Deep tech has become a central concept in innovation, entrepreneurship, and public policy, frequently invoked to describe science-based ventures and breakthrough technologies. Despite its growing prominence, the term remains conceptually ambiguous and theoretically underdeveloped. This article adopts a conceptual–analytical approach to clarify the meaning and role of deep tech within contemporary innovation systems. Drawing on innovation studies, technology entrepreneurship, and science and technology studies, it examines how deep tech is defined, mobilized, and operationalized across technological, business, and policy domains. The analysis shows that deep tech is best understood not as a specific technology category, but as a performative and strategic innovation regime characterized by radical innovation, long time horizons, ecosystem dependence, and mission-oriented public intervention. The article offers conceptual clarity and practical insights for managers and policymakers navigating high-uncertainty, science-based innovation contexts.

Interoperability, as defined by ISO, refers to the ability of systems or applications to exchange information and to mutually use the information that has been exchanged. As technological innovation introduces diverse information systems, the necessity for seamless data exchange has become increasingly critical, particularly with the rise of digital twin ecosystems. However, persistent interoperability challenges continue to result in significant problems that hinder effective communication and collaboration throughout the project lifecycle. To investigate why data interoperability remains a persistent challenge despite various industry efforts, a workshop was held with over 40 experts to identify the practical barriers and enablers from an engineering management perspective. Qualitative analysis identified 12 barriers and 15 enablers, revealing that core impediments are not merely technological but inherently socio-technical, involving complex organizational and process-related factors. Key barriers include proprietary system integration and the difficulty of quantifying ROI, while the adoption of standards for multi-stakeholder collaboration and AI-driven automation serve as corresponding enablers. Based on these findings, six actionable strategies are proposed: (1) Overcome Integration Challenges and Vendor Lock-In, (2) Improve Adoption and Alignment of Standards, (3) Demonstrate and Quantify the Benefits of Interoperability, (4) Build Robust Data Governance and Ownership Frameworks, (5) Address Resistance to Process Change, and (6) Enhance Collaboration and Communication Structures. This study provides strategies addressing distinctive stakeholder priorities, including critical implications for SMEs and startups, thereby offering a roadmap to accelerate the realization of integrated digital twin ecosystems.

Conceptual Background and Gaps– Critical Supply Chains (CSCs) are dynamic networks integrating physical assets and supply chain components—planning, sourcing, manufacturing, and distribution—to ensure continuous delivery of essential goods and services. Due to the increasing digitalization of processes and systems, Next-Generation Complex Supply Chains (NextGen CSCs) resilience increasingly depends on seamless technology integration. Objective– This research identifies and structures Resilience Key Performance Indicators (R-KPIs) for assessing NextGen CSCs. It shifts from cost-oriented KPIs to resilience-focused metrics, ensuring service continuity during disruptions. Methodology- A systematic literature review classifies and ranks resilience-related KPIs, providing a structured framework for their integration into digitalized supply chains. Results and Contributions- This article first summarizes the key points of the quantitative decision-making framework that shows managers precisely how resilience KPIs (R-KPIs) can lead strategic planning, shifting supply-chain thinking from traditional cost optimization to resilience-driven choices. Building on this foundation, a step-by-step implementation road map is laid out that pinpoints where and when each R-KPI should be used in supply-chain decision assessment. Moreover, the scalability of the proposed study approach highlighted its potential for reuse across multiple industrial domains and the key organizational and market challenges companies must address to unlock those cross-sector benefits. Together, these results provide practitioners and researchers with a structured yet transferable pathway for enhancing supply-chain resilience.

This article explores the potential divides from emerging quantum technologies (QTs) on society, covering impacts on science, technology, geopolitics, and societal structures. We aim to challenge the concept of a single “quantum divide” by offering a comprehensive view. We propose four divides: in science, technologies, between countries, and within societies. The “Quantum divide in science” highlights gaps among scientists and access inequalities in research communities. The second divide, “Quantum divide in technologies through path-dependence,” looks at technology adoption processes. The “Quantum divide between countries” examines adoption reasons and outcomes across countries with varying development levels. Finally, the “Quantum divide within societies” addresses societal implications, one of the most discussed in the literature. We propose directions to navigate these divides, noting their interconnectedness and impact on innovation ecosystems. This article guides those interested in the quantum divide, navigation strategies, and QT's influence on scientific, technological, international, and societal institutions.

The role of social media in construction site management is not thoroughly understood, particularly in its capacity to influence team dynamics and performance. This study investigates the impact of social media use (SMU) on team feedback (TF), knowledge sharing (KS), and member support (MS) within construction site teams in Australia. Drawing from existing literature and integrating Social Exchange Theory and the Theory of Communication Visibility, this research develops a conceptual model that illustrates the relationships between SMU and team performance (TP), with coordination (COO) as a moderating variable. The study identifies that SMU enhances TF, KS, and MS, which subsequently improve TP. COO is shown to amplify or mitigate these effects based on task complexity and interdependencies. This research provides a robust framework for understanding the multifaceted influence of SMU on construction teams and sets the stage for empirical validation across diverse project environments. The findings offer valuable insights for leveraging social media to optimize communication and performance on construction site management, while also highlighting areas for future exploration, such as technological integration and comparative regional studies.

Circular supply chains (CSCs) aim to minimize ecological footprints by closing the loop on resource use. However, safety concerns in CSCs, especially in activities, such as recycling and remanufacturing, present significant barriers. Current safety management systems in CSCs are often reactive and limited in scope, failing to address safety concerns in interorganizational complexities. Therefore, we explore how generative artificial intelligence (Gen AI) can revolutionize safety management in CSCs by providing proactive monitoring, predictive analysis, and enhanced training. Nevertheless, integrating this technology requires addressing technical, data security, operational, trust, and ethical issues. Future research and practices should focus on overcoming these barriers to harness Gen AIs potential for enhancing safety in CSCs.

The rise of generative Artificial Intelligence (AI) in traditionally human domains of creativity signifies a profound development in product innovation management. In this article, we investigate the evolving role of innovation managers, emphasizing a transition from problem-solving to sense-making and decision-making. Additionally, we delve into organizational strategies for effective AI integration, advocating for systematic experimentation, comprehensive training, and the development of AI-first innovation frameworks for new product development (NPD). Ultimately, we propose a paradigm shift to AI-first creative processes, where AI is not merely an extension but the foundational mechanism of the innovation workflow.