Orchestrating AI-based circular business models through platform ecosystems: Review, framework, and future directions.

Orchestration Theory of the Multinational Enterprise in Digital Platform and AI Ecosystems

The emergence of digital platforms, business ecosystems, and artificial intelligence (AI) is transforming the nature of multinational enterprise (MNE) activity and challenging established theories of international business. While internalization theory has provided a powerful explanation for the existence and boundaries of the MNE through its focus on market imperfections, transaction costs, and governance choices, its explanatory power is increasingly constrained in environments characterized by ecosystem-based value creation, distributed innovation, and technologically mediated forms of control. Building on earlier work by Pitelis and Teece, this paper advances orchestration theory as a broader framework for understanding the contemporary MNE. We argue that modern MNEs are best viewed not simply as organizations that internalize transactions, but as focal firms that orchestrate globally distributed systems of resources, capabilities, partners, and complementary assets. Orchestration encompasses the creation, co-creation, and capture of value through the deployment of dynamic capabilities and the management of complementarities and co-dependencies across organizational and national boundaries. The digital platform and AI era further reinforces the importance of orchestration as control increasingly derives from technological architectures, data flows, ecosystem positioning, and access to critical complementary assets rather than ownership alone. By integrating insights from internalization theory, dynamic capabilities, and ecosystem research, the paper proposes a reconceptualization of the MNE that better reflects the realities of contemporary international business and provides a foundation for future theoretical development.

Journal of Perspectives in Management

Artificial Intelligence Strategic Lifecycle: A Literature Review-Based Framework

Purpose.  This systematic literature review examines how firms utilise artificial intelligence (AI) as a strategic rather than purely operational resource and develops an integrative conceptual framework of the AI strategic lifecycle.  Design/methodology/approach.  A PRISMA‑guided search identified 147 peer‑reviewed articles published between 2020 and 2025 across major scholarly databases, including Elsevier (Scopus), Emerald, Springer, and Wiley. The evidence is synthesised through five dominant theoretical lenses: dynamic capabilities, resourcebased view (RBV), knowledgebased view (KBV), technology acceptance model (TAM), and disruptive innovation theory (DIT).  Findings.  Dynamic capabilities and RBV explain how organisations mobilise data, algorithms, and AI‑related human capital to build and sustain competitive advantage in sectors such as public administration, energy, human resource management (HRM), and researchintensive industries. KBV highlights the role of absorptive capacity and knowledgesharing routines in transforming AI outputs into innovation, particularly in user‑facing contexts such as healthcare and hospitality. In these sectors, TAM is central, emphasising trust, ease of use, and perceived usefulness as key drivers of adoption. In finance, DIT elucidates competitive disruption and incumbent response strategies triggered by AI‑enabled entrants.  Practical implications.  The review provides recommendations for practitioners, including investing in organisational learning and absorptive capacity and ensuring transparency of AI‑enabled interfaces to translate AI investments into sustainable performance gains.  Originality/value. By integrating five theoretical perspectives, the review develops a model of the AI strategic lifecycle, offering both a consolidated foundation for future research and a forwardlooking agenda for managers seeking to leverage AI as a strategic asset.

Bright sides and governance costs of GenAI in business processes: insights from a multi-case study analysis

This study explores the governance tensions that arise during the adoption of generative artificial intelligence (GenAI), examining how the same mechanisms that create value also introduce frictions and risks. It identifies the Assure-Account-Align (AAA) routine as a process-oriented framework for navigating these tensions, in which observability and authorisation serve as gating constructs that mediate the progression from experimental to embedded GenAI use. The study adopts a qualitative, exploratory multi-case design based on 14 semi-structured interviews with experts and users from three organisations in Northern Italy, complemented by internal documents and direct observation. Data were analysed using a hybrid deductive-inductive coding scheme in NVivo, structured around the integrated Technology–Organisation-Environment (TOE) perspective and the Dynamic Capabilities (DC) framework. The findings reveal that GenAI adoption progresses not through model performance alone, but through a conjunction of observability (provenance, replayability, intervention capability) and authorisation (regulatory, contractual, professional and reputational approval routes). These mechanisms simultaneously enable value creation and introduce governance tensions – workflow overhead, approval bottlenecks and deskilling anxieties – that the AAA routine helps organisations navigate iteratively. The study conceptualises governance tensions in GenAI adoption as inherent paradoxes arising from the same mechanisms that create value. It proposes the AAA routine, on an exploratory basis and grounded in three heterogeneous cases, as a candidate bridge between contextual conditions (TOE) and dynamic action (DC), offering a sensitising device for future research rather than a validated framework.

What Makes A Business Ecosystem Circular? A Multilevel Review

Drawing on multilevel and complexity theories, this research identifies new and reframed core principles of circular business ecosystems (CBEs), thereby clarifying what makes a business ecosystem circular. Although CBEs are increasingly recognised as adaptive systems shaped by interdependencies and non‐linear interactions, how circularity operates across ecosystem levels remains underexplained. Multilevel and complexity theories provide the analytical basis for distinguishing and connecting the CBEs' dimensions and dynamics through which circularity emerges. A systematic literature review was conducted using Coleman's boat model to explore how circular economy principles are integrated into ecosystem configurations. It examines (i) specific dimensions of CBEs related to ecosystem actors (including orchestrators, participants and complementors) and the relational dynamics among them; (ii) the value creation processes and the impacts generated by CBEs; and (iii) contextual factors influencing these systems. Overall, this work advances CBE research by explaining how circularity emerges from the interaction between macro‐ and micro‐level elements, showing how macro‐level association, situational, relational and transformational mechanisms shape these ecosystems.

Rethinking the future

The HCSAIGF contributes to AI governance research by providing an integrated explanatory architecture and offers a conceptual basis for future empirical research and more coherent governance practices.