Leveraging Fuzzy Set Qualitative Comparative Analysis to Explore Determinants of Intention to Use Self-Driving Vehicles in Ghana
Abstract
:1. Introduction
- What are the key conditions that are essential for determining high or low intention to use self-driving vehicles?
- What combinations of factors effectively explain high or not-high intentions to use self-driving vehicles?
- What is the most effective pathway to consider or avoid when explaining high and low intentions to use self-driving vehicles?
2. Theoretical Background and Literature Review
2.1. Perceived Reliability
2.2. Perceived Risk
2.3. Technological Competence
3. Methodology
3.1. Sampling and Data Collection
3.2. Measurement and Survey Instrument
3.3. Asymmetric Data Analysis
4. Results
4.1. Demographic Analysis
4.2. Measurement Model Assessment
4.3. Discriminant Validity
4.4. Contrarian Case Analysis to Justify the Use of fsQCA
4.5. Calibration in Fuzzy Set Qualitative Comparative Analysis
4.6. Analysis of Necessary Conditions (Necessity in Kind)
4.7. Analysis of Sufficiency
PU | PRel | PEOU | PRisk | TC | Number | IU | Raw Consist. | PRI Consist. | SYM Consist. |
---|---|---|---|---|---|---|---|---|---|
1 | 1 | 1 | 1 | 1 | 419 | 1 | 0.965434 | 0.944359 | 0.969274 |
1 | 1 | 1 | 0 | 1 | 21 | 1 | 0.962614 | 0.817092 | 0.81773 |
0 | 1 | 1 | 1 | 1 | 7 | 1 | 0.959368 | 0.742923 | 0.746762 |
1 | 1 | 0 | 0 | 1 | 3 | 1 | 0.95892 | 0.72032 | 0.720321 |
1 | 1 | 1 | 1 | 0 | 11 | 1 | 0.951248 | 0.715526 | 0.719293 |
0 | 1 | 1 | 0 | 1 | 7 | 1 | 0.955442 | 0.666658 | 0.666658 |
1 | 0 | 0 | 0 | 1 | 3 | 1 | 0.956265 | 0.654872 | 0.656809 |
0 | 1 | 0 | 1 | 1 | 3 | 1 | 0.956857 | 0.642587 | 0.642588 |
1 | 1 | 1 | 0 | 0 | 20 | 1 | 0.935009 | 0.640086 | 0.647627 |
0 | 0 | 1 | 1 | 1 | 4 | 1 | 0.950709 | 0.624993 | 0.624993 |
1 | 1 | 0 | 1 | 0 | 3 | 1 | 0.951694 | 0.610331 | 0.610332 |
1 | 1 | 0 | 0 | 0 | 3 | 1 | 0.931329 | 0.557212 | 0.557213 |
0 | 0 | 0 | 0 | 1 | 4 | 1 | 0.936451 | 0.536331 | 0.536332 |
1 | 0 | 0 | 0 | 0 | 9 | 0 | 0.911525 | 0.454325 | 0.454446 |
0 | 1 | 0 | 0 | 0 | 6 | 0 | 0.910579 | 0.446212 | 0.448355 |
0 | 0 | 0 | 1 | 0 | 3 | 0 | 0.924043 | 0.396408 | 0.397774 |
0 | 0 | 0 | 0 | 0 | 412 | 0 | 0.454247 | 0.069767 | 0.071651 |
PU | PRel | PEOU | PRisk | TC | Number | ~IU | Raw Consist. | PRI Consist. | SYM Consist. |
---|---|---|---|---|---|---|---|---|---|
0 | 0 | 0 | 0 | 0 | 412 | 1 | 0.943641 | 0.903936 | 0.928349 |
0 | 0 | 0 | 1 | 0 | 3 | 1 | 0.949683 | 0.600157 | 0.602226 |
0 | 1 | 0 | 0 | 0 | 6 | 1 | 0.927178 | 0.549009 | 0.551645 |
1 | 0 | 0 | 0 | 0 | 9 | 1 | 0.926294 | 0.54541 | 0.545554 |
0 | 0 | 0 | 0 | 1 | 4 | 0 | 0.926492 | 0.463668 | 0.463668 |
1 | 1 | 0 | 0 | 0 | 3 | 0 | 0.913583 | 0.442787 | 0.442788 |
1 | 1 | 0 | 1 | 0 | 3 | 0 | 0.924339 | 0.389667 | 0.389668 |
0 | 0 | 1 | 1 | 1 | 4 | 0 | 0.917852 | 0.375007 | 0.375007 |
