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The study aims to analyze the relationship between teachers’ information and communication technology (ICT) efficacy and the ICT environment in teaching-learning. For this, the current research attempts to explore and understand the role of ICT factors in higher education and develop a framework for future researchers to gain a substantial understanding of the teachers' ICT efficacy and ICT environment.

Teachers’ ICT efficacy has been analyzed in three domains, i.e. technological, content and pedagogical. The ICT environment is measured on training aspects, ICT tools and administrative support. The researcher adopted purposive sampling as a part of the non-probability sampling technique. The questionnaire was circulated among the experts through e-mail to collect the required response sheets. The experts are working in different academic institutes in India, primarily from premier institutes of the country. It covers all the regions of the country, and 22 experts are taken in the research study for collecting the data. The study uses the decision-making trial and evaluation laboratory method to explore the link among identified factors and criteria through the expert’s opinion method to achieve the set objectives within the Indian context.

The results indicate that the content efficacy of a teacher is pivotal to providing sound teaching-learning using digital tools and techniques. The developed model, measuring the cause-effect relationship based on the role of ICT efficacy of teachers in delivering teaching, will enable academic organizations to frame policies and strategies that will focus on enhancing teachers' competencies towards self and organizational growth.

The present research is one of the pioneering works that investigates the factors of ICT in higher education.

Artificial intelligence (AI) is increasingly applied by businesses to optimize their processes and decision-making, develop effective and efficient strategies, and positively influence customer behaviors. Businesses use AI to generate behaviors such as customer retention (CR). The existing literature on “AI and CR” is vastly scattered. The paper aims to review the present research on AI in CR systematically and suggest future research directions to further develop the field.

The Scopus database is used to collect the data for systematic review and bibliometric analysis using the VOSviewer tool. The paper performs the following analysis: (1) year-wise publications and citations, (2) co-authorship analysis of authors, countries, and affiliations, (3) citation analysis of articles and journals, (4) co-occurrence visualization of binding terms, and (5) bibliographic coupling of articles.

Five research themes are identified, namely, (1) AI and customer churn prediction in CR, (2) AI and customer service experience in CR, (3) AI and customer sentiment analysis in CR, (4) AI and customer (big data) analytics in CR, and (5) AI privacy and ethical concerns in CR. Based on the research themes, fifteen future research objectives and a future research framework are suggested.

The paper has important implications for researchers and managers as it reveals vital insights into the latest trends and paths in AI-CR research and practices. It focuses on privacy and ethical issues of AI; hence, it will help the government develop policies for sustainable AI adoption for CR.

To the author's best knowledge, this paper is the first attempt to comprehensively review the existing research on “AI and CR” using bibliometric analysis.

Amid rapid global urbanization, cities confront many complex challenges, including sustainability, waste management, energy consumption and resource allocation. Two emerging paradigms – smart cities and circular economies (CEs) – have shown promise in addressing these issues. Smart cities utilize cutting-edge technologies like internet of things (IoT), artificial intelligence (AI) and big data analytics to create interconnected, efficient urban ecosystems. Meanwhile, the CE model aims for a regenerative system focused on minimizing waste and maximizing the utilization of resources. However, these paradigms have rarely been studied in conjunction, resulting in a gap in the existing literature. This bibliometric analysis aims to bridge this gap by mapping the interdisciplinary research landscape that integrates smart cities and CEs. Specifically, the study identifies key thematic intersections, influential authors, leading academic journals and potential directions for future research. Through this analysis, we provide a comprehensive overview of the existing body of work and lay the groundwork for the evolution of this interdisciplinary domain.

Process mining helps organizations improve their business processes in today’s data-rich environment. However, these processes can change over time due to factors like policy shifts or process trends, impacting model performance. This study examines process behavior in event logs and uses machine learning to detect concept drift.

The trace clustering and change mining techniques have been implemented on two processes, namely loan payment and temporary identity creation, to detect drifts. We use the bag-of-activities and edit distance methods, along with K-Mode and agglomerative hierarchical clustering techniques.

