ANALYSIS OF FACTORS DRIVING PURCHASE INTENTION OF ELECTRIC CARS:
PERSPECTIVE OF THEORY OF PLANNED BEHAVIOR, NORM ACTIVATION MODEL, AND
TECHNOLOGY ACCEPTANCE MODEL
Enggar Handarujati1*
Universitas Indonesia, Jakarta, Indonesia
Email: enggar.handarujati11@ui.ac.id
Abstract
Objective:
The purpose of this study is to empirically investigate the factors that drive
the purchase intention of electric cars from the perspective of the Theory of
Planned Behavior (TPB), Norm Activation Model (NAM),
and Technology Acceptance Model (TAM). Design/Methods/Approach: This study used
quantitative research methods with purposive sampling. The data collection
method used in this research is the survey method, conducted using a
questionnaire distributed online using Google Forms to 253 respondents who do
not own an electric car, have a driver’s license, belong to Socioeconomic
Status A group, and have knowledge related to electric cars..
A 49-item questionnaire was developed, with a five-point Likert scale on each
item. This study used partial least square structural equation modeling (PLS-SEM) for data analysis. Data were analyzed using Smart-PLS 3.2.9 application. Findings:
Perceived Usefulness (PU) and Perceived Behavioral
Control (PBC) positively and significantly impact the intention to purchase
electric cars. Originality/Value: This study contributes to the existing
literature on the purchase intention of electric cars by combining three
theories namely TPB, NAM, and TAM. Practical/Policy
implication: Given the results, government and manufacturers of electric cars
in Indonesia should focus the strategies in the area of the usefulness of
electric cars and consumers’ perceived behavioral control to increase the purchase intention of
electric cars.
Keywords: Electric Car, Norm Activation Model (NAM), dan
Technology Acceptance Model (TAM), Theory of Planned Behavior
(TPB), Purchase Intention
Introduction
Air pollution remains the world's
biggest environmental health threat, accounting for more than six million
deaths yearly (IQAir, 2022). In 2019,
approximately 4.2 million premature deaths worldwide were linked to ambient air
pollution, affecting both urban and rural areas (WHO, 2022). Several air
pollutants reported by the World Health Organization are particle pollution,
ground-level ozone, carbon monoxide, sulfur oxides,
nitrogen oxides, and lead (Manisalidis et al., 2020). The
significant rise in individual car ownership has positioned the transportation
sector as a crucial contributor of greenhouse gas emissions, making it one of the
foremost energy-consuming sectors globally (Xu et al.,
2019),
and substituting conventional vehicles with new energy vehicles is a potential
solution to address environmental issues (Tu &
Yang, 2019).
Over the last two decades, electric
vehicles have emerged as a solution to mitigate emissions and reduce energy
consumption in the transportation sector (Hassouna & Tubaleh, 2020). According
to Subekti et al. (2014), electric
cars utilize an electric motor to convert electrical energy into mechanical
energy, drawing power from rechargeable batteries, which results in zero
emissions and avoids environmental harm (Taghizad-Tavana et al., 2023). Despite the
need for durable batteries containing rare minerals extracted from the Earth,
electric cars prove highly efficient in reducing pollution compared to conventional
vehicles relying on fossil fuels (Malik et
al., 2020).
It is necessary to analyze the factors influencing consumer purchase
intentions to attract consumers to buy electric cars. Several studies have been
conducted using the Theory of Planned Behavior. Adnan et al.
(2018) used
the Theory of Planned Behavior to predict Malaysian
consumers' intention to adopt PHEVs or Plug-In Hybrid Electric Vehicles. Liao (2022) uses
the Theory of Planned Behavior model combined with
perceived risk variables and contextual factors in the form of financial and
non-financial incentives, which are moderated by personality factors in the
form of Consumer Innovativeness and Environmental Self-Identity for consumers
in 3 major cities in China. Asadi et al.
(2021) also
used the Theory of Planned Behavior combined with the
Norm Activation Model to identify factors influencing the intention to use
electric vehicles in Malaysia.
Several studies have been conducted
by developing the Technology Acceptance Model. Wolff and
Madlener (2019) use
the UTAM or Unified Technology Adoption Model, an adaptation of the Technology
Acceptance Model with the Diffusion of Innovations theory, on the use of light
electric vehicles in Germany. Wang et al.
(2018) )
also used the Technology Acceptance Model developed with additional variables
in the form of knowledge, perceived risk, and financial incentive policy in
influencing the intention to adopt electric vehicles in China. Jain et al.
