Exploring Factors Affecting People’s Willingness to Use a Voice-Based In-Car Assistant in Electric Cars: An Empirical Study
Abstract
:1. Introduction
2. Literature Review
2.1. In-Car Interaction
2.2. Voice Assistant
2.3. User Willingness or Acceptance of In-Car Voice Assistants
3. Hypotheses Development and Research Model
3.1. Familiarity
3.2. Privacy Concern
3.3. Anthropomorphism
3.4. Interaction
3.5. Visual Appeal
3.6. Personalisation
3.7. Perceived Trust
3.8. User Satisfaction
3.9. Demographic Factors
4. Methodology
4.1. Measurement Development
4.2. Questionnaire Design and Pilot Study
4.3. Data Collection, Sampling, and Data Analysis
5. Results
5.1. Sample Characteristics
5.2. Reliability, Validity, and Fit Index of the Measurement Model
5.3. The Results of Path Analysis
5.4. Moderating Effects Analysis
6. Discussion and Implementation
7. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Construct | Measurement Item | Reference |
---|---|---|
Familiarity (FAM) | FAM1: I am familiar with voice assistant-related information and knowledge. FAM2: I am familiar with voice assistant brands and products. FAM3: I am familiar with services provided by voice assistants and their functions. FAM4: I am familiar with how to operate voice assistants. | [33] |
Privacy concern (PC) | PC1: I am concerned that voice assistants may collect too much of my personal information and data. PC2: I am concerned that voice assistants may use my personal information and data for other aims without my authorisation. PC3: I am concerned that voice assistants may share my personal information and data with other entities without my authorisation. | [40] |
Anthropomorphism (ANT) | ANT1: Voice assistants have consciousness. ANT2: Voice assistants have a mind of their own. ANT3: Voice assistants have their own free will. ANT4: Voice assistants will experience emotions. | [46] |
Interaction (INT) | INT: I know how to control voice assistants efficiently. INT2: Voice assistants quickly respond to my input and instructions. INT3: Voice assistants provide appropriate auditory and visual feedback (e.g., sounds, images). INT4: All in all, I think voice assistants are very interactive. | [49,51] |
Visual appeal (VA) | VA1: The interface design of voice assistants is appealing. VA2: The interface design of voice assistants is logically structured and designed. VA3: The virtual role image design of voice assistants is well-designed. VA4: All in all, I like the visual design of voice assistants. | [45] |
Personalisation (PER) | PER1: Voice assistants provide personalised services that are based on my information. PER2: Voice assistants personalise my driving experience with vehicles based on my personal preferences. PER3: Voice assistants are tailored to my needs. PER4: Voice assistants are configured according to my wishes and individual needs. | [40] |
Perceived trust (PT) | PT1: I feel voice assistants to be trustworthy. PT2: I feel voice assistants are reliable. PT3: I feel voice assistants are controllable. PT4: I feel voice assistants are competent. | [56] |
