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

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.

1. Introduction

In the ongoing debate over strategic management and technological innovation, the role of Artificial Intelligence (AI) in creating a sustainable competitive advantage remains controversial. Wingate et al. (2025), in their recent MIT Sloan Management Review article, argue that AI—despite its transformative potential—will not provide a lasting competitive edge. Wingate et al. (2025) suggests that AI technologies are quickly commoditized, widely accessible, and easily replicated, thus failing to meet the VRIO criteria (J. B. Barney & Hesterly, 2015) of being valuable, rare, inimitable, and organizationally embedded.

The VRIO framework (J. B. Barney & Hesterly, 2015) is a strategic analysis tool that assesses a firm’s resources and capabilities based on four criteria: whether they are valuable (help capitalize on opportunities or neutralize threats), rare (held by few competitors), inimitable (difficult to copy), and organizationally embedded (supported by the firm’s processes and culture). Resources that meet all four criteria can provide a sustained competitive advantage.

This paper challenges that view by offering a different perspective based on the ARCTIC framework, an extension of J. B. Barney and Hesterly’s (2015) VRIO model. Despite the increased use of AI in mergers and acquisitions (M&A), there is a lack of strong frameworks that show how AI, combined with ESG and OI, can generate synergies that are not only valuable but also rare and difficult to imitate. Existing models, like Barney’s VRIO, present a static, resource-based view that does not fully consider the dynamic, collaborative, and innovative aspects of modern strategic advantages, especially in cross-border M&A scenarios. The paper aims to fill this research gap.

In terms of theoretical novelty and contribution, this paper introduces the ARCTIC framework—an extension of the VRIO model—to address the identified research gap by evaluating AI-ESG synergies in M&A. The ARCTIC framework assesses strategy across six dimensions: Advantages, Relevance, Capacity to Absorb, Time, Implementation, and Cultural Fit. Unlike traditional models, ARCTIC adopts a dynamic, ecosystem-oriented perspective, emphasizing how integrating AI with ESG and Open Innovation (OI) can create new sources of competitive advantage. This approach is supported by empirical case studies and real options valuation, offering a more detailed and practical understanding of AI’s strategic impact.

To further the debate and clarify the contribution, this paper examines the following research questions:

RQ 1. How can the integration of AI, ESG, and Open Innovation generate sustainable competitive advantages in M&A, beyond what traditional resource-based frameworks predict?

RQ 2. How do real-world examples of AI-driven, ESG-aligned M&A transactions demonstrate the practical benefits and limitations of the ARCTIC framework?

RQ 3. How does real options valuation support the ARCTIC framework by offering a quantitative assessment of rare and inimitable collaborative synergies, especially in cross-border AI-driven, ESG-aligned M&A transactions?

By answering these questions, this paper aims to provide both a conceptual and empirical foundation for understanding how AI systems, when embedded in collaborative, innovation-friendly, and sustainability-focused ecosystems (Norton, 2005), can deliver lasting strategic benefits, contrary to the prevailing view that AI’s advantages are inherently transient.

Defining artificial intelligence (AI) is difficult, and there is currently no universally accepted definition (Russell & Norvig, 2020). In a recent Special Issue on “Strategy and Artificial Intelligence” published in the Strategic Management Journal (2024), AI is described as any computer system that perceives its environment (such as numerical data, speech, text, and images), learns from past actions, and takes steps to increase the chance of achieving its goals.

Furthermore, Laamanen et al. (2025) argue that company-specific AI systems can improve an organization’s ability to develop, share, and execute strategy by boosting both intelligence and adaptability. According to a recent article in Long Range Planning, an AI system is defined as “a tailored artificial intelligence infrastructure that leverages machine learning (ML) or generative AI technologies to meet the unique needs and goals of a particular organization” (Laamanen et al., 2025, p. 1).

In the fast-changing world of mergers and acquisitions (M&A), integrating Artificial Intelligence (AI) and Open Innovation (OI) (Chesbrough, 2024) becomes an important strategic move. Traditionally, M&A mainly aims at expanding markets, consolidating resources, and acquiring technology. However, as companies more often seek innovation-driven benefits, the role of AI and OI in creating value after mergers has grown considerably (Holgersson et al., 2024).

Open Innovation, as defined by Chesbrough (2003), involves intentionally sharing knowledge both in and out of an organization to accelerate internal innovation and expand markets for external use of that innovation. In the context of M&A, this approach is especially important as acquiring companies aim to leverage not only tangible assets but also the innovative ecosystems and knowledge networks (Ferraris et al., 2020) of the target firms. AI improves this process by enabling more efficient due diligence, partner selection, and post-merger integration through advanced data analytics, natural language processing, and predictive modeling.

Holgersson et al. (2024) introduce a three-part framework for AI’s role in open innovation (OI): AI-enhancing tools, AI-enabling mechanisms, and AI-replacing technologies. In M&A, AI-enhancing tools assist with IP evaluation, synergy identification, and resource optimization. AI-enabling mechanisms—such as federated learning (Konecný et al., 2016; Yang et al., 2019) and AI-driven marketplaces (Samsukha, 2024)—facilitate the integration of decentralized innovation across merged firms (Al Jasem et al., 2025). Meanwhile, AI-replacing technologies like synthetic data (Nikolenko, 2021) and multi-agent systems (Wooldridge, 2002) automate innovation processes, reducing reliance on traditional collaboration methods.

Furthermore, AI assists in ESG assessments during M&A by enabling real-time monitoring, reducing bias, and providing predictive analytics, all of which are essential for evaluating a deal’s long-term sustainability. From the VRIO framework perspective (J. Barney, 1991), AI and OI together give acquiring firms valuable, rare, inimitable, and well-organized resources that can maintain a competitive advantage after the acquisition.

This paper offers a conceptual framework and empirical analysis of how AI and OI together reshape the strategic reasoning and execution of M&A, providing a forward-looking perspective on innovation-driven dealmaking.

2. Key Literature Review

2.1. AI, Open Innovation, and M&A Strategy

Mergers and acquisitions (M&A) have traditionally focused on acquiring tangible assets and achieving operational synergies. However, in today’s innovation-driven economy, the strategic value of M&A is shifting toward intangible assets, such as knowledge, data, and access to external innovation ecosystems (Burström et al., 2021). This shift challenges the static assumptions of foundational theories like the resource-based view (RBV) and its VRIO framework (J. Barney, 1991), which may not fully capture the dynamic, network-based nature of modern value creation.

This paper argues that combining Artificial Intelligence (AI) and Open Innovation (OI) offers a transformative approach to M&A. Open Innovation, as introduced by Chesbrough (2003), redefines how knowledge flows across organizational boundaries. More recent work by Holgersson et al. (2024) builds on this, describing AI’s role in OI as a tool that can enhance, enable, or even replace traditional innovation practices. The author presents this paper as a significant extension of these ideas, moving beyond simply identifying valuable resources to developing a framework that evaluates a firm’s ability to integrate into new ecosystems, align stakeholders, and scale innovation. The ARCTIC framework (Čirjevskis, 2023a), therefore, provides a more comprehensive perspective for assessing M&A targets.

For example, while the VRIO framework could identify that Microsoft’s acquisition of LinkedIn provided a valuable data resource, it would likely miss the crucial synergies in AI, ESG, and OI that made the deal successful. Similarly, VRIO would struggle to predict the significant cultural and ESG conflicts that arose from Amazon’s acquisition of Whole Foods, which the ARCTIC framework is designed to uncover (Čirjevskis, 2023b).

2.2. Strategic Fit and Innovation Synergies: Value in Exchange vs. Value in Development

Traditional M&A strategies mainly focus on operational and financial synergies, which boost revenue growth and cut costs—a concept known as value in exchange (Hao et al., 2020). However, the literature on innovation-driven M&A highlights a different kind of value: value in development. This involves acquiring complementary technologies, intellectual property, and gaining access to external knowledge networks that support long-term value creation (Hao et al., 2020).

This distinction is essential for establishing our framework. Open Innovation (Chesbrough, 2003, 2024) offers the theoretical basis for this approach, emphasizing the importance of firms leveraging both inbound and outbound knowledge flows. The external innovation connections of the acquired firm are not just resources to absorb but are dynamic capabilities that need to be maintained and improved to develop new competencies.

AI plays a critical and under-examined role in this process. AI-powered due diligence can extend beyond basic balance sheet analysis to uncover hidden innovation synergies by examining patent collections (Morrish, 2021) and scientific publications. Newer studies show how Natural Language Processing (NLP) (Alam et al., 2025) and machine learning can assess strategic fit by analyzing qualitative data, such as internal communications (Fox, 2024), public disclosures, and market sentiment, offering a more detailed and dynamic evaluation than traditional methods provide.

2.3. Post-Merger Integration and Knowledge Sharing

One of the biggest challenges in M&A is post-merger integration (PMI), especially when it comes to effectively combining intangible assets and tacit knowledge (Bodner & Capron, 2018). While older research points this out as a major risk, recent empirical studies show how AI and OI can be valuable tools to address these challenges.

For example, AI-powered platforms can now map knowledge flows (Peng et al., 2023), identify key innovation contributors (Secundo et al., 2025b), and predict integration risks before they occur (Harfouche et al., 2023). This marks a significant theoretical and practical advance, moving beyond merely identifying risks to actively managing them. Additionally, AI-powered platforms can support federated learning and decentralized teamwork (Rehman & Gaber, 2021), enabling collaboration without sacrificing data privacy.

Open Innovation further supports PMI by promoting a shift from an all-encompassing “absorb and internalize” mindset to one of continuous external cooperation. Instead of suppressing the innovative spirit of the acquired firm, acquiring companies can use OI to preserve its agility and openness, fostering an ecosystem of ongoing partnerships (M. Li & Yin, 2024).

2.4. ESG Considerations in M&A

Environmental, Social, and Governance (ESG) factors are now a fundamental part of deal valuation and long-term value creation, not just optional add-ons (Feyisetan et al., 2025; Brownstein et al., 2022). This changing landscape highlights a significant limitation of traditional frameworks like VRIO, which are not well-suited to assess the complex, multi-dimensional nature of ethical, social, and environmental alignment.

AI provides a powerful solution by enabling more advanced ESG due diligence. AI-driven platforms can gather and analyze real-time data from various sources—such as sustainability reports, regulatory filings, and social media (Lee et al., 2025)—to identify and predict ESG risks. This approach goes beyond simple compliance checks to a proactive, forward-looking evaluation of reputational and financial risks.

To address the complexity of ESG integration, this paper suggests that the ARCTIC framework is a necessary development of VRIO. The six dimensions of ARCTIC—Advantages, Relevance, Capacity to Absorb, Time, Implementation, and Cultural Fit—enable a more detailed evaluation. Unlike VRIO’s static view of resources, ARCTIC assesses a target’s AI-ESG synergies as dynamic capabilities within collaborative ecosystems. Additionally, ARCTIC’s integration with real options valuation offers a way to measure the strategic flexibility and risk-adjusted value of these ESG-driven investments, a capability VRIO completely lacks.

2.5. AI, Competitive Advantage, the VRIO Framework, and the ARCTIC Scorecard Extension

The resource-based view (RBV) of the firm and the VRIO framework have long been fundamental tools for assessing sustainable competitive advantage. However, the rise of AI and ESG factors calls for a critical re-evaluation of their underlying assumptions. Wingate et al. (2025) recently argued that AI alone cannot deliver a sustainable competitive advantage because it is becoming increasingly commoditized. This is a key point that this paper addresses directly. The paper suggests that while raw AI technology may eventually become commoditized, its strategic value depends not just on the tool itself but on its integration within ESG-aligned and open innovation ecosystems—a concept that the VRIO framework is too static to fully capture.

Building on the VRIO framework, the analysis shows how AI, OI, and ESG redefine each of its dimensions.

• Value: AI enhances efficiency, while OI introduces diverse ideas (Secundo et al., 2025b; Magli & Amaduzzi, 2025). ESG builds long-term value through reputational capital and regulatory resilience (Ground, 2022).
• Rarity: Proprietary AI models and ESG-driven business models are genuinely difficult to imitate (Soubhari et al., 2025; Alam et al., 2025, p. 37).
• Imitability: Complex AI systems and deeply embedded ESG practices demand cultural change and are hard to replicate (Wynsberghe, 2021; Bani-Khaled et al., 2025).
• Organization: Successfully integrating AI, OI, and ESG demands new organizational structures that foster collaboration and support ethical governance (Secundo et al., 2025b; Bujno & Abrash, 2022).

Although VRIO provides a useful starting point, its limitations in assessing dynamic, ecosystem-based capabilities are notable. This paper directly addresses these issues by introducing the ARCTIC framework as a modern evolution of VRIO. Unlike VRIO, which evaluates resources in isolation, ARCTIC explicitly considers ecosystem integration, collaborative innovation, and ESG factors (Feyisetan et al., 2025). By expanding the VRIO framework, ARCTIC shows that competitive advantage today is co-created through networks and shaped by societal expectations.

