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The growth of the global labor force and business analytics has significantly impacted human resource management (HRM). Human resource (HR) analytics is an emerging field that creates value for employees and organizations. By examining the existing studies on HR analytics, the paper systematically reviews the literature to identify active research areas and establish a roadmap for future studies in HR analytics.

A portfolio of 503 articles collected from the Scopus database was reviewed. The study has adopted a Latent Dirichlet allocation (LDA) topic modeling approach to identify significant themes in the literature.

The HR analytics research domain is classified into four categories: HR functions, statistical techniques, organizational outcomes and employee characteristics. The study has also developed a framework for organizations adopting HR analytics. Linking HR with blockchain technology, explainable artificial intelligence and Metaverse are the areas identified for future researchers.

The framework will assist practitioners in identifying statistical techniques for optimizing various HR functions. The paper discovers that by implementing HR analytics, HR managers and business partners can run reports, make dashboards and visualizations and make evidence-based decision-making.

The previous studies have not applied any machine learning techniques to identify the topics in the extant literature. The paper has applied machine learning tools, making the review more robust and providing an exhaustive understanding of the domain.

Organizations widely adopt knowledge management (KM) to develop and promote technologies and improve business effectiveness. Analytics can aid in KM, further augmenting company performance and decision-making. There has been significant research in the domain of analytics in KM in the past decade. Therefore, this paper aims to examine the current body of literature on the adoption of analytics in KM by offering prominent themes and laying out a research path for future research endeavors in the field of KM analytics.

A comprehensive analysis was conducted on a collection of 123 articles sourced from the Scopus database. The research has used a Latent Dirichlet Allocation methodology for topic modeling and content analysis to discover prominent themes in the literature.

The KM analytics literature is categorized into three clusters of research – KM analytics for optimizing business processes, KM analytics in the industrial context and KM analytics and social media.

Systematizing the literature on KM and analytics has received very minimal attention. The KM analytics view has been examined using complementary topic modeling techniques, including machine-based algorithms, to enable a more reliable, systematic, thorough and objective mapping of this developing field of research.

The purpose of this paper is to integrate academic research on hubristic organizational leaders by proposing a comprehensive conceptual framework and research directions on the hubristic literature.

The paper systematically reviewed 25 years of literature on hubristic organizational leaders based on the PRISMA methodology.

The literature on hubristic leaders is analyzed, and a conceptual framework is presented that highlights the antecedent, consequence, mediators and moderators. Literature has primarily focused on the negative impact of hubris leadership concerning firm performance and destructive behaviors. Few scholars have explored the positive side of hubris leadership, relating it to innovation and product success.

This paper presents the first systematic review of hubristic organizational leaders, to the best of the authors’ knowledge. The review provides an improved grasp of the current status of research, trends and potential future research directions.

In India, 52% of e-commerce consumers actively engage in hyperlocal commerce, representing a substantial increase in online purchasing and hyperlocal services over the past two years. How individuals discover, select, and acquire products and services has been significantly altered due to the COVID-19 pandemic. Digital payments and contactless deliveries have become indispensable in the current dynamic environment, affording consumers enhanced security and convenience. Radio-frequency identification (RFID) technology has enabled the widespread adoption of contactless payments, including Europay, MasterCard, and Visa (EMV) chip cards, which eliminate the need for customers to swipe their cards during physical transactions. Particularly in the fast food, pharmaceutical, and supermarket industries, the symbiotic relationship between e-commerce and contactless payments has experienced significant expansion in India. This chapter investigates the synergies between cashless, contactless, and autonomous purchasing in the digital age. This research examines these technologies’ reciprocal reinforcement and interconnections, focusing on their profound influence on retail transactions and purchasing experiences. The progressive development of payment systems is evidenced by the expanding prevalence of contactless transactions, facilitated by innovations such as the “tap to pay” feature partnership between Paytm and Kotak Mahindra. The year 2023 is anticipated to witness several significant developments, including the expansion of virtual retail, the push for contactless transactions, and the establishment of initiatives such as the open network for digital commerce. The range of applications for frictionless, contactless, and autonomous shopping is extensive, encompassing mobile payment applications such as Apple Pay and Google Pay and proprietary apps developed by retailers to enhance the purchasing experience.