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FACTORS INFLUENCING THE ADOPTION OF ELECTRIC VEHICLE:
THE CASE OF ELECTRIC MOTORCYCLE IN NORTHERN GHANA
Lukuman Wahab1, Haobin Jiang2
1 School of Engineering, Tamale Technical University, Tamale, Ghana
2 School of Automotive and Traffic Engineering, Jiangsu University, Zhenjiang 212013, China
Received 1 November 2018; accepted 26 December 2018
Abstract: Electric motorcycles are one way to reduce fossil fuel consumption and greenhouse gas
emissions in the area of transportation. This survey utilizes both Logit and Probit frameworks
to explore the likely factors influencing the adoption of the electric motorcycle in Northern
Ghana. The outcomes from the two models are consistent with each other; they have similar
signs for every factor except for a slight contrast in the magnitude of the coefficients. A
survey was conducted in Northern Ghana to elicit information from motorcyclists through a
questionnaire. The model takes into consideration motorcyclists’ perceptions about technical
specifications of electric motorcycles, such as charging times, the lifespan of the battery, the
performance of the electric motorcycle, motorcyclists’ perception of the price of the electric
motorcycle, driving range and motorcyclists’ ages, monthly income, among others. The
results reveal that perception of the price of the electric motorcycle, government subsidies,
performance of the electric motorcycle, high usage, and maximum distance has a substantial
impact on motorcyclists’ willingness to adopt electric motorcycles. The findings of this
study will provide constructive advice to diverse stakeholders on the adoption of an electric
motorcycle in Ghana.
Keywords: electric motorcycle, Logit model, Northern Ghana, Probit model, technology
adoption.
1 Corresponding author: wlukuman@tatu.edu.gh
UDC: 629.326.2(667) DOI: http://dx.doi.org/10.7708/ijtte.2019.9(1).03
1. Introduction
Automobile fuels such as gasoline and
diesel produce toxic substances during
combustion. Carbon dioxide and others-
carbon monoxide, hydrocarbon, nitrogen
oxide and, in the case of a diesel engine,
exhaust gas-are discharged by cars. These
pollutants cause global warming and are
culprits of air pollution. According to the
World Health Organization report, a total
of 8.2 million deaths, 16% of global deaths,
were cred ited to air pollution in 2012 (Prùss-
Ustùn et al., 2016; Environmental Protection
Agency, 2016).
The number of newly registered motorcycles
in Ghana annually increased by 315% from
the year 2008 to 2014, whereas the number
of newly registered vehicles increased by 85%
within the same period (Driver and Vehicle
Licensing Authority, 2016).
Motorcycles play a vital role in Ghana’s
transportation system, particularly in
Northern Ghana where they are the most
22
Wahab L. et al. Factors Influencing the Adoption of Electric Vehicle: The Case of Electric Motorcycle in Nor thern Ghana
popular means of transport for both humans
and goods. Due to low cost, convenience,
and ability to maneuver on congested roads,
motorcycles are also becoming attractive
for commercial passenger transport in
major cities in Ghana although they are
not legally permitted to be used as a public
transportation (Akaateba et al., 2 015;
Akaateba et al., 2014). The prominence of
motorcyc les over vehicula r mode of transport
in the nor thern par t of Ghana can be credited
nonexistence of governmental intra-city
public transport system and insufficient
private ones, less motorable roads and the
inability of the people to acquire private
vehicles, a sit uation which is genera lly linked
to the socio-economic characteristics of the
people in the Northern Ghana (Dapilah et
al., 2 017).
Electric vehicles are one way to reduce
emissions in the transport sector because
of their zero-level carbon emissions during
use, low energy consumption, and relatively
simple and mature technology (Wang et al.,
2018). They offer substantial economic and
environmental benefits by substituting gr id-
based electricity for fossil fuels compared
to the internal combustion engine vehicles.
Also, they reduce greenhouse gas and other
emissions, enhance energy security, and
promote the u se of renewable energ y (Larson
et al., 2014; Egbue and Long, 2012).
