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This study investigates the patterns in Microlearning by analyzing the extant literature, uncovering the underlying themes, and proposing future directions in this field for organizations.

A relevant search of the Scopus database was performed on 4th August 2024 which revealed a total of 108 articles. These articles were then analyzed and a network analysis was performed using VOSViewer to uncover the research themes and clusters. Articles pertaining to the domain of Business Management were included for the study.

The findings are based on the published literature from Scopus database and generating an intellectual structure through VOSviewer. Four underlying themes projecting the patterns associated with Microlearning were revealed and future directions based on these patterns were suggested.

This study is a window to the immense possibility of future research in the emerging field of Microlearning. Researchers can conduct empirical studies related to Microlearning with reference to each of the four clusters that emerged from the study. The future directions suggested in the study may also be validated through quantitative research.

Organizations can utilize this study to understand and incorporate Microlearning and base their organizational policies to evolve into inclusive learning organizations.

Microlearning can prove to be a great learning technique to promote gender-agnostic inclusive learning catering to different learning styles.

This paper is a unique study that uncovers the patterns and indicates the future directions in Microlearning – an evolving learning technique appealing to a diverse set of learners.

Educational data mining (EDM) discovers significant patterns from educational data and thus can help understand the relations between learners and their educational settings. However, most previous data mining techniques focus on prediction of learning performance of learners without integrating learning patterns identification techniques.

This study proposes a new framework for identifying learning patterns and predicting learning performance. Two modules, the learning patterns identification module and the deep learning prediction models (DNN), are integrated into this framework to identify the difference of learning performance and predicting learning performance from profiles of students.

Experimental results from survey data indicate that the proposed identifying learning patterns module could facilitate identifying valuable difference (change) patterns from student’s profiles. The proposed learning performance prediction module which adapts DNN also performs better than traditional machine techniques in prediction performance metrics.

To our best knowledge, the framework is the only educational system in the literature for identifying learning patterns and predicting learning performance.

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.

Environment, social and governance (ESG) criteria are a quantum of a company’s performance in the environmental, social and governance aspects. A company’s worth may be determined not only by its earnings but also by its knowledge and sensitivity towards its stakeholders and society. The study aims to rank the companies and determine which company is superior based on ESG criteria. The authors employed the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) in this study. The companies are ranked with this standardized method comprehending which company is the best taking into consideration the various environmental, social and governance factors. The authors have evaluated four companies in the electric utilities and IPPs industry. The results of the study rank these four companies on the basis of ESG criteria. Interestingly, the rankings calculated for ESG criteria are identical to the rankings calculated by a well-known ESG rating agency. To the best of author’s knowledge, this work is among the first to use the TOPSIS method to find rankings of the companies on the basis on ESG criteria. The work provides practical implications regarding convenient to use when finding ESG rankings for companies. This might be the most effective way for investors or other parties to learn which firm is the greatest for sustainable investing.

In today’s environment, electric vehicles (EVs) industries and services provided to consumers are facing many challenges. The public in large numbers is not adopting Electric Vehicles because of the unavailability of suitable EVs and not getting proper charging infrastructure to the consumers. The purpose of this study and research work is to analyze the condition of this industry, its charging infrastructure and E-government policies. Based on the above analysis, frameworks/models are to be designed to assist organizations in formulating strategies for the EV industry and providing proper infrastructure to the consumer.

The Variables for the EV and EVsCI case study are identified from an exhaustive literature review, stakeholders’ perspectives, experts’ opinions, existing EV policies, future policy blueprints and data available for this industry, applying the Situation Actor Process-Learning Action Performance (SAP-LAP) methodology. After that, the Interpretive Ranking Process (IRP) is integrated with the SAP-LAP approach to develop models.

The rankings of Actors Versus Processes and Actions Versus Performances have been obtained and then models have been developed. These models can serve policymakers in framing and implementing effective policies.

Although the SAP-LAP approach is an innovative approach for identifying variables, however, this approach lacks perfect interactive relationships among the variables. It may lead to imperfect decision-making. To avoid this, the IRP approach is integrated with the SAP-LAP approach which provides more accurate models for analysis and providing recommendations to government and organizations.

This study aims to identify the enablers of supply chain resilience (SCR) through a literature review and expert panel input in the context of Pakistan and the post-pandemic era. This study also aims to categorize and rank the identified enablers using expert panel input.

A review of the extant literature was conducted to investigate and identify the factors that contribute to SCR. The relative ranking of the enablers was carried out by a group of industry and academic experts. The expert panel was convened to compare the main categories and each enabler in pairs and to score the enablers using triangular fuzzy numbers.

This study identified 16 critical SCR enablers. Using the fuzzy analytic hierarchy process (AHP), these enablers were divided into three groups and analyzed. The results show that financial enablers, technology enablers and then social enablers are prioritized when it comes to SCR in emerging markets. The robustness of the ranking of enablers is tested through sensitivity analysis.

The results shall be helpful for policymakers and managers to understand the important enablers and also help allocate resources to important enablers. Managers will be able to formulate strategies to achieve SCR in an uncertain environment.

This is one of the first attempts to identify and rank the enablers of SCR in an emerging economy context.

