Employee attrition is a significant concern in today's organizations because it affects productivity, workforce stability, and long-term talent retention. This study aims to develop and validate a Precision HR Diagnostic Framework to identify and interpret the key drivers of employee turnover at both organizational and individual levels. This study uses the IBM Employee Attrition dataset, consisting of 1470 employee records and 35 features on employee demographics, job satisfaction, and performance metrics, and evaluates eight ML algorithms to benchmark our proposed framework. It utilizes Shapley additive explanations (SHAP) and Permutation feature importance (PFI) based explainable Artificial Intelligence (AI) techniques to interpret the ML results. The empirical results show that over time, job satisfaction, environmental satisfaction, stock options, salary, and work-life balance are the key predictors of attrition. It identifies AdaBoost as the top model based on F1 score, accuracy, and interpretability characteristics. Interpreting these results through the Self-Determination Theory (SDT) and Job Demands-Resources (JD-R) frameworks reveals a distinct push-pull mechanism: job demands, such as overtime and work pressure, act as push factors driving turnover, while resources, such as higher salaries and stock options, act as pull factors leading to employee retention. Unlike prior studies that primarily focus on improving predictive accuracy, this work develops a statistically rigorous, explainable machine learning framework that provides a psychologically grounded, theory-driven approach that facilitates employee retention by strategically balancing pull resources against unavoidable push factors using SHAP. ? 2026 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license. http://creativecommons.org/licenses/by/4.0/

Purpose: To compare DEA-based ranking methods and develop a fair approach to evaluate states? efficiency and rankings at the National Games, India's national-level multi-sport event, adjusted for population and wealth, while excluding less relevant macroeconomic factors. Design/methodology/approach: Using data from the 2023 Games (37th edition), we compare three widely applied Data Envelopment Analysis (DEA) models: variable returns to scale (VRS), zero-sum gains (ZSG), and cross-efficiency. Results are validated using data from the 35th edition of the National Games to ensure robustness. Findings: The cross-efficiency method best produces complete rankings without ties. The VRS method identifies peer groups for the seventeen inefficient states out of 29 participating teams. Originality: This study contributes by providing a comparative evaluation comparing DEA models and identifying the most suitable method to assess states? efficiency in promoting athletes at multi-sport events. It also highlights each method?s particularities, suitability, and characteristics. Research Contribution: By combining technical rigor with practical relevance, the paper addresses a gap in the literature by comparatively evaluating VRS, ZSG, and cross-efficiency models, underscoring their methodological distinctiveness and applicability. Practical implications: It provides actionable insights for state sports bodies, organizers, and policymakers to optimize resources, support talent development, and strengthen future National Games performance. ? 2026 Informa UK Limited, trading as Taylor & Francis Group.

This study presents a novel hybrid methodology for multi-attribute group decision-making (MAGDM) under uncertainty using interval-valued q-rung orthopair fuzzy sets (IVq-ROFSs). Each alternative is evaluated in terms of interval-valued membership and non-membership degrees with respect to multiple attributes, allowing a richer representation of expert assessments and inherent hesitation. To objectively determine the influence of decision-makers, correlation coefficients are computed based on deviations from upper and lower outlier assessments. A key feature of the approach is an individualized linear programming (LP) model solved separately for each alternative. This enables the optimization of attribute-wise membership and non-membership values to derive the best achievable score for each alternative under its most favorable attribute weighting, an approach analogous to efficiency estimation in data envelopment analysis. To validate the rankings and integrate attribute importance, an enhanced CRITIC-TOPSIS method is employed. CRITIC captures contrast intensity and inter-criteria correlation to derive objective attribute weights, while TOPSIS ranks alternatives based on proximity to ideal solutions. The applicability and robustness of the proposed method are demonstrated through a sustainable supplier selection problem considering economic, environmental, and social dimensions. Sensitivity analysis, comparative evaluation, and Copeland rank fusion further confirm the consistency and reliability of the decision outcomes. ? 2026 Elsevier B.V.