(2022) use
the UTAUT model or the Unified Theory of Acceptance of Technology previously
proposed by Venkatesh et
al. (2003) and
Sovacool (2017) to
explore the factors that influence electric vehicle adoption intentions in
India. Vafaei-Zadeh et al. (2022) use
the C-TAM-TPB theory, which is a combination of the Technology Acceptance Model
with the Theory of Planned Behavior with the addition
of several variables, namely price value, perceived risk, environmental
self-image, and infrastructure barriers in generation Y electric vehicle
consumers in Malaysia.
x In the
context of consumers' intention to purchase electric vehicles, previous studies
mainly focused on individual theoretical models such as Theory of Planned Behavior (TPB), Norm Activation Model (NAM) or the
technology acceptance model (TAM). Although these models have offered valuable
insights into the factors that influence adoption, there is a notable gap in
the literature regarding the integration of these theories into a unified
framework. Existing research often fails to account for the complex interactions
between psychological, behavioral, and contextual
factors within a single comprehensive model. This study aims to fill this gap
by combining TPB, NAM, and TAM, thereby providing a more comprehensive
understanding of the multifaceted nature of consumer behavior
in the context of electric car adoption. The objectives of this study are to investigate
the impact of behavioral driving factors from each Theory
of Planned Behavior (TPB), Norm Activation Model (NAM ), and the technology
acceptance model (TAM) on individuals' intentions to purchase electric cars.This study intends to adopt the model of Asadi et al.
(2021), a
combination of the Theory of Planned Behavior and the
Norm Activation Model. However, in their study, there are still limitations,
which as using only TPB and NAM in describing the intention to adopt electric
vehicles. Regarding this matter, Asadi et al.
(2021) suggest
that a different theory can be used for further research, such as TAM, or the
Technology Acceptance Model, in which variables of perceived usefulness and perceived
ease of use affect attitude, which then influences purchase intentions. In the
research of Wang et al.
(2018), perceived
usefulness is shown to have a positive effect on attitudes toward buying
electric vehicles and vehicle adoption intentions. Different things were found
in the research of Vafaei-Zadeh et al. (2022), which found
that perceived usefulness has a positive impact on attitudes to buy electric
vehicles but not a significant impact on the intention to adopt electric
vehicles. The same study found that perceived ease of use has the most
substantial influence on consumer behavior.
Therefore, this study analyses the factors that drive the purchase intention of
electric cars using a model consisting of a combination of the Theory of
Planned Behavior (TPB), Norm Activation Model (NAM),
and Technology Acceptance Model (TAM).
Our research contributes significantly to the
existing knowledge base in the field of consumer behavior
and sustainability. By integrating the Theory of Planned Behavior
(TPB), the Norm Activation Model (NAM), and the Technology Acceptance Model
(TAM) into our research, we provide a comprehensive framework taking into
account the various psychological and contextual factors that influence consumers'
purchase intentions for electric cars. This multidimensional approach improves
understanding of the complex decision-making processes involved in adopting
environmentally friendly transportation options. Additionally, our study
extends the applicability of these well-founded theories to the context of
electric vehicle use, shedding light on how factors such as perceived
usefulness and perceived Ease of use interacts with attitudes and intentions in
that particular domain. These findings provide valuable insights for
policymakers, businesses, and researchers interested in promoting sustainable
transportation options and can guide the development of effective strategies to
encourage the use of electric vehicles.
The method used in this research is the survey
method. Partial least square structural equation modeling
(PLS-SEM) were used to test the hypotheses. This article consists of 5 section.
The first section, Introduction, describes the background of the problem. The
second section, Literature Review, where all the theories used are summarized.
The third section, Methods, explaining the research methods used. The fourth section,
Result and Discussion, explains the results of data collection and processing,
as well as the analysis carried out. The last section, Conclusion, gives
conclusions from the results of the research and analysis.
Theory of Planned Behavior, or TPB, is a development of the Theory
of Reasoned Action, or TRA. The main factor in the Theory of Planned Behavior
is a person's intention to perform certain behaviors (Ajzen, 1991). According to the Theory of Planned Behavior (TPB),
human behavior is influenced by three types of considerations: behavioral
beliefs, which are beliefs about the expected outcomes of actions; normative
beliefs, which relate to the perceived social expectations of others; and
control beliefs, which pertain to the belief in factors that can either support
or hinder the execution of behaviors. (Bosnjak et al., 2020). In the Theory of Planned Behavior, the factors that
determine meaning are Attitude toward the Behavior, Subjective Norm, and
Perceived Behavioral Control, as identified by By the general rule, the more positive a person's
attitude towards a behavior, the stronger the individual's intention to carry
out the behavior. Subjective Norm is a social factor that refers to perceived
social pressure to perform or not perform certain behaviors (Ajzen, 1991). As with attitude, the more someone feels the views
of others who agree with the behavior are essential, the more likely the
behavior will occur.
The Norm Activation Model, abbreviated as NAM, explains the
altruistic aspects of environmentally friendly behavior (Schwartz, 1977). According to NAM, people act green when their
standards reflect their moral obligation to be socially and environmentally
responsible (Le & Nguyen, 2022). The three main components of NAM are the ascription
of responsibility, awareness of consequences, and personal norms. . Asadi et al.