This revised perspective directly challenges the premise of Wingate et al. (2025). The paper contends that when AI shifts from being a standalone tool to a part of a larger, collaborative, and ethically governed innovation network, it becomes a strategic asset that is rare, unique, and organizationally integrated. Proprietary AI models, federated learning, and synthetic data improve (Buggineni et al., 2024), enable, or replace traditional innovation methods, making them rare and difficult to replicate in specific contexts. The ARCTIC framework and its operational components offer the essential tools to evaluate this new form of competitive advantage.

2.6. Real Options Valuation (ROV) Integration

To further enhance the strategic value of the framework, the paper incorporates real options valuation (ROV) into each ARCTIC dimension. This is a vital step that converts the qualitative advantages of the ARCTIC framework into quantifiable, financial insights. This method helps firms measure the worth of intangible synergies and make better-informed decisions under uncertainty, a key feature of AI and ESG investments.

By linking each ARCTIC dimension to a specific real option type (e.g., growth, switching, or abandonment), the framework helps decision-makers evaluate strategic flexibility. For example, the “Time” dimension can be connected to a compound option, as shown in the L’Oréal–Aesop case, illustrating the ability to accelerate ESG impact through sequential AI-enhanced OI platforms (Čirjevskis, 2025). This offers a more detailed view of risks and opportunities than VRIO’s simple resource classification. The ROV matrix complements the ARCTIC scorecard by providing a forward-looking assessment of strategic synergies in AI-ESG-driven M&A deals.

2.7. Theoretical Propositions and Supporting Evidence

Based on a critical review of the literature, the paper proposes three theoretical propositions that are more specific than previous work and can be empirically tested. These propositions clarify the nuanced relationships among AI, ESG, and Open Innovation within the context of M&A strategy.

Proposition 1 (P1): A sustainable competitive advantage from AI in M&A is gained not just through the AI technology itself, but through its integration into a target firm’s ESG-aligned and open innovation ecosystems, which together develop a unique, hard-to-duplicate capability.

This proposition directly responds to the argument by Wingate et al. (2025) that AI’s increasing commoditization prevents it from being a source of sustainable advantage. The paper argues that the value of AI is not in its isolated application but in its role as a strategic enabler within a complex, interconnected network. Specifically, when AI is used to facilitate federated learning, ensure ethical governance (Koskinen et al., 2023), and enhance collaborative platforms, it becomes embedded within a unique organizational ecosystem. This ecosystem, shaped by a firm’s specific ESG commitments and OI partnerships, is what becomes truly rare and difficult for competitors to replicate.

Proposition 2 (P2): The ARCTIC framework provides a more comprehensive and predictive evaluation of strategic resources in AI-ESG-driven M&A than the traditional VRIO model by specifically assessing a target firm’s ability to absorb, cultural alignment, and implementation feasibility.

While VRIO effectively assesses the internal features of a resource, it falls short when used in complex, dynamic M&A environments. The ARCTIC framework enhances VRIO’s usefulness by incorporating critical external and organizational aspects. For example, it goes beyond a static view of a resource’s rarity to address practical challenges of integration (Capacity to Absorb and Implementation) and the alignment of values (Cultural Fit). These factors are especially important in cross-border acquisitions where AI and ESG synergies can be easily disrupted by cultural misalignment or hurdles in absorption and implementation.

Proposition 3 (P3): Incorporating real options valuation (ROV) into the ARCTIC framework improves strategic decision-making in AI-ESG-driven M&A by enabling firms to measure the value of strategic flexibility and the rare, hard-to-imitate collaborative synergies that traditional valuation models overlook.

This proposition shows how the ARCTIC framework can serve more than just a qualitative checklist. By connecting each ARCTIC dimension to a specific real option—such as linking “Time” to a compound option or “Capacity to Absorb” to a deferral or abandonment option—merging firms can go beyond simple, one-time valuations. This is vital for AI and ESG investments, which often involve long-term horizons and unpredictable outcomes. The use of ROV makes strategic planning more flexible and adaptable in the fast-changing landscape of AI-ESG innovation that traditional valuation methods cannot provide (Copeland & Tufano, 2004).

3. Research Methodology: Framework Implementation and Scorecard Evaluation

The research methodology describes the overall approach and reasoning behind the study. It explains why specific methods were chosen for data collection and analysis, emphasizing the logic and rationale of the research process. This study uses a conceptual and theory-building research design to expand the VRIO framework, aiming to better understand the strategic value of AI-ESG synergies in M&A scenarios.

The research employs qualitative comparative analysis, drawing insights from strategic management, innovation theory, and sustainability literature. The ARCTIC framework is developed as a multidimensional extension of VRIO, encompassing six evaluation dimensions: Advantages, Relevance, Capacity to Absorb, Time, Implementation, and Cultural Fit.

Table 1. Conceptual model of research: ARCTIC Framework for AI-ESG-OI M&A Synergies Valuation Process.

Stages	Description	Key Focus/Purpose
1. Cross-Border M&A Context	Understanding the global landscape for mergers and acquisitions.	Establishing a complex backdrop for strategy decisions.
2. Influence of AI, ESG, & Open Innovation	Understanding how these key factors influence M&A opportunities and risks.	Identifying the main drivers and obstacles for synergy creation.
3. ARCTIC Framework Application	Starting a systematic assessment using the ARCTIC dimensions.	Structuring the analytical approach for a comprehensive assessment.
4. ARCTIC Dimensions (A, R, C, T, I, C)	Analyzing the deal in terms of Advantages, Relevance, Capacity to Absorb, Time, Implementation, and Cultural Fit.	Leading a detailed investigation into various strategic aspects.
5. Strategic Questions	Posing specific questions related to each ARCTIC dimension, including AI, ESG, and OI.	Gathering specific information and insights for assessment.
6. ARCTIC AI-ESG M&A Scorecard	Applying a structured scoring system (1–5 scale) based on criteria for each dimension.	Measuring the qualitative aspects of AI-ESG-OI synergies.
7. Open Innovation Insights	Identifying patterns of how AI interacts with OI (enhancing, enabling, replacing, or conflicting) and its ESG implications.	Understanding how AI-driven OI models influence synergy potential, ESG alignment, and cultural fit in M&A.
8. Real Options Valuation for Synergies	Applying real options methodology to value the identified collaborative synergies.	Quantifying strategic flexibility, risks, and potential value amid uncertainty.
9. Prediction & Valuation of Collaborative Synergies	Synthesizing all analyses to predict and assign a value to potential synergies.	Reaching an informed decision regarding the M&A transaction’s value.

provides a high-level conceptual framework for assessing and predicting collaborative synergies in cross-border M&A transactions, integrating AI, ESG, and Open Innovation.

Justification for the Structure of the ARCTIC Framework for AI-ESG-OI M&A Synergies Valuation

The ARCTIC Framework is a nine-stage process designed to guide the strategic and financial evaluation of AI-ESG-OI synergies in M&A situations. Each stage builds on the last, ensuring a clear and thorough assessment.

Stage 1 sets the global and cross-border M&A context, offering the essential geopolitical, regulatory, and market background for strategic decisions.

Stage 2 introduces the three main drivers—Artificial Intelligence (AI), Environmental–Social–Governance (ESG), and Open Innovation (OI)—and examines their impact on synergy opportunities and risks.

Stage 3 begins applying the ARCTIC framework, providing a structured way to evaluate strategic fit and potential for integration.

Stage 4 applies the framework through six dimensions: Advantages, Relevance, Capacity to Absorb, Time, Implementation, and Cultural Fit. These dimensions provide a comprehensive analysis of the M&A deal.

Stage 5 enhances the analysis by asking targeted strategic questions for each ARCTIC dimension, customized to AI, ESG, and OI considerations.

Stage 6 introduces a qualitative scorecard to assess each dimension on a standardized scale, allowing for comparative evaluations across deals or scenarios.

Stage 7 synthesizes Open Innovation insights by identifying how AI either enhances, enables, replaces, or conflicts with OI practices, and how these patterns influence ESG alignment and cultural integration. This stage is vital for translating qualitative patterns into strategic insights.

Stage 8 uses real options valuation to measure the flexibility and potential value of identified synergies under uncertainty, connecting strategic insights to financial modeling.

Stage 9 consolidates all findings to estimate and assign a value to collaborative synergies, aiding informed decisions about the M&A transaction.

This organized progression—from contextual analysis to strategic evaluation, insight development, and financial valuation—ensures methodological rigor, strategic depth, and practical significance. The addition of Stage 8 is especially important because it connects qualitative insights with quantitative valuation while also incorporating innovation and sustainability into the core of M&A strategy.

4. Research Design: Extending the VRIO Framework with ARCTIC to Forecast Synergies in M&A Driven by AI, ESG, and Open Innovation

The research methodology details the development and theoretical basis of the ARCTIC framework, which integrates AI, ESG, and innovation. Its elements include combining theories, performing qualitative comparative analysis, and expanding VRIO for multidimensional assessment. Meanwhile, the research design outlines practical applications using a 30-point scorecard and case studies to evaluate M&A strategy. The design emphasizes how to implement the scorecard, analyze strategic fit, and verify findings with real-world M&A examples. The comparison table of the research design and methodology in this paper is presented in

Table 2. Methodology vs. Design in the AI–ESG–OI ARCTIC Model Research.

Aspect	Research Methodology	Research Design
Focus	Why the ARCTIC framework is developed and how it builds on and extends the VRIO model	How the ARCTIC framework is applied through operationalization, scoring, and case study analysis
Purpose	To justify the conceptual and theory-building approach for evaluating AI–ESG–OI synergies in M&A	To provide a structured plan for applying the ARCTIC framework to real-world M&A cases
Scope	Conceptual development of a multidimensional evaluation model (ARCTIC) integrating strategic, innovation, and ESG theory	Application of the model through a 30-point scorecard and six illustrative M&A case studies
Includes	- Integration of strategic management, innovation, and sustainability literature - Qualitative comparative analysis - Development of ARCTIC as an extension of VRIO	- Operationalization of six ARCTIC dimensions via strategic questions - Scorecard-based evaluation - Case study selection and analysis
Flexibility	Methodological stance is stable (conceptual and theory-building)	Design allows adaptation of the scorecard and questions across industries and M&A contexts
Example Question	Why is a multidimensional framework needed to assess AI–ESG–OI synergies in M&A?	How will the ARCTIC scorecard be used to evaluate strategic fit and integration complexity in selected cases?
Example	Developing the ARCTIC framework to capture dynamic, ecosystem-based value in M&A scenarios	Applying the ARCTIC scorecard to analyze case studies (e.g., Microsoft–LinkedIn and Amazon–Whole Foods) for synergy potential

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In this context, research design is the plan or blueprint for carrying out a study. It outlines the methodology used. The paper’s design is exploratory, aiming to address the limitations of static resource-based models and to introduce a dynamic, ecosystem-oriented alternative. It combines conceptual modeling with illustrative case studies to show the framework’s usefulness and predictive capabilities.

This study uses a conceptual and theory-building approach. The ARCTIC framework is implemented with strategic questions and a 30-point scorecard. Each dimension relates to a specific type of real option, enabling the measurement of strategic flexibility. Six M&A cases are examined to demonstrate the framework’s usefulness.

This section introduces the ARCTIC framework and describes each of its six dimensions (Advantages, Relevance, Capacity to Absorb, Time, Implementation, and Cultural Fit), highlighting the key strategic questions for each. To navigate the increasingly complex landscape of M&A, especially during digital transformation and sustainability efforts, strategic decision-makers need a multidimensional evaluation tool. The ARCTIC framework (Čirjevskis, 2023b), which includes Advantages, Relevance, Capacity to Absorb, Time, Implementation, and Cultural Fit, extends the VRIO model by applying its dimensions in a forward-looking, innovation-focused context.

When viewed through the lens of AI, ESG, and Open Innovation, the ARCTIC scorecard becomes a powerful tool for assessing not only the strategic viability of M&A initiatives but also their potential to cultivate collaborative, sustainable, and AI data-driven synergies.

ARCTIC Criteria Analyzed with the VRIO Framework

Advantages refer to the strategic and operational benefits that a target firm can provide. Artificial Intelligence (AI) can enhance these benefits through automation, predictive analytics, and ESG intelligence (Domingo, 2025). Open Innovation (OI) complements this by increasing access to external innovation resources such as patents, platforms, and partnerships (Sanmugam et al., 2023). The evaluation focuses on identifying the target’s unique AI or ESG capabilities, assessing how these capabilities align with the acquirer’s existing innovation ecosystem, and determining whether these advantages can be expanded through open innovation networks.

