The cars industry has undergone significant technological advancements, with data analytics and artificial intelligence (AI) reshaping its operations. This study aims to examine the revolutionary influence of artificial intelligence and data analytics on the cars sector, particularly in terms of supporting sustainable business practices and enhancing profitability. Technology-organization-environment model and the triple bottom line technique were both used in this study to estimate the influence of technological factors, organizational factors, and environmental factors on social, environmental (planet), and economic. The data for this research was collected through a structured questionnaire containing closed questions. A total of 327 participants responded to the questionnaire from different professionals in the cars sector. The study was conducted in the cars industry, where the problem of the study revolved around addressing artificial intelligence in its various aspects and how it can affect sustainable business practices and firms’ profitability. The study highlights that the cars industry sector can be transformed significantly by using AI and data analytics within the TOE framework and with a focus on triple bottom line (TBL) outputs. However, in order to fully benefit from these advantages, new technologies need to be implemented while maintaining moral and legal standards and continuously developing them. This approach has the potential to guide the cars industry towards a future that is environmentally friendly, economically feasible, and socially responsible. The paper’s primary contribution is to assist professionals in the industry in strategically utilizing Artificial Intelligence and data analytics to advance and transform the industry.

1. Abduljabbar, R., Dia, H., Liyanage, S., et al. (2019). Applications of Artificial Intelligence in Transport: An Overview. Sustainability, 11(1), 189. https://doi.org/10.3390/su11010189
2. Acciarini, C., Borelli, F., Capo, F., et al. (2022). Can digitalization favour the emergence of innovative and sustainable business models? A qualitative exploration in the cars sector. Journal of Strategy and Management, 15(3), 335–352. https://doi.org/10.1108/JSMA-02-2021-0033
3. Akter, M. S. (2024). Harnessing Technology for Environmental Sustainability: Utilizing AI to Tackle Global Ecological Challenge. Journal of Artificial Intelligence General Science (JAIGS), 2(1), 61–70. https://doi.org/10.60087/jaigs.v2i1.97
4. Akter, S., Michael, K., Uddin, M. R., et al. (2022). Transforming business using digital innovations: The application of AI, blockchain, cloud and data analytics. Annals of Operations Research, 308(1–2), 7–39. https://doi.org/10.1007/s10479-020-03620-w
5. Alawadhi, S. A., Zowayed, A., Abdulla, H., et al. (2022). Impact of Artificial Intelligence on Information Security in Business. In: Proceedings of the 2022 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems (ICETSIS). https://doi.org/10.1109/icetsis55481.2022.9888871
6. Alenizi, F. A., Abbasi, S., Hussein Mohammed, A., et al. (2023). The artificial intelligence technologies in Industry 4.0: A taxonomy, approaches, and future directions. Computers & Industrial Engineering, 185, 109662. https://doi.org/10.1016/j.cie.2023.109662
7. Ali, B. J. (2022). Integration of Supply Chains and Operational Performance: The Moderating Effects of Knowledge. Information Sciences Letters, 11(04), 1069-1076. doi:10.18576/isl/110407
8. Al-Mobaideen, H. O. (2014). The Impact of Change Management on the Application Enterprise Resource Planning System (ERP) Effectiveness: Field Study in Jordan Bromine Company. Journal of Management Research, 6(4), 79. https://doi.org/10.5296/jmr.v6i4.6137
9. Al-Mobaideen, H., A. Al-Bashabsheh, S., Basioni, E., & Al-khawaldeh, A. (2013). Electronic government services and benefits in Jordan. International Journal of Academic Research, 5(6), 206–215. https://doi.org/10.7813/2075-4124.2013/5-6/a.26
10. Alqaraleh, M. H., Almari, M. O. S., Ali, B., et al. (2022). The mediating role of organizational culture on the relationship between information technology and internal audit effectiveness. Corporate Governance and Organizational Behavior Review, 6(1), 8–18. https://doi.org/10.22495/cgobrv6i1p1
11. Al-Rawashdeh, O. M., Jawabreh, O., & Ali, B. J. (2023). Supply Chain Management and Organizational Performance: The Moderating Effect of Supply Chain Complexity. Information Sciences Letters, 12(3), 1673–1684. https://doi.org/10.18576/isl/120351
12. Alsheiabni, S., Cheung, Y., & Messom, C. (2019). Factors inhibiting the adoption of artificial intelligence at organizational-level: A preliminary investigation.‏‏‎ Available online: https://researchmgt.monash.edu/ws/portalfiles/portal/287736273/287674072_oa.pdf (accessed on 2 June 2012).