(2021) explained that personal norms are
among the most critical predictors of environmentally friendly behavior. Its
activation is accompanied by the formation of an individual's moral commitment,
which directs the individual towards environmentally friendly behavior.
The Technology Acceptance Model, or
TAM, is the most popular theory for predicting and explaining technology
acceptance among potential users (Wang et al.,
2018). Davis (1986) uses this
model to describe user acceptance of new computer technology and information
systems at that time. As technology develops, this model is widely adapted and
designed to see user acceptance of new technology. In TAM, two factors
influence technology acceptance: perceived usefulness and ease of use. Perceived
ease of use significantly affects perceived usefulness because an easy-to-use
system will increase user job performance (Davis, 1986).
Several other factors that are also
important in predicting the intention to buy an electric car, according to Asadi et al.
(2021),
are Perceived Consumer Effectiveness, Perceived Value, and Financial Incentives
Policies. PCEs are considered consumer beliefs about their role in mitigating
the undesirable effects of vehicle use and environmental improvements through
introducing electric vehicles (Asadi et
al., 2021).
According to Moosa and
Hassan (2015),
consumer purchasing decisions are influenced by perceived value, as they will
buy products with high perceived value. Referring to Asadi et al.
(2021),
various financial incentive policies are offered in the era of electric vehicles.
Some of these, including direct purchase subsidies and preferential tax
policies, are available to lower purchase prices and encourage more consumers
to use electric cars.
Method
In this study, quantitative research
methods were used. The sampling method used in this research was
non-probability sampling with purposive sampling. The study sought respondents
based on specific criteria, which included:respondents
do not yet own an electric car, indicating an intention to purchase one; respondents
have a driver's license A,an aspect of driving
legality in Indonesia; respondents fall into the Socioeconomic Status A group
with a monthly family expenditure of over Rp 6,000,000, to ensure the
purchasing power of respondents; respondents have knowledge related to electric
cars, to obtain respondents who were relevant to the items on the
questionnaire.
Indonesia faces significant environmental
challenges, including air pollution and greenhouse gas emissions. Researching
factors influencing electric car purchase intention in Indonesia is
particularly relevant as the country seeks to address these concerns and
transition towards cleaner transportation alternatives. Investigating the motivations
and barriers to adopting electric vehicles can contribute to sustainable
mobility solutions and align with the nation's environmental goals. The
Indonesian government has shown a growing interest in promoting electric
vehicles as part of its efforts to reduce emissions and dependence on fossil
fuels. Researchers can examine the impact of government policies, incentives,
and regulatory frameworks on the purchase intention of electric cars, providing
valuable insights into the effectiveness of these measures in driving EV
adoption. Indonesia's dynamic and diverse economy, coupled with a large
population, presents a significant market potential for electric vehicle manufacturers.
Understanding the economic factors that influence purchase intention is essential
for both policymakers and industry stakeholders. Investigating these aspects in
the Indonesian context can shed light on the country's readiness for electric
vehicle market growth.
The method used in this research is
the survey method. Surveys are used to obtain information by asking respondents
questions about actions, intentions, attitudes, awareness, motivation,
demographics, and lifestyles (Maholtra, 2016).
The survey was conducted using a questionnaire distributed online using Google
Forms. A 49-item questionnaire was adapted from previous TAM TPB and NAM research.
Five-point Likert scale was used on each item (1 = “strongly disagree”; 5 =
“strongly agree”).
The study used partial least square
structural equation modeling (PLS-SEM) to test the
hypotheses. PLS-SEM, a structural equation modeling
method, has many applications in different social science field (Hair et al.,
2022).
Data were analyzed using Smart-PLS 3.2.9 application.
