its title, if it has one, should be
listed separately in the Table of Contents as a first order subdivision under the heading
APPENDICES.

• Tables and figures in the Appendices must be numbered and have captions and also
listed in the List of Tables and List of Figures in the Introduction.

12.0 Plagiarism
Plagiarism, that is, the willful representation of another person’s work, without the
acknowledgement or the deliberate and unacknowledged incorporation in a student’s work
of material derived from the work (published or otherwise) of another, is UNACCEPTABLE
and will incur the penalty of outright failure.

13.0 Declaration and Embargo Request
The following are some guidelines provided by the WOU Library regarding project
declaration and embargo request.

Submission
• The School may deposit in the Library one electronic copy submitted in partial fulfillment

of requirements for a degree at Wawasan Open University.

Declaration of Project Report and Copyright Form (Appendix H)
• Download this form from Wawasan-i for this course.
• Complete the form in duplicate.
• Please ensure that your project supervisor and you sign in black ink.

Copyright
Once the student authorizes the copyright of the project report to the University, it will be
published as open access material. The aim is to promote information sharing for the

common good. Appropriate metadata will be added to your work, before it gets posted on
the Library’s Website.

Embargo
• You may apply for a delay in the release of your Final Project Report for a specified

period.

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• The University permits the embargo of Final Project Reports under the following
conditions :-
– Your work has potential commercial value
– You intend to publish your work
– You need to protect intellectual property rights associated with your work
– You need to protect individual’s rights of privacy or sensitivities, mentioned in your
work

• The Embargo Request Form (Appendix J) is obtainable from the Library’s Website.
• The signed and completed Embargo Request Form must accompany the project paper

upon submission to the Library.
• Please note that the bibliographic information (author, title, subject, other necessary

metadata) of the copy held in the Library will be made accessible to the public on the
Library’s online catalogue.

14.0 Presentation (Assessment 3)

The student’s presentation will be assessed by at least two lecturers from the School and it
is normally will be held one (1) week after the due date of the Project report submission.
The tentative date of the presentation will be published on Wawasan-i. Each student will be
given a period of ten (10) to fifteen (15) minutes for the presentation and ten (10) minutes
for questions and answers (Q&A). Assessment of the student’s presentation will be mainly
based on the contents, style of the presentation and also the ability to answer
questions.

The Marking Rubric for Presentation is in Appendix M

15. Final Grade

The Final total mark is a combination of the Project Proposal (Assignment 1) [20%], the
Project Report (Assignment 2) [60%] and a presentation of your report [20%], and it will
contribute to your student’s grade point average.

The final grade of the Project course will be categorised according to examination
guidelines.

Those who failed will have re-take the Project Course in the next or following semester; in this
case, you will have to pay the full cost of the BLC322/04 Final Year Project Outline (For
Logistics and Supply Chain Management).

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Appendices

Appendix A: Sample Final Project Report Front Cover

Appendix B: Sample Title Page

Appendix C: Sample Acknowledgements

Appendix D: Sample Certificate Of Originality

Appendix E: Sample Plagiarism Statement

Appendix F: Sample Table Of Contents

Appendix G: Sample Abstract

Appendix H: Declaration Of Project Report And Copyright Form

Appendix J: Declaration Of Project Report And Copyright Form

Appendix K: Project Proposal Marking Rubric

Appendix K(a): Project Proposal Marksheet

Appendix L: Project Report Marking Rubric

Appendix L(a): Project Report Marksheet

Appendix M: Marking Rubric For Presentation

Appendix M (a): Presentation Marksheet

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Appendix A. Sample Final Project Report Front Cover

DETERMINANTS OF ADOPTION OF GENERATING
POWER FROM SOLAR PANELS AMONG RESIDENTIAL

HOME IN PENANG
[Note: Project title in full, Font Arial size 18, Bold, Capital Letters]

Tan Hooi Hooi
[Note: Student’s Name as stated in NRIC/Passport, Font Arial size 18, Bold, Capital Letters]

SCHOOL OF BUSINESS AND ADMINISTRATION

WAWASAN OPEN UNIVERSITY
[Font Arial size 12, Bold, Capital Letters]

2018
[Note: Project Report submission year, Font Arial size 12]

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Appendix B. Sample Title Page

NAME Tan Hooi Hooi

DEGREE Bachelor of Business (Hons) in Logistics and Supply Chain Management

SUPERVISOR Dr. Loo Puai Keong

TITLE Determinants of Adoption of Generating Power from Solar Panels Among

Residential Home in Penang

DATE March 2023

INSTITUTION Wawasan Open University (WOU)

Project Report submitted in partial fulfillment

of the requirements for the award of

Bachelor of Business (Hons) in Management

of

Wawasan Open University

Penang, Malaysia

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Appendix C. Sample Acknowledgements

ACKNOWLEDGEMENTS

I would like to take this opportunity to thank my family and friends for all the emotional and financial

support provided to me as it is their support which was instrumental in encouraging me to reach the

end of this journey. During my studies, there were times when work commitments and other

challenges that made me believe that I would not be able to see this journey through. It was during

these times that words of encouragement from my family and friends gave me the necessary

motivation to persist. No words of thanks can adequately express the depth of my appreciation and

gratitude.

I would also like to personally thank my lecturer ……….. for providing me all the guidance required to

successfully complete this research report. His encouragement, support, understanding and, above all,

his prompt, constructive and greatly appreciated criticism and feedback, were invaluable to the

research, writing and completion of this report.

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Appendix D. Sample Certificate of Originality

CERTIFICATE OF ORIGINALITY

This is to certify that the research project, Determinants of Adoption of Generating Power from

Solar Panels Among Residential Home in Penang is an original work of the student and is being

submitted in partial fulfillment of the requirements for the award of Bachelor of Business (Hons)

in Management of Wawasan Open University (WOU). This report has not been submitted earlier

either to this University or to any other University / Institution for the fulfillment of the

requirement of a course of study.

Declaration made by Student:

Tan Hooi Hooi

March 2023

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Appendix E. Sample Plagiarism Statement

PLAGIARISM STATEMENT

I, Tan Hooi Hooi, hereby declare that the attached report is all my own work and all references

contained within it have been correctly cited, the original authors acknowledged and it contains no

plagiarism.

After the completion of the project report, I have scanned the report through TURNITIN software for

plagiarism. The plagiarism report received from the plagiarism detection software indicated that the

work is very likely to be original and that I am satisfied that I had not plagiarised any substantive part

of the report.

Declaration made by Student

————————————————-

[Signature]

Tan Hooi Hooi

44112233

30-Mar-2023

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Appendix F. Sample Table of Contents

TABLE OF CONTENTS

Contents Page

Title Page i

Acknowledgements ii

Certificate of Originality iii

Plagiarism Statement iv

Table of Contents v

List of Tables (if any) vi

List of Figures (if any) vii

List of Symbols (if any) viii

Abbreviations ix

Abstract x

Chapter 1 Introduction to the Study 1

1.1 Introduction to the Problem 1

1.2 Background of the Problem 2

.

.

.

1.9 Summary

Chapter 2 Review of the Literature 5

2.1 Introduction 6

2.2 Review of the Literature 8

2.3 Theoretical Framework 11

.

.

.

2.6 Summary

Chapter 3 Research Methodology 17

3.1 Introduction 17

3.2 Research Design 19

3.3 Methodology 20

.

.

3.8 Summary 25

[Signature]

Tan Hooi Hooi

44112233

30-Mar-2023

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Appendix F. Sample Table of Contents (continued)

TABLE OF CONTENTS(continued)

Contents Page

Chapter 4 Analysis of Results

Profile of Respondents

Goodness of Measures

Descriptive of Analysis

Summary

26

26

29

30

39

Chapter 5 Findings, Conclusions and Recommendations

Recapitulation of the Study Findings

Discussion
Implications

5.8 Summary

40

41

43
44

48

References

Appendices

Appendix A Studies on Solar Panel Demand

Appendix B Interview Structure

Appendix C Interview Transcript

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Appendix G. Sample Abstract

ABSTRACT

(Write your abstract here and the length should not exceed 400 words)

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Appendix H. Declaration of Project Report and Copyright Form

BMG322/04 BUSINESS PROJECT DECLARATION OF PROJECT REPORT

Student ID Student Name

Semester

Project Title

I hereby declare that the work in this project report is my own except for quotations and
summaries which have been duly acknowledged.

I acknowledged that Wawasan Open University reserves the right as follows:

1. The thesis/ project paper is the property of Wawasan Open University.

2. Wawasan Open Library has the right to publish my project report as online access
(full text) and furnish upon request copies in whole or part for the purpose of
research or teaching and learning only.

Name Signature Date

Student’s Name

Project Supervisor

Dean of School

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Appendix J. Project Report Embargo Request Form

BMG322/04 BUSINESS PROJECT EMBARGO REQUEST FORM

Student ID Student Name

Semester Date Embargo
will be Lifted
(dd/mm/yy)

Project Title

An approved embargo period shall be for two years and non-renewable. The University
permits the embargo of project reports under the following conditions:-

i. Your work has potential commercial value
ii. You intend to publish your work
iii. You need to protect intellectual property rights associated with your work
iv. You need to protect individual’s rights of privacy or sensitivities, mentioned in your

work.

Name Signature Date

Student’s Name

Project Supervisor

Dean of School

Justification:

27
BMG322/04 Updated 22 July 2020

Appendix K. Project Proposal Marking Rubric

MARKING RUBRIC FOR BLC322/04 FINAL YEAR PROJECT (FOR LOGISTICS AND SUPPLY CHAIN MANAGEMENT)

PROPOSAL

SEMESTER (MONTH)/YEAR:

MARKS (%) 80 60 – 79 50 – 59 40 – 49 39

DESCRIPTION Distinction Excellent Good Poor (Fail) Very Poor (Fail)

Abstract and Chapter 1:

Introduction to the Study

(30%)

24 – 30 marks

18 – 24 marks

15 – 18 marks

12 – 15 marks

< 12 marks

Criteria to be considered

are:

▪ The quality of

presentation of the

abstract (10%)

▪ The clarity of the

statement of the problem

(6%)

▪ The complexity and

difficulty of the topics

(6%)

▪ Its novelty and relevance

to current business and

management issues (6%)

▪ Others (2%)

▪ Abstract provides a

precise and concise

summary

▪ Readers have very

clear introduction in

which the research

problem, objectives

and research

questions are

logically well

structured and very

clearly presented.

▪ The topic is generally

complicated and

difficult to do

research for average

students

▪ A very interesting and

practical research

problem

▪ Abstract provides a

precise and concise

summary

▪ Readers have a clear

introduction; research

problems, objectives

and research

questions are

logically and well

presented

▪ The topic is quite

complicated but is

quite difficult

▪ An interesting and

quite practical

problem

▪ Abstract provides a

good summary

▪ Readers have a

reasonably clear

introduction; research

problems, objectives

and research

questions are

logically presented.

▪ The topic is not that

difficult and is

manageable

▪ Quite an interesting

research problem and

has some practical

value

▪ Abstract provides a

sufficient summary

▪ Readers have a

readable but barely

sufficient

introduction; research

problems, objectives

and research

questions need more

clarity and to be more

logically consistent.

▪ It is easy to do a

research on this topic

▪ Reasonably

interesting but not

much practical value

▪ Abstract provides

quite a confusing

summary

▪ Readers do not have a

readable introduction;

research problems,

objectives and

research questions are

poorly stated or

partially missing.

▪ It is easy to do

research on this topic

▪ Not an interesting

topic and has limited

practical value if at

all.

28
BMG322/04 Updated 22 July 2020

Chapter 2: Review of the

Literature (30%)
24 – 30 marks 18 – 24 marks 15 – 18 marks 12 – 15 marks < 12 marks

▪ Number of good

references in the literature

(5%)

▪ Currency and

comprehensiveness of the

review of the literature

relevant to the topic (5%)

▪ Research framework

(10%)
▪ Hypothesis (10%)

▪ Between 10-15 good

references

▪ Provide a current and

comprehensive

review of the

literature relevant to

the topic

▪ Well justifiable

research framework

relevent to objectives

▪ Highly accurate

hypotheses and

strongly supported

with theory

▪ Very clear and

appropriate research
design to objectives

▪ Between 5-10 good

references

▪ Provide a current and

good review of the

literature relevant to

the topic

▪ Research framework

generally relevant to

objectives

▪ Accurate hypotheses

and supported with

theory or empirical

evidences

▪ Appropriate research

design to objectives

▪ Between 3-5 good

references

▪ Provide a current and

satisfactory review of

the literature relevant

to the topic

▪ Some relevance of

research framework

to objectives

▪ Accurate hypotheses

and supported with

empirical evidences

only

▪ Some discussion of

research design

▪ Less than 3 good

references

▪ Provide a somewhat

dated and not too

satisfactory review of

the literature relevant

to the topic

▪ Irrelevance of

research framework

to objectives;

▪ Hypotheses cannot be

tested

▪ Insufficient

discussion of research

design

▪ No references

▪ Provide an inadequate

review of the

literature relevant to

the topic

▪ Absence of research

framework;

▪ No hypothesis

▪ No discussion of

research design

Chapter 3: Research
Methodology (30%)

24 – 30 marks 18 – 24 marks 15 – 18 marks 12 – 15 marks < 12 marks

▪ Research methods used

are to be appropriate and

justified (7%)

▪ The sampling plan design

(7%)

▪ Questionnaire (10%)

▪ Analysis techniques (6%)

▪ Research methods

used are appropriate

and justified

▪ They have been

appropriately carried

out

▪ Well explained

sampling methods to

achieve objectives

▪ Analysis techniques

exactly appropriate to
objectives

▪ Research methods

used are quite

justified

▪ They have been

appropriately carried

out

▪ Sampling methods

are complete to

achieve the objectives

▪ Analysis techniques

explained with minor
mistakes.

▪ Research methods

used are quite

justified

▪ They have mostly

been satisfactorily

carried out

▪ Some sampling

methods to achieve

objectives

▪ Analysis techniques

explained with major
mistakes

▪ Research methods

used are quite

inappropriate

▪ They were not carried

out properly

▪ Inappropriate

sampling methods

used

▪ Inappropriate analysis

techniques

▪ Research methods

used are inappropriate

▪ They have been

poorly executed

▪ Wrong sampling

methods

▪ Wrong analysis

techniques

Format & overall

Impression of the

Report (10%)

8 – 10 marks 6 – 8 marks 5 – 6 marks 4 – 5 marks < 4 marks

▪ The Report format should

be in accordance to the

Student Guide for
Project. (2%)

▪ Description of the

Report is very clear.

▪ Great extent of logical

coherence in the

▪ Description of the

Report is clear.

▪ Good extent of

logical coherence in

▪ Description of the

Report is reasonably

clear.

▪ Description of the

Report is vague.

▪ Minor issues with the

logical coherence in

▪ Description of the

Report is vague.

▪ Lack of logical

coherence in the

29
BMG322/04 Updated 22 July 2020

▪ Proper referencing should

be done and in

accordance to the WOU

Citation Guideline. (4%)

▪ Overall clarity in the

description of the Report

(2%)

▪ Extent of logical

coherence in the
arguments in the Report

(2%)

arguments in the

Report.

the arguments in the

Report.

▪ Reasonable extend of

logical coherence in

the Report.

the arguments in the

Report.

arguments in the

Report.

30
BMG322/04 Updated 22 July 2020

Appendix L. Project Report Marking Rubric

MARKING RUBRIC FOR BLC322/04 FINAL YEAR PROJECT (FOR LOGISTICS AND SUPPLY CHAIN MANAGEMENT)

REPORT

SEMESTER (MONTH/YEAR:

MARKS (%) 80 60 – 79 50 – 59 40 – 49 39

DESCRIPTION Distinction Excellent Good Poor (Fail) Very Poor (Fail)

Abstract and Chapter 1:

Introduction to the Study

(15%)

12 – 15 marks

9 – 12 marks

7– 9 marks

5 – 7 marks

< 5 marks

Criteria to be considered

are:

▪ The quality of

presentation of the

abstract (5%)

▪ The clarity of the

statement of the problem

(3%)

▪ The complexity and

difficulty of the topics

(3%)

▪ Its novelty and relevance

to current business and

management issues (3%)

▪ Others (1%)

▪ Abstract provides a

precise and concise

summary

▪ Readers have very

clear introduction in

which the research

problem, objectives

and research

questions are

logically well

structured and very

clearly presented.

▪ The topic is generally

complicated and

difficult to do

research for average

students

▪ A very interesting and

practical research

problem

▪ Abstract provides a

precise and concise

summary

▪ Readers have a clear

introduction; research

problems, objectives

and research

questions are

logically and well

presented

▪ The topic is quite

complicated but is

quite difficult

▪ An interesting and

quite practical

problem

▪ Abstract provides a

good summary

▪ Readers have a

reasonably clear

introduction; research

problems, objectives

and research

questions are

logically presented.

▪ The topic is not that

difficult and is

manageable

▪ Quite an interesting

research problem and

has some practical

value

▪ Abstract provides a

sufficient summary

▪ Readers have a

readable but barely

sufficient

introduction; research

problems, objectives

and research

questions need more

clarity and to be more

logically consistent.

▪ It is easy to do a

research on this topic

▪ Reasonably

interesting but not

much practical value

▪ Abstract provides

quite a confusing

summary

▪ Readers do not have a

readable introduction;

research problems,

objectives and

research questions are

poorly stated or

partially missing.

▪ It is easy to do

research on this topic

▪ Not an interesting

topic and has limited

practical value if at

all.

31
BMG322/04 Updated 22 July 2020

Chapter 2: Review of the

Literature (20%)
16 – 20 marks 12 – 16 marks 10 – 12 marks 8 – 10 marks < 8 marks

▪ Number of good

references in the

literature (5%)

▪ Currency and

comprehensiveness of the

review of the literature

relevant to the topic (7%)

▪ Research framework

(5%)
▪ Hypothesis (3%)

▪ Between 10-15 good

references

▪ Provide a current and

comprehensive

review of the

literature relevant to

the topic

▪ Well justifiable

research framework

relevent to objectives

▪ Highly accurate

hypotheses and

strongly supported

with theory

▪ Very clear and

appropriate research
design to objectives

▪ Between 5-10 good

references

▪ Provide a current and

good review of the

literature relevant to

the topic

▪ Research framework

generally relevant to

objectives

▪ Accurate hypotheses

and supported with

theory or empirical

evidences

▪ Appropriate research

design to objectives

▪ Between 3-5 good

references

▪ Provide a current and

satisfactory review of

the literature relevant

to the topic

▪ Some relevance of

research framework

to objectives

▪ Accurate hypotheses

and supported with

empirical evidences

only

▪ Some discussion of

research design

▪ Less than 3 good

references

▪ Provide a somewhat

dated and not too

satisfactory review of

the literature relevant

to the topic

▪ Irrelevance of

research framework

to objectives;

▪ Hypotheses cannot be

tested

▪ Insufficient

discussion of research

design

▪ No references

▪ Provide an inadequate

review of the

literature relevant to

the topic

▪ Absence of research

framework;

▪ No hypothesis

▪ No discussion of

research design

Chapter 3: Research
Methodology (20%)

16 – 20 marks 12 – 16 marks 10 – 12 marks 8 – 10 marks < 8 marks

▪ Research methods used

are to be appropriate and

justified (8%)

▪ The sampling plan design

(4%)

▪ Questionnaire (4%)

▪ Analysis technique (4%)

▪ Research methods

used are appropriate

and justified

▪ They have been

appropriately carried

out

▪ Well explained

sampling methods to

achieve objectives

▪ Analysis techniques

exactly appropriate to
objectives

▪ Research methods

used are quite

justified

▪ They have been

appropriately carried

out

▪ Sampling methods

are complete to

achieve the objectives

▪ Analysis techniques

explained with minor
mistakes.

▪ Research methods

used are quite

justified

▪ They have mostly

been satisfactorily

carried out

▪ Some sampling

methods to achieve

objectives

▪ Analysis techniques

explained with major
mistakes

▪ Research methods

used are quite

inappropriate

▪ They were not carried

out properly

▪ Inappropriate

sampling methods

used

▪ Inappropriate analysis

techniques

▪ Research methods

used are inappropriate

▪ They have been

poorly executed

▪ Wrong sampling

methods

▪ Wrong analysis

techniques

Chapter 4: Analysis of

Results (20%)
16 – 20 marks 12 – 16 marks 10 – 12 marks 8 – 10 marks < 8 marks

▪ Amount of data collected

(5%)

▪ Manner of data analysis

has to be appropriate

(5%)

▪ No major issues on

the amount of data

collected

▪ Data analysis has

been carried out

appropriately

▪ Only a minor issue in

amount of data

collected

▪ Data analysis has

been carried out quite

appropriately

▪ Only a minor issue in

the amount of data

collected

▪ Data analysis has

been carried out quite

appropriately

▪ Some minor issue in

the amount of data

collected

▪ Data analysis is

poorly carried out

▪ Major issues in the

amount of data

collected

▪ Data analysis is

poorly carried out

32
BMG322/04 Updated 22 July 2020

▪ Interpretation of results

has to be carried out in a

professional and

innovative manner (5%)
▪ Others (5%)

▪ Interpretation of

results has been

adequately provided

▪ Interpretation of

results has been

carried quite

adequately

▪ Interpretation of

results has been

carried somewhat

adequately

▪ Interpretation of

results has not been

carried adequately

▪ Interpretation of

results has been

carried out

incompetently

Chapter 5: Findings,

Conclusions and

Recommendations (15%)

12 – 15 marks

9 – 12 marks

7– 9 marks

5 – 7 marks

< 5 marks

▪ These should relate

directly and fully to the

problems, objectives and

research questions (6%)

▪ Extent of commendable

recommendations or

important implications to

be spelled out clearly

(6%)

▪ Others (3%)

▪ Have related very

tightly to the

problems, objectives

and research

questions

▪ Highly commendable

recommendations or

important

implications have

been spelled out

▪ Have related tightly

to the problems,

objectives and

research questions

▪ Commendable

recommendations or

important

implications have

been spelled out

▪ Have related

adequately to the

problems, objectives

and research

questions

▪ Good

recommendations or

useful implications

have been spelled out

▪ Have some

relationships to the

problems, objectives

and research

questions

▪ Poor

recommendations or

not too useful

implications have

been spelled out

▪ Have no relationships

to the problems,

objectives and

research questions

▪ Very poor or non-

existent

recommendations or

not useful

implications have

been spelled out

Format and References

(10%)

8 – 10 marks 6 – 8 marks 5 – 6 marks 4 – 5 marks < 4 marks

▪ The Report format

should be in accordance

to the Student Guide for

Project. (5%)
▪ Proper referencing

should be done and in

accordance to the WOU

Citation Guideline. (5%)

▪ Report format has

fully complied with

the Student Guide.

▪ Referencing done has

fully complied with

WOU Citation

Guideline.

▪ Report format has

adequately complied

with the Student

Guide.
▪ Referencing done has

adequately complied

with WOU Citation

Guideline.

▪ Report format has

reasonably complied

with the Student

Guide.
▪ Referencing done has

reasonably complied

with WOU Citation

Guideline.

▪ Report format has

minor issues in

compliance to the

Student Guide.
▪ Referencing done has

minor issues in

compliance to WOU

Citation Guideline.

▪ Report format has not

complied with the

Student Guide.

▪ No proper referencing

was done, and has

major issues in

compliance to WOU

Citation Guideline.

33
BMG322/04 Updated 22 July 2020

Appendix M: MARKING RUBRIC FOR BLC322/04 FINAL YEAR PROJECT (FOR LOGISTICS AND SUPPLY CHAIN
MANAGEMENT) PRESENTATION

Criteria Very Poor

<4 marks

Poor
4-5 marks

Good
5-6 marks

Very Good
6-8 marks

Excellent
8-10 marks

Introduction
(10%)

None Confusing Sufficient Good Precise &
concise

Criteria Very Poor
< 8 marks

Poor
8 – 10 marks

Good
10 – 12 marks

Very Good
12 – 16 marks

Excellent
16 – 20 marks

Literature
Review (20%)

None Inadequate Good Well written Comprehensive

Criteria Very Poor
< 5 marks

Poor
5 – 7 marks

Good
7– 9 marks

Very Good
9 – 12 marks

Excellent
12 – 15 marks

Research
Methodology
(15%)

None Too short Short Well written Clearly written
with reference to
LO

Criteria Very Poor
< 8 marks

Poor
8 – 10 marks

Good
10 – 12 marks

Very Good
12 – 16 marks

Excellent
16 – 20 marks

Analysis of
Results (20%)

None Too short Short Well written Clearly written
with reference
to LO

Criteria Very Poor
< 8 marks

Poor
8 – 10 marks

Good
10 – 12 marks

Very Good
12 – 16 marks

Excellent
16 – 20 marks

Findings,
Conclusions &
Recommendations
(20%)

None Too short Short Well written Clearly written
with reference
to LO

Criteria Very Poor
<4 marks

Poor
4-5 marks

Good
5-6 marks

Very Good
6-8 marks

Excellent
8-10 marks

Communication
(10%)

Not
comprehens
ible

Difficulty to
understand

Understandab
le but not
effective

Understandabl
e. A few
mistakes

Clear, correct &
suitable

34
BMG322/04 Updated 22 July 2020

Criteria Very Poor

<1 mark

Poor
1-2 marks

Good
2-3 marks

Very Good
3-4 marks

Excellent
4-5 marks

Overall
Impression (5%)

Vague. Lack
coherence

Vague.
Some
issues with
logical
coherence

Reasonable
clear.
Reasonable
coherence

Presentation is
clear. Good
logical
coherence

Presentation is
very clear.
Great logical
coherence in
presentation.