Thus, this study aims to discover to what
extent some issues are critical to ex plaining
Ghanaian consumers’ willingness to adopt
an electric motorcycle by using a survey and
the information obtained by 537 Ghanaian
respondents. With this aim, t he likeli hood of
consu mers’ stated willingness to consider the
adoption of an elec tric motorcycle is explored
using logistic regression. The study focuses
on motorcyclists’ perception about technical
speci fications of elect ric motorcycles, such as
charging times, their perception of the price
of an electric motorcycle, driv ing range, and
motorcyc lists’ ages, among ot hers. This study
contributes to the literature on this topic in
three ways: (i) it provides an empirical study
about motorcyclists’ decision-making to
buy an electric motorcycle; (ii) it is the first
attempt on this topic in Ghana; and (iii) it
provides useful information to pol icymakers
to discuss the cr itical elements related to the
most appropr iate indust rial polic y which help
to promote the electric motorcycle. The rest
of this paper is structured a s follows. Chapter
2 is a brief review of the previous literature.
Chapter 3 describes the data acquisition
methods, and the research method and
Chapter 4 summarizes the empirical results
and discussion. Finally, Chapter 5 presents
the conclusion of the study.
2. Literature Review
It is necessary to deeply investigate
Ghanaian motorcyclists’ perception of
electric motorcycle and the inf luential
factors that affect their intention to adopt
the electric motorcycle to promote the
sustainable transportation mode of an
electric motorcycle in Ghana. Considering
the innovative characteristics of an electric
motorcycle, in t he current research, we adopt
and the theory of planned behavior (TPB)
(Ajzen, 1991) as the basic theoretical model
to understand the antecedents of consumers’
willingness to adopt innovative technology.
TPB is often considered as a common and
robust model to address consumer adoption
of the electric vehicle (Egbue and Long,
2012; Lane and Potter, 2007; Moons and
de Pelsmacker, 2012; Wang et al., 2016;
Moons and De Pelsmacker, 2015). Several
prior studies have adopted TPB to explore
consumers’ intention to adopt the electric
23
International Journal for Traffic and Transport Engineering, 2019, 9(1): 22 - 37
vehicle and validated its usefulness and
feasibility. For example, Wu et al. (2015)
used the TPB to explore consumers’ green
purchase intentions of Taiwanese to buy
electric motorcycle TPB is appropriate to
explain the inf luential factors of consumer
acceptance of sustainable transportation.
Elect ric vehicles provide a useful comparison
basis for electric motorcycles because they
have several of the same critical elements
including a battery and electric motor-
based powertrain and lower environmental
impacts. As electric vehicles have been
commercially available since the late 1990s,
several studies used revealed preference
data to investigate factors that inf luenced
consu mer uptake for t hose automobi les (Soto
et al., 2018; Wang et al., 2016). To the best
of our knowledge, the available literature
provides a comprehensive assessment of
adoption of electric motorcycles are Chiu
and Tzeng (1999), Guerra (2017), Jones
et al. (2013) and Wu et al. (2015). In the
presence of relatively limited research on
electric motorcycles, we have incorporated
into our study variables that were found
to be significant drivers of electric vehicle
adoption in the literature.
The literature on consumer electric
vehicles adoption has analyzed several
factors affecting the adoption of electric
vehicles. The focus of published studies has
been on various aspects of adoption and
non-adoption behavior. They have utilized
different theories and studied different
electric vehicles in different parts of the
world (Rezvani et al., 2015). Economic
analysis of technology adoption has sought
to explain adoption behavior about buyers’
socio-demographic characteristics such
as age, gender, and level of education,
travel patterns, household attributes such
as income and number of vehicles in the
household (Weinert et al., 2007a; Musti and
Kockelman, 2011; Khan and Kockelman,
2012; Higgins et al., 2012; Egbue and Long,
2012; Liu et al., 2013; Axsen et al., 2017).
Factors driving the market penetration of
e-bikes in China include legislative support,
technologica l improvement, price reduction,
favorable transportation infrastructure,
and favorable socio-economic and cultural
conditions (Weinert et al., 2007a; Weinert et
al., 2008; Weinert et al., 2007b; Cherry and
Cervero, 2007; Cherry, 2007).