0 | 1 | 0 | 1 | 1 | 3 | 0 | 0.922433 | 0.357411 | 0.357412 |
1 | 1 | 1 | 0 | 0 | 20 | 0 | 0.882315 | 0.348269 | 0.352373 |
1 | 0 | 0 | 0 | 1 | 3 | 0 | 0.91664 | 0.34218 | 0.343191 |
0 | 1 | 1 | 0 | 1 | 7 | 0 | 0.910888 | 0.333343 | 0.333342 |
1 | 1 | 0 | 0 | 1 | 3 | 0 | 0.894199 | 0.279679 | 0.279679 |
1 | 1 | 1 | 1 | 0 | 11 | 0 | 0.87648 | 0.279238 | 0.280708 |
0 | 1 | 1 | 1 | 1 | 7 | 0 | 0.881767 | 0.251937 | 0.253238 |
1 | 1 | 1 | 0 | 1 | 21 | 0 | 0.832829 | 0.182127 | 0.18227 |
1 | 1 | 1 | 1 | 1 | 419 | 0 | 0.39736 | 0.029937 | 0.030727 |
4.7.1. Configurations for High Intention to Use Self-Driving Vehicles
4.7.2. Configurations for Not-High Intention to Use Self-Driving Vehicles
4.7.3. Predictive Validity (XY Plots)
5. Discussion
5.1. Conditions Explaining High Intention to Use Self-Driving Vehicles in Ghana
5.2. Conditions Explaining Not-High (Low) Intention to Use Self-Driving Vehicles in Ghana
5.3. Theoretical Contributions
5.4. Practical Implications
5.5. Future Studies
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Abbreviations
SDVs | Self-Driving Vehicles |
AVs | Autonomous Vehicles |
TAM | Technology Acceptance Model |
fsQCA | Fuzzy Set Qualitative Comparative Analysis |
PU | Perceived Usefulness |
PEOU | Perceived Ease of Use |
PRel | Perceived Reliability |
PRisk | Perceived Risk |
TC | Technological Competence |
BI | Behavioral Intention |
IU | Intention to Use |
SEM | Structural Equation Modeling |
MSMEs | Micro-, Small-, and Medium-sized Enterprises |
CR | Composite Reliability |
AVE | Average Variance Extracted |
RI | Raw Consistency |
PRI | Proportional Reduction in Inconsistency |
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Profile | Number | Percentage (%) | |
Gender | Male | 854 | 68.42 |
Female | 394 | 31.57 | |
Age | 18–25 | 181 | 14.5 |
26–35 | 275 | 22.04 | |
36–45 | 454 | 36.38 | |
46–55 | 226 | 18.11 | |
56–65 | 112 | 8.97 | |
Educational Qualification | Senior High School (SHS) | 178 | 14.26 |
Bachelor’s degree | 628 | 50.32 | |
Master’s degree and above | 320 | 25.64 | |
Other | 122 | 9.77 | |
Annual Income (Gh₵) | ≤10,000 | 172 | 13.78 |
10,001–20,000 | 317 | 25.4 | |
20,001–30,000 | 297 | 23.8 | |
30,001–50,000 | 386 | 30.93 | |
≥50,001 | 76 | 6.09 | |
Driving Experience | |||
Car driving license holder | Yes | 1009 | 80.84 |
No | 239 | 19.15 | |
Years of driving experience | <1 year | 52 | 4.17 |
1–3 years | 168 | 13.46 | |
4–6 years | 287 | 23 | |
7–10 years | 486 | 38.94 | |
>10 years | 255 | 20.43 | |
Electric Vehicles (EVs) Experience | Yes | 333 | 26.68 |
No | 915 | 73.32 | |
Cruise Control Experience | Yes | 1002 | 80.29 |
No | 246 | 19.71 |
Code | Measurement Items | Loadings | Cronbach Alpha | CR | AVE |
---|---|---|---|---|---|
Perceived Reliability | |||||
PRel | Autonomous vehicles are reliable. | 0.868 | 0.917 | 0.941 | 0.883 |
PRel | I do not have suspicions about automated vehicles. | 0.846 | |||
