[1] G. Klink, M. Mathur, R. Kidambi, and K. Sen, “Contribution of the automobileindustry to technology and value creation,” Auto Tech Rev., vol. 3, no. 7, pp. 18–23 (2014).
[2] P. Kushwaha, P. Yadav, and J. Prasad, “Impact of enterprise resource planningon human resource management in automobile sector: Statistical analysis,” J. Stat.Manag. Syst., vol. 21, no. 4, pp. 601–615 (2018).
[3] Q. Demlehner, D. Schoemer, and S. Laumer, “How can artificial intelligenceenhance car manufacturing? A Delphi study-based identification and assessment ofgeneral use cases,” Int. J. Inf. Manage., vol. 58, no. 102317, p. 102317 (2021).
[4] P. Pujari, M. Arora, and A. Pandey, “Understanding factorsinfluencing technical inertia in family-run SMEs: A study on technologyadoption challenges,” Journal of Statistics and Management Systems, vol.27, no. 1,pp. 155–168 (2024).
[5] M. Baral, S. Mukherjee, R. Nagariya, S. K. Pal, Chittipaka, V, “Plan of actionrequired for the automobile sectors to deal with disruptions: A resources based andresource dependence view,” Journal of Information & Optimization Sciences, vol. 44, no. 1, pp. 127–141 (2023).
[6] K. P. Patil, D. Pramod, and S. Vijayakumar Bharathi, “MSMEs readiness foradopting artificial intelligence and machine learning,” in 2023 InternationalConference on Advanced Computing Technologies andApplications (ICACTA) (2023).
[7] H. Behrooz, Machine Learning Applications in Surface Transportation Systems: A Systematic Review (2021).
[8] A. K. Sharma, M. Kiran, P. Pauline Sherly Jeba, P. Maheshwari, and V. Divakar, “Demand forecasting using coupling of machine learning and time series models for the automotive aftermarket sector,” in 5th International Conference on Electrical, Electronics, Communication, Computer Technologies and Optimization Techniques (2021).
[9] J. Henseler, G. Hubona, and P. A. Ray, “Using PLS path modeling in new technology research: updated guidelines,” Ind. Manag. Data Syst., vol. 116, no. 1, pp. 2–20 (2016).
[10] L. Tornatzky and M. Fleischer, The process of technology innovation. Lexington, MA (1990).
[11] T. H. Nguyen, X. C. Le, and T. H. L. Vu, “An extended technology organization- environment (TOE) framework for onlineretailing utilization in digital transformation: Empirical evidence from Vietnam,” Journal of Open Innovation Technology Market and Complexity, vol. 8, no. 4 (2022).
[12] B.-N Hwang, C.-Y. Huang and C.-H. Wu, “A TOE approach to establish a green supply chain adoption decision model in the semiconductor industry,” Sustainability, vol. 8 (2016).
[13] D. Gonçalves, M. Bergquist, S. Alänge, and R. Bunk, “How digi-tal tools align with organizational agility and strengthen digital innovation in automotive startups,” Procedia Computer Science, vol. 196, pp. 107–116 (2022).
[14] Y. K. Dwivedi, L. Hughes, E. Ismagilova, G. Aarts, C. Coombs, and T. Crick, “Artificial Intelligence (AI): Multidis-ciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy,” International Journal of Information Management (2021).
[15] N. Verma, A Qualitative Analysis of the Impact of Artificial In-telligence (AI) Adoption (Focusing on Machine Learning (ML)) on the Organizational Capabilities of the Telecom Industry in Sweden and Finland (2023).
[16] M. Iranmanesh, M. Ghobakhloo, B. Foroughi, M. Nilashi, and E. Yadegaridehkordi, “Factors influencing attitude and intention to use autonomous vehicles in Vietnam: findings from PLS-SEM and ANFIS,” Inf. Technol. People, vol. 37, no. 6, pp. 2223–2246 (2024).
[17] V. Oltra and M. Jean, “Sectoral systems of environmental innovation: an application to the French automotive indus-try,” Technological Forecasting and Social Change, vol. 76, no. 4. 
[18] Á. Hőgye-Nagy, G. Kovács, and G. Kurucz, “Acceptance of self-driving cars among the university community: Effects of gender, previous experience, technology adoption propensity, and attitudes toward autonomous vehicles,” Transp. Res. Part F Traffic Psychol. Behav., vol. 94, pp. 353–361, 2023.–583 (2009).
[19] B. Ramdani, D. Chevers, and A. Williams, “SMEs’ adoption of enterpriseapplications: A technology organization environment model,” Journal of Small Business and Enterprise Development, 20, no. 4, 735-753 (2013).
[20] M. A. Hossain, C. Standing, and C. Chan, “The development and validationof a two-staged adoption model of RFID technology in livestock businesses,”Inf. Technol. People, vol. 30, no. 4, pp. 785–808 (2017).
[21] T. Oliveira and M. F. Martins, “Understanding e-business adoption a crossindustries in European countries,” Ind. Manag. Data Syst., vol. 110, no. 9, pp. 1337–1354 (2010).
[22] Y. M. Wang, Y. S. Wang, and Y. F. Yang, “Understanding the determinantsof RFID adoption in the manufacturing industry,” Technological Forecastingand Social Change, vol. 77, no. 5, pp. 803–815 (2010).
[23] K. K. Y. Kuan and P. Y. K. Chau, “A perception-based model for EDIadoption in small businesses using a technology–organization–environmentframework,” Inf. Manag., vol. 38, no. 8, pp. 507–521 (2001).
[24] F. T. S. Chan and A. Y.-L. Chong, “Determinants of mobile supply chainmanagement system diffusion: a structural equation analysis ofmanufacturing firms,” Int. J. Prod. Res., vol. 51, no. 4, pp. 1196–1213 (2013).
[25] F. Faul, E. Erdfelder, A. Buchner, and A.-G. Lang, “Statistical power analysesusing G*Power 3.1: tests for correlation and regression analyses,” Behav. Res.Methods, vol. 41, no. 4, pp. 1149–1160 (2009).
[26] J. F. Hair, G. T. M. Hult, C. M. Ringle, M. Sarstedt, N. P. Danks, and S. Ray,Partial least squares structural equation modeling (PLS-SEM) using R: Aworkbook. Springer Nature (2021).
[27] C. Fornell and D. F. Larcker, “Evaluating structural equation models withunobservable variables and measurement error,” J. Mark. Res., vol. 18, no. 1, p.39 (1981).