Federated Learning (FL) enables privacy-preserving distributed training by avoiding centralized data collection. However, most FL frameworks presume uninterrupted participation from selected clients, which is impractical in edge environments with fluctuating resources. Devices often experience low battery, high CPU or memory load, limited bandwidth, and unstable connectivity, resulting in latency, client dropout, and reduced training performance. To address this issue, we introduce a lightweight, resource-aware participation prediction model based on a HistGradientBoosting Classifier (HGBC), trained solely on device-level resource indicators. A synthetic dataset is constructed through weighted logistic scoring combined with probabilistic Bernoulli sampling to reflect realistic resource variability. Evaluation using standard classification metrics shows strong predictive capability, including a high AUC value. Overall, the results highlight that incorporating an intelligent client selection mechanism aware of real-time resource conditions can significantly improve FL efficiency. It reduces unnecessary computation, lowers the chances of client dropout, and speeds up convergence, all while maintaining scalability and compatibility with a wide range of FL architectures as a modular scheduling component. ? 2026 IEEE.

This study introduces enhancements to three established swarm intelligence-based optimization algorithms: the Improved Crow Search, Dynamic BAT, and Levy Whale Optimization. Mutation operators, traditionally used in evolutionary-based optimization like genetic algorithms, help escape local optima to converge towards a global solution. This work integrates two novel mutation operators into these algorithms, enhancing their ability to effectively explore and exploit the search space. We employed 12 benchmark functions to analyze performance, observing improvements in global optimal values ranging from 29% to 152%. Furthermore, we applied these algorithms as training optimizers for neural networks, aiming to predict pelvic joint location from fingertips and these data have been used to train a multi-layered feedforward neural network. The algorithms without mutation and with mutation operators have been used as optimizers to train neural networks. A significant decrease in the absolute average percentage from experimental data has been observed. These findings highlight the potential of mutation operators to refine the exploration and exploitation capabilities of swarm intelligence algorithms. ? The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.

Customer expectations for quick delivery have shifted significantly. Today, speed is the new currency, with customers wanting faster delivery while prioritising cheaper delivery. Hence, some changes have been made to address this faster delivery process in the retail industry. This research will address what factors need to be considered in adopting the quick-commerce service in retail and address the interdependence among factors and subfactors using supply chain agility. To address these problem the information was gathered from experts in the retailing area from academics and industry and proposed a hybrid multiple criteria decision-making model incorporating the Decision-Making Trial and Evaluation Laboratory (DEMATEL)-based Analytic Network Process (DANP). The findings result in 19 subfactors categorised into five factors from the literature review, which might be important in adopting a q-commerce service. The cause-and-effect relationship has been drawn among the factors and subfactors. The top subfactor, based on the influencing weights, is classified into three tiers, in which the first tier factors are core critical factors, the second tier is for enhancing the efficiency of the organisation, and the third tier is for enhancing customer satisfaction, engagement and trust for q-commerce (quick commerce) service. To successfully adopt q-commerce in retail, artificial intelligence and machine learning facilitate advanced demand forecasting, route optimization, and inventory management, thereby enhancing operational responsiveness in highly dynamic urban environments. Additionally, real-time order tracking, intelligent warehousing, and temperature-controlled transportation are made possible by Internet of Things technology, guaranteeing product quality and supply chain transparency. This knowledge helps to clarify how managers and merchants can use these elements to improve the q-commerce service. This study is novel in that it highlights the factors that contribute to the retail adoption of q-commerce and explores the interdependencies between these aspects. ? 2026 Elsevier B.V.

