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Research excellency has long been the dominant paradigm in assessing academic quality and hence a prime determinant of academic careers. Lately, this approach to academic performance has come under higher scrutiny for its narrow focus on the individual, promoted an exclusive, performance-oriented talent management and inhibiting collaboration, transparency and societal involvement.

As a response to the limitations of the excellency policy, this chapter examines the emergence of open science as a transformative force in the academic world. Open science represents a paradigm shift, emphasizing the importance of transparency, and increased societal engagement in the academic process. It opens up the possibility to include the context dimension, multiple stakeholders and a more diverse set of development and performance indicators.

This chapter stresses the urgent need to realign our system of recognition and rewards with the premise of open science and with talent management. By highlighting the disconnect between current recognition mechanisms and the values of universities, this chapter emphasizes the necessity of transformative changes at institutional and systemic levels.

To provide concrete insights into the implementation of these changes, this chapter explores a case study of Utrecht University. This specific example showcases how strategic decisions at an institute level allow navigation of the complexities of recognizing and rewarding open science practices. The Utrecht University case study serves as an inspiration for other institutions seeking to embrace open science and adapt their policies and practices accordingly.

Due to its high leverage nature, a bank suffers vitally from the credit risk it inherently bears. As a result, managing credit is the ultimate responsibility of a bank. In this chapter, we examine how efficiently banks manage their credit risk via a powerful tool used widely in the decision/management science area called data envelopment analysis (DEA). Among various existing versions, our DEA is a two-stage, dynamic model that captures how each bank performs relative to its peer banks in terms of value creation and credit risk control. Using data from the largest 22 banks in the United States over the period of 1996 till 2013, we have identified leading banks such as First Bank systems and Bank of New York Mellon before and after mergers and acquisitions, respectively. With the goal of preventing financial crises such as the one that occurred in 2008, a conceptual model of credit risk reduction and management (CRR&M) is proposed in the final section of this study. Discussions on strategy formulations at both the individual bank level and the national level are provided. With the help of our two-stage DEA-based decision support systems and CRR&M-driven strategies, policy/decision-makers in a banking sector can identify improvement opportunities regarding value creation and risk mitigation. The effective tool and procedures presented in this work will help banks worldwide manage the unknown and become more resilient to potential credit crises in the 21st century.

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’.

Today the research area on technology acceptance is mainly dependent on the theory of technology acceptance model (TAM). The TAM was used in this study primarily for the purpose of providing a basis for determining the impact of various external variables on the adoption of edtech platforms. The TAM is a theory of information systems that suggests steps for learners to take as they adopt and use new technologies. The primary TAM variables for adoption of edtech platforms are evaluated in this study: perceived usefulness (PU) and perceived ease of use (PEOU) by using the factors – perceived enjoyment (PE), information quality, electronic-word of mouth (e-WOM), perceived compatibility, computer self-efficacy and objective usability. By analyzing and defining the relationship between the external variables with respect to the adoption of edtech platform among students, we hope to contextualize the TAM model. The end result provides a clearer understanding of TAM and its growth as a useful model for technology adoption studies and for clarifying the relationship between the uptake of edtech platforms and technological acceptability. The study employed a qualitative methodology and selected publications and research papers about the adoption of technology. These were then carefully assessed, analyzed and scrutinized for the terms of how students adopted edtech platforms. It was proposed that the adoption of an edtech platform may result from proper training in technology usage and its application to real-world scenarios.

In the mid-2000s, the operator of New York City’s mass transit network committed more than a half-billion dollars to military contractor Lockheed Martin for a security technology capable, in part, of inferring threats based on analysis of data streams, of developing response strategies, and taking automated action toward alerts and calamities in light of evolving circumstances. The project was a failure. This chapter explores the conceptualization and development of this technology – rooted in cybernetics – and compares its conceptual underpinnings with some situated problems of awareness, communication, coordination, and action in emergencies as they unfold in one of the busiest transport systems in the world, the New York subway. The author shows how the technology, with all the theatrical trappings of a “legitimate” security solution, was apparently conceived without a grounded understanding of actual use-cases, and the degree to which the complex interactions which give rise to subway emergency can be anticipated in – and therefore managed through – a technological system. As a case-study, the chapter illustrates the pitfalls of deploying technology against problems which are not well-defined in the first place, to the neglect of investments against much more fundamental problems – such as inadequate communication systems, and unstable relationships with emergency response agencies – which might offer guaranteed benefits, and indeed lay a firm groundwork for future deployment of more ambitious technology.

