The COVID-19 pandemic accelerated the adoption of “hybrid working” arrangements, where (teleworkable) work is carried out from home (‘first place’), the place of work (‘second place’), or other locations (‘third place’). This has led to renewed interest in the impacts of hybrid working on workers’ personal, household, and job spheres, including concepts such as life satisfaction, work-life balance, job satisfaction and job quality. There is little empirical data regarding the effects of hybrid working on people’s life satisfaction, although some academics view hybrid working as the “new normal”. To fill this gap, the paper explores whether and how hybrid working is associated with employees’ life satisfaction during the pandemic (spring 2020 – spring 2022). To achieve this aim, the Eurofound (2022) survey called “Living, Working, and COVID-19” was analysed through econometric models. Our results show that there are gender disparities in the self-reported life satisfaction of hybrid workers, which are exacerbated when children are present. Moreover, being a hybrid worker is positively correlated with higher self-reported life satisfaction when compared to those who work solely on the employer’s or client’s premises.
This research presents a simulation-based optimization approach for heavy industry under propagating energy outages to maintain a risk-neutral viable supply chain. In this paper, we developed a mixed integer linear programming (MILP) model with the objective of minimize total cost and maximize service level while adhering to realistic constraints. We proposed a simulation-based optimization approach to mitigate the disruptions and improve network resilience using multiple cases from cement and steel sectors. A stress test with varied degrees of disruption has been conducted to determine the time to survive (TTS) and time to recover (TTR) for the sectors under consideration. To improve the TTS and TTR, we proposed an inventory strategy and a backup grid strategy to mitigate the effect of disruptions thereby enhancing the network resilience. Consequently, a risk-neutral viable supply chain spectrum/space under interruptions has been established to determine the extreme boundaries of the viability space. Extensive computer testing, including sensitivity analysis and real-world scenarios, was carried out to validate the approach.
Climate change poses a threat to food security by affecting crops, livestock, and altering food distribution systems. It is challenging to accurately forecast and prepare for the effects because of the inherent ambiguities regarding the impacts, such as the location, timing, and intensity of particular events. Climate change directly intensifies pressure on land resources. Hence, food security needs a proactive approach to manage agricultural land to optimize productivity. Because the selection of appropriate land for suitable crops can maximize production and help minimize the risk of food shortage, ensuring sustainable agriculture. To model those inherent ambiguities, this work incorporates the power variations for membership grades in the Q-rung picture fuzzy soft set (Q-RPFSS). This fuzzy soft notion overcomes the limitations of many other extensions of the fuzzy set and supports parametrization by utilizing soft set theory. A class of Yager averaging, geometric, and corresponding ordered AOs is developed. An approach to optimize decision-making (DM) in real-life problems is established and practically implemented to select suitable land as an optimal choice. The results of the approach are consistent with general practices in real-life, as arid and semi-arid lands are the most suitable land types for agriculture under proper management and support large-scale agriculture globally. The sensitivity of the proposed operators is examined by adjusting the index parameter . The results revealed higher variations in membership grades for lower values of and but tend to vanish for higher values. However, the variation did not affect the ranking orders, demonstrating the robustness of the approach. The comparative analysis depicted the stability of the proposed approach.
The adoption of digital technologies is often analysed through intention-based models that assume a direct transition from intention to behaviour. However, empirical evidence increasingly shows that this relationship is neither linear nor automatic. This study investigates the intention–behaviour gap (IBG) in blockchain adoption among agri-food firms located in the Campania region of Italy, integrating Behavioural Reasoning Theory (BRT) and the Technology–Organization–Environment (TOE) framework. Using firm-level data, this study adopts a multi-stage quantitative analytical approach to examine how business values, justifications, and attitudes shape adoption intentions and under which organisational and environmental conditions these intentions translate into actual implementation. The results show that while adoption intention is a significant antecedent of behaviour, structural conditions play a decisive role in enabling or inhibiting adoption. By integrating multiple dimensions of analysis, this study advances the empirical modelling of technological adoption processes and provides insights to support the technological transformation of firms operating in fragmented ecosystems with different organisational and digital evolution paths.