13. Barredo Arrieta, A., Díaz-Rodríguez, N., Del Ser, J., et al. (2020). Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI. Information Fusion, 58, 82–115. https://doi.org/10.1016/j.inffus.2019.12.012
14. Baslom, M. M. M., & Tong, S. (2019). Strategic Management of Organizational Knowledge and Employee’s Awareness About Artificial Intelligence with Mediating Effect of Learning Climate. International Journal of Computational Intelligence Systems, 12(2), 1585–1591. https://doi.org/10.2991/ijcis.d.191025.002
15. Bharadiya, J. P. (2023). Machine Learning and AI in Business Intelligence: Trends and Opportunities. International Journal of Computer (IJC), (48), 123–134.
16. Borges, A. F., Laurindo, F. J., Spínola, M. M., et al. (2021). The strategic use of artificial intelligence in the digital era: Systematic literature review and future research directions. International Journal of Information Management, 57, 102225. https://doi.org/10.1016/j.ijinfomgt.2020.102225
17. Bose, S., Dey, S. K., & Bhattacharjee, S. (2023). Big Data, Data Analytics and Artificial Intelligence in Accounting: An Overview. Handbook of Big Data Research Methods, 32–51. https://doi.org/10.4337/9781800888555.00007
18. Braganza, A., Chen, W., Canhoto, A., et al. (2021). Productive employment and decent work: The impact of AI adoption on psychological contracts, job engagement and employee trust. 0148-2963, Journal of Business Research, 131, 485–494. https://doi.org/10.1016/j.jbusres.2020.08.018
19. Bughin, J., Seong, J., Manyika, J., et al. (2018). Notes from the AI frontier: Modeling the impact of AI on the world economy. Available online: https://www.mckinsey.com/featured-insights/artificial-intelligence/notes-from-the-ai-frontier-modeling-the-impact-of-ai-on-the-world-economy (accessed on 2 July 2013)
20. Cao, L., Sarkar, S., Ramesh, B., et al. (2024). Shift of ambidexterity modes: An empirical investigation of the impact of artificial intelligence in customer service. International Journal of Information Management, 76, 102773. https://doi.org/10.1016/j.ijinfomgt.2024.102773
21. Cebollada, S., Payá, L., Flores, M., et al. (2021). A state-of-the-art review on mobile robotics tasks using artificial intelligence and visual data. Expert Systems with Applications, 167, 114195. https://doi.org/10.1016/j.eswa.2020.114195
22. Chauhan, C., Parida, V., & Dhir, A. (2022). Linking circular economy and digitalisation technologies: A systematic literature review of past achievements and future promises. Technological Forecasting and Social Change, 177, 121508. https://doi.org/10.1016/j.techfore.2022.121508
23. Chen, C. (2021). Deep learning for cars predictive maintenance under Industry 4.0 [School of Engineering]. Available online: https://orca.cardiff.ac.uk/id/eprint/137968/ (accessed on 22 June 2013).
24. Chen, L., Chen, P., & Lin, Z. (2020). Artificial Intelligence in Education: A Review. IEEE Access, 8, 75264–75278. https://doi.org/10.1109/access.2020.2988510
25. Cherviakova, V., & Cherviakova, T. (2018). Value Opportunities for Cars Manufacturers in Conditions of Digital Transformation of the Cars Industry. Journal of Applied Economic Sciences (JAES), (62), 2351–2362.
26. Choi, T. M., Wallace, S. W., & Wang, Y. (2018). Big Data Analytics in Operations Management. Production and Operations Management, 27(10), 1868–1883. https://doi.org/10.1111/poms.12838
27. Cockburn, I. M., Henderson, R., & Stern, S. S. (2018). The impact of artificial intelligence on innovation: An exploratory analysis‏‏. National Bureau of Economic Research. https://doi.org/10.3386/w24449
28. Davenport, T. H., & Ronanki, R. (2018). Artificial intelligence for the real world. Available online: http://blockqai.com/wp-content/uploads/2021/01/analytics-hbr-ai-for-the-real-world.pdf (accessed on 25 June 2013).
29. Delipetrev, B., Tsinaraki, C., & Kostic, U. (2020). Historical Evolution of Artificial Intelligence. Available online: https://eprints.ugd.edu.mk/28050/ (accessed on 12 June 2013).
30. Dobrescu, E. M., & Dobrescu, E. M. (2018). Artificial intelligence (AI)-the technology that shapes the world. Available online: http://www.globeco.ro/wp-content/uploads/vol/split/vol_6_no_2/geo_2018_vol6_no2_art_006.pdf (accessed on 3 June 2013).
