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The widespread uptake of digital technology tools for online teaching and learning reached its peak during the nationwide lockdown triggered by the COVID-19 pandemic. It transformed the higher education institutions (HEIs) marketplace both in developed and developing countries. However, in this process of digital transformation, several HEIs, specifically from developing countries, faced major challenges. That threatened to affect their sustainability and performance. In this vein, this study conducts a bibliometric review to map the challenges during the COVID-19 pandemic and suggest strategies for HEIs to cope with post-pandemic situations in the future.

This comprehensive review encompasses 343 papers published between 2020 and 2023, employing a systematic approach that combines bibliometrics and content analysis to thoroughly evaluate the articles.

The investigation revealed a lack of published work addressing the specific challenges faced by the faculty members affecting their well-being. The study underscores the importance of e-learning technology adoption for higher education sustainability by compelling both students and teachers to rely heavily on social media platforms to maintain social presence and facilitate remote learning. The reduced interpersonal interaction during the pandemic has had negative consequences for academic engagement and professional advancement for both educators and students.

This has implications for policymakers and the management of HEIs, as it may prove useful in reenvisioning and redesigning future curricula. The paper concludes by developing a sustainable learning framework using a blended approach. Additionally, we also provide directions for future research to scholars.

This study has implications for policymakers and HEI management to rethink the delivery of future courses with a focus on education and institute sustainability. Finally, the research also proposes a hybrid learning framework for sustainability and forms a robust foundation for scholars in future research.

The present research work reviews and maps the thematic evolution of the interface between human resource (HR) practices and service quality (SQ) over the last 33 years.

The authors employed systematic literature review (SLR), bibliometric analysis and visualization to comprehensively map 215 papers extracted from the Web of Science and Scopus databases. The present study also helps to document the research themes that evolved through co-occurrence networks and thematic maps.

The study identifies that HR practices are the central drivers for maintaining SQ in an organization and found that teamwork, empowerment, recruitment, selection, training and reward are key for improving the SQ. It concludes the impact of HR practices on SQ, develops the knowledge structure of human resource management (HRM) operations and SQ literature and organizes it under various dimensions as antecedents and outcomes. As its foremost input, the current study proposes human resource practices and service quality (HRPSQ) framework for comprehensive HR practices and SQ in an organization.

The study is unique as it map the journey of HR practices and SQ and proposes a framework for improved performance.

The purpose of this paper is to supplement and update previously published articles about artificial intelligence (AI) instruments and operations in banking sectors with the following objectives in mind: to understand the role of AI in banking sectors; to explore the themes and context in this area based on keywords, co-citations and co-words; and to identify future research direction by evaluating the trend and direction of previous research.

This study adopts a semi-inductive approach with the convolution of bibliometrics and literature review. This study used bibliometrics for the identification of literature across multiple databases and systematic literature review on identified articles to explore heterogeneous sectors within AI in banking and finance.

This study contributes a literature-based model that accounts for both the broadly in AI application in banking and finance: predictive modeling in risk assessment and detection; financial decision-making; client service delivery; and emerging FinTech applications of AI and machine learning.

This study is among the few to address the literature of tools and application of AI in banking through mixed-methods approach and produce a synthesized model for the same.

This paper intends to contribute to the evolving understanding of Indian adolescents as consumers by examining their unique relationships with food brands, focusing specifically on brand love. It further investigates the key drivers that cultivate brand love among adolescents and explores the resulting outcomes of brand love.

About 37 in-depth interviews, including three exercises, were conducted with adolescents aged 11–16. The first and second exercises used projective techniques to explore respondents’ culture-bound love relationships with their favorite brands. Using the laddering technique, the third exercise investigated the critical drivers of respondents’ brand love.

The study reveals that adolescents derive value through attribute-benefit-value linkages from the consumption experience, leading to brand love. The customer value–brand love dynamics result in adolescents’ customer engagement behavior. Additionally, Indian adolescent customers seek brand consumption as a medium to instate their social identity and achieve hedonic pleasure from the experience. The study highlights the role of socialization and attitudinal autonomy in shaping adolescent–brand interactions.

The study could be relevant for both academicians and practitioners as they unveil the consumer psychology of contemporary adolescents in emerging countries like India and how similar or different they are from adult consumers. Also, there are very few adolescent–brand relationship studies in the past that have been deliberated in the context of food brands. Brand managers may design their product development and communication appeals around higher levels of abstraction in the attribute-benefit-value linkages discovered by this study.