This study evaluates the efficiency of 76 commercial banks in Indonesia over the period 2020-2021, using both Data Envelopment Analysis (DEA) and Stochastic Frontier Analysis (SFA). The dataset includes key inputs such as interest cost and labor cost, while outputs for DEA are split into interest income and non-interest income, and the total income is used for SFA. DEA is applied with both Charnes-Cooper-Rhodes (CCR) and Banker-Charnes-Cooper (BCC) models, with output orientation to maximize interest income and non-interest income. The CCR model assumes constant returns to scale (CRS), while the BCC model assumes variable returns to scale (VRS), allowing for scale differences among banks. For SFA, both the Cobb-Douglas and Translog production functions are used to model the relationship between inputs and outputs, with the former assuming a simpler linear relationship and the latter accounting for non-linearities. The results show high correlation between the efficiency scores obtained from both DEA and SFA, suggesting that both methods produce similar rankings of bank performance. However, SFA’s flexibility in capturing inefficiencies through random noise makes it a more robust method for analyzing bank performance in volatile environments. Spearman’s rank correlation is employed to assess the relationship between the efficiency rankings from both methods, revealing strong consistency in their assessments.

This article addresses a two-stage flow-shop scheduling problem (FSSP) considering uncertain job processing and release time. To minimize the worst-case expected makespan, a distributionally robust optimization (DRO) approach is designed. By introducing effective bounds and valid inequalities, the problem is transformed into a mixed integer linear program (MlLP), which is solvable via off-the-shelf commercial solvers. To verify the performance of the model, the DRO model is compared with the stochastic linear program (SLP) and its deterministic counterpart under various parameter settings, where the DRO model exhibits greater robustness and computational efficiency in handling almost all the scenarios. Besides, the DRO model demonstrates a prominent advantage when the uncertain interval of the mean is larger, the release time is less scattered and more jobs are scheduled. It is discovered that the proposed DRO model is capable of providing robust schedules in a highly volatile environment, thereby enhancing the resilience and robustness of manufacturing operations.

Entrepreneurial activity is shaped by the structure and functioning of the entrepreneurial ecosystem, yet there is limited consensus on how entrepreneurial resilience is defined or measured. This study protocol outlines the development of a novel, empirically grounded measure of entrepreneurial resilience, conceptualised as a multidimensional and dynamic capability that enables adaptation under contextual hardship and resource constraints. Guided by a capability-based framework, the study draws on prior literature and employs qualitative methods, including a multi-round Delphi study and stakeholder interviews and focus groups. The study components will identify context-specific mechanisms and translate expert consensus into measurable and policy-relevant indicators. By setting out a transparent and reproducible methodology, this protocol contributes to the entrepreneurship and behavioural economics literature and provides a foundation for future empirical work and targeted policy interventions to support business continuity in volatile environments.

Predictive processing in volatile environments.

This study examines the interrelation among gold, oil, and cryptocurrency markets and their implications for economic growth in the context of geopolitical turmoil. Employing panel data from 2000 to 2023 of exporter, importer, and mixed economies, we employ Nonlinear Autoregressive Distributed Lag (NARDL) and Panel Vector Autoregression (PVAR) to ascertain asymmetric as well as dynamic relations. Evidence shows that oil and gold price shocks exert significant effects on growth with geopolitical risk increasing volatility, while cryptocurrencies are heterogeneously resilient in panels. The results provide fresh evidence of cross-asset linkages, risk transmission mechanisms, and provide policy implications for policymakers and investors under volatile geopolitical environments.