Relevance pertains to the strategic alignment between the target and the acquirer, especially regarding ESG commitments and digital maturity. This aspect connects to the “Value” component of the VRIO framework. OI enhances this assessment by considering the target’s willingness to collaborate and its role within broader innovation ecosystems (Secundo et al., 2025a). Key factors include how relevant the target’s innovation partnerships are to the acquirer’s dynamic capabilities, alignment with ESG and AI roadmaps, and participation in open innovation platforms or consortia.

Capacity to absorb refers to the ability to recognize, assimilate, and apply external knowledge (Cohen & Levinthal, 1990), which is vital for post-merger integration, especially when facing technological, organizational, or regulatory challenges. It facilitates the transfer of technological capabilities and core competencies (e.g., Guisado-González et al., 2018; Van den Bosch et al., 1999; Zahra & George, 2002) and unfolds in three stages: acquisition, absorption, and exploitation (Glabiszewski, 2015).

In AI and Open Innovation (OI) contexts, the capacity to absorb enables firms to leverage external data and collaborate across boundaries. High absorption capacity improves the ability to turn AI-driven insights and OI flows into ESG-aligned innovation. However, OI can also introduce complexity through IP sharing, partner coordination, and governance (Kuzior et al., 2023, p. 9). Assessing this criterion involves identifying technical barriers, evaluating OI relationships, and understanding how AI reshapes innovation processes.

Time concerns how quickly value can be realized after a merger. Although AI can speed up due diligence and integration, ESG and OI initiatives often take longer to show measurable results (PMI, 2023). The ARCTIC framework helps balance short-term gains with long-term innovation value. This dimension involves estimating the timeline for realizing AI and ESG synergies, assessing how quickly the target’s OI networks can be activated, and identifying quick wins that can generate early momentum during integration.

Implementation evaluates the feasibility and scalability of integrating AI, ESG, and OI capabilities (Lee et al., 2025; Kanchibhotla et al., 2024). This involves assessing the technical readiness of both companies, the strength of governance structures, and the level of stakeholder engagement. The review examines the infrastructure needed to deploy AI and ESG tools, the availability of scalable OI models like federated learning or shared platforms (Campoloa et al., 2023), and strategies for managing stakeholder expectations and regulatory compliance.

Cultural fit is crucial for successful integration, especially in AI- and ESG-driven M&A (Fantaguzzi & Handscomb, 2024). It involves openness to experimentation, transparency, and shared values. OI emphasizes the importance of absorptive capacity and a collaborative culture. This criterion includes assessing whether the target promotes a culture of openness and innovation, aligns with ESG values and innovative mindsets, and encourages trust and collaboration between both organizations. The operationalization of six ARCTIC dimensions through strategic questions is provided in Appendix A.1 and Appendix A.2.

Having established the research design and methodology, the following sections present a conceptual research framework and examine its practical implications through case studies, data, and applications.

5. From Framework to Practice: Implementing the ARCTIC Scorecard in AI-ESG-OI M&A Evaluation

Building on the conceptual dimensions of ARCTIC outlined above, this section explores the practical application of the ARCTIC AI-ESG M&A Scorecard.

5.1. Recommended ARCTIC AI-ESG-OI M&A Scorecard

There are several similarities between the Likert scale and the recommended ARCTIC AI-ESG M&A Scorecard. The first is the use of a numerical rating system: both utilize a 1-to-5 scale, where 1 typically indicates a low score and 5 a high score. The second is subjective assessment: both rely on human judgment to evaluate items based on perception, experience, or analysis. The third is a structured format: both are designed as organized tools for collecting data or evaluations across multiple dimensions. The fourth is quantifiable insights: the ratings can be aggregated, averaged, or analyzed to generate insights, support decision-making, or identify trends.

A detailed comparison of the similarities and differences between the standard Likert scale and the ARCTIC AI-ESG M&A Scorecard is presented in

Table 3. Similarities Between the Likert Scale and the Recommended ARCTIC AI-ESG M&A Scorecard.

Feature	Likert Scale	ARCTIC AI-ESG-OI M&A Scorecard
	With 1 representing “Strongly Disagree” and 5 representing “Strongly Agree.”	Rate each criterion from 1 “Low” to 5 “High”
Purpose	Measures attitudes, opinions, or agreement (e.g., “Strongly agree” to “Strongly disagree”)	Evaluates specific AI-ESG-OI related criteria in M&A decisions
Scale Labels	Often uses verbal anchors (e.g., “Strongly disagree”, “Neutral”, “Strongly agree”)	Uses numerical ratings (1 = Low, 5 = High) without verbal anchors
Context	Common in surveys, psychology, social sciences	Tailored for business strategy, AI, ESG, and OI analysis, and M&A evaluation
Interpretation	Focuses on sentiment or opinion	Focuses on performance, relevance, or risk of AI, ESG, and OI factors
Design	Usually generic and standardized	Often customized to fit the ESG framework and strategic goals
Example Comparison	Likert Scale Question: “I believe ESG factors are important in M&A decisions.” Response: 1 (Strongly disagree) to 5 (Strongly agree)	ARCTIC Scorecard Criterion: “Environmental due diligence quality” Rating: 1 (Low quality) to 5 (High quality)

.

The Likert Scale is often used to measure attitudes or opinions, usually from “Strongly Disagree” to “Strongly Agree.” Its limitations include central tendency bias, limited interpretive depth, and challenges in statistical analysis because of its ordinal format. In contrast, the ARCTIC AI-ESG-OI Scorecard is designed to evaluate specific criteria related to AI, ESG, and open innovation in M&A decisions. It uses numerical ratings from 1 (Low) to 5 (High) without verbal anchors, which can reduce ambiguity but may also lack emotional nuance.

While Likert scales are standardized and widely recognized, the ARCTIC Scorecard is more tailored and specific to its context, which improves relevance but might reduce comparability across different assessments. Additionally, the ARCTIC Scorecard emphasizes performance and strategic alignment instead of sentiment, making it more suitable for decision-making but potentially less intuitive for general survey participants.

In this context, understanding the ARCTIC Scorecard involves knowing how each criterion affects the overall ESG (Environmental, Social, Governance), AI, OI, and strategic value of a merger or acquisition. Here is a clear way to interpret the results.

5.2. Evaluating AI-ESG-OI Synergies in M&A: Scorecard Dimensions and Interpretive Framework

To evaluate the strategic significance of mergers and acquisitions using the ARCTIC framework, this section highlights six key aspects that explore the relationship among Artificial Intelligence (AI), Environmental, Social, and Governance (ESG) factors, and Open Innovation (OI). Each aspect is rated on a five-point scale, offering a detailed view of potential synergies, integration challenges, and cultural fit.

Advantages, the first dimension, emphasize competitive strengths derived from the target company’s AI and ESG capabilities. This includes proprietary technologies like carbon accounting algorithms (Gao et al., 2023) or ethical AI governance systems (Law et al., 2025) that provide unique strategic benefits. Open Innovation considerations are also essential, especially when AI is used to boost collaborative innovation, such as through AI-powered patent analytics (T.-Y. Lin & Chou, 2025) or federated learning systems (Q. Li et al., 2021) that enable ESG data sharing. A merger could also create new ESG market opportunities, including entry into green technology or social impact sectors, especially when supported by OI platforms like crowdsourced sustainability initiatives. A notable example is Tesla’s acquisition of SolarCity, which combined AI-driven energy technologies with ESG positioning and open innovation partnerships in the clean tech space.

Relevance, the second dimension, assesses the strategic alignment between acquiring and target companies. This includes how well their AI roadmaps align, such as whether both emphasize AI for sustainability rather than just automation. It also considers whether AI is used to modernize outdated OI practices, for example, by replacing shared datasets with synthetic data. NVIDIA’s synthetic driving datasets for training autonomous vehicles are generated in simulation environments (Washabaugh, 2025). ESG alignment is evaluated through shared commitments to goals like net-zero emissions or diversity, equity, and inclusion (DEI). Joint ventures in circular economy technologies show how ESG priorities can be supported by combined OI strategies. Microsoft’s purchase of GitHub Version 2.14 exemplifies this alignment, blending AI developer tools with ESG goals in open-access education and leveraging crowdsourced innovation within the developer community (Bridgwater, 2024).

The capacity to absorb highlights the third dimension, which underlines the complexity of integrating AI and ESG systems after a merger. Challenges can stem from incompatible data infrastructures, biased algorithms, or isolated ESG datasets. Sometimes, AI may replace traditional OI methods in ways that create governance risks, such as multi-agent systems replacing human collaboration (Moro-Visconti, 2025). ESG integration can also be obstructed by conflicting standards, like different carbon reporting frameworks. Tensions between open-source and proprietary ESG tools further complicate OI alignment. Bayer’s acquisition of Monsanto illustrates these issues, with ESG conflicts over GMO versus organic farming and OI tensions between closed and open innovation ecosystems (Keating, 2016).

Time, the fourth dimension, examines the potential for faster integration through AI and ESG synergies. AI tools like robotic process automation can improve ESG implementation efficiency, while AI-enhanced OI mechanisms may speed up partner searches for ESG projects. Quick wins could include shared renewable energy contracts or AI-driven sentiment analysis of stakeholder feedback (Chakriswaran et al., 2019), which supports ESG alignment. Unilever’s use of AI to integrate Ben & Jerry’s social impact metrics, along with crowdsourced sustainability initiatives (as discussed in Certoma et al., 2015), demonstrates how digital tools and stakeholder engagement can accelerate ESG harmonization. This approach reflects Open Innovation principles by leveraging external ideas and technologies to enhance corporate sustainability performance.

Implementation, the fifth dimension, assesses the strength of the post-merger implementation plan. This includes using AI-powered tools to monitor progress, such as ESG dashboards with predictive alerts, and achieving ESG milestones like transitioning to renewable energy. Open Innovation milestones, such as innovation challenges for ESG technologies, are also part of this evaluation. Danaher’s use of an AI-driven post-merger integration playbook to reduce ESG compliance costs and incorporate OI platforms for R&D demonstrates best practices in this field (F. Lin, 2025).

Cultural fit, the final dimension, involves shared values on ethical AI and ESG. This includes agreement on issues like avoiding biased algorithms in hiring and a mutual willingness to share data through federated learning. Corporate cultures that prioritize worker rights and climate action are more likely to support successful ESG integration. Companies promoting open innovation for ESG often demonstrate higher cultural compatibility in mergers and acquisitions. This is because open innovation encourages transparency, collaboration, and stakeholder engagement, which align with ESG principles. Salesforce’s acquisition strategy, emphasizing its “1-1-1” philanthropy model and open innovation principles, shows how cultural fit and ESG goals can be aligned (e.g., Westwood, 2023).

Insights from Holgersson et al. (2024) further refine the scorecard by clarifying three roles AI can play in relation to OI. AI can improve traditional OI practices, such as better ESG patent analysis; enable entirely new forms of OI, like federated learning for cross-company ESG collaboration; or replace outdated OI mechanisms, such as synthetic data that reduce reliance on shared datasets. These distinctions ensure that the scorecard captures not only the presence of AI-ESG synergies but also AI’s transformative impact on open innovation within M&A contexts.

The total score, based on the six dimensions, ranges from 6 to 30 points. A score between 25 and 30 indicates high synergy potential, where AI, ESG, and OI are well-aligned and scalable, with minimal integration risks. In such cases, companies are advised to proceed confidently, accelerate integration, and use AI-OI tools such as predictive dashboards and ideation platforms.

Scores ranging from 18 to 24 show moderate synergy with noticeable gaps. These gaps may include mismatched OI cultures, AI systems that hinder innovation, or incompatibilities in ESG data. Strategies should involve testing AI-enabled OI platforms, standardizing ESG reporting methods, and training teams in secure collaboration tools like federated learning.

Scores below 18 indicate high risk and poor alignment among AI, ESG, and OI dimensions. These situations may involve AI replacing OI without ethical safeguards, conflicting ESG goals, or isolated data that hinders collaboration. In such cases, firms should reassess their targets or redesign their integration strategies to emphasize better AI-OI enablement, possibly seeking alternative targets with improved alignment.

Recent revisions to the scorecard emphasize the importance of AI-enhanced and AI-enabled OI practices in achieving high scores. Low scores now signal risks where AI could unsustainably substitute for human-driven innovation. Moderate scores highlight the need for bridging strategies, such as crowdsourced ESG prioritization.

Holgersson’s warnings about deskilling and intellectual property risks, especially when AI is trained on copyrighted ESG data, are reflected in the high-risk tier. For example, a score of 22 may lack AI-OI integration but still show strong ESG alignment. In such cases, piloting an AI-enabled OI platform for stakeholder collaboration could reveal hidden value.

Table 4. ARCTIC Framework for AI-ESG-OI M&A Synergies analysis.

