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Industries throughout the world have gone through four phases of the industrial revolution. Today, the authors are encountering the Fourth Industrial Revolution (Industry 4.0 or 4IR), which is characterized by the application of information and communication technologies in the industry. This study aims to evaluate the growth of literature in the context of libraries and the Fourth Industrial Revolution and then identify the active research actors, countries, affiliations and sources and the status of collaboration across various geographical locations.

A systematic literature review was conducted. The data were extracted from the Scopus database incorporating scientometric and altmetric indicators. The search terms were TITLE-ABS-KEY “Industry 4.0” OR “4IR” OR “Fourth Industrial Revolution” AND “Library 4.0” OR “librarian 4.0” OR “libraries” OR “library” OR “library service” without any limitation. The results identified 218 documents out of which 67 core documents are related to libraries. Analysis was performed using MS Excel and VOSviewer.

South Africa, Nigeria and South Korea were the three most productive countries in the field. Library Hi Tech News is the most productive journal. Collaboration among African countries like South Africa, Nigeria and Zimbabwe seems to be good and they constituted the largest share of documents. The research on the Fourth Industrial Revolution in the context of libraries still lacks social media attention.

This study is the first systematic review in this area using a combination of scientometrics and altmetrics. The results will serve as a valuable reference for future researchers and policymakers.

The present chapter discusses evolution, definitions, dimensions, capabilities, and the present state of the art of digital supply chain management (DSCM). The objective of the chapter is to offer a detailed understanding of DSCM, by shedding light on its historical development, exploring its multipronged definitions, and highlighting its core dimensions and capabilities in the contemporary business landscape. The evolution of DSCM appears as a central theme, rooted in the background of industrial revolutions. It starts by relooking at the First Industrial Revolution (IR) with its mechanization and steam power, progresses through the Second IR with electrification and mass production, and arrives at the Third IR, characterized by the rise of computers and the internet. The pivoting transition into the Fourth IR, also called Industry 4.0, marks the start of DSCM with its fusion of digital technologies (DTs) in the supply chain (SC) processes. Analysis of key definitions of DSCMs unveils their role as an enabler of SC collaboration, customer-centric nature, having overarching reliance on DTs. Moreover, the chapter explores the core dimensions of DSCM, exposing its ability to improve SC resilience, sustainability, visibility, efficiency, and agility. These capabilities stem from seamlessly woven DT developments into SC: artificial intelligence (AI), machine learning, the Internet of Things (IoT), and advanced analytics. The chapter concludes by highlighting the present state of the art in DSCM, reflecting its indispensable role in the contemporary turbulent business dynamics. In short, this chapter offers a synthesized view of DSCM's definitions, dimensions, evolution, capabilities, and present status within the larger context of supply chain management (SCM) literature.

The aim of this mixed methods study sought to investigate the preferences and challenges towards fourth industrial revolution (FIR) technologies adoption at hotels in Malaysia.

The study adopted partially-mixed concurrent quantitative-dominant mixed methods. The quantitative part of the study examined the preferred FIR technologies for hotels; whiles the qualitative part explored the challenges towards their adoption.

The results revealed that Internet of things, big data, cloud computing, artificial intelligence and cyber physical systems were the preferred FIR technologies for hotels. The findings revealed significant relationships between respondents' socio-demographic characteristics (age, gender, working experience, etc.) and preferred FIR technologies. Penultimately, challenges that affect the adoption of FIR technologies including investment cost, migration cost, trust, etc., were discussed.

In effect, findings from this study provide valuable information for policy makers (i.e. hotel owners, management, technology providers and future researchers). Most especially, the study unearths specific FIR technologies preferred by the hospitality and tourism industry. Also, the significant interplay of these preferences vis a vis respondents' socio-demographic characteristics, as well as challenges influencing their adoption also have major implications for policy makers. Another valuable contribution of this study lies in the research method utilised, since most studies on technology adoption either use quantitative or qualitative. These contributions for theory and practice have been thoroughly discussed.

