Understanding Behavioral Intention to Adopt Electric Vehicles Among Motorcycle Taxi Pilots: A PLS-SEM Approach
Abstract
1. Introduction
1.1. Role of Electric Vehicles (EVs) in the Transportation Sector
1.2. Current Status of Electric Vehicles (EVs) in India
1.3. Motorcycle Taxis in Goa
2. Theoretical Framework and Hypotheses Development
2.1. Theoretical Framework
2.2. Hypotheses Development
2.2.1. Behavioral Intention (BI) to Adopt the EVs
2.2.2. Charging Infrastructure (CI)
2.2.3. Effort Expectancy (EE)
2.2.4. Price Value (PV)
2.2.5. Performance Expectancy (PE)
2.2.6. Satisfaction with Incentive Policies (SIP)
2.2.7. Social Influence (SI)
3. Materials and Methods
3.1. Questionnaire Design
3.2. Variables Used for the Study
3.3. Sampling Method
3.4. Techniques and Tools Used
4. Results
4.1. Demographic Profile
4.2. Descriptive Statistics
4.3. Measurement Model
4.4. Structural Model
5. Discussion, Managerial Implications, and Future Scope
5.1. Discussion
5.2. Managerial Implications
5.3. Future Scope
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Abbreviations
BI | Behavioral Intention |
CO2 | Carbon Dioxide |
CI | Charging Infrastructure |
EE | Effort Expectancy |
E2W | Electric Two-Wheeler |
EVs | Electric Vehicles |
GHG | Greenhouse Gas |
HTMT | Heterotrait–Monotrait |
ICEV | Internal Combustion Engine Vehicle |
PLS-SEM | Partial Least Squares-Structural Equation Modeling |
PE | Performance Expectancy |
PV | Price Value |
UTAUT | Unified Theory of Acceptance and Use of Technology |
UTAUT 2 | Unified Theory of Acceptance and Use of Technology 2 |
SIP | Satisfaction with Incentive Policies |
SI | Social Influence |
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Constructs | Meaning | Statements | Source |
---|---|---|---|
Behavioral Intention (BI) | It refers to an individual’s or organisation’s expressed interest or plan to use a particular product, service, or technology. | BI 1: I intend to adopt an electric taxi. BI 2: I plan to use an electric taxi whenever possible. BI 3: I predict I will adopt an electric taxi. BI 4: I will always try to adopt an electric taxi for passenger transportation | [2,10,40] |
Charging Infrastructure (CI) | Charging infrastructure relates to the adequacy of the city’s public charging infrastructure. | CI 1: The charging facilities for electric taxis are sufficient. CI 2: Maintenance facilities for electric taxis are sufficient. CI 3: The parking lots with charging piles for electric taxis are sufficient. | [52,53,54] |
Effort Expectancy (EE) | It refers to the degree of ease associated with using the system. | EE 1: Learning to drive an electric taxi will be easy for me. EE 2: I find it will be easy to charge an electric taxi. EE 3: My interaction with electric taxis is clear and understandable. | [38,40] |
Performance Expectancy (PE) | It refers to the effectiveness and benefits that could be gained with innovative applications, e.g., saving time and effort, improving efficiency, accessibility, and convenience, and providing customised services | PE 1: Electric taxis will be an efficient tool for my work. PE 2: I can provide service with electric taxis. PE 3: More passengers will favour my electric taxi service. | [19,40,45,54,55] |
