FUTURES & FORESIGHT SCIENCE最新文献

This study examines the spatial distribution and temporal evolution of technology-driven enterprises in Adelaide, South Australia, from 2019 to 2022, introducing a novel automated foresight methodology that combines natural language processing, machine learning, and geographic visualization. Using web scraping techniques and social media analytics, we analyzed 4001 posts from 856 founder and employee profiles, 20,000 tweets, and 10,000 news articles to map the emergence of technology hotspots across Greater Adelaide. The findings reveal significant clustering patterns in five key technological categories: machine learning and big data analytics, digital health and medical technology, agricultural technology, advanced manufacturing, and renewable energy. Our analysis identifies four primary innovation districts as emerging technology hotspots. The study demonstrates a 40% increase in technology-related activities between 2019 and 2022, with renewable energy showing the most dramatic growth trajectory. The methodology successfully addresses the critical gap between static policy planning and rapidly evolving startup landscapes, providing policymakers and innovation stakeholders with dynamic, fine-grained insights into emerging technology clusters and future innovation trajectories. These findings contribute to understanding regional innovation systems and provide a scalable framework for technology foresight in regional innovation ecosystems.

Anticipating the emergence of radically new technologies poses significant methodological challenges due to high uncertainty surrounding their development and diffusion. Conventional forecasting approaches, which rely on stable relationships and historical data extrapolation, are often ill-suited to such conditions. This editorial examines how different anticipatory methods address uncertainty and what this implies for method selection in technology foresight. Drawing on four case studies—quantum technologies in healthcare, fusion energy, defense technologies, and the emergence of technology clusters—the special issue compares horizon scanning, scenario planning, Delphi-based expert elicitation, and computational weak-signal analysis. Using an emerging-technology framework that treats uncertainty as a defining and evolving attribute rather than a temporary knowledge gap, the editorial shows that method suitability depends on the nature and degree of uncertainty; the time horizon becomes meaningful only under specific uncertainty conditions. Foresight methods that structure exploration across multiple plausible futures remain applicable across uncertainty contexts, whereas forecasting is conditionally applicable and depends on predominantly epistemic uncertainty. The comparison further demonstrates that each method has structural limitations, underscoring the need for strategic combinations under higher uncertainty. By positioning uncertainty as the central organizing principle for methodological choice, this editorial contributes to futures and foresight research and offers guidance for designing anticipatory approaches that remain robust under radical uncertainty.

This study advances Strategic Foresight research by developing and applying a hybrid framework to delineate optimal scenarios that balance exploratory and exploitative strategies, fostering sustainable growth in agri-food Born Global Firms navigating post-internationalization complexity and heightened environmental uncertainty. The framework combines grounded qualitative analysis through inductive methods with advanced quantitative and structural techniques, including Fuzzy-MICMAC for mapping interdependencies, Dynamic Causal Loop Diagrams to quantify feedback dynamics, and morphological analysis complemented by hierarchical clustering. Building on recent participatory and computational foresight innovations, this integrative methodology contributes to the diversification of anticipation methods by enabling the systematic quantification of systemic interdependencies and the exploration of multi-dimensional uncertainties in a computationally rigorous yet context-sensitive manner. The resulting scenarios provide actionable strategic roadmaps for BGFs, emphasizing export-oriented alliances, innovation-driven technological exploration, and adherence to international quality standards. These insights support managers and policymakers in designing adaptive strategies that enhance Organizational Ambidexterity and strengthen competitive positioning in volatile global markets.

This paper develops a theoretical framework for forecasting under Knightian uncertainty, where probabilities are not uniquely defined, and ambiguity fundamentally constrains predictive inference. Traditional forecasting relies on single-model probabilistic structures, yet such approaches are often fragile in environments characterized by structural breaks, limited information, and unforeseen shocks. To address this limitation, the study introduces ambiguity envelopes and set-valued forecasts, formalizing predictions that reflect multiple admissible models rather than a single distribution. Building on decision-theoretic foundations, the paper integrates max–min expected utility, variational preferences, and minimax regret to link forecasts directly to robust decision-making. The mathematical models provide empirical foundations for ambiguity-aware forecasting while highlighting implications for evaluation, communication, and practical implementation. The results indicate that forecasting under Knightian uncertainty requires a paradigm shift: moving from precision-oriented prediction toward robustness and resilience. This framework offers a foundation for applying ambiguity-aware forecasting across economics, finance, and policy domains, while it also complements existing robust decision-making methods by providing a formal structure for ambiguity-aware forecast construction within the broader shift from prediction to robustness.

