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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.

This study aims to explore the impact of one significant threat to the rigor of theory building within supply chain management, namely, the improper development of different measures for the same construct.

Two survey studies are conducted. Study 1 investigates the impact of three firm orientations on five of the most cited supply chain agility (SCA) scales. Study 2 explores the impact of the same five SCA scales on three firm performance indicators.

The findings reveal that the five SCA scales display adequate discriminant validity and thus measure distinct concepts. Further, the relationships between SCA and its antecedents and consequences vary significantly depending on the SCA scale used. In essence, the scale used determines whether a relationship is supported or not, implying that researchers have been loosely applying the same label (i.e. SCA) to distinct constructs.

In essence, the scale used determines whether a relationship is supported or not, implying that researchers have been loosely applying the same label (i.e. SCA) to distinct constructs. The findings indicate the need for further scrutiny and investigation regarding the rigor and validity of theory building within the area of SCA. Importantly, rigorous scale development should be encouraged. Scholars should develop new scales when necessary while carefully distinguishing their proposed constructs and measures from extant ones.

This chapter investigates pandemic impact in a variety of industries, including food, travel, education and pharmaceuticals, considering elements such as isolation, emotions and social influences, which can lead to panic buying. The goal of this research is to ascertain how COVID-19 influences the buying decisions of customers. Additionally, the study aims to identify consumer consumption trends for a spectrum of products and services, including fast-moving consumer goods (FMCGs), entertainment, pharmaceuticals, travel and tourism. A comprehensive review of different research papers is done to conclude. The papers considered are from 2020 to 2022. Different keywords are used to search the relevant papers such as ‘pandemic’, ‘COVID-19’, ‘behaviour’, ‘impulsive’, etc. TCCM framework has been applied while reviewing the articles. During the isolation, consumer behaviour moved to panic buying and stockpiling, favouring organic basics, and encouraging e-commerce, as well as economic nationalism favouring made-in-India products. This study helps in knowing the reasons for change in consumers' behaviour for different products and services due to unforeseeable situations like COVID-19 and can find possible ways to deal with them. Business owners learn about changing consumer purchasing behaviours and how to modify products. The government can change policies to improve medical tourism and social protection.

Purpose: This chapter discusses the role and use of chatbots adopted by the different categories of banks (private and public sector banks) in India. The chapter presents brief essential services offered by Indian chatbots regarding accuracy, technology providers and virtual assistance, ways to connect, etc. This chapter concluded that most of the questions answered by the Indian chatbots are already available on the banks’ websites, and there is a need for enhancement in the capabilities of Indian chatbots.

Need for the Study: The need for the study is based on the working of banking chatbots, customer query handling, and the efficiency of the chatbots in India. The chapter helps to analyze the services offered by various banks.

Methodology: This chapter is based on secondary data collected from banks’ websites and articles from various journals. The study is based on nine banks (both private and public sectors) those are having working chatbots (SBI, HDFC Bank, ICICI Bank, Yes Bank, IndusInd Bank, Kotak Mahindra Bank, Axis Bank, Andhra Bank, Bank of Baroda). The present study is focused on chatbots, their services, and software applications for various customer-handling capacities.

Findings: The research concluded that Indian banks are investing a small amount in using chatbots, yet Indian chatbots are deficient regarding far too provincial administrations as they are adequate just for standard and basic inquiries. Also, Indian customers are not properly aware of chatbots and virtual assistance.

Practical Implications: This study provides an overview of the working chatbots in India (for both public and private sector banks) and their functions, as well as the capacities of these chatbots. The previous conducted studies are based on the uses, importance, and working of chatbots/artificial intelligence (AI) in banking. In this study, after discussing the different services, it is found that Indian banks need to update their AI/Virtual assistance with more features.

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.

This study focuses on the application of reliability-centered maintenance (RCM) to a textile industry steam boiler. The study aims to demonstrate the development and application of RCM to a steam boiler used in the textile industry.

RCM is a structured process that develops maintenance activities needed on physical resources in their operational environment to realize their inherent reliability by logically incorporating an appropriate mixture of reactive, preventive, condition-based and proactive maintenance methods. A detailed analysis of the RCM approach is presented to develop preventive maintenance (PM) program and improve the reliability and availability of the steam boiler system.

The research reveals that the identification of PM tasks is a good indicator of the PM program's efficiency and can serve as an important maintenance-related downtime source. It is also discovered that the majority of maintenance programs that claim to be proactive are, in fact, reactive. This article also shows how RCM may be successfully implemented to any system, resulting in increased system reliability.

