Synergizing Human and AI Factors for Effective Knowledge Transfer in ICE-to-EV Transition
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This study investigates the determinants of knowledge transfer effectiveness during the automotive industry's transition from internal combustion engines (ICEs) to electric vehicles (EVs), examining the interplay between traditional interpersonal knowledge transfer factors and artificial intelligence (AI) factors. Data were collected from 303 professionals in Thailand's automotive and AI sectors using a structured questionnaire. The hypothesized relationships were tested through structural equation modeling (SEM) with confirmatory factor analysis, yielding excellent model fit indices (CFI = 1.00, RMSEA = 0.00, SRMR = 0.006). The results reveal a distinct efficiency-effectiveness dichotomy: traditional knowledge transfer factors primarily drive knowledge application (β = 0.67) and recipient satisfaction (β = 0.76), whereas AI factors function as efficiency accelerators, significantly reducing transfer cost (β = 0.57) and enhancing transfer speed (β = 0.45), while showing negligible influence on satisfaction (β = 0.04). The model explains 65–69% of the variance across all outcome dimensions. This research contributes a statistically validated Dual-Engine Framework demonstrating that AI augments rather than replaces human mentorship, offering organizations a balanced strategy for workforce upskilling during technological transitions.
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[1] Shelar, R. (2024). Accelerating the Shift to Electric: Challenges, Opportunities and Strategies. KTH Royal Institute of Technology, Stockholm, Sweden.
[2] Zhang, A., Tay, H. L., Alvi, M. F., Wang, J. X., & Gong, Y. (2023). Carbon neutrality drivers and implications for firm performance and supply chain management. Business Strategy and the Environment, 32(4), 1966–1980. doi:10.1002/bse.3230.
[3] Ibekwe, K. I., Etukudoh, E. A., Nwokediegwu, Z. Q. S., Umoh, A. A., Adefemi, A., & Ilojianya, V. I. (2024). Energy security in the global context: A comprehensive review of geopolitical dynamics and policies. Engineering Science & Technology Journal, 5(1), 152-168. doi:10.51594/estj.v5i1.741.
[4] Du, J., & Shepotylo, O. (2024). Powering the Future: Aligning Economic Policy for Automotive Sector Resilience in the Face of Critical Material Dependency Challenges. Centre for Business Prosperity, Aston University – Birmingham, United Kingdom.
[5] Jeong, J. H., Kim, C., & Jo, H. J. (2024). Three major challenges in the shift to electric vehicles: Industrial organization, industrial policy, and a just transition. Sociology Compass, 18(5), 13218. doi:10.1111/soc4.13218.
[6] Ide, E. (2025). Automation, AI, and the intergenerational transmission of knowledge. arXiv Preprint, arXiv:2507.16078. doi:10.48550/arXiv.2507.16078.
[7] Osatis, C., & Asavanirandorn, C. (2022). An Exploring Human Resource Development in Small and Medium Enterprises in Response to Electric Vehicle Industry Development. World Electric Vehicle Journal, 13(6), 98. doi:10.3390/wevj13060098.
[8] Felser, K., & Wynn, M. (2023). Managing the Knowledge Deficit in the German Automotive Industry. Knowledge, 3(2), 180–195. doi:10.3390/knowledge3020013.
[9] Argote, L. (2024). Knowledge Transfer Within Organizations: Mechanisms, Motivation, and Consideration. Annual Review of Psychology, 75(1), 405–431. doi:10.1146/annurev-psych-022123-105424.
[10] Nonaka, I. (2009). A dynamic theory of organizational knowledge creation. Knowledge, Groupware and the Internet, 11(7–8), 3–42. doi:10.1287/orsc.5.1.14.
[11] Canonico, P., De Nito, E., Esposito, V., Pezzillo Iacono, M., & Consiglio, S. (2020). Knowledge creation in the automotive industry: Analysing obeya-oriented practices using the SECI model. Journal of Business Research, 112, 450–457. doi:10.1016/j.jbusres.2019.11.047.
[12] Halili, Z. (2020). Identifying and ranking appropriate strategies for effective technology transfer in the automotive industry: Evidence from Iran. Technology in Society, 62, 101264. doi:10.1016/j.techsoc.2020.101264.
[13] Falckenthal, B., Au-Yong-Oliveira, M., & Figueiredo, C. (2025). Intergenerational Tacit Knowledge Transfer: Leveraging AI. Societies, 15(8), 213. doi:10.3390/soc15080213.