Gig work has emerged as a vibrant and fast-expanding context of work, compelling researchers to revisit foundational assumptions about work, workers and working policies. Once treated as a peripheral employment form, it now sits closer to the centre of academic debate within Human Resource Management and Organizational Psychology. Despite its growing prominence, however, the understanding of HRM in gig work remains theoretically fragmented, partial and underdeveloped, necessitating a unifying framework capable of explaining how HRM in gig work emerges, evolves, and acquires legitimacy. Drawing on an integrative conceptual synthesis of the HRM-in-gig-work corpus, this study (a) maps the field's unfolding trajectory across three discernible phases of evolution; (b) interrogates the contemporary landscape of each HR practice, foregrounding neglected perspectives and articulating the interventions now warranted; and (c) develops an HRMGW conceptual framework that theorizes legitimacy as an emergent outcome of synergistic interplay among institutional pressures and institutional complexities, HR practices, and their symbolic and substantive enactments, ultimately crystallizing in legitimacy outcomes. A set of workable propositions, together with a forward-looking research agenda, is laid out to propel the field toward its next frontier. ? 2026 Elsevier Inc.

The implementation of mechanism design in education markets in India and the scope of improvement are explored. The explored implementations are in three different types of markets: the first application is related to admission to the MBA programme at top-tier business schools, the second application is related to admission at the undergraduate level at the University of Delhi, while the last problem is related to admission to higher secondary level schools in the state of Bihar. All three problems are distinct in different aspects, such as the size of the market, the level of stakes in making a decision, the level of competition, etc. We compared the characteristics of the markets with the literature and observed that the Indian markets are suffering from issues of under-admissions, over-admissions, strategic behaviours in agents, congestion and lack of truthfulness. It is established that there is indeed a scope for the implementation of a fair matching mechanism based on deferred acceptance. The existing issues are identified, which lead to undesirable admission assignments, and a solution is discussed for the associated issues in each market. ? 2026 Indian Institute of Management Kozhikode

This study examines the relationship between institutional quality, government expenditure on the environment, innovation, and their combined impact on achieving sustainable food security in 41 developing countries between 1992 and 2021. By using the dynamic panel threshold regression model, kernel regression least squares (KRLS), Driscoll-Kraay standard error (DKSE), and panel-corrected standard error (PCSE), this study identifies the adverse effects of CO2 emissions on food production, posing a threat to food security. When considered alone (interaction with CO2 emission), the quality of governance negatively (positively) impacts food security. Further, excessive government expenditure on environmental protection and non-resident-led innovation compromises food security by diverting resources from agricultural and rural development, whereas resident-led innovation potentially enhances food security. Thus, robust climate change mitigation strategies are crucial to reducing greenhouse gas emissions, building resilience against climate change impacts, enhancing governance frameworks, and promoting participatory decision-making processes for effective food security. An integrated approach to resource management, balancing environmental protection with agriculture and rural development, is necessary for sustainable food security. Policymakers should also encourage innovation in agriculture through research and capacity-building initiatives. ? AESS 2025.

This paper introduces an innovative approach for handling radar data that combines Long Short-Term Memory (LSTM) networks with Joint Probabilistic Data Association (JPDA). To tackle the challenge of clutter in radar signal processing we utilized the sequential data analysis capabilities of LSTM to minimize clutter and enhance measurements quality effectively. Subsequently, JPDA is employed for precise target tracking data association. This collaborative approach results in enhancements of tracking accuracy, completeness, and ambiguity which underscores the approach?s ability to improve radar multi-target tracking performance. This paper thoroughly assesses the proposed approach, backed up by experimental data and comparative analysis. ? The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026.

Tourism transport contributes approximately 8% of total GHG emissions of the World and 60% of that belongs to road transport. In this context, AI plays the crucial role in achieving net-zero emissions and promoting sustainable tourism development. Thus, this study aims to investigate the impacts of artificial intelligence, tourist arrivals, economic growth, recycling of municipal solid waste, and population density on GHG emissions generated from tourism transport in leading tourism economies over the period 1995 to 2020 by utilizing the CS-ARDL model. Results suggest that artificial intelligence and recycling of municipal solid waste are negatively associated with GHGs emissions in both short and long run and significantly contributing to their reduction. Contrary, tourism arrivals, economic growth, and population density are responsible for increasing GHG emissions in both the short and long run. Based on these empirical findings, AI-based applications, modeling mechanisms, and management strategies are proposed to support sustainable tourism development. ? 2026 John Wiley & Sons Ltd.