31. Dou, R., Zhuang, G., Liu, X., et al. (2024). Potential of AI for service performance of manufacturers: Analytical and empirical insights. Advanced Engineering Informatics, 60, 102383. https://doi.org/10.1016/j.aei.2024.102383
32. Duarte, S. P., Lobo, A., Ferreira, S., et al. (2024). Driving as a Service: Promoting a Sustainable Transition to Automated Driving. Sustainability, 16(7), 2809. https://doi.org/10.3390/su16072809
33. Dubey, R., Gunasekaran, A., & Childe, S. J. (2018). Big data analytics capability in supply chain agility. Management Decision, 57(8), 2092–2112. https://doi.org/10.1108/MD-01-2018-0119
34. Dubey, R., Gunasekaran, A., Childe, S. J., et al. (2019). Big Data and Predictive Analytics and Manufacturing Performance: Integrating Institutional Theory, Resource‐Based View and Big Data Culture. British Journal of Management, 30(2), 341–361. https://doi.org/10.1111/1467-8551.12355
35. Dwivedi, A., Moktadir, M. A., Chiappetta Jabbour, C. J., et al. (2022). Integrating the circular economy and industry 4.0 for sustainable development: Implications for responsible footwear production in a big data-driven world. Technological Forecasting and Social Change, 175, 121335. https://doi.org/10.1016/j.techfore.2021.121335
36. Dwivedi, Y. K., Hughes, L., Ismagilova, E., et al. (2021). Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. International Journal of Information Management, 57, 101994. https://doi.org/10.1016/j.ijinfomgt.2019.08.002
37. Dwivedi, Y. K., Kshetri, N., Hughes, L., et al. (2023). Opinion Paper: “So what if ChatGPT wrote it?” Multidisciplinary perspectives on opportunities, challenges and implications of generative conversational AI for research, practice and policy. International Journal of Information Management, 71, 102642. https://doi.org/10.1016/j.ijinfomgt.2023.102642
38. Dwivedi, Y. K., Sharma, A., Rana, N. P., et al. (2023). Evolution of artificial intelligence research in Technological Forecasting and Social Change: Research topics, trends, and future directions. Technological Forecasting and Social Change, 192, 122579. https://doi.org/10.1016/j.techfore.2023.122579
39. Eldem, B., Kluczek, A., & Bagiński, J. (2022). The COVID-19 Impact on Supply Chain Operations of Cars Industry: A Case Study of Sustainability 4.0 Based on Sense-Adapt-Transform Framework. Sustainability, 14(10), 5855. https://doi.org/10.3390/su14105855
40. Enholm, I. M., Papagiannidis, E., Mikalef, P., et al. (2022). Artificial Intelligence and Business Value: A Literature Review. Information Systems Frontiers, 24(5), 1709–1734. https://doi.org/10.1007/s10796-021-10186-w
41. Eriksson, T., Bigi, A., & Bonera, M. (2020). Think with me, or think for me? On the future role of artificial intelligence in marketing strategy formulation. The TQM Journal, 32(4), 795–814. https://doi.org/10.1108/TQM-12-2019-0303
42. Faulks, B., Song, Y., Waiganjo, M., et al. (2021). Impact of Empowering Leadership, Innovative Work, and Organizational Learning Readiness on Sustainable Economic Performance: An Empirical Study of Companies in Russia during the COVID-19 Pandemic. Sustainability, 13(22), 12465. https://doi.org/10.3390/su132212465
43. Fleck, J. (2018). Development and Establishment in Artificial Intelligence. In: Bloomfield, B. P. (editor). The Question of Artificial Intelligence: Philosophical and Sociological Perspectives, 1st ed. Routledge. Taylor and Francis. pp. 106–164. https://doi.org/10.4324/9780429505331-3
44. Foud Ali, A., Zowayed, S. I., Showaiter, D. A., et al. (2022). Artificial intelligence’s potential on Bahrain’s labour market. In: Proceedings of the 2022 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems (ICETSIS).
45. Fredström, A., Wincent, J., Sjödin, D., et al. (2021). Tracking innovation diffusion: AI analysis of large-scale patent data towards an agenda for further research. Technological Forecasting and Social Change, 165, 120524. https://doi.org/10.1016/j.techfore.2020.120524
46. García-Olivares, A., Solé, J., & Osychenko, O. (2018). Transportation in a 100% renewable energy system. Energy Conversion and Management, 158, 266–285. https://doi.org/10.1016/j.enconman.2017.12.053
47. Garikapati, D., & Shetiya, S. S. (2024). Autonomous Vehicles: Evolution of Artificial Intelligence and the Current Industry Landscape. Big Data and Cognitive Computing, 8(4), 42. https://doi.org/10.3390/bdcc8040042
48. Goralski, M. A., & Tan, T. K. (2020). Artificial intelligence and sustainable development. The International Journal of Management Education, 18(1), 100330. https://doi.org/10.1016/j.ijme.2019.100330
49. Groumpos, P. P. (2023). A Critical Historic Overview of Artificial Intelligence: Issues, Challenges, Opportunities and Threats. Artificial Intelligence and Applications, 1(4), 197–213. https://doi.org/10.47852/bonviewAIA3202689
50. Gryth, E., & Rundberg, D. (2018). Am AI ready? Investigating the impacts of Artificial Intelligence on business within the cars industry. Available online: https://gupea.ub.gu.se/handle/2077/57242 (accessed on 14 June 2013).