35
BMG322/04 Updated 22 July 2020

Appendix K (a): Project Proposal Marksheet

PROJECT PROPOSAL MARKSHEET

Semester / Year Name of Marker

Course Code BMG322/04 Course Title BUSINESS PROJECT

Student ID Student Name

Project Title

GENERAL FEEDBACK

Chapter Marks Comments (Strength and Weakness)

Abstract
Chapter 1(30%)

Chapter 2 (30%)

Chapter 3(30%)

Format & Overall
Impression(10% )

Total(100%)

Name & Signature of
Project Marker:

Date

36
BMG322/04 Updated 22 July 2020

Appendix L (a): Project Report Marksheet

PROJECT REPORT MARKSHEET

Semester / Year Name of Marker

Course Code BLC322/04 Course Title FINAL YEAR PROJECT
(FOR LOGISTICS AND
SUPPLY CHAIN
MANAGEMENT)

Student ID Student Name

Project Title

GENERAL FEEDBACK

Chapter Marks Comments (Strength and Weakness)
Abstract
Chapter 1(15%)

Chapter 2 (20%)

Chapter 3(20%)

Chapter 4(20%)

Chapter 5(15%)

Format &
References(10%)

Total(100%)

Name & Signature of
Project Marker:

Date

37
BMG322/04 Updated 22 July 2020

Appendix M (a): Presentation Marksheet

BLC322/04 FINAL YEAR PROJECT (FOR LOGISTICS AND SUPPLY CHAIN
MANAGEMENT) Presentation Marksheet

Semester / Year Name of Marker

Student ID Student Name

Project Title

Please go through the following criteria and write down your awarded marks.

Criteria Marks Comments

Introduction (10%)

Literature Review (20%)

Research Methodology
(15%)

Analysis of Results (20%)

Findings, Conclusions and
Recommendations (20%)

Communication (10%)

Overall Impression (5%)

Total (100%)

Remarks

Name & Signature of
Project Marker:

Date

1


What is the Impact of Advertising on Consumer Behavior in International Markets?

LITERATURE REVIEW

Industry Background

The consumer is presented with various experiences due to advertising, which is a component of communication and information flow. It is generally agreed that advertising has contributed to improving living standards, decreasing the per-unit costs of mass-produced commodities, providing information, and facilitating new products entering the market (Sundaram, Sharma, & Shakya, 2020). To eradicate misleading and dishonest advertisements and commercials, the industry of mass media, as well as consumer groups and government bodies, regulate it.

Advertising has evolved into a communication powerhouse that can meet various demands posed by modern society. Study in advertising can be broken down into three subfields: consumer research, market analysis, and product analysis. When conducting consumer research, advisors comprehend that to sell their product effectively, they need to cater to the requirements and preferences of the target market (Fan, 202). They may accomplish this by correctly estimating the significance of those needs and wants and by delivering the goods that could fulfill those needs and wants. It is imperative that advertisers have to be current on any changes in the geographic positioning of their potential markets. An investigation of the market’s internal and exterior conditions is a component of consumer research. This research focuses on identifying the reason underlying consumer behavior (their buying motives and attitudes). It also analyzes the factors such as motivations, attitudes, paybacks, conducts, and customs that influence purchasing decisions. We discuss product concept testing, the life circle of goods, product users, the packing of products, the positioning of products, and the unit of sales and use of the brand while talking about product analysis. Before putting the product on the market, the maker should do an exhaustive investigation into its qualities to identify those aspects of the product that can be interpreted in an alluring manner as having the capacity to fulfill the customers’ requirements.

Consumer purchasing behavior refers to the procedures followed when target demographic develops these needs, acquire, employ, or dispose of items, services, concepts, or experiences that meet their needs and aspirations (Bray, 2002). A customer’s search for, payment for, use of, evaluation of, and disposal of products and services that they believe will meet their needs. Individual psychology, sociocultural psychology, and cultural anthropology converge to form this discipline of social science. A concept that explains what, when, where, why, and how an individual purchases something; it is especially significant to study consumer buying behavior since it enables businesses to develop and implement improved business strategies

Theoretical Perspectives

Advertisements leverage and reflect conflicting, ever-changing commitments. While advertisements foster a great sense of brand loyalty, they also encourage customers to change loyalties, try something different, and abandon an old product in favor of a brand-new one. Hazel W. Warlaumount, a media historian, contends in Advertising in the 1960s (Rasheed et al.) that advertisements transformed from the 1950s to the 1960s. Many advertisements appeared to support the antiauthoritarian psychedelic counterculture, despite being created and delivered by multinational firms advocating the status quo and corporate interests. Warlaumont claims that advertising appropriated the anti-“ideals, establishment’s leaders, and objectives” for their purposes. The argument of Warlaumont inverts the 1960s notion of detournement, created by activist Guy Debord and others. 1967’s The Society of the Spectacle was written by Debord, the leader of the radical group Situations International and the author of The Society of the Spectacle. Detournement denotes to the repurposing of recognizable pictures by an artist by rearranging circumstances to generate an innovative work with a distinct, frequently contradictory message. Detournement has an anti-art aspect by publicly copying and sabotaging existing pieces, hence reversing the intended message. The concept inspires the culture jamming tactic. Both techniques are viewed as a method of resistance to the glitzier features of capitalist society and as a means of bringing attention to the social impacts of corporate strategies.

Dependent Variable and Independent Variables

Dependent variable- Brand awareness, consumer emotional response to a product, environmental response, and sensory-triggered advertising.

Independent variable- Consumer purchasing behavior

Hypotheses Development

H0: Consumer emotional response element positively affect consumer behavior

H1: The consumer emotional response element does not positively impact consumer behavior

H0: Brand awareness positively affects consumer behavior

H2: Brand awareness does not positively impact consumer behavior

H0: Environmental and sensory elements positively affect consumer behavior

H3: Environmental and sensory elements do not positively affect consumer behavior

Theoretical Framework

When individuals desire to buy a certain product brand, their behavior can be predicted by how they perceive it. Goldsmith and Lafferty (2002) said these things happen when a customer observes an ad for a brand and decides to buy it. Smith and Swinyard (1983), as quoted in Ghulam, Javana Burhan, and Ahmed (2017), said temperament is learned behavior. Adelaar et al. (2003) said that behavior is caused by emotive responses, which are impacted by three independent factors: desire, excitement, and superiority.

RESEARCH METHODOLOGY

Research Design

In this study, a survey research design will be utilized to collect information or data from customers in the Metropolis of Indiana using a questionnaire instrument. All residents of Indiana Metropolis were included in the research’s population for the study.

Population and sampling

Given the numerous restrictions, I will utilize a purposive sampling method that does not rely on probability. The demographics of the consumers in Indiana Metropolis are difficult to ascertain because there is insufficient data. Therefore, the sample will be constructed using data not based on probability. The samples will be taken from 400 customers in Indiana Metropolis who come to various shops and markets to make purchases. To prevent the surveys from being lost, they will collect them as soon as the respondents finish filling them out. The strongly depends on 370 correctly filled out surveys submitted by respondents with education levels equivalent to or higher than secondary school.

Measurement Instruments

A Likert scale of five points, ranging from 1 (strongly disagree) to 5 (strongly agree), will make up the survey. Each variable has a total of four items or elements. The questionnaire will consist of items that the respondent developed. A section of the questionnaire will be devoted to collecting demographic data. Cronbach’s Alpha will evaluate the questionnaire’s validity and reliability. It has been determined that the reliability Coefficient will be 0.79. A value of 0.79 is more than 0.7, which is the minimum threshold for acceptance. This indicates that the data collected are accurate and may be relied upon for further investigation.

Statistical Techniques

There are 5 research elements in the model, four of which are independent, and one is a dependent variable. Because the generated data only contain a single dependent variable but many independent factors, a multiple regression analysis will be carried out, and SPSS 16.0 will be used to obtain the results. The hypothesis will be tested using a technique called multiple regression, and descriptive statistics will be utilized in order to determine percentages and create a frequency table.

Before the questionnaire is distributed to all of the respondents, a pilot test will be conducted on 60 of them to collect their feedback, ensure that the questionnaire is simple and easy to understand, and aid in the development of the questionnaire so that it can be used more effectively. The pilot test findings confirmed that all 60 respondents could comprehend the survey, and the survey was met with positive feedback from those who participated in the study.

Limitations of the Study

It is uncertain how many people make up the consumer population. The sampling method employed was purposive sampling, which cannot be used to generalize results. This is because the precise number of consumers in the Indiana metropolitan cannot be determined due to the currently unavailable data. No probabilistic method can be used to estimate the representativeness of the chosen samples in this sampling design.

Reference

Adelaar, T., Chang, S., Lancendorfer, K. M., Lee, B., & Morimoto, M. (2003). Effects of media formats on emotions and impulse buying intent. 
Journal of Information Technology
18(4), 247-266.
https://www.tandfonline.com/doi/abs/10.1080/0268396032000150799

Bray, J. P. (2008). Consumer behaviour theory: approaches and models.


Fan, B. (2022, March). Research on the Impact of Advertisement on Consumer Behavior. In 
2022 7th International Conference on Financial Innovation and Economic Development (ICFIED 2022) (pp. 2693-2697). Atlantis Press.
https://doi.org/10.2991/aebmr.k.220307.438

Ghulan, S.K.N., Javana, S. Burhan A., & Ahmed, I.H. (2012). Effective advertising and its influence on consumer buying behavior. Information Management and Business Review 14, 114-119
https://ojs.amhinternational.com/index.php/imbr/article/view/971

Goldsmith, R. E., & Lafferty, B. A. (2002). Consumer response to Web sites and their influence on advertising effectiveness. 
Internet research
12(4), 318-328. doi/10.1108/10662240210438407/full/HTML

RASHEED, D. G., DIYAOLU, G. O., & RAJI, A. T. The Impact of Physicians Word of Mouth Advertisements on Consumer Behaviour towards Over-The-Counter Medicine. http://arcnjournals.org/images/NRDA-IAJMM-7-1-12.pdf

Sundaram, R., Sharma, D., & Shakya, D. (2020). Power of digital marketing in building brands: A review of social media advertisement. 
International Journal of Management
11(4). http://www.iaeme.com/ijm/issues.asp?JType=IJM&VType=11&IType=4

6


What is the Impact of Advertising on Consumer Behavior in International Markets?

Introduction

The fundamental objective of advertising is to influence customer spending; although this, it frequently alters or enhances the influence that it has on brands through means of public memory. Customer associations that are connected with the branded version contribute to the formation of brand memory. These brands regularly have an impact on the evaluation, analysis, and final purchase activities. Customers who buy conduct have always placed a significant importance on literary assessments of the effect that advertisements have on the effectiveness of their purchases (Akayleh, 2021). Customers typically make purchases of goods on the basis of the customer’s love or dislike of advertising of the products offered to them. Consumers can be convinced to buy a product by associating with high-quality advertising, whereas low-quality advertising has the opposite effect. In this study a number of different variables are used and an assessment done on how those variables influence consumer behaviors.  This study seeks to identify the factors that have significant impact on customer performance as well as those that have the least. As a result, this enables marketers to concentrate on the appropriate components and make the most of their advertising.

Advertising contributes to the advancement of communications and the environment by presenting the ideal consumer position and encouraging collective behavioral change in order to purchase the product. The expenditure on advertising contributes to the positive image that customers have of a brand. The advertising department is prepared with basic information on each product or service offered by the company. It provides customers the opportunity to learn about the organization or the products before placing an order. There has been a rise in the level of creativity and originality observed in advertising (Singh and Chahal, 2019). Motivation, perception, education, and/or a set of attitudes or beliefs are the four fundamental factors that influence the behavior of customers. Significant changes in consumer behavior can be linked to the influence of unconscious factors. The portion of a customer’s behavior that is not known to the customer is considered unconscious. Many organizations, for instance, have an impact on the attitude of customers in such a way that they do not see why the products are still preferable. Advertising is an essential component of the modern, globalized economy. It is a factor that contributes to the acceleration of a nation’s economic development.

Advertising promotes the growth in employment and production by stimulating more purchases, which helps to satisfy rising requirements and enables each consumer to spend more. In order for a company to flourish in a consumer market that is dominated by advertising, the corporation could choose to indulge in promotional activities to enhance the brand of its product (Kazancoglu and Aydin, 2018). Evaluating the performance of consumers is crucial for effective publicity since it enables one to gain some insight into why customers behave in a particular manner in response to a wide range of scenarios. However, it is crucial to be aware of the factors that impact customer behavior, particularly the economic, social, and mental components. When faced with thousands of different products, customers make an effort to identify their customer loyalty with the most current recollection and to reposition their memories in relation to a company’s identity and a sense of the most recent product. Using this approach, the participants’ recollections can be organized, in particular, according to the price and the brand of the information.

Herhold (2017) asserts that the environment we live in is one that is preoccupied with information. Therefore, the possibilities of a company surviving without participating in any type of marketing are quite low. It is safe to assert that in this day and age, social media has effectively become a weapon and is acting as a substantial part in marketing. The Internet and various forms of social media have brought about considerable shifts in the business world, particularly in the strategies that companies use to increase consumer awareness of the products and services they offer. Because of this, advertisers are always searching for innovative approaches to improve the persuasiveness and efficacy of their advertisements in the congested and competitive media environment of today.

Problem Statement

Consumers respond differently according to organizations marketing strategies. Although some may respond positively while others respond negatively, there is a need to identify the different aspects of consumer behavior and strategies an organization can provide an employee to impact its target consumers positively.

Research Objective(s)

The objective of this dissertation is to inspect the effect that advertising has on consumer behavior and to establish which advertising channels are among the most strong and efficient in terms of their capacity to impose an influence on that behavior. Media platforms like TV’s and the web are to be the primary marketing channels utilized to accomplish the primary objective and is achieved by determining which are the effective channels in impacting the purchasing behavior of consumers as well as comprehending the attitudes of consumers toward advertising.

The second purpose is to demonstrate that for an advertisement to be effective, it has to provide a significant amount of information regarding the product being advertised. In point of fact, this makes the buyer interested in gaining further knowledge. For instance, it is necessary to provide the contact details in order to ensure that the customer may interact with the company through it in the event that the customer has any inquiries regarding the product or need extra information about it. In addition to this, it is essential to give viewers an explanation of the purpose or concept that underpins the product, service, or company that is being promoted.

Research Question

a) What are the different elements of customer behavior?

b) How does advertising help organizations change consumer behaviors?

c) What are the factors affecting consumer behavior in the international market?

Significance of the Study

Understanding consumer behavior is crucial for a business to achieve success with both its current products and the goods it will manufacture in the future. Every consumer has their own distinct way of thinking about and responding in regards to a product. If a company is unable to understand how a product will be received by its target market, it is quite certain that the product will not be successful. It is necessary to have an understanding of consumer behavior in in order to succeed with both presently offered products and new product releases. Marketing normally refers to a technique that differentiates a set of consumers. This makes it easier to achieve the same behavior or behavior that is comparable to that of certain consumer groups when advertising is done. The performance of consumers is important not only to the recruitment of new customers, but also to the maintenance of existing clients (Nuseir, 2018). Customers are more likely to make additional purchases of a certain product if they are pleased with the product overall. As a result, it is necessary to advertise the product in order to persuade customers to make repeat purchases of the goods.

Organisation of the Dissertation

The concept of advertising, its significance, the different styles of advertising, and its purposes will all be covered in the first section of the study, which will also feature a brief study case concerning Hilton Hotel. The second one involves outlining the many varieties of consumer behavior models as well as providing an understanding as to why it is essential to have an understanding of the behavior of consumers. About chapter 4, it will be about the synthesis of the two chapters that came before it in the form of the influence that advertising has on the behavior of consumers. In conclusion, there will be a survey regarding the topic of the thesis as well as the case study that will be presented together with the results.

Summary

Advertising has a very important role in determining the purchasing choices that consumers make by captivating their interest, and generating a desire in them to purchase the products that are being marketed. Since people in different parts of the United States appear to have diverse approaches to thinking and cultural norms, the scope of this dissertation is confined to examining only the impact that advertising has on consumer behavior in those specific parts of the country. For instance, contrasted to Europe, where companies do not typically invest a significant amount in advertisements, American countries have a tendency to spend a substantial amount of money into marketing their products and services.

Reference

Akayleh, F. A. (2021). The influence of social media advertising on consumer behaviour. Middle East Journal of Management, 8(4), 344-366.

Herhold, K. December 7, 2017. How Consumers View Advertising: 2017 Survey. Available at: https://clutch.co/agencies/resources/how-consumers-view-advertising-survey-2017. Accessed 5 November 2020.

Kazancoglu, I., & Aydin, H. (2018). An investigation of consumers’ purchase intentions towards omni-channel shopping: A qualitative exploratory study. International Journal of Retail & Distribution Management.

Nuseir, M. T. (2018). Impact of misleading/false advertisement to consumer behaviour. International Journal of Economics and Business Research, 16(4), 453-465.

Singh, P. P., & Chahal, H. S. (2019). Controversial advertising and consumer behaviour. Asian Journal of Multidimensional Research (AJMR), 8(5), 266-270.

A PROJECT MANAGEMENT CASE STUDY IN

ASSESSING AND IMPLEMENTING SOLAR

PHOTOVOLTAIC PROJECT IN W UNIVERSITY

HUANG MEI TING

SCHOOL OF BUSINESS AND ADMINISTRATION

WAWASAN OPEN UNIVERSITY

2016

NAME Huang Mei Ting

DEGREE Commonwealth Executive Master of Business Administration

SUPERVISOR Mr Loh Chee Seng

TITLE A Project Management Case Study in Assessing and Implementation

Solar Photovoltaic Project in W University

DATE November 2016

INSTITUTION Wawasan Open University (WOU)

Final Project Report submitted in partial fulfilment

of the requirements for the award of

Commonwealth Executive Master of Business Administration (CeMBA)

of

Wawasan Open University

Penang, Malaysia

ii

ACKNOWLEDGEMENTS

I would like to take this opportunity to express my deepest gratitude to all the people who have

been instrumental support in the successful completion of this project report. This project report

owes its existence to the help, support and inspiration from them.

I would first like to express my sincere appreciation and gratitude to my project supervisor Mr

Loh Chee Seng for his guidance during this dissertation. His patience, motivation, enthusiasm,

and immense knowledge has been precious for the development of this project report. He

consistently allowed this paper to be my own work, but steered me in the right direction

whenever he thought I needed it.

Special thanks are given to Dr Quah Hock Soon, the expert who provided me with the advice

and guidance on SPSS data analysis. Also, sincere thanks to my tutors who have assisted and

motivated me during the course of my studies.

I am indebted to my fellow course mates for the stimulating discussions that helped me to focus

on this project report. I have been blessed with friendly and cheerful group of course mates.

Thanks are also due to the participants in my survey, who have willingly shared their precious

time during the process of interviewing and completing the survey form.

Finally, I must express my very profound gratitude to my family for providing me with

unfailing support, patience and continuous encouragement throughout the journey. This

accomplishment would not have been possible without them. Thank you.

Huang Mei Ting

November 2016

v

TABLE OF CONTENTS

Contents Page

Title Page

Acknowledgements ii

Certificate of Originality iii

Plagiarism Statement iv

Table of Contents v

List of Tables x

List of Figures xiii

Abbreviations xiv

Abstract xvi

Chapter 1 Introduction to the Study

1.1 Introduction 1

1.2 Background of the Study 1

1.3 Background of the Case Firm 2

1.4 Problem Statement 4

1.5 Research Objectives 5

1.6 Research Questions 5

1.7 Hypotheses of the Study 6

1.8 Theoretical Framework 7

1.9 Research Methodology 8

1.10 Solar Photovoltaic Energy in Malaysia 9

1.10.1 Preliminary Interviews with Key Informants 10

1.11 Expected Research Resultant Outcomes 10

1.12 Limitation of Research 11

1.13 Summary

11

vi

Contents Page

Chapter 2 Review of the Literature

2.1 Introduction 12

2.2 Innovation Adoption Theories 12

2.2.1 Diffusion of Innovations (DOI) Theory 12

2.2.2 Tornatzky and Klien’s Meta-Analysis 14

2.2.3 Perceived Characteristics of Innovating (PCI) Theory 15

2.2.4 Innovation Characteristics in Green Innovations 16

2.3 Diffusion of Eco-innovations 17

2.4 Cost Attribute 18

2.4.1 Investment Costs 19

2.4.2 Cost Estimation 19

2.4.3 Tax Incentives for Renewable Energy 20

2.4.4 Mechanisms for Financing Solar PV Investment 21

2.5 Relative Advantage 21

2.5.1 Environmental and Climate Change 22

2.5.2 Economic Benefits 22

2.6 Compatibility 23

2.6.1 Compatibility with Values 24

2.6.2 Compatibility with Preferred Work Style 24

2.6.3 Compatibility with Existing Practices 25

2.6.4 Compatibility with Prior Experience 25

2.7 Complexity 26

2.7.1 Awareness and Understanding of Solar PV Technology 26

2.7.2 Ease of Use 27

2.8 Perceived Risk 28

2.8.1 Economic Risk 28

2.8.2 Functional Risk 29

2.8.3 Social Risk 29

2.9 Demographic Variable 29

2.10 Project Management 30

2.10.1 Cost, Time, Quality and Scope 31

2.10.2 Managing Risk 32

vii

Contents Page

2.11 Decision Making Framework 33

2.12 Hypotheses 33

2.13 Theoretical Framework 34

2.14 Summary 35

Chapter 3 Research Methodology

3.1 Introduction 36

3.2 Research Methodology 36

3.3 Survey Instruments 38

3.3.1 Qualitative Approach 38

3.3.2 Quantitative Approach 39

3.4 Population and Sample 41

3.5 Data Collection 42

3.5.1 Key Informants Interviews 42

3.5.2 Administering the Questionnaire 43

3.6 Pilot Study 45

3.7 Data Analysis Plan 45

3.7.1 Analysis of Qualitative Research 45

3.7.2 Analysis of Quantitative Research 47

3.8 Summary 49

Chapter 4 Analysis of Results

4.1 Introduction 50

4.2 Profile of Respondents 50

4.3 Descriptive Analysis 56

4.3.1 Cost Attribute 57

4.3.2 Relative Advantage 59

4.3.3 Compatibility 60

4.3.4 Complexity 62

4.3.5 Perceived Risk 63

4.3.6 Assessment and Implementation 64

viii

Contents Page

4.3.7 Summarised Descriptive Statistic 65

4.4 Reliability Test Analysis 66

4.5 Factor Analysis 71

4.6 Regression Analysis 74

4.6.1 Cost Attribute and Assessment and Implementation 75

4.6.2 Relative Advantage and Assessment and Implementation 77

4.6.3 Compatibility and Assessment and Implementation 78

4.6.4 Complexity and Assessment and Implementation 80

4.6.5 Perceived Risk and Assessment and Implementation 82

4.7 Predictive Model for the Study 84

4.8 Case Study Analysis 86

4.8.1 Technology Analysis 87

4.8.2 Life Cycle Costs Analysis 88

4.9 Summary 91

Chapter 5 Findings, Conclusions and Recommendations

5.1 Introduction 93

5.2 Recapitulation of the Study Findings 93

5.3 Details Analysis and Recommendations 95

5.3.1 Cost Attribute and Assessment and Implementation of Solar

PV System

95

5.3.2 Relative Advantage and Assessment and Implementation of

Solar PV System

96

5.3.3 Compatibility and Assessment and Implementation of Solar

PV System

98

5.3.4 Complexity and Assessment and Implementation of Solar PV

System

99

5.3.5 Perceived Risk and Assessment and Implementation of Solar

PV System

100

5.3.6 Case Study 101

5.4 Answers to Research Questions 102

ix

Contents Page

5.5 Limitations 103

5.6 Direction for Future Research 104

5.7 Conclusions 105

List of References

Appendices

A Survey Questionnaire – English

B Survey Questionnaire – Malay Language

C Preliminary Key Informants Interview Guide

D Malaysia Energy Statistics Handbook 2015: Energy Generation Mix

E Malaysia Energy Statistics Handbook 2015: Electricity Generation Mix in GWh

F Malaysia Energy Statistics Handbook 2015: Total Primary Energy Supply by Fuel

Type

G Malaysia Energy Statistics Handbook 2015: Total Primary Energy Supply by Fuels in

ktoe (kilo tonne of oil equivalent)

H What is Feed-in-Tariff (FiT)?

I What is Net Energy Metering (NEM)?