The current body of scientific literature, despite its depth, fails to provide a comprehensive and high-resolution mapping of the morphological and histological dynamics governing endometrial formation in cattle fetuses, a gap that significantly complicates the development of precision methods for physiological correction and metabolic stimulation during gestation and placental formation. This lack of systematic data is particularly critical for understanding the assembly of the maternal placenta during the estrus phase, where the structural integrity of the tissue determines the success of all subsequent reproductive stages. The intricate process of gametogenesis and the concurrent maturation of reproductive organs are driven by intensive transamination reactions, characterized by a marked elevation in transferase activity where the cytoplasmic-mitochondrial fraction plays a functionally dominant role in maintaining cellular energy balance. It is analytically significant that the estrus stage acts as a metabolic catalyst, triggering a cascade of tissue enzyme activations and systemic metabolic surges, whereas the subsequent phases of inhibition and equilibration are defined by a compensatory reduction in these bioenergetic phenomena to restore basal homeostasis. Contemporary reproductive biology has firmly established that the functional status of uterine glandular epithelial cells and the associated stromal components is characterized by profound heterogeneity throughout the estrous cycle, a state of "dynamic instability" that is paradoxically essential for maintaining long-term tissue homeostasis. Researchers hypothesize that this cellular heterogeneity serves as an evolutionary adaptation, allowing the endometrium to rapidly recruit reserve structural units in response to the volatile hormonal environment of the female organism. In the immediate pre-ovulatory period, the morphometric thickness of the endometrium and the oviductal epithelium reaches its physiological zenith, driven by a precipitous surge in steroid hormone concentrations that stimulates the cervical epithelium to synthesize and discharge maximal volumes of low-viscosity mucus enriched with mucins, glycoproteins, and high-molecular-weight protein complexes. Our rigorous empirical investigations confirm that the estrus phase is distinguished by a statistically significant (p < 0.001) escalation in total serum protein, which increases by 7.6% relative to metestrus and by 8.7% compared to proestrus, reflecting the massive hypertrophic and proliferative expansion of the myometrium and the functional endometrial layer. Glandular epithelial units undergo a series of integrated structural-functional metamorphoses, where the proestrus phase is marked by aggressive proliferation and apical translocation of glycogen reserves within the uterine glands. The temporal duration of these cellular shifts is strictly governed by the maturation rate of the dominant ovarian follicle, ensuring a precise synchronization between ovulation and endometrial receptivity. The application of these localized metabolic findings on a macro-scale will facilitate a substantial increase in herd reproduction indices, thereby optimizing the economic efficiency of meat and dairy industries through a scientifically substantiated model of physiological remodeling.

This study examines how perceived market uncertainty (PMU) influences the strategic adaptation speed (SAS) of small and medium-sized enterprises (SMEs) and how this mechanism contributes to their long-term survival in volatile environments. Drawing on dynamic capabilities theory, we analyze cross-sectional data from 385 Ecuadorian SMEs and apply Structural Equation Modeling (SEM) to test both direct and mediating effects. Results show that higher PMU significantly accelerates SAS (β = 0.42, p < 0.001), and SAS explains most of the positive influence of PMU on SME survival, accounting for 77% of the total effect. Firms exhibiting faster strategic responses, such as redesigning business models, accelerating product modifications, and forming timely alliances, were 2.7 times more likely to withstand economic shocks than slower-adapting firms. These findings reframe uncertainty not merely as a destabilizing force but as a trigger that activates adaptive routines and strengthens organizational continuity. Theoretically, the study provides one of the first empirical validations of the PMU–SAS–Survival pathway in an emerging economy, highlighting the role of time-sensitive strategic decisions in resilience building. A key policy implication emerges from the evidence: programs that combine agile financing instruments with capability-development training can enhance SMEs’ adaptive speed and reinforce their resilience to persistent market turbulence.

ABSTRACT Universities are operating in a volatile, uncertain, complex and ambiguous (VUCA) environment, prompting staff to work across themes rather than rigid structures, drawing on diverse expertise to address complex challenges (McIntosh and Nutt 2022). Within this context, the “third space” has emerged, blending academic and professional roles to connect different parts of the institution (Whitchurch 2008). This space is expected to expand as collaboration and agility become increasingly important (Grant 2021). Future higher education professionals will need to navigate matrix structures, engage with supercomplexity (Barnett 2000), and support complex collaboration (Veles, Carter, and Boon 2019). This article explores career navigation in the third space using mixed methods research on practitioner experiences. Findings highlight two core elements shaping careers: (1) skills, expertise and experience, and (2) values and identity. Additionally, future thinking and systems thinking emerge as critical capabilities for effective collaboration. The article concludes with recommendations to better recognise and support third space roles in higher education.

