Foresight and STI Governance

Human-in-the-Loop: From Complete Automation to Dark Factories

Donald Crestofel Lantu — 2026-02-15

This systematic literature review examines Human-in-the-Loop (HITL) systems within dark factory environments through the lens of Complex Adaptive Systems, investigating how these systems bridge full automation with human expertise to maintain adaptability in highly automated manufacturing settings that operate as complex systems characterized by multiplicity, interdependence, and dynamic interactions where identical conditions can yield different outcomes. Dark factories represent strategic inflection points creating tenfold shifts in manufacturing operations, yet fully automated systems face inherent brittleness when confronting rare events, unintended consequences, and contextual ambiguities that require human cognitive capabilities. Following PRISMA 2020 guidelines, this review analyzed 134 peer-reviewed publications from 2020-2025 through systematic database searches, quality assessment, and thematic analysis to identify patterns, gaps, and emerging trends in HITL implementation. The research reveals that HITL systems have evolved from episodic interventions into strategic design approaches permeating all manufacturing stages, fundamentally transforming human roles from manual operators to cognitive supervisors, exception handlers, and innovation catalysts. Workforce composition shifted from 85% human participation in 2020 to a balanced 40% human – 60% automation ratio by 2025, with HITL systems now accounting for 42% of operations. The study proposes a three-layer HITL collaboration framework spanning operational, tactical, and strategic levels to ensure continuous adaptive human-AI interaction. Critical research gaps identified include the absence of dynamic trust calibration models, manufacturing-specific cognitive load frameworks, and standardized performance metrics. This research contributes original insights into how HITL systems preserve human relevance in Industry 5.0 by creating “bright factories” that optimize both productivity and human well-being, offering sustainable pathways to mitigate job displacement through discovery-driven learning architectures while maintaining manufacturing competitiveness amid accelerating automation.

Bricolage Strategy and SME Performance: The Paradoxical Role of Organizational Ambidexterity and Entrepreneurial Leadership

Galina Shirokova — 2026-06-01

Small and medium-sized enterprises (SMEs) operate under conditions of chronic resource constraints, which makes it important to study strategies capable of transforming available resources into sustainable financial outcomes. One such strategy is entrepreneurial bricolage - the unconventional recombination of existing resources to address organizational challenges. This article examines the impact of bricolage on SME performance, evaluates the mediating role of organizational ambidexterity, and investigates the moderating effect of entrepreneurial leadership. The empirical basis consists of data from a large-scale survey of Russian SMEs combined with objective financial indicators; the model was estimated using the PROCESS macro (Models 4 and 14), controlling for firm characteristics, industry environment, and managerial profiles. The empirical analysis confirms that bricolage has a significant positive effect on SME performance and fosters the development of organizational ambidexterity. At the same time, ambidexterity negatively affects firm performance, which paradoxically results in an indirect negative effect. Entrepreneurial leadership moderates this relationship, and its high level mitigates the adverse impact of ambidexterity. The findings show that in the absence of strong leadership, ambidexterity may reduce the effectiveness of resource-constrained firms. The results contribute to entrepreneurship theory and offer practical implications for SMEs seeking innovation-driven growth.

Economic Growth vs. Environmental Impact: Lessons from China

Farrukh Nawaz — 2026-06-07

Environmental issues were typically overlooked until the late 1980s, but they have secured great interest among economists over the last few decades. In this paper, we explored the impact of the multilevel financial development index (FDINX) along with foreign direct investment (FDI), gross domestic product (GDP), gross domestic product squared (GDP square), and total natural resources (TNRSTN) on the environmental sustainability of China for the period 1991-2022 by using a dynamic autoregressive distributed lag model. We took China as our sample country because it has committed to achieving its net zero emission targets by 2060. The findings reveal that there is a statistically significant relationship between economic growth and carbon emissions (CO2e) both in the short and long term. The EKC hypothesis in China is significant because the GDP squared negatively impacts CO2e in both the short and long run. The estimated parameter of energy efficiency (ENEFC) is negatively significant in the short term, whereas total natural resources significantly impact CO2e in the long run. FDINX has a positive and weakly significant impact on CO2e in the long run, as FDINX increases economic expansion by providing financial resources through cheap loans for dirty industries. The outcomes are significant for foreign direct investment (FDI), which has less impact on environmental sustainability than does FDI in the short run. Our research also supported the pollution haven hypothesis for China in the short run.

The Role of Energy Productivity in Green Transition: Evidence from BRICS Economies

Shahid Ali — 2026-05-29

Improving environmental quality remains a significant challenge for nations worldwide. Energy productivity is widely considered as one of the main indispensable solutions to control greenhouse gas emissions and improving environmental quality. However, the debate between consumption-based accounting and production-based accounting of carbon emissions has gained increasing prominence in climate policy discussions, as the traditional production-based approach often overlooks emissions embodied in international trade. Against this backdrop, this study examines the role of energy productivity as a climate policy instrument in mitigating both consumption-based and territorial (production-based) CO₂ emissions in BRICS economies over the period 1990–2023. This study presents a novel methodology to capture distributional effects, offering new insights into how energy productivity influences environmental sustainability at different emission levels. Using the method moment quantile regression approach, we find that energy productivity is an important determinant of both consumption-based carbon emissions and territorial carbon emissions. Moreover, renewable electricity output and exports reduce both types of carbon emissions. Interestingly, imports increase consumption-based carbon (CCO₂) emissions while decrease territorial-based carbon (TCO₂) emissions in sample countries. Based on the results, it is inferred that increasing energy productivity lower the need for energy utilization, which ultimately improve environmental performance.