J Solar Photovoltaic System Diagram

K Solar PV Investment Analysis for WU

L Project Timeline Gantt Chart

x

LIST OF TABLES

Tables Page

1.1 Organisation of Reports 11

3.2 List of Participants of Preliminary Interview 43

3.3 Reliability of the Pilot Study 45

4.1 Number of Respondents by State 51

4.2 Respondents by Residential Area 51

4.3 Respondents by Nationality 51

4.4 Respondents by Gender 51

4.5 Respondents by Age 52

4.6 Respondents by Education Level 52

4.7 Respondents by Occupation 52

4.8 Respondents by Household Income 53

4.9 SPSS Coding for Variables 56

4.10 Abstract of SPSS Coding for Questions 57

4.11 Descriptive Statistics for Cost Attribute 58

4.12 Descriptive Statistics for Relative Advantage 60

4.13 Descriptive Statistics for Compatibility 61

4.14 Descriptive Statistics for Complexity 62

4.15 Descriptive Statistics for Perceived Risk 64

4.16 Descriptive Statistics for Assessment and Implementation 65

4.17 Descriptive Statistics for Independent Variables 66

4.18 Reliability Statistics for Assessment and Implementation 67

4.19 Reliability Statistics for Cost Attribute 68

4.20 Reliability Statistics for Relative Advantage 68

4.21 Reliability Statistics for Compatibility 69

4.22 Reliability Statistics for Complexity 70

4.23 Reliability Statistics for Perceived Risk 70

4.24 Summary of Reliability Statistics for Variables 71

4.25 KMO and Barlett’s Test Result for Independent Variables 72

4.26 Factor Loadings for the Rotated Factors 73

xi

Tables Page

4.27 Coefficients between Cost Attribute and Assessment and

Implementation

75

4.28 Model Summary between Cost Attribute and Assessment and

Implementation

76

4.29 ANOVA between Cost Attribute and Assessment and Implementation 76

4.30 Coefficients between Relative Advantage and Assessment and

Implementation

77

4.31 Model Summary between Relative Advantage and Assessment and

Implementation

78

4.32 ANOVA between Relative Advantage and Assessment and

Implementation

78

4.33 Coefficients between Compatibility and Assessment and Implementation 79

4.34 Model Summary between Compatibility and Assessment and

Implementation

80

4.35 ANOVA between Compatibility and Assessment and Implementation 80

4.36 Coefficients between Complexity and Assessment and Implementation 81

4.37 Model Summary between Complexity and Assessment and

Implementation

81

4.38 ANOVA between Complexity and Assessment and Imp 82

4.39 Coefficients between Perceived Risk and Assessment and

Implementation

83

4.40 Model Summary between Perceived Risk and Assessment and

Implementation

83

4.41 ANOVA between Compatibility and Assessment and Implementation 84

4.42 Mean, Standard Deviations, and Intercorrelations for Assessment and

Implementation

85

4.43 Simultaneous Multiple Regression Analysis Summary for Innovation

Characteristics Predicting Assessment and Implementation

86

4.44 Technology Identification for Solar PV System for W University 88

4.45 Solar Power System Yield for WU 90

4.46 Summary of Hypotheses Testing 91

xii

Tables Page

5.1 Summary of Major Findings 94

5.2 Answer to Research Questions 103

xiii

LIST OF FIGURES

Figures Page

1.1 Risk Event Graph 3

1.2 Aerial View of Kuala Lumpur Campus of Case Firm 3

1.3 Theoretical Framework 7

1.4 Three important Renewable Technologies: PV, Wind and Wave 9

2.1 Diffusion of Innovation Model 14

2.2 Proposed Conceptual Model for Green Innovation 17

2.3 Federal Renewable Energy Decision Model 31

2.4 Risk Related to Renewable Energy Projects 32

2.5 Theoretical Framework 35

3.1 Research Methodology Flow Chart 37

3.2 Breakdown of Procedure and Timeframe 44

3.3 Typology of Qualitative Data Analysis Techniques 47

4.1 Number of respondents by state 53

4.2 Respondents by Residential Area 53

4.3 Respondents by Nationality 54

4.4 Respondents by Gender 54

4.5 Number of respondents by age 54

4.6 Number of respondents by education level 55

4.7 Number of respondents by occupation 55

4.8 Number of respondents by monthly household income 56

4.9 Aerial View of Kuala Lumpur Campus of Case Firm 87

4.10 Solar Investment Analysis of WU (LCCA) 90

xiv

ABBREVIATIONS

ARR accounting average rate of return

BOS balance-of system

CAPEX capital expenditures

2 carbon dioxide

DCF discounted cash flow

DOI diffusion of innovations

EE energy efficiency

EFA Exploratory factor analysis

EIA U.S. Energy Information Administration

EU European Union

FB Facebook

FiT Feed-in Tariff

GHG greenhouse gasses

H Hypothesis

IT information technology

ITA Investment Tax Allowance

KLC KL Campus

KLRO KL Regional Office

KMO Kaiser’s Measure of Sampling Adequacy

LCCA life cycle cost analysis

LCOE levelized cost of electricity

MIDA Malaysian Investment Development Authority

MM mixed methods

MS Excel Microsoft Excel 2013

NEM Net Energy Metering

NPV net present value

NIMBY not-in-my-backyard

OPEX operational expenditures

PA principal axis

PBP payback period

PCI perceived characteristics of innovating

xv

ABBREVIATIONS

PIA Promotion of Investment Act

PV photovoltaic

RE renewable energy

SD standard deviation

SEDA Sustainable Energy Development Authority Malaysia

SEIA Solar Energy Industries Association

SESB Sabah Electricity Sdn. Bhd.

SESCO Sarawak Energy Supply Corporation

SPSS IBM SPSS version 24 for Windows

TAM Technology Acceptance Model

TNB Tenaga Nasional Berhad

Subscripts

GW Gigawatt

kWh kilowatt hour

kWp kilowatt peak

ktoe kilotonne of oil equivalent

m2 square meters

MJ/m
2
mega joule per meter square

MW megawatt

R2 coefficient of determination

W watt

Wp peak watt

xvi

NAME Huang Mei Ting

DEGREE Commonwealth Executive Master of Business Administration

SUPERVISOR Mr Loh Chee Seng

TITLE A Project Management Case Study in Assessing and Implementing

Solar Photovoltaic Project in W University

DATE November 2016

ABSTRACT

This study examines case study of implementing a local university solar Photovoltaic (PV)

system as a project.

Green electricity derives clean and sustainable energy from the sun, reduces dependency on

fossil fuel, lower greenhouse gas emission, as an approach to mitigate global climate change.

However, statistics indicate that solar PV accounted for less than 1% of total generated

electricity in Malaysia.

This study explores the five innovations characteristics (cost attribute, relative advantage,

compatibility, complexity and perceived risk) that will affect the assessment and

implementation of solar PV system. The feasibility of implementing the solar PV system is

also evaluated. A mix-method of both quantitative and qualitative approaches are used in this

in-depth study.

The findings indicate a significant relationship between innovation characteristics towards

assessing and implementation of such solar PV system. The study reveals that cost, relative

advantage, compatibility and perceived risk are significant determinants to project handling in

the case firm’s solar PV system. The results of this case study evaluation demonstrate a

xvii

favourable decision making justification that this case firm would have a feasible project in

hand.

Several recommendations were highlighted for future study, which may be of value to

decision makers of diverse interests and expertise in industry, government and adopters

towards the solar PV system assessment and implementation.

1

Chapter 1 Introduction to the Study

1.1 Introduction

This chapter presents the outline of the project. Besides, it also includes a brief explanation of

solar Photovoltaic (PV) overview related to this study.

1.2 Background of the Study

The solar energy industry hailed a milestone by surpassing 1 million solar PV projects

installation, representing to 27.5 GW of operating capacity in the U.S., as compared to 1,000

such projects 16 years ago, according to the Solar Energy Industries Association (SEIA)

(Kann, et al. 2016). However, those 1 million installations account to just 1% of electricity in

the U.S. (Unger 2016), and the figure is about the same globally (Energy Post, 2015).

Malaysia is situated at the equatorial region with an average solar irradiation of 400–600

MJ/m
2
per month (Mekhilef, et al. 2011). However its current annual generation of

Renewable Energy (RE) is only 194.9 MW, which represents less than 1% of the country’s

fuel mix, as compare to the technology feasible of generation of 2,080 MW of RE, or 11% of

our demand by 2020 (Fong 2014).

With this in mind, this research focuses to assess and examine the feasibility of installing a

solar PV system in W University (WU) as well as to explore the innovation characteristics

that impact the solar PV assessment and implementation. Implementing solar panels on WU

campus is an effective and easy way to introduce clean energy with proven technology. Solar

panels offer both an environmental and economic benefit, especially at universities where

energy consumption is high. Buildings consuming 40% of the world’s energy and two-thirds

of its electricity, energy is a substantial and widely recognized cost of buildings that makes up

2

a significant portion of their whole life costs and that, if reduced, could lead to substantial

savings (Issa, et al. 2010) especially in operating costs.

1.3 Background of the Case Firm

W University (WU), the case firm, is currently undergoing Feasibility Studies in the pre-

implementing evaluation stage at defining (see Figure 1.1) the implementing a solar PV

project in its campus. The objective of the case-firm is to deploy RE such as solar PV that

efficiently produces electricity and contribute to greenhouse-gas reduction efforts, and

subsequently to reduce the electricity cost. The site of the solar PV project is proposed to be

installed on the rooftop of WU’s Kuala Lumpur Campus (KLC) (see Figure 1.2). Alongside

with the commitment, WU also recognizes its special accountability to the future, i.e., the

responsibility in driving for environmental sustainability.

According to Larson and Gray (2011), the chances of risk events occuring are greatest in the

concept, planning, and start-up phase of the project (see Figure 1.1 Risk Event Graph). Hence,

it is prudent for the case firm to identify risk events and decide a response before the project

begin.

3

Figure 1.1

Risk Event Graph

Source: Adopted and adapted from Larson and Gray (2011)

Figure 1.2

Aerial View of Kuala Lumpur Campus of Case Firm

Source: Adopted and adapted from Google Earth Pro (2016) (Google Earth Pro 2016)

4

1.4 Problem Statement

Going green and reducing the carbon footprint has been the goal of many organisations

recently. Educational facilities and universities around the globe are also supporting this

important endeavour.

According to Mekhilef et al. (2011), in order to develop solar energy as one of the significant

sources of energy, the Malaysian government has announced Malaysian Building Integrated

Photovoltaic (MBIPV) project in 2005, which consists of national “SURIA1000” programme

that aimed to install solar PV system to 1,000 roofs by 2010, with a financial incentives of

capital rebates up to 60% . The barriers for solar energy is the ecomonic barrier (required high

capital investment), awareness and understanding of solar PV technology where strong

government policy is crucial for development in solar energy (Mekhilef, et al. 2011).

Solangi et al. (2015) studied the social acceptance and level of human interest in solar energy.

People are highly interested in solar energy, however is hindranced by high initial costs, lack

of information on solar energy and lack of government funding in solar power plant

establishment (Solangi, et al. 2015).

The study itends to conduct a case study of assessing the implementation of solar PV system

in WU campus. In this case firm study, capital budgeting and financial tools such as payback

period (PBP) and life cycle cost analysis (LCCA) shall be applied in the investment decision-

making processes. This study effort shall further explore and identify the innovation

characteristics that affect the assessing and implemening solar PV technology from the

perspective of difussion of innovation theory.

The determinants from this study would possibly provide a justification ground for the case

firm on the investment descisions, not limited to a mere go/ or no-go prospect beore the

implementation of the solar PV system.

5

1.5 Research Objectives

The research objectives of this study are as follows:

1. To investigate the current usage of RE in Malaysia.

2. To investigate and examine the innovation characteristics which influence the

acceptance of solar PV system.

3. To analyse and measure the correlation coefficients and regression between the

innovation characteristics identified and the assessment and implementation of solar

PV system, in order to obtain a better understanding for innovation users.

4. To estimate savings of electricity cost of WU by implementing solar PV system.

5. To calculate the payback period of investment of the solar PV system.

1.6 Research Questions

The basic research questions of this study are:

1. What is the current usage of RE in Malaysia?

2. What are the innovation characteristics which influence the acceptance of solar PV

system?

3. Is there any relationship between each innovation characteristic identified and the

assessment and implementation of solar PV system?

4. Can solar PV systems reduce electricity cost of WU?

5. How long is the payback period for the solar PV system investment to recover its

initial outlay?

6

Based on review of literature, innovation characteristics and other characteristics of

innovation that affect the diffusion of solar PV technology are identified. In this study, a case

study to assess the implementation of solar PV project will be evaluated.

1.7 Hypotheses of the Study

The following hypotheses were developed and explored in this study:

Hypothesis 1 (H1): Cost attribute of solar PV system significantly influence the likelihood of

assessment and implementation of solar PV system.

Hypothesis 2 (H2): The higher the perceived relative advantage of a solar PV system, the

greater is the likelihood that the solar PV system will be assessed and implemented.

Hypothesis 3 (H3): Beliefs about the compatibility of solar PV energy are expected to

significantly influence the assessment and implementation of solar PV system.

Hypothesis 4 (H4): The more users think solar PV power is difficult to acquire and integrate

into their daily practices, the lower the adoption and implementation of solar PV system will

be.

Hypothesis 5 (H5): Lower perceived risk associations with the use of solar PV equipment are

expected to positively influence the adoption and implementation of solar PV system.

Hypothesis 6 (H6): The assessment and implementation of solar PV system is significantly

associated with innovation characteristics.

7

1.8 Theoretical Framework

This study focuses on the diffusion of green innovations and proposes to test this set of

characteristics in the context of the assessing and implementation of solar PV system in the

case firm and its community. The study of the interactions among the perceived attributes of

innovation helps establishment of a general theory (Moore and Benbasat 1991).

The schematic theoretical frame work for this study is presented in Figure 1.3. The

independent variables are extracted from diffusion of innovation (DOI) theory which consists

of cost attribute, relative advantage, compatibility, complexity and perceived risk. There is

only a single dependent variable, i.e., assessing and implementing solar PV project.

Figure 1.3

Theoretical Framework

8

1.9 Research Methodology

This study will be conducted using the mixed method (MM) approach, which combined the

quantitative and qualitative approach (Johnson, Onwuegbuzie and Turner 2007). The

researcher applies exploratory sequential MM approach that begins with a qualitative research

phase, explores the view of participants, and then the data were analysed and built into a

second quantitative phase (Creswell 2014).

This study will be separated into 2 phases. Firstly, the researcher performs literature review

and conducts preliminary interviews with key informants. This qualitative research enables

collection of information which relates to solar PV innovations and obtain key informants’

perspectives on the acceptance level of current and potential solar PV implementation. The

information obtained from these key informants will be applied to the development and

construction of survey questionnaire and the case study in assessing and implementing of

solar PV project. Case study enables a researcher to closely examine the data within a specific

context (Zainal 2007).

The data set collected from survey questionnaire will be transferred into computer software

such as Microsoft Excel 2013 (MS Excel) and IBM SPSS version 24 for Windows (SPSS).

Descriptive statistics and inferential statistics will be applied to analyse the data. The survey

questionnaire will be tested on its reliability and consistency followed by analysing the data

using statistics such as factor analysis, correlation, ANOVA and regression. Additionally, a

case study analysis will be conducted to assess and evaluate the implementation of solar PV

system.

The Gantt chart of this project research timeline and its component is shown in Appendix L.

9

1.10 Solar Photovoltaic Energy in Malaysia

The government and private sector in Malaysia has been keen to promote RE as an essential

part of the 21st century’s energy mix since 2005 by the launching of the Fifth-Fuel Policy of

the 8th Malaysia Plan (Shah Alam, et al. 2012). According to Shah Alam, et al. (2012), there

was a wide gap between policy and implementation of the Fifth-Fuel Policy whereby there

was only 12 MW (2.4%) out of the targeted 500 MW electricity generated from renewable

sources to the national grid of the 8th Malaysian Plan. According to Malaysia Energy

Commission (2015), RE merely consisted of 1,318 GWh (0.93%) of electricity generation

mix in 2013 (see Appendix D and Appendix E), aimed to achieve the target of 5.5% by 2015

(Solangi, et al. 2015). Furthermore, total primary energy supply contributed by solar energy

was merely 38 ktoe (kilotonne of oil equivalent), equivalent to 0.04% in 2013 (Malaysia

Energy Commission 2015) (see Appendix F and Appendix G). The three important renewable

technologies are PV, wind and wave (see Figure 1.4) as well as biogas and biomass.

Figure 1.4

Three important Renewable Technologies: PV, Wind and Wave

Source: Adopted from Lynn 2010 (Lynn 2010)

10

1.10.1 Preliminary Interviews with Key Informants

The preliminary interview through phone was conducted with an officer from Sustainable

Energy Development Authority Malaysia (SEDA) to collect information regarding the feed-in

tariffs (FIT) mechanism which provides monthly income to renewable energy developers (see

Appendix H). The researcher was made known that Net Energy Metering (NEM) programme

(see Appendix I) will be implemented commencing Nov 2016 until 2010 with 100 MW

capacity limit a year, whereby the energy produced from the solar PV system installed will be

consumed first, and any excess to be exported and sold to the distribution licensee (such as

TNB /SESB).

Interviews with the industry experts provide information regarding the economic and

technical (see Appendix J) perspectives of assessment and implementation of solar PV

system. The project manager was even willing to assist in providing evaluation on the solar

PV investment by using proprietary investment module for this case study.

The response from these respondents will be useful to construct the questionnaire (see

Appendix A and Appendix B) which is then used for quantitative research.

1.11 Expected Research Resultant Outcome

It is anticipated that this study will be of value for case firm to evaluate the feasibility of the

project as well as to quantify the benefits of proposed project prior to implementation. The

information from this study could result in the development project plan which could allow

the case firm to move forward with the solar PV project.

This study will be able to find out the relationship between the dependent variables and the

independent variable as well as the demographic characteristics.

11

1.12 Limitations of Research

The limitation of this study could be sampling error due to the small sample size of

respondents as compare to the size of population, hence restricting the generalising of the

result findings. Another limitation could be non-sampling error where by the willingness of

the respondents to answer the survey questionnaire seriously was regarded as un-controllable

variable. Furthermore, time and cost are constraints and as such, this study shall target on a

single university perspective only.

1.13 Summary

This study is to demonstrate the innovation characteristics that affect the assessment and

implementation of a solar PV project; and a case study of solar PV project in WU. This study

will be presented in 5 chapters as shown in Table 1.1.

Table 1.1

Organisation of Reports

Chapter Descriptions

Chapter 1 Introduction of Study

Chapter 2 Literature Review

Chapter 3 Research Methodology

Chapter 4 Analysis of Results

Chapter 5 Findings, Conclusions and Recommendation

12

Chapter 2 Review of the Literatures

2.1 Introduction

This chapter reviews selective literatures related to model of diffusions of innovation (DOI),

innovations characteristics, demographic variables and project management variables of solar

PV project.

2.2 Innovation Adoption Theories

According to the Oslo Manual (OECD/Eurostat 2005), innovation is perceived and

understood as “the implementation of a new or significantly improved product (good or

service), or process, a new marketing method, or a new organisational method in business

practices, workplace organisation or external relations”. A typical example of innovation

would be the installation of a solar PV system within the case firm.

Works on innovation adoption such as Rogers’ (1983) DOI theory, Tornatzky and Klien’s

(1982) Meta-Analysis, and Moore and Benbasat’s (1991) Perceived Characteristics of

Innovating (PCI) theory are reviewed herein. Further, model for green innovation from

Kapoor, Dwivedi and Williams (2014) that examined various innovations characteristics and

establised a proposed conceptual model towards green innovations implementation shall also

be reviewed.

2.2.1 Diffusion of Innovations (DOI) Theory

The measuring of potential adopters’ perceptions of the innovation has been termed a “classic

issue” in the DOI, where Rogers (1983) defined DOI as “the process whereby an innovation is

13

communicated through certain channels over time among the membes of a social system”,

which is summarised in Figure 2.1. The DOI framework explained the five stages in the

innovation-decision process: initial knowledge of the innovation, persuasion (attitute

formation), decision, implementation (use of the innovation), confirmation of the innovation

decision by continue usage (Jansson 2011).

Rogers (1983) identified five perceived attributes of innovations and described them as: (i)

relative advantage – the degree to which an innovation is perceived as better than the idea it

supersedes; (ii) compatibility – the degree to which an innovation is perceived as being

consistent with the existing values, past experiences, and needs of potential adopters; (iii)

complexity – the degree to which an innovation is perceived as difficult to understand and use;

(iv) trialability – the degree to which an innovation may be experimented with on a limited

basis; and (v) observability – the degree to which the results of an innovation are visible to

others.

DOI theory studied the perceived attributes of innovation that influence the rate and direction

of the adoption of an innovation. Rogers’ (1983) DOI theory suggested that individual’s

decision on adoption (or non-adoption) of a particular innovation is by evaluating the

characteristics of the innovation itself.

14

Figure 2.1

Diffusion of Innovation Model

Source: Adopted from Rogers (1995)

2.2.2 Tornatzky and Klien’s Meta-Analysis

If solar energy is being promoted as a vital source in generating power-supply, then the

attributes of any adoption of new technologies need to be assessed. On one side, through

comprehensive literature review and preliminary meta-analysis, Tornatzky and Klien (1982)

examined the relationship between the attribues or characteristics of an innovation to the

adoption or implementation of that innovation. Out of thirty different innovations-attributes

from the seventy-five articles reviewed, they studied in detailed the ten most frequently

15

addressed attributes, notably compatibility, relative advantage, complexity, cost,

communicability, divisibility, profitability, social approval, trialability and obserability.

Tornatzky and Klien (1982) found that three innovation chracteristics, namely compatibility,

relative advantage and complexity had the most consistent significant relationships to

innovation adoption across a broad range of innovation types.

These innovation characteristics studies are appriopriate to this project as they greatly impact

the intention and adoption decisions of innovation such as solar PV.

2.2.3 Perceived Characteristics of Innovating (PCI) Theory

At another angle, however, Moore and Benbasat (1991) redefined the innovation

characteristics as Perceived Characteristics of Innovating (PCI) and aimed to develope an

instrument or tool designed to measure individual’s perceptions of adopting an information

technology (IT) innovation. In addtion to Rogers’ (1983) five characteristics of DOI, three

new characteristics were developed in their study: image, voluntariness and result

demonstrability (Moore and Benbasat 1991).

Moore and Benbasat (1991) argued that the key to whether the innovation diffuses is not the

potential adopters’ perceptions of the innovation itself, but rather their perception of using the

innovation. Hence, they created that an overall instrument to measure perceptions of using an

IT innovation.

16

2.2.4 Innovation Characteristics in Green Innovations

It is further stated that in order to achieve increment in adoption of green innovations such as

of household solar innovations, Kapoor, Dwivedi and Williams (2014) had developed a

conceptual model for green innovation (see Figure 2.2) to understand the replationship

between the shortlisted fourteen innovation-attribute and the behavioral intention. The

framework attempted to integrate the innovation characteristics from the three well-

recognised research in innovation-adoption, which are Rogers’ DOI theory, Tornatzky and

Klien’s Meta-Analysis, and Moore and Benbasat’s PCI theory.

Kapoor, Dwivedi and Williams (2014) introduced a framework that is organized and

theoretically sound medium that can be used to empirically examine the adoption of green

innovations.

17

Figure 2.2

Proposed conceptual model for green innovations

Source: Adopted and Adapted from Davis (1986); Moore and Benbasat (1991); Rogers

(2003); Tornatzky and Klein (1982) cited by Kapoor, Dwivedi and Williams (2014)

2.3 Diffusion of Eco-innovations

In the literature propounded on efficiency of solar energy, one has to focus of the

environmental effect, too. Environmental concerns for innovation or eco-innovation, is a

specific form of innovation aiming at reducing the impact of products and production

processes on the natural environment (Ozusaglam 2012). According to the Porter’s

Hypothesis, eco-innovation addresses environmental impacts which can also lead to an

increase of product performance and quality (Porter and Van Der Linde 1995).

18

Kemp and Pearson (2007) proposed the following definiition for eco-innovation in their

Measuring Eco-inovation Project:

“Eco-innovation is the production, assimilation or exploitation of a product, production

process, service or management or business method that is novel to the organisation

(developing or adopting it) and which results, throughout its life cycle, in a reduction of

environmental risk, pollution and other negative impacts of resources use (including energy

use) compared to relevant alternatives.”

An example of diffusion of eco-innovation is shown in the PV Parity Project (Lettner and

Auer 2012) amongst the EU nations; Germany had achieved PV grid parity in 2012, with an

average share of self-consumption between 38- 42% of the PV electricity generation. As

compared to Malaysia, the diffusion of eco-innovation of RE showed that its current annual

generation of RE is only 194.9 MW (Fong 2014), which represents less than 1% of the

country’s fuel mix (see Appendix F and Appendix G).

2.4 Cost Attribute

Other considerations in adopting solar energy have to be taken into accounts; one important

aspect is the cost estimation in any project handling in the implementation of solar

photovoltaic system. Gillingham and Sweeney (2012) found that the cost of the technology,

and in particular, the private costs, is the most important barrier to a larger-scale

implementation of technologies.