User satisfaction (US) | US1: The use of voice assistants gives me pleasure. US2: I am satisfied with the functions of voice assistants. US3: I am satisfied with the range of services offered by voice assistants. US4: All in all, I am satisfied with voice assistants. | [49] |
Willingness to use (WTU) | WTU1: I am willing to receive services delivered by voice assistants. WTU2: I am willing to use voice assistants in the future. WTU3: I plan to use voice assistants continuously in the future. WTU4: I am willing to recommend voice assistants to my friends. | [40,49] |
Attribute | Value | Frequency | Percent |
---|---|---|---|
Gender | Male | 213 | 49.9% |
Female | 214 | 50.1% | |
Age | Below 20 | 65 | 15.2% |
21–30 | 101 | 23.7% | |
31–40 | 84 | 19.7% | |
41–50 | 88 | 20.6% | |
Above 50 | 89 | 20.8% | |
Educational level | Under Junior high school | 44 | 10.3% |
High school | 67 | 15.7% | |
Diploma | 143 | 33.5% | |
Bachelor’s degree | 124 | 29.0% | |
Master’s degree and above | 49 | 11.5% | |
Electric car driving experience (years) | <1 | 109 | 25.5% |
1–3 | 101 | 23.7% | |
3–5 | 106 | 24.8% | |
>5 | 111 | 26.0% | |
The impact of voice assistants on in-car interaction | Very low | 74 | 17.3% |
Low | 69 | 16.2% | |
Moderate | 84 | 19.7% | |
High | 114 | 26.7% | |
Very high | 86 | 20.1% |
Construct | Cronbach’s Alpha | Variable | Standardised Factor Loading | AVE | Composite Reliability |
---|---|---|---|---|---|
Familiarity (FAM) | 0.886 | FAM1 | 0.868 | 0.663 | 0.887 |
FAM2 | 0.779 | ||||
FAM3 | 0.786 | ||||
FAM4 | 0.820 | ||||
Privacy concern (PC) | 0.885 | PC1 | 0.859 | 0.722 | 0.886 |
PC2 | 0.877 | ||||
PC3 | 0.811 | ||||
Anthropomorphism (ANT) | 0.899 | ANT1 | 0.797 | 0.689 | 0.899 |
ANT2 | 0.852 | ||||
ANT3 | 0.832 | ||||
ANT4 | 0.839 | ||||
Interaction (INT) | 0.884 | INT1 | 0.789 | 0.656 | 0.884 |
INT2 | 0.812 | ||||
INT3 | 0.817 | ||||
INT4 | 0.822 | ||||
Visual appeal (VA) | 0.876 | VA1 | 0.782 | 0.639 | 0.876 |
VA2 | 0.827 | ||||
VA3 | 0.793 | ||||
VA4 | 0.795 | ||||
Personalisation (PER) | 0.890 | PER1 | 0.820 | 0.670 | 0.890 |
PER2 | 0.811 | ||||
PER3 | 0.817 | ||||
PER4 | 0.826 | ||||
Perceived trust (PT) | 0.881 | PT1 | 0.801 | 0.651 | 0.882 |
PT2 | 0.818 | ||||
PT3 | 0.821 | ||||
PT4 | 0.787 | ||||
User satisfaction (US) | 0.877 | US1 | 0.777 | 0.641 | 0.877 |
US2 | 0.807 | ||||
US3 | 0.818 | ||||
US4 | 0.799 | ||||
Willingness to use (WTU) | 0.874 | WTU1 | 0.800 | 0.634 | 0.874 |
WTU2 | 0.791 | ||||
WTU3 | 0.826 | ||||
WTU4 | 0.768 |
Construct | AVE | FAM | PC | ANT | INT | VA | PER | PT | US | WTU |
---|---|---|---|---|---|---|---|---|---|---|
FAM | 0.663 | (0.814) | ||||||||
PC | 0.722 | 0.182 ** | (0.850) | |||||||
ANT | 0.689 | 0.554 *** | 0.248 ** | (0.830) | ||||||
INT | 0.656 | 0.248 *** | 0.092 | 0.512 *** | (0.810) | |||||
VA | 0.639 | 0.717 *** | 0.174 ** | 0.572 *** | 0.729 *** | (0.799) | ||||