Table I. Definition of Operational Variables
|
Construct |
Definition |
Item
Code |
Item |
Source |
|
Personal
Norms |
Moral obligation to perform or refrain from certain
actions |
PN1 |
I have a moral
obligation to purchase an electric car |
Song
et al. (2019)
& Singh
et al. (2023) |
|
PN2 |
I have a sense
of guilt if I don't purchase an electric car |
|||
|
PN3 |
I am the type
of person who wants to purchase an electric car to solve the problem of air
pollution |
|||
|
PN4 |
I think it is
important to travel as little as possible by conventional car |
|||
|
Awareness
of Consequences |
An individual's awareness of the negative consequences
for others when not performing a certain behavior |
AC1 |
Purchasing an
electric car can reduce fossil fuel consumption |
Song
et al. (2019) & Zhao
et al. (2019) |
|
AC2 |
Purchasing an
electric car can reduce harm to the environment |
|||
|
AC3 |
Purchasing an
electric car can solve air pollution problems |
|||
|
AC4 |
Overall,
purchasing an electric car can have several positive consequences |
|||
|
Ascription
of Responsibility |
An individual's sense of responsibility for the adverse
consequences caused by not performing a certain behavior |
AR1 |
I feel
responsible for the environmental damage caused by using conventional cars. |
Song
et al. (2019) & Singh
et al. (2023) |
|
AR2 |
I feel
responsible for air pollution problems caused by using conventional cars. |
|||
|
AR3 |
I think
everyone is partly responsible for the environmental problems caused by
conventional cars. |
|||
|
AR4 |
I have a
responsibility to influence the automobile industry towards more
environmentally friendly solutions. |
|||
|
Perceived
Consumer Effectiveness |
The level of consumer confidence that their behavior
can protect the environment |
PCE1 |
Everyone can
make a positive impact on the environment by buying an electric car |
Song
et al. (2019) & Matharu
(2019) |
|
PCE2 |
I can help
solve air pollution problems through my consumption behavior |
|||
|
PCE3 |
I can support
environmental protection by buying an electric car |
|||
|
PCE4 |
When I buy an
electric car, I try to understand how its use will impact the environment and
other consumers |
|||
|
Perceived
Value |
Functions of various benefits and tradeoffs |
PV1 |
Buying an
electric car provides benefits to me, which are worth the price I pay |
Kim
and Park (2019)
& Deng
et al. (2014) |
|
PV2 |
Buying an
electric car provides benefits to the environment, which are worth the price
I pay |
|||
|
PV3 |
Electric car
technology (use of batteries as a source of driving energy) provides benefits
that are worth the price. |
|||
|
PV4 |
Electric car
technology (use of batteries as a source of driving energy) provides
advantages over conventional car technology |
|||
|
PV5 |
Buying an
electric car is more beneficial than
buying a conventional car |
|||
|
Attitude
to Purchase Electric Car |
A person's level of assessment of a particular behavior |
ATT1 |
In general, I
think it is a very good thing to buy an electric car. |
Vafaei-Zadeh et
al. (2022)
& Joshi
and Rahman (2019) |
|
ATT2 |
In general, I
think buying an electric car is a very wise decision |
|||
|
ATT3 |
In general, I
think buying an electric car is a very satisfying decision. |
|||
|
ATT4 |
I feel good
about myself when I buy an electric car |
|||
|
Perceived
Behavioral Control |
Behavioral control that describes a person's perception
of their ability to perform certain behaviors |
PBC1 |
I can decide
for myself if I want to buy an electric car in the future |
Vafaei-Zadeh et
al. (2022) & Paul
et al. (2016) |
|
PBC2 |
I feel
confident that in the future I will be able to buy an electric car |
|||
|
PBC3 |
I feel
confident that in the future I will have the money to buy an electric car |
|||
|
PBC4 |
There will
likely be many opportunities for me to buy an electric car |
|||
|
Subjective
Norms |
The social pressure felt as a result of doing or not doing
a certain behavior |
SN1 |
People who are
important to me think that I should buy an electric car in the future |
Vafaei-Zadeh et
al. (2022) & Hasan
(2021) |
|
SN2 |
People who are
important to me want me to buy an electric car in the future |
|||
|
SN3 |
People who are
important to me would prefer that I buy an electric car |
|||
|
SN4 |
People who are
important to me think that electric cars support a sustainable transportation
system |
|||
|
Financial
Incentive Policies |
Strategies and programs implemented to encourage
and incentivize individuals to purchase and use electric vehicles |
FIP1 |
I feel that
the subsidy policy for purchasing electric cars is adequate. |