Organizational change is a key mechanism to ensure the sustainability of healthcare systems. However, healthcare organizations are persistently difficult to change, and literature is riddled with examples of failed change endeavors. In this chapter, we attempt to unravel the underlying causes for failed organizational change. We distinguish three types of change with different levels of depth that require different change approaches. Transformations are the deepest forms of change where beliefs and principles need to be modified to successfully influence routines. Renewals are deep forms of change where principles need to be modified to successfully influence routines. Improvements are shallow forms of change where only modifications at the level of routines are needed. Using deoxyribonucleic acid (DNA) as our metaphor, we propose a theory of “organizational DNA” to understand organizations and these three types of organizational changes. We posit that organizations are made up of a double helix consisting of a so-called “social string,” which contains the “soft” interaction or communication among the organization's members, and a so-called “technical string,” which contains “hard” organizational aspects such as structure and technology. Ladders of organizational nucleotides (i.e., Routines, Principles, and Beliefs) connect this double helix in various combinations. Together, the double helix and accompanying nucleotides make up the DNA of an organization. Without knowledge of the architecture of organizational DNA and whether a change addresses beliefs, principles, and/or routines, we believe that organizational change is constrained and based on luck rather than change management expertise. Following this metaphor, we show that organizational change fails when it attempts to change one part of the DNA (e.g., routines) in a way that renders it incompatible with the connecting components (e.g., principles and beliefs). We discuss how the theory can be applied in practice using an exemplar case.

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.

Escalation in the number of online food ordering platforms, along with extensive junk food marketing, lucrative offers and discounts, innovation in food flavors, and doorstep delivery of food, have triggered the consumption of high-calorie and unhealthy food products which pose serious threats to the health and future well-being of individuals by making them more obese. To date, several public policy frameworks have been developed to confront obesity; however, their efficacy seems debatable. Directionally, the objective of this study is to highlight the potential influence of “digital nudging” which aims at steering individuals in desired directions, at the same time delimiting their freedom of choice. The study also establishes the effectiveness of digital nudges promoting a healthy lifestyle by steering individuals toward healthier food choices. The author strongly believes that this conceptual perusal will offer immense inputs to healthy food marketers and researchers alike in addressing the matters of obesity. Addressing the menace of obesity calls for joint efforts of the government, the public, researchers, and more specifically food product manufacturers/marketers who should incorporate healthier food options into their portfolios. E-tailers are also urged to adopt such practices in virtual markets and promote healthier food options to effectively tackle obesity.

Disruptive technologies are accelerating global growth. Artificial intelligence (AI) has the potential to transform the idea of delivering value to end users. On the other hand, the growth of Industry 5.0 has given rise to the concept of humanizing technology, and AI is a promising technology with the potential to contribute to business success. Nevertheless, the idea of value creation in the field of AI is novel, so it is necessary to define the meaning of value by understanding the context of AI applicability in different environments and industries. In this chapter, the author uses the Scientific Procedures and Rationales for Systematic Literature Reviews (SPAR-4-SLR) procedure to conduct an SLR that provides interesting insights into the focus, industries, and methodologies and approaches used in existing research. Following the initial literature review on the state of the art of AI and value creation, the author also offers a reflection on the strategic implications of AI in the field of marketing, postulating a macrovalue creation framework that addresses the existence of implications on three different levels: emerging markets, Sustainable Development Goals, and adoption issues. Therefore, this chapter examines the value creation perspectives of AI to understand the current research focus and future directions.