The usage of AI-powered chatbots and virtual assistants facilitates seamless communication, offering instant responses to inquiries and enhancing customer satisfaction. In Japan, the hospitality industry is at the forefront of this AI-driven transformation and through collaborations with technology companies, hotels are deploying AI-powered concierge services, smart room automation, and language translation systems to cater to diverse guest needs. The integration of AI in Japan's tourism sector not only enhances operational efficiency but also showcases the country's commitment to innovation and delivering exceptional customer experiences. As Japan embraces AI in its hospitality industry, it navigates the delicate balance between leveraging technology and preserving human interaction and by combining the efficiency and accuracy of AI with the warmth and personal touch of human hospitality, Japan aims to redefine the future of tourism. Moreover, AI streamlines operations by automating repetitive tasks, optimising resource allocation, and improving efficiency in areas such as reservation management, inventory control, and demand forecasting. However, along with these benefits, there are significant challenges to consider. Privacy concerns arise as AI systems collect and process personal data, necessitating robust security measures to protect sensitive information. Ethical considerations must also be addressed, as the use of AI raises questions about transparency, bias, and accountability. Furthermore, while AI enhances efficiency, there is a concern about losing the human touch that has long been a hallmark of the hospitality industry. Balancing the benefits of AI with maintaining personalised and authentic guest experiences becomes a crucial challenge.

This study aims to identify the effects of artificial intelligence (AI) on university libraries and to reveal challenges associated with the adoption of AI-powered applications in libraries.

A systematic literature review (SLR) was applied to address the study’s objectives. The 25 most relevant seminal studies published in Scopus- and Web of Science-indexed journals were selected to conduct the study.

Findings revealed that AI has strong positive effects on university libraries. These effects included efficiency and promotion of library products, innovative library services, alignment of library services with the fourth industrial revolution (4IR), collection management and user services and transformation of library systems. Results also manifested that skills and knowledge barriers, financial and resource constraints and resistance to change created challenges to adopt AI-based services in university libraries.

This study has added valuable literature to the existing body of knowledge by conducting SLR on the basis of 25 most relevant research articles published in cite score and impact factor journals. It has provided practical implications by offering recommendations to adopt AI in university libraries. The study is a benchmark for policymakers, AI applications developers, higher education bodies, government representatives, university administration and library leadership to devise effective strategies and methods for the efficient adoption of AI in libraries. The study has also provided a framework to adopt AI applications in library settings.

The domains of Industry 4.0 and Smart Farming encompass the application of digitization, automation, and data-driven decision-making principles to revolutionize conventional sectors. The intersection of these two fields has numerous opportunities for industry, society, science, technology and research. Relatively, this intersection is new, and still, many grey areas need to be identified. This research is a step toward identifying research areas and current trends.

The present study examines prevailing research patterns and prospective research prospects within Industry 4.0 and Smart Farming. This is accomplished by utilizing the Latent Dirichlet Allocation (LDA) methodology applied to the data procured from the Scopus database.

By examining the available literature extensively, the researchers have successfully discovered and developed three separate research questions. The questions mentioned above were afterward examined with great attention to detail after using LDA on the dataset. The paper highlights a notable finding on the lack of existing scholarly research in the examined combined field. The existing database consists of a restricted collection of 51 scholarly papers. Nevertheless, the forthcoming terrain harbors immense possibilities for exploration and offers a plethora of prospects for additional investigation and cerebral evaluation.

This study examines the Industrial Revolution's and Smart Farming's practical effects, focusing on Industry 4.0 research. The proposed method could help agricultural practitioners implement Industry 4.0 technology. It could additionally counsel technology developers on innovation and ease technology transfer. Research on regulatory frameworks, incentive programs and resource conservation may help policymakers and government agencies.

The paper proposes that the incorporation of Industry 4.0 technology into agricultural operations can enhance efficiency, production and sustainability. Furthermore, it highlights the significance of creating user-friendly solutions specifically tailored for farmers and companies. The study indicates that the implementation of supportive legislative frameworks, incentive programmes and resource conservation methods might encourage the adoption of smart agricultural technologies, resulting in the adoption of more sustainable practices.