Gallagher and Muehlegger (2011) studied
the relative efficacy of state sales tax waivers,
income tax credits, and non-tax incentives
and found that the type of tax incentive
offered is as imperative as the generosity of
the incentive. Consequently, plug-in hybrid
vehicle penetration is shown to be strongly
dependent on permanent tax rebates,
subsid ies, and sales tax exemptions. Dia mond
(2009) examines the impact of government
incentives policies designed to promote the
adoption of hybrid-electric vehicles and
found that the relationship between gasol ine
prices and hybrid adoption is strong, but a
much feebler connection between incentive
policies and hybrid adoption and incentives
that provide payments upfront also appear
to be the most effective.
Among the most significant barriers
hindering the electric vehicles deployment,
literature identifies cost competitiveness;
whether regarding the total cost of ownership
or purchase price (Morganti and Browne,
2018). Surveys show that many consumers
express a willingness to pay a price premium
for a more fuel-ef ficient vehicle (Lieven et al. ,
2011; Eppstein et al., 2011; Graham-Rowe et
al., 2012; Krupa et al., 2014). Electric vehicle
24
Wahab L. et al. Factors Influencing the Adoption of Electric Vehicle: The Case of Electric Motorcycle in Nor thern Ghana
purchase prices, which a re heavily dependent
on battery costs, have been identified as
being the most significant factors in electric
vehicle adoption (Sierzchula et al., 2 014).
Barth et al. (2016) find that the purchasing
price is the most important factor related
to the adoption of electric vehicles. Electric
vehicles are usually more expensive
to be bought. However, energy-saving
technologies could be net-cost savers in the
long run (Junquera et al., 2016).
A key determinant of the adoption of
the electric vehicle is an environmental
concern because adoption of the electric
vehicle is considered as an environmental
protection action (Rezvani et al ., 2015). The
issues of energy security, concerns about
the environment, and the obtainability of
alternative fuels, along with demographic
characteristics, have significant effects
on consumer purchase expectations for
alternative-fuel vehicles (Li et al., 2 013).
Krupa et al. (2014) find that those who are
most concerned about climate change have a
greater willingness to adopt electric vehicles.
This study is consistent wit h other studies of
Schuitema et al. (2013) and Yadav and Pathak
(2016). Contrasts of electric bikes with cars
and buses have reported that per kilometer
traveled, even tak ing into account the longer
lifetime of the automobiles, the electr ic bikes
are very energy efficient and cleaner than
cars on all metrics, except those using lead-
acid batteries (Weinert et al., 2007b; Cherry,
20 0 7).
Charging time plays a critical role in the
adoption process of electric vehicles. Many
authors have analyzed charging time as one
of the vital determinants of electric vehicle
adoption (Hidrue et al ., 2011; Neubauer et al .,
2012; Beggs et al., 1981; Bunch et al., 1993;
Chéron and Zins, 1997; Lieven et al., 2011;
Zhang et al., 2011; Egbue and Long, 2012).
Whereas most internal combustion engine
vehicles can refuel in roughly 4 min, electric
vehicles require approximate 30 min at a
fast charging station and up to several (>10)
h for charging from a 110 or 220 V outlet,
dependent on battery size (Saxton, 2011).
Lengthy charging times are considered a
crit ical handicap to improve the market sha re
of electric vehicles (Pilkington and Dyerson,
2002; Hård and Knie, 2001). An additional
factor which inf luences consumer adoption
of electric vehicles is the availability of
charging stations (Yeh, 2007; Egbue and
Long, 2012; Tran et al ., 2012; Neubauer and
Wood, 2014).
In several studies, fuel (gasoline or diesel)
prices have been identified as one of the most
potent predictors of electric vehicle adoption
(Diamond, 2009; Beresteanu and Li, 2011;
Gallagher and Muehlegger, 2011). Related
to fuel prices, although less commonly
considered in analyses, is electricity costs.
Those two factors combine to determine a
majority of electric vehicle operating costs
which in turn have an impact on adoption
rates (Zubaryeva et al ., 2012; Dijk et al ., 2 013).