51. Günther, M., Kacperski, C., & Krems, J. F. (2020). Can electric vehicle drivers be persuaded to eco-drive? A field study of feedback, gamification and financial rewards in Germany. Energy Research & Social Science, 63, 101407. https://doi.org/10.1016/j.erss.2019.101407
52. Gurjar, K., Jangra, A., Baber, H., et al. (2024). An Analytical Review on the Impact of Artificial Intelligence on the Business Industry: Applications, Trends, and Challenges. IEEE Engineering Management Review, 52(2), 84–102. https://doi.org/10.1109/emr.2024.3355973
53. Gürlek, M., & Tuna, M. (2018). Reinforcing competitive advantage through green organizational culture and green innovation. The Service Industries Journal, 38(7–8), 467–491. https://doi.org/10.1080/02642069.2017.1402889
54. Hair, J. F., Hult, G. T. M., Ringle, C. M., et al. (2017). A primer on partial least squares structural equation modeling (PLS-SEM), 2nd ed. Sage.
55. Hassan, W. M., Aldoseri, D. T., Saeed, M. M., et al. (2022). Utilization of Artificial Intelligence and Robotics Technology in Business. In: Proceedings of the 2022 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems (ICETSIS). https://doi.org/10.1109/icetsis55481.2022.9888895
56. Hradecky, D., Kennell, J., Cai, W., et al. (2022). Organizational readiness to adopt artificial intelligence in the exhibition sector in Western Europe. International Journal of Information Management, 65, 102497. https://doi.org/10.1016/j.ijinfomgt.2022.102497
57. Huang, M. H., & Rust, R. T. (2018). Artificial Intelligence in Service. Journal of Service Research, 21(2), 155–172. https://doi.org/10.1177/1094670517752459
58. Jahangir, H., Tayarani, H., Ahmadian, A., et al. (2019). Charging demand of Plug-in Electric Vehicles: Forecasting travel behavior based on a novel Rough Artificial Neural Network approach. Journal of Cleaner Production, 229, 1029–1044. https://doi.org/10.1016/j.jclepro.2019.04.345
59. Jamwal, A., Agrawal, R., Sharma, M., et al. (2021). Industry 4.0 Technologies for Manufacturing Sustainability: A Systematic Review and Future Research Directions. Applied Sciences, 11(12), 5725. https://doi.org/10.3390/app11125725
60. Javaid, M., Haleem, A., Singh, R. P., et al. (2021). Significance of Quality 4.0 towards comprehensive enhancement in manufacturing sector. Sensors International, 2, 100109. https://doi.org/10.1016/j.sintl.2021.100109
61. Javaid, M., Haleem, A., Singh, R. P., et al. (2022). Artificial Intelligence Applications for Industry 4.0: A Literature-Based Study. Journal of Industrial Integration and Management, 07(01), 83–111. https://doi.org/10.1142/S2424862221300040
62. Jawabreh, O., Shniekat, N., Saleh, M. M. A., et al. (2022). The strategic deployment of information systems attributes and financial performance in the hospitality industry. Information Sciences Letters, 11(5), https://doi.org/10.18576/isl/110510
63. Jöhnk, J., Weißert, M., & Wyrtki, K. (2021). Ready or Not, AI Comes—An Interview Study of Organizational AI Readiness Factors. Business & Information Systems Engineering, 63(1), 5–20. https://doi.org/10.1007/s12599-020-00676-7
64. Judijanto, L., Pugu, M. R., Prayoga, A. S., et al. (2024). Economic Optimization in the Age of Artificial Intelligence: Opportunities and Challenges. International Journal of Economic Literature (INJOLE), 2(6), 1590–1600. https://injole.joln.org/index.php/ijle/article/view/187
65. Jung, K. B., Kang, S. W., & Choi, S. B. (2020). Empowering Leadership, Risk-Taking Behavior, and Employees’ Commitment to Organizational Change: The Mediated Moderating Role of Task Complexity. Sustainability, 12(6), 2340. https://doi.org/10.3390/su12062340
66. Khayyam, H., Javadi, B., Jalili, M., et al. (2020). Artificial Intelligence and Internet of Things for Autonomous Vehicles. In: Jazar, R. N. & Dai, L. (editors). Nonlinear Approaches in Engineering Applications: Cars Applications of Engineering Problems, 1st ed. Springer International Publishing. pp. 39–68. https://doi.org/10.1007/978-3-030-18963-1_2
67. Kolasani, S. (2024). Connected Cars and Autonomous Vehicles: Personalizing Owner/Customer Experiences and Innovation using AI, IoT, Blockchain, and Big Data. International Numeric Journal of Machine Learning and Robots, 8(8), 1–17.