PRel | I would engage in other tasks while riding in an automated vehicle. | 0.765 | |||
PRel | I feel hesitant about using an automated vehicle. | 0.932 | |||
Technological Competence | |||||
TC | I believe autonomous vehicles are equipped with advanced technology that can handle various driving situations. | 0.783 | 0.867 | 0.921 | 0.801 |
TC | I trust that the technology behind autonomous vehicles is reliable and efficient. | 0.836 | |||
TC | I feel confident that autonomous vehicles can perform tasks without human intervention. | 0.682 | |||
TC | I am convinced that autonomous vehicle technology is capable of adapting to different road conditions and environments. | 0.941 | |||
Perceived Ease of Use | |||||
PEOU | Learning to operate an autonomous vehicle would be easy for me. | 0.742 | 0.975 | 0.977 | 0.938 |
PEOU | I would find it easy to get an autonomous vehicle to do what I want. | 0.956 | |||
PEOU | Interacting with an autonomous vehicle would not require much mental effort. | 0.923 | |||
PEOU | I would find it easy to become skilled at using autonomous vehicles. | 0.895 | |||
Perceived Risk | |||||
PRisk | Autonomous vehicles are more likely to lead me to a fatal accident. | 0.732 | 0.929 | 0.928 | 0.824 |
PRisk | Autonomous vehicles might not perform well and could crash when faced with small problems. | 0.922 | |||
PRisk | Using autonomous vehicles would be risky. | 0.901 | |||
PRisk | I am concerned about equipment and system failures in autonomous vehicles. | 0.881 | |||
Intention to Use | |||||
IU | I plan to use an autonomous vehicle in the future. | 0.955 | 0.983 | 0.975 | 0.961 |
IU | I expect to use an autonomous vehicle in the future. | 0.958 | |||
IU | I intend to use an autonomous vehicle in the future. | 0.981 | |||
IU | I would recommend that my family members and friends ride in an autonomous vehicle. | 0.978 | |||
Perceived Usefulness | |||||
PU | Using autonomous vehicles will boost my productivity. | 0.682 | 0.884 | 0.926 | 0.792 |
PU | Automated vehicles will help alleviate traffic congestion. | 0.837 | |||
PU | Automated vehicles will assist with parking. | 0.853 | |||
PU | The use of AVs will lead to a decrease in accidents. | 0.913 |
Constructs | PU | PEOU | PRel | TC | PRisk | IU |
---|---|---|---|---|---|---|
PU | 0.882 | 0.837 | 0.591 | 0.673 | 0.569 | 0.774 |
PEOU | 0.628 | 0.953 | 0.537 | 0.692 | 0.516 | 0.793 |
PRel | 0.736 | 0.812 | 0.830 | 0.707 | 0.537 | 0.763 |
TC | 0.874 | 0.803 | 0.853 | 0.908 | 0.522 | 0.833 |
PRisk | −0.451 | −0.371 | −0.454 | −0.547 | 0.883 | 0.587 |
IU | 0.736 | 0.831 | 0.754 | 0.772 | −0.491 | 0.983 |
Outcome: Intention to Use Self-Driving Vehicles | ||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
Perceived Usefulness | Perceived Reliability | |||||||||||
1 | 2 | 3 | 4 | 5 | 1 | 2 | 3 | 4 | 5 | |||
1 | 208 | 29 | 4 | 4 | 4 | 1 | 212 | 29 | 8 | 4 | 0 | |