This study examines the Industrial Revolution's and Smart Farming's practical effects, focusing on Industry 4.0 research. The proposed method could help agricultural practitioners implement Industry 4.0 technology. It could additionally counsel technology developers on innovation and ease technology transfer. Research on regulatory frameworks, incentive programs and resource conservation may help policymakers and government agencies.

Based on a thorough examination of existing literature, it has been established that there is a lack of research specifically focusing on the convergence of Industry 4.0 and Smart Farming. However, notable progress has been achieved in the field of seclusion. To date, the provided dataset has not been subjected to analysis using the LDA technique by any researcher.

Artificial intelligence and augmented reality are two key tools gaining importance in the digital era due to their wide range of applications in different fields and sectors. Industry 4.0 lays emphasis principally on the technology used to help the business remain competitive and sustainable. Sustainable development goals are another important objective of the UN which has laid responsibility for every business to support addressing the global challenges. Purpose: This chapter essentially aims to present the standpoint of artificial intelligence and augmented reality in meeting the sustainability perspective of organizations. Information about the study is gathered through secondary approaches, critically reviewing published literature, scientific reports, and statistical data accessible through business reports, and corporate websites. Further analyzed to present the perspectives of the authors in the study. Globally artificial intelligence market size is predicted to reach $190 billion by 2025, while the funding for startups doubled during the period 2011–2020 globally. The investment in artificial intelligence is going to reach $500 by 2024 resulting in substantial revenue returns. The augmented reality market size could reach $97 billion by 2028. Artificial intelligence today is increasingly used in many fields and is attracting multiple applications in many sectors such as manufacturing, retail, education, IT, and health care and has also contributed to sustainable development the same time by providing energy conservation options, optimization, and reduction of resources, minimizing wastage, offering timely assistance on maintenance schedules, practices which are enabling organizations to reach closer to sustainability and transformation.

The study aims to develop a model that supports the application of data science techniques for real estate professionals in the fourth industrial revolution (4IR) era. The present 4IR era gave birth to big data sets and is beyond real estate professionals' analysis techniques. This has led to a situation where most real estate professionals rely on their intuition while neglecting a rigorous analysis for real estate investment appraisals. The heavy reliance on their intuition has been responsible for the under-performance of real estate investment, especially in Africa.

This study utilised a survey questionnaire to randomly source data from real estate professionals. The questionnaire was analysed using a combination of Statistical package for social science (SPSS) V24 and Analysis of a Moment Structures (AMOS) graphics V27 software. Exploratory factor analysis was employed to break down the variables (drivers) into meaningful dimensions helpful in developing the conceptual framework. The framework was validated using covariance-based structural equation modelling. The model was validated using fit indices like discriminant validity, standardised root mean square (SRMR), comparative fit index (CFI), Normed Fit Index (NFI), etc.

The model revealed that an inclusive educational system, decentralised real estate market and data management system are the major drivers for applying data science techniques to real estate professionals. Also, real estate professionals' application of the drivers will guarantee an effective data analysis of real estate investments.

Numerous studies have clamoured for adopting data science techniques for real estate professionals. There is a lack of studies on the drivers that will guarantee the successful adoption of data science techniques. A modern form of data analysis for real estate professionals was also proposed in the study.

In the context of the journey toward digital transformation and the realization of a fully connected factory, concepts such as data science, artificial intelligence (AI), machine learning (ML) and even predictive models emerge as indispensable pillars. Given the relevance of these topics, the present study focused on the analysis of customer complaint data, employing ML techniques to anticipate complaint accountability. The primary objective was to enhance data accessibility, harnessing the potential of ML models to optimize the complaint handling process and thereby positively contribute to data-driven decision-making. This approach aimed not only to reduce the number of units to be analyzed and customer response time but also to underscore the pressing need for a paradigm shift in quality management. The application of AI techniques sought to enhance not only the efficiency of the complaint handling process and data accessibility but also to demonstrate how the integration of these innovative approaches could profoundly transform the way quality is conceived and managed within organizations.

