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Purpose: As a result of the transition from the paradigm of ‘knowledge and skills’ learning to the university of uncertainty, the concept of VUCA has grown for the revision of various adaptive models of educational practices.

Need for Study: The primary goal is to explore the research field of the educational system and learning environments; the investigation of scientific knowledge is enabled by bibliometric analysis, revealing through it the fluctuations of the literature.

Methodology: To better view the historical evolution of publications in the educational system field, two data samples were integrated into this study, with the focus of the chapter being on the authors, keywords, articles, journals, subject analysis, word cloud analysis, and cluster analysis. The first includes 1,620 Web of Science-recorded documents published between 1991 and 2022, and the second sample comprises 159 Scopus-recorded papers published between 1978 and 2022.

Findings: The first empirical results show that interest in this subject escalated around 2008. The main concerns around this research field are the labour market, teaching-learning, technology, economic development, the medical field, and sustainability. After 2020, a new subject took amplitude, seemingly connected to the educational system and learning environment, that subject being ’COVID-19.

Practical Implications: The relationship between authors, keywords, and sources is illustrated through Sankey diagrams, from which valuable information can be extracted: nine of the Scopus authors have published articles in the ‘Journal of Higher Education Policy and Management’ documents that present the following list of keywords: ‘higher education’, ‘education’, ‘management’, ‘leadership’, and ‘tertiary education’.

Bailey et al. (2001) queried accounting researchers concerning admitted fraudulent research practices, their beliefs about the prevalence of such practices among their peers, and their perceptions of the causal factors. They used a randomized response technique that assures anonymity, and it remains the only published study to ask these questions explicitly. Over the past two decades, publication pressures have increased, and accounting academia has experienced a shocking instance of fraud. The current study replicates Bailey et al. (2001) and extends the study by asking new questions about the adequacy of participants’ graduate training, the perceived attitudes and practices of mentors and coauthors, and whether their awareness and concern have evolved. Participants’ comments provide insights about the accounting research environment. Importantly, they indicate a lack of consensus about the legitimacy of research practices.

India's manufacturing exports are heavily tilted toward primary and resource-based products, while its Asian peers have a significant proportion of high and mid-tech products in their export baskets. An attempt is thus made to understand the hurdles faced by technology-intensive exports by India, using gravity panel estimates on its high, mid, and low-tech exports, using data across 130 countries from 2001 to 2019. In line with the Knowledge Theories of trade, which postulate that technical and scientific knowledge and innovation provide trade advantages, this chapter also tries to understand how India fares on this front. We use Principal Component Analysis to construct an index which provides a relative understanding of India's technical and scientific knowledge base.

We conclude that nontariff measures (NTMs) are a stringent hurdle faced by Indian exports, especially in the European Union. Tariffs have the most debilitating effect on its mid-tech exports and the least on high-tech exports. Regional Trade Agreements (RTAs) are most effective in creating trade for mid-tech exports and least in case of low-tech exports. The index for ascertaining India's relative knowledge base shows that while India ranked 6th in 2017, much higher than its Asian peers, its high and mid-tech exports lagged behind these countries. This puzzle is explained by the fact that scientific research in India has very little industry collaboration and thus is out of sync with market needs. Hence, the prevalent scientific and technical knowledge in India does not have the expected favorable impact on its technology-intensive exports.

Purpose: Innovation is a multi-dimensional phenomenon influenced at the organisational level by internal and external factors that can determine how innovative an organisation can be, determining a firm’s business performance. This chapter measures and predicts how innovative a company can be, considering key internal factors using modern data analytics/science.

Need for Study: The increasing challenge of modern business operations is affected by how quickly, sustainably, effectively, and efficiently companies can innovate to mitigate the dynamic challenges of current business environments and evolving customer needs. The ability to predict, measure, and manage innovation becomes necessary to ensure that businesses are fit for purpose.

Methodology: A model was designed following the study hypotheses and statistically tested. A historical data sample from the OECD global industry dataset for eight years was used for the analysis. The ordinary least square method was used to test for model fit. Also, in machine learning engineering, predictive analysis using the multivariate linear regression analysis method was carried out.

Findings: The results support the hypotheses that an organisation’s capacity to be innovative can be measured and predicted, and it is influenced by a good number of internal factors or independent variables at various degrees.

Practical Implications: Managers must understand how to measure and predict innovation metrics to manage innovation better, ultimately leading to better business outcomes and performance. Also proposed are new measurement matrices for innovation management: innovation capacity (I_C), business innovation value (BIV), innovation creation factor (ICF), and a practical data-driven innovation management and prediction system.