The range is widely identified as a
significant concern by potential electric
vehicle buyers. The electric vehicle is
powered solely by a rechargeable electric
battery and can travel for up to 100 miles
on one full charge (Pilk ington and D yerson,
2002; Hardman et al., 2017; Van Haaren,
2012; Egbue and Long, 2012). Franke and
Krems (2013) in their study treat range as a
barrier to adoption and find that experience
from driving all-electric vehicles produce
the adaptation, which reduces the practical
constraints of range. Consequently, range
limitation can be considered as the adaption
demand or the needed change or behavior
25
International Journal for Traffic and Transport Engineering, 2019, 9(1): 22 - 37
relative to conventional internal combustion
engine cars. Moreover, such changes in
behavior make consumers resistant to the
acceptance of battery electric vehicles
(Caperello and Kurani, 2012; Lane and
Potter, 2007). The current electric vehicle
becomes less suitable when the daily trip
distance of the user is more than 200 km
(K r umm, 201 2).
Rezvani et al. (2015) carried out a
comprehensive overview of the drivers for
and barriers against consumer adoption
of plug-in electric vehicles, in addition to
a review of the theoretical perspectives
that have been applied for understanding
consumer intentions and adoption behav ior
towards electric vehicles. They argued
that various factors inf luence the adoption
process. These factors are: Technical (e.g.,
instrumental, functional electric vehicle
attr ibutes); Contextua l (e.g., pol icy, chargi ng
infrastructure), Cost (e.g., purchase price,
fuel costs), indiv idual and social factors (e.g.,
knowledge, perceived behavioral control,
emotions, the symbolic meaning of the
electric vehicle, subjective social norm) are
all associated with battery electric vehicle
adoption.
3. Materials and Methods
In this study, Tamale which doubles as the
capital town of the Tamale Metropolitan
Assembly and the regional capital of the
Northern Region of Ghana was selected
purposively for the research. The Tamale
Metropol itan Assembly is t he most populous
district in the region, with a population
of 371,351, representing 15 percent of
the region’s population. This massive
concentration may be because Tamale
is the capital of the region and is also
centrally located. Commercial activities,
job opportunities, as well as educational
institutions in the metropolis are attracting
people from other pa rts of the region. Tama le
is selected because is one of the few cities
in Ghana where the use of a motorcycle
as a means of transport is widespread
(Ackaah and Afukaar, 2010). Face-to-face
interviews with questionnaires were used
to solicit a response from the motorcycle
owners. The questionnaire was divided
into two parts where the first part is about
demographic information, including will
adopt electric motorcycle, gender, age,
education background, monthly income,
and household. The second part focuses on
the attitude factors that may influence the
adoption of an electric motorcycle, and this
made up of statements that were used to
explore consumer perception of barriers to
electric motorcycle adoption. Eleven factors
were chosen as possible barriers to electric
motorcycle adoption from the review of the
literature. Explanations of the potential
barriers were provided to respondents to
ensure that the respondents had a consistent
understanding of the barriers. In this
study, the Logit and Probit models and the
associated odds ratios are estimated using
Stata (version 14.0).
Table 1 below shows the variable with their
definitions and a prior expectation. Among
these variables is the “maximum education”
of the respondent as described: 1=Primary
school graduate, 2=Junior high school
graduate, 3= Senior high school graduate,
4= Undergraduate degree, 5=Postgraduate
degree and was modeled as a categorical
variable. Another variable investigated
in this study is “monthly income” and is
defined as a categorical variable as well. This
research aggregates monthly incomes into
26
Wahab L. et al. Factors Influencing the Adoption of Electric Vehicle: The Case of Electric Motorcycle in Nor thern Ghana
six categories: 1=less than ₵1,000, 2=₵1,000
to ₵2,000, 3=₵2,001 to ₵3,000, 4=₵3,001
to ₵4,000, 5=₵4,001 to₵5,000, 6=greater
than ₵5,000. The Ghana Cedi and Euros
ratio is 1 Ghana Cedi is equivalent to 0.18
Euro (XE Corporation, 2018).