This study makes two important findings: trace clustering is a popular choice for detecting drifts, and the bag-of-activities method using K-Mode clustering and hamming distance proved highly effective at spotting drifts in various event logs. It also identifies different types of drifts occurring simultaneously in the processes.

The drifts discovered in different processes provide a real-world example of concept drift in the domains of loans and university administrations. This contributes to improving operational efficiency and overall organizational performance based on these detected drifts and assists in enhancing the process design.

This study is the first to employ a hybrid method of trace clustering and change mining to detect process changes. It is also the first to simultaneously detect sudden and recurring drift in the field of trace clustering in process mining. Furthermore, it stands out for investigating and comparing the performance of multiple clustering methods, in contrast to prior research that used a single technique. Additionally, it is pioneering in applying machine learning methods to detect drift in the domain of loan processes.

This study explores the role of human resource management (HRM) practices in advancing sustainable development goals (SDGs) within emerging markets. Specifically, we examine how HRM practices in financial institutions in the emerging markets of India and China promote SDGs 8 (decent work and economic growth), 10 (reduced inequalities) and 13 (climate action). We also propose a framework integrating these key SDG goals with core HRM functions.

Secondary data analysis was employed using data from sustainability reports of the top five Indian and Chinese banks listed in Forbes – the Global (2000) ranking for 2022–2023. These sustainability reports were analysed based on their reporting of indicators from the Global Reporting Initiative GRI 400 series, aligned with the SDGs 8, 10 and 13.

The result of the comparative analysis indicates that both Indian and Chinese banks use HRM practices of recruitment and selection, rewards and payments, workplace health and safety, and training and development to meet SDGs 8 (decent work and economic growth), 10 (reduced inequalities) and 13 (climate action). Regarding the reporting and disclosure of HRM practices in diversity, equity and inclusion, Indian banks outperform Chinese banks, and these practices contribute significantly to SDGs 8, 10 and 13. The dominance of state-owned initiatives in China dictates the alignment of HRM strategies with economic priorities at the national level, highlighting the challenge of balancing global sustainability initiatives with a centralised management system.

The study provides a comprehensive examination of sustainability reports with a specific focus on HRM practices and their role in advancing SDGs. It applies institutional theory to understand the differences in the reporting and implementation of HRM practices that contribute to the achievement of SDGs.

This study aims to understand the current production scenario emphasizing the significance of green manufacturing in achieving economic and environmental sustainability goals to fulfil future needs; to determine the viability of particular strategies and actions performed to increase the process efficiency of electrical discharge machining; and to uphold the values of sustainability in the nonconventional manufacturing sector and to identify future works in this regard.

A thorough analysis of numerous experimental studies and findings is conducted. This prominent nontraditional machining process’s potential machinability and sustainability challenges are discussed, along with the current research to alleviate them. The focus is placed on modifications to the dielectric fluid, choosing affordable substitutes and treating consumable tool electrodes.

Trans-esterified vegetable oils, which are biodegradable and can be used as a substitute for conventional dielectric fluids, provide pollution-free machining with enhanced surface finish and material removal rates. Modifying the dielectric fluid with specific nanomaterials could increase the machining rate and demonstrate a decrease in machining flaws such as micropores, globules and microcracks. Tool electrodes subjected to cryogenic treatment have shown reduced tool metal consumption and downtime for the setup.

The findings suggested eco-friendly machining techniques and optimized control settings that reduce energy consumption, lowering operating expenses and carbon footprints. Using eco-friendly dielectrics, including vegetable oils or biodegradable dielectric fluids, might lessen the adverse effects of the electrical discharge machine operations on the environment. Adopting sustainable practices might enhance a business’s reputation with the public, shareholders and clients because sustainability is becoming increasingly significant across various industries.

A detailed general review of green nontraditional electrical discharge machining process is provided, from high-quality indexed journals. The findings and results contemplated in this review paper can lead the research community to collectively apply it in sustainable techniques to enhance machinability and reduce environmental effects.