ARCTIC Dimension	VRIO Link	Core Concept & AI/ESG/OI Integration	Key Strategic Questions	Scorecard Criteria (Sample) & Holgersson et al. (2024) Insights
A: Advantages (Strategic & Operational Gains)	Rarity & Imitability	How AI enhances gains through predictive analytics & ESG intelligence. OI enhances AI by enabling access to external innovation assets, thereby gaining benefits.	What unique AI or ESG capabilities do the target offer? How do these fit into our OI ecosystem? Can we scale via OI networks?	AI: Does the target’s AI/IP (for example, carbon accounting algorithms) provide unique advantages? OI Consideration: Does AI enhance OI (for example, AI-powered patent analytics) or establish new OI methods (such as federated learning for ESG data collaboration)? ESG: Does the merger open up ESG market opportunities? OI Consideration: Are there synergies with OI platforms (e.g., crowd-sourcing sustainability solutions)?
R: Relevance (Strategic Alignment)	Value	How well the target aligns with the acquirer’s strategic goals, including ESG commitments and digital maturity. OI enables AI and adds a layer by assessing collaboration openness & role in innovation ecosystems.	How relevant are their innovative partnerships to our dynamic capabilities? Does the target align with our ESG and AI roadmaps? Is the target active in OI platforms?	AI: How do companies’ AI and technology roadmaps align (e.g., AI for sustainability versus profit-only automation)? OI Consideration: Do both firms use AI to replace outdated OI practices, such as synthetic data replacing shared datasets? ESG: Do ESG priorities, like net-zero targets, support each other? OI Consideration: Are ESG goals supported by combined OI efforts (e.g., joint ventures for circular economy tech)?
C: Capacity to Absorb (Integration Challenges)	Organization	Challenges in integrating technology, organizations, and regulations. OI adds complexity with IP sharing, partner coordination, & ecosystem governance.	What are the technical challenges to integration? How intricate are the target’s OI relationships and data-sharing practices? Does AI reshape established OI forms?	AI: Are there data or AI system incompatibilities, such as biased algorithms or the absence of centralized ESG data platforms? OI Consideration: Does AI replace traditional OI in ways that create governance risks, like multi-agent systems replacing human collaboration? ESG: Do ESG standards conflict, such as with different carbon reporting frameworks? OI Consideration: Are there OI-driven conflicts, such as between open-source and proprietary ESG tools?
T: Time (Speed of Value Realization)	Organization	AI can accelerate due diligence and integration, while ESG and OI initiatives may require longer timelines. ARCTIC evaluates the balance between immediate benefits and long-term innovation value.	What is the expected timeline for achieving AI/ESG synergies? How quickly can we activate the target’s OI networks? Are there quick wins?	AI: Can AI tools accelerate ESG process integration? OI Consideration: Does AI allow for quicker OI, such as AI-accelerated partner searches for ESG projects? ESG: Are there any “quick win” ESG synergies? OI Consideration: Can AI-enhanced OI streamline ESG alignment?
I: Implementation (Feasibility & Scalability)	Organization	Feasibility of integrating AI, ESG, and OI capabilities, including technical readiness, governance structures, and stakeholder engagement.	What infrastructure is required to implement AI/ESG tools? Are scalable OI models available? How will we handle stakeholder expectations and ensure compliance?	AI: Is there an AI-powered PMI tracker (e.g., ESG KPI dashboards with predictive alerts)? OI Consideration: Does the plan include AI-enhancing OI (e.g., AI-driven IP utilization for ESG patents)? ESG: Are ESG milestones integrated into the timeline? OI Consideration: Are OI milestones (e.g., open innovation challenges for ESG tech) incorporated?
C: Cultural Fit (Innovation & Sustainability Mindsets)	Organization	Critical for successful integration, including openness to experimentation, transparency, and shared values. OI emphasizes absorptive capacity and a culture of collaboration.	Does the target demonstrate a culture of openness and innovation? How aligned are our ESG values and innovation mindsets? Can we foster trust and collaboration?	AI: Do both firms prioritize ethical AI? OI Consideration: Are OI cultures aligned (e.g., willingness to share AI/ESG data through federated learning)? ESG: Are corporate cultures aligned with ESG? OI Consideration: Do both firms support OI for ESG (e.g., open-source climate data) models)?

explains the ARCTIC framework, which builds on the VRIO model by applying its dimensions to an innovation-driven context, specifically for evaluating M&A synergies influenced by AI, ESG, and Open Innovation (OI).

The concern about operationalizing abstract ideas like corporate culture and climate action within the ARCTIC framework is valid. These concepts are complex and highly dependent on context. Nonetheless, the ARCTIC framework is specifically created to tackle this issue by providing a structured yet adaptable method that combines conceptual clarity with practical use.

To implement these dimensions, the framework combines multi-level indicators with case-based criteria, as shown in Table 4. For instance, corporate culture is evaluated using observable proxies such as openness to data sharing, ethical AI practices, and participation in open innovation (OI) platforms—elements that are increasingly documented in ESG disclosures, stakeholder reports, and integration plans. Similarly, climate action is assessed through concrete metrics like carbon accounting algorithms, net-zero targets, and engagement in circular economy initiatives, which are often incorporated into AI and ESG strategies.

The framework draws on established literature to support this operationalization. For example, the Capacity to Absorb dimension builds on Cohen and Levinthal’s (1990) absorptive capacity model and is further explained through AI-OI integration challenges (such as data compatibility, IP governance, and federated learning). The Cultural Fit dimension includes insights from Brunswicker and Chesbrough (2018) and Holgersson et al. (2024), connecting innovation mindsets and ESG values to post-merger integration success.

While some subjectivity is unavoidable in assessing qualitative aspects, the ARCTIC Scorecard offers structured criteria and guiding questions to promote consistency and transparency in evaluation. Additionally, the framework is designed to adapt over time with empirical validation. Future research (as outlined in Section 9) will aim to improve these indicators through quantitative ESG-M&A studies, cross-industry benchmarking, and AI-driven decision-support tools.

Variations in scorecard assessments highlight the vital role of AI-enhanced and AI-enabled open innovation practices in achieving high synergy scores. Lower scores increasingly indicate potential risks, especially when AI technologies replace human-centered innovation processes in unsustainable ways. Mid-range scores show partial alignment and emphasize the need for strategic actions, such as using crowdsourced ESG prioritization to address existing gaps.

The concerns raised by Holgersson et al. (2024) regarding deskilling and intellectual property risks—especially when AI systems are trained using copyrighted ESG data—are reflected in how high-risk situations are classified. For example, a target firm with a score of 22 might demonstrate strong ESG alignment but lack meaningful integration between AI and open innovation. In such cases, deploying an AI-enabled open innovation platform to encourage stakeholder collaboration could help unlock hidden strategic value.

In summary, the ARCTIC framework does not aim to remove ambiguity but to organize the assessment of complex, interconnected factors in AI-ESG-OI-driven M&A. Its advantage is providing a repeatable structure that can be tailored to various industry settings while staying rooted in both theory and practice.

6. Data Analysis and Interpretation: Empirical Evidence from Case Study Research

This section provides a qualitative analysis of selected M&A transactions, using the ARCTIC framework and complemented by insights from Open Innovation literature, particularly the work of Holgersson et al. (2024). The analysis aims to demonstrate how the interaction among Artificial Intelligence (AI), Environmental, Social, and Governance (ESG) factors, and Open Innovation (OI) influences the strategic results of mergers and acquisitions. The cases chosen cover a variety of industries, including retail, technology, beauty, logistics, healthcare, and automotive, offering a comprehensive view of sector-specific dynamics.

The reason for selecting these cases was to capture a variety of AI-ESG-OI synergies. Some cases, like L’Oréal’s acquisition of Aesop, exhibit high levels of strategic alignment across all three areas, while others, such as Amazon’s acquisition of Whole Foods, serve as cautionary examples where ESG integration was weaker. The analysis also examines different strategic archetypes. For instance, Microsoft’s acquisition of LinkedIn shows a technology-oriented approach where AI supports ESG objectives, while Ahold Delhaize emphasizes sustainability over technological advancement. Amazon’s acquisition of Zoox is viewed as a long-term strategic move, with ESG benefits expected to develop gradually.

Comparative analysis is central to this study. Comparing L’Oréal/Aesop, which achieved a nearly perfect ARCTIC score of 28 out of 30, with Amazon/Whole Foods, which scored 16 out of 30, highlights the gap between best practices and integration challenges. Samsung’s acquisition of Harman is also analyzed to explore the complex connections between IoT, AI, and ESG within the automotive industry.

The structure of the case study analysis begins with a contextual introduction of the ARCTIC framework as it applies to each transaction. High-synergy cases like L’Oréal/Aesop and Microsoft/LinkedIn serve as benchmarks for successful integration. In contrast, moderate and low-synergy cases, such as Amazon’s acquisitions of Zoox and Whole Foods, are analyzed to identify barriers to effective ESG and innovation alignment. Sector-specific comparisons are also included, such as the comparison between Ahold Delhaize and Whole Foods in retail, and LinkedIn versus Harman in the technology sector.

The section concludes by emphasizing recurring patterns, strategic trade-offs, and implications for enhancing the ARCTIC scorecard as a predictive tool. Supplemental cases, such as Tesco–Carrefour and Amazon/Souq.com, are briefly mentioned to demonstrate limitations in AI-ESG scope or thematic redundancy and are excluded from the main analysis. It also incorporates open innovation (OI) insights from Holgersson et al. (2024), explaining how AI-ESG-OI synergies (or gaps) influence M&A outcomes, as shown in

Table 5. Recommended Case Studies & Justifications.

Case	Why Include?	Key AI-ESG Insights
1. L’Oréal/Aesop (2023)	Gold standard for high AI-ESG synergy (Modi Face AI + B Corp ESG). Perfect benchmark.	How Luxury Beauty Aligns AI Personalization with Radical Sustainability.
2. Microsoft/LinkedIn (2016)	Demonstrates data-driven AI (Azure) and social ESG (skills training). Shows strong cultural fit.	Scaling ESG impact with AI-driven expertise networks.
3. Amazon/Whole Foods (2017)	Cautionary tale of cultural and ESG clashes despite retail tech potential.	Labor disputes and slow AI adoption weakened ESG value.
4. Amazon/Zoox (2020)	Tests long-term bets (AVs) versus immediate ESG returns. Highly complex.	Balancing R&D timelines with climate commitments (net-zero logistics).
5. Ahold Delhaize (2016)	ESG-focused merger with poor AI integration. Contrasts with tech-driven deals.	Can sustainability leadership make up for slow AI innovation?
6. Samsung/Harman (2016)	IoT/AI combined with automotive ESG (e.g., electric vehicles). Although understudied strategic.	Embedded AI in connected cars enables sustainable mobility.

and

Table 6. Supplemental Cases for Contrast.

Case	Role in Paper: Key AI-ESG Insights
Tesco–Carrefour (2018–2021)	Displays procurement-focused alliances with limited AI-ESG ambitions (baseline comparison).
Amazon/One Medical (2022)	Highlights health tech’s unique AI-ESG potential (privacy versus social impact tradeoffs).
Amazon/Souq.com (2017)	Illustrates geographic expansion pitfalls where AI-ESG is deprioritized.

.

The selected case studies span a wide range of industries, including retail, technology, beauty, logistics, healthcare, and automotive. This variety demonstrates how different sectors implement artificial intelligence (AI) and environmental, social, and governance (ESG) principles. The author has analyzed these cases over the past five years, with research findings published in journals such as Knowledge Management Research & Practice (Taylor & Francis), Journal of Risk and Financial Management (MDPI), Administrative Sciences (MDPI), Journal of Open Innovation: Technology, Markets, and Complexity (Elsevier), International Journal of Applied Management Science (Inderscience Publishers), and others.

These examples illustrate a spectrum of AI-ESG-OI alignment. Some, like L’Oréal’s acquisition of Aesop, show strong synergy between technological innovation and sustainability goals (Čirjevskis, 2025). Others, such as Amazon’s acquisition of Whole Foods, highlight challenges and risks in ESG performance after a tech-driven acquisition (Čirjevskis, 2023a).

The cases also illustrate different strategic approaches. Microsoft’s acquisition of LinkedIn exemplifies a technology-oriented strategy where AI supports ESG outcomes (Čirjevskis, 2023a). Ahold Delhaize emphasizes ESG priorities, focusing on sustainability rather than technological advancement (Čirjevskis, 2020). Zoox represents a long-term investment where ESG benefits are expected to develop gradually, especially through autonomous and electric mobility (Čirjevskis, 2024b).

Comparative analysis is also feasible. For instance, L’Oréal and Aesop rank high in AI-ESG alignment, whereas Amazon and Whole Foods rank much lower, illustrating a stark contrast between leading and lagging practices (Čirjevskis, 2023b). Samsung’s acquisition of Harman offers a unique chance to examine how AI and ESG converge in automotive innovation and IoT technologies (Čirjevskis, 2021).

To improve contrast and methodological rigor, the comparative case study analysis follows a clear sequence. First, two highly complementary AI-ESG-OI mergers and acquisitions (M&A) will be compared: Microsoft’s acquisition of LinkedIn and Samsung’s acquisition of Harman International Industries (Table 6). These cases will be assessed using the ARTIC scorecard, which uses a five-point numerical scale and includes a final verdict and insights on post-M&A realities.