Pharmaceutical manufacturing stands at a decisive inflection point. The convergence of artificial intelligence with increasingly complex therapeutic modalities including, gene therapies, cell- based products, and advanced biologics, has created both extraordinary opportunity and profound ambiguity. In this volatile, uncertain, complex, and ambiguous (VUCA) environment, competitive advantage will not be secured through technology alone. It will emerge from something more deliberate: the orchestration of cooperative intelligence, a disciplined integration of AI capability with elevated human agency.

In today’s volatile, uncertain, complex and ambiguous environment, international trade relationships are in constant flux as they are shaped by changing political tides at a global level and through changes in economic policies. This research looks to examine what is changing in the trade and investment climate between India and the UK,which are at the heart of the Indian --UK Free Trade Agreement issue, which is a large part of a larger effort at better bilateral relations and economic growth. We look at how India’s export and investment strategies have played out during the FTA talks, and we look into what policies may help both countries to better fortify their trade in an uncertain world.The study draws upon secondary sources such as government reports, World Trade Organization publications, UN Conference on Trade and Development publications, the Reserve Bank of India, and the UK Department for Business and Trade. Analysis of trade performance for the years 2019 to 2024 captures trends before negotiations and after. According to the findings, there is growing diversification in the composition of Indian exports, with the pharmaceuticals, textiles, IT, and services sectors witnessing particularly robust growth. India-UK bilateral investments have also increased, suggesting higher confidence and further economic interdependence between the two countries. The negotiation of the India-UK Free Trade Agreement (FTA) underscores potential expansion of exports, long-term investments, and diversification of trade for the two countries. Institutional collaboration consolidation, trade infrastructure and development, and transparent and predictable policy enforcement, however, will be required to maximize these prospects.

This study addresses the integrated collection–disassembly–distribution problem under fuzzy demand. The problem provides a roadmap for effectively managing reverse supply chains under uncertain conditions. It involves collecting end-of-life products, disassembling them, and distributing the components obtained from the disassembly process to demand centers under fuzzy demand. The reuse of valuable components not only provides cost savings but also supports sustainable supply chain practices. A nonlinear mathematical model is developed to represent the problem and is tested through various scenario analyses. The results show that the proposed model provides a realistic decision-support framework that accounts for the vagueness faced by decision-makers and manufacturers. Moreover, the findings indicate that it is possible to achieve cost-effectiveness and efficient resource utilization even in the presence of demand ambiguity, highlighting the model’s applicability to industries operating in volatile and unpredictable market environments.

Purpose This article conceptualizes retail unsafety as a narratively negotiated experience. It investigates how retail stakeholders face experiences of unsafety in vulnerable retailscapes marked by high crime rates and insecurity. Design/methodology/approach Employing a narrative approach, the study uses in-depth interviews with staff in and around retail stores to show how storying experiences of unsafety organize and give meaning to retail work in vulnerable areas in Sweden. Findings The analysis shows how stakeholders navigate unsafety by narratively organising their experiences around story plots of threatening customers, volatile environments, and fragile local communities. By foregrounding everyday work practices, the study highlights the relational and context-dependent nature of unsafety within vulnerable retailscapes. Practical implications The study highlights the importance of developing context-sensitive in-store policies and targeted training that prepare frontline staff to handle safety-related challenges, including encounters with aggressive or violent customers. It emphasizes the value of fostering a collaborative workplace culture, redesigning store layouts to improve visibility and minimize staff isolation, and building local partnerships that enable retailers and community actors to address safety concerns collectively. Originality/value The article advances research on retail safety by shifting attention from store-level features to the wider retailscape, where safety emerges through ongoing interactions among stakeholders. It underscores the experiential and relational dimensions of retail unsafety and the mutually shaping relationship between retailers and the neighbourhoods they serve.