Socially sustainable manufacturing in supply chains (SCs) is difficult to achieve for buyer-supplier-networks with competing priorities. Considering this, FAHP and FDEMATEL methods are used to identify and interconnect prominent Socially Sustainable Supply Chain Enablers (SSSCEs) and FVIKOR help to understand how well buyer-supplier-networks are performing in achieving social sustainability. The proposed approach was applied and demonstrated through a case study conducted in an organization reflecting typical textile industry characteristics. The findings indicate that companies should give priority to social auditing, business continuity, social inclusivity, and sustainable practice training. After analysis, technologically enabled accountability mechanisms, mutual site visits and supply evaluations, and CSR initiatives were found to be major causes. Thus, these initiatives? effects are most noticeable in resource allocation, social security infrastructure, and social inclusion. ? 2026 The Author. Published by Elsevier Ltd. This is an open access article under the CC BY-NC license. http://creativecommons.org/licenses/by-nc/4.0/

Purpose ? The present study aims to evaluate and rank Critical Success Factors (CSFs) necessary for the adoption of Industry 5.0 (I5.0) in the manufacturing sector using a decision-making approach. Specifically, it investigates how human-centric, environmentally sustainable and digitally integrated enablers, such as system interoperability, real-time communication, upskilling, renewable energy integration and circular product design can be prioritized to support effective implementation strategies. Design/methodology/approach ? The model is structured to help decision-makers recognize the critical CSFs even in the context of uncertainty and vagueness. This study adopted the Trapezoidal intuitionistic fuzzy technique for order preference by similarity to the ideal solution (TraInFuTOPSIS) method as a solution methodology to assess and rank 18 critical success factors (CSFs) based on six criteria. Six criteria include operational flexibility (C1), human-centric integration (C2), customization (C3), operational efficiency (C4), innovativeness (C5) and sustainability (C6). A panel of experts assigned linguistic expressions for the criteria weights and factor ratings. Findings ? The findings of the study revealed ?Supply chain modularity (CSF6), ? ?Sustainable thinking (CSF3)? and ?Employee technical assistance (CSF13)? as top-priority success factors, which highlighted the need for flexibility, environmental consciousness and human?machine cooperation to adopt I5.0 with a sustainable focus. Therefore, these CSFs are essential for establishing sustainable and robust pathways for I5.0 in manufacturing. Practical implications ? The study delivers a structured methodology to recognize and prioritize CSFs that can help industry practitioners and managers identify which factors are most important to successfully implement I5.0 and industry sustainability goals. Originality/value ? The originality of the study is the introduction of trapezoidal intuitionistic fuzzy numbers to the decision-making strategy in the critical evaluation of success factors in I5.0 adoption. The study is a systematic decision-making model for organizations aiming at adopting I5.0, ensuring sustainability. ? 2025 Emerald Publishing Limited

In the branch banking system, branches with surplus funds cannot be profitable without an internal fund transfer pricing (FTP) system. Therefore, a dynamic FTP model is critical to enhance a bank?s sustainability. This article has designed a market-oriented multiple FTP model by factoring in liquidity risk, market interest rate, credit demand, and portfolio diversification. The FTP rates are estimated using the 151 branch-level daily data of a bank in India. We reduced the endogeneity issue related to variable selection with the 2SLS system equation. The findings suggest that dual-functioning branches are profitable if they reduce their fund borrowings from the corporate head office (HO). Corporate treasury provides liquidity support to banks, but their profit gets impacted by the increase in the policy and HO-transfer rate. The study showed that corporate spread is significantly affected by increased liquidity cost and market borrowing rate. It is also empirically established that internal FTP pricing may hamper branches? earnings when branch managers mobilize funds beyond the cost of lending as per FTP rates. The dynamic FTP model is linked to the yield curve, making it market oriented and enabling managers to price products effectively. ? 2026 Institute of Financial Management and Research