68. Kopka, A., & Grashof, N. (2022). Artificial intelligence: Catalyst or barrier on the path to sustainability? Technological Forecasting and Social Change, 175, 121318. https://doi.org/10.1016/j.techfore.2021.121318
69. Kumar, G., & Altalbe, A. (2024). Artificial intelligence (AI) advancements for transportation security: In-depth insights into electric and aerial vehicle systems. Environment, Development and Sustainability, 1–51. https://doi.org/10.1007/s10668-024-04790-4
70. Lee, M. (2020). An analysis of the effects of artificial intelligence on electric vehicle technology innovation using patent data. World Patent Information, 63, 102002. https://doi.org/10.1016/j.wpi.2020.102002
71. Li, J, Liu, Y., Yue, L., et al. (2019). Artificial Intelligence Governed by Laws and Regulations. In: Jin, D. (editor). Springer. pp. 61–97. https://doi.org/10.1007/978-981-13-2209-9_3
72. Li, J., Cheng, H., Guo, H., et al. (2018). Survey on Artificial Intelligence for Vehicles. Cars Innovation, 1(1), 2–14. https://doi.org/10.1007/s42154-018-0009-9
73. Li, J., Li, M., Wang, X., et al. (2021). Strategic Directions for AI: The Role of CIOs and Boards of Directors. MIS Quarterly, 45(3), 1603–1644. https://doi.org/10.25300/MISQ/2021/16523
74. Li, J., Ye, Z., & Zhang, C. (2022). Study on the interaction between big data and artificial intelligence. Systems Research and Behavioral Science, 39(3), 641–648. https://doi.org/10.1002/sres.2878
75. Liu, C., Huang, W., & Yang, C. (2017). The evolutionary dynamics of China’s electric vehicle industry—Taxes vs. subsidies. Computers & Industrial Engineering, 113, 103–122. https://doi.org/10.1016/j.cie.2017.08.026
76. Liu, J., Kong, X., Xia, F., et al. (2018). Artificial Intelligence in the 21st Century. IEEE Access, 6, 34403–34421. https://doi.org/10.1109/access.2018.2819688
77. Llopis-Albert, C., Rubio, F., & Valero, F. (2021). Impact of digital transformation on the cars industry. Technological Forecasting and Social Change, 162, 120343. https://doi.org/10.1016/j.techfore.2020.120343
78. Lü, X., Wu, Y., Lian, J., et al. (2020). Energy management of hybrid electric vehicles: A review of energy optimization of fuel cell hybrid power system based on genetic algorithm. Energy Conversion and Management, 205, 112474. https://doi.org/10.1016/j.enconman.2020.112474
79. Lukin, E., Krajnović, A., & Bosna, J. (2022). Sustainability Strategies and Achieving SDGs: A Comparative Analysis of Leading Companies in the Cars Industry. Sustainability, 14(7), 4000. https://doi.org/10.3390/su14074000
80. Makarius, E. E., Mukherjee, D., Fox, J. D., et al. (2020). Rising with the machines: A sociotechnical framework for bringing artificial intelligence into the organization. Journal of Business Research, 120, 262–273. https://doi.org/10.1016/j.jbusres.2020.07.045
81. Mayan, S. N. A., & Nor, R. M. (2017). Prospects and Challenges of Ecotourism Sector and Poverty Eradication in Sabah: The Case of Orangutans and Mabul Island. Global Journal of Social Sciences Studies, 3(1), 1–12. https://doi.org/10.20448/807.3.1.1.12
82. Mbaidin, H. (2022). The mediating effect of entrepreneurial orientation on the impact of knowledge management processes on successful organizational performance at industrial sector in Jordan. Journal of Intelligence Studies in Business, 12(1), 20–33. https://doi.org/10.37380/jisib.v12i1.916
83. Mbaidin, H. (2024). E-learning’s influence on organizational excellence in UAE universities: Exploring the moderating role of demographic variables. International Journal of Data and Network Science, 8(1), 609–616. https://doi.org/10.5267/j.ijdns.2023.8.024
84. Mbaidin, H. O. (2020). Human Resources Information Systems and Their Impact on Employee Performance Assessment Strategy: A Practical Study on Jordan Telecom Company in the Hashemite Kingdom of Jordan. In: Proceedings of fifth International Congress on Information and Communication Technology. Springer. pp. 54–74. https://doi.org/10.1007/978-981-15-5856-6_6
85. Mbaidin, H. O., Alsmairat, M. A., & Al-Adaileh, R. (2023). Blockchain adoption for sustainable development in developing countries: Challenges and opportunities in the banking sector. International Journal of Information Management Data Insights, 3(2), 100199. https://doi.org/10.1016/j.jjimei.2023.100199