19

2.4.1 Investment Costs

Investment costs are expenditure incurred to install a solar PV system, consisting of many

individual solar cells that absorb and turn sunlight (solar photon) directly into electricity,

which is then integrated with balance-of system (BOS) hardware component (Schmalensee, et

al. 2015), in order to supply electricity power to a building. It is literally a quantum

technology of “photons in, electrons out” (Lynn 2010). While cost of solar PV system has

been reducing steadily, the cost per-kilowatt hour (kWh) of the levelized cost of electricity

(LCOE) remains relatively higher as compare to fossil technology (Schmalensee, et al. 2015).

2.4.2 Cost Estimation

It is required to establish whether a project is viable financially during feasibility study stage.

The cost estimation can be done using analogous estimating (top-down estimating), bottom-

up estimating or vendor bid analysis (Snyder 2013).

Cost estimation develops an approximate of monetary resources such as direct costs, overhead

costs, general and administrative costs and etc. required to complete a project. Project cost

management includes the processes involved in planning, estimating, budgeting, financing,

funding, managing, and controlling costs so that the project can be completed within the

approved budget (Snyder 2013).

In a solar PV project, an evaluation of cost-effectiveness involves a cost estimate of how

much it will cost to install the system, an estimate of utility cost savings and operation and

maintenance costs (A. Walker 2013). The cost of installing a solar PV system is divided into

two parts: the cost of solar module and the balance-of-system (BOS) costs, which include

20

costs of inverters, racking and installation hardware, along with other expenses involved in

design, engineering and physical installation (Schmalensee, et al. 2015).

Solar PV project is evaluated by using life cycle cost analysis (LCCA) because of the

characteristics of a high initial costs but follows by a low operating costs over the life of the

system; LCCA discounts all future costs to their present value so that they can be compared

(A. Walker 2013).

2.4.3 Tax Incentives for Renewable Energy

Another emerging issue is that of incentives to business entities in adopting solar-powered

energy in their daily operations. Herein, we shall consider – through a comprehensive

literature review – the various financial incentives available for solar-powered RE.

According to Gillingham and Sweeney (2012), policy intervention can improve the economy

efficiency in implementing low carbon technologies. The green technology incentive such as

Investment Tax Allowance (ITA) has been the most important federal-level mechanism for

subsidising solar energy deployment since it was announced in Budget 2014 related to the RE

and energy efficiency (EE) projects under the Promotion of Investment Act (PIA), 1986

(MIDA n.d.). Owners of solar PV system, who consist of companies, can claim the green

technology incentive under ITA of 100% qualifying capital expenditure incurred on a green

technology project or asset for five years to be offset against 100% of the statutory income

(SEDA 2009), in addition of the existing capital tax allowance under general plant and

machinery.

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2.4.4 Mechanisms for Financing Solar PV Investment

Solangi, et al. (2015) studied Malaysia users’ perspective and found that 80% of the

respondents are highly interested in solar energy, however majority of the respondents are

restraint by the expensive up-front costs of solar PV system. According to Schmalensee, et al.

(2015), most of the financing for solar PV projects in the US consist of tax equity deal

structure such as partnership or “partnership flip”, sale-leaseback and inverted lease.

Access to capital for solar PV project in Malaysia are limited as local financial institutions

tend to limit their interest in solar PV projects financing especially in residential and

commercial solar PV system. So far, two banks that collaborated with SEDA to provide solar

PV financing include Alliance Bank Bhd’s Home Complete Plus Solar Panel Financing (The

Star Online 2013) and the country’s first-ever Shariah-compliant solar PV financing scheme

offerred by Bank Muamalat Malaysia Bhd (Archibald 2013).

H1 Cost attribute of solar PV system significantly influence the likelihood of

assessment and implementation of solar PV system.

2.5 Relative Advantage

Rogers (1983) defined relative advantage as the degree to which an innovation is perceived as

better than the idea it supercedes. Relative advantage attribute has been found to positively

infuence intention or adoption of internet technology innovations such electronic channel in

marketing (Choudhury and Karahanna 2008), e-government internet voting (Carter and

Campbell 2011) as well as eco-innovation (Jansson 2011).

22

2.5.1 Environmental and Climate Change

The non-renewable energy sources such as coal, petroleum, natural gas and others which took

millions of years to form are on their way to extinction, whereas RE such as solar energy is

sustainable without significantly depleting the Earth’s capital resources or causing

environmental damage (Lynn 2010).

Solar power’s importance in displacing the direct use of fossil fuels (coal, oil and gas) for

generating electricity derives from the threat of global warming caused by greenhouse gasses

(GHG) emissions from burning fossil fuel (Melillo, Richmond and Yohe 2014), where 78% of

the GHG consist of carbon dioxide ( 2). Melillo, Richmond and Yohe (2014) identified one

of the measures to reduce future climate change, i.e., by reducing emissions of GHG and

particles into the atmosphere. Solar power is considered as a tool to reduce globlal 2

emmissions and serve to mitigate changes in climate (Schmalensee, et al. 2015), in which

solar PV is a “carbon free” technology that turns sunllight directly into electricity without

fuel, moving parts, or waste product (Lynn 2010).

2.5.2 Economic Benefits

According to U.S. Energy Information Administration (EIA) (2016), electricity accounts for

61% of all energy consumed in commercial buildings for heating, ventilation and air

conditioning. Furthermore the price of electricity is projected to increase in the near future,

with Tenaga Nasional Bhd (TNB) raising electricity charges by an average of 15% on Jan

2014 (Borneo Post Online 2013) and elecicity tariff by 2% on Jan 2016 (The Rakyat Post

2015). Going for solar PV system hedges consumers’ price of electricity for decades as the

expected life span of Solar PV system is 20-25 years (Schmalensee, et al. 2015), hence a cost

23

savings in electricity bill. Solar PV system also create energy independence by reducing

consumers’ dependency on big utility corporations and semi-monopolies (Cost of Solar

2013).

PBP is used in project-evaluation to obtain the expected length of time for an investement to

return its initial costs and according to this method, the investment is consiered viable if

payback is sufficiently fast (Boyle and Guthrie 2006). Nasirov, Silva and Agostini (2015)

claimed that RE technology projects have longer PBP. Base on proprietary data on Solar PV

system investment ayalysis, the PBP of a typical commaercial solar PV system in Malaysia is

approximately 5 to 6 years after taking into considertation of green technology incentive,

capital tax allowance and the saving of electricity bill.

H2 The higher the perceived relative advantage of a solar PV system, the

greater is the likelihood that the solar PV system will be assessed and

implemented.

2.6 Compatibility

Rogers (1983) defined compatibility as the degree to which an innovation is perceived as

being consistent with the existing socialcultural values and beliefs, past experiences, and

needs of adopters. Tornatzky and Klein (1982) argued for two types of compatibility

interpretation: (i) normative or cognitive compatibility that relate with what people feel or

think about a technology; and (ii) practical or operational compatibility that refer to what

people do. Jansson (2011) claimed that an innovation that is incompatible with the values and

norms of a social system will not be adopted as fast as an compatible innovation.

Compatibility has been found to be positively related to adoption of mobile banking (Dash,

Bhusan and Samal 2014) and contactless credit card (Wang 2008). In eco-innovation, Labay

24

and Kinnear (1981) found that adopters are perceived to have greater compatibility than non-

adopters in as solar energy.

Karahanna, Agarwal and Angst (2006) introduced a comprehensive concept of compatibility

in four dimensions: compatibility with values; compatibility with preferred work style;

compatibility with existing work practices; and compatibility with prior experience which is

relevant for assessing and implementing solar PV system.

2.6.1 Compatibility with Values

Climate change and environmental degradation are global problems, Harvard University has

modelled an institutional pathway toward a more sustainable future by creating a University-

wide Sustainability Plan (Harvard University 2014). Harvard University has adopted a variety

of RE systems which are able to generate 14% of its electricity, to reduce fuel purchases and

therefore reduce GHG emissions (Harvard University 2015), which represents the match

between the possibilities offered by technology and the users’ dominant value system

(Karahanna, Agarwal and Angst 2006).

2.6.2 Compatibility with Preferred Work Style

Karahanna, Agarwal and Angst (2006) defined compatibility with preferred work style as

capturing the possibility offered by the technology of being consistent with a desired work

style. Claudy, Michelsen and O’Driscoll (2011) argued that potential adopters of

microgeneration technologies such as solar PV might worry that they were required to change

daily practices to operate heating and electricity production, as previously generating

25

electricity is usually detached from people’s daily practices, whether the adopters prefer

electricity being generated on their rooftop.

2.6.3 Compatibility with Existing Practices

Wang (2008) believed that consumers will have a favorable impression if usage of innovation

fits their habits, lifestyle and needs. Cho and Kim (2001-2002) found that technological

compatibility of object-oriented technology that are not consistent with the existing way of

thinking, procedure, experiences, skill, and the need of receivers are the reasons of slow

acceptance. Schmalensee, et al. (2015) suggested that recent innovation in solar PV

technologies, which include higher efficiency, lower material used and improved in

manufactuability, have met the adopters’ needs of convenient electricity supply which is

compatible with fossil fuel electricity supply, as proposed by (Karahanna, Agarwal and Angst

2006) of the extent to which a technology “fits” with user’s current work process.

2.6.4 Compatibility with Prior Experience

According to Karahanna, Agarwal and Angst (2006) compatibility with prior experience

reflects a fit between the target technology and a variety of users’ past encounters with

technology. Green electricity study by Ozaki (2011) discovered that the way innovation

reflect respondents’ identity, image, values and norms can motivate the adoption of green

energy, where respondents expressed their experience of green energy as being social

responsible, not compromising quality of life and deriving hapiness.

26

H3 Beliefs about the compatibility of solar PV energy are expected to

significantly influence the assessment and implementation of solar PV

system.

2.7 Complexity

Rogers (1983) defined complexity as the degree to which an innovation is perceived as

difficult to understand and use, in which an individual will be more attracted to an innovation

that they feel more comfortable to use with.

2.7.1 Awareness and Understanding of Solar PV Technology

Kapoor, Dwivedi and Williams (2014) found that the perception of complexity associated

with an individual’s knowledge and the related skill required to use that innovation. Faiers

and Neame (2006) investigated the adoption of solar power between a group of “early

adopters” and another group of “early majority” related to their product knowledge,

awareness and the adoption.

In Malaysia, low adoption of solar energy is due to lack of public awareness and

understanding of solar PV technology (Mekhilef, et al. 2011) as well as lack of correct

information about solar energy utilization (Solangi, et al. 2015). Ozaki (2011) realized that

information relating to green tariffs in not easily available and repondents do not possess

accurate information for them to make descision to adopt the sustainable innovation.

Kebede and Mitsufuji (2014) sought to address capability problems associated with the

availability of skills and knowledge from another angle, which is the industry players that

affect the diffusion of solar energy in Ethiopia, and found the barriers of adoption as: lack of

27

skilled manpower for mainenance services, lack of technical know-how of policy-makers and

customs oficers and lack of capacities of rural users to prevent or fix minor problems.

2.7.2 Ease of Use

Davis (1986) defined perceived ease of use as the degree to which an individual believes that

using a particular system would be free of physical and mental effort in his Technology

Acceptance Model (TAM) and hyphothesized that perceived ease of use to be one of the

fundamental determinants of user acceptance of IT (Davis 1989). In an IT study, Venkatesh

(2000) suggested that users’ perceptions about ease of use would be determined by various

general computer beliefs about computer use, however after direct experience, the perceptions

would be adjusted to reflect various aspects of the experience.

Arkesteijn and Oerlemans (2005) examined the ease of using green power in households

which is related to system complexity factors, i.e., the difficulties that individuals can

encounter in understanding and using an innovation. In the green electricity for domestic

study, Arkesteijn and Oerlemans (2005) found that a high level of complexity will be

transformed into a low level of internal complexity if a decision maker trusted the product,

brand name or producer.

H4 The more users think solar PV power is difficult to acquire and integrate

into their daily practices, the lower the adoption and implementation of

solar PV system will be.

28

2.8 Perceived Risk

Midgley and Dowling (1978) considered the fact that innovation involve an element of

uncertainty or risk for the adopter and suggested to include perceived risk to Rogers’ (1983)

DOI. Kleijnen, Lee and Wetzels (2009) stated that perceived risk constituted to consumers’

evluation of the likelihood of negative outcomes. Meuter, et al. (2005) studied on self-servive

technology revealed that as perceived risk increases, the less motivated the individuals are to

adopt the innovation. Labay and Kinnear (1981) defined perceived risk as the expected

probability of economic or social loss resulting from innovation, and found that lower

perceived financial riskiness and lower perceived social riskiness positively impact users’

adoption on solar energy systems. Claudy, Michelsen and O’Driscoll (2011) studied

economcic risk, functional risk and social risk related to microgeneration technologies

adoption.

2.8.1 Economic Risk

Economic risk reflects the fear of wasting financial resource for adopting an innovation

(Claudy, Michelsen and O’Driscoll 2011). Tietjen, Pahle and Fuss (2016) observed a

considerable investment risks in the weather-dependent RE such as solar and wind, due to its

high capital intensity and uncertain production volumes. Auverlot, et al. (2014) found that

low-carbon technology are changing the cost structure of the energy market due to its high

capital expenditures (CAPEX) and very low operational expenditures (OPEX), where the

invesment might not provide sufficient revenue to cover the CAPEX.

29

2.8.2 Functional Risk

According to Claudy, Michelsen and O’Driscoll (2011), functional risk refers to performance

uncertainties of a new product, which relates to its reliability. Arkesteijn and Oerlemans

(2005) suggested that system reliability in green electricity is important as users expect a

continuous supply of electricity in term of solar system quality. Ozaki (2011) explained that

uncertainty of the efficiency and reliability of green electricity affect potential adopters’

decision.

2.8.3 Social Risk

Claudy, Michelsen and O’Driscoll (2011) suggested that social risk reflects uncertainty as to

how adopting the innovation might be perceived by relevant others. Kleijnen, Lee and

Wetzels (2009) mentioned that social risk refers to whether or not consumers feel that their

social environment or reference groups will accept or support their adoption. Noothout, et al.

(2016) studied that social risk on RE related to lack of awareness on the positive effects of RE

or whether local communities benefit from the project as well as negative impacts on RE

installtion from “not-in-my-backyard” (NIMBY) metallity effects.

H5 Lower perceived risk associations with the use of solar PV equipment are

expected to positively influence the adoption and implementation of solar

PV system.

2.9 Demographic Variable

Base on pass innovation investigation, Rogers (1983) suggested that demography variables

that have been correlated with individual innovativeness include formal education, size of

30

operation, income, cosmopoliteness and mass media exposure. Rogers (1983) stated that

characteristics of “early adopters” are more educated and enable them to obtain “how-to”

knowledge of an innovation. Diamantopoulos, et al. (2003) found that demographics are

useful in profiling “green consumers” and understand their perceptions, knowledge and

attitudes towards environment.

Studies which found that demographics variables have significant impact include

demographic measure comparison between adopters and non-adopters of solar energy systems

(Labay and Kinnear 1981); consumer attitudes towards domestic solar power system (Faiers

and Neame 2006); S-P-P Model in profiling environmental sustainability-conscious consumer

(Ukenna, et al. 2012); willingness to sign up green electricity (Ozaki 2011); adoption timing

of solar PV for household electricity generation (Islam and Meade 2013); and barriers to the

adoption of PV systems (Karakaya and Sriwannawit 2015).

However, there are a few exceptions such as: adoption of technology that showed no

significant differences on demographic variables (Compeau, Meister and Higgins 2007); net

disposable income do not impact on the likelihood of adoption of green energy (Arkesteijn

and Oerlemans 2005); influence of demographic is less clear on home owners’ willingness to

pay for micro-generation technologies (Claudy, Michelsen and O’Driscoll 2011); and Kapoor,

Dwivedi and Williams (2014) that did not considered demographic factors in the study of

consumer acceptance of green innovation.

2.10 Project Management

According to Kathy O. Roper (2009) there are five primary stages to RE project decision in

new or existing buildings: project identification, feasibility, financing, contract award and

project completion as shown in Figure 2.3.

31

Figure 2.3

Federal Renewable Energy Decision Model

Source: Adopted from Castro-Lacouture and Roper (2009)

2.10.1 Cost, Time, Quality and Scope

Larson and Gray (2011) suggested that quality and the ultimate success of a project as

meeting and/ or exceeding the expextations of the customer in term of cost (budget), time

(schedule) and performance (scope) of the project. Often, project managers are required to

manage the trade-offs among time, cost and performance. Kral and Mildeova (2012) analysed

the relationship between project parameters: time, budget and scope, in terms of the types of

projects.

32

2.10.2 Managing Risk

According to Wuester, et al. (2016), constrains of the development and financing of RE

projects are the underlying market barriers as well as a perception of high risk that adds a risk

premium to the cost of capital, which in turn limits the access to affordable capital. Poject risk

for RE include country risk, social acceptance risk, financing risk, administrative riskpolitical

and regulatory risk, counterparty, grid and transmission link risk, technical and management

risk as well as market design and regulatory risk as shown in Figure 2.4. (Noothout, et al.

2016). Gaurav, Chileshe and Ma (2011) found that failure to identify and manage risks can

be held accountable for the delays in the advancement of current and future solar projects.

Figure 2.4

Risk Related to RE Projects

Source: Adopted from Noothout, et al. (2016)

33

2.11 Decision Making Framework

Afonso and Cunha (2009) identified various type of capital investment mehods used by firms

for decision making such as non-discounting cash flows metchod, i.e, PBP and accounting

average rate of return (ARR), as well as discounted cash flow (DCF) methods, i.e., NPV and

internal rate of return (IRR). Larson and Gray (2011) argued that non-financial appraisal

methods, such as checklist model and multi-weighted scoring model, are used to appraise

non-financial criteria of projects that contribute to the most impostant strategic objectives.

In solar PV investment, PBP, NPV and IRR are used to estimate whether an investment is

financially viable, investors are also required to consider the trade-off between risk and return

in such large upfront investment, but low working/ operating capital type of project

(Noothout, et al. 2016).

2.12 Hypotheses

The hypoytheses developed based on literature review are as follows:

H1 Cost attribute of solar PV system significantly influence the likelihood of

assessment and implementation of solar PV system.

H2 The higher the perceived relative advantage of a solar PV system, the

greater is the likelihood that the solar PV system will be assessed and

implemented.

H3 Beliefs about the compatibility of solar PV energy are expected to

significantly influence the assessment and implementation of solar PV

system.

34

H4 The more users think solar PV power is difficult to acquire and integrate

into their daily practices, the lower the adoption and implementation of

solar PV system will be.

H5 Lower perceived risk associations with the use of solar PV equipment are

expected to positively influence the adoption and implementation of solar

PV system.

H6 The assessment and implementation of solar PV system is significantly

associated with innovation characteristics.

2.13 Theoretical Framework

A theoretical framework is a structure that guides research by relying on a formal theory; i.e.,

the framework is constructed by using an established, coherent explanation of certain

phenomena and relationships (Eisenhart 1991). A theoretical framework for this study has

been constructed as shown in Figure 2.5. The independent variables are cost attribute, relative

advantage, compatibility, complexity and perceived risk; and the dependent variable is

assessing and implementing of solar PV project.

35

Figure 2.5

Theoretical Framework

2.14 Summary

This section discussed articles and journals that explored characteristics of innovation that

affect the assessing and implementing of solar PV system. Among the innovation

characteristics explored are cost, relative advantage, compatibility, complexity and perceived

risk as well as other factors such as demographic and project management feasibility.

The following chapters consist of research methodology, data collection and data analysis.

36

Chapter 3 Research Methodology

3.1 Introduction

This chapter presents the detail of research methodology and study approaches chosen for this

study.

3.2 Research Methodology

This research is an exploratory case study research, whereby it entailed the detailed and

intensive analysis (Bryman 2012), to be conducted on a single case. Rowley (2002) suggested

case study as a useful tool for the preliminary, exploratory stage of a research project, as a

basis for the development of the ‘more structured’ tools that are necessary in surveys and

experiments. This research is exploratory in nature as the study of social acceptance of solar

PV energy is a relatively new field especially to the case of a university-based solar PV

project. Exploratory studies are important for obtaining a good grasp of the phenomena of

interest and advancing knowledge through subsequent theory building and hypothesis testing

(Saunders, Lewis and Thornhill 2009; Sekaran 2003).

The purpose of this study is to understand how solar PV system is beneficial both financially

and socially to the local universities. The scope of case study was further narrowed down to

the university in the case. The approaches used in this study included to: demonstrate project

management case study in the case firm; undergo preliminary interviews; develop economic/

financial model of the solar PV system; as well as develop a survey to understand the

relationship between five innovation characteristics (cost attribute, relative advantage,

compatibility, complexity and perceived risk) and the dependent variable (assessing and

implementing of a solar PV system).

37

This research was conducted in two distinct phases, i.e., the two-phase approach as shown in

Figure 3.1.

Figure 3.1

Research Methodology Flow Chart

Source: Adopted and Adapted from Beckstead (2008)

In Phase 1, a detailed literature review and preliminary key informants interviews were

conducted to identify the common characteristics of a successful solar PV project as well as

the barriers experienced in implementing solar PV projects. Common business models of

solar PV system and government tax incentives for solar PV projects in Malaysia were also

explored.

38

Results from Phase 1 were used to establish the design of the case study and detailed survey

questionnaires which were distributed to the stakeholders and other members of the

community in Phase 2. In Phase 2, a case study of solar PV project was developed and

evaluated. The questionnaire survey was cross-sectional, i.e., the study of a particular

phenomenon at a particular time (Saunders, Lewis and Thornhill 2009).

This research used exploratory sequential mixed methods (MM) approach to obtain a more

comprehensive view of the topic. Exploratory sequential approach firstly began with a

qualitative research phase and explored the view of participants, then the data were analysed

and the information were used to build into a second, the qualitative phase (Creswell 2014).

Case study research can be based on and applied in any mix of quantitative and qualitative

approaches (Gog 2015; Rowley 2002). MM researchers use and integrate both qualitative and

quantitative research techniques, approaches, methods, concepts or language that involve

collecting, analysing and interpreting quantitative and qualitative data (Creswell 2014;

Johnson, Onwuegbuzie and Turner 2007; Tashakkori and Creswell 2007; Teddlie and Yu

2007) into a single study or a set of related studies.

3.3 Survey Instruments

The MM approach of this study was conducted as follows:

3.3.1 Qualitative Approach

The qualitative approach was employed in Phase 1 prior to development of the survey

questionnaire and to obtain information for the case study. Kumar (1989) stated that in

preliminary studies during the design of a comprehensive quantitative study, key informants

39

interview could help define the parameter of survey questionnaires. Key informants were

engaged through face-to-face interviews, telephonic interviews or email conversations,

subject to their availability. Research participants included one (1) project manager of a solar

PV system provider, one (1) sales manager of a solar PV system vendor, one (1) official from

SEDA, and two (2) representatives from the case firm. The interview sessions were either

recorded or noted down by hands. These interviews provided a better understanding on the

background for solar PV installation, calculation processes involved, and challenges which

might impede the implementation of this kind of project. According to Bryman (2012),

qualitative interviewing provides insight into what the interviewee sees as relevant and

important, furthermore new questions that follow up the interviewee’s reply can be asked to

obtain detailed answers. Hence, qualitative exploration enabled the researcher to build a

shorter and more focused surveys by discovering the underlying factors that might be missed,

thus eliminating “dead ends” from research prior to commencement of qualitative research.

3.3.2 Quantitative Approach

The quantitative approach was utilized in the self-administered survey questionnaire of Phase

2. Since this study assessed the implementation of solar PV system, a survey questionnaire

was appropriate in order to reach larger audience size and a more dispersed geographical area.

The survey questionnaire (see Appendix A and Appendix B) was conducted in September

2016. Preston and Colman (2000) observed that the scales with 5, 7 and 10-point were most

preferred in the “ease of use” criteria, a scale if it is too difficult (scale of more that 11-point)

to use or too simple (scale of 2, 3 and 4-point) to allow respondents to express themselves

tend to frustrate, demotivate and decrease their response rate. In this study, a 5-point Likert

40

scale was used to measure the respondents’ rating, i.e., 1. Strongly Disagree; 2. Disagree; 3.

Neutral; 4. Agree; and 5. Strongly Agree.

The questionnaire consisted of a total of 34 questions divided into seven distinct sections. The

variables measurement and scale were adopted and adapted from Arkesteijn and Oerlemans

(2005); Carter and Campbell (2011); Choudhury and Karahanna (2008); Claudy, Michelsen

and O’Driscoll (2011); Jansson (2011); Karahanna, Agarwal and Angst (2006); Kebede and

Mitsufuji (2014); Loo (2013); Nasirov, Silva and Agostini (2015) and Ozaki (2011).

The first six sections consisted of 26 questions of 5-point Likert Scale: 23 questions

requesting the respondents to indicate how they felt towards innovation characteristics of

solar PV; and 3 questions asking the opinion of respondents on the adoption of the

innovations, as follows:

 Section I: Cost Attribute – 5 Questions

 Section II: Relative Advantage – 5 Questions

 Section III: Compatibility – 4 Questions

 Section IV: Complexity – 4 Questions

 Section V: Perceived Risk – 5 Questions

 Section VI: Assessment and Implementation – 3 Questions

The researcher would like to find out the relationship between the five independent variables

and the dependent variable that determine various characteristics to solar PV innovations in

Malaysia.