Achieving gender equality is crucial both as a fundamental human right and as a cornerstone of sustainable development. This study presents a decision-making framework to support stakeholders, particularly policymakers, in achieving Sustainable Development Goal 5 (SDG 5) by identifying and addressing the barriers that perpetuate discrimination against women. The framework enables informed policy and intervention strategies by rank-ordering potential barriers and analysing causal relationships among them. Employing a multi-criteria decision-making (MCDM) approach, the framework integrates Fermatean fuzzy sets to manage uncertainty, the logarithmic percentage change-driven objective weighting (LOPCOW) method for weighting expert opinions, and the decision-making trial and evaluation laboratory (DEMATEL) method for prioritization and causal analysis of the barriers, effectively capturing multi-attitudinal perspectives on the issue. Key findings highlight that ?prevalence of traditional viewpoints? and ?lack of awareness regarding women's rights and policies? are primary barriers, with causal impact analysis indicating that factors ?lack of awareness regarding women's rights and policies,? ?economic inequality? and ?lack of access to education and training? stem from the causes ?prevalence of traditional viewpoints,? ?lack of effective laws and policy interventions?, ?explicit and implicit biases,? and ?lack of male engagement in gender equality.? Sensitivity analysis confirms the decision framework's robustness and helps determine the barriers' final rank ordering. The framework proposed not only advances decision-making in gender studies but also offers a readily applicable tool for policymakers focused on gender equality, making a valuable contribution to research on discrimination reduction strategies. ? 2025 ERP Environment and John Wiley & Sons Ltd.

Smart campus as a research domain has been gaining attention due to technological advancements. These changes have called for a holistic development of these educational hubs. The literature on smart campuses is still fragmented, so an effort has been made to improve it. The factors in an intelligent campus have been analyzed in this work. A hierarchy of factors and sub factors has been developed for a proper analysis. The analysis used best and worst methods, which showed consistency over traditional methods like the analytical hierarchy process. The fuzzy form of BWM helps the decision-maker enhance efficiency even in uncertain situations. The factors used are learning and teaching, Infrastructure and operations, governance and administration, life health, engagement and sustainability. Infrastructure and operations are found to be critical drivers with higher weightage. Among the sub factors, personalized learning, Connectivity, e-governance platforms, mental and physical health and environmental sustainability are the key drivers. The paper shows the importance of the best and worst methods with enhanced consistency and the fuzzy form in uncertain decisions. ? The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026.

This research investigates the interlinkages and risk spillovers among clean energy stocks, water stocks, green finance instruments (green bonds and green stocks), and the sustainability index over the period November 1, 2013 to October 20, 2023. We adapt and extend the novel dynamic frequency connectedness framework of Chatziantoniou et al. (2023) by introducing a medium-term horizon in addition to short- and long-term bands and by explicitly accounting for the asymmetric effects of positive and negative return shocks. Our empirical findings reveal that the sustainability index is the main transmitter of shocks across the system, while the global bond market plays a relatively minor role. We also show that asymmetric shocks significantly alter spillover dynamics, with positive shocks dominating the short run and negative shocks exerting stronger and longer-lasting impacts during crisis periods such as the COVID-19 pandemic and the Russia-Ukraine war. By uncovering the heterogeneous time-varying nature of these connectedness patterns, the paper advances the sustainable finance literature and offers novel insights for asset allocation, hedging strategies, and systemic risk monitoring in ESG-oriented portfolios. ? 2026 Elsevier B.V.

We studied how Artificial Intelligence (AI) and Human Intelligence (HI) can substitute (automate) and complement (augment) each other over time. Through the adaptation of the Lotka?Volterra predator?prey model, we provide a mathematical framework to depict the cyclical interaction. These models are supported by real-world examples that highlight how AI and HI co-evolve, continuously optimizing task efficiency. The findings challenge the ?either-or? view of automation and augmentation, presenting a nuanced perspective on how AI?HI mutually enhance each other over time. ? 2025 Elsevier Ltd

Solar energy, a form of clean energy, is gaining popularity among the micro, small, and medium enterprises (MSMEs), despite the challenges. This paper seeks to understand the challenges of adoption and to rank these challenges for better planning and implementation by modelling solar energy adoption uncertainty flexibly and establishing the causal relationships among the entities. The data is interpreted as Hyperbolic Fuzzy Values (HyFVs). Cronbach's ? and DEMATEL are extended to the HyFVs to reliably obtain the weights of the experts and to prioritize the challenges arising from the causal relationships of the entity attributes. A case example of MSME solar energy adoption is presented to exemplify the practical use of the proposed HyFV-Cronbach's ?-DEMATEL solution framework. The results suggest that the lack of a skilled workforce (4.345), high capital costs (4.046), and a lack of political commitment (4.034) are the top three barriers that impede MSME adoption of solar energy. ? 2026 Elsevier B.V.