Wang
et al. (2018) & Wang
et al. (2017) |
|
FIP2 |
I understand
the subsidy policy for purchasing electric cars. |
|||
|
FIP3 |
Subsidy
policies and tax breaks are important to me in purchasing an electric car |
|||
|
FIP4 |
The purchase
tax exemption is very helpful for me to buy an electric car |
|||
|
Perceived
Usefulness |
The extent to which an individual believes that using a
particular system will improve his or her job performance |
PU1 |
Electric cars
are useful in reducing carbon emissions and overcoming the energy crisis |
Wang
et al. (2018) & Wu
et al. (2019) |
|
PU2 |
Electric cars
are useful for reducing my family's transportation expenses |
|||
|
PU3 |
Electric cars
can increase my travel efficiency and improve my quality of life |
|||
|
PU4 |
I believe that
using an electric car can make me healthier |
|||
|
Perceived
Ease of Use |
The extent to which an individual believes that using a
particular system requires no physical or mental effort |
PEU1 |
I think
electric car features (e.g. home charging) are easy to use. |
Vafaei-Zadeh et
al. (2022) & Park
et al. (2015) |
|
PEU2 |
I think the
electric car is easy to drive wherever I want to go |
|||
|
PEU3 |
My
interactions with the electric car (maintenance, usage, charging, etc.) will
be understandable. |
|||
|
PEU4 |
Using an
electric car does not require much mental or physical effort on my part. |
|||
|
Purchase
Intention of Electric Car |
A form of consumer behavior that has the desire to buy
a product based on desire, usage experience, and desire for the product. |
PIEC1 |
When I must or
will buy a new car, I am willing to buy an electric car |
Vafaei-Zadeh et
al. (2022) & Park
et al. (2015) |
|
PIEC2 |
When I must or
will buy a new car, I plan to buy an electric car |
|||
|
PIEC3 |
When I have to
or will buy a new car, I will buy an electric car |
|||
|
PIEC4 |
I expect to
buy an electric car because of its positive contribution to the environment |
Results and Discussion
Demographic Analysis
The demographic analysis for the study shows in Table
I. The total sample size is 253 which are 60.1% were males and 39.9% were
females. This study divides the age range into four groups. Most respondents
were 17-26 years old, with a proportion of 55.7%. Respondents who fall into the
age group of 27-36 years, 37-46 years, and above 46 years are 24.5%, 11.5%, and
8.3%, respectively. Most respondents in this study lived in Jabodetabek,
77.9% or 197 respondents, followed by respondents who lived on Java Island
outside Jabodetabek, 17%, and the rest lived outside
Java Island. Regarding educational status, most respondents in this study had
an undergraduate education background, with a portion of 78.3%. Only 11.1% and
10.3% of respondents have a high school and master's degree, respectively. The
least amount, namely 0.3% of respondents, have a doctoral background. Regarding
the range of respondents' monthly income, respondents who have income above
Rp8,000,000 are 47% or 119 respondents, who have income in the range of
Rp6,000,001-Rp8,000,000 is 26.9%, Rp4,000,000-Rp6,000,000 is 14.6%, and below
Rp4,000,000 is 11.5%.
|
Items |
Frequency |
Percentage |
|
|
Gender |
Male |
152 |
60.1% |
|
Female |
101 |
39.9% |
|
|
Age |
17-26 years old |
141 |
55.7% |
|
27-36 years old |
62 |
24.5% |
|
|
37-46 years old |
29 |
11.5% |
|
|
Above 46 years old |
21 |
8.3% |
|
|
Domicile |
Jabodetabek |
197 |
77.9% |
|
Java Island
outside Jabodetabek |
43 |
17.0% |
|
|
Outside Java
Island |
13 |
5.1% |
|
|
Education |
High School |
28 |
11.1% |
|
Undergraduate |
198 |
78.3% |
|
|
Master’s Degree |
26 |
10.3% |
|
|
Doctoral |
1 |
0.3% |
|
|
Income |
<IDR4.000.000 |
29 |
11.5% |
|
IDR4.000.000-IDR6.000.000 |
37 |
14.6% |
|
|
IDR6.000.001-IDR8.000.000 |
68 |
26.9% |
|
|
>IDR8.000.000 |
119 |
47.0% |
Outer Model
Before
hypothesis testing, validity and reliability assessments of the variables and
items were conducted. According to Table 2, all items were valid as the factor
loadings of each item were higher than 0.50 (Hair
et al., 2019), ranging from 0.791 to 0.896. This study used the
Average Variance Extracted (AVE) approach to examine the construct validity.
The AVE scores for all constructs ranged from 0.656 to 0.770, greater than
0.50, as proposed by Hair
et al. (2019), indicating the suitability of convergent validity
for the constructs. The Cronbach’s alpha ranged from 0.826 to 0.921, while the
composite reliability values ranged from 0.884 to 0.931. As a result, the
reliability of each variable was acceptable because Cronbach’s alpha and
composite reliability were at least at a value of 0.70 (Hair
et al., 2019).