To conduct this study, real customer complaint data from an automotive company was utilized. Our main objective was to highlight the importance of artificial intelligence (AI) techniques in the context of quality. To achieve this, we adopted a methodology consisting of 10 distinct phases: business analysis and understanding; project plan definition; sample definition; data exploration; data processing and pre-processing; feature selection; acquisition of predictive models; evaluation of the models; presentation of the results; and implementation. This methodology was adapted from data mining methodologies referenced in the literature, taking into account the specific reality of the company under study. This ensured that the obtained results were applicable and replicable across different fields, thereby strengthening the relevance and generalizability of our research findings.

The achieved results not only demonstrated the ability of ML models to predict complaint accountability with an accuracy of 64%, but also underscored the significance of the adopted approach within the context of Quality 4.0 (Q4.0). This study served as a proof of concept in complaint analysis, enabling process automation and the development of a guide applicable across various areas of the company. The successful integration of AI techniques and Q4.0 principles highlighted the pressing need to apply concepts of digitization and artificial intelligence in quality management. Furthermore, it emphasized the critical importance of data, its organization, analysis and availability in driving digital transformation and enhancing operational efficiency across all company domains. In summary, this work not only showcased the advancements achieved through ML application but also emphasized the pivotal role of data and digitization in the ongoing evolution of Quality 4.0.

This study presents a significant contribution by exploring complaint data within the organization, an area lacking investigation in real-world contexts, particularly focusing on practical applications. The development of standardized processes for data handling and the application of predictions for classification models not only demonstrated the viability of this approach but also provided a valuable proof of concept for the company. Most importantly, this work was designed to be replicable in other areas of the factory, serving as a fundamental basis for the company’s data scientists. Until then, limited data access and lack of automation in its treatment and analysis represented significant challenges. In the context of Quality 4.0, this study highlights not only the immediate advantages for decision-making and predicting complaint outcomes but also the long-term benefits, including clearer and standardized processes, data-driven decision-making and improved analysis time. Thus, this study not only underscores the importance of data and the application of AI techniques in the era of quality but also fills a knowledge gap by providing an innovative and replicable approach to complaint analysis within the organization. In terms of originality, this article stands out for addressing an underexplored area and providing a tangible and applicable solution for the company, highlighting the intrinsic value of aligning quality with AI and digitization.

In the fourth industrial revolution, where business accounting integrates with automation through artificial intelligence (AI) and information communication technology (ICT), auditors must be able to access and analyze vast data and information to identify potential risks and issues. Using data analytics and AI to study significant amounts of data linked to audits, this study aims to investigate auditing practices by leveraging ICT and AI to enhance the audit process.

Bibliometric and quantitative research techniques have been used in the study’s mixed-method process. The theoretical underpinnings of AI have been investigated using the bibliometric research method, and the challenge of implementing ICT-enabled auditing practices among auditing professionals has been studied using the quantitative research method. Surveys, interviews and bibliometric analysis have all been used as data-gathering techniques.

Research in AI and auditing has a broad worldwide scope, involving developed and developing nations. ICT perceived benefits have no direct effect on auditing practices. However, ICT training has a mediating effect on the relationship between ICT perceived benefits and auditing practices. ICT adoption has no moderating effect on the relationship between ICT training and auditing practices.

Findings have significance for lead auditors, policymakers and the Institute of Chartered Accountants of India (ICAI), who are keenly interested in upgrading the auditing practice of accounting professionals in India by incorporating AI and ICT determinants.

This research makes a significant contribution by offering a thorough framework for improving the knowledge management of practising auditors regarding ICT adoption, training and perceived benefits, a crucial component of auditing practices in the digital age. In addition, it provides insightful information about how AI affects accounting practices, which may point the way for further study in this area.

This research has significant implications for auditing firms in India. It can inform ICAI, policymakers and regulators in their attempts to foster the incorporation of AI and ICTs in auditing practice.