Strategic Decision-Making Models Using Artificial Intelligence In The Electric Vehicle Industry: A Systematic Literature Review And Bibliometric Analysis
Keywords:
Artificial Intelligence, Strategic Decision-Making, Electric Vehicle Industry, Systematic Literature Review, Bibliometric Analysis.
Abstract
The rapid growth of the electric vehicle (EV) industry has increased the complexity of strategic decision-making related to production planning, resource allocation, technology investment, and sustainability. Artificial Intelligence (AI) has become an important enabler of data-driven decision-making through machine learning, predictive analytics, decision support systems, and optimization techniques. However, existing studies remain fragmented and primarily focus on specific applications rather than integrated strategic frameworks. This study systematically reviews and maps the development of AI-enabled strategic decision-making research in the EV industry using a combination of Systematic Literature Review (SLR) and bibliometric analysis. Following the PRISMA 2020 guidelines, 32 relevant studies were selected from Scopus, ScienceDirect, and SpringerLink. The findings identify three dominant research streams: AI-based decision support systems, predictive analytics and engineering decision-making, and fuzzy logic and optimization models. The study highlights the lack of an integrated framework and proposes an Input–Process–Output (IPO)-based conceptual framework to support strategic decision-making and sustainable development in the EV industry
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Aiudi, A., Francioni, B., Kyrdoda, Y., & Amerio, S. (2025). The role of artificial intelligence in international strategic decision-making for SMEs. Journal of Strategy and Management. https://doi.org/10.1108/JSMA-06-2025-0207
Akinnagbe, O. (2024). The future of artificial intelligence: Trends and predictions. Mikailalsys Journal of Advanced Engineering International, 1(3), 249–261. https://doi.org/10.58578/mjaei.v1i3.4125
Al-Khamees, H. A. A., et al. (2026). Advancing decision-making: A comprehensive review of intelligent systems, applications, and challenges. Intelligent Systems with Applications, 21, 200631. https://doi.org/10.1016/j.iswa.2026.200631
Alaskar, T. H. (2025). Integrated AI and business analytics for sustaining data-driven and technological innovation. Sustainability, 17(19). https://doi.org/10.3390/su17198749
Ashtar, D., Mohammadi Ziabari, S. S., & Alsahag, A. M. M. (2025). Hybrid forecasting for sustainable electricity demand in the Netherlands using SARIMAX, SARIMAX-LSTM, and sequence-to-sequence deep learning models. Sustainability, 17(16). https://doi.org/10.3390/su17167192
Badmus, O., Rajput, S. A., Arogundade, J. B., & Williams, M. (2024). AI-driven business analytics and decision making. World Journal of Advanced Research and Reviews, 24(1), 616–633. https://doi.org/10.30574/wjarr.2024.24.1.3093
Binsar, F., Wahyudi, I., Atmoko, G. D. P., Nurdin, F., & Santoso, T. N. (2026). Optimizing business competitiveness through artificial intelligence: A framework for digital innovation and operational efficiency. Advances in Business and Management Research, 1, 100002. https://doi.org/10.1016/j.abmr.2026.100002
Bousia, A. (2026). The use of business intelligence and analytics in electric vehicle technology: A comprehensive survey. Electronics, 15(2), 366. https://doi.org/10.3390/electronics15020366
Carter, O. (2025). Strategic decision-making in modern organizations: Frameworks, challenges, and performance implications.
Cavus, M., Dissanayake, D., & Bell, M. (2025). Next generation of electric vehicles: AI-driven approaches for predictive maintenance and battery management. Energies, 18(5). https://doi.org/10.3390/en18051041
Chakraborty, C., Bhattacharya, M., Pal, S., & Lee, S. S. (2024). From machine learning to deep learning: Advances of the recent data-driven paradigm shift in medicine and healthcare. Current Research in Biotechnology, 7, 100164. https://doi.org/10.1016/j.crbiot.2023.100164
Collins, C., Dennehy, D., Conboy, K., & Mikalef, P. (2021). Artificial intelligence in information systems research: A systematic literature review and research agenda. International Journal of Information Management, 60, 102383. https://doi.org/10.1016/j.ijinfomgt.2021.102383
Fomude, A. H., Yang, C., Makafui, O., & Agordzo, G. K. (2026). AI model to enhance organizational decision-making for accurate predictions using a machine learning algorithm. International Journal of Information Technology and Computer Engineering, 14(1), 310–339.