Additive manufacturing (AM), particularly Laser Powder Bed Fusion (L-PBF), has transformed mold and core fabrication in foundry applications by enabling complex geometries, rapid prototyping, and patternless production. Despite these advancements, the consistent achievement of high-dimensional accuracy, mechanical strength, and surface integrity in sand-based AM remains challenging due to intricate thermal?process parameter interactions. To address these issues, the present study aims to optimize the process parameters of an indigenously developed Sand-based Additive Manufacturing (SAAM) apparatus based on a flying-optics CO2 laser for fabricating sand molds without conventional tooling. Four key parameters: laser power, layer thickness, scan speed, and overlap were analyzed for their influence on dimensional accuracy (diameter and height), compressive strength, surface porosity, and areal surface roughness. A Central Composite Design (CCD) under Response Surface Methodology (RSM) was used for modelling, followed by single- and multi-objective optimization using the Teaching Learning-Based Optimization (TLBO) algorithm. The regression models showed high predictive accuracy (R? >96% for all responses), while the optimized process parameters yielded less than 4.24% deviation between predicted and experimental outcomes. The multi-objective optimization produced an optimal parameter set (t = 0.3946?mm, P = 14.94?W, v = 64.84?mm/s, W = 19.52%) that enhanced mold quality and structural integrity, enabling the successful patternless casting of A356 aluminium alloy. Overall, this work provides an experimentally validated framework for parameter optimization in SAAM, highlighting its industrial potential for cost-effective, high-precision, and sustainable mold fabrication. ? The Author(s), under exclusive licence to Springer-Verlag France SAS, part of Springer Nature 2025.

Efficient operation on the factory floor has been hindered by data latency and communication delays, leading to production downtime and associated costs. This research investigates the use of an IoT-enabled Andon system integrated with a vendor-neutral Business Intelligence (BI) visualisation layer, within a genset manufacturing business. Through a sociotechnical intervention, this work examines how integration of real-time monitoring, data visualisation, and visual management influences operational performance. The single-case study method confirms the effect of implementing an IoT-enabled Andon: a 55.55% reduction in unscheduled downtime and a 4% increase in Overall Equipment Effectiveness. The effectiveness of the Andon and Power BI system was due to ?technology? and, importantly, its empowerment of operators to make responsive and aligned production decisions. The research provides empirical validation of an integrated Industry 4.0 intervention through a sociotechnical lens. It highlights the synergy between digital tools and human decision-making, offering strategic directions for digital transformation. ? 2026 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

It has been scientifically established that the transportation and logistics sectors are critical in the determination of sustainable global initiatives because of their immense effects on the environment. This chapter uses a holistic round scheme to examine how sustainability principles are incorporated into transport and logistics. This model identifies these components, Catalysts, Conversions, Outputs, and Impacts. Necessarily, these include alternative fuel vehicles (AFVs), routing software for optimal decisions together, and collaboration. Improving the routes taken when making deliveries, optimizing vehicle loads, and changing modes of transport are examples of catalysts that have been implemented in real-world systems through operationalization. These steps then give rise to things like less pollution, less fuel usage, or more successful supply chain operations. The successful yield, in turn, has cumulative significant impacts that result in better environmental performance, cost savings, and happy customers. ? 2026 selection and editorial matter, Dr. Ashu Sharma, Dr Veena Vohra and Dr. Rose Antony; individual chapters, the contributors.

Cost minimization and reducing impacts have become essential business priorities in today's rapidly evolving regulatory environment. The Carbon Border Adjustment Mechanism (CBAM) is one of the emerging mechanisms that helps drive this momentum and underscores the importance of sustainable practices in achieving both environmental and economic objectives. The manufacturing industry must assess its sustainable performance to ensure long-term viability. This study conducts a Life Cycle Costing (LCC) and Eco-Efficiency (EE) analysis for four Fertilizer Industries (FIs) engaged in Urea and NPK fertilizer manufacturing in emerging markets. A quantitative study, supplemented by qualitative validation, utilizes ISO 14040/44 standards, integrating the International Reference Life Cycle Data System (ILCD DN) and the Mechanical Licensing Collective (MLC) databases for comprehensive cost analysis. Key cost drivers, including raw material usage, energy consumption, labor, and maintenance, are examined to assess their economic impacts during fertilizer production. The EE analysis is integrated with cost-environmental impacts, emphasizing carbon dioxide (CO2) emissions and resource depletion metrics. The findings show that raw materials and electricity dominate costs and carbon footprints; raw inputs account for more than 60% of production expenses and are the primary source of CO2 emissions and resource depletion. Scenario mapping shows that more innovative use of resources, greener feedstocks, and energy-saving measures can slash costs and emissions, giving the best EE score for FI investing in cleaner power and optimizing their material mix. The study can facilitate FIs' compliance with emerging environmental regulations, minimize operational costs, and enhance sustainable performance, ensuring both economic and ecological resilience. The study findings will help policymakers and decision-makers by providing a clear view of the most cost-effective, low-carbon strategies for this hard-to-abate sector. ? 2026 ERP Environment and John Wiley & Sons Ltd.

