Inhibitors of Battery Electric Vehicle Adoption in Morocco
Abstract
:1. Introduction
1.1. Background
- SDG 3: to establish and ensure good health and well-being.
- SDG 7: to provide and ensure access to affordable and clean energy.
- SDG 11: to organize sustainable communities and cities.
- SDG 13: to combat climate change and its impacts on the planet.
1.2. Worldwide CO2 Emissions: Transportation Sector
1.3. An Overview of Morocco’s Automotive Industry
1.4. Understanding the Barriers to BEV Adoption in Morocco
2. Literature Review and Contribution
2.1. Literature Review on BEV Adoption in Various Countries and Regions
2.2. The Common Barriers to the Adoption of BEVs
- Infrastructure-related issues: The availability, positioning, and speed of charging stations are significant barriers to BEV adoption. The lack of reliable public charging options and the disparity between the number of BEVs and the number of public charging stations contribute to the challenge of addressing range anxiety and increasing BEV ownership [15,38,39]. Moreover, the ability to retrofit existing infrastructure to support BEVs also poses an issue [33].
- High upfront costs: The upfront purchase cost of BEVs is a barrier to adoption. The cost of purchasing a BEV, especially compared to internal combustion engine vehicles, remains a concern for many potential buyers [15].
2.3. Focus on the Key Challenges and Opportunities for the Adoption of BEVs in Morocco
2.4. Paper Contribution
3. Materials and Methods
3.1. Conceptual Framework
3.1.1. Motivation
3.1.2. Research Scope
- (i)
- The study strictly analyzes data regarding battery electric vehicles and does not include electric scooters, buses, bikes, hybrid electric vehicles, or plug-in hybrid electric vehicles.
- (ii)
- The study targets individuals who reside in Morocco and does not include any individuals residing in any other country.
3.1.3. Research Questions
- (i)
- What are the constructs that inhibit or discourage the increased adoption of battery electric vehicles in Morocco?
- (ii)
- What are the constructs that encourage the adoption of battery electric vehicles in the country?
- (iii)
- Are there any misconceptions or lack of knowledge within the topic of battery electric vehicles in the country?
3.1.4. Research Objectives
- (i)
- To assess the knowledge and intentions of individuals residing within Morocco regarding BEVs and their adoption.
- (ii)
- To determine both the constructs that encourage and inhibit the adoption of BEVs in Morocco.
- (iii)
- To evaluate the relationship between constructs related to the adoption of BEVs in Morocco using structural equation modelling.
3.1.5. Hypotheses
3.2. Data Collection
3.2.1. Instruments and Measures
3.2.2. Participants
3.2.3. Procedures
3.2.4. Limitations of the Data Collection Procedure
3.2.5. Smart-PLS 4 Methods and Procedures
4. Results and Discussion
4.1. Questionnaire Results
4.1.1. First Part of the Questionnaire—Demographic Profiles
4.1.2. Second Part of the Questionnaire—Main Study Questions
4.2. Statistical Analysis and Results of Smart-PLS 4
4.2.1. Data Assessment for Reliability and Validity
4.2.2. Hypothesis Testing
4.2.3. Importance–Performance Map Analysis (IPMA)
4.3. Discussions and Implications of the Study
4.4. Limitations of the Study
5. Conclusions
- The population of Morocco should be made aware of more affordable options available within the country as only being aware of expensive BEVs would not allow them to seriously consider adopting one.
- A larger selection of BEVs should be made available for individuals with a focus on design variety. There should also be a focus on producing and offering BEVs that are more affordable to the average Moroccan.
- Focus should be placed on opening facilities that can maintain BEVs and the necessary technicians must be trained to be equipped to handle maintenance issues.
- The government should continue its effort in increasing the number of charging stations in the country to reduce range anxiety. Moreover, continued efforts to adapt the infrastructure for the adoption of BEVs are encouraged.
- The government should also create more incentives for owning and operating BEVs and make the population aware of them.