Gao, J., Qiu, Y., & Chen, Z. (2025). Systemic integration of EV and autonomous driving technologies: A study of China’s intelligent mobility transition. World Electric Vehicle Journal, 16(10). https://doi.org/10.3390/wevj16100574
Gupta, P. (2024). AI driven decision support systems for business operations. International Journal of Transformations in Business Management, 14, 94–100.
Hassani, H., Silva, E. S., Unger, S., TajMazinani, M., & Mac Feely, S. (2020). Artificial intelligence (AI) or intelligence augmentation (IA): What is the future? AI, 1(2), 143–155. https://doi.org/10.3390/ai1020008
Ho, D. B., Duong, C. D., Tran, M. L., Luong, T. S., & Tran, T. P. H. (2025). Big data analytics powered by artificial intelligence and entrepreneurial resilience. Journal of Open Innovation: Technology, Market, and Complexity, 11(3), 100611. https://doi.org/10.1016/j.joitmc.2025.100611
Idrees, S. M., Alam, M. A., Agarwal, P., & Ansari, L. (2019). Effective predictive analytics and modeling based on historical data. In Communications in Computer and Information Science (pp. 552–564). Springer. https://doi.org/10.1007/978-981-13-9942-8_52
Janiesch, C., Zschech, P., & Heinrich, K. (2021). Machine learning and deep learning. Electronic Markets, 31(3), 685–695. https://doi.org/10.1007/s12525-021-00475-2
Jordan, S., & Messner, M. (2020). The use of forecast accuracy indicators to improve planning quality. European Accounting Review, 29(2), 337–359. https://doi.org/10.1080/09638180.2019.1577150
Kassa, B. Y., & Worku, E. K. (2025). The impact of artificial intelligence on organizational performance: The mediating role of employee productivity. Journal of Open Innovation: Technology, Market, and Complexity, 11(1), 100474. https://doi.org/10.1016/j.joitmc.2025.100474
Kermansaravi, A., Refaat, S. S., Trabelsi, M., & Vahedi, H. (2025). AI-based energy management strategies for electric vehicles: Challenges and future directions. Energy Reports, 11, 101–118. https://doi.org/10.1016/j.egyr.2025.04.053
Kong, Y., et al. (2024). Unlocking the power of LSTM for long-term time series forecasting. arXiv. https://arxiv.org/abs/2408.xxxxx
Krichen, M., & Mihoub, A. (2025). Long short-term memory networks: A comprehensive survey. AI, 6(9). https://doi.org/10.3390/ai6090215
Kovari, A. (2024). AI for decision support: Balancing accuracy, transparency, and trust across sectors. Information, 15(11), 725. https://doi.org/10.3390/info15110725
Kozioł-Nadolna, K., & Beyer, K. (2021). Determinants of the decision-making process in organizations. Procedia Computer Science, 192, 2375–2384. https://doi.org/10.1016/j.procs.2021.09.006
Mojumder, M. R. H., Nakib, T. H., Hasanuzzaman, M., & Abdel-Khalik, A. S. (2025). Advancements, challenges, and prospects of cost-effective electric vehicles: A review. Alexandria Engineering Journal.
Pugliese, R., Regondi, S., & Marini, R. (2021). Machine learning-based approach: Global trends, research directions, and regulatory standpoints. Data Science and Management, 4, 19–29. https://doi.org/10.1016/j.dsm.2021.12.002
Raina, K., Sharma, G. D., Taheri, B., Dev, D., & Chavriya, S. (2026). Artificial intelligence-driven management: Bridging innovation, knowledge creation, and sustainable business practices. Journal of Innovation and Knowledge, 11, 100860. https://doi.org/10.1016/j.jik.2025.100860
Published
2026-06-18
How to Cite
Rizky Stighfarrinata, Aryuanto Soetedjo, Nelly Budiarti, & Nanta Sigit. (2026). Strategic Decision-Making Models Using Artificial Intelligence In The Electric Vehicle Industry: A Systematic Literature Review And Bibliometric Analysis. International Journal of Economics, Business and Innovation Research, 5(04), 1491-1511. https://doi.org/10.63922/ijebir.v5i04.4379
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