Behavioural dimensions of risk have often been overlooked in supply chain research, particularly in downstream contexts where human decisions directly shape demand responsiveness, coordination, and customer service. This study systematically reviews downstream supply chain risk management (D-SCRM) through a behavioural lens. It aims to develop a theory-driven taxonomy and explore its implications for risk mitigation, resilience, and performance. Drawing on a structured coding framework grounded in behavioural theory and industry practice, the review identifies key themes, classifies them across decision-making levels, and assesses their downstream performance impact. The analysis highlights that behavioural risks are central to downstream disruptions. A novel multi-level taxonomy is proposed, mapping risks across individual, relational, and organizational levels. The findings emphasize that integrating behavioural theories into D-SCRM significantly enhances decision-making under uncertainty. The study calls for a paradigm shift in research and practice by embedding behavioural insights into performance metrics, decision-support tools, and governance structures. It advocates for a holistic SCRM approach that combines cognitive, relational, organizational, and cultural dimensions. This review contributes to theory development and provides practical tools for managing behavioural risks, offering a foundational step toward more resilient and responsive downstream supply chains. ? The Author(s), under exclusive licence to Springer Nature Switzerland AG 2026.

This study examines how preferential buyer?supplier relationships contribute to net-zero carbon supply chain performance by focusing on the relational and capability-based mechanisms that enable supplier-level decarbonization. Drawing on the resource-based view (RBV) and stakeholder theory, the study addresses a critical gap in the literature, where supplier collaboration, motivation, and capability have largely been examined in isolation rather than as interdependent pathways. Using survey data collected from 324 supply chain professionals across emission-intensive sectors, the proposed conceptual framework is empirically tested through confirmatory factor analysis and regression-based conditional process modelling. The findings demonstrate that supplier preparedness and capability play a mediating role, fully transmitting the effects of collaboration and motivation for zero-carbon operations on net-zero carbon performance. Preferential relationship status indirectly enhances performance by fostering supplier collaboration and strengthening intrinsic motivation, which together enable capability development. In contrast, supply chain visibility and transparency do not significantly moderate these relationships, suggesting that monitoring technologies alone are insufficient without parallel investments in supplier capability-building. The study contributes to sustainability and supply chain management literature by clarifying how preferential relationships facilitate a shift from compliance-oriented approaches toward capability-driven partnerships, offering actionable insights for firms pursuing net-zero targets across complex supply networks. ? 2026 ERP Environment and John Wiley & Sons Ltd.

Purpose ? The arrival of the Metaverse is expected to revolutionize organizational practices, which substantially impact sustainability in logistics and supply chain. In addition, prescriptive analytics-based methodological improvements might make Metaverse self-sustaining. This study assesses the current reflective discussion about the function of prescriptive analytics in Metaverse. It proposes alternative streams for additional research in this area so that we can understand the relationship between Metaverse, prescriptive analytics, sustainable operations and supply chain. Design/methodology/approach ? We use structural topic modeling (STM), a text-mining approach, to critically assess the literature and analyze 161 articles. Findings ? Primary and secondary topics were developed using STM findings for comparison. Also, a research framework is created by sketching out the study following the findings of the review. Finally, we conclude with a list of unanswered research issues that might serve as a starting point for future investigations into the role of prescriptive analytics in empowering Metaverse for sustainable operations. Originality/value ? This study provides original insights into how prescriptive analytics can drive sustainable operations through Metaverse, offering a roadmap for future empirical research in this emerging area. ? 2025 Emerald Publishing Limited

Our study is a novel attempt to examine the sub-industry sectors of conventional energy (i.e., oil & gas, crude production, oil refinery & marketing, and oil equipment and services) and the sub-industry sector of green energy (i.e., renewable energy) to analyse the systemic risk and commonality. The results show that the systemic risk for green energy has decreased since 2006, indicating a shift towards net-zero emissions. Further, we develop a systemic contagion index (SCI) for green and conventional energy. The SCI shows that green energy has lower contagion than conventional energy. However, conventional energy appears to create lower contagion during crises, indicating its herding characteristics and investors' preference for conventional energy during crises. A mild systemic risk commonality between green and conventional energy provides an opportunity to shift towards green energy, thus enhancing the possibility of achieving net-zero emissions. These findings provide guidance to policymakers to build the global green and conventional energy policy to achieve net-zero emissions in 2050. ? 2024 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license. http://creativecommons.org/licenses/by/4.0/