PER | 0.67 | 0.733 *** | 0.170 ** | 0.497 *** | 0.684 *** | 0.658 *** | (0.819) | |||
PT | 0.651 | 0.653 ** | 0.071 | 0.548 *** | 0.682 *** | 0.740 *** | 0.709 *** | (0.807) | ||
US | 0.641 | 0.759 *** | 0.099 | 0.476 *** | 0.681 *** | 0.699 *** | 0.685 *** | 0.670 *** | (0.801) | |
WTU | 0.634 | 0.618 *** | 0.203 *** | 0.510 *** | 0.642 *** | 0.731 *** | 0.583 *** | 0.724 *** | 0.595 *** | (0.796) |
Research Model | Chi-Square | df | Chi-Square/df | TLI | CFI | RMSEA | SRMR |
---|---|---|---|---|---|---|---|
Benchmark value | / | / | 1–5 | >0.9 | >0.9 | <0.08 | <0.08 |
Measurement model | 597.463 | 524 | 1.094 | 0.992 | 0.993 | 0.018 | 0.026 |
Structural model | 931.344 | 679 | 1.372 | 0.973 | 0.975 | 0.030 | 0.058 |
Hypothesis | Path Direction | Standardised Coefficient | Standard Error | T Statistics | p-Value | Result |
---|---|---|---|---|---|---|
H1 | FAM ⟶ PT | 0.715 | 0.040 | 18.027 | 0.000 | Accepted |
H2 | PC ⟶ PT | −0.093 | 0.039 | −2.388 | 0.017 | Accepted |
H3 | ANT ⟶ PT | 0.195 | 0.048 | 4.065 | 0.000 | Accepted |
H4 | INT ⟶ US | 0.209 | 0.070 | 2.960 | 0.003 | Accepted |
H5 | VA ⟶ US | 0.288 | 0.073 | 3.957 | 0.000 | Accepted |
H6 | PER ⟶ US | 0.275 | 0.063 | 4.396 | 0.000 | Accepted |
H7 | PT ⟶ WTU | 0.577 | 0.053 | 10.810 | 0.000 | Accepted |
H8 | US ⟶ WTU | 0.245 | 0.058 | 4.200 | 0.000 | Accepted |
H9 | PT ⟶ US | 0.126 | 0.068 | 1.863 | 0.062 | Rejected |
H10a | AGE ⟶ WTU | 0.014 | 0.038 | 0.378 | 0.705 | Rejected |
H10b | EXPERIENCE ⟶ WTU | 0.096 | 0.038 | 2.506 | 0.012 | Accepted |
Path Direction | Group 1 (Male) | Group 2 (Female) | Sig. Diffi. |
---|---|---|---|
FAM ⟶ PT | 0.763 *** | 0.669 *** | 0.053 |
PC ⟶ PT | −0.059 | −0.120 | 0.061 |
ANT ⟶ PT | 0.224 *** | 0.171 * | 0.063 |
INT ⟶ US | 0.240 * | 0.117 | 0.126 |
VA ⟶ US | 0.150 | 0.420 *** | −0.288 |
PER ⟶ US | 0.253 ** | 0.322 *** | −0.055 |
Path Direction | Group 1 (Low Level) | Group 2 (High Level) | Sig. Diffi. |
---|---|---|---|
FAM ⟶ PT | 0.945 *** | 0.670 *** | 0.227 |
PC ⟶ PT | 0.041 | −0.210 ** | 0.253 ** |
ANT ⟶ PT | 0.038 | 0.296 ** | −0.298 |
INT ⟶ US | 0.556 | 0.170 | 0.378 |
VA ⟶ US | 0.385 | 0.302 | 0.038 |
PER ⟶ US | 1.062 | 0.286 | 0.800 |
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Liu, J.; Wan, F.; Zou, J.; Zhang, J. Exploring Factors Affecting People’s Willingness to Use a Voice-Based In-Car Assistant in Electric Cars: An Empirical Study. World Electr. Veh. J. 2023, 14, 73. https://doi.org/10.3390/wevj14030073
Liu J, Wan F, Zou J, Zhang J. Exploring Factors Affecting People’s Willingness to Use a Voice-Based In-Car Assistant in Electric Cars: An Empirical Study. World Electric Vehicle Journal. 2023; 14(3):73. https://doi.org/10.3390/wevj14030073
Chicago/Turabian StyleLiu, Jing, Fucheng Wan, Jinzhi Zou, and Jiaqi Zhang. 2023. "Exploring Factors Affecting People’s Willingness to Use a Voice-Based In-Car Assistant in Electric Cars: An Empirical Study" World Electric Vehicle Journal 14, no. 3: 73. https://doi.org/10.3390/wevj14030073