Confirmatory Factors of Battery Electric Vehicle Technology Acceptance Model (BEVTAM) of Thai Consumers
Keywords:
The Battery Electric Vehicle Technology Acceptance Model (BEVTAM), Perceived Compatibility, Perceived Financial Resource, Perceived Security Risk, Battery Electric VehicleAbstract
Background and Objectives: The popularity of electric vehicles around the world is rapidly growing. The use of electric vehicles is also becoming popular in Thailand. Nevertheless, there are still obstacles from such factors as limited driving distance per charge, lack of confidence in the technology and safety of electric vehicles as well as inadequate knowledge of and understanding of electric vehicles. The objectives of the present research were to analyze confirmatory factors of the battery electric vehicle technology acceptance model (BEVTAM) and to prioritize such factors.
Methodology: Questionnaires were used to gather the data from 440 Thai consumers aged between 25-65 years who were the target group to buy battery electric vehicles (BEV) during 2022-2025. Data were analyzed in terms of the mean, standard deviation, chi-square, degree of freedom, p-value, root mean square error of approximation (RMSEA), standardized root mean square residual (SRMR), goodness of fit index (GFI), adjusted goodness of fit index (AGFI) and comparative fit index (CFI).
Main Results: It is possible to rank 6 elements involved in the battery electric vehicle technology acceptance by their importance of the elements from the highest to the lowest as follows: 1) Perceived Ease of Use (PEU), 2) Perceived Usefulness (PCU), 3) Perceived Security Risk (PSR), 4) Perceived Trust (PCT), 5) Perceived Compatibility (PCC) and 6) Perceived Financial Resources (PFR). The component weights were 0.98, 0.90, 0.88, 0.85, 0.85 and 0.76, respectively. The proposed Battery Electric Vehicle Technology Acceptance Model (BEVTAM) represents a new finding as all available previous researches focused only on the components of technology acceptance models; BEVTAM had never been used in research studies in the context of battery electric vehicle technology before, especially in Thailand.
Conclusions: The 6-dimension electric vehicle technology acceptance measure represents a component of technology acceptance measurement in the context of BEV electric vehicles in Thailand. The results are consistent with the empirical data.
Practical Application: The obtained results provide primary information that can be used to formulate a framework for manufacturers and distributors of battery electric vehicles to improve the technology to be more stable. Such improvement includes the development of an intelligent system that can help drivers operate their vehicles more efficiently and safely. Software that responds to modern usage should also be developed, along with a battery technology that is efficient for long-term use and allows for longer driving distance per charge. Faster chargeability also deserves attention. Such developments would give consumers more confidence in the use of battery-electric vehicles.
References
Jaiswal, D., Kaushal, V., Deshmukh, A.K., Kant, R. and Kautish, P., 2022, “What Drives Electric Vehicles in An Emerging Market?,” Marketing Intelligence and Planning, 40 (6), pp. 738-754. https://doi.org/10.1108/MIP-11-2021-0406
Huang, X., Lin, Y., Lim, M.K., Tseng, M.L. and Zhou, F., 2021, “The Influence of Knowledge Management on Adoption Intention of Electric Vehicles: Perspective on Technological Knowledge,” Industrial Management & Data Systems, 121 (7), pp. 1481-1495. https://doi.org/10.1108/IMDS-07-2020-0411
Baccarella, C.V., Wagner, T.F., Scheiner, C.W., Maier, L. and Voigt, K.I., 2021, “Investigating Consumer Acceptance of Autonomous Technologies: The Case of Self-driving Automobiles,” European Journal of Innovation Management, 24 (4), pp. 1210-1232. https://doi.org/10.1108/EJIM-09-2019-0245
Rogers, E. and Shoemaker, F., 1978, Communication of Innovations: A Cross–cultural Approach, Free Press, New York, 476 p.
Davis, F.D., Bagozzi, R.P. and Warshaw, P.R., 1989, “User Acceptance of Computer Technology: A Comparison of Two Theoretical Models,” Management Science, 35 (8), pp. 982–1003. http://dx.doi.org/10.1287/mnsc.35.8.982
Ooi, K.B. and Tan, G.W.H., 2016, “Mobile Technology Acceptance Model: An Investigation using Mobile users to Explore Smartphone Credit Card,” Expert Systems with Applications, 59, pp. 33–46. https://doi.org/10.1016/j.eswa.2016.04.015
Singh, S. and Srivastava, P., 2019, “Social Media for Outbound Leisure Travel: a Framework based on Technology Acceptance Model (TAM),” Journal of Tourism Futures, 5 (1), pp. 43-61. https://doi.org/10.1108/JTF-10-2018-0058
Aref, M.M. and Okasha, A.E., 2020, “Evaluating the Online Shopping Behavior among Egyptian College-educated Community,” Review of Economics and Political Science, 5 (1), pp. 21-37. https://doi.org/10.1108/REPS-10-2018-0013
Dubey, P. and Sahu, K.K., 2023, “Mediation Analysis of Students' Perceived Benefits in Predicting their Satisfaction to Technology-Enhanced Learning,” Journal of Research in Innovative Teaching and Learning, 16 (1), pp. 82-99. https://doi.org/10.1108/JRIT-11-2021-0074
Nourallah, M., Öhman, P. and Amin, M., 2023, “No Trust, No Use: How Young Retail Investors Build Initial Trust in Financial Robo-advisors,” Journal of Financial Reporting and Accounting, 21 (1), pp. 60-82. https://doi.org/10.1108/JFRA-12-2021-0451
Schumacker, R.E. and Lomax, R.G., 1996, A Beginner's Guide to Structural Equation Modeling, Mahwah, Lawrence Erlbaum Associates, New Jersey, 510 p.
Lindeman, R.H., Merenda, P.F. and Gold, R.Z., 1980, Introduction to Bivariate and Multivariate Analysis, Glenview, IL: Scott, Foresman, Published in Dutch in 1981, Vereniging voor Statistiek Bulletin, 14 (7/8), pp. 11-14.
Hair, J.F., Black, W.C., Babin, B.J. and Anderson, R.E., 2010, Multivariate Data Analysis, 7th ed., Prentice Hall, New Jersey, 761 p.
Hair, J.F., Black, W.C., Babin, B.J., Anderson, R.E. and Tatham, R., 2006, Multivariate Data Analysis, 6th ed., Prentice Hall, New Jersey, 761 p.
Nunnally, J.C., 1978, Psychometric Theory, 2nd ed., McGraw-Hill, New York, 701 p.
Kaemkate, W., 2008, Behavioral Science Research Methodology, 2nd ed., Printing of Chulalongkorn University, Bangkok, 565 p. (In Thai)
Chantavanich, S., 2008, Qualitative Methods, 8th ed., Chulalongkorn University Printing House, Bangkok, 233 p. (In Thai)
Berdie, D.R., Anderson, J.F. and Niebuhr, M.A., 1986, Questionnaires: Design and Use, 2nd ed., Scarecrow Press, New Jersey, 330 p.
Tabachnick, B.G. and Fidell, L.S., 2007, Using Multivariate Statistics, 5th ed., Allyn and Bacon, New York, 983 p.
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