Purpose This study aims to investigate the transformative impact of the COVID-19 pandemic on over-the-top (OTT) platform adoption and digital entertainment consumption among Indian consumers. By integrating the technology acceptance model (TAM), uses and gratifications (UG) theory and crisis-driven behavioural frameworks, the research seeks to identify core determinants, mediators and moderators influencing user adoption and to quantify the relative contribution of technological, motivational and contextual drivers in shaping digital media behaviour during periods of acute disruption. Design/methodology/approach A cross-sectional survey was conducted with 412 respondents selected through multi-stage stratified sampling to ensure representativeness across age, income, gender and region. The 64-item instrument, pilot-tested and validated for reliability, measured TAM, UG and crisis constructs. Data were analysed using descriptive and inferential statistics, multivariate regression, multinomial logistic regression and structural equation modelling (SEM) to test direct, mediated and moderated pathways among key variables, providing a robust empirical assessment of the integrated conceptual framework. Findings Results reveal a 180% increase in OTT subscriptions and a dramatic rise in streaming hours post-pandemic. SEM analysis confirms perceived usefulness (ß = 0.54, p < 0.001) and UG motives (ß = 0.37, p < 0.001) as significant predictors of behavioural intention, with crisis exposure moderating the intention–usage relationship. The combined model explained 69% of behavioural variance. Distinct adoption and usage patterns emerge for urban youth and high-income groups, while platform choice is shaped by content, pricing and regional offerings. Research limitations/implications The cross-sectional design limits causal inference, and urban, digitally savvy youth are overrepresented, constraining generalizability to rural or older populations. Self-reporting introduces potential for recall and social desirability bias, and online sampling may exclude less-connected demographics. The study focuses on major OTT providers, omitting niche and/or regional platforms. Future research should employ longitudinal, probability-based and mixed-method designs to capture behavioural persistence and deeper motivational dynamics. Practical implications OTT providers can optimize segmentation by targeting high-intention, crisis-sensitive users with tailored content and pricing strategies. Investments in localized, diverse content and data-driven hybrid subscription models will enhance user retention. Policymakers should prioritize digital infrastructure expansion and actively promote regional content to strengthen industry resilience. Platform developers should implement adaptive interface and recommendation systems responsive to both technological and motivational drivers illuminated here. Social implications Widespread OTT adoption during crisis deepens digital inclusion for urban and youth segments but risks widening access gaps for marginalized groups. Enhanced content diversity and regional programming have the potential to foster cultural expression and cross-demographic engagement. Policy support for affordable access and digital literacy is vital to equitable benefits from accelerated media digitalization post-pandemic. Originality/value This paper advances knowledge by empirically validating an integrated TAM–UG–crisis framework for digital platform adoption in an emerging market under pandemic-induced upheaval. The multi-theoretical approach demonstrates the value of fusing technological, psychological and contextual perspectives to model adoption behaviour, offering actionable insights for researchers, industry practitioners and policymakers seeking to understand and shape digital transformation in volatile environments.

Futures thinking is a crucial skill for organisations navigating volatile, uncertain, complex, and ambiguous environments. Nonetheless, some researchers and practitioners argue that the insights futures thinking can yield appear obvious, trivial, or overly abstract and stress the need to move towards futures making. This paper examines how early-stage and punctuated prototyping can enhance futures making within urban transformation processes, supporting the co-creation, experimentation and decision-making on futures from local stakeholders. Drawing from the case study of T-Factor, a four-year project aiming to envision and prototype future uses in urban spaces, we find that two essential dimensions of prototyping—filtering and manifesting—can aid stakeholders in thinking about futures throughout the design process and, eventually, enable futures making. The content analysis of our data shows how these two dimensions contribute to futures making by facilitating the materialisation of more concrete, actionable, and engaging representations of the future that can be experienced, performed, assessed, and refined. The paper concludes by proposing practical recommendations for organisations and communities aiming to envision and realise sustainable urban futures through prototyping.

