Exploring AI-ESG-Driven Synergies in M&A Transactions: Open Innovation and Real Options Approaches
Bibliographic record
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".