The paper focuses on a pilot study of the development and implementation of the RCM technique to a textile industry steam boiler. It is suggested that the developed RCM model can be applied to the entire plant.

The paper presents a comprehensive RCM model framework as well as an RCM decision framework, providing maintenance managers and engineers with a step-by-step approach to RCM implementation. The proposed framework is significant in that it may be utilized for both qualitative and quantitative analysis at the same time.

The unified payment interface (UPI) is in its early stages of adoption for baby boomers. This study explores the factors affecting the behavioral intention of baby boomers to adopt UPI. UTAUT was adopted as theoretical lens of the study and extended with ubiquity, privacy risk and perceived security. The impact of an external factor – effect of COVID-19 was also examined in this study.

A consumer intercept survey was used to collect data from baby boomers via a self-administered structured questionnaire. Structural equation modeling was used to establish the relationships among latent variables. Further, using bootstrap re-sampling technique, the role of perceived security as a mediator between risk, ubiquity and behavioral intention was examined.

The study confirmed that COVID-19 was the most influential external factor for baby boomers to adopt UPI, followed by performance expectancy, social influence, ubiquity, effort expectancy and perceived security. Apropos of UPI adoption by baby boomers, privacy risk negatively influenced perceived security, whereas perceived security fully mediated the relationship between risk, ubiquity and behavioral intention.

The study focused only on baby boomers and their intention to adopt UPI. Hence the results cannot be generalized to all age groups and are specific to the cohort.

The present study aims to establish research findings on predicting antecedents of adopting a newly introduced payment mechanism and an exemplary Indian digital innovation, UPI, by baby boomers. This study is first to empirically explore intention of baby boomers toward adoption of UPI.

Introduction: All countries are interested in attracting foreign direct investment (FDI) as it provides for productivity gains and modernisation for attaining sustainable development goals. Multinational corporations (MNCs) collect a vast volume of structured and unstructured big data when seeking international expansion by the FDI route in the insurance sector, but concluding these data may not be practically feasible. So nowadays, for finalising their FDI ventures, MNCs depend on machine-based algorithms for quick analysis of big data sets.

Purpose: This chapter explores how emerging big data analytics and predictive modelling fields can scale and speed up FDI decisions in the insurance sector.

Methodology: The author used a descriptive study based on secondary data from sources like World Bank, The Organisation for Economic Co-operation and Development (OECD), World Trade Organisation (WTO), and International Finance Corporation (IFC) data repositories to identify variables such as risks, costs, trade agreements, regulatory policies, and gross domestic product (GDP) that affect FDI movements. This chapter highlights the process flow that can be beneficial to convert big data sets using statistical tools and computer software such as Statistical Analytics Software (SAS), IBM SPSS Statistics.

Findings: The application of artificial intelligence-based statistical tools on FDI variables can help derive time-series graphs and forecast revenues. The authors found that foreign investors can narrow their prospect search for industry or product to manageable from varied investment opportunities in host countries. Advancements in big data analysis offer cost-effective methods to improve decision-making and resource management for enterprises.

Introduction: AI technologies are transforming the industrial sectors, and the impact of AI technologies does not leave behind human resource management (HRM). From recruitment to development and payroll, AI has its own impact. In recruitment, selection, training and development, compensation, and remuneration AI play an important role.

Purpose: The main purpose of this chapter is to analyse the challenges in adopting AI in HRM. This chapter investigates the various strategies to overcome challenges encountered by companies while adopting AI in HR practices. Moreover, this chapter also examines the role of AI in HR practices.

Methodology: To achieve the purpose of this chapter, various case studies were analysed and literature studies with emphasis on what types of challenges are face in adopting AI in HR practices. The authors of various newspaper articles, books, published journals, and websites on AI analyse the various newspaper articles, books, published journals, and websites on AI in human resources (HR). Electronic databases are used as the most effective technique to begin a literature search for the current overview, specifically Science Direct, Google Scholar, and Emerald. In addition to this, the application of AI in HRM practices used by leading organisations globally is also reviewed. The analysis used four keywords: HRM, AI, challenges and adopting. This research has been conducted by searching scholarly papers and relevant studies using similar keywords.

Findings: The adoption of AI technologies is continuously increasing in HR practices. AI performs various HR functions such as recruitment, selection, training and development practices, scanning resume, etc. Organisations can be benefitted in many ways by adopting AI in HRM practices, such as better employees engagement and relations, accelerating competitive advantages and effectively utilising HR budgeting. The findings show that many companies like IBM, Deloitte, Amazon, etc., adopt AI in their HR practices.