The last section, Section VII consisted of 8 questions to collect demographic data of the

respondents such as gender, age group, monthly household income, education level,

occupation, residing area, nationality and states of respondents (indicated by postcode filled in

41

by respondents). In order to maintain privacy and to avoid the respondents of being reluctant

to provide sensitive data, the researcher used band range of information for age and monthly

household income.

In view to reduce carbon footprints, hardcopy questionnaire forms were avoided whenever

possible. Web form questionnaire was created and hosted at Google Forms, an online survey

tools. A link was provided through email, social media such as Facebook (FB) and LinkedIn,

cross platform messaging applications such as WhatsApp, Line and FB Messenger. In this

study, the questionnaire were available in two languages, i.e., English and Malay Language,

for the respondents to select their preferred language, in accordance to the definition of MM

research by mixing of languages (Johnson, Onwuegbuzie and Turner 2007).

3.4 Population and Sample

This research was carried out within the WU and its communities which comprised of

students, academic staffs, administrative and management staffs, parents, neighbours and

residents within the communities. WU has campuses/ regional offices in several states such as

Penang, Perak, Selangor, Kuala Lumpur, Johor Bahru and Sarawak. This was inspired by

Devine-Wright’s (2007) work that suggested public acceptance is recognised as an important

issue in shaping the widespread implementation of RE technology. Walker, Devine-Wright

and Evans’ (2006) research showed that pursuing a community approach to sustainable

technology diffusion enable experimentation with different models of project development

that fit local circumstances and needs. The demographic characteristics of the sample were

identified in “Section VII” of the questionnaire (see Appendix A and Appendix B) which

includes gender, age, education level, occupation and residential area.

42

This research was carried out by using convenience sampling. This method was selected due

to ease of the participants volunteering ability and easy access. The advantages of

convenience sampling are the availability and the quickness with which data can be gathered

(Business Dictionary 2016).

3.5 Data Collection

The data collection methods comprise of setting boundaries for the study, collecting

information through semi structured interviews, documents and visual materials, survey

questionnaire as well as establishing the protocol for recording and collecting information

(Creswell 2014).

3.5.1 Key Informants Interviews

Interviews were arranged with the key informants either face to face or by phone subject to

the time convenient to the key informants, while email conversations helped to received

further data from the respondents. Kumar (1989) suggested that the key informants should be

selected based on the possession of knowledge of the subject on which they would be

interviewed (see Table 3.1 for the List of Participants of Preliminary Interview).

An interview guide (see Appendix C) was prepared to list down the topics and issues to be

covered during an interview, the researcher could rephrase questions according to different

informant categories in this study. The interviews assembled information related to

installation, practical understanding and observation related to solar PV projects.

43

The interviews started by establishing rapport with the key informants, and then proceeded

with factual questions followed by questions requiring opinions and judgements (Kumar

1989). Interviews were recorded by notes taking and tape recording where key informants’

permissions were sought beforehand. In face to face interviews, the key informants’

nonverbal behaviours were noted as well. The researcher used follow-up email conversations

to seek clarification on the subject after the interviews.

Table 3.2

List of Participants of Preliminary Interview

Type of Organisation Number of key Informant Occupation

1 Solar PV system company 1 Project Manager

2 Solar PV system vendor 1 Sales Manager

3 SEDA 1 Officer

4 Private University 1 Managerial Staffs

3.5.2 Administering the Questionnaire

The researcher sent out 25 invitations through email, 240 messages through instant messaging

applications such as WhatsApp, Line and FB Messenger and 39 by hardcopy. The researcher

had used social media in order to ensure that the survey was available to other members of the

community by posting the questionnaire to the following FB pages with the permission from

the FB page owners: Cochrane Road School Alumni; Malaysian Greenbook; Kuantan

Environment Lover Club (KELC); Green Technology Business Sharing and FB groups

created by students from the researcher’s university (WOU MBA Project – July 2016; WOU

CEMBA Project Course; WOU Economic Environment of Business; WOU Business Law;

44

WOU Research Methods; WOU Strategic Management; and WOU Quantitative Techniques

WOU Quality Management).

The initial participants were selected through previously known contacts either personal or

professional. Then the researcher used snowballing samples by asking the contacts to identify

and invite members of their network which might also participate in this research (Brewis

2014). The researcher used convenience sampling in this study in view of time and cost

constraints to complete this study, therefore the study may not adequately represent the whole

population (Business Dictionary 2016). The breakdown of procedure and timeframe is shown

in Figure 3.3.

Figure 3.3

Breakdown of Procedure and Timeframe

45

3.6 Pilot Study

Prior to the actual survey, a pilot study was conducted in early September 2016 which

involved 12 students and staffs of the researcher’s university at the KL Regional Office

(KLRO). Pilot testing is important to establish the content validity of scores on the instrument

and to improve questions, format and scales (Creswell 2014). The Cronbach’s alpha were

computed and valued at .83, indicated that the instrument had a good (over .80) reliable

internal consistency (Mooi and Sarstedt 2011; Sekaran 2003).

Table 3.4

Reliability of the Pilot Study

Cronbach’s Alpha Number of Items

.826 26

3.7 Data Analysis Plan

Data analysis plan outlines the plan for preparing the data for analysis, statistic that will be

used to analyse and interpret data in order to test the research hypotheses and draw valid

inferences (Marczyk, De Matteo and Festinger 2005). This research was a cross-sectional

design because it entailed the collection of data at a single point in time (Bryman 2012).

3.7.1 Analysis of Qualitative Research

According to Dougherty (2002), qualitative analysis aims to build theory, i.e., grounded

theory building, it does not test or verify theory. Kohlbacher (2005) suggested that qualitative

content analysis can be used as a method of text analysis (for interpreting interview transcripts

and other documents) in case study research.

46

Topology of qualitative data analysis is shown in Figure 3.2. Data collected from literature

review was examined and categorised to develop preliminary hypotheses and research design.

Mayring’s (2000) qualitative content analysis, i.e., summary, structuring and explication

(cited by Kohlbacher 2005), was applied to analyse data collected from the interviews and

follow up emails.

 Summary: Material is reduced to create a manageable corpus by paraphrasing,

generalized or abstracted so as to preserve the essential content;

 Explication: Data in the material is “explicatory paraphrased” by explaining,

clarifying and annotating the material, then examined with reference to the total

context; and;

 Structuring: The text is structured according to form and scaling. Dimension of the

case study structure is established.

47

Figure 3.5

Typology of Qualitative Data Analysis Techniques

Source: Adopted from Ryan and Bernard (2000)

3.7.2 Analysis of Quantitative Research

Data collected from questionnaire in hardcopies was screened for accuracy to detect

omissions and errors, only questionnaire which was correctly filled be considered for data

analysis. Using web form questionnaire in this study had simplified and expedited the data

screening process because it was programmed to check blank fields or skipped items, thus

only completed responses could be submitted to the Google Form host. Research data

collected from survey questionnaire readied for analysis was initially captured electronically

in software application such as Microsoft Excel 2016 before being transferred into IBM SPSS

version 24 for Windows (SPSS) to proceed with statistical analysis.

48

SPSS is possibly the most widely used computer software for the analysis of quantitative data

(Bryman 2012). The two major areas which the data set will be analysed in this research

consist of using descriptive and inferential statistics.

Descriptive analysis is applied to describe the characteristics of both independent and

dependent variables by measuring the central tendency, dispersion and standardising data

(Chua 2013). The descriptive statistics used are frequency, percentage, mean, variance and

standard deviation, where the respondents’ perception towards the variables can be interpreted

next.

Inferential statistics are used to describe the relationship between variables with the aim of

generalising the research results of a research sample to the population (Chua 2013), as

explained in the following paragraphs.

Reliability of a measure is an indication of stability and consistency, to the extent to which the

instrument measured is without bias, consistent across time and across the various items in the

instrument (Sekaran 2003). The reliability of a scale is the degree to which the items that

make up the scale are all measuring the same underlying attribute, which is indicated by

Cronbach’s coefficient alpha or Cronbach’s alpha (Pallant 2011). Cronbach’s alpha ranges

from 0 to 1.00, it is considered acceptable to have a reliability coefficient of .60 (Mooi and

Sarstedt 2011; Sekaran 2003); those value over .80 is considered good reliability of the

instrument measurement (Sekaran 2003).

Factor analysis analyses the structure of the interrelationships (correlations) among a large

number of variables (suach as test items and questionnirre responses) by defining sets of

variables that are highly interralated (Hair, et al. 2006). Principal axis (PA) factor analysis

replaces the correlation matrix with a “communality” which is able to measure the test item’s

relation to other items in order to understand the covariation among variables (Leech, Barrrett

49

and Morgan 2008). Factors with an eigenvalue of 1.0 or more (known as Kaiser’s criteion) are

retained for further investigation (Pallant 2011).

A correlation analysis describes the relationship between two variables (Morgan, et al. 2011).

Pearson product-moment correlation coefficient or Pearson correlation is used to test the

strength of the “relationship” between the dependent variable and the independent variables.

Pearson correlation expresses the strength of association indicated by value between -1.0 and

+1.0, with 0 representing no effect and +1 or -1 representing the maximum effect (Morgan, et

al. 2011).

Regression analysis is used to predict a single dependent variable from the knowledge of one

(simple regression) or more (multiple regression) independent variables (Hair, et al. 2006).

Multiple regression analysis can be used to predict the changes in the dependent variable in

response to changes in the independents variables. Each independent variable is weighted to

denote the relative contribution to form the regression variate, in order to enable prediction

and interpretation of the regression equation or model (Hair, et al. 2006). The level of

predictive accuracy of the regression model is presented by the coefficient of determination

( 2).

3.8 Summary

This chapter summarises the research method and survey instruments adopted in this study. A

detailed data analysis and interpretation will be presented in the next chapter: Chapter 4

Analysis of Results.

50

Chapter 4 Analysis of Results

4.1 Introduction

In this chapter, the researcher analyse the quantitative and qualitative data and results were

compiled based on several data analysis approaches.

4.2 Profile of Respondents

Of the 265 respondents who participated in the survey questionnaire, 264 were completed and

usable. The respondents were from difference states in Malaysia, but most of them came from

Selangor and Federal Territory (Kuala Lumpur and Putrajaya), which accounted for 78.0% of

respondents. The majority of the respondents stayed in urban area (85.2%) and were

Malaysian (98.9%).

The respondents who participated consist of 57.2% male and 42.8% female. The majority of

the respondents were aged between 26 and 45 (78.1%), which inclusive of those respondents

aged between 36 and 45 as the largest group of respondents (45.5%), followed by those aged

26 and 35 (32.6%). Half of the respondents had a Bachelor degree (50.8%) and slightly more

than one-fifth had a Master degree (22.7%). In this survey, Executive constituted of 41.3%

followed by Senior Management 14.3%. Students, University Academic Staff and University

Administrative Staff merely accounted for 6.4% of the respondents. Slightly more than half of

the respondents earned at least RM6,001 of total household income (54.9%).

The demographic profiles of the respondents are shown in Table 4.1 to 4.8 and Figure 4.1 to

4.8.

51

Table 4.1

Number of Respondents by State

State Frequency Per cent %

Kedah 2 0.8

Pulau Pinang 12 4.5

Perak 7 2.7

Selangor 116 43.9

Negeri Sembilan 13 4.9

Melaka 4 1.5

Pahang 7 2.7

Johor 10 3.8

Terengganu 1 0.4

Kuala Lumpur 89 33.7

Putrajaya 1 0.4

Sabah 1 0.4

Sarawak 1 0.4

Total 264 100.0

Table 4.2

Respondents by Residential Area

Area Frequency Per cent %

Urban 225 85.2

Sub-urban or Rural 39 14.8

Total 264 100.0

Table 4.3

Respondents by Nationality

Nationality Frequency Per cent %

Malaysian 261 98.9

Non-Malaysian 3 1.1

Total 264 100.0

Table 4.4

Respondents by Gender

Gender Frequency Per cent %

Male 151 57.2

Female 113 42.8

Total 264 100.0

52

Table 4.5

Respondents by Age

Age Group Frequency Per cent %

25 or below 17 6.4

26 – 35 86 32.6

36 – 45 120 45.5

46 – 55 30 11.4

50 – 65 7 2.7

66 or older 4 1.5

Total 264 100.0

Table 4.6

Respondents by Education Level

Education Level Frequency Per cent %

Secondary 14 5.3

Certificate or Diploma 38 14.4

Bachelor’s Degree 134 50.8

Master’s Degree 60 22.7

Doctoral Degree 2 0.8

Professional Qualifications 16 6.1

Total 264 100.0

Table 4.7

Respondents by Occupation

Education Frequency Per cent %

Non-executive 14 5.3

Executive 109 41.3

Senior Management 38 14.4

Professional / Specialist 35 13.3

Self-employed 30 11.4

Home Maker 2 .8

Retired 7 2.7

Unemployed 5 1.9

Students 10 3.8

University Academic Staff 4 1.5

University Administrative Staff 3 1.1

Others 7 2.7

Total 264 100.0

53

Table 4.8

Respondents by Household Income

Income Frequency Per cent %

Less than RM3,000 39 14.8

RM3,001 – RM6,000 80 30.3

RM6,001 – RM10,000 75 28.4

More than RM10,000 70 26.5

Total 264 100.0

0 20 40 60 80 100 120 140

Sarawak

Sabah

Putrajaya

Terengganu

Kedah

Melaka

Perak

Pahang

Johor

Pulau Pinang

Negeri Sembilan

Kuala Lumpur

Selangor

Figure 4.1

Number of respondents by state

Urban
225
85%

Sub-urban or
Rural

39
15%

Figure 4.2

Respondents by Residential Area

Urban

Sub-urban or Rural

54

Malaysian
261
99%

Non-
Malaysian

3
1%

Figure 4.3

Respondents by Nationality

Malaysian

Non-Malaysian

Male
151
57%

Female
113
43%

Figure 4.4

Respondents by Gender

Male

Female

0 20 40 60 80 100 120 140

25 or below

26 – 35

36 – 45

46 – 55

56 – 65

66 or older

Figure 4.5

Number of respondents by age

55

0 20 40 60 80 100 120 140 160

Secondary

Certificate or Diploma

Bachelor’s Degree

Master’s Degree

Doctoral Degree

Professional Qualification

Figure 4.6

Number of respondents by education level

0 20 40 60 80 100 120

Non-executive

Executive

Senior Management

Professional / Specialist

Self-employed

Home Maker

Retired

Unemployed

Students

University Academic Staff

University Administrative Staff

Others

Figure 4.7

Number of respondents by occupation

56

4.3 Descriptive Analysis

As mentioned, the 26 survey questions required the respondents to rate to what extend they

agree or disagree on the factors pertaining to five variables related to assessment and

implementation of solar PV system such as cost attribute, relative advantage, compatibility,

complexity and perceived risk. Descriptive statistics was applied to describe and summarise

the basic feature of data collected from the survey questionnaire.

In this research, SPSS was used to perform the data analysis. The independent variables and

dependent variable from each section of the questionnaire were coded as COS, ADV, CPT,

CPX, RIS and ANI as illustrated in Table 4.9. The numeric number after the code illustrated

the numbering of the question as shown in Table 4.10.

Table 4.9

SPSS Coding for Variables

Section Variables SPSS Coding

I Cost Attribute COS

II Relative Advantage ADV

III Compatibility CPT

IV Complexity CPX

V Perceived Risk RIS

VI Assessment and Implementation ANI

0 20 40 60 80 100

Less than RM3,000

RM3,001 – RM6,000

RM6,001 – RM10,000

More than RM10,000

Figure 4.8

Number of respondents by monthly household

income

57

Table 4.10

Abstract of SPSS Coding for Questions

Question No. Variables SPSS Coding

1 The initial costs of installing a solar PV system would be high

for me.

COS1

6 I would help to improve my local environment by installing a

solar PV system.

ADV2

14 To use electricity generated from sunlight is in line with my

values

CPT4

15 Solar PV systems are very complex products. CPX1

23 I am concern with the payback period of investing in solar PV

system.

RIS5

25 I will purchase a solar PV system. ANI2

Source: Extracted from Appendix and Appendix B

Then, descriptive statistic measure such as frequency, per cent, mean, standard variation and

variance for all the variables were computed to describe and summarise the dataset.

4.3.1 Cost Attribute

Table 4.11 indicates the descriptive statistics for cost attribute variable. 86.0% of the

respondents were of the opinion that initial costs of installing a solar PV is high (COS1 – “The

initial costs of installing a solar PV system would be high for me”. mean = 4.11, SD = .815).

83.3% of the respondents supported government tax incentives in reducing cost to produce

RE (COS3 – “Government Tax Incentives to encourage producing electricity using solar PV

is a good thing”. mean = 4.15, SD = .917).70.1% of the respondents expressed a concern of

financial strain to install a solar PV system (COS2 – “I would find it a financial strain to

install a solar PV system”. mean = 3.79, SD = .871).

58

Availability of solar PV system financing were moderately accepted (63.7%) by the

respondents (COS4 – “Availability of finance/ loan especially for solar PV energy from banks

is a good thing”. mean = 3.66, SD = 1.100). The respondents were neutral (mean = 3.4, SD =

1.023) when asked on the additional costs required to work on existing building in order to

install a solar PV system (COS5 – “A solar PV system could only be installed on my house/

organisation with major additional/ renovation work”).

Table 4.11

Descriptive Statistics for Cost Attribute

Item Likert’s Scale Frequency Percent % Mean

Std.

Deviation Variance

COS1 1 Strongly Disagree 7 2.7 4.11 .815 .664

2 Disagree 1 .4

3 Neutral 29 11.0

4 Agree 145 54.9

5 Strongly Agree 82 31.1

Total 264 100.0

COS2 1 Strongly Disagree 6 2.3 3.79 .871 .759

2 Disagree 12 4.5

3 Neutral 61 23.1

4 Agree 137 51.9

5 Strongly Agree 48 18.2

Total 264 100.0

COS3 1 Strongly Disagree 8 3.0 4.15 .917 .841

2 Disagree 5 1.9

3 Neutral 31 11.7

4 Agree 116 43.9

5 Strongly Agree 104 39.4

Total 264 100.0

COS4 1 Strongly Disagree 13 4.9 3.66 1.049 1.100

2 Disagree 22 8.3

3 Neutral 61 23.1

4 Agree 114 43.2

5 Strongly Agree 54 20.5

Total 264 100.0

COS5 1 Strongly Disagree 11 4.2 3.40 1.023 1.047

2 Disagree 42 15.9

3 Neutral 73 27.7

4 Agree 107 40.5

5 Strongly Agree 31 11.7

Total 264 100.0

59

4.3.2 Relative Advantage

Table 4.12 indicates the descriptive statistics for relative advantage variable. 88.3% of the

respondents agreed that their electricity bill will be reduced (ADV3 – “I would reduce my

electricity bill if I use solar power to generate electricity”. mean = 4.22, SD = .801); but they

are not sure whether they can be independent from national energy provider by generating

own electricity from solar PV system (ADV4 – “Installing solar PV system would make me

independent from national energy providers”. mean = 3.43, SD = 1.076).

Although 79.9% of the respondents would help to reduce greenhouse gasses (ADV1 – “I

would help to significantly reduce greenhouse gases by installing a solar PV system”mean =

4.22, SD = .801) and 74.6% of the respondents would help to improve local environment,

ADV2, (mean = 3.92, SD = .816), the respondents were not so ready to make monetary

sacrifice to preserve the environment (ADV5 – “I would give first priority to the quality of the

environment, even if it cost me more money”. mean = 3.10, SD = .927).

60

Table 4.12

Descriptive Statistics for Relative Advantage

Item Likert’s Scale Frequency

Per

cent % Mean

Std.

Deviation Variance

ADV1 1 Strongly Disagree 3 1.1 4.04 .786 .618

2 Disagree 4 1.5

3 Neutral 46 17.4

4 Agree 137 51.9

5 Strongly Agree 74 28.0

Total 264 100.0

ADV2 1 Strongly Disagree 4 1.5 3.92 .816 .667

2 Disagree 6 2.3

3 Neutral 57 21.6

4 Agree 137 51.9

5 Strongly Agree 60 22.7

Total 264 100.0

ADV3 1 Strongly Disagree 5 1.9 4.22 .801 .641

2 Disagree 3 1.1

3 Neutral 23 8.7

4 Agree 132 50.0

5 Strongly Agree 101 38.3

Total 264 100.0

ADV4 1 Strongly Disagree 12 4.5 3.43 1.076 1.158

2 Disagree 45 17.0

3 Neutral 64 24.2

4 Agree 104 39.4

5 Strongly Agree 39 14.8

Total 264 100.0

ADV5 1 Strongly Disagree 11 4.2 3.10 .927 .860

2 Disagree 51 19.3

3 Neutral 119 45.1

4 Agree 66 25.0

5 Strongly Agree 17 6.4

Total 264 100.0

4.3.3 Compatibility

Table 4.13 indicates the descriptive statistics for compatibility variable. 84.5% of the

respondents expressed that it will be a different experience for them to use solar PV electricity

(CPT3 – “Using solar PV electricity would be a new power generating experience for me”.

mean = 4.00, SD = .768) and 78.8% of the respondents agreed that it is in line with their

61

values (CPT4 – “To use electricity generated from sunlight is in line with my values”. mean =

3.86, SD = .732).

The respondents show a week level of agreement (mean = 3.59, SD = .719) in compatibility

of solar PV system with their daily life (CPT2 – “Using a solar PV system would be

compatible with most aspects of my domestic life”). They were unsure (mean = 3.44, SD

= .891) whether there will be any significant changes required in their existing daily routine to

use a solar PV is used to generate electricity life (CPT1 – “To use a solar PV system would

not require significant changes in my existing daily routines.”).

Table 4.13

Descriptive Statistics for Compatibility

Item Likert’s Scale Frequency

Per

cent % Mean

Std.

Deviation Variance

CPT1 1 Strongly Disagree 10 3.8 3.44 .891 .794

2 Disagree 22 8.3

3 Neutral 95 36.0

4 Agree 117 44.3

5 Strongly Agree 20 7.6

Total 264 100.0

CPT2 1 Strongly Disagree 4 1.5 3.59 .719 .517

2 Disagree 9 3.4

3 Neutral 94 35.6

4 Agree 142 53.8

5 Strongly Agree 15 5.7

Total 264 100.0

CPT3 1 Strongly Disagree 5 1.9 4.00 .768 .589

2 Disagree 6 2.3

3 Neutral 30 11.4

4 Agree 167 63.3

5 Strongly Agree 56 21.2

Total 264 100.0

CPT4 1 Strongly Disagree 2 .8 3.86 .732 .536

2 Disagree 2 .8

3 Neutral 73 27.7

4 Agree 140 53.0

5 Strongly Agree 47 17.8

Total 264 100.0

62

4.3.4 Complexity

Table 4.14 indicates the descriptive statistics for complexity variable. The respondents were

not sure of the complexity (CPX1 – “Solar PV systems are very complex products”. mean =

3.06, SD = .935), difficulty (CPX2 – “Solar PV systems would be difficult to use”. mean =

2.59, SD = .831) and knowledge requirement (CPX3 – “Solar PV systems require a lot of

knowledge to use”. mean = 2.70, SD = .954) to use a solar PV system. They were unsure

(mean = 3.33, SD = .949) whether it is difficult to find a service provider to install solar PV

system (CPX4).

Table 4.14

Descriptive Statistics for Complexity

Item Likert’s Scale Frequency

Per

cent % Mean

Std.

Deviation Variance

CPX1 1 Strongly Disagree 6 2.3 3.06 .935 .875

2 Disagree 74 28.0

3 Neutral 99 37.5

4 Agree 69 26.1

5 Strongly Agree 16 6.1

Total 264 100.0

CPX2 1 Strongly Disagree 17 6.4 2.59 .831 .691

2 Disagree 111 42.0

3 Neutral 104 39.4

4 Agree 27 10.2

5 Strongly Agree 5 1.9

Total 264 100.0

CPX3 1 Strongly Disagree 22 8.3 2.70 .954 .910

2 Disagree 99 37.5

3 Neutral 85 32.2

4 Agree 52 19.7

5 Strongly Agree 6 2.3

Total 264 100.0

CPX4 1 Strongly Disagree 6 2.3 3.33 .949 .900

2 Disagree 47 17.8

3 Neutral 88 33.3

4 Agree 99 37.5

5 Strongly Agree 24 9.1

Total 264 100.0

63

4.3.5 Perceived Risk

Table 4.15 indicates the descriptive statistics for perceived risk variable. 66.7% of the

respondents expressed their concern with the PBP of investing in solar PV system, RIS5

(mean = 3.79, SD = .948). Slightly more than half of the respondents are concern of the

function (53.8%) (RIS1 – “I am worry about how dependable and reliable solar PV system

will be”. mean = 3.48, SD = .954) and expected benefits (52.3%) (RIS3 – “I am concern that

solar PV system will not provide the level of benefits expected”. mean = 3.48, SD = .954) of

the solar PV system.

The respondent were neutral of the safeness of solar PV technology (RIS2 – “I am worry

about the safeness of solar PV technology”.mean = 3.25, SD = 1.021) and social risk of (RIS4

– “I am concern that some people whose opinion I value would think that I am just being

showy”. mean = 2.56, SD = .942).

