AI between ambitious improvements and accelerated idiocy: a framework for capturing the advantages and disadvantages of AI projects

Artificial intelligence and business model: a bibliometric analysis of strategic and entrepreneurial trends

Artificial intelligence in business: challenges and prospects for development

This study aims to identify the key challenges and development prospects of AI implementation to enhance business efficiency and ensure sustainable development, providing strategic guidance for organizations to balance technological innovation with responsibility and risk management.

NEW TRENDS IN CONTEMPORARY ECONOMICS, BUSINESS AND MANAGEMENT

Generative Artificial Intelligence in Scientific Research: Individual Benefits, Collective Risks, and a Framework for Responsible Research with AI

This paper examines the tension between the benefits of generative artificial intelligence (AI) for scientific research and the unresolved governance questions that accompany its rapid adoption. Drawing on an academic roundtable held at the AI for Science and Innovation Workshop (Scuola IMT Alti Studi Lucca, April 2026) and on a fast-expanding empirical literature, it maps the disagreement within the research community across four stages of the research process: funding, research tasks, publication and peer review, and use and uptake. The empirical case for AI's productivity, augmentation, and democratization effects has strengthened. The picture changes once productivity is disaggregated: AI-assisted work shows measurable gains in publication volume and citation share, while the evidence on novelty, disruption, and breakthrough output remains ambiguous or negative. We argue that the divergence between private and social returns arises through three analytically distinct mechanisms, namely information asymmetry, negative externalities on a shared knowledge base, and depletion of research capacity, and that each calls for a different governance instrument. We propose Responsible Research with AI (RRAI), an extension of the Responsible Research and Innovation tradition organized around four principles that operate at different levels of the research system: disclosure, differentiation, narrative, and proportionality. RRAI builds on existing institutional scaffolding, including the EU AI Act, UNESCO, and the OECD, and aims to preserve AI's productivity gains while addressing systemic risks that individual researchers can neither observe nor manage on their own.

AIS Electronic

This study contributes a unified research model for stakeholder-specific AI adoption, demonstrates a mixed quantitative-qualitative approach, and establishes a scalable foundation beginning with engineers and extending to other stakeholders through future empirical validation.

A Relational View on Artificial Intelligence Business Value Manifestation

The rapid advancement of Artificial Intelligence (AI) has prompted many organizations to explore how they can leverage AI. As a result, AI business value has emerged as a central topic in both scholarly and practitioner discourse. As AI technologies grow increasingly complex, organizations often lack the capabilities to develop and deploy them independently and therefore turn to interorganizational collaborations as a means of creating and capturing AI-enabled value. However, interorganizational partnerships are characterized by distinctive dynamics that stem from interactions among heterogeneous organizations, each comprising stakeholders with differing perceptions, interests, and expectations regarding AI business value. Information Systems (IS) literature provides rich insights into how AI business value manifests, yet current knowledge predominantly focuses on intraorganizational settings, which creates an analytical blind spot that obscures how partnership dynamics affect the manifestation of AI business value. Without a clear understanding of these dynamics, it remains difficult to explain the challenges organizations face when partnering around AI, including misaligned expectations, mismatches in partner resources and capabilities, and opportunistic behaviors among partners. This dissertation addresses this need and explores the manifestation of AI business value in interorganizational settings. Adopting a relational view, it sheds light on how partnership dynamics shape the manifestation of AI business value and, ultimately, the business value perceived by different stakeholders. The dissertation consists of four studies. The first study focuses on stakeholders’ perceptions of AI business value, uncovering connections between partnership conditions and how stakeholders perceive the business value. The second study builds on this by examining how stakeholders’ values shape their perceptions of AI business value. The third study advances the analysis by exploring how AI business value is communicated across partner organizations. Finally, the fourth study establishes the relational determinants that shape the manifestation of AI business value in interorganizational partnerships. The dissertation synthesizes the findings of the individual studies into an analytical framework that offers explanatory insights on the manifestation of AI business value in interorganizational settings. Specifically, the framework establishes the relevance of the relational dynamics, the need to examine the manifestation as a phenomenon operating across individual and (inter)organizational levels of analysis, and the importance of accounting for the distinctive characteristics of AI, which enable novel dynamics in interorganizational settings. Through this synthesis, the dissertation contributes to the AI business value literature by offering both novel insights and new analytical directions for future IS research.