[14] Taherdoost, H., & Madanchian, M. (2023). Artificial Intelligence and Knowledge Management: Impacts, Benefits, and Implementation. Computers, 12(4), 72. doi:10.3390/computers12040072.
[15] Rane, N., Paramesha, M., Choudhary, S., & Rane, J. (2024). Artificial Intelligence, Machine Learning, and Deep Learning for Advanced Business Strategies: a Review. SSRN Electronic Journal, 2(3), 147–171. doi:10.2139/ssrn.4835661.
[16] Olan, F., Ogiemwonyi Arakpogun, E., Suklan, J., Nakpodia, F., Damij, N., & Jayawickrama, U. (2022). Artificial intelligence and knowledge sharing: Contributing factors to organizational performance. Journal of Business Research, 145, 605–615. doi:10.1016/j.jbusres.2022.03.008.
[17] Visweswaran, R. (2025). Retaining Tacit Knowledge in High-Turnover Indian Automotive Embedded Engineering Companies: An Integrated Theoretical and Practical Framework. Anusandhanvallari, 581-589.
[18] Argote, L., Guo, J., Park, S. S., & Hahl, O. (2022). The Mechanisms and Components of Knowledge Transfer: The Virtual Special Issue on Knowledge Transfer Within Organizations. Organization Science, 33(3), 1232–1249. doi:10.1287/orsc.2022.1590.
[19] Salazar-Fierro, P., Bayardo, J. M., Guaderrama, A. I. M., & Arroyo, J. C. (2023). Analysis of factors that affect the intention and transfer of knowledge within organizations among employees of the automotive industry in the northern border of Mexico. Contaduria y Administracion, 68(2), 225–249. doi:10.22201/fca.24488410e.2023.2997.
[20] Sabeti, M., Hashemzadeh, G. Reza, Gelard, P., & Rabiei, M. (2020). Designing a knowledge based technology transfer model using the fourth generation approach in the automotive industry. Revista Gestão & Tecnologia, 20, 106–125. doi:10.20397/2177-6652/2020.v20i0.1864.
[21] Saleet, H., Aldamsah, A., Banikhaled, M., Abu-Baker, A., Damseh, R. A., Al-Smadi, M., Mostafa, A., Adaileh, W., Alahmer, A., Al-Salaymeh, A., Al Twassi, S., AlBeek, R., & Hassouneh, K. (2023). Importance and Barriers of Establishing Educational/Training Programs in Electric Vehicles/Hybrid-Electric Vehicles in Jordan. World Electric Vehicle Journal, 14(9), 232. doi:10.3390/wevj14090232.
[22] Tokarz, M., & Rosiński, J. (2024). Transfer of tacit knowledge: exploring effective methods and techniques of tacit knowledge across generations in cultural institutions – a case study. Scientific Papers of Silesian University of Technology. Organization and Management Series, 199, 619. doi:10.29119/1641-3466.2024.199.46.
[23] Dien Vy, P., & Tam, P. T. (2025). Policy Recommendations for Enhancing the Green Banking and Sustainable Development. Emerging Science Journal, 9(6), 3062–3086. doi:10.28991/ESJ-2025-09-06-011.
[24] Lopes, A., Mamede, H. S., Reis, L., & Santos, A. (2024). Common Techniques, Success Attack Factors and Obstacles to Social Engineering: A Systematic Literature Review. Emerging Science Journal, 8(2), 761-794. doi:10.28991/ESJ-2024-08-02-025.
[25] Lee, N. J., Lombardi, J. M., & Lehman, R. A. (2023). Artificial Intelligence and Machine Learning Applications in Spine Surgery. International Journal of Spine Surgery, 17(4), S18–S25. doi:10.14444/8503.
[26] Hou, X., Zhao, Y., Liu, Y., Yang, Z., Wang, K., Li, L., Luo, X., Lo, D., Grundy, J., & Wang, H. (2024). Large Language Models for Software Engineering: A Systematic Literature Review. ACM Transactions on Software Engineering and Methodology, 33(8), 1–79. doi:10.1145/3695988.
[27] Del Rio-Chanona, R. M., Laurentsyeva, N., & Wachs, J. (2024). Large language models reduce public knowledge sharing on online Q&A platforms. PNAS Nexus, 3(9), 400. doi:10.1093/pnasnexus/pgae400.