Next, the highly synergistic L’Oréal–Aesop deal will be compared with the moderately synergistic Amazon–Whole Foods acquisition (

Table 7. Comparative analysis of ARCTIC case studies: Samsung/Harman (2016) versus Microsoft/LinkedIn (2016).

Criterion	Samsung/Harman (2016)	Score (/5)	Microsoft/LinkedIn (2016)	Score (/5)
Advantages	AI: Harman’s automotive AI (infotainment, ADAS) complements Samsung’s IoT/5G capabilities. ESG: Harman’s eco-friendly audio tech aligns with Samsung’s sustainability goals.	5/5	AI: LinkedIn’s data and Microsoft’s Azure AI created synergies, such as talent analytics. ESG: LinkedIn’s “Green Skills” taxonomy aligns with Microsoft’s carbon-negative initiatives goals.	4/5
Relevance	AI: Shared focus on connected cars and smart cities. ESG: Both are committed to reducing e-waste (Harman’s recyclable materials, Samsung’s Circular Economy pledge).	5/5	AI: Strong fit (cloud/AI + professional data). ESG: Shared digital inclusion goals (e.g., LinkedIn Learning for underserved communities).	5/5
Capacity to Absorb	AI: Minimal tech hurdles (Harman already uses Samsung chips). ESG: Harmonized supply chain sustainability standards.	4/5	AI: Data privacy and API integration challenges. ESG: Few conflicts, both prioritize transparency.	3/5
Time of Integration	AI: Quickly integrating Harman’s audio AI into Samsung’s Bixby ecosystem. ESG: Implemented Harman’s energy-efficient manufacturing methods by 2018.	5/5	AI: Rapid Azure integration (e.g., AI-powered LinkedIn recommendations). ESG: Swift alignment on skills-for-climate-jobs initiatives.	4/5
Implementation Plan	AI: Clear roadmap for AI-driven in-car experiences. ESG: Public 2020 sustainability targets for the automotive division.	4/5	AI: Clear roadmap (e.g., Dynamics 365 + LinkedIn Sales Navigator). ESG: Public pledges (e.g., 250K green skills trained by 2025).	4/5
Cultural Fit	AI: Collaborative R&D culture (e.g., joint innovation labs). ESG: Mutual emphasis on green tech and ethical sourcing.	4/5	AI: Shared ethics (e.g., responsible AI principles). ESG: Both committed to workforce upskilling and DEI.	5/5
Total Score		27/30		25/30
Verdict	High synergy—Tech/ESG alignment accelerated auto-tech growth.		High synergy—AI-driven product integration, ESG-enhanced social impact.
Post-M&A Reality	AI: Harman’s tech powered Samsung’s Digital Cockpit, leading to a 30% revenue growth by 2020. ESG: Achieved 95% recyclable materials in Harman products by 2022. AI-ESG: Harman’s automotive AI combined with Samsung’s scale enabled rapid market penetration.		AI: LinkedIn’s premium subscription revenue increased by 25% in 2023, reaching $1.7 billion, mainly driven by the adoption of AI-powered features. ESG: “Global Skills Initiative” trained over 80 million people in green and digital skills.

). The analysis then continues with Amazon’s high-tech advantage from acquiring One Medical, compared to its moderately synergistic purchases of Zoox and Souq.com (

Table 8. AI-ESG M&A Scorecard Comparison: Amazon/Whole Foods vs. L’Oréal/Aesop.

Criterion	Amazon/Whole Foods (2017)	Score (/5)	L’Oréal/Aesop (2023)	Score (/5)
Advantages	- AI: Amazon’s logistics AI combined with Whole Foods’ physical stores creates omnichannel dominance. - ESG: Whole Foods’ organic supply chain enhances Amazon’s sustainability credibility.	4/5	- AI: L’Oréal’s AI beauty tech (ModiFace) plus Aesop’s cult following equals hyper-personalization. - ESG: Aesop’s vegan, sustainable branding boosts L’Oréal’s ESG metrics.	5/5
Relevance	- AI: High alignment (Amazon’s tech + Whole Foods’ premium retail). - ESG: Partial fit (Amazon’s labor controversies vs. Whole Foods’ “conscious capitalism”).	3/5	- AI: Perfect fit (L’Oréal’s digital tools and Aesop’s experiential retail). - ESG: Aesop’s B Corp status aligns with L’Oréal’s “Green Beauty” goals.	5/5
Capacity to Absorb	- AI: Integrating Amazon’s cashierless technology into Whole Foods faced operational hurdles. - ESG: Culture clash over worker wages and unionization.	2/5	- AI: Minimal—Aesop’s minimalist operations integrate easily. - ESG: Shared cruelty-free and sustainable sourcing standards.	4/5
Time of Integration	- AI: Rapid rollout of Prime discounts, but full tech integration took years. - ESG: Slow progress in harmonizing labor standards.	3/5	- AI: Rapid rollout of AI-powered Aesop skincare recommendations. - ESG: Immediate boost to L’Oréal’s ESG credibility.	5/5
Implementation Plan	- AI: Utilized data analytics to enhance Whole Foods’ inventory management. - ESG: Lacks a clear ESG roadmap after the merger.	2/5	- AI: Leveraged L’Oréal’s AI to boost Aesop’s e-commerce growth. - ESG: Published a public roadmap to expand Aesop’s sustainability practices worldwide.	4/5
Cultural Fit	- AI: Whole Foods resisted Amazon’s automation-heavy culture. - ESG: Worker protests over pay disparities.	2/5	- AI: Both value tech-enabled, human-centered beauty. - ESG: Shared focus on ethical sourcing and storytelling.	5/5
Total Score		16/30		28/30
Verdict	Moderate risk—ESG and cultural gaps diminish synergies.		High synergy—AI/ESG driving accelerated growth.
Post-M&A Reality	- AI Success: Prime integration boosted sales, but technology adoption lagged behind expectations. - ESG Failures: Labor disputes and brand decline damage reputation.		AI/ESG Win: Aesop’s sales increased 50% in 2023, driven by AI personalization and ESG storytelling as main factors drivers.

). Finally, to highlight the contrast between failed and successful retail collaborations, the Ahold Delhaize merger will be contrasted with the problematic strategic alliance between Tesco and Carrefour (

Table 9. AI-ESG M&A Scorecard: Amazon’s Acquisition of Zoox (2020), Souq.com (2017) and One Medical (2022).

Criterion	Amazon/Zoox (2020)	Score (/5)	Amazon/Souq.com (2017)	Score (/5)	Amazon/One Medical (2022)	Score (/5)
Advantages	AI: Zoox’s autonomous vehicle (AV) AI complements Amazon’s logistics. ESG: Potential for zero-emission last-mile delivery.	4/5	AI: Limited immediate AI integration; potential for Amazon’s AI tools in MENA e-commerce. ESG: Access to emerging market with sustainability growth potential.	3/5	AI: Health-tech potential. ESG: Healthcare access.	4/5
Relevance	AI: Strong fit (Amazon’s robotics + Zoox’s AV tech). ESG: Aligns with Amazon’s Climate Pledge (net-zero by 2040).	4/5	AI: Basic e-commerce AI alignment (recommendation engines). ESG: Some alignment in digital inclusion for MENA region.	3/5	AI: Strong (AI diagnostics). ESG: Social impact alignment.	5/5
Capacity to Absorb	AI: High (integrating Zoox’s AI with Amazon’s systems). ESG: Regulatory hurdles in sustainable transport.	3/5	AI: Minimal initial technical integration required. ESG: Cultural and regulatory differences in ESG standards.	4/5	AI: Data privacy risks. ESG: Regulatory concerns compliance.	3/5
Time of Integration	AI: Slow (AV tech requires lengthy testing). ESG: Long-term ESG benefits (e.g., EV fleets).	3/5	AI: Gradual rollout of Amazon’s AI tools. ESG: Slow ESG alignment because of market differences.	3/5	AI: Moderate (API integration). ESG: Focus on long-term health equity.	4/5
Implementation Plan	AI: Gradual rollout, such as pilot programs in select cities. ESG: No clear short-term ESG milestones.	3/5	AI: Lacks a clear public roadmap for AI integration. ESG: Shows basic adoption of Amazon’s sustainability practices.	2/5	AI: EHR integration. ESG: Public health goals.	4/5
Cultural Fit	AI: Both emphasize innovation but differ in their risk appetite. ESG: Zoox’s sustainability focus aligns with Amazon’s Climate Pledge.	4/5	AI: Both focus on e-commerce but operate at different scales. ESG: Partial alignment on digital accessibility.	3/5	AI: Ethics alignment. ESG: Shared (Diversity, Equity, Inclusion) DEI goals.	4/5
Total Score		21/30		18/30		24/30
Verdict	Moderate potential—long-term AI-ESG benefits but high complexity and slow integration.		Moderate potential—focus on market expansion with limited initial AI-ESG integration.		High synergy: AI-ESG in healthcare.
Post-M&A Reality	- AI: Zoox testing AVs in over 3 U.S. cities (2024). - ESG: No significant ESG impact yet; possible future synergy with Amazon’s EV fleet.		- AI: Gradual adoption of Amazon’s recommendation algorithms. - ESG: Some sustainability practices adopted, but no major initiatives.		- AI: Growth in health analytics.—ESG: Wins in accessibility.

).

This sequence offers a layered understanding of how AI and ESG synergies vary across industries and strategic objectives. For researchers, it provides a reproducible method to evaluate post-merger integration and strategic alignment using structured comparison logic and scoring tools. For readers, it presents a clear framework to assess M&A outcomes beyond financial metrics. The comparative ARCTIC case study analysis of Harman Industries International by Samsung Electronics in 2016 and the LinkedIn acquisition by Microsoft is shown in Table 6.

The data analysis for the Samsung/Harman and Microsoft/LinkedIn acquisitions primarily relies on two of the author’s publications: “Managing Competence-Based Synergy in Acquisition Processes: Empirical Evidence from the ICT and Global Cosmetic Industries” (Čirjevskis, 2023a) and “Exploring the Link of Real Options Theory with the Dynamic Capabilities Framework in Open Innovation-Type Merger and Acquisition Deals” (Čirjevskis, 2021).

Readers are encouraged to consult these publications for more details and insights. The information and data were gathered and analyzed from these sources, along with relevant literature, and used in the analysis presented in Table 7.

Samsung’s acquisition of Harman in 2016 showcased strong strategic synergy, especially in integrating AI and ESG goals. Harman’s automotive AI features, like infotainment systems and advanced driver-assistance systems (ADAS), complemented Samsung’s strengths in IoT and 5G. This synergy spurred innovation in connected car solutions and smart mobility. From an ESG view, Harman’s eco-friendly audio technologies aligned well with Samsung’s sustainability aims.

Both companies shared a commitment to reducing electronic waste, with Harman using recyclable materials and Samsung promoting circular economy principles. This shared vision made the merger more environmentally meaningful. Technologically, the integration was seamless. Harman already used Samsung’s semiconductor components, which lowered technical barriers.

On ESG, the companies aligned their supply chain sustainability standards, making integration easier. The timeline was efficient. Harman’s audio AI was quickly integrated into Samsung’s Bixby ecosystem, and by 2018, Samsung adopted Harman’s energy-efficient manufacturing practices. This quick alignment demonstrated strong operational compatibility. The implementation plan was clear and focused on the future. Samsung outlined a roadmap for AI-driven in-car experiences and set public sustainability targets for its automotive division by 2020.

These commitments showed a proactive approach to technological and ESG goals. Culturally, the merger benefited from a collaborative R&D environment, including joint innovation labs. Both companies emphasized green technology and ethical sourcing, supporting a strong cultural fit. Overall, the Samsung–Harman deal scored 27 out of 30, reflecting high synergy. The alignment of technological capabilities and ESG values greatly enhanced Samsung’s growth in the automotive tech sector.

Microsoft’s acquisition of LinkedIn successfully integrated AI, mainly through Azure’s improvements to LinkedIn’s recruitment analytics tools like “Talent Insights.” However, the deal also revealed a missed chance for deeper collaboration on AI ethics, especially in reducing bias in hiring algorithms. From an ESG perspective, LinkedIn’s platform supported Microsoft’s climate education initiatives on a large scale. Nonetheless, there was a gap in using AI to track ESG metrics more effectively, such as monitoring the carbon footprint linked to remote work trends.

Compared to other M&A deals, the Microsoft–LinkedIn acquisition resembles the L’Oréal–Aesop deal in terms of strategic alignment between AI and ESG, both achieving high synergy scores. Unlike Amazon’s acquisition of Whole Foods, Microsoft avoided cultural clashes by aligning technological platforms and corporate values, as shown in the following comparative analysis.