This study addresses the critical issue of transparency within foodservice supply chains, which are typically inefficient, susceptible to fraud, and difficult to trace due to their complexity and fragmentation. However, blockchain technology can enable all supply chain participants to track and share data securely and in real-time; this presents a promising solution for the aforementioned issues. The three pillars of this study, food distribution, sourcing, and safety, have notable transparency gaps. Using the Neutrosophic weighted influence non-linear gauge system (WINGS) multiple-criteria decision-making (MCDM) approach, we analyze how blockchain technology can prioritize and address the following challenges: reducing post-harvest losses, ensuring the ethical sourcing of raw materials, and improving food safety standards in foodservice operations. Our findings offer actionable implications for the managers of Agri-foodservice supply chains, such as prioritizing security solutions to minimize post-harvest losses, theft, and stockpiling. Blockchain-based tracking systems and Internet of Things (IoT) sensors can enhance visibility and traceability and reduce transit losses and logistical disruptions. To improve coordination and resource utilization, we further recommend that supply chain actors strengthen collaborative data-sharing and automate compliance processes using smart contracts. While maintaining regulatory compliance, raw material procurement variability can be eliminated by standardizing sourcing practices and implementing quality assurance procedures. The novelty of this study lies in its comprehensive analysis of the functions of blockchain technology in all significant facets of Agri-foodservice supply chains. This comprehensive analysis provides a robust framework for blockchain adoption and highlights implications for regulatory compliance and fraud prevention, which can lead to increased consumer confidence. ? 2025 Taylor & Francis Group, LLC.

Purpose ? This study endeavours to examine the impact of an inclusive climate on organizational citizenship behaviours at the workplace. The study also explores how the underlying mechanisms, namely, allyship and psychological well-being, impact this relationship. Design/methodology/approach ? Data were collected through survey method via a structured questionnaire administered to management students with work experience across various industries. The sample comprised of 360 participants. AMOS was used to conduct confirmatory factor analysis and PROCESS (Hayes, 2015) for testing the hypothesized model. Findings ? The findings reveal that allyship mediates the relationship between inclusive climate and psychological well-being, and psychological well-being, in turn, mediates the relationship between inclusive climate and organizational citizenship behaviours. These results suggest a sequential mediation effect, where the presence of an inclusive climate fosters allyship, which enhances psychological well-being, ultimately leading to higher levels of organizational citizenship behaviours among employees. Originality/value ? There is a dearth of studies exploring the role of allyship as a mediator and no study in our knowledge studies its impact along with psychological well-being through self-determination theory lens on discretionary behaviours. ? Emerald Publishing Limited

The study reviews the sustainable consumption literature using bibliometrics, content analysis, and structured review methodology. The analysis included reviewing 3117 peer-reviewed journal articles on sustainable consumption utilizing citation networks, bibliographic coupling, thematic analysis, and publication trends. The study maps the intellectual structure of sustainable consumption along with the gaps. Most popular authors, journals, countries, articles, and emerging themes in sustainable consumption were highlighted. The study elaborates on the key emerging areas in sustainable consumption, like education for generating awareness, government incentives, labeling, and partnerships, along with details about implementation challenges. The study also outlines a research agenda for scholars working in this domain. ? The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.