Table II. Validity and Reliability Test Results
|
Variables |
Items |
Factor Loading |
Cronbach's Alpha |
Composite
Reliability |
AVE |
|
Awareness of
Consequences |
AC1 |
0.817 |
0.866 |
0.909 |
0.715 |
|
AC2 |
0.854 |
||||
|
AC3 |
0.852 |
||||
|
AC4 |
0.854 |
||||
|
Ascription of Responsibility |
AR1 |
0.865 |
0.867 |
0.909 |
0.762 |
|
AR2 |
0.869 |
||||
|
AR3 |
0.841 |
||||
|
AR4 |
0.806 |
||||
|
Attitude |
ATT1 |
0.863 |
0.896 |
0.928 |
0.713 |
|
ATT2 |
0.874 |
||||
|
ATT3 |
0.873 |
||||
|
ATT4 |
0.882 |
||||
|
Financial Incentive
Policies |
FIP1 |
0.773 |
0.826 |
0.884 |
0.656 |
|
FIP2 |
0.807 |
||||
|
FIP3 |
0.798 |
||||
|
FIP4 |
0.859 |
||||
|
Perceived Behavioral
Control |
PBC1 |
0.863 |
0.886 |
0.921 |
0.744 |
|
PBC2 |
0.861 |
||||
|
PBC3 |
0.867 |
||||
|
PBC4 |
0.86 |
||||
|
Perceived Consumer
Effectiveness |
PCE1 |
0.831 |
0.847 |
0.897 |
0.685 |
|
PCE2 |
0.838 |
||||
|
PCE3 |
0.822 |
||||
|
PCE4 |
0.82 |
||||
|
Perceived Ease of
Use |
PEU1 |
0.828 |
0.84 |
0.893 |
0.676 |
|
PEU2 |
0.806 |
||||
|
PEU3 |
0.828 |
||||
|
PEU4 |
0.826 |
||||
|
Purchase Intention
of Electric Cars |
PIEC1 |
0.838 |
0.87 |
0.912 |
0.721 |
|
PIEC2 |
0.887 |
||||
|
PIEC3 |
0.875 |
||||
|
PIEC4 |
0.794 |
||||
|
Personal Norms |
PN1 |
0.859 |
0.841 |
0.894 |
0.678 |
|
PN2 |
0.826 |
||||
|
PN3 |
0.845 |
||||
|
PN4 |
0.761 |
||||
|
Perceived
Usefulness |
PU1 |
0.783 |
0.85 |
0.899 |
0.691 |
|
PU2 |
0.844 |
||||
|
PU3 |
0.844 |
||||
|
PU4 |
0.853 |
||||
|
Perceived Value |
PV1 |
0.836 |
0.896 |
0.923 |
0.706 |
|
PV2 |
0.855 |
||||
|
PV3 |
0.852 |
||||
|
PV4 |
0.833 |
||||
|
PV5 |
0.826 |
||||
|
Subjective Norm |
SN1 |
0.896 |
0.901 |
0.931 |
0.77 |
|
SN2 |
0.888 |
||||
|
SN3 |
0.878 |
||||
|
SN4 |
0.849 |
The
coefficient of determination R2 of 0.731 for the purchase intention
variable was obtained in this study. This can be interpreted that the exogenous
variables used in this study could explain the purchase intention variable by
73.1%, which means that the remaining 26.9% is explained by other variables not
examined in this study. The
model in this study had a Predictive Relevance (Q2) value of 0.510
which indicated to have strong predictive power. The NFI value in this research
model was 0.763, so it can be said that the applicability of the model used in
this study was 76.3%. The Standardised Root Mean
Square Residual (SRMR) value in this research model was 0.084, indicating that
the structural model in this study was defined according to standards and was
feasible to use.
Through bootstrapping 5000 samples for hypothesis
testing, this study yielded results (Table IV and Figure II) showing that among
the 15 hypotheses proposed, eight were accepted while seven were rejected. H1
and H2 were not supported as personal norms and awareness of consequences
didn't significantly impact electric car purchase intentions. H3 and H4 were
confirmed, indicating that awareness of consequences influenced the ascription
of responsibility, which, in turn, impacted personal norms significantly. H5
was supported, illustrating that perceived consumer effectiveness positively
affected personal norms, but H6 was rejected as it had no significant effect on
purchase intention. H7 was backed by a positive association between perceived
value and attitudes. However, H8 was rejected as perceived value didn't
significantly affect electric car purchase intentions. H9 was rejected,
signifying that attitude did not significantly affect purchase intentions. H10
was supported, revealing that perceived behavioral control
positively influenced purchase intentions. H11 and H12 were not accepted as
subjective norms and financial incentive policies didn't significantly affect
purchase intentions. H14 and H15 was supported as perceived usefulness and ease
of use both positively and significantly affected attitudes toward electric car
purchase intentions. Lastly, H13 was supported, demonstrating that perceived
usefulness significantly influenced consumers' purchase intentions regarding
electric cars.