86. Mer, A., & Srivastava, A. (2023). Employee Engagement in the New Normal: Artificial Intelligence as a Buzzword or a Game Changer? The Adoption and Effect of Artificial Intelligence on Human Resources Management, 15–46. https://doi.org/10.1108/978-1-80382-027-920231002
87. Mhlanga, D. (2021). Artificial Intelligence in the Industry 4.0, and Its Impact on Poverty, Innovation, Infrastructure Development, and the Sustainable Development Goals: Lessons from Emerging Economies? Sustainability, 13(11), 5788. https://doi.org/10.3390/su13115788
88. Mihet-Popa, L., & Saponara, S. (2018). Toward Green Vehicles Digitalization for the Next Generation of Connected and Electrified Transport Systems. Energies, 11(11), 3124. https://doi.org/10.3390/en11113124
89. Mikalef, P., & Gupta, M. (2021). Artificial intelligence capability: Conceptualization, measurement calibration, and empirical study on its impact on organizational creativity and firm performance. Information & Management, 58(3), 103434. https://doi.org/10.1016/j.im.2021.103434
90. Mishra, A. N., & Pani, A. K. (2021). Business value appropriation roadmap for artificial intelligence. VINE Journal of Information and Knowledge Management Systems, 51(3), 353–368. https://doi.org/10.1108/VJIKMS-07-2019-0107
91. Moslehpour, M., Chau, K. Y., Tu, Y. T., et al. (2022). Impact of corporate sustainable practices, government initiative, technology usage, and organizational culture on cars industry sustainable performance. Environmental Science and Pollution Research, 29(55), 83907–83920. https://doi.org/10.1007/s11356-022-21591-2
92. Nazat, S., Li, L., & Abdallah, M. (2024). XAI-ADS: An Explainable Artificial Intelligence Framework for Enhancing Anomaly Detection in Autonomous Driving Systems. IEEE Access, 12, 48583–48607. https://doi.org/10.1109/access.2024.3383431
93. Neumann, O., Guirguis, K., & Steiner, R. (2022). Exploring artificial intelligence adoption in public organizations: a comparative case study. Public Management Review, 26(1), 114–141. https://doi.org/10.1080/14719037.2022.2048685
94. Ofosu-Ampong, K. (2024). Artificial intelligence research: A review on dominant themes, methods, frameworks and future research directions. Telematics and Informatics Reports, 14, 100127. https://doi.org/10.1016/j.teler.2024.100127
95. Oh, S. I., & Kang, H. B. (2017). Object Detection and Classification by Decision-Level Fusion for Intelligent Vehicle Systems. Sensors, 17(1), 207. https://doi.org/10.3390/s17010207
96. Olan, F., Arakpogun, E. O., Suklan, J., et al. (2022). Artificial intelligence and knowledge sharing: Contributing factors to organizational performance. Journal of Business Research, 145, 605–615. https://doi.org/10.1016/j.jbusres.2022.03.008
97. Paluch, S., Antons, D., Brettel, M., et al. (2020). Stage-gate and agile development in the digital age: Promises, perils, and boundary conditions. Journal of Business Research, 110, 495–501. https://doi.org/10.1016/j.jbusres.2019.01.063
98. Papadopoulos, T., Baltas, K. N., & Balta, M. E. (2020). The use of digital technologies by small and medium enterprises during COVID-19: Implications for theory and practice. International Journal of Information Management, 55, 102192. https://doi.org/10.1016/j.ijinfomgt.2020.102192
99. Parida, V., Sjödin, D., & Reim, W. (2019). Reviewing Literature on Digitalization, Business Model Innovation, and Sustainable Industry: Past Achievements and Future Promises. Sustainability, 11(2), 391. https://doi.org/10.3390/su11020391
100. Peng, Y., Ahmad, S. F., Ahmad Y. A., et al. (2023). Riding the Waves of Artificial Intelligence in Advancing Accounting and Its Implications for Sustainable Development Goals. Sustainability, 15(19), 14165. https://doi.org/10.3390/su151914165
101. Pietronudo, M. C., Croidieu, G., & Schiavone, F. (2022). A solution looking for problems? A systematic literature review of the rationalizing influence of artificial intelligence on decision-making in innovation management. Technological Forecasting and Social Change, 182, 121828. https://doi.org/10.1016/j.techfore.2022.121828
102. Pumplun, L., Tauchert, C., & Heidt, M. (2019). A new organizational chassis for artificial intelligence-exploring organizational readiness factors‏‏. In: Proceedings of the European Conference on Information Systems.
