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The present research identifies and prioritizes the critical success factors (CSFs) for Lean 4.0 (L4.0) implementation in small and medium enterprises (SMEs). L4.0 integrates Lean principles with Industry 4.0 (I4.0) technologies, for instance wireless networks, Internet of things (IoT), big data, cloud computing (CC), etc., offering significant opportunities to enhance operational efficiency by reducing non-value-adding activities.

This research adopts the “Fuzzy Decision-Making Trial and Evaluation Laboratory (FDEMATEL)” methodology to examine and assess the connections between CSFs for L4.0 implementation. Data were gathered from SMEs using qualitative and quantitative approaches to ensure comprehensive insights into the critical enablers of L4.0 adoption.

The study identifies Top Management Support and Commitment, Employee Training and Financial Capabilities as the most important CSFs for L4.0 adoption in SMEs. These factors significantly impact the adoption process, providing actionable insights for SME leaders to overcome challenges and optimize implementation strategies.

This study contributes to the growing knowledge of L4.0 by highlighting key CSFs relevant to SMEs, a sector often constrained by resources but crucial for economic development. The findings provide a practical roadmap for SME entrepreneurs to achieve operational excellence and competitiveness through effective L4.0 adoption.

This study aims to develop a neurocomputational approach using the Levenberg–Marquardt artificial neural network (LM-ANN) to analyze flow and heat transfer characteristics in mixed convection involving radiative magnetohydrodynamic hybrid nanofluids. The focus is on the influence of morphological nanolayers at the fluid–nanoparticle interface, which significantly impacts coupled heat and mass transfer processes.

This research simplifies a complex system of higher-order nonlinear coupled partial differential equations governing the flow between orthogonal coaxially porous disks into ordinary differential equations via similarity transformations. These equations are solved using the shooting method, and parametric studies are conducted to observe the impact of varying important parameters. The resulting data sets are used to train, validate and test the LM-ANN model, which ensures high predictive accuracy. Machine learning and curve-fitting techniques further enhance the model’s capability to generate detailed visualizations.

The findings of this study indicate that increased nanolayer thickness (0.4–1.6) significantly improves thermal performance, while changes in the chemical reaction parameter (0.2–1) have a notable effect on enhancing the Sherwood number. These results highlight the critical role of morphological nanolayers in optimizing thermal and mass transfer efficiency in MHD nanofluids.

This research provides a novel neurocomputational framework for understanding the thermal and mass transfer dynamics in MHD nanofluids by incorporating the effects of interfacial nanolayers, an aspect often overlooked in conventional studies. The use of LM-ANN trained on computational data sets enables high-fidelity predictive analysis, offering new insights into the enhancement of thermal and mass transfer efficiency in hybrid nanofluid systems.

Purpose: In this study, monolith analysis methods, microservice identification, and decomposition methods proposed for the transition to microservice architectures that enable the development of appropriate solutions by adapting to the complex demands that will shape the technological infrastructure of the future are evaluated.

Need for the study: Decomposition from monolithic architectures to microservices has become a popular approach in organizations and companies with Industry 5.0. This transformation of Industry 5.0 enables businesses to gain a competitive advantage and can provide a quick solution to personalized problems such as personal service systems.

Methodology: The study, decomposition from monolith to microservice, initially includes monolith analysis, followed by microservice decomposition review. Various classification methods have been proposed for microservice identification and decomposition and are aligned with Industry 5.0 principles, focusing on artificial intelligence (AI)-based approaches, especially human-centered AI.

Findings: Four analysis methods (domain, static, dynamic, and version) are identified for monolith analysis, with static and dynamic being the most common. Version analysis is not typically used alone. In the decomposition phase, clustering-based methods are prevalent due to the uncertain dimensions of microservices. Rule-based and unsupervised methods are identified for decomposition, with AI algorithms like affinity propagation, Kmeans clustering, hierarchical clustering, Hungarian algorithm, genetic algorithm, latent Dirichlet allocation (LDA), and minimum spanning tree (MST) being employed.

Practical implications: Microservice architecture enables flexibility, scalability, and resilience compared to monolithic structures. Decomposing large-scale monolith projects into microservices is challenging, requiring selection of appropriate monolith analysis methods based on project details (e.g., domain analysis for detailed Unified Modelling Language (UML) diagrams) before proceeding with decomposition. This transformation improves deployment, maintenance, fault isolation, and scalability, while allowing for diverse service-specific databases and programming languages.

Supply chains need to be made viable in this volatile and competitive market, which could be possible through digitalization. This study is an attempt to explore the role of Industry 4.0, smart supply chain, supply chain agility and supply chain resilience on sustainable business performance from the lens of natural resource-based view.

