TIM-7072 v1

Shansuddoha, M., Ahmed, K. E., Hosan, C. M., &Nasir, T. (2025). Revolutionizing supply chains: Unleashing the power of AI-driven intelligent automation and real-time information flow. Information. 16(1) p.26.
This paper identifies critical success factors and challenges in adopting intelligent automation by analyzing real-world industry implementations. The findings propose a strategic framework for organizations seeking to leverage AI to achieve operational excellence, agility, and real-time information flow, thereby enabling effective decision-making.

Artificial Intelligence in Supply Chain and Operations Management: A Multiple Case Study Research. Violetta Giada Cannas, Maria Pia Ciano, Mattia Saltalamacchia, & Raffaele Secchi. (2024). Artificial intelligence in supply chain and operations management: A multiple case study research. International Journal of Production Research, 62(9).
Although the nature of the study is not suitable for wide generalization, it offers clear guidance for practitioners facing AI dilemmas in specific Supply Chain Operations Reference (SCOR) processes. It provides the basis for future research.

Balan, G. S., Kumar, V. S., & Raj, S. A. (2025). Machine learning and artificial intelligence methods and applications for post-crisis supply chain resiliency and recovery. Journal of Supply Chain Analytics 10(100121).
This study investigates the utilization of artificial intelligence and machine learning methodologies to enhance supply chain resilience and recovery in the aftermath of these crises. Leveraging data-driven methods, these technologies provide opportunities to enhance the overall resilience of the supply chain, optimize resource allocation, and improve decision-making processes. Proposed newer measures to protect economies, national security, lives, and a more resilient future are discussed in this study.

Klophaus, R. (2025). AI-generated SWOT analysis of emerging technologies in air transportation: Potential and limitations. Journal of Research in Transportation, Business & Management. 50(101316).
This paper employs artificial intelligence (AI) to conduct a comparative SWOT (Strengths, Weaknesses, Opportunities, Threats) analysis of key emerging technologies within the domain of air transportation. The technologies under consideration are urban air mobility (UAM), unmanned aerial vehicles (UAVs), electric aircraft, supersonic aircraft, and sustainable aviation fuels (SAFs).

Perals, P. O., Rambaud, S. C., & Garcia, J. S. (2025). Does AI private investment really matter for financial institutions efficiency? Evidence from a country panel. Finance Research Open,1(2).
The main objective of this paper is to disclose the real impact that private AI investment has on different dimensions of the financial institution efficiency index at a country level. The dataset analyzed corresponds to three dimensions that the International Monetary Fund uses to measure a country's financial institution’s efficiency. Twenty-four countries were analyzed between 2014 and 2021. The methodology employed in this paper is dynamic panel data, which, after applying several tests, was found to be the best option for treating the dataset.

Saeed Awadh Bin-Nashwan, Jackie Zhanbiao Li, HaiChang Jiang, Anas Rasheed Bajary, & Muhammad M. Ma'aji (2025). Does AI adoption redefine financial reporting accuracy, auditing efficiency, and information asymmetry? An integrated model of TOE-TAM-RDT and big data governance. Computers in Human Behavior Reports,17(100572).
This study aims to empirically explore what shapes AI adoption among AAFs and what its potential role is in financial reporting accuracy, auditing efficiency, and information asymmetry. By applying a validated model of TOE-TAM-RDT and analyzing it using PLS-SEM, the findings are that AI adoption was shaped by competitive pressure, vendor ecosystem, top management support, relative advantage, AI readiness, and innovation climate. The adoption of AI-driven systems among AAFs has exerted a positive impact on financial reporting accuracy and auditing efficiency, while having a negative impact on information asymmetry. Results further reveal a significant moderating effect of big data governance, demonstrating that proper management, quality, and ethical use of data within AAFs can enhance the benefits of AI adoption, thereby improving financial reporting accuracy and auditing efficiency and mitigating information asymmetries between AAFs and stakeholders. These outcomes not only advance scholarly conversations on AI adoption in the financial and accounting landscape but also deliver actionable strategies for stakeholders to maximize the benefits of AAFs from this emerging revolutionary technology.