The current study examines how digital transformation reshapes the economics of medical tourism within a North–South framework, focusing on its effects on inequality and quality of life. This model features a dynamic setup in which a Southern-based Multinational Medical Tourism Service Provider (MMTSP) engages in price differentiation and digitally enabled quality innovation amid income disparities and cross-border market integration. Unlike typical trade models, this analysis places medical tourism within a rapidly digitalizing ecosystem characterized by platform intermediation, algorithmic pricing, and AI-enabled diagnostics. This shift from physically mediated exchange to digitally coordinated healthcare markets reflects a larger move from Homo Sapiens to Homo Digitalis, raising important concerns about access, quality of life, and the distribution of welfare benefits. The model includes state-led income redistribution in the South to explore how domestic equity policies interact with international competitiveness. Additionally, Monte Carlo simulations are used to assess how changes in spending capacity, patient mobility, and regional cost differences influence quality of life, equilibrium prices, and market reach. The results show that digital mobility and improved domestic health access strengthen incentives for digitally enabled quality innovation and enhance trade viability. However, without redistributive and regulatory safeguards, digital transformation might worsen inequality by expanding health access gaps and concentrating market power. Overall, the findings suggest that the advantages of digital medical tourism rely on inclusive policies that promote equitable healthcare access and support sustainable improvements in quality of life.
Due to due diligence requirements, social sustainability is receiving more attention in global supply chains. In the context of sourcing, companies seek sustainable supply sources, engage in supplier development, or invest in raw material mining projects to meet social standards. However, existing literature on supply chain planning rarely studies and oversimplifies socially sustainable raw material sourcing. To fill this gap, we develop a novel approach to support optimal raw material sourcing, combining social life cycle assessment, activity analysis, and multi-objective optimization. The developed multi-period model decides how much raw material to source from multiple sites with varying levels of costs and social risks. To meet social standards and fulfill demand, different measures are explored: commitment to long-term supply contracts, investment in mining projects, and implementation of social supplier development measures. The net present value of these decisions is maximized in the economic objective function, while social risks are limited by constraints or used as a second objective function. The augmented ε-constraint method is used to solve the model for raw material extraction in the supply chain of lithium-ion batteries for electric vehicles. The results reveal the complex and intricate interdependencies between the decisions about supply sources, sourcing options, and supplier development in socially sustainable raw material sourcing. Depending on the objective function, different sourcing options as well as different locations for the implementation of supplier development measures are obtained. Additionally, decisions are influenced by the social impacts of indirect suppliers outside the focal firm's control.
When supply chain disruptions propagate through multimodal transportation networks, their impacts are often spatially correlated and socio-economically uneven, yet most restoration models optimize for aggregate efficiency without controlling who benefits and who bears residual risk. This paper develops an analytics-to-optimization framework for post-disruption restoration planning that makes equity a transparent, auditable policy lever rather than a post-hoc reporting metric. We construct a disruption stress-testing set using arc-level risk indicators and a spatial correlation structure that produces clustered co-failure scenarios reflecting stylized but structurally diverse hazard-driven patterns. We then formulate a risk-averse stochastic mixed-integer linear program that jointly selects restoration actions and reallocates multimodal flows to minimize expected system cost (with an optional CVaR tail-risk penalty) while enforcing an explicit inter-group service-disparity bound across commodity sectors. We additionally provide a parameter-free lexicographic max–min (leximin) formulation and its ordered weighted averaging generalization, in the sense of equitable optimization, which corroborates the threshold-based frontier without requiring subjective weights or disparity targets. A case study on the Colombian multimodal supply chain network yields three main findings. First, the efficiency–equity frontier shows that the inter-group service gap can be reduced from 0.70 to 0.20 at a price of fairness (aggregate efficiency loss) of only about 1.2% under the proposed metric, though the marginal cost accelerates sharply as protection tightens. Second, stochastic planning produces a measurable value of the stochastic solution (1.8% of expected cost), confirming that uncertainty modeling changes restoration decisions rather than only reporting outcomes. Third, out-of-sample stress testing reveals that policies tuned on training scenarios can lose half their service level under unseen disruption severity, even when the formal equity constraint remains satisfied, demonstrating that equity protection is necessary but not sufficient for resilience. These results provide decision-makers with an auditable workflow for selecting, validating, and communicating restoration policies that balance efficiency, equity, and robustness under disruption uncertainty.