64

Table 4.15

Descriptive Statistics for Perceived Risk

Item Likert’s Scale Frequency

Per

cent % Mean

Std.

Deviation Variance

RIS1 1 Strongly Disagree 5 1.9 3.48 .906 .821

2 Disagree 32 12.1

3 Neutral 85 32.2

4 Agree 114 43.2

5 Strongly Agree 28 10.6

Total 264 100.0

RIS2 1 Strongly Disagree 10 3.8 3.25 1.021 1.042

2 Disagree 61 23.1

3 Neutral 66 25.0

4 Agree 106 40.2

5 Strongly Agree 21 8.0

Total 264 100.0

RIS3 1 Strongly Disagree 9 3.4 3.37 1.031 1.063

2 Disagree 52 19.7

3 Neutral 65 24.6

4 Agree 108 40.9

5 Strongly Agree 30 11.4

Total 264 100.0

RIS4 1 Strongly Disagree 34 12.9 2.56 .942 1.886

2 Disagree 95 36.0

3 Neutral 93 35.2

4 Agree 38 14.4

5 Strongly Agree 4 1.5

Total 264 100.0

RIS5 1 Strongly Disagree 4 1.5 3.79 .948 .898

2 Disagree 22 8.3

3 Neutral 62 23.5

4 Agree 114 43.2

5 Strongly Agree 62 23.5

Total 264 100.0

4.3.6 Assessment and Implementation

Table 4.16 indicates the descriptive statistics for assessment and implementation variable.

80.7% of the respondents agree to implement solar PV system in the future (ANI1 – “I will try

out solar PV system in the future”. mean = 3.94, SD = .689) while 55.7% agreed to

recommend it to others (ANI3 – “I will recommend solar PV system to others”. mean = 3.63,

65

SD = .743). However, the respondents were uncertain (mean = 3.48, SD = .745) in purchasing

it (ANI2 – “I will purchase a solar PV system”).

Table 4.16

Descriptive Statistics for Assessment and Implementation

Item Likert’s Scale Frequency

Per

cent % Mean

Std.

Deviation Variance

ANI1 1 Strongly Disagree 2 .8 3.94 .689 .475

2 Disagree 5 1.9

3 Neutral 44 16.7

4 Agree 169 64.0

5 Strongly Agree 44 16.7

Total 264 100.0

ANI2 1 Strongly Disagree 3 1.1 3.48 .745 .555

2 Disagree 10 3.8

3 Neutral 128 48.5

4 Agree 102 38.6

5 Strongly Agree 21 8.0

Total 264 100.0

ANI3 1 Strongly Disagree 2 .8 3.63 .743 .553

2 Disagree 6 2.3

3 Neutral 109 41.3

4 Agree 117 44.3

5 Strongly Agree 30 11.4

Total 264 100.0

4.3.7 Summarised Descriptive Statistic

Table 4.17 indicates the descriptive statistics for all five independent variables. The researcher

noted that the respondents agreed (approximately close to mean = 4) that cost attribute (mean

= 3.82, SD = .576), relative advantage (mean = 3.74, SD = .612) and compatibility (mean =

3.72, SD = .544) were the independent variables that contribute to assessment and

implementation of solar PV system. The respondents were neutral (approximately close to

mean = 3) that perceived risk (mean = 3.29, SD = .698) and complexity (mean = 2.92, SD

66

= .720) of the independent variables that contribute to assessment and implementation of solar

PV system.

Complexity was the least important variable because of the low mean score of 2.92. Cost

attribute is the most important variable to the acceptance of solar PV system, carrying the

highest mean of 3.82.

Table 4.17

Descriptive Statistics for Independent Variables

Item N Minimum Maximum Mean

Std.

Deviation Variance

Cost Attribute 264 1.00 5.00 3.82 .576 .332

Relative Advantage 264 1.00 5.00 3.74 .612 .375

Compatibility 264 1.00 5.00 3.72 .544 .296

Complexity 264 1.00 5.00 2.92 .720 .518

Perceived Risk 264 1.00 4.80 3.29 .698 .487

4.4 Reliability Test Analysis

In order to measure the internal consistency reliability between the 26 questions of Section I

to VI of the questionnaire, the researcher computed Cronbach’s coefficient alpha (by using

SPSS). These questions were related to variables under study that include assessment and

implementaiton, cost attribute, relative advantage, compatibility and complexity.

Table 4.18 indicates the Cronbach’s alpha value of .80 for assessment and implementation

score, which indicated good internal consistency reliability. However, it was not necessary to

slightly increase the alpha value to .81 by deleting ANI1, because the corrected item-total

correlation for item ANI1 was .54, which was considered moderately high (.40 or above)

(Leech, Barrrett and Morgan 2008) and moderately correlated with the other items. Therefore,

67

items ANI1 – ANI3 showed measures for the same construct and can directly be summarised

to form a reliable scale.

Table 4.18

Reliability Statistics for Assessment and Implementation

Cronbach’s

Alpha

Cronbach’s Alpha Based

on Standardized Items Number of Items

.795 .794 3

Item-Total Statistics

Item

Scale Mean if

Item Deleted

Scale

Variance

if Item

Deleted

Corrected

Item-Total

Correlation

Squared

Multiple

Correlation

Cronbach’s

Alpha if

Item

Deleted

ANI1 7.1174 1.868 .544 . 302 .814

ANI2 7.5720 1.508 .714 .528 .637

ANI3 7.4242 1.576 .665 .487 .692

Table 4.19 indicates the Cronbach’s alpha value of .59 for cost attribute, which was not

sufficient (lower than .60) to form a scale that has reasonable internal consistency reliability.

The alpha value would be increased to .60 if item COS5, which had the corrected item-total

correlation of .22 which was too low (less than .3) (Leech, Barrrett and Morgan 2008) was

deleted. Alpha is highly dependent on the number of items in the summated scale, hence for a

four-item scale, the alpha value of .60 was acceptable (Mooi and Sarstedt 2011), which

indicated minimally adequate reliability. Therefore, items COS1 – COS5 showed measures

for the same construct and can be summarised, indicated minimally adequate reliability.

.

68

Table 4.19

Reliability Statistics for Cost Attribute

Cronbach’s

Alpha

Cronbach’s Alpha Based

on Standardized Items Number of Items

.585 .602 5

Item-Total Statistics

Item

Scale Mean

if Item

Deleted

Scale

Variance if

Item

Deleted

Corrected

Item-Total

Correlation

Squared

Multiple

Correlation

Cronbach’s

Alpha if

Item

Deleted

COS1 14.9962 5.760 .479 .461 .466

COS2 15.3182 5.670 .450 .428 .475

COS3 14.9621 6.097 .300 .219 .553

COS4 15.4508 5.617 .317 .214 .548

COS5 15.7121 6.153 .216 .105 .604

Table 4.20 shows the Cronbach’s alpha value of .72 for relative advantage score, which

indicated reasonable internal consistency reliability (exceeding .60). Therefore, items ADV1 –

ADV5 showed measures for the same construct and can be summarised to form a reliable

scale.

Table 4.20

Reliability Statistics for Relative Advantage

Cronbach’s

Alpha

Cronbach’s Alpha Based

on Standardized Items Number of Items

.724 .740 5

Item-Total Statistics

Item

Scale

Mean if

Item

Deleted

Scale

Variance

if Item

Deleted

Corrected

Item-Total

Correlation

Squared

Multiple

Correlation

Cronbach’s

Alpha if

Item

Deleted

ADV1 14.6667 6.147 .670 .631 .612

ADV2 14.7879 6.282 .594 .593 .638

ADV3 14.4924 6.867 .446 .244 .692

ADV4 15.2803 6.043 .412 .195 .719

ADV5 15.6061 6.742 .369 .152 .723

69

Table 4.21 showed the Cronbach’s alpha value of .65 for compatibility score, which indicated

minimally adequate reliability a for a four-item scale, due to the fact that alpha value is highly

dependent on the number of items in the summated scale. Therefore, items CPT1 – CPT4

showed measures for the same construct and can be summarised, indicated minimally

adequate reliability.

Table 4.21

Reliability Statistics for Compatibility

Cronbach’s

Alpha

Cronbach’s Alpha Based

on Standardized Items Number of Items

.648 .657 4

Item-Total Statistics

Item

Scale

Mean if

Item

Deleted

Scale

Variance

if Item

Deleted

Corrected

Item-Total

Correlation

Squared

Multiple

Correlation

Cronbach’s

Alpha if

Item

Deleted

CPT1 11.4470 2.803 .384 .237 .621

CPT2 11.2955 2.886 .548 .337 .502

CPT3 10.8864 3.249 .327 .161 .647

CPT4 11.0189 2.984 .483 .272 .545

Table 4.22 shows the Cronbach’s alpha value of .79 for complexity score, which indicated

reasonable internal consistency reliability (exceeding .60). Therefore, items CPX1 – CPX4

showed measures for the same construct and can be summarised to form a reliable scale.

70

Table 4.22

Reliability Statistics for Complexity

Cronbach’s

Alpha

Cronbach’s Alpha Based

on Standardized Items Number of Items

.790 .793 4

Item-Total Statistics

Item

Scale

Mean if

Item

Deleted

Scale

Variance

if Item

Deleted

Corrected

Item-Total

Correlation

Squared

Multiple

Correlation

Cronbach’s

Alpha if

Item

Deleted

CPX1 8.6250 4.775 .645 .419 .714

CPX2 9.0909 5.140 .651 .490 .716

CPX3 8.9811 4.695 .648 .481 .712
CPX4 8.3485 5.338 .467 .237 .804

Table 4.23 shows the Cronbach’s alpha value of .77 for perceived risk score, which indicated

a reasonable internal consistency reliability (exceeding .60). Therefore, items RIS1 – RIS5

showed measures for the same construct and can be summarised to form a reliable scale.

Table 4.23

Reliability Statistics for Perceived Risk

Cronbach’s

Alpha

Cronbach’s Alpha Based

on Standardized Items Number of Items

.766 .763 5

Item-Total Statistics

Item

Scale

Mean if

Item

Deleted

Scale

Variance

if Item

Deleted

Corrected

Item-Total

Correlation

Squared

Multiple

Correlation

Cronbach’s

Alpha if

Item

Deleted

RIS1 1.000 .680 .555 .226 .342

RIS2 .680 1.000 .597 .319 .339

RIS3 .555 .597 1.000 .248 .482

RIS4 .226 .319 .248 1.000 .133

RIS5 .342 .339 .482 .133 1.000

71

The results presented in Table 4.24 showed that in this study, Cronbach’s alpha for the

variables, except cost attribute was found in the range of .65 to .80, indicating that the data

were relatively reliable. Cronbach’s alpha value .59 for cost attribute could be increased to .60

if item COS5 was deleted from the scale to indicate minimally adequate reliability. Hence, it

is concluded that the variables show measures for the same construct and can be summarised

for further analysis.

Table 4.24

Summary of Reliability Statistics for Variables

Variable Cronbach’s Alpha Number of Items

Assessment and

Implementation

.795 3

Cost Attribute .585

Note: Alpha could be increased to .604 by

deleting of item COS5

5

Relative Advantage .724 5

Compatibility .648 4

Complexity .790 4

Perceived Risk .766 5

4.5 Factor Analysis

Exploratory factor analysis (EFA) and principal axis (PA) factor analysis are conducted to

identify the structure underlying a group of reliable variables. Indication of the usefulness of

EFA is given by Kaiser’s Measure of Sampling Adequacy (KMO) and the Barlett’s test of

sphericity. The recommended measure for KMO value is at least .50 (Leech, Barrrett and

Morgan 2008), indicates that the correlations between variables can be explained by the other

variables in the dataset. Barlett’s test of sphericity compares and checks the correlation matrix

in order to summarise the redundancy of the factors. Then PA with varimax rotation was

conducted to reduce a number of variables or items in the dataset to result lower dimensional

dataset that gives the best approximation to the larger set of variables (Charry, et al. 2016).

72

In this research, the twenty-three items for the five independent variables of the questionnaire

were subject to PA factor analysis using SPSS. Prior to performing PA factor analysis, the

suitability of data for factor analysis was assessed. Inspection of the correlation matrix reveals

that some variables did not meet the assumption of being correlated at a moderate level of .30

and above. Thus, the result should be viewed with caution. The result of KMO and Barlett’s

test of sphericity is presented in Table 4.25. The KMO was .78, exceeding the recommended

value of .50, which indicated the construct validity of the variables. The Barlett’s test of

sphericity reached statistical significance ( 2 = 2199.2, p < .05), supporting the factorability

of the correlation matrix. According to these two tests, the EFA was considered to be

appropriate and useful.

Table 4.25

KMO and Barlett’s Test Result for Independent Variables

Kaiser-Meyer-Olkin Measure of Sampling Adequacy. .776

Bartlett’s Test of Sphericity Approx. Chi-Square 2199.194

df 253

Sig. .000

PA factor analysis with varimax rotation was conducted to assess the underlying structure for

the twenty-three items of the questionnaire. Five factors were requested, based on the fact that

the items were designed to index five independent variables: cost attribute, relative advantage,

compatibility, complexity and perceived risk.

After rotation, the five-factor solution explained 45.4% of the variance, with the first factor

accounted for 13.9%, the second factor accounted for 11.7%, and the third to the fifth factor

account for 8.9%, 6.3% and 4.6% respectively as presented in Table 4.26.

73

Table 4.26

Factor Loadings for the Rotated Factors

Item

Factor Loading Communa-

lity 1 2 3 4 5

ADV1 I would help to significantly reduce greenhouse

gases by installing a solar PV system.

.862 .666

ADV2 I would help to improve my local environment by

installing a solar PV system.

.780 .654

CPT4 To use electricity generated from sunlight is in line

with my values.

.602 .440

ADV3 I would reduce my electricity bill if I use solar power

to generate electricity.
.559 .360

CPT3 Using solar PV electricity would be a new power

generating experience for me.

.437 .291

ADV4 Installing solar PV system would make me

independent from national energy providers.

.437 .342 .417

COS3 Government Tax Incentives to encourage producing

electricity using solar PV is a good thing.

.435 .362

ADV5 I would give first priority to the quality of the

environment, even if it cost me more money.

.415 .314

CPX3 Solar PV systems require a lot of knowledge to use. .745 .534

CPX2 Solar PV systems would be difficult to use. .738 .567

CPX1 Solar PV systems are very complex products. .707 .472

CPX4 It is difficult to find a service provider to install solar

PV system.

.477 .392

RIS4 I am concern that some people whose opinion I value

would think that I am just being showy.

.325 .264

COS5 A solar PV system could only be installed on my

house/ organisation with major additional/

renovation work.

.302 .256

RIS3 I am concern that solar PV system will not provide

the level of benefits expected.

.804 .567

RIS2 I am worry about the safeness of solar PV

technology.

.514 .637 .641

RIS1 I am worry about how dependable and reliable solar

PV system will be.

.378 .618 .554

RIS5 I am concern with the payback period of investing in

solar PV system.

.572 .336

COS1 The initial costs of installing a solar PV system

would be high for me.

.791 .572

COS2 I would find it a financial strain to install a solar PV

system.

.704 .510

CPT2 Using a solar PV system would be compatible with

most aspects of my domestic life.

.426 .612 .443

CPT1 To use a solar PV system would not require

significant changes in my existing daily routines.

.508 .320

COS4 Availability of finance/ loan especially for solar PV

energy from banks is a good thing.

.346

Eigenvalues 3.208 2.698 2.040 1.438 1.059

% of variance 13.947 11.732 8.871 6.252 4.605

74

Table 4.26 displays the items and factor loadings for the rotated factors. The first factor,

which seemed to index relative advantage had strong loading on the first eight items. ADV4

(“Installing solar PV system would make me independent from national energy providers”)

had its highest loading from the first factor, but had a low cross-loading of .34 on the

complexity factor. The second factor, which seemed index complexity, had high loadings on

the next six items. The third factor, which seemed index perceived risk, loaded highly on the

next four items in the table. RIS2 (“I am worry about the safeness of solar PV technology”)

and RIS1 (“I am worry about how dependable and reliable solar PV system will be”) had its

highest loading from the third factor, but also had a strong and moderate loading respectively

from the complexity factor. The fourth factor which seemed cost attribute loaded highly on

the next two items. The fifth factor which seemed compatibility loaded highly on the last two

items. CPT2 (“Using a solar PV system would be compatible with most aspects of my

domestic life”) had its highest loading from the fifth factor, but also had a strong loading from

the relative advantage factor. Lastly, COS4 (“Availability of finance/ loan especially for solar

PV energy from banks is a good thing”) had no loading within the five factors.

The results of this analysis support the use of relative advantage, complexity, perceived risk,

cost attribute and compatibility as separate scales of independent variables.

4.6 Regression Analysis

Regression analysis is a general statistical technique used to analyse the relationship between

a single dependent variable and single/ several independent variables (Hair, et al. 2006). In

addition, it is also used to predict and explore the impact the variables relative to another. In

this study, regression analysis and analysis of variance (ANOVA) were used to perform

hypotheses testing, followed by multiple regression to establish predictive model for this

75

study. There was no multicollinearity problem in the dataset by assessing that the variance

inflation factor (VIF) value of less than 5 and tolerance value of more than .20 (Charry, et al.

2016). Durbin-Watson value range from 1.59 – 1.76 (between 1.5 and 2.5) indicated no

autocorrelation error.

4.6.1 Cost Attribute and Assessment and Implementation

Hypotheses 1 proposed that cost attribute significantly influence the likelihood of adoption

and implementation of solar PV system:

H1: Cost attribute of solar PV system significantly influence the likelihood of assessment

and implementation of solar PV system.

Linear regression analysis was used to study this relationship. Table 4.27 shows that beta

coefficient was valued at .13, p< 0.05, which show a small (less than .29) correlations

according to Cohen (1992). There was a weak and positive relationship between cost attribute

perception and adoption and implementation of solar PV system. Lower cost attribute

perceptions was correlated with increased of the likelihood of adoption and implementation of

solar PV system.

Table 4.27

Coefficients between Cost Attribute and Assessment and Implementation

Model

Unstandardized

Coefficients

Standardize

d

Coefficients

t Sig.

Collinearity

Statistics

B Std. Error Beta Tolerance VIF

1 (Constant) 3.159 .251 12.562 .000

Cost Attribute .138 .065 .130 2.117 .035 1.000 1.000

a. Dependent Variable: Assessment & Implementation

76

Table 4.28 shows that the R value of .13 indicates weak relationship between the two

variables. As indicated by 2 value of .02, this meant that only 2.0% of the variance in

assessment and implementation of solar PV system could be predicted by cost attribute.

However, variables were not rejected solely of the small 2 value.

Table 4.28

Model Summary between Cost Attribute and Assessment and Implementation

Model R

Adjusted

Std.

Error of

the

Estimate

Change Statistics Durbin

Watso

n

Change

F

Change

df

1 df2

Sig. F

Chang

e

1 .130a .017 .013 .60785 .017 4.483 1 262 .035 1.665

a. Predictors: (Constant), Cost Attribute

b. Dependent Variable: Assessment & Implementation

Table 4.29 shows that cost attribute, F (1,262) = 4.49, was significant (p < .05). This indicated

that there was a significant relationship between cost attribute and assessment and

implementation. Despite the small 2 value, the regression coefficient p-value was

statistically significant. According to Colton and Bower 2012, such a relationship between

predictors and response may be very important, even though it may not explain a large

amount of variation in the response. Hence, this did not mean that cost attribute was not

important because of the small 2 value. As a result, H1 was accepted, where lower cost

perceptions increased the likelihood of adoption and implementation of solar PV system.

Table 4.29

ANOVA between Cost Attribute and Assessment and Implementation

Model

Sum of

Squares df

Mean

Square F Sig.

1 Regression 1.656 1 1.656 4.483 .035b

Residual 96.804 262 .369

Total 98.461 263

a. Dependent Variable: Assessment & Implementation

b. Predictors: (Constant), Cost Attribute

77

4.6.2 Relative Advantage and Assessment and Implementation

Hypotheses 2 proposed that higher perceived relative advantage increase the likelihood of

adoption and implementation of solar PV system:

H2: The higher the perceived relative advantage of a solar PV system, the greater is the

likelihood that the solar PV system will be adopted and implemented.

In order to study the relationship, regression analysis was used. Table 4.30 shows that beta

coefficient was valued at .36, p< 0.05, which show a medium (0.30 – 0.49) correlations

according to Cohen (1992). There was a moderate and positive relationship between relative

advantage and adoption and implementation of solar PV system. Increase of relative

advantage perceptions was correlated with increased of the likelihood of adoption and

implementation of solar PV system.

Table 4.30

Coefficients between Relative Advantage and Assessment and Implementation

Model

Unstandardized

Coefficients

Standardized

Coefficients

t Sig.

Collinearity

Statistics

B Std. Error Beta Tolerance VIF

1 (Constant) 2.352 .219

10.76

1

.000

Relative

Advantage

.356 .058 .357 6.181 .000 1.000 1.000

a. Dependent Variable: Assessment & Implementation

Table 4.31 shows that the R value of .36 indicates medium relationship between the two

variables, while 2 value of .13 indicated that 13.0% of the variance in assessment and

implementation could be predicted by relative advantage. Variables were not rejected solely

of the small 2 value.

78

Table 4.31

Model Summary between Relative Advantage and Assessment and Implementation

Model R

Adjusted

Std.

Error of

the

Estimate

Change Statistics Durbin

Watso

n

Change

F

Change

df

1 df2

Sig. F

Change

1 .357a .127 .124 .57270 .127 38.199 1 262 .000 1.599

a. Predictors: (Constant), Relative Advantage

b. Dependent Variable: Assessment & Implementation

Table 4.32 shows that relative advantage, F (1,262) = 38.20, was significant (p < .05). This

indicated that there was a significant relationship between relative advantage and assessment

and implementation. Despite the small 2 value, the regression coefficient p-value was

statistically significant and such a relationship between predictors and response may be

important (Colton and Bower 2012). As a result, H2 was accepted, where the higher the

perceived relative advantage of a solar PV system, the greater the likelihood that the solar PV

system will be adopted and implemented.

Table 4.32

ANOVA between Relative Advantage and Assessment and Implementation

Model

Sum of

Squares df

Mean

Square F Sig.

1 Regression 12.529 1 12.529 38.199 .000b

Residual 85.932 262 .328

Total 98.461 263

a. Dependent Variable: Assessment & Implementation

b. Predictors: (Constant), Relative Advantage

4.6.3 Compatibility and Assessment and Implementation

Hypotheses 3 proposed that compatibility of solar PV energy significantly influence adoption

and implementation of solar PV system:

79

H3: Beliefs about the compatibility of solar PV energy are expected to significantly

influence the adoption and implementation of solar PV system.

In order to study the relationship, regression analysis was used. Table 4.33 shows that beta

coefficient was valued at .50, p< 0.05, which show a large (.50 or more) correlations

according to Cohen (1992). There was a strong and positive relationship between

compatibility and adoption and implementation of solar PV system. Increase of beliefs of

compatibility of solar PV energy is correlated with increased of the likelihood of adoption and

implementation of solar PV system.

Table 4.33

Coefficients between Compatibility and Assessment and Implementation

Model

Unstandardized

Coefficients

Standardized

Coefficients

t Sig.

Collinearity

Statistics

B Std. Error Beta Tolerance VIF

1 (Constant) 1.603 .226

7.08

0

.000

Compatibility .560 .060 .498 9.30

0

.000 1.000 1.000

a. Dependent Variable: Assessment & Implementation

Table 4.34 shows that the R value of .50 indicates strong relationship between the two

variables, while 2 value of .25 indicated that 25.0% of the variance in assessment and

implementation could be explained by compatibility.

80

Table 4.34

Model Summary between Compatibility and Assessment and Implementation

Model R

Adjusted

Std.

Error of

the

Estimate

Change Statistics

Durbin-

Watson

Change

F

Change

df

1 df2

Sig. F

Change

1 .498a .248 .245 .53154 .248 86.488 1 262 .000 1.763

a. Predictors: (Constant), Compatibility
b. Dependent Variable: Assessment & Implementation

Table 4.35 shows that compatibility, F (1,262) = 86.49, was significant (p < .05). This

indicated that there was a significant relationship between compatibility and assessment and

implementation. As a result, H3 was accepted, where the higher the beliefs of compatibility of

solar energy, the greater the likelihood that the solar PV system will be adopted and

implemented.

Table 4.35

ANOVA between Compatibility and Assessment and Implementation

Model

Sum of

Squares df

Mean

Square F Sig.

1 Regression 24.436 1 24.436 86.488 .000b

Residual 74.025 262 .283

Total 98.461 263

a. Dependent Variable: Assessment & Implementation

b. Predictors: (Constant), Compatibility

4.6.4 Complexity and Assessment and Implementation

Hypotheses 4 proposed that the complexity of the solar PV will reduce the likelihood of

adoption and implementation of solar PV system:

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H4:
The more users think solar PV power is difficult to acquire and integrate into their

daily practices, the lower the adoption and implementation of solar PV system will

be.

