The convergence of artificial intelligence (AI) and digital platforms is reconfiguring how organizations create, deliver, and capture value. As platform-based business models proliferate across industries, AI introduces new mechanisms of learning, data-driven value creation, and ecosystem evolution within two-sided and multi-sided platforms. This special issue assembles eight studies that examine AI-enabled platform business model innovation from diverse theoretical, methodological, and sectoral perspectives, including agri-food sustainability, semiconductor manufacturing, ESG rating platforms, open-source ecosystems, B2B platforms, global innovation dynamics, entertainment industry leadership, and blockchain-enabled decentralized systems. This editorial situates these contributions within the broader research trajectory on digital transformation and platform thinking and develops a structured synthesis that highlights recurring patterns across studies. Building on this synthesis, it proposes an integrative conceptual Framework for AI-driven Platform Business Model Transformation, conceptualizing the dynamic alignment and co-evolution among AI roles, platform configurations, and value creation mechanisms. In particular, the framework advances a processual understanding of platform transformation and identifies data externalities as central driver of learning-based value creation. Finally, the editorial outlines a future research agenda grounded in the insights of the special issue, emphasizing the need for further investigation of co-evolutionary dynamics, governance mechanisms, and the theoretical and methodological approaches required to explain AI-enabled platform innovation.

Guest Editorial - Transforming business models across digital platforms: Exploring the role of artificial intelligence / Elena Latino, M., Marzi, G., Trabucchi, D., Grippa, F., Ladd, T.. - In: JOURNAL OF ENGINEERING AND TECHNOLOGY MANAGEMENT. - ISSN 0923-4748. - In Press:(2026). [10.1016/j.jengtecman.2026.101983]

Guest Editorial - Transforming business models across digital platforms: Exploring the role of artificial intelligence

Giacomo Marzi;
2026

Abstract

The convergence of artificial intelligence (AI) and digital platforms is reconfiguring how organizations create, deliver, and capture value. As platform-based business models proliferate across industries, AI introduces new mechanisms of learning, data-driven value creation, and ecosystem evolution within two-sided and multi-sided platforms. This special issue assembles eight studies that examine AI-enabled platform business model innovation from diverse theoretical, methodological, and sectoral perspectives, including agri-food sustainability, semiconductor manufacturing, ESG rating platforms, open-source ecosystems, B2B platforms, global innovation dynamics, entertainment industry leadership, and blockchain-enabled decentralized systems. This editorial situates these contributions within the broader research trajectory on digital transformation and platform thinking and develops a structured synthesis that highlights recurring patterns across studies. Building on this synthesis, it proposes an integrative conceptual Framework for AI-driven Platform Business Model Transformation, conceptualizing the dynamic alignment and co-evolution among AI roles, platform configurations, and value creation mechanisms. In particular, the framework advances a processual understanding of platform transformation and identifies data externalities as central driver of learning-based value creation. Finally, the editorial outlines a future research agenda grounded in the insights of the special issue, emphasizing the need for further investigation of co-evolutionary dynamics, governance mechanisms, and the theoretical and methodological approaches required to explain AI-enabled platform innovation.
2026
Artificial Intelligence, Digital platforms, Business model innovation, Platform thinking, Two-sided platforms, Multi-sided platforms, Data externalities, Twin transition
File in questo prodotto:
File Dimensione Formato  
JET-M_Editorial.pdf

non disponibili

Tipologia: Versione Editoriale (PDF)
Licenza: Copyright dell'editore
Dimensione 2.52 MB
Formato Adobe PDF
2.52 MB Adobe PDF   Visualizza/Apri   Richiedi una copia

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.11771/42018
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus ND
  • OpenAlex ND
social impact