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Purpose: Progression is an unpreventable reality of presence, and banking is no exclusion. Cash transformers and moneylenders from times gone past are great agents today. Cash held in trust became store taking, and money advancing became credit making; over an extended time, banks transformed into a need, and the occupation of banks, transformed into a critical piece of monetary reality. Banks’ turn of events and headway has been mind-boggling, with the latest frenzy being intuitive media banking. The chapter additionally framed the amazing open doors and dangers for banks because of the presentation of innovations and how banks are making the most of the open doors and endeavouring to cure the risks. The financial area in India is a lifesaver for the country. Indian banks could become the fifth most prominent on earth by 2020 and the third most prominent by 2025.

Methodology: This study has given auxiliary information. Furthermore, it’s gathered from the holding bank of India concerning utilisation by various banks. Which utilised graphic review including mean mode middle.

Finding: After the review, we find that sight, sound, and green banking have become fantastic assets for the baking area. During COVID-19, the utilisation of mixed media expanded in contrast to a year ago.

Significance: The review featured sight and sound and green banking sealed spine in COVID -19 and is utilised expanded after this pandemic.

The purpose of this paper is to examine whether experiential offerings from two types of retailers play a significant role in consumer responses toward Indian malls. Specifically, this study examined the relationships between consumer perceptions of experience economy 4E constructs (i.e. educational, entertainment, escapist, and esthetic experiences) and experiential value associated with merchandise retailers and service retailers in Indian shopping malls, and between perceived experiential value and mall patronage intention.

A mall intercept survey conducted in two shopping malls in India resulted in 552 useable responses. Structural equation modeling was used to test the hypothesized relationships.

Experience economy constructs (i.e. entertainment, escapist, and esthetic experiences) contributed to the experiential value associated with merchandise retailers and/or service retailers in the mall. Experiential value associated with both merchandise retailers and service retailers in the mall positively influenced mall patronage intention.

The results have practical implications for mall retailers, mall managers, and mall developers; particular experiential strategies for both merchandise retailers and service retailers may improve patronage intentions toward the mall, which includes a measure of purchase intentions.

Although academic articles support the idea that retailers can obtain benefits by offering experiences to consumers, this is the first study to empirically validate the role of specific consumer experiences, the 4Es, resulting from both merchandise retailers and service retailers, in a non-Western mall context on value creation for shoppers and the consequent influence on patronage intentions.

Introduction: The rapid growth of high technology has urged many organisations to dynamically look for innovative ways, ideas, testing, and ingenious solution in improving their current product, process, system, and technology. For contemporary business, artificial intelligence (AI)-based people analytics is an instrument currently employed to develop a better prosperous future.

Purpose: The study aims to investigate the usage of AI in human resource management (HRM) practices. It also examines the benefits and challenges of using AI in implementing people analytics in organisations.

Methodology: This chapter contains a systemic review of articles and papers on analytics. The presented qualitative study did a literature review based on the articles published in the last five years and extracted from the Scopus database.

Findings: This chapter indicated that AI-based people analytics is on the verge of changing various aspects of HRM practices better to furnish it for a vibrant, ever-changing workplace. It concludes different usage of AI in people analytics for better managing human resources (HR) at the workplace. Also explored the benefits and challenges of implementing AI in the people analytics domain.

Implications: This chapter will help understand ongoing practices of AI-enabled process benefits and challenges. This insight will help develop a better AI-enabled function for a better decision-making system. The future scope of the study is how to overcome the challenges.

The faculties are crucial to education. They should have enough training facilities and be encouraged to actively contribute to high-quality education and successful learning. Faculty engagement and development activities should be explored and included in learning organizations and employee engagement in India. This paper aims to describe higher education as a learning organization. The research will also assess how faculty development programs affect faculty engagement behaviors in these institutions and if professional development mediates this effect, which has not been previously examined.

This study was conducted with quantitative data collected from 267 faculties through reliable and validated adapted questionnaires. Semistructured interviews were conducted with heads and professor-level faculties to gain insights into faculty development and engagement. Partial least squares structural equation modeling technique (PLS-SEM 3.3.6 version) was used to test the conceptually drafted model.

Faculty professional development programs shown to improve higher education faculty engagement and professional progress. The studies also showed that higher education institutions must prioritize faculty development to become learning organizations. Professional development reduced the direct effects of faculty development program (FDP) on faculty engagement. This suggests that professional growth mediates the research.