Table 1
Variable with Their Definitions and a Prior Expectation
Var i a b l e Definition Expected Sign
Will adopt electric motorcycle 1 yes; 0 otherwise +
Gender 1 if the respondent is a male; 0 otherwise +/-
Age Number of years +/-
Maximum education Level of formal education by the respondent +/-
Monthly income Monthly earnings of the respondent in Ghana Cedis +/-
Household size Total number of people in the household +/-
Charging time of the battery Time to recharge the battery of electric motorcycle in an
hour
+/-
Lifespan of battery The lifespan of the electric motorcycle battery in years +/-
Riding pleasure 1 if the respondent likes riding; 0 otherwise +
Operating cost 1 if the respondent thinks the operating cost of the electric
motorcycle is less than that of the gasoline-powered
motorcycle; 0 otherwise
+
Perception of the price 1 if the respondent considers the price of an electric
motorcycle is higher than that of the gasoline-powered
motorcycle; 0 otherwise
+
Environmental concern 1 if the respondent thinks the pollution by electric
motorcycle is lower to a gasoline-powered motorcycle; 0
otherwise
+
Government subsidies 1 if the respondent will buy an electric motorcycle if there
are subsidies on an electric motorcycle; 0 otherwise
+
Performance of electric motorcycle 1 if the respondent thinks the performance (acceleration,
Speeding, and so forth.) is better than a gasoline-powered
motorcycle; 0 otherwise
+
High usage to cover 200 Km 1 if the respondent uses the motorcycle with high frequency
to cover 200 Km; 0 otherwise
-
Public charging of infrastructure 1 if the respondent will adopt electric motorcycle if public
charging infrastructure is provided; 0 otherwise
+
A multistage sampling technique was used in
select ing the motorcycle ow ners for the study.
Motorcycle owners who have been riding a
motorcyc le continuousl y for the past five years
were pur posively selected. Th is technique was
to avoid new motorc yclist, since they may not
have adequate knowledge about the usage of
the motorc ycle. Af ter purposive sampling, the
simple random technique was used to select
the requ ired number of motorcycle owners for
the interv iew. Six-hundred and twenty-seven
motorcycle owners were interviewed for the
study. The field survey started in June and
ended in September 2017.
27
International Journal for Traffic and Transport Engineering, 2019, 9(1): 22 - 37
3.1. Statistical Model Specification
Methodologies used in this study follow the
processes desc ribed by other researchers such
as (Zhang et al. , 2011; Bunch et al ., 1993; Ax sen
and Kurani, 2011; Junquera et al., 2016; Lin
and Wu, 2018; Javid and Nejat, 2017; Soto et
al., 2018). The stati stical modeling fra mework
employed in this study to determine the
possible factors influencing the adoption
of the electric motorcycle was the Logit
model. The choice of this type of model was
inf luenced by the dichotomous nature of the
response variable. This model is derived
under the postulation that the error term ɛ
is the Independent, Identically Distributed
(IID) extreme value, which has a logistic
distribution (Train, 20 09). A Logit model will
produce results like Probit reg ression, which
uses a standard normal distribution for the
error term. T he choice of Probit versus Log it
depends mostly on individual preferences.
Since the dependent variable, or endogenous
variable, is a 0 or 1 variable, we employ the
Logit regression model. W hile the application
of the Logit reg ression is the emphasis of this
paper, the results of the Probit model are also
provided for comparison. In this section, we
describe mathematical formulations for the
Logit regression model.
In this study, we use the multiple Logit
models as described by (1) where P(x) is
the predicted probability of y=1 for a given
value of xk (k=1, 2…, P) (Hosmer et al.,
2013). The coefficients a and bk (k=1, 2…,
P) are determined according to a maximum
likelihood approach, and it allows us to
estimate the P(x) (Hosmer et al., 2013), Eq.
(1) and Eq. (2).
1
()
log 1 ()
P
kk
k
Px a bx
Px
=
= +
−
∑
(1)
Solving for P(x) gives the equation (2):
()
1
() 1
kkk
a bx
Px e
− +Σ
=+
(2)
For each data-point (i =1, 2…, n) we have a
vector of features, xi, and an observed class,
yi. The probability of that class was either
P(x i), if yi=1, or 1-P(x i), if yi=0. The likelihood
function, L (a, b) is presented by Eq. (3), and
the resulting log-likelihood function, L′ (a ,
b) is shown by Eq. (4). Placing Eq. (1) into
Eq. (4) and differentiating the loglikelihood
concerning the parameters will result in Eq.
(5). The derivative in Eq. (5) is set to zero
and solved to determine the coefficients a
and bk (k=1, 2…, P).