Annex

A Scoping Review of Energy Consumption and Sustainability Benefits in Renewable Energy Applications

Sofian Lusa — 2026-05-29

The rapid integration of artificial intelligence (AI) in renewable energy systems presents a paradox: while AI optimizes energy efficiency and forecasting accuracy, its computational demands impose substantial environmental costs. From this perspective, the approaches proposed by researchers to address this issue are of interest, as they aim to ensure that the benefits outweigh the costs. Progress in their implementation will determine whether AI ultimately accelerates or hinders renewable energy transitions and transforms from a potentially double-edged technology into a genuinely sustainable catalyst for decarbonization. This scoping review addresses a critical knowledge gap at the intersection of digital innovation and environmental sustainability. It synthesizes evidence from 76 peer-reviewed studies (2014–2025) to examine AI's energy footprint, operational benefits, and trade-off dynamics. These findings challenge simplistic narratives about AI as either uniformly beneficial or harmful for sustainability in relation to the studied sector. AI’s energy consumption is not evenly distributed across the various stages of the value chain, and the benefits of increased equipment efficiency are offset by the growing complexity of the AI models. The study proposes a framework for balancing AI energy consumption and sustainability, providing evidence-based guidance for policymakers and practitioners navigating AI deployment decisions in renewable energy transitions.

A Methodological Benchmarking Approach for Digital Maturity Models

Cesar Viloria-Nuñez — 2026-05-29

Digital maturity models are increasingly used by organizations to evaluate their transformation capabilities and define strategic roadmaps. However, despite their popularity, little is known about the consistency and psychometric soundness of the most widely adopted self-assessment instruments. This study has two objectives: (1) to empirically demonstrate that the results obtained from different digital maturity questionnaires vary significantly for the same organization, and (2) to establish an objective benchmarking framework to evaluate the quality of these tools. The analysis focuses on three prominent models: PwC, MinTIC & iNNpulsa, and MIT & Capgemini. Based on a structured case study and survey responses from 90 non-expert participants and 15 digital transformation experts, we assess each instrument using four key criteria: uncertainty, internal consistency, theoretical validity, and suitability for self-assessment. Results reveal statistically significant discrepancies between instruments and highlight the limitations of relying on unvalidated tools for strategic decision-making. The proposed benchmarking framework offers a practical and replicable approach to guide the selection and development of digital maturity instruments, especially in resource-constrained contexts such as small and medium-sized enterprises (SMEs).

Identifying robust ecosystem identities through scenario-based wind tunneling

Cristian-Germán Hernández — 2026-04-16

Scenario-based foresight has become an important analytical approach for exploring the long-term evolution of innovation systems and entrepreneurial ecosystems under conditions of uncertainty. However, many scenario exercises remain primarily descriptive and provide limited guidance regarding the systemic orientation that socio-economic systems can realistically sustain across multiple plausible futures. This study addresses this limitation by applying the Identity Wind Tunneling approach to examine the robustness of alternative ecosystem identities across contrasting future environments. The analysis is conducted using the Ecuadorian entrepreneurial ecosystem as an empirical case. Building on a previously developed scenario architecture describing five possible trajectories of ecosystem development toward 2040, five candidate ecosystem identities were evaluated through an identity–scenario compatibility matrix. The analysis considered four structural dimensions that characterize both ecosystem identities and scenario environments: governance coordination, innovation capability, financing structures, and market integration. Identity robustness was assessed by examining the number of scenarios in which each identity remained viable. The results indicate that an innovation-driven entrepreneurial ecosystem demonstrates the highest level of robustness across the scenario set. By extending Identity Wind Tunneling from the organizational level to the ecosystem level, the study introduces the concept of robust ecosystem identity as a foresight-informed framework for guiding long-term ecosystem development under uncertainty.

Scenario Planning Evolution in Two Parallel Paths: French School vs. English School

Sepehr Ghazinoory — 2026-03-19

Although a consensus about scenario planning exists among the experts in futures studies, less comparative analysis has been done on different schools of thought in the field. The two mainstream schools of thought identified in this case study, which is the focus of this paper, are the French La-Prospective School and the English Shell School. Historical and discourse analysis methods are being used for this purpose. At first, the history and evolution of each of these two schools are investigated in three different generations. This is followed by introducing the key thinkers of each school. Similarities such as attracting participation, consensus, and approaching strategy are discussed. After that, the genealogy, terminology, methodology, and epistemology of the two schools are examined. Finally, we look at the future of the two schools and their integration. The models and diagrams developed here will be a good guide for future researchers.