Despite rising net-zero commitments, many carbon-intensive firms struggle to translate ambition into change in everyday work. This study examines how organizational learning operates as a coordination meta-capability linking strategic net-zero intent to operational transformation. Using Gioia-style grounded theorizing, we analyse 25 semi-structured, multi-level interviews from five large Indian firms in the energy, oil and gas, steel, cement, and automotive sectors. We inductively identify four micro-foundations and six cross-cutting learning routines that together constitute organizational learning as a coordination system. We then introduced capability fluency and proposed a Distribution?Reflexivity?Integration (D-R-I) rubric to operationalize the quality of this system. The rubric assesses how widely routines are deployed, whether periodic reviews trigger within-cycle changes, and how tightly micro-foundations are linked. Two firms in our sample exhibit high capability fluency, two moderate, and one low. Variation in net-zero learning stems less from the presence of routines than from their D?R?I quality and the strength of coupling mechanisms. We further translate the Dynamic Capabilities View into a measurable, auditable architecture, offering a simple D?R?I rubric, count-based indicators, and a learning maturity roadmap that provides managers and policymakers with concrete diagnostics and low-cost design options for integrating net-zero into day-to-day decisions in emerging economies. ? 2025 Elsevier Ltd.

Global supply chains face unprecedented disruption due to political conflicts, natural disasters, and the COVID-19 pandemic. These instances uncover weaknesses in conventional supplier selection methods, where the emphasis has always been on low cost and efficiency rather than on resilience. This chapter presents an integrated decision-support model called DD-GDA-GT-EMSS?Data-Driven, Grey Decision Analysis, Game Theory, and Enhanced Multi-Stage Selection?for the evaluation of suppliers under uncertain environments and the strengthening of supply chain resilience. The model, which combines strategic collaboration using game theory and iterative refinement using multi-stage selection, contrasts weaknesses found in traditional approaches. The efficacy of the model is proven in a case study, and a robust methodology for the dynamic selection of suppliers is proposed. Findings unveil key factors for strengthening resilience, such as financial stability, collaboration, and continuity in operations during disruptions through providing high-quality goods and services. Sector-specific implementations and real-time integration of big data can be considered in future studies for further strengthening the resilient selection of suppliers. ? 2026 selection and editorial matter, Rajesh S. Prabhu Gaonkar; individual chapters, the contributors.

Organizational healing is an important construct examining how organizations repair and strengthen their collective social fabric and functioning after experiencing trauma, crisis, or significant disruption. However, while this field is gaining attention, current research is largely rooted in Western contexts and based on studies of specific crises, which may not have universal applicability. This paper proposes that a profound and culturally rich understanding of organizational healing IP: 5.10.31.210 can be achieved On: Thu, by incorporating 22 Jan 202 perspectives from Indian knowledge Delivered systems. by Ingenta In Indian indigenous traditions, healing is viewed holistically, going beyond merely curing a condition to encompass a process that restores harmony, balance, and peace, fostering an inner sense of well-being and connection between mind, body, and spirit. Presenting excerpts from various ancient Indian texts and drawing primarily from the Santi Parva of Mahabharata, we explain how these systems often emphasize relational processes and collective support as crucial for healing. By integrating this Indianness into the study of organizational healing, we can illuminate unique insights into how organizations might navigate adversity. This theoretical exploration suggests that culturally grounded approaches can enhance collective well-being and robustness, offering a valuable alternative or complement to existing frameworks and paving the way for culturally sensitive organizational practices and future research. ? 2026 Association of Management, Spirituality & Religion.