A comparative ARCTIC scorecard analysis of Amazon’s acquisition of Whole Foods in 2017 and L’Oréal’s acquisition of Aesop in 2023 is presented in Table 8. The data for these cases mainly come from two of the author’s publications: “Measuring Dynamic Capabilities-Based Synergies Using Real Options in M&A Deals: Amazon’s Acquisition of Whole Foods” (Čirjevskis, 2023b), and “Exploring Parallel Compound Real Options in MNCs International Transactions (Čirjevskis, 2025)

Readers are encouraged to consult these publications for additional insights and detailed analysis. The data and information were collected and analyzed from these sources, along with relevant literature, to support the analysis presented in Table 8.

One of the main insights from the comparative analysis is that alignment between AI capabilities and ESG values significantly influences post-merger success. The L’Oréal–Aesop acquisition, which scored 28 out of 30, demonstrates how shared commitments to personalization technologies and sustainability can result in a quick return on investment. Conversely, Amazon’s acquisition of Whole Foods, which scored only 16 out of 30, encountered challenges due to cultural and ESG misalignment, which slowed down integration despite promising AI potential.

Whole Foods Market needed a modern, scalable, and high-performance solution to simplify and optimize business intelligence (BI) to improve employee experience and overall performance because the previous tool was becoming difficult to manage, time-consuming, and costly (Reddy & Shah, 2024). The success of the “One Grocery” strategy depends on maintaining Whole Foods’ premium brand identity while integrating Amazon’s operational discipline—a balance that, if maintained, could reshape the grocery sector’s competitive landscape (West, 2025).

To address such disparities, mitigation strategies are essential. In cases with lower synergy scores, conducting pre-close audits to assess AI-ESG compatibility and implementing joint training programs focused on ethical AI practices can help bridge strategic gaps. For high-synergy deals, companies should actively leverage AI tools like ModiFace to enhance personalized customer experiences and bolster ESG branding through certifications such as B Corp to maximize strategic impact.

Besides internal alignment, open innovation is crucial for improving post-merger results. By engaging external partners—such as startups, universities, and NGOs focused on sustainability—companies can accelerate the development of AI-ESG solutions that internal R&D alone might not produce. For instance, Samsung and Harman’s joint innovation labs demonstrate how collaborative ecosystems can lead to breakthroughs in automotive AI and green technologies. Similarly, Microsoft’s integration of LinkedIn benefited from open data partnerships that expanded ESG education and workforce analytics.

Open innovation also promotes adaptive learning during mergers. It enables firms to test and refine ESG strategies in real time, incorporating diverse perspectives and external feedback. This approach not only boosts technological relevance but also enhances resilience in ESG implementation, especially in fast-changing sectors such as healthcare, mobility, and retail, as shown in the next table on several Amazon acquisitions.

A comparative ARCTIC scorecard analysis of Amazon’s acquisitions of Souq.com (2017), Zoox (2020), and One Medical (2022) is shown in Table 9. The data for these cases mainly come from three of the author’s publications: “Predicting Explicit and Valuing Tacit Synergies of High-Tech Based Transactions: Amazon.com’s Acquisition of Dubai-Based Souq.com” (Čirjevskis, 2023c); “Valuation of Patent-Based Collaborative Synergies under Strategic Settings with Multiple Uncertainties: Rainbow Real Options Approach” (Čirjevskis, 2024b), and “Exploring Competence-Based Synergism in Strategic Collaborations: Evidence from the Global Healthcare Industry” (Čirjevskis, 2024a).

Readers are encouraged to review these publications for more detailed insights and additional context. The data and information were collected and analyzed from these sources, including relevant academic literature, to support the analysis presented in Table 9.

The acquisition of Zoox highlights significant AI potential, particularly in autonomous vehicle technology that could transform Amazon’s logistics. However, implementing this technology faces technical and regulatory challenges, and the pace of AI adoption—such as warehouse automation—has been slower than anticipated. From an ESG standpoint, Zoox holds future promise with zero-emission delivery solutions, but there are no immediate sustainability achievements. A major gap is the absence of publicly announced ESG goals, like measurable targets for converting to electric vehicle fleets.

This case is similar to Ahold Delhaize in having a strong long-term ESG vision but slower implementation. Unlike the L’Oréal–Aesop deal, Zoox does not have immediate AI-ESG synergy, which limits its short-term strategic impact. To improve results, Amazon should accelerate autonomous vehicle pilots in dense urban areas and set clear ESG goals, such as committing to a specific percentage of electric vehicle fleet conversion by a certain year.

In the case of Souq.com, the acquisition was primarily focused on expanding geographically rather than driving innovation. Amazon missed opportunities to leverage Souq’s local data to develop regionally tailored AI models. ESG initiatives also lagged behind, despite the increasing potential for sustainability in the Middle East and North Africa (MENA). To address these gaps, Amazon could implement its personalization algorithms customized to regional preferences and introduce MENA-specific sustainability initiatives, such as Arabic-language climate education. A quick win would be adopting Amazon’s packaging waste reduction programs within Souq’s operations.

Across these cases, several patterns become evident. There are clear trade-offs between AI and ESG priorities. One Medical stands out with a dual focus on AI diagnostics and health equity, while Whole Foods and Souq.com prioritize operational expansion over innovation. The speed of integration also varies, with One Medical providing the fastest return on investment through health APIs, whereas Zoox remains in a long-term research and development phase.

Cultural challenges are common in all deals, with none scoring above four in cultural fit, showing Amazon’s difficulty in adapting its scale to different organizational cultures. ESG performance also varies, with One Medical having a strong social impact and Souq.com lacking regional ESG leadership.

Strategically, the most successful deals are those with native alignment between AI and ESG goals, such as in health tech and social good. In contrast, geographic expansions like Souq often overlook localized ESG strategies. Long-term investments like Zoox reflect Amazon’s patient capital approach but need clearer short-term ESG metrics to demonstrate progress.

For future transactions, Amazon should conduct pre-merger audits to assess AI-ESG compatibility, especially in sensitive sectors like healthcare. Launching visible ESG initiatives early in the integration process—such as climate education programs—can build public trust. Ultimately, creating cultural bridge teams might help ease integration friction, as seen in the union-related issues following the Whole Foods acquisition.

This analysis sets the stage for the next comparative case study, which shifts focus from Amazon’s tech-driven acquisitions to the retail sector. The upcoming review of Ahold Delhaize’s merger and the strategic alliance between Tesco and Carrefour will explore how ESG-focused strategies and cultural integration impact outcomes in traditional consumer markets. These cases offer a contrasting perspective to assess long-term sustainability commitments, operational synergies, and the challenges of cross-border collaboration in retail.

A comparative ARCTIC scorecard analysis of the Ahold Delhaize merger (2016) and the Tesco–Carrefour strategic alliance (2018–2021) is presented in

Table 10. AI-ESG M&A Scorecard Comparison: Ahold Delhaize vs. Tesco–Carrefour.

Criterion	Ahold Delhaize (2016 Merger)	Score (/5)	Tesco–Carrefour (2018–2021 Alliance)	Score (/5)
Advantages	AI: Supply chain optimization. ESG: Emphasis on strong sustainability (e.g., circular economy).	4/5	AI: Minimal AI use. ESG: Coordinated purchasing to save costs, limited ESG innovation.	3/5
Relevance	AI: Moderate alignment with a focus on efficiency. ESG: High shared priorities such as net-zero and ethical sourcing.	4/5	AI: No strategic AI alignment. ESG: Partial overlap (sustainable sourcing).	2/5
Capacity to Absorb	AI: Data silos in logistics. ESG: Regional reporting differences (EU/US).	3/5	AI: No integration. ESG: Varying supplier standards (UK/EU).	1/5
Time of Integration	AI: Potential for automated ESG reporting. ESG: Rapid procurement wins.	4/5	AI: No AI acceleration. ESG: Slow ESG collaboration.	2/5
Implementation Plan	AI: Lacks AI-driven PMI tools. ESG: Has public goals but no AI integration.	3/5	AI: No AI roadmap. ESG: No joint ESG milestones.	1/5
Cultural Fit	AI: Ethical AI alignment. ESG: Strong shared values (worker rights, climate).	5/5	AI: Lacks AI culture. ESG: Only superficial sustainability efforts.	2/5
Total Score		23/30		11/30
Verdict	Moderate potential for synergy—Strong ESG but requires AI integration.		Limited synergy—Procurement focus causes missed AI-ESG opportunities.
Post-M&A Reality	- AI: Improved logistics but no breakthrough innovation. - ESG: Achieved 2025 carbon goals early.		- AI: No noticeable AI impact. - ESG: Cost savings but lacks ESG leadership.

. The data for these cases primarily comes from two of the author’s publications: “Valuing Collaborative Synergies with Real Options Application: A Dynamic Political Capabilities Perspective” (Čirjevskis, 2022), and “Do Synergies Pop Up Magically in Digital Transformation-Based Retail M&A? Valuing Synergies with Real Options Application” (Čirjevskis, 2020).

Readers are encouraged to review these works for more detailed insights and additional context. The data and information were collected and analyzed from these sources, along with relevant academic literature, to support the analysis in Table 10.

The merger between Ahold and Delhaize is notable as an ESG-driven integration with strong cultural alignment. The companies shared sustainability values and operational philosophies, which helped ensure a smooth transition. However, the merger lagged behind in AI innovation. One missed opportunity was the adoption of AI-powered carbon accounting systems, which could have enhanced transparency and reporting across their supply chains.

In contrast, the strategic alliance between Tesco and Carrefour mainly concentrated on procurement efficiencies. While the partnership achieved cost savings through joint sourcing, it did not include significant AI and ESG initiatives. For example, the alliance could have explored blockchain technologies to improve ethical sourcing and traceability, but such innovations were not pursued.

A broader pattern emerges when comparing these cases to others in the study. High-scoring deals, like L’Oréal’s acquisition of Aesop, tend to integrate AI and ESG strategically, resulting in stronger post-merger performance. Low-scoring deals, such as Tesco–Carrefour, often treat ESG and AI as secondary issues, which limits their long-term impact and innovation potential.

Furthermore, open innovation requires both cultural readiness and strategic alignment. When companies lack a collaborative mindset or fail to integrate external ideas into their core strategy, open innovation efforts can become disjointed or superficial. This was evident in the Tesco–Carrefour partnership, where procurement efficiencies were achieved, but no significant AI or ESG innovation occurred—despite opportunities to collaborate with ethical sourcing platforms or sustainability-focused startups.

In contrast, successful M&A deals like L’Oréal–Aesop demonstrate how open innovation can be integrated into brand strategy, product development, and ESG storytelling. By collaborating with AI startups like ModiFace and obtaining ESG certifications like B Corp, L’Oréal expanded its innovation ecosystem beyond internal R&D, creating a more resilient and responsive post-merger platform.

The case studies in Appendix B balance depth, originality, and practical relevance for AI–ESG–OI-driven M&A strategies.

Table A1. Case studies provide a balance of depth, novelty, and practical insights for AI-ESG-OI-based M&A strategy.

Cases	Key AI-ESG-OI Insights
1. L’Oréal/Aesop (2023)	AI enhances OI through crowdsourced eco-design (e.g., Green Sciences Platform), while ESG commitments draw in OI partners.
2. Microsoft/LinkedIn (2016)	AI enables new OI markets (e.g., GitHub Copilot for ESG tech collaboration), aligning with social impact goals.
3. Amazon/Whole Foods (2017)	Failed to leverage AI for OI, such as sharing synthetic data with suppliers, or for ESG transparency.
4. Amazon/Zoox (2020)	AI replaces traditional OI (e.g., in-house R&D over open AV alliances), which creates ESG integration risks.
5. Ahold Delhaize (2016)	Key AI-ESG-OI insights show that sustainability leadership could not make up for lagging AI-OI (e.g., no federated learning for supply chain ESG data).
6. Samsung/Harman (2016)	Embedded AI (connected cars) enables OI ecosystems for sustainable mobility (e.g., open EV battery patents).

and

Table A2. Supplemental Cases for Contrast.

Case	Key AI-ESG-OI Insights
Tesco–Carrefour (2018–2021)	Baseline for low AI-ESG-OI ambition: Procurement alliance without AI/OI tools for ESG (e.g., shared carbon accounting).
Amazon/One Medical (2022)	Highlights AI-ESG-OI tradeoffs: Health tech’s privacy risks (synthetic data potential) versus social impact (open-access diagnostics).
Amazon/Souq.com (2017)	Geographic expansion challenges: AI-ESG is deprioritized in favor of market share, leading to missed OI opportunities (e.g., local sustainability crowdsourcing).

show how the AI–ESG-OI ARCTIC Scorecard is practically used by analyzing six main and three supplementary merger and acquisition cases. These evaluations examine how AI, ESG, and OI factors have influenced—or could influence—the potential for strategic synergy in each scenario.

