FUTURES & FORESIGHT SCIENCE最新文献

Quantum technologies, driven by principles of quantum mechanics like superposition and entanglement, have shown transformative potential in drug discovery, medical diagnosis, precision medicine, and other therapeutic interventions. However, the research on emerging quantum technologies at early to late stages of development for healthcare applications is limited. The main objective of this study was to identify emerging quantum technologies such as quantum computing, diagnostics, and therapeutics, with a focus on specific applications within healthcare, such as drug discovery, diagnosis assistance, precision medicine, and treatment interventions. We conducted a comprehensive review of this landscape by analyzing data from clinical trials, published literature, and soft intelligence sources. The analysis revealed 116 quantum technologies such as computing algorithms, therapeutics, sensors, and imaging applications that are currently in development or already in the market. Diagnosis assistance-related technologies, including technologies such as magnetoencephalography and quantum dots, constituted the majority of the technologies, while quantum computing-related machine learning and algorithms were significant in drug discovery and precision medicine applications. The integration of quantum technologies into healthcare faces challenges such as infrastructure demands, regulatory frameworks, and the need for professional training. However, with ongoing advancements, quantum technologies are uniquely positioned to revolutionize diagnostic accuracy, computational capacity for drug design, and precision medicine. This horizon scan highlights the current innovation landscape of emerging quantum technologies in healthcare and the challenges in facilitating the integration of these technologies into healthcare systems.

This article explores the potential of discriminative Artificial Intelligence (AI) to enhance scenario planning, a widely used methodology in strategic planning. Like others, scenario planning also faces the challenge of efficiently integrating available information. We address this challenge by investigating two research questions: First, how is discriminative AI currently applied in scenario planning? Second, how could discriminative AI techniques additionally be used to support the stakeholders of scenario planning? A systematic literature review identifies 58 relevant documents that illustrate the application of discriminative AI in several stages of the scenario process. We present six key findings in relation to the purpose of discriminative AI, the data used and the spectrum of topics. We then formulate seven research propositions that serve as a research agenda and highlight further potential for the utilization of discriminative AI. Our contribution to science is that we show how the roles of stakeholders are going to change. For management, we demonstrate the numerous opportunities offered by discriminative AI to improve the quality of scenario planning.

This paper examines time and temporality as central but often overlooked dimensions that shape the futures of work. While workplace transformation discussions often emphasise spatial aspects of working environments, our study reveals that temporal structures fundamentally determine how work is organised, experienced, and valued. In a two-round Delphi study conducted in 2024, we used a novel Delphi approach with provocations and paradox probing. As a result, we gained understanding on how temporal (i.e., time-related) issues function as critical elements in future work environments. Our findings indicate that novel temporal arrangements and understandings are associated with transformations in working environments. However, the possibility of novel temporal arrangements and their utilisation often follow existing power structures, which create inequities between different sectors, roles, and people. To conceptualise time as an active element rather than neutral background, we contextualise the analysis to literature on time in futures studies and related fields. In this way, our research contributes through the Delphi study to understanding how changing temporalities might affect whether workplace innovations succeed in fostering productivity, well-being, and equity or whether they create novel problems and new forms of exclusion. The results of our Delphi study are particularly timely given how workplaces change in the context of the so-called double twin transition of digital/green and virtual/physical transforming our societies. Where and when cannot be separated when it comes to the futures of work, given the double twin transition.

Future research, including participatory research, can help to explore stakeholder perspectives. This capability is observable in scenario methods such as the cross-impact balance analysis that aggregates factors from which scenarios and further research opportunities are derivable. In this methodologically oriented paper, a modeling idea is explored, in which consequences are considered as flexible factors in the modeling process, rather than drawing on fixed impact-consequence linkages. The study proposes an extension that explicitly models implicitly acting consequences to increase stakeholder involvement and system understanding. In this regard, the manuscript argues that this approach enables reflection on the modeling and potential results during this process, as they are constructed around these consequences or, more generally, system indicators. Thus, this approach provides an opportunity to integrate cross-impact assessment more effectively into stakeholder dialog by facilitating the tracking and discussion of structures. The study also illustrates the methodological approach by highlighting its application to the topic of water governance. In addition to the potential for utilizing this approach in workshops and participatory co-modeling, the most significant initial finding of this study is that the explication of consequences could serve as a suitable foundation for further studies focusing on stakeholder involvement or impact analysis.

This paper presents a sustainable design framework for cellular agriculture innovations by integrating foresight methods and design thinking. Importantly, the research connected design thinking, foresight methods and sustainable innovations. The framework systematically aligns foresight methods with the design thinking process, thus enabling cellular agriculture innovators to anticipate long-term challenges and navigate uncertainties. The strengths and limitations of either design thinking or foresight are explored thus rationalizing the need to integrate them. A key research question of the study is: How can foresight methods be systematically integrated with design thinking to enhance the sustainability of Cellular Agriculture innovations? A user-friendly framework is developed by matching foresight methods capabilities and aims of design thinking stages. The paper discusses theoretical and practical implications of the framework while pointing direction of further research in shaping sustainable food systems.

In sectors based on renewable resources, foresight studies have often been macro-scale, quantitative outlook studies. As a result, in the complex operating environment of the forest bioeconomy, the organizations and professionals of the emerging bioeconomic sector have not been able to fully utilize futures information in their decision-making processes. This study sought to find solutions to this issue by utilizing a nested business environment framework (NEST framework) in signal interpretation as part of piloting interactive horizon scanning of the forest bioeconomy. In interaction with a diverse group of forest bioeconomy stakeholders, the horizon scanning exercise collected and interpreted signals of change. The aim was to increase forest bioeconomy actors' sense of ownership towards signals information. This study discusses strengths and weaknesses of interactive horizon scanning in the context of the forest bioeconomy and raises awareness of the opportunities and challenges of using a NEST framework in signal interpretation. The study contributes to the analytical tools and has practical implications for interpreting signals of change in the forest bioeconomy and other sectors based on renewable resources. According to the results, the NEST framework used in signal interpretation has strengths, but revealing its full potential requires further development. These developments may include the application of the framework in concrete, timely stakeholder contexts, both involving forest bioeconomy actors and a necessary outside-in perspective from other sectors.