Growing environmental volatility, increasing societal expectations, and resource scarcity have prompted organizations to redesign supply chains in line with circular economy (CE) principles. Closed-loop supply chains (CLSCs) enable product recovery and material recirculation but operate under significant uncertainty in demand, return flows, costs, and environmental factors. Despite extensive research, limited studies systematically compare alternative uncertainty-management paradigms within a unified CE-based CLSC framework. This study develops a multi-period bi-objective mathematical model for CLSC network design that minimizes total cost and carbon emissions under uncertainty. A scenario-based framework captures variability in demand, return rates, transportation costs, emission parameters, and secondary market conditions. From a business analytics perspective, the proposed framework functions as an advanced decision-support tool that enables data-driven evaluation of alternative uncertainty-management strategies for resilient and sustainable circular supply chains. The study contributes to frontier technology-enabled supply chain planning by integrating robust and stochastic optimization techniques for strategic network design under uncertainty. Four uncertainty-handling approaches Risk Neutral, Worst Case (Risk Averse), Hybrid, and Mulvey Robust Optimization (MRO) are comparatively evaluated. The Augmented Epsilon Constraint (AEC) method generates Pareto-optimal solutions, and the Relative Ideal Distance (RID) index identifies the preferred trade-off. Computational results show that MRO provides more stable performance and lower cost variability than alternative approaches, while maintaining favorable environmental outcomes. Sensitivity analysis highlights the dominant influence of recovery efficiency and return rates on system performance. The findings offer decision-support insights for designing cost-efficient, robust, and environmentally sustainable CLSC networks under uncertainty.
Global closed-loop supply chains (GCLSCs) face growing complexity from the interplay between recycling regulations and international trade policies. This study investigates how minimum recovery rate (MRR) requirements and tariffs jointly shape supply chain equilibrium. Using variational inequality theory and a modified projection algorithm, we model a multi-tier GCLSC network that captures the interdependent decisions of suppliers, manufacturers, retailers, recyclers, and demand markets across regions. Our findings reveal that while MRR policy can enhance environmental benefits and consumer welfare, it may adversely affect the economic returns of supply chain participants. Raw material tariffs are found to encourage recycling and remanufacturing activities but also lead to a reduction in consumer welfare. In contrast, tariffs on finished products significantly undermine both economic and environmental performance. Furthermore, remanufacturing capabilities are found to bolster manufacturers' resilience against tariff-related risks. These findings carry clear managerial implications. For firms, strengthening remanufacturing capacity is not merely an environmental initiative but also a strategic hedge against trade policy uncertainty. For policymakers, coordinating minimum recovery rate requirements with stage-specific tariffs is essential to avoid unintended trade-offs and to align recycling goals with broader economic and environmental objectives.
The ever-rising Influx of Metaverse and generative artificial intelligence-based technologies, such as ChatGPT, across business operations presents a unique domain for exploring their role in enhancing supply chain resilience. Despite varied scholarly perspectives on the impact of emerging technologies on overall corporate agility and resilience, there is no consensus on their role in enhancing firms’ ability to operate more effectively. Therefore, the present study develops a research framework to investigate the anticipated impact of metaverse and generative artificial intelligence technologies on overall supply chain agility and resilience. The study also examines the given theoretical formation in the presence of technology readiness as a moderator. Using a cross-sectional survey, data were collected from 372 modern-day manufacturing corporations across Pakistan. The empirical data were assessed and analyzed through structural equation modelling (SEM). The study tested the interrelation between construct through the prism of RBV, dynamic capability and socio-technical systems. The results revealed that metaverse technologies and generative artificial intelligence have a significant and positive influence on supply chain agility, with no direct role of generative AI for SCR. The results further highlighted the interrelationship between supply chain agility and supply chain resilience. Likewise, technology readiness proves to be a moderator of the relationship between emerging technologies (such as the metaverse/artificial intelligence) and supply chain agility. The study holds notable empirical and practical recommendations for scholars, practitioners, and industry professionals.