Anesthesiology is experiencing simultaneous crises of workforce depletion, moral distress, rising complexity, and economic constraint-manifestations of a volatile, uncertain, complex, and ambiguous environment. Traditional, efficiency-driven solutions have failed to restore balance and have deepened instability. Anesthesiology 5.0 provides a framework for redesigning perioperative systems around human capacity, shifting responsibility from individual resilience to organizational design. Drawing on Industry 5.0, it emphasizes three interdependent pillars-human-centricity, sustainability, and resilience-as the foundation for reimagining how care is organized and delivered. Provider well-being becomes a prerequisite for patient safety, not an ancillary goal. Technology is reframed as an amplifier of judgment, not a substitute for it. Economic models align with value, not volume. By reframing burnout, moral injury, and disengagement as systemic, not individual failures, Anesthesiology 5.0 offers a strategic lens to restore meaning and reliability in periprocedural care while enabling anesthesiology to thrive amid volatility, uncertainty, complexity, and ambiguity.

The ambrosia fungus-beetle symbiosis is an obligate nutritional association, yet the specific metabolic contributions of fungal partners to beetle nutrition remain incompletely characterised. We compared several ambrosia fungal species with closely related non-ambrosia species to investigate their metabolic capabilities, carbon-nitrogen stoichiometry and uric acid catabolism. Ambrosia fungi displayed distinct but lineage-specific metabolic features that were largely influenced by phylogeny. Although both groups shared overarching metabolic similarities, ambrosia fungi such as Ambrosiella roeperi (Ceratocystidaceae) and Irpex subulatus (Irpicaceae) exhibited enriched amino acid biosynthesis pathways, especially for L-histidine and L-phenylalanine. Formaldehyde assimilation pathway was detected in ambrosia fungi, specifically A. roeperi (Ceratocystidaceae) and Harringtonia lauricola (Ophiostomataceae), reflecting strategies for coping with volatile environments of stressed host trees. Elemental stoichiometry revealed elevated C:N ratios in ambrosia fungi, particularly A. roeperi and I. subulatus, despite no significant differences in carbon or nitrogen when assessed independently. However, nitrogen content was not significantly enhanced, suggesting that nitrogen provisioning may not be conserved as an adaptive trait in ambrosia symbiosis. Similarly, fungal growth on uric acid as the sole nitrogen source did not differ between groups, indicating that nitrogen provisioning through recycling of beetle wastes may reflect lineage-specific patterns rather than a convergent trait across ambrosia fungi.

Subject. In the context of the digital transformation of the construction industry, the key barrier to increasing economic efficiency is not the lack of data, but the inability of existing management systems to integrate and analyze heterogeneous information flows to build reliable forecasts. The separation of static information models (BIM) and dynamic equipment telemetry (IoT) generates an asymmetry of management decisions, leading to systematic budget overruns and delays in the implementation of investment and construction projects. Objectives. Development of an economic and mathematical methodology for predictive management of construction processes, which minimizes operational risks and increases the investment attractiveness of projects by integrating deep machine learning methods into the production planning contour. Methods. The research is based on the synthesis of methods of systems analysis, theory of economic risks and technologies of intellectual data processing. Using Monte Carlo stochastic modeling algorithms, a unique dataset has been generated that combines the attribute characteristics of BIM models (IFC classes, material volumes), telemetry flows of construction equipment, and external factors (weather conditions, logistical delays). Recurrent neural networks of the LSTM architecture with mathematical formalization of the optimization process of the objective function of economic losses are used to identify hidden nonlinear dependencies. Results. A methodology for integrating heterogeneous data has been developed and verified, including strict rules for time synchronization (Δt = 1 hour) and semantic binding via globalId identifiers. A comparative analysis of four architectures (linear regression, Random Forest, XGBoost, LSTM) on a test sample demonstrated a statistically significant superiority of recurrent networks: the average absolute prediction error was reduced to 1.04 days (MAPE = 4.1%) with a coefficient of determination R2 = 0.94, which is 3 times more accurate than traditional methods and 38% more accurate than ensemble methods. algorithms of gradient boosting. Conclusions. The introduction of intelligent predictive management systems integrating BIM and IoT ensures the transition from reactive response to proactive risk management, creating an economic effect by reducing unproductive downtime, optimizing resource use and minimizing penalties for failure to comply with contractual obligations. The proposed methodology lays the foundation for the creation of self-learning digital counterparts of construction sites and can be scaled to enterprises of the investment and construction complex operating in a highly volatile environment.