In order to study the relationship, regression analysis was used. Table 4.36 shows that beta

coefficient was valued at -.06, p = .31, indicated zero correlations and p-value was more than

the significant level of .05. Hence, there was zero linear relationship between complexity and

adoption and implementation of solar PV system.

Table 4.36

Coefficients between Complexity and Assessment and Implementation

Model

Unstandardized

Coefficients

Standardized

Coefficients

t Sig.

Collinearity

Statistics

B Std. Error Beta Tolerance VIF

1 (Constant) 3.841 .158 24.365 .000

Complexity -.053 .052 -.063 -1.018 .310 1.000 1.000

a. Dependent Variable: Assessment & Implementation

Table 4.37 shows that the R value of .06 indicates no relationship between the two variables,

while 2 value of .00 indicated that variance in assessment and implementation could not be

predicted by complexity.

Table 4.37

Model Summary between Complexity and Assessment and Implementation

Model R

Adjusted

Std.

Error of

the

Estimate

Change Statistics

Durbin-

Watson

Change

F

Change

df

1 df2

Sig. F

Change

1 .063a .004 .000 .61182 .004 1.036 1 262 .310 1.605

a. Predictors: (Constant), Complexity

b. Dependent Variable: Assessment & Implementation

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Table 4.38 shows that relative advantage, F (1,262) = 1.04, was not significant (p > .05). This

indicated that there was no significant relationship between complexity and assessment and

implementation. As a result, H4 was rejected, where the more users think solar PV power is

difficult to acquire and integrate into their daily practices, the lower the adoption and

implementation of solar PV system will be.

Table 4.38

ANOVA between Complexity and Assessment and Implementation

Model

Sum of

Squares df

Mean

Square F Sig.

1 Regression .388 1 .388 1.036 .310b

Residual 98.073 262 .374

Total 98.461 263

a. Dependent Variable: Assessment & Implementation

b. Predictors: (Constant), Complexity

4.6.5 Perceived Risk and Assessment and Implementation

Hypotheses 5 proposed that lower perceived risk association to solar PV usage increase the

adoption and implementation of solar PV system:

H5: Lower perceived risk associations with the use of solar PV equipment are expected to

positively influence the adoption and implementation of solar PV system.

In order to study the relationship, regression analysis was used. Table 4.39 shows that beta

coefficient was valued at -.21, p< 0.05, which show a small (less than .29) correlations

according to Cohen (1992). There was a weak and negative relationship between perceived

risk and adoption and implementation of solar PV system. Lower perceived risk perceptions

was correlated with increased of the likelihood of adoption and implementation of solar PV

system.

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Table 4.39

Coefficients between Perceived Risk and Assessment and Implementation

Model

Unstandardized

Coefficients

Standardized

Coefficients

t Sig.

Collinearity

Statistics

B Std. Error Beta Tolerance VIF

1 (Constant) 4.304 .178 24.175 .000

Perceived Risk -.188 .053 -.214 -3.551 .000 1.000 1.000

a. Dependent Variable: Assessment & Implementation

Table 4.40 shows that the R value of .21 indicates weak relationship between the two

variables. As indicated by 2 value of .05, this meant that only 5.0% of the variance in

assessment and implementation of solar PV system could be predicted by perceived risk.

However, variables were not rejected solely of the small 2 value.

Table 4.40

Model Summary between Perceived Risk and Assessment and Implementation

Model R

Adjusted

Std.

Error of

the

Estimate

Change Statistics

Durbin-

Watson

Change

F

Change

df

1 df2

Sig. F

Change

1 .214a .046 .042 .59879 .046 12.610 1 262 .000 1.588

a. Predictors: (Constant), Perceived Risk

b. Dependent Variable: Assessment & Implementation

Table 4.41 shows that compatibility, F (1,262) = 86.49, was significant (p < .05). This

indicated that there was a significant relationship between compatibility and assessment and

implementation. As a result, H5 was accepted, where lower the perceived risk related to solar

PV usage, the greater the likelihood that the solar PV system will be adopted and

implemented.

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Table 4.41

ANOVA between Compatibility and Assessment and Implementation

Model

Sum of

Squares df

Mean

Square F Sig.

1 Regression 24.436 1 24.436 86.488 .000b

Residual 74.025 262 .283

Total 98.461 263

a. Dependent Variable: Assessment & Implementation

4.7 Predictive Model for the Study

Multiple linear regression was conducted to determine the best linear combination of

innovation characteristics (cost attribute, relative advantage, compatibility, complexity and

perceived risk) for predicting assessment and implementation of solar PV system:

H6: The assessment and implementation of solar PV system is significantly associated

with innovation characteristics.

The relationship between dependent variable (assessment and implementation) and

independent variables (innovation characteristics: cost attribute, relative advantage,

compatibility, complexity and perceived risk) was investigated using Pearson product-

moment correlation coefficient. Preliminary analyses were performed to ensure no violation

of the assumption of normality, linearity and homoscedasticity.

Table 4.22 shows that four of the five pairs of variables were significantly correlated (between

dependent variables and independent variables). The strongest positive correlation, which

would be considered a large effect size according to Cohen (1992), was between compatibility

and assessment and implementation, r (262) = .50, p < .01. Assessment and implementation

was also positively correlated with cost attribute (r = .13, p < .05) and relative advantage test

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score (r = .36, p< .01); these are small to medium size correlations according to Cohen (1992).

There was a negative correlation, which would be considered a small effect size between

perceived risk and assessment and implementation, r (262) = -.214, p < 0.01. However, there

was no correlation between complexity and assessment and implementation, r (262) = -.06,

p >0.05.

Table 4.42

Mean, Standard Deviations, and Intercorrelations for Assessment and Implementation

(N=264)

Variable M SD 1 2 3 4 5

Asst. &

Implementation

3.69 .612 .130* .357** .498** -.063 -.214**

Predictor Variable

1. Cost Attribute 3.82 .576 – .307** .313** .241** .166**

2. Relative

Advantage

3.74 .612 – – .571** .013 -.124*

3. Compatibility 3.72 .544 – – – -.050 -.138*

4. Complexity 2.92 .720 – – – – .481**

5. Perceived Risk 3.29 .698 – – – – –

* p < .05; ** p < .01

Table 4.43 shows that combination of variables significantly predicted assessment and

implementation of solar PV, F(5,258) = 19.80, p < .001, with two variables (complexity and

perceived risk) significantly contributing to the prediction. The adjusted 2 value was .26.

This indicated that 26% of the assessment and implementation was explained by the model.

According to Cohen (1992), this is a medium effect. The beta weights suggested that

compatibility and perceived risk contributed to predicting the assessment and implementation

of solar PV. As a result, H6 was accepted, where the assessment and implementation of solar

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PV system is significantly associated with innovation characteristics. The predictive model

was constructed as:

= 1.965 + 0.428 − 0.158 +

Where refers to the dependent variable, i.e., assessment and implementation. The

independent variables and represent compatibility and perceived risk, respectively.

Table 4.43

Simultaneous Multiple Regression Analysis Summary for Innovation Characteristics

Predicting Assessment and Implementation (N=264)

Variable

Unstandardized Coefficients
Standardized Coefficients

B Std. Error
Beta

Asst. & Implementation 1.965 .324 –

Cost Attribute -.018 .063 -.017

Relative Advantage .097 .066 .097

Compatibility .481 .074 .428**

Complexity .032 .052 .037

Perceived Risk -.139 .054 -.158*

Note. 2 = .28; Adjusted 2 = .26; F(5,258) = 19.80, p < .001

* p < .05; ** p < .01

4.8 Case Study Analysis

The case firm was undergoing feasibility study to assess and implement solar PV system

project on the roof top of WU’s KLC. The aerial view of the building is shown in Figure 4.9.

Area A was estimated to be approximately 1,080 square meters (m2) by using ruler function

in Google Earth Pro.

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Figure 4.9

Aerial View of Kuala Lumpur Campus of Case Firm

Source: Adopted and adapted from Google Earth Pro (2016) (Google Earth Pro 2016)

4.8.1 Technology Analysis

The technical analysis is presented in Table 4.44. The rooftop area “A” of WU KLC campus

(see Figure 4.9) will be able to host a 150 kWh Solar PV System. The solar PV system

consists of 480 pieces of Multicrystalline solar PV retro-fit roof mount, inverters and Wifi

monitoring system.

The rate of solar PV installation is expected to be RM7 per Watt. The estimated green

electricity is 196,560 kwh / year, and the first year electricity cost saving is estimated to be

RM100,843. This solar PV system will be able to reduce 2 emission by 138.18 tonnes per

year.

A

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Table 4.44

Technology Identification for Solar PV System for W University

Technology Specifications

System 150 kWh Solar PV System

kWp Capacity 151.2 k

Solar Module Type Multicrystalline 315 .

Quantity 480 pieces

Covered Roof Space 960 m2

Mounting Retro-fit roof mount

Inverters 3 X 3-Phase Output

Monitoring System Wifi Monitoring System

Rate of Installation RM7 / W

Estimate Generation 196,560 kwh / year

Estimate Electricity Cost Savings RM100,843 / year (*first year cost savings, yearly

degrading)

2 Emission Reduction 138.18 tonnes/ year

Category of Building Commercial – University Campus

System Integrator Qualified RE integrator registered with SEDA

4.8.2 Life Cycle Costs Analysis

The researcher applied the proprietary method provided by the industry expert during the key

informant interview to perform life cycle costs analysis (LCCA) on all associated costs and

benefits observed from the system. This was to answer the research problem, i.e., are solar PV

systems efficient to offset electricity bill of WU.

LCCA evaluates the economic benefits of solar PV system over its entire life. The analysis

consisted of project costs and financial benefits over the life of the PV system, where it

enabled balancing and measurement of initial capital investment with its long term electricity

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savings, tax incentives such as initial allowance, annual allowance and investment tax

allowance (ITA of 100% of qualifying capital expenditure on green technology project) for

owning the equipment. Assumptions and considerations made in the analysis include: utilising

simple payback method; maintenance costs was minimal hence was not accounted; no

changes to the current the tax incentives rates; inflation rate of electricity and time value of

money were not taken into account.

The LCCA of implementation of solar PV system is presented in in the form of investment

analysis in excel spread sheet (see Appendix K). Simple payback method was used as a tool to

analyse the cash flow trends for the project. The analysis combined detailed of cash flow for

LCCA during its 25 years combining return and return on investment in monetary term. The

solar PV system yield calculation is presented in Table 4.46, which indicated the estimated

annual electricity cost savings of RM100,843 per year for the first after taking consideration

of 1,300 hours of yearly sunlight yield in KL and the solar PV system capacity of 150 k .

The results shows that the PBP for the solar PV project was expected to be 6 years, see Figure

4.10. The estimated electricity cost savings for the first year was expected to be RM100,843

with reducing amount for the remaining of the life cycle due to degrading efficiency of the

solar PV system (see Appendix K).

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Figure 4.10

Solar Investment Analysis of WU (LCCA)

Table 4.45

Solar Power System Yield for WU

Solar Power System Yield

Annual Specific Yield 1,300 Hours

k Capacity 151.2 k

Annual Yield k Capacity x Annual Specific Yield

=151.2 k x 1,300 hours

= 196,560 kWh

Annual Cost Saving Annual Yield x TNB Tariff

= RM100,843

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4.9 Summary

This chapter discussed the research findings based data analysis by applying SPSS and case

study analysis. This research study is intended to examine the relationship of the dependent

variables and independent variables with regards to the assessment and implementation of

solar PV system in WU, as well as the cost savings effect of the implementation.

Research data was collected from 264 respondents who completed the research instrument.

The following table summarised the findings of the research hypotheses testing. Predictive

model that was established illustrated that respondents emphasized on compatibility and

perceived risk issues for assessment and implementation of solar PV system.

Table 4.46

Summary of Hypotheses Testing

Hypothesis Description Technique Applied Results

H1 Lower cost perceptions increase the

likelihood of adoption and implementation of

solar PV system.

Correlation,

Regression

Analysis and

ANOVA

Accepted

H2 The higher the perceived relative advantage

of a solar PV system, the greater the

likelihood that the solar PV system will be

adopted and implemented.

Correlation,

Regression

Analysis and

ANOVA

Accepted

H3 Beliefs about the compatibility of solar PV

energy are expected to significantly influence

the adoption and implementation of solar PV

system.

Correlation,

Regression

Analysis and

ANOVA

Accepted

H4 The more users think solar PV power is

difficult to acquire and integrate into their

daily practices, the lower the adoption and

implementation of solar PV system will be.

Correlation,

Regression

Analysis and

ANOVA

Rejected

H5 Lower perceived risk associations with the

use of solar PV equipment are expected to

positively influence the adoption and

implementation of solar PV system.

Correlation,

Regression

Analysis and

ANOVA

Accepted

H6 The assessment and implementation of solar

PV system is significantly associated with

innovation characteristics.

Multiple regression

Analysis and

ANOVA

Accepted

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The case study results showed that implementing a solar PV system was able to reduce the

cost of electricity throughout the life cycle of the system with the PBP for the solar PV project

of 6 years.

The research finding, conclusion and recommendation will be presented in Chapter 5.

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Chapter 5 Findings, Conclusions and Recommendations

5.1 Introduction

This chapter discusses and summarises the findings gained from this research. The findings

are aligned with the research objectives and hypotheses presented in Chapter 1 as well as

results of the hypotheses test and case study which were presented in Chapter 4. Then, it is

followed by the limitation of the study, implication and recommendations for future research.

5.2 Recapitulation of the Study Findings

This study investigated assessment and implementation of solar PV system from the

perspective of innovation characteristics, namely cost attribute, relative advantage,

compatibility, complexity and perceived risk. The findings are summarised in Table 5.1. The

regression analysis showed that four out of five innovation characteristics (cost attribute,

relative advantage, compatibility and perceived risk) are related to the assessment and

implementation of solar PV system. While the simultaneous multiple regression showed that

compatibility and perceived risk are able to predict assessment and implementation of solar

PV system. Furthermore, the case study analysis showed that the solar PV PBP is 6 years and

indicated annual electricity cost savings throughout the life cycle of the system.

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Table 5.1

Summary of Major Findings
No Hypotheses Past study This Study

Authors Relationship Relationship Supported

1 Lower cost perceptions increase

the likelihood of adoption and

implementation of solar PV

system.

Mekhilef, et al. (2011) Solar energy Positive Positive Yes

Ali, et al. (2009) Solar hot water heating systems Positive

Solangi, et al. (2015) Solar PV Positive

Ozaki (2011) Green electricity Positive

2 The higher the perceived

relative advantage of a solar PV

system, the greater the

likelihood that the solar PV

system will be adopted and

implemented.

Mekhilef, et al. (2011) Solar energy Positive Positive Yes

Labay and Kinnear (1981) Solar energy Positive

Jansson (2011) Alternative fuel vehicle Positive

Faiers and Neame (2006) Solar power System Positive

3 Beliefs about the compatibility

of solar PV energy are expected

to significantly influence the

adoption and implementation of

solar PV system.

Faiers and Neame (2006) Solar power System Positive Positive Yes

Labay and Kinnear (1981) Solar energy Positive

Ozaki (2011) Green electricity Positive

Jansson (2011) Alternative fuel vehicle Positive

4 The more users think solar PV

power is difficult to acquire and

integrate into their daily

practices, the lower the adoption

and implementation of solar PV

system will be.

Solangi, et al. (2015) Solar PV Positive No

relationship

No

Faiers and Neame (2006) Solar PV No correlation

Ozaki (2011) Green electricity Positive

5 Lower perceived risk

associations with the use of

solar PV equipment are

expected to positively influence

the adoption and

implementation of solar PV

system.

Solangi, et al. (2015) Solar PV Negative Negative Yes

Labay and Kinnear (1981) Solar energy Negative

Ozaki (2011) Green electricity No correlation

Jansson (2011) Alternative fuel vehicle Negative

6 The assessment and

implementation of solar PV

system is significantly

associated with innovation

characteristics.

Ozaki (2011) Green electricity Significant Significant Yes

Kapoor, Dwivedi and Williams

(2014)

Conceptual model for green

innovation

Significant

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5.3 Details Analysis and Recommendations

Six hypotheses were proposed to examine the relationship between independent variables

(i.e., innovation characteristics) and dependent variable (assessment and implementation of

solar PV system). Five hypotheses out of six were supported by the results presented in

Chapter 4.

5.3.1 Cost Attribute and Assessment and Implementation of Solar PV System

Investment costs for solar PV system consists of solar module and BOS costs (Schmalensee,

et al. 2015). Cost attribute exhibited a significant positive relationship with assessment and

implementation of solar PV system, however it indicated a small strength positive

relationship. This dimension has a significant impact and was an important variable towards

implementation of solar PV system, where the mean value for cost attribute was 3.82

(approximately “Agree” in a 5-point Likert-scale).

Base on the result in Chapter 4, 227 out of the 264 respondents felt that the investment costs

of solar PV system are high. 185 out of the 264 respondents would find it a financial strain to

install a solar PV system and 168 out of the 264 respondents agreed on the availability of

solar PV system loan from the banks. This finding is aligned with Ali, et al. (2009), Mekhilef,

et al. (2011) and (Solangi, et al. 2015), that high capital investment has been a major barrier

for solar energy adoption. 138 out of the 264 respondents opined that the solar PV system

could only be installed with additional renovation work done to the buildings. According to

Mekhilef, et al. (2011), due to inappropriate roof structure, 10% of commercial building and

20% of residential roof structrue were not suitable to install solar PV system. Additional

work done on the roof will increase the investment costs.

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220 out of the 264 respondents welcomed tax incentives from the government to encourage

usage of solar PV system. The government tax incentives for green industry such as provided

incentives in the form of investment tax allowance for the purchase of green technology

assets and income tax exemption for the use of green technology services and system (MIDA

2016).

With Malaysia being the world’s third-largest producer of solar equipment (Bradsher 2014)

and government broaden scope of green technology for incentives activities (MIDA 2016),

the investment costs of solar PV is expected to decrease. In order to lower the hourly cost of

electricity generated by solar PV to be comparable to fossil fuel through per-kilo-watt hour

(kWh) cost of electricity, the government should consider policies to reward generation of

electricity rather than investment and to extend the green technology incentives to residential

solar PV system and. The industry players could collaborate with local universities to

research and develop low-cost, scalable energy storage technologies and improve

manufacturing processes. Other than loan, the financial institutions could explore into energy

investing sector to provide financial business model such as lease, sale-leaseback

arrangement and other financial innovations (Schmalensee, et al. 2015).

5.3.2 Relative Advantage and Assessment and Implementation of Solar PV System

Relative advantage describes the marginal advantage an innovation has over existing

products (Faiers and Neame 2006). Relative advantage exhibited a significant positive

relationship with assessment and implementation of solar PV system, it indicated a medium

strength positive relationship. This dimension has a significant impact and was an important

variable towards implementation of solar PV system, where the mean value for relative

advantage was 3.74 (approximately “Agree” in a 5-point Likert-scale).

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Base on the result in Chapter 4, 211 out of the 264 respondents would help to reduce

greenhouse gasses by installing the solar PV system. 197 out of the 264 respondents would

help to improve the local environment by installing the solar PV. 233 out of the 264

respondents agreed that their electricity bill would be reduced if they use solar power to

generate electricity. These findings were confirmed by Labay and Kinnear (1981) that

relative advantage related to product and economic factor were the highest concern in

adopting solar energy. Further, Jansson (2011) found that relative advantage was significant

to eco-innovation adopters.

Currently, three agencies are responsible for regulating the supply of electricity in Malaysia,

i.e., Tenaga Nasional Berhad (TNB), Sabah Electricity Sdn. Bhd. (SESB) and Syarikat

SESCO (former known as Sarawak Energy Supply Corporation (SESCO)) which hold

monopoly on power transmission and distribution, over Peninsular Malaysia, Sabah and

Sarawak, respectively (Mekhilef, et al. 2011). The survey has shown that 143 out of the 264

respondents opined that the solar PV system could make them independent from national

energy providers. This showed that respondents were willing to produce own electricity by

generating electricity on their rooftops, hence reducing the dependency to the energy

provider.

However, 119 out of the 264 respondents were neutral towards giving first priority to the

quality of the environment, even if it cost more money. Although the respondents agreed that

solar PV could help to improve the environment, they were not so ready to “sacrifice” their

current lifestyle and not so willing to commit to practice such as implementing the solar PV

systems.

The relative advantage dimension is recognised as significant in adopting solar PV system, as

it offered variety of benefits include intention to reduce carbon pollution, mitigate climate

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change and providing other societal benefits (Melillo, Richmond and Yohe 2014). In this

study, there could be possibilities that the respondents poorly understood the important

interaction between the quality of environment and monies sacrificed towards the

environment are noble attributes (Loo 2013). As a managerial consequence, industry players

and researchers need to look into collaboration to research a model that better quantify the

benefits of solar PV system to environment against installation costs incurred.

5.3.3 Compatibility and Assessment and Implementation of Solar PV System

Compatibility describes how the innovation fits with an adopter’s values, attitudes and

behaviour (Faiers and Neame 2006). Compatibility exhibited a significant strong positive

relationship with assessment and implementation of solar PV system. This dimension has a

significant impact and was an important variable towards implementation of solar PV system,

where the mean value for compatibility was 3.72 (approximately “Agree” in a 5-point Likert-

scale).

Base on the result in Chapter 4, 137 out of the 264 respondents felt that they did not have to

change their existing routine in order to use solar PV system and 157 out of the 264

respondents agree that solar PV system would be compatible with the most aspects of their

domestic life. This is aligned with Faiers and Neame’s (2006) finding that solar PV system

was compatible with current energy consumption trends. Ozaki (2011) also supported that the

“easy-to-adopt” required minimal behavioral change for adopters to integrate into their

everyday practices. 223 of the 264 respondents agree that there is different between using

solar PV system and present system. Whereas, 187 out of the 264 respondents agreed that

generating electricity from solar energy is in line with their values. This finding is confirmed

by Labay and Kinnear (1981) and Ozaki (2011) that adopters evaluated solar-energy systems

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as compatible with their value systems. Further, Jansson (2011) found that compatibility was

significant to eco-innovation adopters.

Most people are busy in their daily lives, and do not prefer drastic change in their daily lives.

Hence, solar PV service providers should widen their services to include change management

consultation, so that the switch-over procedure from fossil electricty to solar electricity is

easier and the transition period is smooth. Furthermore, school education and consumer

education play an important role to encourage sustainability and plant the values from young.

It would foster public recognition of the positive consequences of adopting solar PV

and create shared societal norms among consumers (Ozaki 2011).

5.3.4 Complexity and Assessment and Implementation of Solar PV System

Complexity is the degree to which consumers perceived that the innovation is difficult to be

understood and used (Rogers, Diffusion of Innovations, 3rd ed. 1983). Complexity did not

exhibited significant relationship with assessment and implementation of solar PV system.

This dimension did not have a significant impact and was not considered as an important

variable towards implementation of solar PV system, where the mean value for complexity

was 2.92 (approximately “Neutral” in a 5-point Likert-scale).

Base on the result in Chapter 4, 99 out of the 264 respondents were neutral on the complexity

of solar PV systems. 128 out of the 264 disagree that solar PV system would be difficult to

use and 121 of the 264 respondents disagree that using solar PV system require a lot of

knowledge. 123 out of the 264 respondents also agreed that it will be difficult to find a

service provider to install solar PV system. This finding is not aligned with Solangi, et al.

(2015), which found that consumer barrier for adopting solar PV system include lack of

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knowledge, required more information, demanded for additional/ professional assistance and

had difficulty to trust in the solar PV system provider. However, Faiers and Neame (2006)

found that respondents which were unfamiliar with the technology will not be able to

understand how the systems operate on a day-to-day basis.

The researcher noted that the respondents of this survey were highly educated, i.e., 73.5% of

the respondent have at least a bachelor and 6.1% are professional, furthermore some of the

respondents invited from environmental related interest group such as Malaysian Greenbook,

KELC and Green Technology Business Sharing through social media such as FB. These

respondents were presumed to have interest in environmental related issues, were aware and

have some knowledge related to solar PV system. Hence, the complexity of the solar PV

system is not a significant dimension for descision making on assessment and implementation

of solar PV system. This showed that the complexity of solar PV technology could not

predict the implimentation of soalar PV system of a consumer.

This finding, which is contrary to the conceptual model for green innovation developed by

Kapoor, Dwivedi and Williams (2014), suggests that for solar PV sytem, as public become

more familiar with the innovation, the less novelty the innovation becomes, thus perceived it

as being less complex.

5.3.5 Perceived Risk and Assessment and Implementation of Solar PV System

Perceived risk is adopters’ uncertainty about the quality of green electricity which often cause

anxiety and influence their adoption decision (Ozaki 2011). Perceived risk exhibited a

significant negative relationship with assessment and implementation of solar PV system,

however it indicated a medium strength negative relationship. This dimension has a

significant impact and was an important variable towards implementation of solar PV system,

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where the mean value for perceived risk was 3.29 (approximately “Neutral” in a 5-point

Likert-scale).