[28] Vadisetty, R., & Polamarasetti, A. (2024). AI-Augmented Skill Development Roadmaps: Tailoring 12-Month Learning Paths for Future-Ready Careers in Education 4.0 and Industry 4.0. Proceedings of the 2024 13th International Conference on System Modeling and Advancement in Research Trends, SMART 2024, 655–661. doi:10.1109/SMART63812.2024.10882483.
[29] Nguyen-Van, H., Nguyen, L. D., Pham-Hong, T., Nguyen, H. T. T., Hoang, D. N. T., Nguyen, H. T., & Nguyen, H. L. T. (2025). Ecotourism Choice Drivers: Role of Green Marketing, Green Innovation, and Receptivity to Green Communication. Journal of Human, Earth, and Future, 6(3), 505–520. doi:10.28991/HEF-2025-06-03-02.
[30] Qu, J., Batool, H., & Ullah, K. (2025). A Study of the Effects of Knowledge Management on Enterprise Innovation Performance. Emerging Science Journal, 9(4), 2275–2291. doi:10.28991/ESJ-2025-09-04-030.
[31] Pan, S. J., & Yang, Q. (2010). A survey on transfer learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345–1359. doi:10.1109/TKDE.2009.191.
[32] Lei, H., Gui, L., & Le, P. B. (2020). Linking transformational leadership and frugal innovation: the mediating role of tacit and explicit knowledge sharing. Journal of Knowledge Management, 25(7), 1832–1852. doi:10.1108/JKM-04-2020-0247.
[33] Nawaz, A., Gilal, F. G., Soomro, M. I., Gilal, R. G., & Channa, K. A. (2024). Driving innovation through servant leadership in higher education: Unveiling the serial mediating mechanisms of subjective well-being and knowledge sharing. Knowledge and Process Management, 31(1), 3–16. doi:10.1002/kpm.1761.
[34] Jayaram, N., & Bhatta, N. M. K. (2023). Tacit Knowledge Management in Engineering Industries: Systematic Literature Review and Development of a Conceptual Model. Journal of Information and Knowledge Management, 22(4), 2350012. doi:10.1142/S0219649223500120.
[35] Dangjaroen, Y., Wiriyapinit, M., & Sinthupinyo, S. (2023). Knowledge Transfer Factors for Internal Combustion Engine (ICE) Industry to Electric Vehicle (EV) Industry by Artificial Intelligent: Machine Learning. International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management, IC3K - Proceedings, 142–149. doi:10.5220/0012162800003598.
[36] Hair, J. F., & Brunsveld, N. (2019). Essentials of business research methods. Routledge, New Jersey, United States. doi:10.4324/9780429203374.
[37] Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2019). Multivariate data analysis. Cengage Learning, Singapore.
[38] Kalkbrenner, M. T. (2023). Alpha, Omega, and H Internal Consistency Reliability Estimates: Reviewing These Options and When to Use Them. Counseling Outcome Research and Evaluation, 14(1), 77–88. doi:10.1080/21501378.2021.1940118.
[39] Kennedy, I. (2022). Sample Size Determination in Test-Retest and Cronbach Alpha Reliability Estimates. British Journal of Contemporary Education, 2(1), 17–29. doi:10.52589/bjce-fy266hk9.
[40] Sathyanarayana, S., & Mohanasundaram, T. (2024). Fit Indices in Structural Equation Modeling and Confirmatory Factor Analysis: Reporting Guidelines. Asian Journal of Economics, Business and Accounting, 24(7), 561–577. doi:10.9734/ajeba/2024/v24i71430.
[41] Demir, S. (2022). Comparison of Normality Tests in Terms of Sample Sizes under Different Skewness and Kurtosis Coefficients. International Journal of Assessment Tools in Education, 9(2), 397–409. doi:10.21449/ijate.1101295.
[42] Hatem, G., Zeidan, J., Goossens, M., & Moreira, C. (2022). Normality Testing Methods and the Importance of Skewness and Kurtosis in Statistical Analysis. BAU Journal - Science and Technology, 3(2), 7. doi:10.54729/ktpe9512.
[43] Ng, J., Wah, K., Fitriana, M., & Arumugam, T. (2023). Assumptions for structure equation modeling (SEM), normality of data distribution analysis & model fit measures. Normality of Data Distribution Analysis & Model Fit Measures, 4, 1-5.