Hussam Al Halbusi, Simona Popa, Pedro Soto-Acosta, & Mohammad Alshallaqi. (2025). The nexus of managerial and technical AI knowledge, disruptive innovation and the circular economy: The role of organizational change capability and financial resilience. Technology in Society, 82.
This study is based on two-wave data collected from 242 general, IT, and operations managers in firms located within a central hub for the technology and manufacturing industries in Baghdad. The findings indicate that managerial and technical AI knowledge significantly boost disruptive innovation, which, in turn, enhances the circular economy. Moreover, organizational change capability and financial resilience strengthen the relationship between disruptive innovation and the circular economy. This research contributes toAI capabilities by highlighting their role in fostering disruptive innovation and promoting sustainability through the circular economy. Additionally, it provides insights into how organizational and financial resilience, alongside AI skills and knowledge, can support sustainable business models. Practically, the study emphasizes the importance for technology and manufacturing companies to invest in both managerial and technical AI skills while prioritizing robust organizational change capabilities and financial resilience to maximize the benefits of disruptive innovation and support a circular economy.

Idrissi, Z. K., Lachgar, M., & Hrimech, H. (2024). Blockchain, IoT and AI in logistics and transportation: A systematic review. Transport Economics and Management, Transport Economics and Management 2275-285.
This paper aims to conduct a systematic review of the academic literature on Blockchain, IoT, and AI in the context of Smart Logistics.

Ficherov, R., Stoev, P., Georgiev, M., & Stoeva, M. (2024). Design of a micromobility transportation unit with AI. IFAC – Papers online 58(3),215-220.
This paper proposes a micro-mobile cyber-physical system for personal transport with AI, facilitating faster city access. It outlines system components, autonomous route planning, and 3D modeling algorithms. Research on micro-mobility robots with integrated AI explores benefits and drawbacks among potential users in the US, Germany, and Bulgaria. By implementing these transport devices, the goal is to mitigate traffic and pollution challenges in cities, advancing urban transport quality toward achieving the 15-minute city model.

Lakshmi Shankar Iyer (2021). AI enabled applications towards intelligent transportation. Transportation Engineering,5(100083).
This study aims to discuss the applications of Artificial Intelligence in the field of transportation on the road to building a sustainable society. Four sub-systems of Intelligent Transportation Systems – Traffic Management, Public Transport, Safety Management, Manufacturing, and Logistics are considered for this analysis, where the role and benefit of AI are captured. The study has also compiled various AI applications across different cities and corporations, presenting them as a ready reckoner for future decision-makers.

Aldasoro, I., Gambacorta, L.,Korinek, A., Shreeti,V., & Stein, M. (2024). Intelligent financial system: How AI is transforming finance. Centre for the Governance of AI.
This article investigates the potential spillover effects of AI on the real economy, examining both an optimistic and a disruptive AI scenario. To address the transformative impact of advances in AI on the financial system, we propose a framework for upgrading financial regulation based on well-established general principles for AI governance.

Emmanuel Baffour Gyau, Michael Appiah, Bright Akwasi Gyamfi, Theodoria Achie, & Muhammad Abubakr Naeem. (2024). Transforming banking: Examining the role of AI technology innovation in boosting banks' financial performance. International Review of Financial Analysis, 96(8).
This study highlights the essential role of artificial intelligence technology innovation in banking and finance, emphasizing the need to consider economic and technological factors for maximizing its benefits in enhancing financial performance. Policy recommendations include promoting an artificial intelligence innovation ecosystem, adapting regulatory frameworks, and investing in information and communication technology infrastructure to harness artificial intelligence innovation's benefits while addressing associated challenges.

Ozili, P. K.(2024). Artificial intelligence (AI), financial stability and financial crisis. Reference Module in Social Sciences.
This study shows that artificial intelligence systems can undermine financial stability by increasing cybersecurity risk and concentration risk. It also increases the risk of interdependencies, market distortion, and manipulation. The study also shows that artificial intelligence systems can trigger a financial crisis through its potential to increase herding behavior, offer misleading interpretation of AI-assisted decisions, worsen an existing crisis, increase the risk and speed of digital bank runs, increase the speed of decision making which may amplify stress in bad times and trigger a crisis.

Falak khan, Gabriela Pajtinková Bartáková, Ahmad Almadhor, Amna Qayyum, Kainaat Abeer, & Aman Durrani. (2025). Evaluating the capacity and limitations of generative AI in financial decision making. Computer Standards & Interfaces, 93.
This qualitative study is based on 50 interviews, and themes are formed using qualitative SQL software. The findings reveal that consumers are quite vulnerable due to the lack of financial and digital literacy, bank cooperation, trust issues, and regulatory discrepancies. The study also makes recommendations for practitioners and policymakers for better and sustainable financial inclusion.