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Exploring AI-ESG-Driven Synergies in M&A Transactions: Open Innovation and Real Options Approaches

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  • Andrejs Čirjevskis

    (Faculty of Business and Economics, RISEBA University of Applied Sciences, Meza Street 3, LV-1048 Riga, Latvia)

Abstract

This study aims to explore the intersection of Artificial Intelligence (AI), Environmental, Social, and Governance (ESG) factors, and Open Innovation (OI) within the context of mergers and acquisitions (M&A). As ESG considerations increasingly influence corporate strategy and valuation, integrating AI offers powerful tools for enhancing due diligence, reducing risks, and creating long-term value. Building on the ARCTIC framework, an extension of the VRIO framework and real options theory, this paper introduces a new method for measuring AI-ESG-OI-driven synergies in mergers and acquisitions. It highlights the crucial role of Open Innovation in facilitating cross-boundary knowledge exchange, federated learning, and collaborative ESG data analysis. Based on recent advances in AI-ESG-enabled OI practices, such as multi-agent systems, synthetic data, and decentralized innovation, this paper shows how companies can address ESG complexity and cultural integration challenges. The findings indicate that incorporating OI principles into AI-ESG strategies not only enhances decision-making but also aligns M&A activities with evolving investor expectations and sustainability goals. The study concludes with practical insights and directions for future research in AI-driven, ESG-aligned corporate innovation.

Suggested Citation

  • Andrejs Čirjevskis, 2025. "Exploring AI-ESG-Driven Synergies in M&A Transactions: Open Innovation and Real Options Approaches," JRFM, MDPI, vol. 18(10), pages 1-40, October.
    • Handle: RePEc:gam:jjrfmx:v:18:y:2025:i:10:p:561-:d:1764519
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    References listed on IDEAS

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    1. Mahfooz Alam & Zaid Ahmad Ansari & Syed Hasan Jafar, 2025. "The Technological Landscape of AI and Sustainable Finance: An Exploration," Springer Books, in: Shakeb Akhtar & Mahfooz Alam & Nassir Ul Haq Wani & Syed Hasan Jafar (ed.), Green Horizons, chapter 0, pages 37-53, Springer.
    2. Giustina Secundo & Claudia Spilotro & Johanna Gast & Vincenzo Corvello, 2025. "The transformative power of artificial intelligence within innovation ecosystems: a review and a conceptual framework," Review of Managerial Science, Springer, vol. 19(9), pages 2697-2728, September.
    3. Andrejs Čirjevskis, 2021. "Exploring the Link of Real Options Theory with Dynamic Capabilities Framework in Open Innovation-Type Merger and Acquisition Deals," JRFM, MDPI, vol. 14(4), pages 1-16, April.
    4. Sakhr Bani-Khaled & Graça Azevedo & Jonas Oliveira, 2025. "Environmental, social, and governance (ESG) factors and firm value: A systematic literature review of theories and empirical evidence," AMS Review, Springer;Academy of Marketing Science, vol. 15(1), pages 228-260, June.
    5. Sergey I. Nikolenko, 2021. "Synthetic Data for Deep Learning," Springer Optimization and Its Applications, Springer, number 978-3-030-75178-4, December.
    6. Burström, Thommie & Parida, Vinit & Lahti, Tom & Wincent, Joakim, 2021. "AI-enabled business-model innovation and transformation in industrial ecosystems: A framework, model and outline for further research," Journal of Business Research, Elsevier, vol. 127(C), pages 85-95.
    7. Manuel Guisado-González & Jennifer González-Blanco & José Luís Coca-Pérez & Manuel Guisado-Tato, 2018. "Assessing the relationship between R&D subsidy, R&D cooperation and absorptive capacity: an investigation on the manufacturing Spanish case," The Journal of Technology Transfer, Springer, vol. 43(6), pages 1647-1666, December.
    8. Antoine Harfouche & Bernard Quinio & Mario Saba & Peter Bou Saba, 2023. "The Recursive Theory of Knowledge Augmentation: Integrating human intuition and knowledge in Artificial Intelligence to augment organizational knowledge," Information Systems Frontiers, Springer, vol. 25(1), pages 55-70, February.
    9. Andrejs Čirjevskis, 2023. "Predicting Explicit and Valuing Tacit Synergies of High-Tech Based Transactions: Amazon.com’s Acquisition of Dubai-Based Souq.com," JRFM, MDPI, vol. 16(2), pages 1-11, February.
    10. Julia Bodner & Laurence Capron, 2018. "Post-merger integration," Journal of Organization Design, Springer;Organizational Design Community, vol. 7(1), pages 1-20, December.
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