103. Rafiey, M. (2024). Using artificial intelligence to analyze and improve financial decisions using big data modeling and analysis [Master’s thesis]. Politecnico di Torino.
104. Rahmani, A. M., Azhir, E., Ali, S., et al. (2021). Artificial intelligence approaches and mechanisms for big data analytics: A systematic study. PeerJ Computer Science, 7, e488. https://doi.org/10.7717/peerj-cs.488
105. Rajput, S., & Singh, S. P. (2019). Connecting circular economy and industry 4.0. International Journal of Information Management, 49, 98–113. https://doi.org/10.1016/j.ijinfomgt.2019.03.002
106. Rehan, H. (2024). The Future of Electric Vehicles: Navigating the Intersection of AI, Cloud Technology, and Cybersecurity. International Journal of Scientific Research and Management (IJSRM), 12(04), 1127–1143. https://doi.org/10.18535/ijsrm/v12i04.ec04
107. Reu, W. A. (2023). Strategies Information Technology Managers Use to Retain Qualified Information Technology Employees ‏[PhD thesis]. Walden University.
108. Roscoe, S., Subramanian, N., Jabbour, C. J., et al. (2019). Green human resource management and the enablers of green organisational culture: Enhancing a firm’s environmental performance for sustainable development. Business Strategy and the Environment, 28(5), 737–749. https://doi.org/10.1002/bse.2277
109. Rubio, F., Llopis-Albert, C., Valero, F., et al. (2020). Sustainability and optimization in the cars sector for adaptation to government vehicle pollutant emission regulations. Journal of Business Research, 112, 561–566. https://doi.org/10.1016/j.jbusres.2019.10.050
110. Sachdeva, N., Rathore, A. K., Sondhi, N., et al. (2023). Manifestation of customer value co-creation behaviour in the cars industry: A perspective from Twitter analytics. Electronic Commerce Research, 1–38. https://doi.org/10.1007/s10660-023-09740-2
111. Sakala, C. M., & Bwalya, S. M. (2023). The Role of Artificial Intelligence in Optimizing Supply Chain Performance. Journal of Procurement and Supply Chain Management, 2(1), 1–14. https://gprjournals.org/journals/index.php/JPSCM/article/view/215
112. Salhab, H., Allahham, M., Abu-AlSondos, I., et al. (2023). Inventory competition, artificial intelligence, and quality improvement decisions in supply chains with digital marketing. Uncertain Supply Chain Management, 11(4), 1915-1924. https://doi.org/10.5267/j.uscm.2023.8.009
113. Schöggl, J. P., Baumgartner, R. J., & Hofer, D. (2017). Improving sustainability performance in early phases of product design: A checklist for sustainable product development tested in the cars industry. Journal of Cleaner Production, 140, 1602–1617. https://doi.org/10.1016/j.jclepro.2016.09.195
114. Secinaro, S., Calandra, D., Secinaro, A., et al. (2021). The role of artificial intelligence in healthcare: A structured literature review. BMC Medical Informatics and Decision Making, 21(1), 125. https://doi.org/10.1186/s12911-021-01488-9
115. Selimović, J., Pilav-Velić, A., & Krndžija, L. (2021). Digital workplace transformation in the financial service sector: Investigating the relationship between employees’ expectations and intentions. Technology in Society, 66, 101640. https://doi.org/10.1016/j.techsoc.2021.101640
116. Shahadat, M. M. H., Nekmahmud, M., Ebrahimi, P., et al (2023). Digital Technology Adoption in SMEs: What Technological, Environmental and Organizational Factors Influence in Emerging Countries? Global Business Review, 097215092211371. https://doi.org/10.1177/09721509221137199
117. Shan, R., Xiao, X., Dong, G., et al. (2022). The influence of accounting computer information processing technology on enterprise internal control under panel data simultaneous equation. Applied Mathematics and Nonlinear Sciences, 8(1), 1–9. doi:10.2478/amns.2022.2.0157
118. Shang, G., Low, S. P., & Lim, X. Y. V. (2023). Prospects, drivers of and barriers to artificial intelligence adoption in project management. Built Environment Project and Asset Management, 13(5), 629–645. https://doi.org/10.1108/BEPAM-12-2022-0195
119. Skrúcaný, T., Kendra, M., Stopka, O., et al. (2019). Impact of the Electric Mobility Implementation on the Greenhouse Gases Production in Central European Countries. Sustainability, 11(18), 4948. https://doi.org/10.3390/su11184948
120. Thuneibat, N. S. M., Ali, B., Alqaraleh, M. H., et al. (2022). The Mediating Role of Innovation on the Relationship Between Information Technologies and Reducing Tax Evasion. Information Sciences Letters, 11(2), 13–23. doi:10.18576/isl/110505
121. Toniolo, K., Masiero, E., Massaro, M., et al. (2020). Sustainable Business Models and Artificial Intelligence: Opportunities and Challenges. In: Knowledge, People, and Digital Transformation. Springer. pp. 103–117. https://doi.org/10.1007/978-3-030-40390-4_8
122. Usman, M., Khan, R., & Moinuddin, M. (2024). Assessing the Impact of Artificial Intelligence Adoption on Organizational Performance in the Manufacturing Sector. Revista Española De Documentación Científica, 18(2), 95–124.