1
1
(,) [()(1 ()) ]
i
n
y
yi
ii
i
Lab Px Px
−
=
= −
∏
(3)
1
'( , ) [( log( (( )) (1 ) log(1 ( ))]
n
iii i
i
L ab y P x y P x
=
= +− −
∑
(4)
1
'[ ( )) ]
n
i i ik
i
k
Ly Px x
b
=
∂= −
∂
∑
(5)
(whe re k=1,2,3,…,P)
3.2. Marginal Effects
The inferences about the effect of a
variable on the outcome are determined
by its marginal effect. Marginal effects
are estimates of the change in an outcome
for a change in one independent variable,
holding all other variables constant (Long
and Freese, 2014).
Following the discussion in (Shaheed and
Gkritza, 2014; Greene, 2012), the direct
and cross-marginal effects are calculated
following Eq. (6) and Eq. (7), respectively:
28
Wahab L. et al. Factors Influencing the Adoption of Electric Vehicle: The Case of Electric Motorcycle in Nor thern Ghana
(1 )
ij
jk ij ij
ijk
PPP
x
β
∂= −
∂
(6)
ij
jk ij iq
ijk
PPP
x
β
∂= −
∂
(7)
The direct marginal ef fect Eq. (6) represents
the effect that a unit change in xijk has on the
probabil ity of outcome j (denoted by Pij). The
cross-marginal effec t Eq. (7) shows the ef fect
of a unit change in variable k of alternative
j (j≠q) on the probability (Piq) of outcome
q. For indicator variables, the marginal
effects are computed as the difference in
the estimated probabilities with the indicator
variables changing from zero to one (rather
than a unit change). The final ma rginal effect
of a var iable is calculated as the sum mation of
the marginal effects for each class weighted
by their posterior latent class probabilities.
4. Results and Discussion
Table 2 below shows the summary and the
results of the var iance inf lation factor (VIF)
test of the variables in this study. Based on
the data sample, 74.9% are males; this means
that more males ride a motorcycle than
females. The mean age of the motorcycl ists is
approximately 40 years wit h 19 years been the
minimum and 60 years the maximum. This
result shows that that riding of motorcycle
cut across the youth and the aged. On the
max imum education of the respondents, it was
revealed that an average educational level in
the study is senior high school graduate. On
average t he households conta in approxi mately
three persons with one person been the
minimum and seven persons the max imum.
The average charging time of battery 3.13
hours and the average lifespan of the battery
is 3.02 years. This study employs the VIF to
determine the multicollinearity problem in
the model. Kutner et al. (2004) and Khatoon
et al. (2013) suggested that multicollinearit y is
only se vere when VI F is greater than 10. I n this
study, the reported VIFs are each less than 10,
which indicate no mult icollinea rity among t he
ex planator y variable s as shown in Table 2 below.
Table 2
Summary and the Results of the VIF Test of the Variables
Var iabl e Mean S td. De v. Min Max VIF 1/VIF
Gender 0.749 0.434 0 1 1.03 0.97
Age 39.695 10.718 19 60 1.14 0.88
Maximum education 2.732 1.232 1 5 1.14 0.88
Monthly income 2.721 1.456 1 6 1.15 0.87
Household size 2.417 1.521 1 7 1.04 0.96
Charging time of a battery 3.134 1.515 1 6 1.13 0.89
Lifespan of battery 3.017 1.356 1 7 1.04 0.96
Riding pleasure 0.963 0.190 0 1 1.11 0.90
Operating cost 0.449 0.498 0 1 1.20 0.83
Perception of the price 0.631 0.483 0 1 1.05 0.95
Environmental condition 0.490 0.500 0 1 1.08 0.93
Government subsidies 0.749 0.434 0 1 1.08 0.93
Performance of electric
motorcycle 0.428 0.495 0 1 1.29 0.78
High usage to cover 200 Km 0.523 0.500 0 1 1.09 0.91
Public charging infrastructure 0.499 0.500 0 1 1.22 0.82
Maximum distance is less
than 100 Km
0.446 0.498 0 1 1.45 0.69
Sample number 537
29
International Journal for Traffic and Transport Engineering, 2019, 9(1): 22 - 37
The estimated coefficients, odds ratios, and
marg inal effect s of the fitted Logit and Probit
models of adoption of electric motorc ycle are
presented in Table III below. Although the
coefficients are different, the odds ratios-
the ratio of the probability of y=1 and the
probability of y=0 are almost the same for
Logit and Probit models (Long and Freese,
2014).