Table III. Hypothesises Results
|
Hypothesis |
Path |
Hypothesis
test |
Result |
|
|
Path
coefficient |
p-value |
|||
|
H1 |
PN-PIEC |
0.052 |
0.222 |
Not
Supported |
|
H2 |
AC-PN |
0.057 |
0.238 |
Not
Supported |
|
H3 |
AC-AR |
0.711 |
0.000 |
Supported |
|
H4 |
AR-PN |
0.408 |
0.000 |
Supported |
|
H5 |
PCE-PN |
0.335 |
0.000 |
Supported |
|
H6 |
PCE-PIEC |
0.101 |
0.099 |
Not
Supported |
|
H7 |
PV-ATT |
0.257 |
0.003 |
Supported |
|
H8 |
PV-PIEC |
0.138 |
0.071 |
Not
Supported |
|
H9 |
ATT-PIEC |
-0.025 |
0.402 |
Not
Supported |
|
H10 |
PBC-PIEC |
0.305 |
0.000 |
Supported |
|
H11 |
SN-PIEC |
0.047 |
0.271 |
Not
Supported |
|
H12 |
FIP-PIEC |
0.112 |
0.060 |
Not Supported |
|
H13 |
PU-PIEC |
0.376 |
0.000 |
Supported |
|
H14 |
PU-ATT |
0.232 |
0.002 |
Supported |
|
H15 |
PEU-ATT |
0.289 |
0.000 |
Supported |
Figure II. Results
of the Structural Model
From the
perspective of NAM, personal norms insignificantly influence the purchase
intention of electric car consumers in Indonesia. This finding is different
from what was explained by Asadi et al. (2021), namely that personal norms are the main predictor of
intention to show environmentally friendly behavior.
For consumers in Indonesia, the moral obligation to buy an electric car is not
strong enough to encourage the intention to buy an electric car, which may be
due to personal factors such as limited funds. It could be due to situational
factors such as dependents and other interests that are considered more urgent
than buying an electric car to improve the environment. For young adults aged
17-26 which is the majority of respondents, competing interests such as
education expenses, career development, and social activities often take
precedence over purchasing an electric car for environmental reasons. Awareness
of consequences positively affects consumer personal norms in Indonesia but is
not significant. This finding is not in accordance with previous studies, which
found that awareness of consequences is significant in positively influencing
personal norms (Asadi et al., 2021; Rezaei et al., 2019; Xiaojie
Zhang et al., 2018).
Although consumers know the positive consequences of buying an electric car, it
does not encourage the emergence of moral obligations. On the other hand,
awareness of consequences positively and significantly affects the ascription
of responsibility for electric car consumers in Indonesia. This finding is in
accordance with some previous studies that also found a significant association
between these two variables (Asadi et al., 2021; He & Zhan, 2018; Song et al.,
2019).
De Groot and Steg (2009) also explained that individuals must be aware of the
consequences of behavior before feeling responsible
for it. So the higher the awareness of the consequences associated with a
particular behavior, the more individuals feel
responsible for that behavior (Rezaei et al., 2019). Understanding the positive consequences of buying an
electric car and the negative consequences of a conventional car gives rise to
a sense of consumer responsibility for the environmental damage caused.
Ascription of responsibility positively and significantly affects the personal
norms of electric car consumers in Indonesia. An individual's sense of
responsibility for the adverse consequences of ignoring environmentally
friendly behavior gives individuals a sense of moral
obligation to follow such behavior (López-Mosquera et al., 2014). Similarly, stated by Rezaei et al. (2019) that AR plays an important role in generating and
strengthening personal norms, and only when these conditions are met, personal
norms will be active. A sense of responsibility for environmental damage caused
by conventional cars triggers the emergence of norms that oblige them to act
according to the moral values they hold to be able to buy an electric car.
Perceived consumer effectiveness positively and
significantly affects the personal norms of electric car consumers in
Indonesia. This finding is consistent with previous research that perceived
consumer effectiveness positively affects personal norms (Asadi et al., 2021; Song et al., 2019).
Consumers who have the perception that they are able to create better air
conditions from their consumption behavior create a
moral obligation to buy an electric car. However, the relationship between
perceived consumer effectiveness and purchase intention of electric cars did
not prove significant. This finding contradicts previous studies, which found
that perceived consumer effectiveness has a positive and significant effect on
intention (Kabadayı et al., 2015;
Vermeir & Verbeke, 2008).
Even though consumers know that buying an electric car can solve the air
pollution problem, it is not realized in a plan to buy an electric car. This
can be caused by individual factors, namely limited funds, or it can be due to
situational factors, such as the existence of needs that are considered more
urgent than buying an electric car to solve air pollution problems.
Perceived value positively and significantly affects
the attitude of electric car consumers in Indonesia. This finding is in
accordance with previous research that perceived value positively affects
attitude (Asadi et al., 2021; Kim & Park, 2019).
In generating a positive attitude towards purchasing an electric car, a
cognition process occurs that involves beliefs that buying an electric car
provides benefits that are worth it when compared to the sacrifice of money
spent. However, the relationship between perceived value and purchase intention
of electric cars did not prove significant. This finding contradicts previous
studies, which found that perceived value has a positive and significant effect
on intention (Aini et al., 2019; Chen et al., 2012; Li & Shang,
2020).
In the High Involvement Hierarchy of Effects, the process of attitude formation
starts from cognition, affect, and behavior.
Consumers do not feel affection for electric cars, so there is no intention to
plan to buy an electric car.