This research emphasizes and professional development to boost teacher involvement in B-Schools. Management must design faculty development programs to construct professional development and learning organizations, according to the results. Developing and writing rules that encourage faculty engagement in such internal and external programs would also enhance their academic and administrative abilities and assist higher education institutions become learning organizations.

The study is one of the few to examine the impact of faculty development programs and professional development on faculty engagement in higher education institutions, particularly B-Schools, and its competitive mediating role.

Introduction: Artificial intelligence (AI) is being extensively used to solve complex problems in the industry. AI provides several benefits such as providing visibility in the processes, reducing time, improving accuracy, saving time, helping in the decision-making process, etc. Due to the range of benefits of AI technologies, organisations readily adopt this technology. However, there are several challenges that the organisation faces during the implementation of AI. These challenges are in context to human resource (HR) development for successful implementation of AI across different functions and are discussed in this chapter.

Purpose: Although we know that AI technology is widely accepted in human resource management (HRM) due to its various benefits. But the organisations face many challenges during the implementation of AI. The focus of the study is to explore the literature on AI in HRM, identify the challenges of implementing AI and provide potential future research direction based on a systematic literature review.

Methodology: To explore the literature on AI in HRM, the study undertakes a systematic literature review. The study identifies, analyse and classifies the literature to provide a holistic view of HR challenges in implementing AI. The study is built on a review of 47 documents, including the articles, book chapters and conference papers using the Scopus database for the past 10 years (2012–27 January 2022).

Findings: The study provides an overview of the documents published in Scopus in this area through a systematic literature review. The study reveals that a significant amount of growth in the publication has been shown in the past 10 years. The maximum and continuous growth is shown after 2017. The maximum number of papers are published in India, the USA and China. The study identifies major eight challenges of AI implementation in HRM. The study also provides a secondary case to deep dive in this area based on a systematic literature review.

Research Limitation/Implication: The challenges identified in the study are not empirically tested. Each of the identified challenges should be empirically examined. This study has expanded the body of knowledge of AI in HRM. This study will help the academicians and practitioners work on the identified challenges and help the organisations ease in adopting AI.

Originality/Value: This study represents the first work that integrates AI implementation challenges in HRM.

Introduction: Human resource management (HRM) is going through a transformation phase due to the pandemic. The COVID-19 crisis compelled the employees to work virtually. To mitigate the effects of COVID-19, several organisations heavily invested in artificial intelligence (AI) in the realm of HRM.

Purpose: With limited studies on the paradigm shift in HRM post-pandemic and the role of AI, the study investigates and proposes a conceptual framework for the paradigm shift in HRM practices post-COVID-19 pandemic and the significance of AI. Furthermore, the study investigates the outcomes of the use of AI in HRM for organisations and employees.

Methodology: A comprehensive review of the literature based on the guidelines of Tranfield, Denyer, and Smart (2003) and Crossan and Apaydin (2010) has been followed. A systematic literature review assisted in critically analysing, synthesising, and mapping the extant literature by identifying the broad themes involved.

Findings: COVID-19-related economic disruption has led to a paradigm shift in HRM practices. AI-enabled HRM practices are now centred around remote and contingent workforce management, mindfulness, social capital, increasing employee engagement, reskilling and upskilling towards new competencies, etc. AI is making remote work seamless through smooth recruitment and selection process, onboarding, career and development, tracking and managing the performance, facilitating learning, and talent management. Post-pandemic, AI-powered tools based on data mining (DM), predictive analytics, big data analytics, natural language processing (NLP), intelligent robots, machine learning (ML), virtual (VR)/augmented reality (AR), etc., have paved the way for managing the HRM practices effectively, thereby leading to enhanced organisational performance, employee well-being, automation, and reduced cost.

This paper aims to examine how interfirm transactional and relational assets drive firm performance (FP) in digitally integrated supply chains.

The authors combine the Transaction Cost Economics (TCE) and Relational Exchange Theory (RET) frameworks to hypothesize that FP will be a function of Asset Specificity (AS), Digital Technology Usage (DTU) and Collaborative Information Sharing (CIS). In addition, the authors hypothesize that Supply Chain Integration (SCI) will partially mediate the effect of DTU and fully mediate the impact of AS and CIS on FP. A cross-sectional survey of supply chain managers is used to test the hypotheses.