The results identify that perception of the
price, govern ment subsidies, the per formance
of the electric motorcycle, high usage to
cover 200 Km, and maximum distance is
less than 100 Km can significantly impact
electric motorcycle adoption behavior.
Based on both the Logit and Probit models,
other variables in the model: gender, age,
maximum education, monthly income,
household size, charging time of battery,
lifespan of battery, riding pleasure, operat ing
cost, environmental condition, and public
charging infrastructure are not statistically
significant to explain the willingness to
adopt electric motorcycle. The estimated
coefficients of the independent variables
do not show the dynamics among the
outcomes. To deal with these questions,
the exponentiated values of the estimated
coefficient eβ referred to as the odds ratio
can be used to explore how variables affect
the choice of one outcome compared with
another outcome (Long and Freese, 2014).
Because the coefficient result only tells the
direction of change and not the probability
or magnitude of change (Long and Freese,
2014), marginal effects are analyzed and
included in Table 3.
The relative overall fit indices for the models
are shown in Table 3. The chi-squared
values, p-values, correct classification and
area under receiver operating characteristics
(ROC) curves were considered to measure
the goodness of fit of the models. These fit
indices provide values that imply a good
model fit to the data set. Only the parameters
of the sig nifica nt variables in the L ogit model
were used to simplif y the presentation results
and discussion.
The odds ratio value associated with the
perception of the price is 0.260. Hence,
when motorcyclist consider that the price
of an electric motorcycle is higher than the
price of a combust ion engine motorcycle, the
odds ratio of willingness to buy an electric
motorcycle decreases by 3.846 (1/0.260)
and, therefore, motorcyclists are 3.846 less
times likely to buy an electric motorcycle,
when other variables are controlled.
According to marginal effects, setting all
the other variables the same, the predicted
probability of adopting electric motorcycle
is decreased by 18.2% for a unit increase in
the cost of the electric motorcycle.
The results of the fitted model indicate
that the presence of government subsidies
will be more likely to increase the odds
ratio of willingness to adopt the electric
motorcycle by almost 3.357 times compared
to combustion engine motorcycle when
other variables are constant. On the average,
the government subsidies increased the
predicted probability of adopting electric
motorcycle by 16.3%.
30
Wahab L. et al. Factors Influencing the Adoption of Electric Vehicle: The Case of Electric Motorcycle in Nor thern Ghana
Table 3
Estimated Parameters of Logit and Probit Models
Var iabl e Logit
Coefficient
Probit
Coefficient
Logit
Odds
Ratio
Probit
Odds
Ratio
Marg inal
effec t
(Log it)
Marg inal
effec t
(Probit)
Gender 0.078 (0.280) 0.044 (0.160) 1.081 1.045 0.010 0.010
Age -0.020 (0011) -0.011 (0.006) 0.980 0.989 -0.003 -0.003
Maximum education -0.153 (0.103) -0.085 (0.059) 0.858 0.918 -0.021 -0.020
Monthly income 0.140 (0.086) 0.080 (0.049) 1.150 1.084 0.019 0.019
Household size 0.070 (0.081) 0.048 (0.047) 1.072 1.049 0.009 0.011
Charging time of battery 0.125 (0.084) 0.072 (0.048) 1.133 1.074 0.017 0.017
Lifespan of battery -0.032 (0.087) -0.022 (0.050) 0.969 0.979 -0.004 -0.005
Riding pleasure -0.505 (0.685) -0.336 (0.384) 0.603 0.714 -0.068 -0.080
Operating cost 0.267 (0.260) 0.155 (0.149) 1.306 1.168 0.036 0.037
Perception of the price -1.346 (0.268) ** -0.794 (0.152) ** 0.260 0.452 -0.182 -0.188
Environmental condition -0.001 (0.245) -0.019 (0.140) 0.999 0.981 -0.000 -0.005
Government subsidies 1.211 (0.283) ** 0.685 (0.162) ** 3.357 1.984 0.163 0.162
Performance of electric
motorcycle 1.986 (0.302) ** 1.105 (0.161) ** 7.283 3.019 0.268 0.262
High usage to cover 200 Km -0.553 (0.256) * -0.292 (0.143) * 0.575 0.747 -0.075 -0.069
Public charging
infrastructure -0.242 (0.256) -0.148 (0.148) 0.785 0.863 -0.033 -0.035
Maximum distance is less
than 100 Km 1.469 (0.292) ** 0.857 (0.163) ** 4.345 2.355 0.198 0.203
Constant 0.873 (0.994) 0.522 (0.567)
