This study explores the critical role of boundary objects in the context of people-centred smart cities, a new paradigm in urban development that emphasises citizen participation in planning and decision-making. Boundary objects—artefacts, documents, or concepts that facilitate communication and collaboration across different knowledge domains—are increasingly recognized as essential tools in the complex, multi-stakeholder environment of urban governance. Despite extensive research on boundary objects in organizational contexts, their specific application in smart cities remains underexplored. This study addresses this gap by investigating how BOs, particularly collaborative tools and spaces, contribute to innovation, engagement, and knowledge-sharing in people-centred smart cities. Employing a Systematic Literature Review following the PRISMA protocol, this research synthesizes key insights from scholarly articles to comprehensively understand boundary objects' role in urban governance. This study offers a theoretical framework for leveraging boundary objects to enhance the inclusivity and sustainability of smart cities. It suggests avenues for future research, including empirical validation and exploration of boundary objects in diverse geographic and cultural contexts.

Digital transformation is reshaping entrepreneurial ecosystems by redefining value creation, stakeholder dynamics, and innovation trajectories. While digital technologies can enhance efficiency and foster innovation, their benefits are often unevenly distributed, reinforcing structural inequalities between regions and sectors. These disparities underscore the need for governance systems that are both adaptive and inclusive, capable of evolving with technological change while addressing social and territorial asymmetries. This research unveils the Nested-Cyclical Model (NeCyM) for Entrepreneurial Innovation, a framework that integrates governance, innovation, and entrepreneurship as co-evolving forces within dynamic ecosystems. Drawing on interdisciplinary literature, the model positions governance not as a static structure but as a generative actor that enables iterative learning, stakeholder co-creation, and institutional adaptability. The NeCyM challenges linear and state-centric paradigms by foregrounding feedback loops, decentralised collaboration, and systems-level transformation. Theoretically, the model contributes to innovation and governance studies by offering a processual perspective suited to volatile and digitalised environments. Practically, it provides a foundation for designing governance systems that support inclusive, context-sensitive, and sustainability-oriented innovation. The paper concludes with implications for researchers and policymakers, offering pathways for empirical validation and operational application across urban innovation districts, regional ecosystems, and collaborative governance platforms.

Purpose: This study explores how stakeholders perceive and evaluate Artificial Intelligence (AI) in sustainable agriculture. While AI is often promoted as a solution to food security and climate challenges, its adoption raises ethical, social, and institutional concerns that remain underexplored. Design/methodology/approach: Using Q methodology, the study captures subjective viewpoints from 20 stakeholders, including farmers, nutritionists, journalists, and health professionals. A Q sample of 30 statements was ranked and analysed through inverted factor analysis, revealing four distinct perspectives: “The Concerned Skeptic”, “The Critical Adopter”, “The Responsible Environmentalist”, and “The Technological Optimist”. Despite differences, all groups expressed concern over the digital divide and access inequalities. Findings: The findings challenge linear models of technology adoption and highlight the value of context-sensitive, inclusive governance. Originality/value: By integrating the Social Construction of Technology framework and extended Technology Acceptance Models, the study contributes a structured and interpretive understanding of how artificial intelligence is socially constructed in agriculture, offering practical insights for more equitable and responsible innovation.