A PROJECT MANAGEMENT CASE STUDY IN ASSESSING AND

ASSESSING AND IMPLEMENTING SOLAR
HUANG MEI TING
WAWASAN OPEN UNIVERSITY
SUPERVISOR Mr Loh Chee Seng
TITLE A Project Management Case Study in Assessing and Implementation
Solar Photovoltaic Project in W University
DATE November 2016
Final Project Report submitted in partial fulfilment
of the requirements for the award of
Commonwealth Executive Master of Business Administration (CeMBA)
of
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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
1.1 Introduction 1
1.3 Background of the Case Firm 2
1.4 Problem Statement 4
1.5 Research Objectives 5
1.6 Research Questions 5
1.8 Theoretical Framework 7
1.9 Research Methodology 8
1.11 Expected Research Resultant Outcomes 10
1.12 Limitation of Research 11
1.13 Summary
2.1 Introduction 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
2.4.4 Mechanisms for Financing Solar PV Investment 21
2.5 Relative Advantage 21
2.5.2 Economic Benefits 22
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
2.7.2 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
2.10.2 Managing Risk 32
2.12 Hypotheses 33
3.5 Data Collection 42
3.6 Pilot Study 45
3.7.1 Analysis of Qualitative Research 45
3.7.2 Analysis of Quantitative Research 47
3.8 Summary 49
4.1 Introduction 50
4.3 Descriptive Analysis 56
4.3.1 Cost Attribute 57
4.3.2 Relative Advantage 59
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4.5 Factor Analysis 71
4.6 Regression Analysis 74
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
4.9 Summary 91
5.1 Introduction 93
5.3 Details Analysis and Recommendations 95
5.3.1 Cost Attribute and Assessment and Implementation of Solar
PV System
Solar PV System
PV System
System
99
PV System
ix
5.7 Conclusions 105
List of References
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
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
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Implementation
75
Implementation
76
4.29 ANOVA between Cost Attribute and Assessment and Implementation 76
4.30 Coefficients between Relative Advantage and Assessment and
Implementation
77
Implementation
78
Implementation
78
4.34 Model Summary between Compatibility and Assessment and
Implementation
80
4.37 Model Summary between Complexity and Assessment and
Implementation
81
4.39 Coefficients between Perceived Risk and Assessment and
Implementation
83
Implementation
83
Implementation
85
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
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xiii
1.1 Risk Event Graph 3
1.2 Aerial View of Kuala Lumpur Campus of Case Firm 3
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
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
BOS balance-of system
CAPEX capital expenditures
2 carbon dioxide
EU European Union
LCCA life cycle cost analysis
LCOE levelized cost of electricity
MIDA Malaysian Investment Development Authority
MM mixed methods
NEM Net Energy Metering
NPV net present value
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ABBREVIATIONS
PV photovoltaic
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
m2 square meters
MW megawatt
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.
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
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 1.2
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. 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
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?
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.
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
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
Source: Adopted from Lynn 2010 (Lynn 2010)
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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.
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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
Chapter 2 Literature Review
Chapter 3 Research Methodology
Chapter 5 Findings, Conclusions and Recommendation
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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
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
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.
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.
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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.
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
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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.
21
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
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.
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.
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
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
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).
The hypoytheses developed based on literature review are as follows:
H1 Cost attribute of solar PV system significantly influence the likelihood of
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
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
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
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 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
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
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.
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
.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
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.
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
State Frequency Per cent %
Total 264 100.0
26 – 35 86 32.6
36 – 45 120 45.5
46 – 55 30 11.4
50 - 65 7 2.7
Total 264 100.0
Secondary 14 5.3
Bachelor's Degree 134 50.8
Master's Degree 60 22.7
Doctoral Degree 2 0.8
Professional Qualifications 16 6.1
Others 7 2.7
Total 264 100.0
RM3,001 – RM6,000 80 30.3
RM6,001 – RM10,000 75 28.4
Total 264 100.0
Sarawak
Sabah
Putrajaya
Terengganu
Kedah
Melaka
Perak
Pahang
Johor
Urban 225 85%
Sub-urban or Rural
25 or below
55
Secondary
0 20 40 60 80 100 120
Non-executive
Executive
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
0 20 40 60 80 100
Less than RM3,000
income
57
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
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
Item Likert’s Scale Frequency Percent % Mean
Std.
2 Disagree 1 .4
3 Neutral 29 11.0
4 Agree 145 54.9
Total 264 100.0
2 Disagree 12 4.5
3 Neutral 61 23.1
4 Agree 137 51.9
Total 264 100.0
2 Disagree 5 1.9
3 Neutral 31 11.7
4 Agree 116 43.9
Total 264 100.0
2 Disagree 22 8.3
3 Neutral 61 23.1
4 Agree 114 43.2
Total 264 100.0
2 Disagree 42 15.9
3 Neutral 73 27.7
4 Agree 107 40.5
Total 264 100.0
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
Per
2 Disagree 4 1.5
3 Neutral 46 17.4
4 Agree 137 51.9
Total 264 100.0
2 Disagree 6 2.3
3 Neutral 57 21.6
4 Agree 137 51.9
Total 264 100.0
2 Disagree 3 1.1
3 Neutral 23 8.7
4 Agree 132 50.0
Total 264 100.0
2 Disagree 45 17.0
3 Neutral 64 24.2
4 Agree 104 39.4
Total 264 100.0
2 Disagree 51 19.3
3 Neutral 119 45.1
4 Agree 66 25.0
Total 264 100.0
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
Per
2 Disagree 22 8.3
3 Neutral 95 36.0
4 Agree 117 44.3
Total 264 100.0
2 Disagree 9 3.4
3 Neutral 94 35.6
4 Agree 142 53.8
Total 264 100.0
2 Disagree 6 2.3
3 Neutral 30 11.4
4 Agree 167 63.3
Total 264 100.0
2 Disagree 2 .8
3 Neutral 73 27.7
4 Agree 140 53.0
Total 264 100.0
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
Per
2 Disagree 74 28.0
3 Neutral 99 37.5
4 Agree 69 26.1
Total 264 100.0
2 Disagree 111 42.0
3 Neutral 104 39.4
4 Agree 27 10.2
Total 264 100.0
2 Disagree 99 37.5
3 Neutral 85 32.2
4 Agree 52 19.7
Total 264 100.0
2 Disagree 47 17.8
3 Neutral 88 33.3
4 Agree 99 37.5
Total 264 100.0
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 =

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