Factors Influencing Purchase of Advanced Intelligent Driving Vehicles in China: A Perspective of Value-Based Adoption Model
Abstract
:1. Introduction
- What factors influence consumers’ perceived value in the context of AIDVs?
- Does brand credibility play a facilitative role in enhancing perceived value and purchase intention?
2. Conceptual Framework
2.1. Value-Based Adoption Model
2.2. Brand Credibility
3. Research Hypotheses and Model
3.1. Perceived Benefit
3.1.1. Perceived Usefulness
3.1.2. Perceived Enjoyment
3.2. Perceived Sacrifice
3.2.1. Perceived Risk
3.2.2. Perceived Fee
3.3. Perceived Value
3.4. Brand Credibility, Pereived Value and Purchase Intention
4. Research Methodology
4.1. Instrument Design
4.2. Data Collection
4.3. Structural Equation Modeling (SEM) Analysis
4.3.1. Measurement Modeling
4.3.2. Path Analysis
5. Discussion and Implication
5.1. Determinants of Perceived Value
5.2. The Non-Significant Effect of Perceived Risk
5.3. The Relationship Between Perceived Value and Purchase Intention
5.4. The Role of Brand Credibility
5.5. Implications
5.6. Limitations and Future Research
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Appendix A
References
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Research Context | Perceived Value | Scholar | |
---|---|---|---|
Perceived Benefit | Perceived Sacrifice | ||
AI-based intelligent products | Usefulness; Enjoyment | Technicality; Perceived Fee | Sohn and Kwon [19] |
Smart Home Service | Facilitating Conditions Usefulness; Enjoyment | Privacy Risk; Perceived Fee Innovation Resistance; Technicality | Kim et al. [23] |
Sharing Platform | Ease of Use | Transaction Costs; Risk | Liang et al. [26] |
Fitness Wearables | Perceived Usefulness Perceived Social Image Perceived Health Increase Perceived Enjoyment | Perceived Privacy Risk Perceived Fee | Mathavan et al. [27] |
Smart Product-Service Systems | Perceived Usefulness Perceived Flexibility Perceived Reliability | Perceived Fee Perceived Technicality | Yu and Sung [28] |
Internet Health Care Technology | Perceived Usefulness Perceived Enjoyment | Perceived Complexity Perceived Risk | Bian et al. [29] |
E-Learning | Perceived Usefulness Perceived Enjoyment | Perceived Fee Perceived Risk | Liao et al. [30] |
Chatbot Technology | Perceived Usefulness Perceived Enjoyment | Perceived Risk | Al-Abdullatif [31] |
E-wallet | Perceived usefulness Enjoyment Facilitating condition | Privacy Risk; Monetary Risk Innovative Resistance Technicality | Ju et al. [32] |
PropTech Service | Usefulness; Enjoyment | Technicality: Perceived fee | Kim and Kim [33] |
Financial Robo-advisors | Perceived Financial Benefit | Perceived Financial Risk Perceived Privacy Risk | Hong et al. [34] |
Constructs | Description | References |
---|---|---|
Perceived Usefulness (PU) |
| Liao et al. [30] Kim and Kyung [45] |
Perceived Enjoyment (PE) |
| Kim and Kim [33] Chi et al. [46] |
Perceived Risk (PR) |
| Yang et al. [11] Hong et al. [33] |
Perceived Fee (PF) |