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.

Introduction: As Internet usage increases, so does widespread concern about surveillance and privacy. While most of the research primarily focuses on a particular digital setting, these problems cut beyond national boundaries and impact economies everywhere.

Purpose: This study critically analyses the Data Protection Bill 2019’s effectiveness within the context of surveillance and privacy in India’s digital economy. Investigating critical provisions of the bill, comparing it to international privacy laws and standards, and identifying potential gaps and weaknesses, this study provides insights into the bill’s ability to protect personal data and limit surveillance practices.

Methodology: The chapter is based on secondary sources of data, including academic articles, government reports, and news articles on the topics of surveillance, privacy, and the Data Protection Bill 2019 in India, involving content and critical discourse analyses.

Findings: The Data Protection Bill 2019 evaluation reveals a set of provisions with the overarching intent to safeguard citizens’ privacy worldwide and curtail undue surveillance practices exercised by both governmental bodies and private enterprises. Intricately delineates the entitlements of individuals concerning their data, encompassing vital aspects such as the right to access, rectify, and erase their data, the bill mandates stringent adherence to the principle of explicit consent when collecting and processing personal data.

Nevertheless, a comprehensive analysis also reveals several gaps and constraints inherent in the bill’s framework. One such area is the inclusion of exemptions for governmental entities, an aspect that raises international concerns regarding potential disparities in data protection practices.

In academia, assessment is often narrow in its focus on research productivity, its application of a limited number of standardised metrics and its summative approach aimed at selection. This approach, corresponding to an exclusive, subject-oriented concept of talent management, can be thought of as at odds with a broader view of the role of academic institutions as accelerating and improving science and scholarship and its societal impact. In recent years, open science practices as well as research integrity issues have increased awareness of the need for a more inclusive approach to assessment and talent management in academia, broadening assessment to reward the full spectrum of academic activities and, within that spectrum, deepening assessment by critically reflecting on the processes and indicators involved (both qualitative and quantitative). In terms of talent management, this would mean a move from research-focused assessment to assessment including all academic activities (including education, professional performance and leadership), a shift from focus on the individual to a focus on collaboration in teams (recognising contributions of both academic and support staff), increased attention for formative assessment and greater agency for those being evaluated, as well as around the data, tools and platforms used in assessment. Together, this represents a more inclusive, subject-oriented approach to talent management. Implementation of such changes requires involvement from university management, human resource management and academic and support staff at all career levels, and universities would benefit from participation in mutual learning initiatives currently taking shape in various regions of the world.

Purpose: Since its inception in 1987, the literature on the VUCA that represents the volatile, uncertain, complex, and ambiguous nature of the business environment has progressively increased. This study systematically evaluates the vast literature on the VUCA world. Since review-based studies have been criticised as biased, this study uniquely amalgamates bibliometric analysis with content analysis, thereby taking a research triangulation discourse.

Need of the Study: This study was conducted to consolidate the literature about the VUCA environment and uncover the foundational and emerging themes for future research agendas.

Design/Methodology/Approach: This study conducts a performance analysis and science mapping of 193 carefully selected articles (using PRISMA) published in various sources during 2012–2023. Articles for the study were procured from the SCOPUS database. While performance analysis focused on analysing publication and citation evolution, thematic evolution, leading publications, country publication analysis, and most relevant authors and sources. On the other hand, science mapping revealed conceptual structures (keyword plus co-occurrence analysis and thematic maps) and intellectual structures (co-authorship analysis). VOSviewer and Biblioshiny (R-tool for comprehensive science mapping) were used for this study’s choice of application.

Findings: This chapter concludes with future research agendas using content analysis of the ‘scope for the future research’ section of selected publications and bibliographic coupling (to unearth emerging themes).

Practical Implications: The work presented in this chapter will help the researchers gain a structured conceptual, intellectual, and social understanding of the vast literature on the VUCA environment.

This chapter is focussed on the macro context of higher education and describes the historical developments in higher education and how these developments affect academic jobs and academic work. When we sketch the development of higher education with a few broad strokes of the pen, we see (1) a development from a small-scale elite institution to broad training (and research) institutes; (2) a struggle over control of higher education; and (3) a movement in which higher education is professionalized and increasingly assigned a societal task, with a series of consequences for education, research and impact. These developments contribute to a field of tension in which old traditions of academic behaviour must be reconciled with demands that are placed on higher education by society. This makes talent management, both on an individual and collective level, no easy task.