The study tests the proposed model using a covariance-based structural equation modelling and further investigates the ranking of each construct using the artificial neural networks approach in AMOS and SPSS respectively. A total of 234 respondents selected using purposive sampling aided in capturing the industry practices across supply chains in the UK. The full collinearity test was carried out to study the common method bias and the content validity was carried out using the item content validity index and scale content validity index. The convergent and discriminant validity of the constructs and mediation study was carried out in SPSS and AMOS V.23.

The results are overtly inferring the significant impact of Industry 4.0 practices on creating smart and ultimately sustainable supply chains. A partial relationship is established between Industry 4.0 and supply chain agility through a smart supply chain. This work empirically reinstates the combined significance of green practices, Industry 4.0, smart supply chain, supply chain agility and supply chain resilience on sustainable business value. The study also uses the ANN approach to determine the relative importance of each significant variable found in SEM analysis. ANN determines the ranking among the significant variables, i.e. supply chain resilience > green practices > Industry 4.0> smart supply chain > supply chain agility presented in descending order.

This study is a novel attempt to establish the role of digitalization in SCs for attaining sustainable business value, providing empirical support to the mediating role of supply chain agility, supply chain resilience and smart supply chain and manifests a significant integrated framework. This work reinforces the integrated model that combines all the constructs dealt with in silos so far in prior literature.

Logistics service provider (LSP) selection involves multiple criteria, alternatives and decision makers. Group decision-making involves vagueness and uncertainty. This paper aims to propose a novel fuzzy method for assessing and selecting agile, resilient and sustainable LSP, taking care of the inconsistency and uncertainty in subjective group ratings.

Eighteen agile, resilient, operational, economic, environmental and social sustainability criteria were identified from the literature and discussion with experts. Interval-valued Fermatean fuzzy (IVFF) sets are more flexible and accurate for handling complex uncertainty, impreciseness and inconsistency in group ratings. The IVFF PIvot Pairwise RElative Criteria Importance Assessment Simplified (IVFF-PIPRECIAS) and IVFF weighted aggregated sum product assessment (IVFF-WASPAS) methods are applied to determine criteria weights and LSP evaluation, respectively.

Collaboration and partnership, range of services, capacity flexibility, geographic coverage, cost of service and environmental safeguard are found to have a greater influence on the LSP selection, as per this study. The LSP (L3) with the highest score (0.949) is the best agile, resilient and sustainable LSP in the manufacturing industry.

Hybrid IVFF-based PIPRECIAS and WASPAS methods are proposed for the selection of agile, resilient and sustainable LSP in the manufacturing industry.

The model can help supply chain managers in the manufacturing industry to easily adopt the hybrid model for agile, resilient and sustainable LSP selection.

The paper also contributes to the social sustainability of logistics workers.

To the best of the authors’ knowledge, IVFF-PIPRECIAS and IVFF-WASPAS methods are applied for the first time to select the best agile, resilient and sustainable LSP in a developing economy context.

In the present era, artificial intelligence (AI) is transforming and redefining the lifestyles of society through its applications, such as chatbots. Chatbot has shown tremendous growth and has been used in almost every field. The purpose of this study is to identify and prioritize the factors that influence millennial’s technology acceptance of chatbots.

For the present research, data were collected from 432 respondents (millennials) from Punjab. A fuzzy analytical hierarchy process was used to prioritize the factors influencing millennials’ technology acceptance of chatbots. The key factors considered for the study were information, entertainment, media appeal, social presence and perceived privacy risk

The findings of the study revealed media appeal as the top-ranked prioritized factor influencing millennial technology acceptance of chatbots. In contrast, perceived privacy risk appeared as the least important factor. Ranking of the global weights reveals that I3 and I2 are the two most important sub-criteria.

Data were gathered from the millennial population of Punjab, and only a few factors that influence the technology acceptance of chatbots were considered for analysis which has been considered as a limitation of this study.

The findings of this study will provide valuable insights about consumer behaviour to the business firm, and it will help them to make competitive strategies accordingly.

Existing literature has investigated the factors influencing millennials’ technology acceptance of chatbots. At the same time, this study has used the multi-criteria decision-making technique to deliver valuable insights for marketers, practitioners and academicians about the drivers of millennials’ technology acceptance regarding chatbots which will add value to the prevailing knowledge base.

The purpose of this study is to provide a holistic understanding of the factors that either promote or hinder the adoption of artificial intelligence (AI) in supply chain management (SCM) and operations management (OM). By segmenting the AI lifecycle and examining the interactions between critical success factors and critical failure factors, this study aims to offer predictive insights that can help in proactively managing these factors, ultimately reducing the risk of failure, and facilitating a smoother transition into AI-enabled SCM and OM.

This study develops a knowledge graph model of the AI lifecycle, divided into pre-development, deployment and post-development stages. The methodology combines a comprehensive literature review for ontology extraction and expert surveys to establish relationships among ontologies. Using exploratory factor analysis, composite reliability and average variance extracted ensures the validity of constructed dimensions. Pearson correlation analysis is applied to quantify the strength and significance of relationships between entities, providing metrics for labeling the edges in the resource description framework.