123. Vaio, A. D., Palladino, R., Hassan, R., et al. (2020). Artificial intelligence and business models in the sustainable development goals perspective: A systematic literature review. ournal of Business Research, 121, 283–314. https://doi.org/10.1016/j.jbusres.2020.08.019
124. Walz, A., & Firth-Butterfield, K. (2019). Implementing Ethics into Artificial Intelligence: A Contribution, from a Legal Perspective, to the Development of an AI Governance Regime. Duke Law & Technology Review, 18, 176.
125. Wamba-Taguimdje, S. L., Wamba, S. F., Kamdjoug, J. R. K., et al. (2020). Influence of artificial intelligence (AI) on firm performance: The business value of AI-based transformation projects. Business Process Management Journal, 26(7), 1893–1924. https://doi.org/10.1108/BPMJ-10-2019-0411
126. Wang, Y. C., & Uysal, M. (2024). Artificial intelligence-assisted mindfulness in tourism, hospitality, and events. International Journal of Contemporary Hospitality Management, 36(4), 1262–1278. https://doi.org/10.1108/IJCHM-11-2022-1444
127. Wang, Z. (2023). Application of Data Analytics in Cars Manufacturing—Evidence from Existing Literature. Advances in Engineering Technology Research, 5(1), 428. https://doi.org/10.56028/aetr.5.1.428.2023
128. Wessel, L., Baiyere, A., Ologeanu-Taddei, R., et al. (2021). Unpacking the Difference Between Digital Transformation and IT-Enabled Organizational Transformation. Journal of the Association for Information Systems, 22(1), 102–129. https://doi.org/10.17705/1jais.00655
129. Wodecki, A. (2019). Artificial Intelligence in Value Creation: Improving Competitive Advantage, 1st ed. Springer International Publishing. https://doi.org/10.1007/978-3-319-91596-8
130. Wulandari, N. P., Mariatin, E., & Hadiyani, S. (2023). The Effect of Transformational Leadership Style and Organizational Culture on Organizational Commitment in Employees of Faculty of Tourism and Hospitality. International Journal of Progressive Sciences and Technologies, 38(2), 278. https://doi.org/10.52155/ijpsat.v38.2.5327
131. Yang, L., & Ali, B. (2022). Mathematical Statistics Technology in the Educational Grading System of Preschool Students. Applied Mathematics and Nonlinear Sciences, 8(1), 1–9. https://doi.org/10.2478/amns.2022.2.0044
132. Yang, Z., Sun, J., Zhang, Y., et al. (2017). Green, Green, It’s Green: A Triad Model of Technology, Culture, and Innovation for Corporate Sustainability. Sustainability, 9(8), 1369. https://doi.org/10.3390/su9081369
133. Yu, X., Xu, S., & Ashton, M. (2023). Antecedents and outcomes of artificial intelligence adoption and application in the workplace: the socio-technical system theory perspective. Information Technology & People, 36(1), 454–474. https://doi.org/10.1108/ITP-04-2021-0254
134. Zameer, H., Wang, Y., & Yasmeen, H. (2020). Reinforcing green competitive advantage through green production, creativity and green brand image: Implications for cleaner production in China. Journal of Cleaner Production, 247, 119119. https://doi.org/10.1016/j.jclepro.2019.119119
135. Zhao, W. W. (2018a). How to improve corporate social responsibility in the era of artificial intelligence? Available online: https://iopscience.iop.org/article/10.1088/1755-1315/186/6/012036/pdf (accessed on 26 June 2023).
136. Zhao, W. W. (2018b). Improving Social Responsibility of Artificial Intelligence by Using ISO 26000. Available online: https://iopscience.iop.org/article/10.1088/1757-899X/428/1/012049/pdf (accessed on 26 June 2023).