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Purpose: Nowadays, many terms like computer vision, deep learning, and machine learning have all been made possible by recent artificial intelligence (AI) advances. As new types of employment have risen significantly, there has been significant growth in adopting AI technology in enterprises. Despite the anticipated benefits of AI adoption, many businesses are still struggling to make progress. This research article focuses on the influence of elements affecting the acceptance procedure of AI in organisations.

Design/Methodology/Approach: To achieve this objective, propose a hierarchical paradigm for the same by developing an Interpretive Structural Modelling (ISM). This paper reveals the barriers obstructing AI adoption in organisations and reflects the contextual association and interaction amongst those barriers by emerging a categorised model using the ISM approach. In the next step, cross-impact matrix multiplication is applied for classification analysis to find dependent, independent and linkages.

Findings: As India is now focusing on the implementation of AI adoption, therefore, it is essential to identify these barriers to AI to conceptualise it systematically. These findings can play a significant role in identifying essential points that affect AI adoption in organisations. Results show that low regulations are the most critical factor and functional as the root cause and further lack of IT infrastructure is the barrier. These two factors require the most attention by the government of India to improve AI adoption.

Implications: This study may be utilised by organisations, academic institutions, Universities, and research scholars to fill the academic gap and faster implementation of AI.

The extant literature provides much-needed support to understand marketing accountability and how marketing actions are related to financial performance (FP). However, we have limited understanding of the relationships between marketing actions and firms' social performance (SP) and environmental performance (EP). Understanding these links is critical to enhancing sustainable FP, SP, and EP. Moreover, the literature provides limited understanding of the measures by which SP and EP may be operationalized, or the data necessary to reach a conclusion. This study bridges these gaps by extensively reviewing the extant literature to offer a set of measures and data sources to operationalize SP and EP, and empirically show their relationships with marketing actions. We find that greenhouse gas (GHG) emission, environmental disclosure score, waste reduction, energy consumption, and recycling are prominent measures of EP, and that social disclosure score, philanthropy or community spending, and diversity of gender and race are prominent measures of SP. The KLD, ASSET4, and Bloomberg are prominent sources of data that can be used to operationalize SP, to which CDP may be added for EP. We also show that marketing actions positively affect EP and SP. This study contributes to the extant literature on SP and EP by identifying measures and data sources and linking marketing actions to both performance types. It contributes to policy development by identifying the importance of EP and SP and how marketing actions can help achieve such performance.

Purpose: This study aims to employ bibliometric analysis to condense multiple studies into a single publication that not only gives insights into the growth and advancement of the research area but also establishes a future research agenda. This study provides a summary of advances in academic research on money laundering. The research includes bibliometric analysis and visualisation of bibliographic data using the Scopus database. The results of the study show that there has been a significant increase in the number of publications in the field of money laundering research, with topics focussed on specific areas. This study will also benchmark existing and preliminary themes, designs, and methodological choices for future money laundering research.

Methodology: With the help of the ‘visualisation of similarities’ (VOS) viewer open-source software, bibliometric analysis was performed using Scopus data. Citation analysis, topic mapping, country collaboration, co-citation analysis, and keyword co-occurrence analysis are some of the approaches used in bibliometric analysis.

Findings: Based on a bibliometric analysis of 1,391 research papers retrieved from the Scopus database over the past three decades (1990–2021), the study identified the most prominent authors, studies, journals, affiliations, and countries in the field of money laundering, as well as the most co-cited authors and journals. The writers also highlight future study issues in the field of money laundering.

Practical implications: The study’s findings might provide academics and practitioners with information on the present state of money laundering research and trend subjects. It can also be used as a guideline for identifying possible research gaps in the existing literature.

Purpose: This chapter aims to evaluate the impact of money laundering and terrorism financing on the Indian economy and to study the effectiveness of prevention of money laundering acts and terrorist financing as per the guidance of the financial protection task force.

Need for the study: Developing countries like India have been more vulnerable to terrorism and financial scams over the last four decades. Despite the establishment of regulating bodies and anti-money laundering acts, this problem continued to be a national threat. Therefore, examining the impact of money laundering and terrorism finance on the Indian economy is necessary.

Methodology: This study is based on secondary data gathered from the web portals of government agencies and international organisations dealing with money laundering and terror funding. Newspapers, journals, and annual reports are reviewed to identify the modus operandi of money laundering operators and their impact on the economy.

Findings: Money laundering and terrorism financing significantly threaten the Indian economy and national security. Despite different anti-money laundering laws and multiple regulating authorities, the system has pitfalls that allow economic fraud and money transactions for terrorist activities. There is a need for cyber security, and integrated enforcement agencies to combat money laundering at national and international levels.

Practical implications: This study would be helpful for academicians and policymakers to understand the nexus of money laundering and terrorism financing and its impacts on the Indian economy.

The purpose of the chapter is to highlight the role of strategic human resources in building resilient organisations. Our research also aims to highlight how resilient organisations are poised better to take on constantly changing business environment and deliver superior products and service experiences to the target market. With the help of an extensive literature review and real-world cases, our research focusses on key elements of strategic human resources and then establishes the linkages between the contribution of individual employees and HR policies towards the achievement of these crucial elements. The findings suggest that the aggregation of individual contributions and HR practices lead to the development of resilience in organisations and helps them to deliver better on all critical indicator of performance. The research has important implications for all important stakeholders including scholars, practitioners, and policymakers, among others. Organisations are also crucial for ensuring the well-being of the masses as their contribution in terms of taxes and duties becomes instrumental in funding various social welfare programs run by governments across the globe. The research is unique in the sense that it draws the linkages between organisations’ resilience and their performance through the creation, development, and management of strategic human resources.

Blockchain technology was once only associated with the financial industry, but it is now being used in a variety of industries, including education. Researchers all over the world take a keen interest in studying the various applications of blockchain technology for the last 4–5 years. The current study is a review of previously published studies on blockchain technology’s applicability in the sector of education. The systematic review was used to conduct the qualitative analysis using the PRISMA Framework (Preferred Reporting Items for Systematic Review and Meta-Analysis). For this comprehensive literature review analysis, 99 publications were chosen in the final stage of selection. Bibliometric analysis is employed to analyse the collected data. Authorship analysis, co-authorship analysis, keyword co-occurrences, and important applications of blockchain in education are the primary parts in which the literature’s findings are organised. Important directions are given for researchers and academicians involved in blockchain-related research who may use the bibliometric analysis of the present study as a reference.

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.