This article develops a multi-layered framework for understanding how strategic foresight can function as a form of policy infrastructure within financial governance systems, with a particular focus on emerging economies. Drawing on recent literature on anticipatory governance, data-driven regulation, and institutional capacity-building, the paper argues that effective foresight depends not only on methodological tools but on the alignment of political, technical, participatory, and regulatory conditions. A structured review of secondary sources (2020–2024), complemented by descriptive analysis of the experiences of Singapore, India, and the OECD, is used to highlight common patterns in how financial authorities incorporate anticipation into their decision-making environments. These insights are then applied to the Iranian banking sector to illustrate how the proposed framework can organize existing information and reveal practical constraints and opportunities. The paper also outlines three exploratory scenarios for Iran in 2030, derived from the interplay of key technological, regulatory, and geopolitical drivers identified in the study. Rather than offering predictive claims, the analysis provides a structured way of interpreting institutional dynamics and clarifying the types of capabilities required for anticipatory governance in constrained settings. The findings underscore the importance of coherent political sponsorship, reliable data ecosystems, participatory mechanisms, and channels for translating foresight insights into regulatory action. The framework offers policymakers a way to diagnose systemic gaps and consider how foresight can be embedded more effectively within financial governance architectures.

Despite its usefulness as an innovation tool for strategic foresight, conventional roadmapping has several limitations in practice, such as linear tendency, poor articulation of value, difficulty and delay in implementation, and implicit certainty and persistent prejudice, as identified in existing literature. These limitations are further exacerbated in the digital transformation era due to radical and disruptive changes that fundamentally alter systems, calling for more innovative roadmapping practices for strategic foresight to navigate transformative changes. This study examines the potential of integrating design thinking principles and tools with roadmapping to address these challenges. An improved roadmapping process is proposed, by combining conceptual theories from a literature review of strategic roadmapping and design thinking. This process model is tested and further developed via an in-depth case study of an airline company undergoing digital transformation, based on participant observation. Feedbacks and verification interviews demonstrate the benefits of the new roadmapping approach, which incorporates principles of design thinking (e.g., human-centeredness, creative reframing, learning by doing, and divergence and convergence) and relevant tools (e.g., persona, customer journey map, and ideas menu) that are effective for forward-oriented strategic decision making under uncertainty. The proposed roadmapping process can improve firms' strategic foresight by providing informed and practical guidance for systematically designing, organizing, and executing roadmaps in rapidly evolving and highly disruptive environments of digital transformation. This study advances the literature on strategic roadmapping, and enables academic researchers to further explore how design thinking principles and tool-kits can enhance firms' foresight processes and capabilities in managing digital transformation.

Vietnam's rapid digital transformation presents a complex paradox: whilst the digital economy demonstrates exceptional growth exceeding 20% annually, the labour market exhibits deepening precarity and structural inequalities. This study employs a comprehensive Futures Studies approach, utilising Causal Layered Analysis (CLA), scenario planning, and the Futures Wheel, to examine Vietnam's digital labour dynamics through in-depth interviews with 28 stakeholders across five categories: workers, employers, educators, policymakers, and futures experts. Grounded in Inayatullah's Six Pillars Framework for futures thinking, the research reveals four distinct layers of causality: surface trends showing accelerated digitisation and gig economy expansion; systemic causes including policy-driven development and legal grey zones; worldviews characterised by pervasive techno-optimism and partnership illusions; and deep myths positioning Vietnam as an “Asian Tiger” pursuing a “Digital Leapfrog.” Futures Wheel analysis maps the cascading impacts of widespread AI adoption, while scenario planning identifies four potential pathways, ranging from inclusive digital prosperity to stratified precarity. The findings demonstrate how surface-level economic success can coexist with microeconomic vulnerability when social infrastructure fails to keep pace with technological advancements. This rigorous application of established Futures Studies methods provides empirical insights for managing digital labour transitions in emerging economies, contributing substantial case study evidence and practical policy insights for ensuring equitable development outcomes whilst leveraging technological opportunities.