Base on the result in Chapter 4, 142 out of the 264 respondents worried about the dependable

and reliable of solar PV system and 127 out of the 264 respondents concerned about the

safeness and 220 out of the 264 respondents concerned that solar PV system will not provide

the level of expected benefit. This is aligned with Ozaki’s (2011) finding that respondents

were less certain about its efficiency, also people are uncertain about the quality of green

energy, nature of system and the system costs had caused anxiety which led to rejection. 176

out of the 264 respondents concerned on the PBP of investing in solar PV system. However,

129 out of the 264 respondents disagreed that other people would think is it being showy to

install a solar PV system. According to Labay and Kinnear (1981), the respondents evaluated

solar systems to be less social risky and social factor was of far less consequence in the

adoption decision process.

Potential adopters who have green awareness require accurate information to evaluate and

make a decision, the perceived risk is reduce with the increace of information accuracy.

Industries players, researchers and financial instituition should take action to explore

potential opportunities that may emerge form the perceived risk, such as developing and

expending better products and services. Furthermore, greater incentives are needed to allow

consumers to overcome the uncertainty to the PBP of the system.

5.3.6 Case Study

The main task within the study is the assessment and implementation of solar PV system. The

case firm adopted PBP method and LCCA to evaluate the solar PV system project. This

analysis included various uncertainties such as efficiency of the solar PV system and average

102

sun irradiation available. As determined in the LCCA, the electricity cost savings calculation

was estimated by using proprietary method provided by the industry expert during the key-

informant interview. The electricity cost savings from solar PV system are dependent on the

sun hours received by the system. The more sun hours yield by the system, the more it offsets

electricity. The exact electricity cost savings could only be tracked by a monitoring system,

hence, the cost savings in this case study was an approximate estimates. The case firm

evaluated the project by using PBP method, i.e., the expected length of time for an

investment to return its initial costs (Boyle and Guthrie 2006). However, this method ignores

both the time value of money and cashflows that occur subsequent to payback. Various

studies found that between 40% – 90% of firms in US ([Gilbert and Reichert (1995); Gitman

and Forrester (1977); Oblak and Helm (1980); Stanley and Block (1984); Visscher and

Stansfieks (1997)] cited by Boyle and Guthrie (2006)) and Asia, Canada, Europe and Ocenia

( [Jog and Srivastava (1995); Kester, et al. (1999); Patterson (1989); Shao and Shao (1993)]

cited by Boyle and Guthrie (2006)) used payback as a capital budgeting technique.

Based on the case study investment analysis report evaluation on the feasibility of the project,

it is recommended that the case firm to proceed to planning stage (see Figure 1.1) which is to

develope a project plan.

5.4 Answers to Research Questions

In this study, there were five research questions identified. The answer to the first and second

research questions are found in Chapter 2. The answer to the third, fourth and the fifth

questions can be found in Chapter 4.

103

Table 5.2

Answer to Research Questions

No Research Question Answer

1 What is the current usage of RE in

Malaysia?

RE usage is 1,318 GWh (0.93%) of

electricity generation.

Solar is 38 ktoe, equivalent to 0.04%

2 What are the innovation characteristics

which influence the acceptance of solar

PV system?

In this study, the innovation characteristics

identified are cost attribute, relative

advantage, compatibility, complexity and

perceived risk.

3 Is there any relationship between each

innovation characteristic identified and

the assessment and implementation of

solar PV system?

In this study, the relationship between the

dimensions and assessment and

implementation of solar PV system are: (i)

significant positive relationship for cost

attribute, relative advantage, compatibility

and assessment; (ii) zero relationship for

complexity; and lastly (iii) significant

negative relationship for perceived risk.

4 Can solar PV systems reduce

electricity cost of WU?

In this study, solar PV systems are able to

reduce electricity expenditure of WU.

5 How long is the payback period for the

solar PV system investment to recover

its initial outlay?

In this study, the payback period of the

solar PV system investment is 6 years.

5.5 Limitations

As in other research, this study has its limitation and shortcoming. There could be sampling

error due to the small sample size of respondents as compare to the size of population,

furthermore researcher used non-probability convenient sampling instead of random

sampling in view of the time frame and cost factors during the study.

104

This case study was only conducted in a single unversity, WU, i.e., of the case firm. The

respondents were mainly from the Klang Valley (78%) and does not cover all the community

from other states where other campus/ regional office of WU which are situated. Hence, the

generalisation of the findings may be limited and does not represent the whole population of

Malaysia.

Non-sampling errors might creep into the research process as there might be unwillingness of

respondents to answer the questionnaire honestly or lack of motivation to answer the

questionnaire seriously. There could be situation that the respondents do not have basic

knowledge of solar PV technology to participate fully in this study. This is regard as un-

controllable variables as the researcher has no way to measure non-sampling errors.

The questionnaire was mostly distributed through online web form through email and social

media. It was limited to those respondents who possessed internet access. The feedback of

non-internet users was limited to hardcopy questionnaire distributed in the researcher’s

university campus. Furthermore, the level of understanding of solar PV system was not

considered in distribution of the questionnaire to the respondents.

5.6 Direction for Future Research

The findings from this study could be used for upcoming future research in renewable energy

RE in order to “diffuse” the innovation in acceptance of solar PV energy to contribute to the

well-being of the society and enhance better quality of life in the future.

In this study, only five innovation characteristics, i.e., cost attribute, relative advantage,

complexity, compatibility and perceived risk were selected from the conceptual green

innovation model proposed by Kapoor, Dwivedi and Williams (2014). Future research could

105

probe other dimensions of green innovation model such as image, observability, result

demonstrability, social approval, ease of use, etc. to understand their impact and influence to

assessment and implementation of solar PV system.

This research instrument were adopted and applied from other studies. The future research

could consider to create self-designed scales for the questionnaire, in order to imply the exact

meaning of the text base on coomprehension of the local context.

A conjoint analysis method could be used to determine how respondents perceive different

business model or financial capital funding model of solar PV system.

Lastly, this case study is only limited to one unversity. It is suggested to widen the study area

to cover other universities. Also, sample size should also be gathered at a larger size for

statisitcal analysis in order to produce a critical statistical outcome.

5.7 Conclusions

This study attempts to identify the impact of innovation characteristics over assessment and

implementation of solar PV system in universities. It also aimed to estimate the savings of

electricity cost and PBP of the solar PV system. The identified innovation characteristics

were cost attribute, relative advantage, compatibility, complexity and perceived risk. In this

study, the data was gathered by applying non-probability convenient sampling method, where

the questionnaire was distributed by hardcopy as well as through online web survey via email

and social media.

As evidenced from the results, four of the five identified innovation characteristics exhibit

relationships, i.e., cost attribute, relative advantage and compatibility exhibited their positive

relationship while perceived risk exhibited negative relationships towards solar PV

106

assessment and implementation. However, the respondents’ adoption of solar PV system

could be only predicted significantly by two innovation characteristics (compatibility and

perceived risk) form the predictive model. Case study analysis also showed savings of

electrical cost throughout the solar PV system life cycle.

In order to improve the solar PV system implementation rate, several recommendations were

suggested. The challenge for government, industry players, researchers who wish to promote

green electricity, therefore, is how to fill the gap between intentions and actual behaviour.

Generation of electricity through renewable sources which are abundant and free, will

definitely help in mitigating the emission of greenhouse gasses, hence improve the quality of

the environment through reducing the effect of global warming.

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APPENDICES

Appendix A: Questionnaire – English

Survey Questionnaires

Assessment and implementation of solar photovoltaic (PV) system to generate

electricity

The following statements are about your perceptions, understanding, motivation and

benefits on solar PV energy.

The result of this survey shall be presented as the main part of a ceMBA thesis, Wawasan

Open University. Please be assured that your responses remain confidential and solely for

academic purpose.

If you could take a few minutes to complete the questionnaire I would be very grateful.

Please indicate by circling the degree to which you agree or disagree with each statement

presented below, ranging from 1 (Strongly Disagree) to 5 (Strongly Agree).

Should you have any questions regarding this research, I can be contacted via email:

[email protected]

Overview of solar PV energy:

Solar PV is the “carbon-free” technology that converts sunlight directly into electricity.

The word photovoltaic comes from “photo,” meaning light, and “voltaic,” which refers to

producing electricity. Solar radiation is a type of renewable energy that is sustainable

without depleting the Earth’s capital resources.

Solar PV technology offers consumers the ability to generate electricity in a clean, quiet

and reliable way. Environmentally, solar PV reduces emissions of greenhouse gases and

helps to mitigate global warming and other climatic changes.

Appendix A (Continued)

Section I : Cost Attribute

How do you agree with the following statements towards cost of using solar PV

energy?

Code No Statements Strongly

Disagree

Dis-

agree
Neutral Agree

Strongly

Agree

COS1 1. The initial cost of

installing a solar PV

system would be high

for me.

1 2 3 4 5

COS2 2. I would find it a

financial strain to

install a solar PV

system.

1 2 3 4 5

COS3 3. Government Tax

Incentives to

encourage producing

electricity using solar

PV is a good thing.

1 2 3 4 5

COS4 4. Availability of

finance/ loan

especially for solar

PV energy from banks

is a good thing.

1 2 3 4 5

COS5 5. A solar PV system

could only be installed

on my house/

organisation with

major additional/

renovation work.

1 2 3 4 5

Section II : Relative Advantage

How do you agree with the following statements towards advantage of using

solar PV energy?

Code No Statements Strongly

Disagree

Dis-

agree
Neutral Agree

Strongly

Agree

ADV1 6. I would help to

significantly reduce

greenhouse gases by

installing a solar PV

system.

1 2 3 4 5

Appendix A (Continued)

Code No Statements Strongly

Disagree

Dis-

agree

Neutral Agree Strongly

Agree

ADV2 7. I would help to improve

my local environment

by installing a solar PV

system.

1 2 3 4 5

ADV3 8. I would reduce my

electricity bill if I use

solar power to generate

electricity.

1 2 3 4 5

ADV4 9. Installing solar PV

system would make me

independent from

national energy

providers.

1 2 3 4 5

ADV5 10. I would give first

priority to the quality of

the environment, even

if it cost me more

money.

1 2 3 4 5

Section III : Compatibility

How do you agree with the following statements towards consistency of solar PV

energy?

Code No Statements Strongly

Disagree

Dis-

agree
Neutral Agree

Strongly

Agree

CPT1 11. To use a solar PV

system would not

require significant

changes in my

existing daily

routines.

1 2 3 4 5

CPT2 12. Using a solar PV

system would be

compatible with most

aspects of my

domestic life.

1 2 3 4 5

CPT3 13. Using solar PV

electricity would be a

new power generating

experience for me.

1 2 3 4 5

Appendix A (Continued)

Code No Statements Strongly

Disagree

Dis-

agree
Neutral Agree

Strongly

Agree

CPT4 14. To use electricity

generated from

sunlight is in line with

my values.

1 2 3 4 5

Section V : Risk

How do you agree with the following statements towards the risk of solar PV

energy usage?

Code No Statements Strongly

Disagree

Dis-

agree
Neutral Agree

Strongly

Agree

RIS1 19. I am worry about how

dependable and reliable

solar PV system will

be.

1 2 3 4 5

RIS2 20 I am worry about the

safeness of solar PV

technology.

1 2 3 4 5

RIS3 21. I am concern that solar

PV system will not

provide the level of

benefits expected.

1 2 3 4 5

Section IV : Complexity

How do you agree with the following statements towards the complexity to use

solar PV energy?

Code No Statements Strongly

Disagree

Dis-

agree
Neutral Agree

Strongly

Agree

CPX1 15. Solar PV systems are

very complex products.

1 2 3 4 5

CPX2 16. Solar PV systems

would be difficult to

use.

1 2 3 4 5

CPX3 17. Solar PV systems

require a lot of

knowledge to use.

1 2 3 4 5

CPX4 18. It is difficult to find a

service provider to

install solar PV system.

1 2 3 4 5

Appendix A (Continued)

Code No Statements Strongly

Disagree

Dis-

agree

Neutral Agree Strongly

Agree

RIS4 22. I am concern that some

people whose opinion I

value would think that I

am just being showy.

1 2 3 4 5

RIS5 23. I am concern with the

payback period of

investing in solar PV

system.

1 2 3 4 5

Section VI : Assessment and Implementation

How do you agree with the following statements towards generating electricity

using solar PV system?

Code No Statements Strongly

Disagree

Dis-

agree
Neutral Agree

Strongly

Agree

ANI1 24. I will try out solar PV

system in the future.

1 2 3 4 5

ANI2 25. I will purchase a solar

PV system.

1 2 3 4 5

ANI3 26. I will recommend solar

PV system to others.

1 2 3 4 5

Section VII : Profile

Please tick (√) in the box provided.

27. Gender : Male Female

28. Age Group : < 25 46 – 55

(years old) 26 – 35 56 – 65

36 – 45 > 65

29. Household Income : > 10,001 3,001 – 6,000

(RM / Monthly) 6,001 – 10,000 < 3000

Appendix A (Continued)

30. Education Level : Secondary Master’s Degree

Certificate or Diploma Doctoral Degree

Bachelor’s Degree Professional

Qualifications

Others (please specify):

31. Occupation : Non-executive Students

Executive University Academic Staff

Senior Management University Admin. Staff

Home Maker University Managerial Staff

Retired Professional/ Specialist

Unemployed Self-employed

Others (please specify):

32. Residential Area : Urban Sub-urban or Rural

33. Are you a Malaysian? : Yes No

34. Where do you stay (please provide postcode only) :

THANK YOU VERY MUCH FOR YOUR TIME AND PARTICIPATION!

Appendix B: Questionnaire – Malay Language

Kajian Soal Selidik

Kajian Penilaian dan Perlaksanaan Penjanaan Elektrik Melalui Sistem Solar

Fotovoltaik (PV)

Kenyataan di bahagian seterusnya adalah mengenai persepsi anda, kefahaman , motivasi

dan manfaat terhadap tenaga solar PV.

Hasil kaji selidik ini akan dibentangkan sebagai bahagian utama tesis ceMBA , Universiti

Terbuka Wawasan (Wawasan Open University). Adalah dimaklumkan bahawa jawapan

anda adalah kekal sulit dan semata-mata untuk tujuan akademik.

Saya berasa amat bersyukur jikalau anda boleh meluangkan beberapa minit untuk

melengkapkan soal selidik ini. Sila nyatakan / klik sejauh mana anda bersetuju atau tidak

bersetuju dengan setiap kenyataan yang dibentangkan di bahagian seterusnya , yang

terdiri daripada 1 ( Sangat Tidak Bersetuju) hingga 5 ( Sangat Bersetuju ).

Sekiranya anda mempunyai sebarang pertanyaan mengenai kajian ini , saya boleh

dihubungi melalui emel: : [email protected]

Tenaga Solar PV:

Solar PV adalah teknologi ” silap mata ” yang menukarkan cahaya matahari terus kepada

tenaga elektrik, ia “bebas karbon”. Perkataan “fotovoltaik” (PV) terdiri daripada ” foto “,

bermaksud cahaya , dan ” voltaik ,” yang merujuk kepada menjanakan tenaga elektrik.

Sinaran matahari adalah sejenis tenaga boleh diperbaharui yang mampan tanpa

mengurangkan sumber Bumi. Solar PV menawarkan keupayaan untuk menjana tenaga

elektrik dengan cara yang bersih , senyap dan boleh dipercayai kepada pengguna. Solar

PV mengurangkan pelepasan gas-gas rumah hijau dan membantu untuk mengurangkan

pemanasan global dan perubahan iklim.

Appendix B (Continued)

Bahagian I : Kos

Bagaimanakah anda bersetuju atau tidak bersetuju terhadap pernyataan di bawah

terhadap: kos penggunaan tenaga sistem solar PV?

Code No Kenyataan Sangat

Tidak

Bersetuju

Tidak

Bersetuju

Neu-

tral

Ber-

setuju

Sangat

Ber-

setuju

COS1 1. Kos awalan memasang

sistem PV solar adalah

tinggi untuk saya.

1 2 3 4 5

COS2 2. Saya akan merasa bebanan

kewangan untuk memasang

sistem solar PV.

1 2 3 4 5

COS3 3. Insentif Cukai Kerajaan

untuk menggalakkan

penjanaan elektrik

menggunakan solar PV

adalah satu perkara yang

baik

1 2 3 4 5

COS4 4. Kewujudan pinjaman khas

untuk pemasangan tenaga

solar PV daripada bank

adalah satu perkara yang

baik.

1 2 3 4 5

COS5 5. Sistem solar PV hanya boleh

dipasang di rumah /

organisasi saya dengan

kerja-kerja tambahan /

pengubahsuaian utama.

1 2 3 4 5

Bahagian II : Kebaikan

Bagaimanakah anda bersetuju atau tidak bersetuju terhadap pernyataan di bawah

terhadap: kebaikan pengunaan tenaga solar PV?

Code No Kenyataan Sangat

Tidak

Bersetuju

Tidak

Bersetuju

Neu-

tral

Ber-

setuju

Sangat

Ber-

setuju

ADV1 6. Saya akan membantu untuk

mengurangkan gas-gas

rumah hijau dengan

memasang sistem solar PV.

1 2 3 4 5

ADV2 7. Saya akan membantu

meningkatkan keadaan

persekitaran tempatan saya

dengan memasang sistem

solar PV.

1 2 3 4 5

Appendix B (Continued)

Code No Kenyataan Sangat

Tidak

Bersetuju

Tidak

Bersetuju

Neu-

tral

Ber-

setuju

Sangat

Ber-

setuju

ADV3 8. Saya akan mengurangkan

bil elektrik saya jika saya

menggunakan kuasa solar

untuk menjana elektrik.

1 2 3 4 5

ADV4 9. Memasang sistem PV solar

akan membuat saya bebas

daripada bergantung kepada

pembekal tenaga nasional.

1 2 3 4 5

ADV5 10. Saya akan memberikan

keutamaan kepada kualiti

alam sekitar, walaupun ia

kos saya lebih banyak wang.

1 2 3 4 5

Bahagian III : Keserasian

Bagaimanakah anda bersetuju atau tidak bersetuju terhadap pernyataan di bawah

terhadap: Keserasian pengunaan tenaga solar PV?

Code No Kenyataan Sangat

Tidak

Bersetuju

Tidak

Bersetuju

Neu-

tral

Ber-

setuju

Sangat

Ber-

setuju

CPT1 11. Penggunakan sistem

solar PV tidak

memerlukan perubahan

penting dalam rutin

harian saya yang sedia

ada.

1 2 3 4 5

CPT2 12. Menggunakan sistem

solar PV akan menjadi

serasi dengan

kebanyakan aspek

kehidupan harian saya.

1 2 3 4 5

CPT3 13. Menggunakan elektrik

solar PV akan menjadi

pengalaman penjanaan

kuasa baru bagi saya.

1 2 3 4 5

CPT4 14. Penggunakan tenaga

elektrik daripada

cahaya matahari adalah

selaras dengan nilai-

nilai diri saya.

1 2 3 4 5

Appendix B (Continued)

Bahagian V : Risiko

Bagaimanakah anda bersetuju atau tidak bersetuju terhadap pernyataan di bawah

terhadap: risiko penggunaan tenaga solar PV?

Code No Kenyataan Sangat

Tidak

Bersetuju

Tidak

Bersetuju

Neu-

tral

Ber-

setuju

Sangat

Ber-rsetuju

RIS1 19. Saya bimbang sama ada

system solar PV boleh

dipercayai atau tidak.

1 2 3 4 5

RIS2 20 Saya bimbang tentang

keselamatan

penggunaan teknologi

solar PV.

1 2 3 4 5

RIS3 21. Saya kebimbangan

bahawa sistem solar PV

tidak akan memberikan

tahap faedah yang

dijangkakan.

1 2 3 4 5

Bahagian IV : Kerumitan

Bagaimanakah anda bersetuju atau tidak bersetuju terhadap pernyataan di bawah

terhadap: kerumitan penggunaan sistem solar PV?

Code No Kenyataan Sangat

Tidak

Bersetuju

Tidak

Bersetuju

Neu-

tral

Ber-

setuju

Sangat

Ber-

setuju

CPX1 15. Sistem PV solar adalah

produk yang sangat

kompleks.

1 2 3 4 5

CPX2 16. Sistem solar PV akan

menjadi sukar untuk

digunakan.

1 2 3 4 5

CPX3 17. Sistem Solar PV

memerlukan banyak

pengetahuan untuk

digunakan.

1 2 3 4 5

CPX4 18. Adalah sukar untuk

mencari pembekal

perkhidmatan untuk

memasang sistem PV

solar.

1 2 3 4 5

Appendix B (Continued)

Code No Kenyataan Sangat

Tidak

Bersetuju

Tidak

Bersetuju

Neu-

tral

Ber-

setuju

Sangat

Ber-rsetuju

RIS4 22. Saya kebimbangan

bahawa sesetengah

orang yang

pendapatnya saya

pentingkan, akan

menganggap saya

sebagai menunjuk-

nunjuk.

1 2 3 4 5

RIS5 23. Saya bimbang dengan

tempoh bayar balik

untuk melabur dalam

sistem PV solar.

1 2 3 4 5

Bahagian VI : Penilaian dan Pelaksanaan

How do you agree with the following statements towards generating electricity

using solar PV system?

Code No Kenyataan Sangat

Tidak

Bersetuju

Tidak

Berset

uju

Neu-tral
Ber-

setuju

Sangat

Ber-

rsetuju

ANI1 24. Saya akan mencuba

sistem solar PV pada

masa hadapan.

1 2 3 4 5

ANI2 25. Saya akan membeli

sistem solar PV.

1 2 3 4 5

ANI3 26. Saya akan

mengesyorkan sistem

solar PV kepada orang

lain.

1 2 3 4 5

Appendix B (Continued)

Bahagian VII : Profil

Sila tanda (√) di dalam kotak.

27. Jantina : Lelaki Perempuan

28. Umur (tahun) : < 25 46 – 55

26 – 35 56 – 65

36 – 45 > 65

29. Pendapatan isi rumah : > 10,001 3,001 – 6,000

(RM / Bulanan) 6,001 – 10,000 < 3000

30. Tahap : Sekolah Menengah Ijazah Sarjana

Pendidikan Sijil atau Diploma Ijazah Kedoktoran

Ijazah Sarjana Muda Kelayakan Profesional

Lain-lain (Sila nyatakan):

31. Pekerjaan : Bukan eksekutif Pelajar

Eksekutif Staf Akademik Universiti

Pengurusan Kanan Staf Tadbir Universiti

Suri Rumah Staf Pengurusan Universiti

Bersara Profesional/ Pakar

Menganggur Tuan Sendiri

Lain-lain (Sila nyatakan):

32. Kawasan penetapan : Bandar Luar Bandar

33. Adakah anda rakyat Malaysia? : Ya Tidak

34. Di manakah anda tinggal (sila isikan postkod sahaja) :

TERIMA KASIH ATAS MASA DAN PENYERTAAN ANDA!

Appendix C: Preliminary Key Informants Interview Guide

1. Please explain the motivation for installing solar PV projects?

2. Please explain in brief the business model available to solar PV systems?

3. Are solar PV system installations effective in the long run?

4. How are savings of electricity cost calculated?

5. State the incentives provided by the government?

6. Please express practical opinion regarding benefits observed?

7. Please explain any other issues, topics, cases regarding practical implication of solar

PV systems?

8. Do you have any questions for me?

Appendix D: Malaysia Energy Statistics Handbook 2015: Energy Generation Mix

Source: Adopted and adapted from Malaysia Energy Commission (2015)

Appendix E: Malaysia Energy Statistics Handbook 2015: Electricity Generation Mix in

GWh

Source: Adopted and adapted from Malaysia Energy Commission (2015)

Appendix F: Malaysia Energy Statistics Handbook 2015: Total Primary Energy Supply

by Fuel Type

Source: Adopted and adapted from Malaysia Energy Commission (2015)

Appendix G: Malaysia Energy Statistics Handbook 2015: Total Primary Energy Supply

by Fuels in ktoe (kilo tonne of oil equivalent)

Source: Adopted and adapted from Malaysia Energy Commission (2015)

Appendix H: What is Feed-in-Tariff (FiT)?

Source: Adopted and adapted from SEDA Corporate Website (SEDA 2016)

Appendix I: What is Net Energy Metering (NEM)

 A mechanism where an eligible consumer installs a solar PV system primarily

for his own use and excess energy to be exported to the grid for which credit to

be received that may be used to offset part of the electricity bill for the energy

provided by the Distribution Licensee (TNB/SESB) to the electricity consumer

during the applicable billing period.

 The credit (excess energy) to NEM consumer will be based on prevailing Displaced

Cost for the relevant supply voltage level at the Point of Common Coupling. The

calculation for the net billing of electricity will be based on the following

calculation.

 Net billing = [ Energy Consumed from DL (kWh) x Gazetted Tariff ] –

[ Energy Exported to DL (kWh) x Displaced Cost ]

 The net billing or credit shall be allowed to roll over for a maximum of 24 months.

Any available credits after 24 months will be forfeited.

Source: Adopted and adapted from SEDA Corporate Website (SEDA 2016)

Appendix J: Solar Photovoltaic System Diagram

Source: Adopted and adapted from Solarstream Technology Sdn. Bhd. (2016)

Appendix K: Solar PV Investment Analysis for WU

Appendix L: Project Timeline Gantt Chart

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