The Role of Digital Intelligence in Shaping Purchase Behavior for New Energy Vehicles: A Study in Bangkok
Article Sidebar
Main Article Content
Abstract
In the digital intelligence era, consumers' vehicle purchasing behavior is increasingly shaped by smart technologies and interactive information systems. This study investigates the determinants of purchase behavior toward New Energy Vehicles (NEVs) in Bangkok, Thailand—an emerging NEV market facing both opportunities and challenges. Drawing upon the Stimulus–Organism–Response (SOR) framework, this research integrates the Motivation–Opportunity–Ability (MOA) theory as external stimuli and the Technology Acceptance Model (TAM) as internal psychological processing mechanisms to construct a comprehensive model. Using a mixed-methods approach, the study first conducts in-depth interviews with 14 early NEV adopters to identify context-specific factors, followed by a quantitative survey of 443 respondents analyzed via Structural Equation Modeling (SEM). The findings reveal that consumer motivation and purchasing ability significantly enhance perceived usefulness and perceived ease of use, while reducing perceived risk. While market opportunity factors such as government incentives and charging infrastructure exert statistically significant effects, their overall influence is relatively limited compared to motivational and ability-based factors. Notably, digital intelligence features—such as autonomous driving, smart connectivity, and remote control—serve as powerful stimuli that elevate consumers' perceptions of NEV utility, particularly among younger, tech-oriented users. From a practical standpoint, the study recommends that policymakers strengthen purchase incentives and directly address perceived risks through extended government-backed battery warranty programs and transparent service guarantees. These strategies can reduce uncertainty and accelerate NEV adoption. The study contributes to the literature by extending the SOR framework to include digital intelligence as a novel stimulus in shaping consumer cognition and behavior in the NEV sector.
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
บทความวิจัยนี้เป็นของลิขสิทธิ์
References
Abro, G. E. M., Zulkifli, S. A. B., Kumar, K., El Ouanjli, N., Asirvadam, V. S., & Mossa, M. A. (2023). Comprehensive review of recent advancements in battery technology, propulsion, power interfaces, and vehicle network systems for intelligent autonomous and connected electric vehicles. Energies, 16(6), 2925.
Agency, I. E. (2024). Global EV Outlook 2024: Trends in Electric Cars. https://www.iea.org/reports/global-ev-outlook-2024/trends-in-electric-cars
André, Q., Carmon, Z., Wertenbroch, K., Crum, A., Frank, D., Goldstein, W., Huber, J., Van Boven, L., Weber, B., & Yang, H. (2018). Consumer choice and autonomy in the age of artificial intelligence and big data. Customer needs and solutions, 5, 28-37.
Arts, J. W., Frambach, R. T., & Bijmolt, T. H. (2011). Generalizations on consumer innovation adoption: A meta-analysis on drivers of intention and behavior. International Journal of Research in Marketing, 28(2), 134-144.
Cai, Z., Gold, M., & Brannan, R. (2019). An exploratory analysis of US consumer preferences for North American pawpaw. Agroforestry Systems, 93, 1673-1685.
Chen, J., Liu, X., Sun, X., & Yang, Y. (2022). The Purchase Behavior of New Energy Vehicle in Carbon Trading Market. 2022 International Conference on Creative Industry and Knowledge Economy (CIKE 2022),
Chen, Z. (2013). The formation, development and core concept of MOA model. Library Science Research, (13), 53-57.
Chengde, C., & Yuwei, Z. (2011). The impact of consumer emotions on perceived risk and behavioral intention. Shanghai Management Science, (2), 44-50.
Cicchino, J. B. (2022). Effects of automatic emergency braking systems on pedestrian crash risk. Accident Analysis & Prevention, 172, 106686.
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS quarterly, 319-340.
Featherman, M. S., & Pavlou, P. A. (2003). Predicting e-services adoption: a perceived risk facets perspective. International journal of human-computer studies, 59(4), 451-474.
Fishbein, M., & Ajzen, I. (1977). Belief, attitude, intention, and behavior: An introduction to theory and research.
Forsythe, S., Liu, C., Shannon, D., & Gardner, L. C. (2006). Development of a scale to measure the perceived benefits and risks of online shopping. Journal of interactive marketing, 20(2), 55-75.
Gefen, D. (2003). Trust and tam in online shopping: An integrated model1. MIS quarterly, 27(1), 51-90.
Hoyer, W. D., Kroschke, M., Schmitt, B., Kraume, K., & Shankar, V. (2020). Transforming the customer experience through new technologies. Journal of interactive marketing, 51(1), 57-71.