| Xu et al. [57] Kim and Kyung [45] |
Perceived Value (PV) |
| Zhang et al. [62] Hu et al. [63] |
Brand Credibility (BC) |
| Guo and Luo [22] Erdem and Swait [35] |
Purchase Intention (PI) |
| Wang et al. [60] Zhang et al. [62] |
Items | Types | Numbers | Percentage (%) |
---|---|---|---|
Gender | Male | 198 | 58.75 |
Female | 139 | 41.25 | |
Age | 20–30 years old | 132 | 39.2 |
31–40 years old | 116 | 34.4 | |
41–50 years old | 70 | 20.8 | |
Above 50 years old | 19 | 5.6 | |
Education level | High school graduates | 16 | 4.7 |
Junior colleges | 153 | 45.4 | |
Four-year colleges | 133 | 39.5 | |
Graduate schools and above | 35 | 10.4 | |
Average monthly Income | Below USD 1000 | 60 | 17.8 |
USD 1000~USD 2000 | 189 | 56.1 | |
USD 2000~USD 3000 | 65 | 19.3 | |
More than USD 3000 | 23 | 6.8 | |
Brand type | Huawei | 91 | 27 |
Lixiang | 78 | 23.1 | |
Tesila | 59 | 17.5 | |
Xiaopeng | 53 | 15.8 | |
Weilai | 31 | 9.2 | |
Other | 25 | 7.4 |
Construct | Component | ||||||
---|---|---|---|---|---|---|---|
PI | PV | PE | PU | PF | BC | PR | |
PI1 | 0.806 | 0.102 | 0.129 | 0.148 | −0.105 | 0.112 | 0.078 |
PI2 | 0.806 | 0.231 | 0.102 | 0.051 | −0.112 | 0.204 | 0.124 |
PI4 | 0.777 | 0.205 | 0.140 | 0.132 | −0.083 | 0.151 | 0.154 |
PI3 | 0.723 | 0.121 | 0.124 | 0.185 | −0.130 | 0.200 | 0.208 |
PV4 | 0.131 | 0.845 | 0.071 | 0.121 | −0.127 | 0.027 | 0.084 |
PV3 | 0.153 | 0.811 | 0.160 | 0.239 | −0.130 | 0.093 | 0.117 |
PV1 | 0.198 | 0.767 | 0.153 | 0.211 | −0.176 | 0.104 | 0.036 |
PV2 | 0.165 | 0.710 | 0.172 | 0.188 | −0.214 | 0.069 | −0.001 |
PE2 | 0.050 | 0.164 | 0.829 | −0.004 | −0.013 | 0.056 | 0.031 |
PE1 | 0.044 | 0.092 | 0.828 | 0.066 | −0.064 | 0.040 | 0.033 |
PE4 | 0.152 | 0.128 | 0.749 | 0.139 | −0.115 | 0.090 | 0.148 |
PE3 | 0.262 | 0.088 | 0.708 | 0.208 | −0.148 | 0.120 | 0.090 |
PU2 | 0.095 | 0.168 | −0.001 | 0.783 | −0.088 | 0.108 | 0.125 |
PU4 | 0.159 | 0.087 | 0.078 | 0.753 | −0.124 | 0.174 | 0.113 |
PU1 | 0.166 | 0.241 | 0.122 | 0.730 | −0.119 | 0.194 | 0.128 |
PU3 | 0.075 | 0.238 | 0.212 | 0.701 | −0.126 | −0.002 | 0.001 |
PF3 | −0.028 | −0.130 | −0.026 | −0.100 | 0.805 | −0.031 | 0.063 |
PF4 | −0.071 | −0.159 | −0.042 | −0.017 | 0.801 | 0.048 | −0.205 |
PF1 | −0.094 | −0.118 | −0.099 | −0.138 | 0.757 | −0.138 | −0.087 |
PF2 | −0.193 | −0.139 | −0.149 | −0.162 | 0.672 | −0.029 | −0.027 |
BC3 | 0.132 | 0.053 | 0.078 | 0.136 | −0.059 | 0.855 | 0.079 |
BC1 | 0.180 | 0.086 | 0.140 | 0.141 | −0.070 | 0.834 | 0.083 |
BC2 | 0.239 | 0.094 | 0.050 | 0.121 | −0.006 | 0.793 | 0.140 |
PR1 | 0.134 | 0.103 | 0.062 | 0.003 | −0.123 | 0.127 | 0.817 |
PR2 | 0.164 | 0.041 | 0.066 | 0.108 | −0.029 | 0.133 | 0.809 |
PR3 | 0.123 | 0.037 | 0.118 | 0.195 | −0.056 | 0.026 | 0.769 |
Eigenvalues | 2.914 | 2.900 | 2.731 | 2.658 | 2.591 | 2.362 | 2.187 |
Variance % | 11.209 | 11.155 | 10.504 | 10.223 | 9.966 | 9.084 | 8.413 |
Cumulative% | 11.209 | 22.364 | 32.868 | 43.091 | 53.057 | 62.140 | 70.553 |
Construct | Items | λ | CR | AVE | α |
---|---|---|---|---|---|
(PU) | PU1 | 0.827 | 0.821 | 0.536 | 0.820 |