The article explores the conceptual and practical foundations of forming the state budget policy within the framework of profound socio-economic transformations, which is particularly relevant given the imperatives of martial law and the subsequent challenges of post war recovery in Ukraine. In the current volatile environment budget policy transcends its traditional role as a mere financial ledger, evolving into a sophisticated instrument for macroeconomic stabilization and the activation of social development. The relevance of this study is rooted in the necessity to define strategic priorities for an adaptive fiscal framework capable of responding effectively to both external shocks and internal structural shifts. The analysis of Ukraine`s budgetary system through 2024-2025 characterizes the current state as one of managed deficit, highlighting the critical implementation of National Revenue Strategy through 2030. This strategy includes the digitalization of tax administration and the reform of excise policies, alongside temporary fiscal measures like increased military fees and taxes on windfall bank profits. A significant shift is observed in the financing structure, with a decreasing share of non-repayable grants from international partners in favor of concessional loans, which heightens the requirements for internal budgetary management efficiency. The study identifies the budget deficit-to-GDP ratio and the debt-to-GDP ratio as the primary indicators of fiscal predictability. The research proposes a transition toward a productive expenditures model focusing on sectors with high multiplicative effects such as energy, logistics and human capital. This approach aims to secure future tax revenues and enhance fiscal sovereignty, ensuring that the budget policy acts as a resilient institutional mechanism for long-term social and economic stability. Special attention is paid to the synchronization of budget and tax instruments to foster a transparent, client-oriented fiscal environment. Furthermore, the research explores the institutional principles of budget policy such as mobility, adaptability and availability of financial reserves as essential prerequisites for responding to internal and external shocks.

Edge computing provides resources for IoT workloads at the network edge. Monitoring systems are vital for efficiently managing resources and application workloads by collecting, storing, and providing relevant information about the state of the resources. However, traditional monitoring systems have a centralized architecture for both data plane and control plane, which increases latency, creates a failure bottleneck, and faces challenges in providing quick and trustworthy data in volatile edge environments, especially where infrastructures are often built upon failure-prone, unsophisticated computing and network resources. Thus, we propose DEMon, a decentralized, self-adaptive monitoring system for edge. DEMon leverages the stochastic gossip communication protocol at its core. It develops efficient protocols for information dissemination, communication, and retrieval, avoiding a single point of failure and ensuring fast and trustworthy data access. Its decentralized control enables self-adaptive management of monitoring parameters, addressing the tradeoffs between the quality of service of monitoring and resource consumption. We implement the proposed system as a lightweight and portable container-based system and evaluate it through experiments. We also present a use case demonstrating its feasibility. The results show that DEMon efficiently disseminates and retrieves the monitoring information, addressing the challenges of edge monitoring.

Financial markets in emerging economies such as Nigeria often exhibit nonlinear dynamics, volatility clustering, and regime shifts that traditional linear models failed to capture. To address these complexities, this study developed a Bayesian Regime-Switching Threshold Autoregressive (RS-TAR) model to analyze daily All-Share Index (ASI) returns in the Nigerian Stock Exchange. The model employed Markov Chain Monte Carlo (MCMC) methods to estimate posterior parameters, transition probabilities, and regime-specific volatility. Results revealed two distinct regimes: a low-volatility state characterized by faster mean reversion and a high-volatility state marked by strong persistence, with the probability of remaining in the crisis regime exceeding ( > 0.8500), indicating prolonged instability once shocks occur. Bayesian posterior predictive distributions were used to compute Value-at-Risk (VaR) and Expected Shortfall (ES), providing robust measures of downside risk. Comparative analysis showed that RS-TAR significantly outperformed benchmark autoregressive (AR) and GARCH models, with Diebold–Mariano tests confirming superior predictive accuracy. The study concludes that RS-TAR is effective in detecting structural breaks, modeling nonlinear market behavior, and enhancing risk forecasting in the Nigerian financial market. These findings underscore the importance of adopting advanced nonlinear and regime-switching approaches in emerging economies, offering practical insights for investors, regulators, and portfolio managers seeking to better understand and manage risks in volatile environments.