[44] Zhao, Q. (2024). A Leptokurtic Distribution Explains Volatility Skew and Smile. Journal of Derivatives, 32(1), 81–108. doi:10.3905/jod.2024.1.211.
[45] Jorgensen, T. D., & Johnson, A. R. (2022). How to Derive Expected Values of Structural Equation Model Parameters when Treating Discrete Data as Continuous. Structural Equation Modeling, 29(4), 639–650. doi:10.1080/10705511.2021.1988609.
[46] Verma, J., & Verma, P. (2023). Understanding Structural Equation Modeling. Springer, Cham, Switzerland. doi:10.1007/978-981-15-3793-6.
[47] Adhikari, G. P. (2022). Interpreting the Basic Results of Multiple Linear Regression. Scholars’ Journal, 22–37. doi:10.3126/scholars.v5i1.55775.
[48] Dash, G., & Paul, J. (2021). CB-SEM vs PLS-SEM methods for research in social sciences and technology forecasting. Technological Forecasting and Social Change, 173, 121092. doi:10.1016/j.techfore.2021.121092.
[49] Gokmen, S., Dagalp, R., & Kilickaplan, S. (2022). Multicollinearity in measurement error models. Communications in Statistics - Theory and Methods, 51(2), 474–485. doi:10.1080/03610926.2020.1750654.
[50] Sureshchandar, G. S. (2023). Quality 4.0 – a measurement model using the confirmatory factor analysis (CFA) approach. International Journal of Quality and Reliability Management, 40(1), 280–303. doi:10.1108/IJQRM-06-2021-0172.
[51] Groskurth, K., Bluemke, M., & Lechner, C. M. (2024). Why we need to abandon fixed cutoffs for goodness-of-fit indices: An extensive simulation and possible solutions. Behavior Research Methods, 56(4), 3891-3914. doi:10.3758/s13428-023-02193-3.
[52] Marsh, H., & Alamer, A. (2024). When and how to use set-exploratory structural equation modelling to test structural models: A tutorial using the R package Lavaan. British Journal of Mathematical and Statistical Psychology, 77(3), 459–476. doi:10.1111/bmsp.12336.
[53] Chicco, D., Sichenze, A., & Jurman, G. (2025). A simple guide to the use of Student’s t-test, Mann-Whitney U test, Chi-squared test, and Kruskal-Wallis test in biostatistics. BioData Mining, 18(1), 56. doi:10.1186/s13040-025-00465-6.
[54] Cai, L., Chung, S. W., & Lee, T. (2023). Incremental Model Fit Assessment in the Case of Categorical Data: Tucker–Lewis Index for Item Response Theory Modeling. Prevention Science, 24(3), 455–466. doi:10.1007/s11121-021-01253-4.
[55] Yin, Y., Shi, D., & Fairchild, A. J. (2023). The Effect of Model Size on the Root Mean Square Error of Approximation (RMSEA): The Nonnormal Case. Structural Equation Modeling, 30(3), 378–392. doi:10.1080/10705511.2022.2127729.
[56] Pavlov, G., Maydeu-Olivares, A., & Shi, D. (2021). Using the Standardized Root Mean Squared Residual (SRMR) to Assess Exact Fit in Structural Equation Models. Educational and Psychological Measurement, 81(1), 110–130. doi:10.1177/0013164420926231.
[57] Cheung, M. W. L. (2022). Synthesizing Indirect Effects in Mediation Models with Meta-Analytic Methods. Alcohol and Alcoholism, 57(1), 5–15. doi:10.1093/alcalc/agab044.
[58] Berggren, M. (2024). Coefficients of Determination Measured on the Same Scale as the Outcome: Alternatives to R2 That Use Standard Deviations Instead of Explained Variance. Psychological Methods. doi:10.1037/met0000681.
[59] Iheaka, V. C. (2025). Diagnosing and correcting violations of normality and constant variance assumptions in multiple linear regression analysis. International Journal of Advanced Statistics and Probability, 12(1), 17–27. doi:10.14419/8bnsc148.
[60] West, S. G., Taylor, A. B., & Wu, W. (2012). Model fit and model selection in structural equation modeling. Handbook of Structural Equation Modeling, 1(1), 209-231.