16.7% | 2.3% | 0.3% | 0.3% | 0.3% | 17.0% | 2.3% | 0.6% | 0.3% | 0.0% | |||
2 | 37 | 106 | 66 | 28 | 3 | 2 | 34 | 115 | 72 | 30 | 8 | |
3.0% | 8.5% | 5.3% | 2.2% | 0.2% | 2.7% | 9.2% | 5.8% | 2.4% | 0.6% | |||
3 | 2 | 36 | 143 | 85 | 12 | 3 | 1 | 25 | 129 | 65 | 8 | |
0.2% | 2.9% | 11.5% | 6.8% | 1.0% | 0.1% | 2.0% | 10.3% | 5.2% | 0.6% | |||
4 | 0 | 8 | 37 | 151 | 39 | 4 | 0 | 11 | 39 | 167 | 33 | |
0.0% | 0.6% | 3.0% | 12.1% | 3.1% | 0.0% | 0.9% | 3.1% | 13.4% | 2.6% | |||
5 | 0 | 1 | 3 | 19 | 223 | 5 | 0 | 0 | 5 | 21 | 232 | |
0.0% | 0.1% | 0.2% | 1.5% | 17.9% | 0.0% | 0.0% | 0.4% | 1.7% | 18.6% | |||
Perceived Ease of Use | Perceived Risk | |||||||||||
1 | 2 | 3 | 4 | 5 | 1 | 2 | 3 | 4 | 5 | |||
1 | 216 | 27 | 6 | 4 | 0 | 1 | 181 | 28 | 6 | 4 | 0 | |
17.3% | 2.2% | 0.5% | 0.3% | 0.0% | 14.5% | 2.2% | 0.5% | 0.3% | 0.0% | |||
2 | 30 | 110 | 66 | 49 | 5 | 2 | 63 | 105 | 67 | 42 | 5 | |
2.4% | 8.8% | 5.3% | 3.9% | 0.4% | 5.0% | 8.4% | 5.4% | 3.4% | 0.4% | |||
3 | 1 | 29 | 140 | 56 | 7 | 3 | 2 | 35 | 148 | 62 | 13 | |
0.1% | 2.3% | 11.2% | 4.5% | 0.6% | 0.2% | 2.8% | 11.9% | 5.0% | 1.0% | |||
4 | 0 | 12 | 38 | 163 | 31 | 4 | 0 | 10 | 29 | 169 | 30 | |
0.0% | 1.0% | 3.0% | 13.1% | 2.5% | 0.0% | 0.8% | 2.3% | 13.5% | 2.4% | |||
5 | 0 | 2 | 3 | 15 | 238 | 5 | 1 | 2 | 3 | 10 | 233 | |
0.0% | 0.2% | 0.2% | 1.2% | 19.1% | 0.1% | 0.2% | 0.2% | 0.8% | 18.7% | |||
Technological Competence | ||||||||||||
1 | 2 | 3 | 4 | 5 | ||||||||
1 | 223 | 27 | 3 | 2 | 0 | Cases in bold shapes represent contrarian cases. Cases in dotted shapes represent the main effects. | ||||||
17.9% | 2.2% | 0.2% | 0.2% | 0.0% | ||||||||
2 | 23 | 115 | 59 | 40 | 1 | |||||||
1.8% | 9.2% | 4.7% | 3.2% | 0.1% | ||||||||
3 | 1 | 32 | 167 | 54 | 9 | The sets of contrarian cases are counter to the main effect size. | ||||||
0.1% | 2.6% | 13.4% | 4.3% | 0.7% | ||||||||
4 | 0 | 4 | 22 | 171 | 35 | |||||||
0.0% | 0.3% | 1.8% | 13.7% | 2.8% | ||||||||
5 | 0 | 2 | 2 | 20 | 236 | |||||||
0.0% | 0.2% | 0.2% | 1.6% | 18.9% |
Outcome: High Intention to Use | Outcome: Not-High Intention to Use | |||||
---|---|---|---|---|---|---|
Conditions | Consistency | Coverage | Conditions | Consistency | Coverage | |
PU | 0.891553 | 0.912386 | PU | 0.456957 | 0.396507 | |
PRel | 0.908374 | 0.910356 | PRel | 0.463208 | 0.393611 | |
PEOU | 0.900674 | 0.913031 | PEOU | 0.460781 | 0.396057 | |
PRisk | 0.836975 | 0.933805 | PRisk | 0.421106 | 0.398363 | |
TC | 0.879382 | 0.940683 | TC | 0.437853 | 0.397135 | |
~PU | 0.410284 | 0.471193 | ~PU | 0.899028 | 0.875453 | |
~PRel | 0.394929 | 0.464581 | ~PRel | 0.894505 | 0.892214 | |
~PEOU | 0.404227 | 0.469249 | ~PEOU | 0.898819 | 0.884697 | |
~PRisk | 0.460744 | 0.484181 | ~PRisk | 0.830025 | 0.828681 | |
~TC | 0.436418 | 0.477969 | ~TC | 0.934600 | 0.867897 |
Outcome: High Intention to Use Self-Driving Vehicles | |||||||