- A campaign should be created to correct misinformation regarding the benefits of BEVs to the environment and to spread awareness regarding the importance of the environment.
- Marketing of BEVs should include an element that shines a light on positive social reinforcement as it drives individuals to purchasing BEVs.
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
Abbreviations
Aut | Autonomy |
AVE | Average Variance Extracted |
Bac | Baccalaureate |
BehF | Behavioral Factor |
Carbon Dioxide | |
Des | Design |
EnvC | Environmental Concern |
BEV | Battery Electric Vehicle |
FinA | Financial Attributes |
HTMT | Heterotrait–Monotrait |
Inc | Incentives |
IPMA | Importance–Performance Map Analysis |
Maint | Maintenance |
NDC | Nationally Determined Contribution |
PLS-SEM | Partial Least Squares Structural Equation Modelling |
SDG | Sustainable Development Goal |
SEM | Structural Equation Modelling |
SocR | Social Reinforcement |
Will | Willingness to Adopt EV |
Appendix A. Copy of the Questionnaire in English
- ○
- Female
- ○
- Male
- ○
- 18–25
- ○
- 26–30
- ○
- 31–40
- ○
- 41–50
- ○
- 51–60
- ○
- 60+
- ○
- Tangier
- ○
- Casablanca
- ○
- Rabat
- ○
- Fes
- ○
- Meknes
- ○
- Kenitra
- ○
- Marrakech
- ○
- Agadir
- ○
- Other:
- ○
- High School Diploma (Baccalaureate)
- ○
- Baccalaureate +2
- ○
- Baccalaureate +3
- ○
- Bachelor’s Degree (Baccalaureate+4)
- ○
- Master’s Degree
- ○
- Doctorate Degree
- ○
- Educational
- ○
- Health Services
- ○
- Manufacturing
- ○
- Information Technology
- ○
- Engineering
- ○
- Agricultural
- ○
- Administration
- ○
- Social/Governmental
- ○
- Business Owner
- ○
- Student
- ○
- No employment
- ○
- Other:
- ○
- 3000–8000 MAD
- ○
- 8001–13,000 MAD
- ○
- 13,001–18,000 MAD
- ○
- 18,001–25,000 MAD
- ○
- More than 25,000 MAD
- ○
- No income
- ○
- I do not drive
- ○
- Less than once a week (when necessary)
- ○
- 2–3 times a week
- ○
- 4–6 times a week
- ○
- Every day of the week
- ○
- Less than 200,000 MAD
- ○
- 200,000–400,000 MAD
- ○
- 400,001–600,000 MAD
- ○
- 600,001–800,000 MAD
- ○
- 800,001–1,000,000 MAD
- ○
- More than 1,000,000 MAD
- ○
- 200,000–400,000 MAD
- ○
- 400,001–600,000 MAD
- ○
- 600,001–800,000 MAD
- ○
- 800,001–1,000,000 MAD
- ○
- 1,000,001–1,600,000 MAD
- ○
- Strongly Disagree
- ○
- Disagree
- ○
- Neutral
- ○
- Strongly Agree
- ○
- Agree
- ○
- Strongly Disagree
- ○
- Disagree
- ○
- Neutral
- ○
- Strongly Agree
- ○
- Agree
Affects My Decision | Does Not Affect My Decision | I Don’t Know | |
Time to charge | o | o | o |
Number of available charging stations | o | o | o |
Cost of vehicle | o | o | o |
Maintenance | o | o | o |
Maximum Speed | o | o | o |
Safety | o | o | o |
Travel Range * of the vehicle | o | o | o |
Style/Design | o | o | o |