Hung, K., Sirakaya-Turk, E., & Ingram, L. J. (2011). Testing the efficacy of an integrative model for community participation. Journal of Travel Research, 50(3), 276-288.
Jing, C., Ruiqiu, O., & Quan, C. (2016). The impact mechanism of distributed innovation participants' motivation on behavior: Taking the open source community as an example. Scientific and technological progress and countermeasures, 33(24), 17-23.
Kurniawan, I. A., Mugiono, M., & Wijayanti, R. (2022). The effect of Perceived Usefulness, Perceived Ease of Use, and social influence toward intention to use mediated by Trust. Jurnal Aplikasi Manajemen, 20(1), 117-127.
Li, C., Ye, L., & Wang, L. (2021). The impact of new energy vehicle consumption promotion policies on potential consumers’ purchase intention. Chinese management science, 29(10), 151-164.
Mouloudj, K., Bouarar, A. C., Bovsh, L. A., Zikii, N., & Kebab, M. (2023). Investigating the Antecedents of Eco-Friendly Vehicle Purchase Intentions: An Extension of the Theory of Planned Behavior. In Sustainable Marketing, Branding, and Reputation Management: Strategies for a Greener Future (pp. 1-16). IGI Global.
Paudel, A., Pinthurat, W., & Marungsri, B. (2023). Impact of large-scale electric vehicles’ promotion in Thailand considering energy mix, peak load, and greenhouse gas emissions. Smart Cities, 6(5), 2619-2638.
Rezvani, Z., Jansson, J., & Bodin, J. (2015). Advances in consumer electric vehicle adoption research: A review and research agenda. Transportation research part D: transport and environment, 34, 122-136.
Secinaro, S., Calandra, D., Lanzalonga, F., & Ferraris, A. (2022). Electric vehicles’ consumer behaviours: Mapping the field and providing a research agenda. Journal of Business Research, 150, 399-416.
Sicheng, W. (2024). Thailand Embraces the "Golden Era" of Electric Vehicles. Guangming Daily. Retrieved March 17, 2025 from https://cn.wicinternet.org/2024-08/13/content_37497738.htm
Singh, V., Singh, V., & Vaibhav, S. (2020). A review and simple meta-analysis of factors influencing adoption of electric vehicles. Transportation research part D: transport and environment, 86, 102436.
Strauss, A., & Corbin, J. (1998). Basics of qualitative research techniques.
Sundararjun, J., & Wongbandit, A. (2021). Policies and regulations to promote electric vehicles in Thailand Doctoral dissertation, Thammasat University].
Tian, J., Wang, P., & Zhu, D. (2024). Overview of Chinese new energy vehicle industry and policy development. Green Energy and Resources, 100075.
Venkatesh, V., & Davis, F. D. (1996). A model of the antecedents of perceived ease of use: Development and test. Decision sciences, 27(3), 451-481.
Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS quarterly, 425-478.
Venkatesh, V., Thong, J. Y., & Xu, X. (2016). Unified theory of acceptance and use of technology: A synthesis and the road ahead. Journal of the association for Information Systems, 17(5), 328-376.
Wang, Z., & Rong, D. (2011). A study on the impact of interpersonal trust and self-efficacy on knowledge sharing and participation in virtual communities. Intelligence Theory and Practice, 34(10), 71-74.
Xu, L., Lin, L., & Rong, Y. (2018). Econometric analysis of factors influencing consumers' purchasing intention of new energy vehicles. Times Auto, 10.
Yang, L., Xu, G., & Jianzhong, X. (2022). Research on the Impact of Consumer Innovation on Purchase Intention of New Energy Vehicles—Regulated Intermediary Effect. Asia-Pacific Conference on Economics & Finance,
Yuanyuan, H., Krishnan, V., & Pentang, J. T. (2024). Key Factors Shaping Purchase Intentions for New Energy Vehicles in Urumqi, China. Journal of Ecohumanism, 3(8), 9629–9643-9629–9643.
Zhang, L., Tong, H., Liang, Y., & Qin, Q. (2023). Consumer purchase intention of new energy vehicles with an extended technology acceptance model: The role of attitudinal ambivalence. Transportation Research Part A: Policy and Practice, 174, 103742.
Zhou, Y., Li, J., Adel, G., & Liu, C. (2024). Examination of the adoption intention of new energy vehicles from the perspective of functional attributes and media richness. Heliyon, 10(4).