[61] Momin, M. M., Lee, S., Wray, N. R., & Lee, S. H. (2023). Significance tests for R2 of out-of-sample prediction using polygenic scores. American Journal of Human Genetics, 110(2), 349–358. doi:10.1016/j.ajhg.2023.01.004.
[62] Deng, L., & Yuan, K. H. (2023). Which method is more powerful in testing the relationship of theoretical constructs? A meta comparison of structural equation modeling and path analysis with weighted composites. Behavior Research Methods, 55(3), 1460–1479. doi:10.3758/s13428-022-01838-z.
[63] Brosseau, M. (2025). Electrical Vehicles and Li-ion Battery Manufacturing - What are the actual risks for workers? IEEE IAS Electrical Safety Workshop, 1–8. doi:10.1109/ESW58401.2025.11159135.
[64] Shen, Y., & Zhou, P. (2024). Technological anxiety: Analysis of the impact of industrial intelligence on employment in China. Chinese Journal of Population Resources and Environment, 22(3), 343–355. doi:10.1016/j.cjpre.2024.09.013.
[65] Singh, N. (2024). Developing psychological safety in organizations. Journal of Psychology & Clinical Psychiatry, 15(1), 9–11. doi:10.15406/jpcpy.2024.15.00754.
[66] Lee, C. (2024). Navigating Change: Understanding Employee Attitudes and Behaviors during Periods of Management Transition. International Journal of Multidisciplinary Studies and Innovative Research, 12(1), 1769–1775.
[67] Dégallier-Rochat, S., Kurpicz-Briki, M., Endrissat, N., & Yatsenko, O. (2022). Human augmentation, not replacement: A research agenda for AI and robotics in the industry. Frontiers in Robotics and AI, 9, 997386. doi:10.3389/frobt.2022.997386.
[68] Al-Alawi, A. I., & Al-Ahmed, S. A. (2025, April). Integration of AI in capturing tacit knowledge of employees leading to innovation in organizational learning: a literature review. 2024 International Conference on IT Innovation and Knowledge Discovery (ITIKD), 1-8. doi:10.1109/ITIKD63574.2025.11005249.
[69] Huang, X., Lin, Y., Lim, M. K., Tseng, M. L., & Zhou, F. (2021). The influence of knowledge management on adoption intention of electric vehicles: perspective on technological knowledge. Industrial Management and Data Systems, 121(7), 1481–1495. doi:10.1108/IMDS-07-2020-0411.
[70] Lind, S. J. (2025). Can AI-powered avatars replace human trainers? An empirical test of synthetic humanlike spokesperson applications. Journal of Workplace Learning, 37(1), 19–40. doi:10.1108/JWL-04-2024-0075.
[71] Rosilius, M., Hügel, L., Wirsing, B., Geuen, M., von Eitzen, I., Bräutigam, V., & Ludwig, B. (2024). Development and Evaluation of Training Scenarios for the Use of Immersive Assistance Systems. Applied System Innovation, 7(5), 73. doi:10.3390/asi7050073.
[72] Waykar, Y. (2023). Tacit Knowledge: Understanding the Enigmatic Underpinnings of Human Expertise. International Journal of Scientific Research in Engineering and Management, 7(1), 1–12.
[73] Pongnumkul, S., & Motohashi, K. (2025). Measuring technology catching up of automotive industry in Thailand by text mining of patent information. Asia Pacific Journal of Innovation and Entrepreneurship, 19(4), 316–328. doi:10.1108/apjie-09-2024-0187.
[74] Kuzior, A. (2022). Technological Unemployment in the Perspective of Industry 4.0 Development. Virtual Economics, 5(1), 7–23. doi:10.34021/VE.2022.05.01(1).
[75] Muzam, J. (2023). The Challenges of Modern Economy on the Competencies of Knowledge Workers. Journal of the Knowledge Economy, 14(2), 1635–1671. doi:10.1007/s13132-022-00979-y.
[76] Shi, L. (2025). Global Perspectives on AI Competence Development: Analyzing National AI Strategies in Education and Workforce Policies. Human Resource Development Review, 24(4), 447–476. doi:10.1177/15344843251332360.
[77] Main, J. B., Pantoja, C., Lord, S. M., Mobley, C., & Brawner, C. (2025). Transitioning into engineering education: Diverse pathways of military veterans. Journal of Engineering Education, 114(2), 20628. doi:10.1002/jee.20628.
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