- ○
- Positively Impact
- ○
- Negatively Impact
- ○
- No Impact
- ○
- Not Important
- ○
- Somewhat Important
- ○
- Neutral
- ○
- Important
- ○
- Very Important
- ○
- Yes
- ○
- No
- ○
- Yes
- ○
- No
- ○
- Strongly Disagree
- ○
- Disagree
- ○
- Neutral
- ○
- Strongly Agree
- ○
- Agree
- ○
- Yes
- ○
- No
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Grid Reinforcement | |
---|---|
2019 | 2020 |
Installation of a third transformer of 400/225 kV | Setup of a 3-phase line of 400 kV (300 km) |
Installation of a third autotransformer of 400/225 kV | Setup of a 3-phase line of 400 kV (300 km) |
Setup of a 3-phase line of 400 kV (55 km) | Setup of a 3-phase line of 400 kV |
Setup of a 3-phase line of 400 kV (400 km) | Addition of a new post of 400/225 kV |
Replacement of a 225/60 kV transformer by a 400/225 kV transformer |
Constructs | Indicators/Items | Question from Survey |
---|---|---|
Age | Age | 2 |
Autonomy | Aut1 | 12.1 |
Aut2 | 12.2 | |
Aut3 | 12.7 | |
Behavioral Factor | BehF | 7 |
Design | Des1 | 12.5 |
Des2 | 12.6 | |
Des3 | 12.8 | |
Education | Education | 4 |
Environmental Concern 1 | EnvC1 | 14 |
Environmental Concern 2 | EnvC2 | 15 |
Financial Attributes | FinA1 | 8 |
FinA2 | 9 | |
FinA3 | 10 | |
FinA4 | 12.3 | |
Gender | Gender | 1 |
Incentive 1 | Inc1 | 16 |
Incentive 2 | Inc2 | 17 |
Income | Income | 6 |
Maintenance | Maint1 | 11 |
Maint2 | 12.3 | |
Social Reinforcement | SocR | 13 |
Willingness to Adopt BEVs | Will | 18 |
Variables | Frequency | Percentage |
---|---|---|
Gender | ||
Male | 112 | 54% |
Female | 97 | 46% |
Age | ||
18–25 | 137 | 65% |
26–30 | 12 | 6% |
31–40 | 19 | 9% |
41–50 | 13 | 6% |
60 or more | 28 | 14% |
Educational Level | ||
High School Diploma (Baccalaureate) | 57 | 27% |
Baccalaureate +2 | 15 | 7% |
Baccalaureate +3 | 11 | 5% |
Bachelor’s Degree | 58 | 28% |
Master’s Degree | 45 | 22% |
Doctorate Degree | 23 | 11% |
Monthly Income | ||
No Income | 99 | 47% |
MAD 3000–8000 | 41 | 20% |
MAD 8001–13,000 | 26 | 13% |
MAD 13,000–18,000 | 11 | 5% |
MAD 18,001–25,000 | 9 | 4% |
More than MAD 25,000 | 23 | 11% |
Question | Factors | Affects | Does Not Affect | I Do Not Know |
---|---|---|---|---|
12.1 | Time to change | 151 | 36 | 22 |
12.2 | Number of available charging stations | 172 | 20 | 17 |
12.3 | Cost of vehicle | 150 | 39 | 20 |
12.4 | Maintenance | 159 | 37 | 13 |
12.5 | Maximum speed | 69 | 120 | 21 |
12.6 | Safety | 155 | 42 | 12 |
12.7 | Travel range of vehicle | 165 | 28 | 16 |
12.8 | Style and design | 126 | 68 | 15 |
Constructs | Indicators/Items | Factor Loadings | Cronbach’s Alpha | Composite Reliability (ρc) | Average Variance Extracted (AVE) |
---|---|---|---|---|---|
Age | Age | 1.000 | 1.000 | 1.000 | 1.000 |