This study identifies 11 dimensions critical for AI integration in SCM and OM: (1) setting clear goals and standards; (2) ensuring accountable AI with leadership-driven strategies; (3) activating leadership to bridge expertise gaps; (4) gaining a competitive edge through expert partnerships and advanced IT infrastructure; (5) improving data quality through customer demand; (6) overcoming AI resistance via awareness of benefits; (7) linking domain knowledge to infrastructure robustness; (8) enhancing stakeholder engagement through effective communication; (9) strengthening AI robustness and change management via training and governance; (10) using key performance indicators-driven reviews for AI performance management; (11) ensuring AI accountability and copyright integrity through governance.

This study enhances decision-making by developing a knowledge graph model that segments the AI lifecycle into pre-development, deployment and post-development stages, introducing a novel approach in SCM and OM research. By incorporating a predictive element that uses knowledge graphs to anticipate outcomes from interactions between ontologies. These insights assist practitioners in making informed decisions about AI use, improving the overall quality of decisions in managing AI integration and ensuring a smoother transition into AI-enabled SCM and OM.

Restrictions imposed on freedom of movement and interaction with others due to the COVID-19 pandemic have had the effect of causing many people, especially students, to become addicted to social media. This study aims to investigate the effect of social media addiction on the academic performance of Sri Lankan government university students during the COVID-19 pandemic.

A convenience sampling technique was used to conduct a quantitative cross-sectional survey. The survey involved 570 respondents from nine state universities in Sri Lanka. The raw data from the completed questionnaires were coded and processed using SPSS for descriptive and inferential statistical analysis.

The findings of this study indicated that the overall time spent on social networking increased dramatically during COVID-19. Based on the results, this study found that there was no association between the time spent on social media and the academic performance of students before COVID-19 came on the scene. However, a significant association was found between the time spent on social media and students’ performance during the pandemic. The authors concluded that overblown social media use, leading to addiction, significantly negatively affects academic performance.

This study helps to understand the impact of social media use on the academic performance of students during COVID-19. Restrictions imposed by COVID-19 have changed the typical lifestyle of the students. Therefore, social media usage should be reassessed during the COVID-19 pandemic. The findings of the study will comprise these new insights, and they may well show how to adapt social media to contribute to academic work in meaningful ways.

This study aims to identify and model deterrents to adopt and institutionalize analytics and artificial intelligence in modern human resource (HR) using interpretive structural modelling (ISM) and cross-impact matrix multiplication applied to classification (MICMAC) approach.

A comprehensive investigation of the literature and feedback from experts led to the identification of 16 deterrents in this study. After that, the ISM tool is used to find connections between the identified deterrents in the HR ecosystem and MICMAC which helps in categorising deterrents on the basis of driving and dependence power and provides deeper insights into their roles and significance.

Employee resistance and HR transformation are highly influenced by other factors but exert minimal driving power. Data availability, leadership support, communication and collaboration, legal, ethical and regulatory compliance, and infrastructure and resources exhibit strong influence and dependence, making them highly sensitive and crucial. Training and development, learning culture and change management, and data privacy and security have strong driving power with minimal dependence, indicating their foundational role in shaping HR transformation.

This study will assist policymakers and owners/managers in the HR ecosystem in recognising and comprehending the importance and applicability of analytics and AI obstacles while developing HR strategies.

This study explicitly focuses on data analytics and AI technology in the current scenario. It also explores the relationship between deterrents and their driving and dependence powers.

The current study focuses on the relationship between total quality management (TQM) and sustainable performance (SP) and examines how TQM practices can facilitate firms' achievement of sustainable performance. Knowledge management (KM), with its four dimensions, i.e. knowledge creation (KCR), knowledge acquisition (KAC), knowledge sharing (KSH) and knowledge application (KAP), is also an essential factor for organizations. Therefore, this study also focuses on the mediating role of KM in the relationship between TQM and sustainable performance.

This study used a survey method to collect data from the managers of 485 manufacturing SMEs working in five major industrial cities in Pakistan. Collected data were analyzed through PLS-SEM with the help of smart-PLS.

The study's findings reveal that TQM practices positively influence the environmental and economic sustainability of the firm. At the same time, there is no evidence that TQM practices positively affect the social sustainability of the firm. Results further elaborate that TQM practices significantly affect all four dimensions of KM. Moreover, KM positively affects the two dimensions of SP, i.e. economic and social sustainability, but surprisingly, the impact of KM on environmental sustainability is not found. Finally, results indicate the significant mediating role of KM between TQM and SP.

This study contributes to bridging research gaps in the literature and advances how TQM, directly and indirectly, helps firms improve sustainable performance via the mediating role of KM.