PU2 | 0.718 | ||||
PU3 | 0.648 | ||||
PU4 | 0.725 | ||||
(PE) | PE1 | 0.734 | 0.834 | 0.553 | 0.834 |
PE2 | 0.744 | ||||
PE3 | 0.752 | ||||
PE4 | 0.757 | ||||
(PR) | PR1 | 0.752 | 0.785 | 0.55 | 0.783 |
PR2 | 0.778 | ||||
PR3 | 0.692 | ||||
(PF) | PF1 | 0.736 | 0.803 | 0.507 | 0.801 |
PF2 | 0.654 | ||||
PF3 | 0.701 | ||||
PF4 | 0.755 | ||||
(PV) | PV1 | 0.816 | 0.880 | 0.647 | 0.878 |
PV2 | 0.731 | ||||
PV3 | 0.872 | ||||
PV4 | 0.794 | ||||
(BC) | BC1 | 0.834 | 0.849 | 0.652 | 0.843 |
BC2 | 0.772 | ||||
BC3 | 0.815 | ||||
(PI) | PI1 | 0.769 | 0.873 | 0.637 | 0.873 |
PI2 | 0.846 | ||||
PI3 | 0.767 | ||||
PI4 | 0.809 |
PI | BC | PV | PF | PR | PU | PE | |
---|---|---|---|---|---|---|---|
PI | 0.798 | ||||||
BC | 0.523 | 0.807 | |||||
PV | 0.521 | 0.312 | 0.805 | ||||
PF | −0.374 | −0.209 | −0.475 | 0.747 | |||
PR | 0.464 | 0.358 | 0.288 | −0.281 | 0.742 | ||
PU | 0.481 | 0.45 | 0.594 | −0.408 | 0.376 | 0.732 | |
PE | 0.436 | 0.32 | 0.442 | −0.318 | 0.306 | 0.401 | 0.747 |
Model Fit Indicators | Optimization Criteria | Statistical Value | Fit |
---|---|---|---|
CMIN | —— | 542.476 | —— |
DF | —— | 286 | —— |
CMIN/DF | <3 | 1.897 | Good |
NFI | >0.8 | 0.877 | Good |
RFI | >0.8 | 0.860 | Good |
IFI | >0.9 | 0.938 | Good |
TLI | >0.9 | 0.929 | Good |
CFI | >0.9 | 0.937 | Good |
RMSEA | <0.08 | 0.052 | Good |
Path | β | S.E. | C.R. | p-Value | Hypothesis | |||
---|---|---|---|---|---|---|---|---|
H1 | PV | <--- | PU | 0.408 | 0.069 | 5.651 | *** | Accepted |
H2 | PV | PE | 0.202 | 0.056 | 3.315 | *** | Accepted | |
H3 | PV | <--- | PR | 0.013 | 0.062 | 0.205 | 0.838 | Rejected |
H4 | PV | <--- | PF | −0.247 | 0.054 | −3.590 | *** | Accepted |
H5 | PI | <--- | PV | 0.451 | 0.064 | 7.374 | *** | Accepted |
H6 | PV | <--- | BC | 0.032 | 0.050 | 0.560 | 0.576 | Rejected |
H7 | PI | <--- | BC | 0.402 | 0.056 | 6.530 | *** | Accepted |
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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/).
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Yang, Y.; Wang, Y.; Bi, X. Factors Influencing Purchase of Advanced Intelligent Driving Vehicles in China: A Perspective of Value-Based Adoption Model. World Electr. Veh. J. 2025, 16, 154. https://doi.org/10.3390/wevj16030154
Yang Y, Wang Y, Bi X. Factors Influencing Purchase of Advanced Intelligent Driving Vehicles in China: A Perspective of Value-Based Adoption Model. World Electric Vehicle Journal. 2025; 16(3):154. https://doi.org/10.3390/wevj16030154
Chicago/Turabian StyleYang, Yanlu, Yiyuan Wang, and Xiaohan Bi. 2025. "Factors Influencing Purchase of Advanced Intelligent Driving Vehicles in China: A Perspective of Value-Based Adoption Model" World Electric Vehicle Journal 16, no. 3: 154. https://doi.org/10.3390/wevj16030154
APA StyleYang, Y., Wang, Y., & Bi, X. (2025). Factors Influencing Purchase of Advanced Intelligent Driving Vehicles in China: A Perspective of Value-Based Adoption Model. World Electric Vehicle Journal, 16(3), 154. https://doi.org/10.3390/wevj16030154