Investors in India buy financial products from asset management companies (AMCs) to secure their financial future using various platforms for investing. To achieve sustained performance, AMCs need to explore technological advancements, methodologies to make their operations efficient and effective to provide seamless transaction experience across its various investment platforms. This study investigates the factors essential in achieving operational excellence in asset management companies (AMCs). The study uses 300 valid responses from a survey collected from eight prominent Indian AMCs. The analysis was conducted using SEM approach. The findings indicate that business process management, Industry 4.0, Lean Six Sigma enable achievement of operational excellence in Indian AMCs. This study is among the few empirical studies which examines the enablers of operational excellence in Indian AMCs. ? 2026 Econjournals. All rights reserved.

Purpose ? This study grounded in social learning theory examines the impact of instrumental and integrative responsible leadership behavioral styles (RLBS) on employees' innovative work behavior (IWB) and turnover intention. Organisational citizenship behavior (OCB) and meaningful work (MFW) are mediators between RLBS and outcome variables. Design/methodology/approach ? Data from 602 manufacturing and service sector employees in India were analyzed using structural equation modeling and mediation analysis with AMOS software. A multigroup invariance analysis compared the application of RLBS across the two sectors. Findings ? RLBS significantly enhances IWB and reduces turnover intention. OCB and MFW fully mediate the RLBS-IWB relationship, but their impact on turnover intention remains partial. The multigroup analysis confirms that RLBS operates consistently across sectors, reinforcing its universal applicability. Practical implications ? Organisations aiming to enhance innovation and reduce turnover should invest in developing responsible leadership practices that balance instrumental and integrative styles. By fostering a leadership culture that promotes quick decision-making, employee engagement and MFW, companies can strengthen employee motivation, increase OCB and enhance overall retention and innovation outcomes. Originality/value ? This study introduces a novel RLBS model integrating instrumental and integrative leadership styles, offering empirical validation within a South Asian context. The findings provide actionable insights for fostering innovation and employee retention through RLBS. ? 2026 Emerald Publishing Limited

The paper focused on enhancing packaging efficiency in the Fast-Moving Consumer Goods (FMCG) sector, specifically for powder-form products. The primary challenge faced by FMCG companies is the precise filling of product packets during machine-based packaging. This paper explored the factors contributing to underfilling and overfilling and proposed techniques and strategies to improve packaging efficiency. The study employed causality analysis, multivariate regression, and statistical methods to uncover the root causes of packaging variations. The findings also highlighted the importance of reducing overfilling to minimize material wastage and environmental impact. Two key propositions were presented and examined in the paper in order to investigate and support the objective. The first proposition examined the impact of external environmental factors on packaging efficiency, including lump formation, temperature, humidity, and rainfall. The second proposition investigated the effect of time difference on packaging efficiency. The reduction of overfilling not only reduces material wastage but also aligns with sustainability practices, portrays companies as responsible environmental stewards and increases revenue for the company. This paper discussed some actionable recommendations for improving the packaging process, reducing waste, and enhancing industry efficiency. The findings have implications for similar products and contribute to the broader goal of sustainability. ? The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026.

Purpose: This study explores how AI can support sustainable and efficient textile design, identify AI-based factors, analyse their interdependencies, and categorize them into cause-and-effect groups using a structured decision-making approach. Design/methodology/approach: The study adopted a three-step process: (1) conducting a systematic literature review to identify AI applications in textile design, (2) applying the Fuzzy Delphi Method (FDM) to screen and cross-validate key factors based on expert opinions, and (3) using the Decision Making Trail and Evaluation Laboratory (DEMATEL) approach to analyse the cause-and-effect relationships between the screened factors. Findings: The study found that AI-enabled generative Design for Material Efficiency and Predictive Colour and Trend Forecasting for Design Planning are primary causal factors influencing other AI-driven innovations, while AI-Powered Fit Prediction and Customization Algorithms and AI-Based Circular Design Recommendations are ranked as effect factors (i.e., they are more dependent on the impact of other factors). The results demonstrate the interdependent role of AI technologies in textile design and their influence on sustainability and efficiency in operations. Originality and implications: The study provides new insight by mapping the influence of textile design AI technologies in a systematic way using an integrated FDM-DEMATEL framework, and the findings offer useful guidelines for designers, producers, and policymakers to set priorities for AI-based innovations and to make strategic investments into AI applications for increasing efficiency, circularity, and sustainability in the textile sector. ? 2026 Informa UK Limited, trading as Taylor & Francis Group.