Furthermore, Appendix B Table A1 and Table A2 provide a framework for understanding how strategic intent and execution impact the outcomes of mergers and alliances. They also emphasize the importance of integrating AI, ESG, and OI principles from the beginning—not just as operational tools but as essential drivers of long-term value creation.

Key Open Innovation Insights from Case Studies

Building on the approach described by Holgersson et al. (2024), several trends emerge regarding AI’s role in shaping open innovation practices in mergers and acquisitions (M&A) contexts. In some cases, AI helps improve traditional OI methods. For example, L’Oréal’s use of ModiFace and Microsoft’s deployment of GitHub Copilot demonstrate how AI tools can boost existing collaborative models like crowdsourcing and open APIs. These improvements are often linked to ESG transparency, which then attracts new innovation partners. Certifications such as B Corp, for instance, have been shown to support green technology collaborations by signaling ESG commitment.

In other cases, AI facilitates entirely new types of open innovation. Samsung and Microsoft show how technologies like federated learning and open patent ecosystems can create new avenues for ESG data collaboration and innovation. These developments are supported by shared digital infrastructures—such as Microsoft Azure—that help expand ESG impact through initiatives like skills training and carbon tracking.

However, not all AI applications in M&A foster open innovation. Sometimes, AI replaces traditional open innovation methods, which can introduce new risks. Amazon’s acquisition of Zoox exemplifies this trend, where proprietary autonomous vehicle technologies replaced participation in open mobility alliances. This shift raises concerns about accountability and transparency in ESG results.

Cultural alignment is crucial for successful AI-ESG-OI integration. The example of Amazon and Whole Foods shows how misalignments, such as using closed AI systems combined with an anti-union corporate culture, can damage both ESG value and innovation potential (Stambor, 2025).

Conversely, Microsoft’s focus on open skills development and inclusive innovation practices illustrates how cultural fit can boost the effectiveness of AI and OI strategies in reaching ESG goals.

Table 11. Patterns of AI-Driven Open Innovation and ESG Implications in M&A: Insights from Case Studies.

OI Pattern	Representative Cases	AI Role	ESG Link	Insight Summary
OI-Enhancing AI	L’Oréal, Microsoft	AI tools such as ModiFace and GitHub Copilot enhance traditional OI practices like crowdsourcing and open application programming interfaces (APIs).	ESG transparency (e.g., B Corp certification) attracts new OI partners and green tech collaborations.	AI strengthens existing OI mechanisms and supports ESG through openness and trust.
OI-Enabling AI	Samsung, Microsoft	AI facilitates new forms of OI, such as federated learning for ESG data and open patent ecosystems.	Shared infrastructure (e.g., Azure) enhances ESG impact through skills training and carbon tracking.	AI creates novel OI pathways that expand ESG collaboration and scalability.
OI-Replacing AI	Amazon/Zoox	Proprietary AI systems replace traditional OI, like autonomous vehicles replacing open mobility alliances.	Increases ESG risks due to less accountability and transparency.	AI might undermine OI and ESG goals if it replaces collaborative ecosystems.
OI-Cultural Fit	Amazon/Whole Foods vs. Microsoft	Closed AI systems and anti-union culture conflict with OI and ESG values.	Microsoft’s open-skills approach contrasts with Amazon’s restrictive policies practices.	Cultural misalignment between AI and OI values can weaken ESG and innovation potential.

highlights key OI insights from case studies by Holgersson et al. (2024).

Furthermore, adding real options valuation (ROV) of collaborative synergies to the AI-ESG ARCTIC Scorecard framework can significantly enhance the accuracy of strategic and financial analyses. The suggested ROV synergy mapping is shown in

Table 12. Mapping Real Options Valuation to ARCTIC Criteria for Assessing Strategic Flexibility in AI-ESG-OI M&A Synergies.

ARCTIC Criterion	ROV Opportunity	Valuation Approach	Allocated Case Studies	Key Risk Factors	Risk Mitigation Strategies
Advantages	Option to scale AI-ESG tech across markets	Growth option pricing	L’Oréal/Aesop: Scalable AI personalization and ESG branding. Microsoft/LinkedIn: AI-powered ESG training and technology collaboration. Amazon/One Medical: Potential to expand AI diagnostics and ESG healthcare access.	L’Oréal/Aesop: Low risk; ESG branding and AI personalization are broadly appealing worldwide, but luxury market ESG expectations differ. Microsoft/LinkedIn: Low to moderate risk; global expansion may be affected by regulatory and cultural ESG differences. Amazon/One Medical: Moderate risk; healthcare regulations and privacy issues might restrict ESG growth across regions.	Conduct ESG localization audits; co-develop ESG standards with regional partners; pilot AI-ESG tools in diverse regulatory environments before full rollout
Relevance	Option to pivot AI for ESG adjacencies	Switching option valuation	Microsoft/LinkedIn: Shifting AI tools to focus on ESG tech collaboration. Samsung/Harman: Redirecting IoT/AI toward sustainable mobility. Amazon/Souq.com: Missed chance to shift toward local ESG/OI initiatives.	Microsoft/LinkedIn: Low risk; flexible AI tools and strong ESG culture support strategic pivots. Samsung/Harman: Moderate risk; ESG mobility pivots depend on regional infrastructure and policy support. Amazon/Souq.com: High risk; expansion prioritizes scale over ESG/OI localization, missing regional innovation potential.	Establish regional ESG-OI innovation hubs, engage local stakeholders in ESG co-design, and integrate local sustainability metrics into AI platforms.
Capacity to Absorb	Option to delay or abandon if integration fails	Deferral (American call)/Abandonment (American put) options	Amazon/Zoox: High R&D complexity and ESG uncertainty. Amazon/Zoox: High risk; long R&D cycles and lack of open alliances increase ESG uncertainty. Amazon/Whole Foods: High risk; labor tensions and closed AI systems hinder ESG transparency and stakeholder trust. Amazon/One Medical: Moderate to high risk; ethical AI and data privacy concerns challenge ESG integration in health tech. Amazon/Whole Foods: Cultural and ESG misalignment. Amazon/One Medical: Privacy versus social impact tradeoffs complicate ESG integration.	Amazon/Zoox: High risk; long R&D cycles and lack of open alliances increase ESG uncertainty. Amazon/Whole Foods: High risk; labor tensions and closed AI systems obstruct ESG transparency and stakeholder trust. Amazon/One Medical: Moderate to high risk; ethical AI and data privacy concerns challenge ESG integration in health tech.	Use modular integration plans with exit checkpoints; implement AI ethics boards; harmonize ESG standards through third-party frameworks (e.g., Global Reporting Initiative(GRI),Sustainability Accounting Standards Board (SASB)
Time of Integration	Compound option to accelerate with AI tools	Compound timing options	Microsoft/LinkedIn: AI accelerates ESG scaling. Samsung/Harman: AI-enabled mobility platforms speed ESG deployment.	Microsoft/LinkedIn: Low risk; mature AI infrastructure enables quick ESG rollout. Samsung/Harman: Moderate risk; ESG acceleration could be delayed due to regulatory timelines and ecosystem readiness.	Align ESG timelines with AI deployment cycles; use AI to simulate ESG impact scenarios; phase ESG implementation alongside AI rollouts.
Implementation Plan	Sequential (staged) investment options	Compounded real options	Samsung/Harman: Staged rollout of connected car ESG platforms. Ahold Delhaize: ESG milestones are present, but AI integration is weak. Amazon/Zoox: Long-term staged investment in AV and ESG logistics.	Samsung/Harman: Low to moderate risk; success depends on ecosystem adoption and open standards. Ahold Delhaize: Moderate risk; ESG goals lack AI support, risking execution delays. Amazon/Zoox: High risk; long-term ESG returns are uncertain, and a lack of OI partnerships increases isolation.	Invest in AI infrastructure before scaling ESG efforts; integrate ESG KPIs into AI dashboards; leverage open innovation platforms to crowdsource ESG implementation ideas.
Cultural Fit	Deferral options to align values for long-term ESG-OI success or Abandonment options	Deferral (LearniMicrosoft/LinkedIn: Strong cultural alignment around open innovation and ESG. Amazon/Whole Foods: Cultural misalignment hampers ESG-OI potential. Tesco/Carrefour: Weak cultural and strategic fit for AI-ESG-OI ambitions.ng)/Abandonment real options	Microsoft/LinkedIn: Strong cultural alignment around open innovation and ESG. Amazon/Whole Foods: Cultural misalignment undermines ESG-OI potential. Tesco/Carrefour: Weak cultural and strategic fit for AI-ESG-OI ambitions.	Microsoft/LinkedIn: Low risk; shared values and open innovation culture reinforce ESG integration. Amazon/Whole Foods: Moderate to high risk; anti-union stance and closed systems hinder ESG-OI alignment. Tesco/Carrefour: High risk; procurement-driven culture lacks innovation mindset and strategic ESG integration.	Conduct cultural due diligence pre-merger; launch cross-cultural ESG-OI training programs; co-create ESG-OI charters with employees and partners

.

7. Discussion: Open Innovation Patterns and Strategic Flexibility in AI-ESG-OI M&A Deals with Real Options

This study identifies four patterns of AI-driven Open Innovation (OI) in M&A—OI-enhancing, OI-enabling, OI-replacing, and OI-Cultural Fit patterns—based on nine comparative case studies shown in Table 11. These patterns are then assessed through the ARCTIC framework using real options reasoning to forecast strategic flexibility and risk in AI-ESG-OI integration.

L’Oréal/Aesop exemplifies the pattern that enhances open innovation (OI), where AI personalization (ModiFace) complements ESG leadership (B Corp), reinforcing Chesbrough and Bogers’ (2014) view that digital tools can scale OI. Microsoft/LinkedIn illustrates AI that enables OI, where Azure infrastructure supports ESG-aligned skills development and open collaboration, consistent with Lee et al. (2025).

In contrast, Amazon/Zoox exemplifies AI that replaces open innovation, where proprietary systems take precedence over open mobility alliances, increasing ESG risks and echoing concerns from Moro-Visconti (2025). The cultural difference between Tesco/Carrfour and Amazon/Whole Foods highlights the importance of cultural fit in maintaining ESG-OI synergies, supporting Brunswicker and Chesbrough (2018).

Table 12 aligns these patterns with real options logic. For example, L’Oréal/Aesop and Microsoft/LinkedIn provide growth and switching options, reflecting scalable and flexible AI-ESG strategies. Tesco/Carrefour and Amazon/Whole Foods, however, encounter high uncertainty in ESG integration due to limited capacity to absorb AI and cultural misalignment, which justifies deferral or abandonment options. Samsung/Harman and Ahold Delhaize demonstrate staged investment options, where ESG goals exist but depend on AI readiness and ecosystem maturity.

Supplemental cases such as Amazon/Souq.com and Amazon/One Medical further illustrate the risks of deprioritizing local ESG-OI adaptation or underestimating privacy concerns in health-tech. Tesco–Carrefour, with limited AI-ESG ambition, acts as a baseline comparison.

Furthermore, the theoretical basis for combining ARCTIC measures with real options is backed by several practical examples published by the author in the last five years. These empirical case studies demonstrate how the frameworks can be used to predict and value collaborative synergies that traditional models often miss.

L’Oréal’s Acquisition of Aesop: As outlined in Čirjevskis (2025), this case demonstrates the use of parallel compound real options. L’Oréal’s acquisition created two concurrent options: a growth option through expanding Aesop’s brand internationally, and a deferral option by enabling the company to leverage brand portfolio and distribution )synergies over time. The analysis emphasizes that the true value of the deal lies in the strategic flexibility to exercise these options at the best possible time.

Amazon’s Acquisition of One Medical: This case, described in Čirjevskis (2024a), exemplifies valuing tacit competence-based synergies. The ARCTIC framework was initially used to estimate the synergy potential by evaluating how Amazon’s core competencies could combine with One Medical’s healthcare services. The subsequent valuation was carried out using three different real options models (Binomial, Black-Scholes, and Monte Carlo) to quantify the value of this strategic flexibility, which included the option to incorporate One Medical’s services into Amazon’s broader business ecosystem.

Ahold-Delhaize Merger: In this case (Čirjevskis, 2020), the framework showed how synergies from digital transformation could be valued. The merger of two major grocery retailers created synergies through integrating their digital platforms and data analytics capabilities. The real option was to invest in and expand a new digital service model, capturing the value of the digital assets as a flexible investment opportunity.

This paper does not include detailed analysis or calculations for the other companies mentioned (Amazon/Souq.co, Amazon/Whole Foods, Amazon/Zoox, and Tesco Carrefour), as the focus is on the specific examples that best illustrate the theoretical framework. Overall, this framework supports prior research on OI and strategic flexibility while extending it by explicitly incorporating ESG and AI dimensions. Linking methods directly to the research objectives improves rigor and provides a deeper understanding of how M&A outcomes are shaped by the interaction of innovation models, sustainability goals, and organizational capacity to adapt to change.