Price Value (PV) | It is defined as the consumer’s cognitive trade-off between the perceived benefits and the cost of using various applications. | PV 1: The price of using an electric taxi is reasonable. PV 2: Using an electric taxi is worth the money. PV 3: Electric taxis have a high use value at current prices. | [17,37,38] |
Satisfaction with Incentive Policies (SIP) | It is defined as the degree to which people are satisfied with the incentive policies for electric taxis. | SIP 1: I will be satisfied with the purchase subsidy policies for electric taxis. SIP 2: I will be satisfied with the operation subsidy policies for electric taxis. SIP 3: I will be satisfied with the information provision policies of electric taxis. SIP 4: I will be satisfied with the facilitation policies of electric taxis. | [8,37,54] |
Social Influence (SI) | It refers to the degree to which others believe the user should adopt the new system or technology. | SI 1: People who are important to me think I should use an electric taxi. SI 2: Drivers using electric taxis will be considered environmentally friendly. SI 3: Drivers around me consider it appropriate to use electric taxis. | [17,40,45] |
District | Place of Registration Offices | No. of Registered Pilots | Target Sample | Actual Data Collected |
---|---|---|---|---|
North Goa | Panaji | 389 | 61 | 50 |
Bicholim | 101 | 16 | 17 | |
Mapusa | 339 | 53 | 34 | |
South Goa | Margao | 803 | 126 | 80 |
Ponda | 216 | 34 | 41 | |
Vasco | 211 | 33 | 20 | |
Total | 2059 | 324 | 242 |
Demographic | Frequency | % | |
---|---|---|---|
Education | Up to Primary | 60 | 24.80 |
Secondary | 166 | 68.60 | |
Higher Secondary | 16 | 6.60 | |
Place of residence | North Goa | 101 | 41.70 |
South Goa | 141 | 58.30 | |
Source of funds to purchase vehicles | Own Savings | 74 | 30.60 |
Help from Friends/Family | 14 | 5.80 | |
Funds Under the Scheme | 24 | 9.90 | |
Loan | 129 | 53.30 | |
Others | 1 | 0.40 |
Minimum | Maximum | Mean | Standard Deviation | |
---|---|---|---|---|
Age (Years) | 25 | 80 | 52.43 | 10.02 |
Number of dependent members in the family | 1 | 10 | 4.20 | 1.53 |
Working experience (Years) | 1 | 54 | 21.93 | 11.98 |
Working hours per day | 3 | 18 | 9.65 | 2.48 |
Daily income (INR) | 150 | 1000 | 563.84 | 188.97 |
Constructs | Variable Code | Variance Inflation Factor (VIF) | Factor Loading | Cronbach’s Alpha | Composite Reliability (CR) | Average Variance Extracted (AVE) |
Behavioral Intention (BI) | BI 1 | 1.485 | 0.749 | 0.877 | 0.881 | 0.733 |
BI 2 | 2.966 | 0.903 | ||||
BI 3 | 2.984 | 0.877 | ||||
BI 4 | 3.305 | 0.887 | ||||
Charging Infrastructure (CI) | CI 1 | 1.895 | 0.889 | 0.839 | 0.871 | 0.753 |
CI 2 | 2.089 | 0.846 | ||||
CI 3 | 1.971 | 0.868 | ||||
Effort Expectancy (EE) | EE 1 | 1.754 | 0.875 | 0.821 | 0.842 | 0.734 |
EE 2 | 1.856 | 0.829 | ||||
EE 3 | 1.926 | 0.866 | ||||
Performance Expectancy (PE) | PE 1 | 2.394 | 0.901 | 0.869 | 0.871 | 0.792 |
PE 2 | 2.389 | 0.889 | ||||
PE 3 | 2.114 | 0.879 | ||||
Price Value (PV) | PV 1 | 1.860 | 0.868 | 0.803 | 0.826 | 0.717 |
PV 2 | 1.971 | 0.890 | ||||
PV 3 | 1.544 | 0.779 | ||||
Satisfaction with Incentive Policies (SIP) | SIP 1 | 2.427 | 0.873 | 0.881 | 0.922 | 0.729 |
SIP 2 | 2.684 | 0.851 | ||||
SIP 3 | 2.666 | 0.846 | ||||
SIP 4 | 1.755 | 0.846 | ||||
Social Influence (SI) | SI 1 | 2.414 | 0.887 | 0.872 | 0.876 | 0.795 |