Number of Observations: 537 537
Log-likelihood at Zero: -327.097 -327.097
Log-likelihood at
Convergence: -224.934 -225.662
LR Chi-Square Test: 204.325 202.869
p-value: 0.000 0.000
Pseudo R-squared: 0.312 0.310
Akaike’s Inf. Criterion 483.868 485.325
Correct classifications 82.50% 82.12%
The area under the ROC
curve 0.8516 0.8518
* >95% level of significance. ** >99% level of significance
31
International Journal for Traffic and Transport Engineering, 2019, 9(1): 22 - 37
The odds ratio value associated with the
performance of an electric motorcycle
is 7.283. Hence, when motorcyclist
consider that the performance of an
electric motorcycle is better than that
of a combustion engine motorcycle, the
odds ratio of willingness to buy an electric
motorcycle increases by 7.283 and, therefore,
motorcyclists are 7.283 more times likely
to adopt an electric motorcycle, holding
all other variables constant. The most
significant var iable is the performance of the
electric motorcycle, which results in a 26.8%
increment in the probability of adopting an
electric motorcycle.
The odds ratio of t he high usage to cover 200
Km is 0.575. Th is result means that when the
usage is high to cover 200 K m motorcyclist,
the odds ratio of willingness to adopt an
electric motorcycle decreases by 1.739
(1/0.575), so motorcyclists are 1.739 less
time likely to adopt an electric motorcycle,
holding all other variables constant. On the
average, the high usage to cover 200 Km
decreased the probability of adopting electric
motorcycle by 7.5%.
The odds ratio value associated with the
maximum distance is less than 100 Km is
4.345. Hence, when motorcyclists thi nk that
the distance which an electric motorcycle
can go without recharging is less than 100
km, the odds ratio of willingness to adopt
an electric motorcycle increases by 4.345
and, therefore, motorcyclists are 4.345 more
times likely to buy an electric motorcycle,
when other var iables are constant. According
to marginal effects, setting all the other
variables the same, the predicted probability
of adopting electric motorcycle is increased
by 19.8%.
5. Conclusion
The primary aim of this study is to discover
to what extent some factors are critical
to explaining Ghanaian motorcyclists’
willingness to adopt an electric motorcycle.
A survey is used to obtain a motorcyclist
profile’s factors together with other barriers
to adoption electric motorcycle by using
multistage sampling techniques. When
designing the su rvey questionnaire, prev ious
literature and the realistic situation of Ghana
were considered. T he explanatory variables
considered in this study are Gender, Age,
Maximum education, Monthly income,
Household size, Charging time of battery,
Lifespan of battery, Riding pleasure,
Operating cost, Perception of the price,
Environmental concern, Government
subsidies, Public charging of infrastructure,
Performance of the electric motorcycle,
High usage to cover 200 Km, and Maximum
distance is less t han 100 Km. Both Logit a nd
Probit regression ana lyses were conduc ted to
explain the willingness to adopt an electric
motorcycle for 537 motorcyclists these
variables. Both models gave the same signs/
direction of change; the differences in the
coefficients are not much and could not a lter
the interpretation of the results.
The estimated models indicate that factors
such as; perception of the price, government
subsidies, the performance of the electric
motorcycle, high usage to cover 200 Km,
and maximum distance is less than 100 Km
were found to have statistical significance
in motorcyclists’ adoption of an electric
motorcycle. Other factors such as; gender,
age, maximum education, monthly income,
household size, charging time of the battery,
the lifespan of the battery, riding pleasure,
32
Wahab L. et al. Factors Influencing the Adoption of Electric Vehicle: The Case of Electric Motorcycle in Nor thern Ghana
operating cost, environmental condition,
and public charging infrastructure was not
statistically significant. Based on these
findings, both policymakers and electric
motorcycle manufacturers might design
specific strategies for inducing Ghanaian
consu mers to be potentia l electric motorcyc le
adopters.
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