Hyperlocal delivery (HLD) refers to a novel model for delivering goods or services to customers within a very limited geographic area, typically within a few miles of their location, in a very short time. With more and more online retailers moving to HLD, it is imperative to analyse the impact of HLD on the pricing decisions of both online channels and modern trade outlets. For this, we assume a framework consisting of a manufacturer employing two downstream channels: modern trade outlets and online platforms, to reach the product to the end customer. The Stackelberg game is used to model the interplay between the manufacturer and downstream channel partners, while the horizontal Nash game captures the dynamics within the downstream channel partners. The downstream channel partners are also assumed to be risk-averse, and we used the Mean-Variance Approach for modelling. The study indicates that HLD provides a competitive edge to the online channel; however, strategic product selection enables modern trade outlets to counter this competition effectively. The study underscores the importance of aligning HLD costs with production costs for online channels to maintain profitability. Furthermore, it reveals that the risk aversion of downstream partners and channel competition significantly influence the profitability of both downstream and upstream partners. The findings will assist management professionals in their quest for profitability by providing them with actionable insights on choosing the right product and optimising hyperlocal delivery facilities. ? 2025 China Science Publishing & Media Ltd.

Purpose ? This study aims to identify and analyse key barriers to digitalization in maritime logistics using the Decision-Making Trial and Evaluation Laboratory (DEMATEL) methodology to establish their cause?effect relationships. Design/methodology/approach ? The study conducts a detailed literature review and collects expert opinions, and as a result, 18 significant barriers to digitalization in maritime logistics are identified. The DEMATEL technique determines the cause-and-effect relationship between the identified barriers based on their significance. Findings ? The present study concluded that three barriers, namely, ?Lack of trained professional human resources, ? ?Employee?s and Manager?s resistance? and ?Linking all the stakeholders with upgraded technology, ? are the most significant factors influencing the digitalization of maritime logistics. Research limitations/implications ? This study uses a DEMATEL model by considering inputs from domain experts to guide maritime stakeholders and government institutions in developing new regulations or updating old rules to digitalize maritime logistics effectively. Originality/value ? This study contributes methodologically by applying the DEMATEL technique to uncover cause?effect relationships among 18 digitalization barriers and theoretically by contextualizing findings within innovation diffusion and institutional theory, offering new insights from the Indian maritime logistics sector. ? 2025 Emerald Publishing Limited

Prompt engineering involves manual design and optimization of text-based instructions or queries, enabling precise control over outputs generated by pre-trained large language models (LLMs) and ensuring alignment with desired responses. However, substantial computational costs and energy footprint of prompt inferencing process remain critical challenges while building generative AI applications. The energy efficiency of LLM inferences is particularly impacted by suboptimal prompts, which may require multiple iterations, thereby escalating energy consumption and the associated carbon footprint. To address these challenges, we propose a series of practices and guidelines designed to enhance the likelihood of obtaining desired responses from LLMs with minimal reiterations. Empirical evaluation demonstrates that, across a range of LLMs and test scenarios, energy consumption and corresponding operational greenhouse gas emissions were reduced by 32?48% when best practices were applied. Drawing upon these insights, our proposed best practices can be seamlessly integrated into the design frameworks of generative AI applications, thereby enhancing the energy efficiency of prompt inferencing. By addressing the challenge of establishing a cohesive framework for energy-efficient prompt design and inferencing, this paper advocates for the sustainable and effective deployment of generative AI technologies. ? 2026 The Authors. Published by Elsevier B.V. on behalf of Chinese Society for Environmental Sciences, Harbin Institute of Technology, Chinese Research Academy of Environmental Sciences. This is an open access article under the CC BY-NC-ND license. http://creativecommons.org/licenses/by-nc-nd/4.0/