The chapter explored the critical components, challenges, and technological advancements in construction supply chain management (CSCM), focusing on stealth construction (STC). It delved into STC encompassing nature, highlighting its unique challenges in its supply chain management and the necessity for adaptive technologies. It further discussed the benefits of tailoring supply chain management specifically for STC, emphasising the importance of developing the building’s cross-section, managing visibility, controlling energy transmission, and implementing countermeasures. Practical applications of CSCM in STC are also examined. This chapter sheds light on the complexities of managing supply chains in STC contexts and offers insights into strategies and technologies to address these challenges effectively.

Industry 4.0 (I4.0) influences the supply chain, including innovation, structure, availability and communication. It influences the supply chain by modifying how products are designed, manufactured, delivered and discarded for the sustainability of an organization. Sustainability emphasizes the use of optimal raw materials, efficient storage and delivery, on-time supply, productivity development, recycling, remanufacturing and refurbishing. The aim of this study is to identify and establish causal relationships among the key aspects that contribute to the success of I4.0-enabled sustainable supply chain (I4eSSC). The objective is to classify these components into discrete categories according to their causal and consequential linkages.

This paper presents a three-phase hybrid Criteria Importance Through Inter-criteria Correlation and Grey Decision-Making Trial and Evaluation Laboratory (G-DEMATEL) framework to achieve the stated objectives. A study is conducted to empirically examine the Indian automobile sector in order to illustrate the suitability of the suggested framework.

During the initial stage, a total of 26 success factors have been identified for the I4eSSC implementation. During the second part of the study, a total of 20 success factors were found and deemed critical success factors (CSFs). During the concluding stage, it was seen that out of the total of 20 chosen CSFs, nine were categorized in the cause group, whereas the remaining 11 were classified under the effect group. The analysis revealed that the element of “adoption of emerging technology for sustainable product and process development” was assigned the greatest level of importance.

This study aims to motivate professionals to enhance the integration of I4.0 into sustainable supply chains to maximize the advantages offered by these two separate concepts. It is imperative to devote genuine consideration to the examination of causal group variables in the context of I4eSSC, since these elements possess a direct influence on the overall performance of systems and exert a significant impact on the components within the effect group.

This study presents an extensive compilation of CSFs pertaining to the implementation of I4eSSC. The primary objective is to determine the relative relevance of these factors and explore the intricate causal relationships that exist among them. Furthermore, this study stands out due to its novel application of a specific decision-making methodology within the field of I4eSSC.

Purpose: This piece delves into the transformative potential of artificial intelligence (AI) in the healthcare field within the emerging realm of Industry 5.0, highlighting a people-focused and eco-friendly approach.

Need for the study: While Industry 4.0 set the foundation for digitization in healthcare, it frequently overlooked the human factor and concerns about sustainability. Industry 5.0 tackles these deficiencies by giving importance to human welfare, efficiency in resource usage, and societal consequences alongside technological progress.

Methodology: This research utilizes a survey of existing written works on Industry 5.0, AI in healthcare, and associated empowering technologies. It also leans on insights from recent investigations and business actions to pinpoint current patterns and future paths.

Findings: This chapter showcases how AI-driven solutions can greatly alter various facets of healthcare. Some of these healthcare facets encompass personalized medicine and treatment, intelligent diagnostics and decision support, robot-supported surgery and care, and enhanced availability and affordability.

Practical applications: This piece offers valuable perspectives for healthcare investors. These investors cover healthcare suppliers, technology creators, rule creators, and patients. By embracing the standards of Industry 5.0, the merging of AI into healthcare brings significant potential for crafting a more competent, sustainable, and people-centered healthcare network that benefits both patients and society as a complete unit. This research investigates the stance, viewpoints, and potential impacts of machine intelligence (MI) in health with an emphasis on Industry 5.0.

This study aims to address the critical need for innovation in the power grid sector, driven by global carbon reduction commitments. It highlights the pivotal role of critical success factors (CSFs) in enhancing system adaptability and environmental mitigation within India’s power industry.

This research is grounded on transition management theory to identify and validate the CSFs necessary to integrate energy storage systems (ESS). Here, exploratory factor analysis (EFA) and total interpretive structural modeling (TISM) are integrated to evaluate the model’s effectiveness in reducing CO₂ emissions while ensuring grid stability and flexibility.

The research develops a seven-level hierarchical model illustrating the interaction of ESS components for a stable power grid, clean energy and a profitable electric industry. It emphasizes the strategic significance of managing key factors to reduce CO₂ emissions and ensure grid stability. The study recommends continuous monitoring at tactical and operational levels to enhance overall performance.

The study provides policymakers with strategic insights for the successful implementation of smart grid initiatives, facilitating effective decarbonization of the electricity industry. Additionally, it offers a comprehensive framework for minimizing the environmental impact associated with electricity generation, thereby enhancing overall operational sustainability and efficiency.

The originality of this study lies in its integration of EFA and TISM for robust model assessment and the application of transition management theory to identify and validate CSFs in the integration of ESS. This approach offers a novel perspective on enhancing the sustainability and efficiency of power grids.