From the perspective of TPB, the only factor
positively and significantly affects the intention to purchase electric car is Perceived
behavioral control. This finding is consistent with
previous research that perceived behavioral control
positively influences the purchase intention of electric cars (Wang et al., 2016), and in line with the Theory of Planned Behavior that perceived behavioral
control affects intention (Ajzen, 1991). The easier a person is or has control over buying an
electric car, the stronger the intention to buy it. This proves that the
availability of funds is very important in encouraging the realization of a
plan to buy an electric car. On the other hands, attitude negatively but
insignificantly affects the purchase intention of electric car consumers in
Indonesia. This finding contradicts previous research, which found that attitude
has a positive and significant effect on intention (Asadi et al., 2021; Shi et al., 2017; Xiang Zhang et
al., 2018).
This finding can be interpreted that even though a consumer has a positive
attitude towards the intention to buy an electric car, it is not able to be
realised into an intention to buy an electric car, even weaken it. Subjective
norm positively affects the purchase intention of electric car consumers in
Indonesia but is not significant. This finding contradicts the results of
previous research, which found that subjective norm has a positive effect on
the purchase intention of electric cars (Asadi et al., 2021). This marks that others’ opnion
does not influence the intention to buy an electric car. Even though the
individual is important, it still cannot significantly influence the purchase
intention of an electric car from consumers.
Financial incentive policies positively influence the
purchase intention of electric car consumers in Indonesia but are not
significant. This finding contradicts the results of previous research, which
found that financial incentive policies negatively affect the purchase
intention of electric cars (Asadi et al., 2021). This finding also contradicts the results of Lin and Wu (2018), that found the subsidies from the government for the
purchase will have a strong impact to purchase intention. But this finding
confirms Wang et al. (2018), that found the effect of financial
incentive policy on consumer’s intention to adopt EVs was not significant
in China consumers. When looking at the form of incentives provided by the
government to prospective electric car buyers, the amount that must be spent by
consumers is still quite expensive. So that incentives are not significant in
encouraging the intention to buy an electric car.
In the perspective of TAM, perceived usefulness
positively and significantly affects the purchase intention of electric car
consumers in Indonesia. This finding is consistent with previous research where
perceived usefulness positively influences the purchase intention of electric
cars (Wang et al., 2018; Wu et al., 2019).
In this study, the perception held by consumers in Indonesia that electric cars
are useful in improving their health is strong in influencing future electric
car purchase plans. Perceived usefulness positively and significantly affects
the attitude of electric car consumers in Indonesia. This finding is consistent
with previous research where perceived usefulness positively affects attitude (Vafaei-Zadeh et al., 2022;
Wang et al., 2018; Wu et al., 2019). The process of consumer cognition in
Indonesia related to the positive impact of electric cars on health creates
positive consumer feelings toward electric cars, which leads to a positive
attitude toward purchasing electric cars. Perceived ease of use positively and
significantly affects the attitude of electric car consumers in Indonesia. This
finding is consistent with previous research where perceived ease of use
positively affects attitude (Vafaei-Zadeh et al., 2022;
Wu et al., 2019).
With the various conveniences offered by electric cars in their use, consumers
in Indonesia believe that using electric cars can improve the quality of their
health.
Conclusion
This study investigated how the
factors that drive the intention from Theory of Planned Behavior
(TPB), Norm Activation Model (NAM), and Technology Acceptance Model (TAM)
affecting the purchase intention of electric cars. Other factors outside the
three theories were obtained from previous research. Based on the results,
perceived usefulness from TAM and perceived behavioral
control from TPB were the two factors positively and significantly affect
purchase intention of electric cars.
The research findings reveal significant
departures from prior studies, highlighting crucial theoretical implications.
Notably, two key drivers derived from the Theory of Planned Behavior,
subjective norm and attitude, were found to be insignificant in shaping the
intention to purchase electric cars in the Indonesian context. This suggests
that even figures considered influential in society cannot stimulate the desire
to buy electric cars, and attitudes do not readily translate into behavioral changes within this group of potential
consumers. Furthermore, personal norms, as per the Norm Activation Model
theory, were also deemed ineffective in influencing purchasing intentions,
despite a moral inclination to buy electric cars. This disparity may be
attributed to personal financial constraints hindering the realization of this
obligation. In addition, the research indicated that financial incentive
policies provided by the government failed to significantly impact purchase
intentions, primarily due to the continued high cost of electric cars even with
incentives. Moreover, perceived value was found to be a non-significant factor
in shaping the intention to buy electric cars, suggesting that despite
consumers' cognitive awareness of electric cars, they lack a genuine affinity
for the technology, resulting in a lack of intent to purchase. These findings
have far-reaching implications for the development of strategies to promote
electric car adoption in Indonesia, emphasizing the need to consider distinct
socio-cultural, economic, and attitudinal factors in policy and marketing
initiatives.
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