Findings indicate that specific investments in digitally integrated supply chains would increase FP. In addition, SCI fully mediates the relationships between AS and FP and CIS and FP, while SCI partially mediates the influence of DTU on FP.

Managers could strategically engage in the technologies that effectively fit within the firm’s supply chain strategies and seek to develop a pragmatic expertise that enables the effective use of technology in a comprehensive setting.

The study enriches the extant literature by incorporating TCE and RET as contradictory viewpoints on AS and investigating how transactional and relational assets affect FP in digitally integrated supply chains.

The importance of nutraceuticals and functional foods has been a topic of interest in nutrition research for many years. This review aims to summarize the findings on the nutritive value and health benefits of chia, as well as its use as a food fortificant.

Published literature on the nutritive value and therapeutic properties of chia has been reviewed.

Chia, an ancient grain, belongs to the mint family (Lamiaceae) and was cultivated in Mexico and Guatemala by the Mayas and Aztecs of a pre-Columbian era. In addition to being gluten-free, chia seeds are concentrated source of omega-3 fatty acids (mainly α-linolenic acid), fiber (insoluble) and polyphenolic compounds (myricetin, quercetin, kaempferol, chlorogenic and caffeic acids), which were found to be comparatively higher than many other grains, cereals and oily seeds. Chia supplementation has potential to lower incidence of cardiovascular disease, obesity, hypertension, cancer, diabetes, pruritus and celiac disease. Because of its nutraceutical and physiochemical properties, chia has been widely used as a whole seed, flour, seed mucilage, gel and oil for developing various enriched food products, such as bread, pasta, cakes, cookies, chips, cheese, yoghurt, meat, fish and poultry.

With advancement in nutrition research, chia would have a great future perspective as feed, food and medicine. However, further research is needed to validate the potential therapeutic effect of chia supplementation on human health.

Artificial Intelligence (AI) is one of the newest technology that is quickly advancing and can be utilised to improve human resource competence in the age of rapid digital transformation. The present competitive scenario demands accurate data that need to be collected and analysed for organisational growth.

Purpose: The research examines the applications and usage of AI in performance management and further analyses the future of PM from the perspectives of AI.

Methodology: The study is conceptual and relies on secondary data from research papers, publications, HR blogs, survey reports and other sources. Employee performance and attitudes were monitored using digital technologies, big data analytics and AI. The quality of employee performance continues to increase with the integration of AI, enabling predictive analytics to increase employee performance.

Research Implication: In employee performance appraisal, a digital performance management system leads to openness and honesty with time, effort and sincerity. It is based on the performance management system’s practical usefulness.

Theoretical Implication: The study’s findings provide HR managers, academics, IT professionals and practitioners with an understanding of how AI may be used for performance management and its consequences on their operations. In addition, the connection between the HR devolution theory on performance management and AI is discussed.

Need of the Study: Digitalisation, machine learning, and artificial intelligence (AI) is changing at a swift pace, significantly uplifting the role of information technology. The present human resource (HR) aspect transpires AI-based resolution, are gradually more effective with HR process, time-consumption and a complex tasks surrounded by the HRM functionalities.

Purpose: This study attempts to investigate the adoption and diffusion of human resource management (HRM) with the phenomenon of AI-based applications. Hence, this study has emphasised the predictors of AI adoption like competitive pressure, performance expectancy, top management support, strategic partner, employee champion, etc. Moreover, how the AI predictors are connected with HR practices. The research sample focused on 207 HR managers and senior managers from various industries.

Methodology: This study is based on a quantitative research technique encompassing mean, standard deviation, exploratory factor analysis (EFA), Confirmatory Factor Analysis (CFA), Average Variance Extracted (AVE), and Dependent Variable (DV).

Findings: The study’s empirical findings show that higher performance expectations and higher management support are both major predictors of AI adoption. In contrast, competitive pressure did not show a significant relationship with such an intention, and the ‘employee champion’ role has a negative impact on AI adoption.

Implications: AI diffusion and implementation show a significant research gap. In previous studies, adoption in HRM was overlooked. The study’s results provide a comprehensive picture of the situation. The framework and a major contribution to the study of the phenomenon in relation to its possible role in AI’s effectiveness and quality in HRM. The research inspires a debate among service providers, policy-makers, and stakeholders, and builds an efficient workplace.