SI 2 | 2.193 | 0.892 | ||||
SI 3 | 2.368 | 0.896 |
BI | CI | EE | PE | PV | SIP | SI | |
---|---|---|---|---|---|---|---|
Behavioral Intention (BI) | 0.856 | ||||||
Charging Infrastructure (CI) | 0.239 | 0.868 | |||||
Effort Expectancy (EE) | 0.270 | 0.674 | 0.857 | ||||
Performance Expectancy (PE) | 0.434 | 0.492 | 0.451 | 0.890 | |||
Price Value (PV) | 0.557 | 0.377 | 0.349 | 0.516 | 0.847 | ||
Satisfaction with Incentive Policies (SIP) | 0.249 | 0.446 | 0.400 | 0.436 | 0.381 | 0.854 | |
Social Influence (SI) | 0.363 | 0.338 | 0.248 | 0.614 | 0.420 | 0.350 | 0.892 |
BI | CI | EE | PE | PV | SIP | SI | |
Behavioral Intention (BI) | |||||||
Charging Infrastructure (CI) | 0.270 | ||||||
Effort Expectancy (EE) | 0.312 | 0.815 | |||||
Performance Expectancy (PE) | 0.494 | 0.570 | 0.534 | ||||
Price Value (PV) | 0.652 | 0.456 | 0.430 | 0.627 | |||
Satisfaction with Incentive Policies (SIP) | 0.264 | 0.512 | 0.468 | 0.482 | 0.428 | ||
Social Influence (SI) | 0.411 | 0.393 | 0.290 | 0.705 | 0.512 | 0.378 |
Relationships | β | T-Statistics | p-Values | Inference |
---|---|---|---|---|
H1: Charging Infrastructure -> Behavioral Intention | −0.089 | 1.251 | 0.211 | Unsupported |
H2: Effort Expectancy-> Behavioral Intention | 0.086 | 1.224 | 0.221 | Unsupported |
H3: Performance Expectancy -> Behavioral Intention | 0.160 | 2.056 | 0.040 | Supported |
H4: Price Value-> Behavioral Intention | 0.447 | 5.030 | 0.000 | Supported |
H5: Satisfaction with Incentive Policies -> Behavioral Intention | −0.019 | 0.362 | 0.718 | Unsupported |
H6: Social Influence -> Behavioral Intention | 0.090 | 1.260 | 0.208 | Unsupported |
R-Squared | Effect Size (f2) | Rating |
---|---|---|
Behavioral Intention: | ||
Charging Infrastructure | 0.006 | Small |
Effort Expectancy | 0.006 | Small |
Performance Expectancy | 0.018 | Small |
Price Value | 0.208 | Moderate |
Satisfaction with Incentive Policies | 0.000 | Small |
Social Influence | 0.008 | Small |
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© 2025 by the authors. Published by MDPI on behalf of the World Electric Vehicle Association. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Sukthankar, S.; Fernandes, R.; Korde, S.; Gaonkar, S.; Kurtikar, D. Understanding Behavioral Intention to Adopt Electric Vehicles Among Motorcycle Taxi Pilots: A PLS-SEM Approach. World Electr. Veh. J. 2025, 16, 309. https://doi.org/10.3390/wevj16060309
Sukthankar S, Fernandes R, Korde S, Gaonkar S, Kurtikar D. Understanding Behavioral Intention to Adopt Electric Vehicles Among Motorcycle Taxi Pilots: A PLS-SEM Approach. World Electric Vehicle Journal. 2025; 16(6):309. https://doi.org/10.3390/wevj16060309
Chicago/Turabian StyleSukthankar, Sitaram, Relita Fernandes, Shilpa Korde, Sadanand Gaonkar, and Disha Kurtikar. 2025. "Understanding Behavioral Intention to Adopt Electric Vehicles Among Motorcycle Taxi Pilots: A PLS-SEM Approach" World Electric Vehicle Journal 16, no. 6: 309. https://doi.org/10.3390/wevj16060309
APA StyleSukthankar, S., Fernandes, R., Korde, S., Gaonkar, S., & Kurtikar, D. (2025). Understanding Behavioral Intention to Adopt Electric Vehicles Among Motorcycle Taxi Pilots: A PLS-SEM Approach. World Electric Vehicle Journal, 16(6), 309. https://doi.org/10.3390/wevj16060309