Autonomy | Aut1 | 0.894 | 0.651 | 0.787 | 0.557 |
Aut2 | 0.664 | ||||
Aut3 | 0.656 | ||||
Behavioral Factor | BehF | 1.000 | 1.000 | 1.000 | 1.000 |
Design | Des1 | 0.876 | 0.497 | 0.795 | 0.661 |
Des3 | 0.745 | ||||
Education | Education | 1.000 | 1.000 | 1.000 | 1.000 |
Environmental Concern 1 | EnvC1 | 1.000 | 1.000 | 1.000 | 1.000 |
Environmental Concern 2 | EnvC2 | 1.000 | 1.000 | 1.000 | 1.000 |
Financial Attributes | FinA2 | 0.589 | 0.306 | 0.725 | 0.580 |
FinA3 | 0.901 | ||||
Gender | Gender | 1.000 | 1.000 | 1.000 | 1.000 |
Incentive 1 | Inc1 | 1.000 | 1.000 | 1.000 | 1.000 |
Incentive 2 | Inc2 | 1.000 | 1.000 | 1.000 | 1.000 |
Income | Income | 1.000 | 1.000 | 1.000 | 1.000 |
Maintenance | Maint1 | 1.000 | 1.000 | 1.000 | 1.000 |
Maint2 | 1.000 | 1.000 | 1.000 | 1.000 | |
Social Reinforcement | SocR | 1.000 | 1.000 | 1.000 | 1.000 |
Willingness to Adopt EVs | Will | 1.000 | 1.000 | 1.000 | 1.000 |
Indicators/Items | VIF |
---|---|
Age | 1.000 |
Aut1 | 1.242 |
Aut2 | 1.388 |
Aut3 | 1.251 |
BehF | 1.000 |
Des1 | 1.123 |
Des3 | |
Education | 1.000 |
EnvC1 | |
EnvC2 | |
FinA2 | 1.034 |
FinA3 | |
Gender | 1.000 |
Inc1 | |
Inc2 | |
Income | |
Maint1 | |
Maint2 | |
SocR | |
Will |
Path of Constructs | VIF |
---|---|
Age → Social Reinforcement | 1.000 |
Age → Willingness to Adopt EV | 1.984 |
Autonomy → Willingness to Adopt EV | 1.347 |
Behavioral Factor → Willingness to Adopt EV | 1.701 |
Design → Willingness to Adopt EV | 1.220 |
Education → Environmental Concern 1 | 1.000 |
Education → Environmental Concern 2 | 1.000 |
Education → Willingness to Adopt EV | 1.718 |
Environmental Concern 1 → Willingness to Adopt EV | 1.419 |
Environmental Concern 2 → Willingness to Adopt EV | 1.483 |
Financial Attributes → Willingness to Adopt EV | 1.358 |
Gender → Behavioral Factor | 1.000 |
Gender → Willingness to Adopt EV | 1.110 |
Incentive 1 → Willingness to Adopt EV | 1.201 |
Incentive 2 → Willingness to Adopt EV | 1.220 |
Income → Willingness to Adopt EV | 2.259 |
Maintenance → Willingness to Adopt EV | 1.425 |
Social Reinforcement → Willingness to Adopt EV | 1.243 |
Constructs | Age | Aut | BehF | Des | Education | EnvC1 | EnvC2 | FinA | Gender | Inc 1 | Inc 2 | Income | Maint | SocR | Will |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Age | 1.000 | ||||||||||||||
Aut | −0.156 | 0.746 | |||||||||||||
BehF | 0.492 | −0.091 | 1.000 | ||||||||||||
Des | −0.163 | 0.249 | −0.009 | 0.813 | |||||||||||
Education | 0.427 | −0.091 | 0.366 | −0.144 | 1.000 | ||||||||||
EnvC1 | 0.158 | 0.047 | −0.110 | −0.059 | 0.216 | 1.000 | |||||||||
EnvC2 | 0.075 | −0.132 | 0.005 | −0.144 | 0.148 | 0.403 | 1.000 | ||||||||
FinA | −0.117 | −0.090 | −0.065 | 0.022 | 0.017 | 0.149 | 0.369 | 0.761 | |||||||