This study contributes to the Circular Economy (CE) literature by simultaneously examining how general environmental concerns, perceptions of local environmental issues, social capital, and demographic and socio-economic characteristics relate to CE behaviors. Using data from the Aspects of Daily Life survey conducted by the Italian National Statistics Office (2019–2021), and applying ordered probit models with robustness checks, the paper reports the following key findings. Higher levels of egoistic, altruistic, and biospheric environmental concerns are positively associated with a greater probability of declaring CE practices, by approximately 0.3 percentage points (p.p.). Perceptions of local environmental problems (e.g., dirtiness, unpleasant smells) and environmental dissatisfaction display a negative relationship with participation by 0.1 and 0.2 p.p., respectively, while the absence of nearby parks or green spaces is positively correlated with engagement in CE behaviors (by 0.7 p.p.). Social capital factors such as civic sense and trust in municipal government show positive associations with CE behaviors (by 6.5 and 0.1 p.p., respectively), whereas social trust exhibits a negative one (by 0.8 p.p.). Demographics reveal that females, married individuals, and older persons are more likely to adopt CE practices, while higher education, larger household size, and better economic conditions are negatively related. The findings provide valuable insights into individual motivations and barriers, informing policy strategies aimed at promoting sustainable consumption and reinforcing the role environmental and social factors in advancing the circular economy at the consumer level.
This study analyses the determinants of income inequality in Southern Africa by combining interpretable ensemble machine learning with panel econometric models. The study uses an un-balanced panel dataset covering eleven Southern African countries over the period 1991–2019. The analysis is anchored in an adapted Social Determinants of Health framework, which helps organise the selected variables into structural, institutional, and intermediary socio-economic domains. Four ensemble models, namely Categorical Boosting, Extreme Gradient Boosting, Gradient Boosting Machine, and Adaptive Boosting, are used to capture non-linear relationships and interaction effects, while random effects and two-way fixed effects models provide econometric benchmarks. The machine learning models show strong internal predictive performance, with an out-of-sample R2 ≈ 0.98, although the training and testing design is based on a stratified random split across pooled country-year observations. Therefore, the reported test results are interpreted as internal validation within the observed Southern African panel rather than evidence of full generalisation to new countries or future periods. SHapley Additive exPlanations results show that unemployment is the most influential predictor of income inequality across all four ensemble models, followed by gross domestic product per capita and institutional quality. The comparison with panel econometric models shows partial convergence, particularly for gross domestic product per capita, years of schooling, and natural resource rents, while differences in unemployment and democracy suggest that their effects may be non-linear or interaction-dependent. The findings indicate that inequality in Southern Africa is shaped by labour market exclusion, uneven economic growth, institutional conditions, education-related disparities, and resource dependence. The results support policies that promote inclusive employment, improve the quality and equity of education, strengthen accountable institutions, and ensure transparent management of natural resource revenues. The study contributes by showing that interpretable machine learning can complement, rather than replace, conventional panel econometrics in explaining complex inequality dynamics.
This study explores the determinants of the Circular Bioeconomy (CBE) within a core–periphery framework, with a particular focus on the factors influencing the localisation of biorefineries across European regions. Drawing on a panel data approach at the NUTS-2 level, the research investigates the socio-economic, techno-territorial, and resource-endowment determinants shaping the spatial distribution and uptake of biorefineries, with particular attention to facilities utilizing secondary biomass and cleaner conversion technologies. The findings reveal marked regional heterogeneity: core regions benefit from well-developed technological infrastructures and strong innovation capacity, whereas semi-peripheral and peripheral regions are constrained by limited resources and technical expertise. The analysis highlights the importance of tailored, place-based policy interventions to address territorial disparities and support equitable CBE implementation. By elucidating these spatial dynamics, the study contributes to the growing literature on regional sustainability transitions and offers actionable insights for EU policymakers seeking to mitigate economic and spatial inequalities while fostering circularity.
This paper examines eco-productivity convergence across European Union regions while explicitly incorporating institutional quality within a multilevel governance framework.Using a panel of 216 NUTS-2 regions over the period 2010–2023, we analyse whether regions converge in their ability to generate economic output while limiting environmental pressures, and whether the estimated convergence patterns differ once both national and regional Quality of Government (QoG) are taken into account. Eco-productivity is measured using a nonparametric frontier approach based on Data Envelopment Analysis, with labour and capital as inputs, GDP as a desirable output, and Greenhouse Gas (GHG) emissions , transformed into an output-oriented ‘GHG savings’ indicator. The results show that average eco-productivity levels are around 8% higher when QoG is accounted for, cross-regional dispersion is considerably lower, and β-convergence is consistently stronger and statistically significant across all subperiods. Overall, eco-productivity convergence is associated with QoG, suggesting that sustainable regional catch-up is more likely where green investment is matched by improvements in institutional quality.