To address these challenges, the framework proposes specific mitigation strategies tailored to each risk type. These include modular integration plans, regional ESG co-design, AI ethics governance, and cultural onboarding programs. These measures not only reduce downside risk but also reveal hidden strategic value by aligning technological capabilities with sustainability goals and collaborative innovation practices, as shown in Figure 1.

This integrated approach shows how ROV identifies the hidden flexibility in AI-ESG deals, turning the ARCTIC scorecard into a flexible strategic tool. Ultimately, the ARCTIC–ROV framework provides a real-time decision-making resource for M&A strategists, ESG analysts, and innovation managers. It links financial valuation with strategic foresight, helping firms handle complexity, speed up integration, and build resilient, future-ready ecosystems.

8. Research Findings, Contributions, and Implications

Research findings reveal the limitations of traditional strategic frameworks in today’s M&A trends and introduce a new, more comprehensive approach with the ARCTIC framework.

8.1. ARCTIC Uncovers Strategic Opportunities Beyond VRIO

Applying the ARCTIC framework to multiple M&A case studies shows its superior ability to identify strategic synergies that the traditional VRIO model might miss. While VRIO effectively assesses whether a resource is valuable, rare, inimitable, and well-organized (J. Barney, 1991), it does not consider the dynamic interactions among AI, ESG, and Open Innovation (OI) in today’s strategic landscape.

For example, in Microsoft’s acquisition of LinkedIn, ARCTIC emphasized the alignment of AI capabilities, ESG commitments, and OI practices, revealing a synergy that VRIO would classify as only “valuable” and “organized,” but lacking the crucial cultural and ecosystem fit (Holgersson et al., 2024).

8.2. Cultural Fit and Capacity to Absorb as Critical Differentiators

Two ARCTIC dimensions—Cultural Fit and Capacity to Absorb—proved especially crucial in predicting post-merger success. In the Amazon–Whole Foods case, despite operational potential, the lack of cultural alignment and ESG transparency caused substantial integration challenges and reputational risks (Kim, 2025). VRIO did not capture these nuances, while ARCTIC identified them through low scores in Capacity to Absorb and Cultural Fit, indicating high integration risk.

This finding supports the idea that competitive advantage in the AI-ESG era is not just resource-based but also depends on context, requiring frameworks that evaluate organizational readiness, stakeholder alignment, and ethical appropriateness (Feyisetan et al., 2025).

8.3. Real Options Reasoning Improves Strategic Flexibility

By linking each ARCTIC dimension to a real options valuation (ROV) type—such as growth, switching, or abandonment options—the framework assists firms in quantifying strategic flexibility. For example, the “Time” dimension in the L’Oréal–Aesop case was associated with compound options, representing the ability to accelerate ESG impact through AI-enhanced OI platforms. This valuation method provides a more detailed understanding of risk and opportunity than VRIO’s binary resource classification.

8.4. ARCTIC as a Predictive and Prescriptive Tool

The ARCTIC scorecard not only evaluates current synergies but also guides strategic planning. Firms with scores in the moderate range (18–24) can use ARCTIC to identify gaps and develop mitigation strategies, such as federated learning pilots or ESG reporting harmonization. This prescriptive ability makes ARCTIC a decision-support tool for M&A strategy, innovation management, and ESG integration.

8.5. Research Question, Theoretical Propositions, and Supporting Examples

The findings show that ARCTIC enhances the VRIO framework by incorporating ecosystem dynamics, ethical issues, and the scalability of innovation. It aligns with the evolving strategic environment where AI and ESG are not separate skills but interconnected factors that drive value creation. This research empirically addresses the research questions and supports the theoretical propositions as follows.

RQ 1. How can the integration of AI, ESG, and Open Innovation generate sustainable competitive advantages in M&A, beyond what is predicted by traditional resource-based frameworks?

Proposition 1. 

A sustainable competitive advantage from AI in M&A is gained not by the AI technology itself, but through its integration into a target firm’s ESG-aligned and open innovation ecosystems, which together create a unique, hard-to-imitate capability.

Supporting Example: Microsoft–LinkedIn integrated AI (Azure, GitHub Copilot) with ESG initiatives and OI platforms, creating a unique ecosystem. In contrast, Amazon–Whole Foods lacked ESG alignment and an innovation culture, leading to value erosion (Čirjevskis, 2023a, 2023b).

RQ 2. How do empirical cases of AI-driven, ESG-aligned M&A transactions demonstrate the practical value and limitations of the ARCTIC framework?

Proposition 2. 

The ARCTIC framework provides a more comprehensive and predictive evaluation of strategic resources in AI-ESG-driven M&A than the traditional VRIO model by explicitly assessing a target firm’s capabilities in terms of its ability to absorb, cultural compatibility, and implementation feasibility.

Supporting Example: L’Oréal–Aesop demonstrated strategic fit across ARCTIC dimensions, such as ESG branding and OI practices, which VRIO alone could not fully capture (Čirjevskis, 2025).

RQ 3. How does real options valuation support the ARCTIC framework by offering a quantitative evaluation of rare and inimitable collaborative synergies, especially in cross-border AI-driven, ESG-aligned M&A transactions?

Proposition 3. 

Applying real options valuation (ROV) within the ARCTIC framework improves strategic decision-making in AI-ESG-driven mergers and acquisitions (M&A) by enabling firms to measure the value of strategic flexibility and the rare, hard-to-imitate collaborative synergies that traditional valuation models often miss.

Supporting Example: Samsung–Harman’s scalable AI-ESG mobility solutions align with growth and multiple expansion options, demonstrating how ROV can model strategic flexibility (Čirjevskis, 2021).

8.6. Implications for Corporations

This research paper offers a clear, practical framework for corporate leaders. Instead of relying solely on traditional financial or operational metrics, we recommend that M&A practitioners utilize the ARCTIC framework in their due diligence process. This allows for a deeper evaluation of intangible assets and potential synergies related to AI and ESG, helping to uncover hidden value and mitigate integration risks.

The findings emphasize the importance of evaluating cultural fit and absorptive capacity as key factors for post-merger success. Managers should adopt a proactive approach to integration, emphasizing alignment of values and communication strategies from the beginning. Additionally, by connecting ARCTIC dimensions to real options valuation, firms can develop a more advanced understanding of strategic flexibility and better support long-term investments in AI and ESG.

8.7. Implications for Policymakers

This research highlights the importance for policymakers to foster an environment that promotes sustainable and transparent business practices in M&A. We suggest creating standardized ESG reporting frameworks to address current inconsistencies, which hinder the evaluation and comparison of potential acquisition targets.

Policymakers should also focus on how to motivate the use of AI and OI in corporate governance and M&A due diligence. By encouraging ethical AI and transparent OI practices, they can inspire companies to make more responsible and sustainable acquisition decisions that benefit all stakeholders, including employees and local communities.

8.8. Implications for Society

The paper’s findings go beyond corporate strategy to impact society at large. By encouraging firms to adopt a framework like ARCTIC, which emphasizes ESG factors and cultural compatibility, we can promote a business environment where M&A deals are not just profit-focused but also ethically responsible.

This method can result in several beneficial social outcomes, including:

• Enhanced Labor Practices: Focusing on cultural fit helps prevent post-merger layoffs and improves working conditions by ensuring alignment with social values.
• Environmental Stewardship: Emphasizing ESG encourages the adoption of sustainable practices and reduces the environmental footprint of integrated companies.
• Greater Transparency: Using AI to analyze ESG data and open innovation practices can lead to more transparent corporate operations and increased accountability to the public.

By adopting a multi-dimensional perspective, society can benefit from M&A deals that generate long-term value, not only for shareholders but for all stakeholders.

9. Conclusions, Contributions, Limitations, and Future Directions

This study critically examined the limitations of the VRIO framework in evaluating strategic resources in the era of artificial intelligence (AI), Environmental, Social, and Governance (ESG) imperatives, and Open Innovation (OI). In response to Wingate et al.’s (2025) assertion that AI cannot offer a sustainable competitive advantage due to its commoditization, the paper introduced the ARCTIC framework as a dynamic extension of VRIO. ARCTIC operationalizes strategic evaluation through six dimensions—Advantages, Relevance, Capacity to Absorb, Time, Implementation, and Cultural Fit—each designed to capture the multifaceted nature of AI-ESG synergies in M&A contexts.

Empirical case analysis demonstrated that ARCTIC offers a more detailed and predictive tool for assessing post-merger integration success, stakeholder alignment, and innovation scalability. By incorporating real options valuation and ecosystem-based thinking, ARCTIC assists firms in navigating complexity, measuring strategic flexibility, and meeting the evolving expectations of investors and society.

9.1. Contributions

This paper makes several key contributions to the strategic management literature. First, it expands the VRIO framework by introducing ARCTIC, a multidimensional model that incorporates ESG and open innovation (OI) considerations into strategic resource evaluation. Second, it offers methodological innovation by creating a scorecard-based assessment tool that connects each ARCTIC dimension to real options valuation.

This enables the measurement of intangible synergies and strategic flexibility. Third, through comparative case studies, the paper offers empirical insights into how ARCTIC highlights integration risks and cultural misalignments that the traditional VRIO framework often misses. Finally, the framework remains highly relevant for managers, providing decision-makers with a practical tool to assess AI-ESG fit, develop integration roadmaps, and minimize post-merger risks.

9.2. Limitations

Although the ARCTIC framework provides a strong conceptual model, several limitations should be acknowledged. It mainly emphasizes theory development and has not yet been thoroughly tested through large-scale empirical studies across various industries and regions. The scorecard relies on managerial judgment, which can lead to subjectivity and inconsistency in evaluation. Additionally, the framework may require adjustments for sectors with unique regulatory or technological challenges, such as healthcare or defense.

9.3. Future Research Directions

Future research should focus on empirical validation through cross-industry studies to assess the predictive accuracy of ARCTIC scores regarding post-merger performance and ESG outcomes. It would also be helpful to reference more independent empirical studies, especially quantitative analyses on ESG and M&A, to strengthen the evidence base and the generalizability of findings. Recent quantitative research has started to clarify the complex relationship between ESG performance and M&A outcomes.

For example, large-scale event studies and panel data analyses have shown that while strong ESG profiles can boost long-term value creation and lower post-merger risks, the short-term financial impact of ESG integration remains mixed and depends on the context. Some studies indicate that high ESG scores in both acquirer and target firms are linked to fewer negative announcement returns and higher post-merger value, while others find that ESG factors do not always have a statistically significant effect on deal completion risk or arbitrage spreads (Hiltunen & King, 2025).

Meta-analyses and global surveys further show that ESG considerations are increasingly influencing M&A strategies, with more organizations pausing or abandoning deals due to ESG red flags (Deloitte, 2024). Quantitative data also indicates that ESG integration is more impactful in certain sectors (e.g., financial services, healthcare) and regions (Europe, Middle East) than others (Deloitte, 2024). However, the literature emphasizes the need for more detailed, sector-specific, and long-term studies to fully understand the causal links between ESG and M&A success.

Additionally, research should explore behavioral integration, focusing on how organizational culture, leadership styles, and stakeholder engagement influence ARCTIC dimensions, particularly cultural fit and implementation. Another important direction is developing tools to visualize and measure open innovation networks and ESG data flows, which would enhance the accuracy of ARCTIC assessments. Integrating ARCTIC into AI-powered decision-support systems could also support real-time evaluation of M&A opportunities and ongoing ESG monitoring.

Incorporating Environmental, Social, and Governance (ESG) factors into Artificial Intelligence (AI) initiatives is essential for maximizing benefits in Mergers and Acquisitions (M&A). This alignment not only minimizes risks but also fosters long-term value creation by ensuring AI investments are ethical and sustainable, particularly from the investor’s perspective.

However, to fully unlock the potential of AI-ESG synergies, organizations must adopt Open Innovation (OI) as a strategic driver. As Holgersson et al. (2024) emphasize, AI is not only transforming innovation processes but also reshaping the core of OI by enhancing, enabling, and sometimes replacing traditional practices. Within the ARCTIC framework, OI provides the collaborative infrastructure needed to integrate diverse ESG data sources, foster cross-sector partnerships, and co-create sustainable value.

Engaging with industry practitioners and experts is crucial for creating a structured way to combine AI with ESG factors. Open Innovation encourages this engagement by facilitating knowledge sharing among organizations, supporting federated learning, and using synthetic data to address privacy and data-sharing challenges. These features are especially useful during M&A activities, where due diligence, cultural integration, and stakeholder alignment are vital.

In conclusion, the co-evolution of AI, ESG, and Open Innovation presents a transformative opportunity for M&A strategies. By utilizing the VRIO-based ARCTIC framework as a guiding tool for structuring M&A transactions and embedding OI principles at its core, organizations can better manage complexity, foster sustainable growth, and meet the evolving expectations of investors, regulators, and society at large. It provides a methodology for future research, including using real options to evaluate synergies. As a theory-building tool, it can serve as a foundation for further empirical research.