---|---|---|---|---|---|---|---|
Configuration/Solutions | 1 | 2 | 3 | 4 | 5 | 6 | 7 |
Perceived Usefulness | ⬤ | ◯ | ◯ | ⊗ | ⊗ | ⬤ | ⬤ |
Perceived Ease of Use | ◯ | ● | ⊗ | ◯ | ● | ◯ | ⊗ |
Perceived Reliability | ⬤ | ⬤ | ⊗ | ⬤ | ◯ | ⬤ | ◯ |
Perceived Risk | ◯ | ◯ | ⊗ | ● | ● | ⊗ | ⊗ |
Technological Competence | ⊗ | ⬤ | ⬤ | ⬤ | ⬤ | ◯ | ⬤ |
Consistency | 0.924 | 0.956 | 0.935 | 0.956 | 0.951 | 0.929 | 0.955 |
Raw Coverage | 0.373 | 0.834 | 0.319 | 0.316 | 0.321 | 0.388 | 0.322 |
Unique Coverage | 0.004 | 0.437 | 0.008 | 0.0007 | 0.004 | 0.0023 | 0.00013 |
Overall Solution Consistency | 0.933223 | ||||||
Overall Solution Coverage | 0.891242 |
Outcome: Not-High Intention to Use Self-Driving Vehicles | ||
---|---|---|
Configuration/Solutions | 1 | 2 |
Perceived Usefulness | ◯ | ⊗ |
Perceived Ease of Use | ⊗ | ⊗ |
Perceived Reliability | ⊗ | ◯ |
Perceived Risk | ● | ● |
Technological Competence | ⊗ | ⊗ |
Consistency | 0.933845 | 0.934296 |
Raw Coverage | 0.863771 | 0.858445 |
Unique Coverage | 0.0170264 | 0.0117004 |
Overall Solution Consistency | 0.926261 | |
Overall Solution Coverage | 0.875471 |
Outcome: High IU | |||
---|---|---|---|
Models | Raw Coverage | Unique Coverage | Consistency |
PU*PRel*PEOU | 0.860004 | 0.0409976 | 0.943911 |
PRel*PEOU*TC | 0.838461 | 0.0134037 | 0.959257 |
~PU*~PRel*~PEOU*~PRisk*TC | 0.302896 | 0.0176091 | 0.943099 |
solution coverage | 0.897068 | ||
solution consistency | 0.932857 | ||
Outcome: Not-High IU | |||
Models | Raw Coverage | Unique Coverage | Consistency |
~PU*~PEOU*~PRisk*~TC | 0.862213 | 0.862213 | 0.94008 |
solution coverage | 0.862213 | ||
solution consistency | 0.94008 |
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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
Opoku-Mensah, N.; Qin, Z.; Opoku-Mensah, E.; Ankrah, S.T. Leveraging Fuzzy Set Qualitative Comparative Analysis to Explore Determinants of Intention to Use Self-Driving Vehicles in Ghana. World Electr. Veh. J. 2025, 16, 323. https://doi.org/10.3390/wevj16060323
Opoku-Mensah N, Qin Z, Opoku-Mensah E, Ankrah ST. Leveraging Fuzzy Set Qualitative Comparative Analysis to Explore Determinants of Intention to Use Self-Driving Vehicles in Ghana. World Electric Vehicle Journal. 2025; 16(6):323. https://doi.org/10.3390/wevj16060323
Chicago/Turabian StyleOpoku-Mensah, Nelson, Zhiguang Qin, Evans Opoku-Mensah, and Shadrach Twumasi Ankrah. 2025. "Leveraging Fuzzy Set Qualitative Comparative Analysis to Explore Determinants of Intention to Use Self-Driving Vehicles in Ghana" World Electric Vehicle Journal 16, no. 6: 323. https://doi.org/10.3390/wevj16060323
APA StyleOpoku-Mensah, N., Qin, Z., Opoku-Mensah, E., & Ankrah, S. T. (2025). Leveraging Fuzzy Set Qualitative Comparative Analysis to Explore Determinants of Intention to Use Self-Driving Vehicles in Ghana. World Electric Vehicle Journal, 16(6), 323. https://doi.org/10.3390/wevj16060323