Gender | 0.019 | −0.042 | −0.138 | 0.027 | −0.054 | 0.088 | 0.072 | −0.045 | 1.000 | ||||||
Inc 1 | −0.022 | 0.067 | 0.183 | 0.145 | −0.097 | −0.048 | −0.022 | 0.164 | −0.183 | 1.000 | |||||
Inc 2 | 0.027 | 0.082 | −0.178 | 0.003 | 0.046 | 0.259 | 0.220 | 0.224 | 0.084 | −0.054 | 1.000 | ||||
Income | 0.611 | −0.082 | 0.485 | −0.193 | 0.588 | 0.083 | 0.108 | 0.035 | −0.145 | 0.038 | 0.027 | 1.000 | |||
Maint | 0.117 | −0.355 | 0.067 | 0.069 | 0.148 | 0.168 | 0.145 | 0.256 | −0.073 | 0.149 | 0.018 | 0.082 | 1.000 | ||
SocR | 0.059 | −0.117 | 0.021 | −0.122 | 0.109 | 0.165 | 0.307 | 0.217 | 0.038 | 0.089 | 0.220 | 0.053 | 0.266 | 1.000 | |
Will | 0.134 | −0.157 | −0.045 | −0.184 | 0.174 | 0.316 | 0.442 | 0.374 | 0.042 | 0.129 | 0.229 | 0.111 | 0.277 | 0.367 | 1.000 |
Hypothesis | Path of Constructs | Hypothesis Test | |||
---|---|---|---|---|---|
Path Coefficient | p-Value | t-Statistic | Results | ||
H1 | Age → SocR | 0.059 | 0.339 | 0.956 | Not supported |
H2 | Age → Will | 0.055 | 0.116 | 1.574 | Not supported |
H3 | Aut → Will | −0.011 | 0.624 | 0.491 | Not supported |
H4 | BehF → Will | −0.052 | 0.118 | 1.563 | Not supported |
H5 | Des → Will | −0.054 | 0.036 | 2.094 | Supported |
H6 | Education → EnvC1 | 0.216 | 0.000 | 3.506 | Supported |
H7 | Education → EnvC2 | 0.050 | 0.031 | 2.154 | Supported |
H8 | Education → Will | 0.041 | 0.202 | 1.276 | Not supported |
H9 | EnvC1 → Will | 0.036 | 0.244 | 1.164 | Not supported |
H10 | EnvC2 → Will | 0.275 | 0.015 | 2.426 | Supported |
H11 | FinA → Will | 0.080 | 0.006 | 2.767 | Supported |
H12 | Gender → BehF | −0.276 | 0.047 | 1.989 | Supported |
H13 | Gender → Will | 0.025 | 0.617 | 0.500 | Not supported |
H14 | Inc1 → Will | 0.122 | 0.010 | 2.592 | Supported |
H15 | Inc2 → Will | 0.024 | 0.395 | 0.850 | Not supported |
H16 | Income → Will | −0.022 | 0.587 | 0.543 | Not supported |
H17 | Maint → Will | 0.042 | 0.111 | 1.594 | Not supported |
H18 | SocR → Will | 0.066 | 0.027 | 2.210 | Supported |
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Share and Cite
Nasreddin, D.; El Hafdaoui, H.; Jelti, F.; Boumelha, A.; Khallaayoun, A. Inhibitors of Battery Electric Vehicle Adoption in Morocco. World Electr. Veh. J. 2024, 15, 6. https://doi.org/10.3390/wevj15010006
Nasreddin D, El Hafdaoui H, Jelti F, Boumelha A, Khallaayoun A. Inhibitors of Battery Electric Vehicle Adoption in Morocco. World Electric Vehicle Journal. 2024; 15(1):6. https://doi.org/10.3390/wevj15010006
Chicago/Turabian StyleNasreddin, Dalal, Hamza El Hafdaoui, Faissal Jelti, Aya Boumelha, and Ahmed Khallaayoun. 2024. "Inhibitors of Battery Electric Vehicle Adoption in Morocco" World Electric Vehicle Journal 15, no. 1: 6. https://doi.org/10.3390/wevj15010006