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The purpose of this study is to critically analyze the key factors contributing to gender disparities in the science, technology, engineering and mathematics (STEM) fields and propose creative solutions to mitigate these differences. Despite the significance of this issue, it has not received sufficient attention owing to the absence of clarity regarding the factors that exacerbate the gender gap.

This study used a qualitative methodology that combined the viewpoints of social psychology and educational research to pinpoint and evaluate essential elements. Using a grounded theory approach, semistructured interviews were analyzed, and the obtained data were coded and categorized using ATLAS.ti software.

This qualitative research identified three key areas: internal and external factors influencing the gender gap, as well as strategic actions within higher education to address these disparities. The innovative contribution of this study lies in its development of a comprehensive theoretical framework that enables the diagnosis, quantification and understanding of these factors and proposes practical measures to mitigate these gender disparities. By promoting greater gender diversity, the proposed model can contribute to more inclusive and sustainable development, which is consistent with the 2030 agenda.

This study highlights the need for a multidimensional approach to address the gender gap in higher education, fills a crucial knowledge gap and provides a theoretical model to guide effective university policies.

In recent years, there has been significant interest in adopting XR (extended reality) technologies such as VR (virtual reality) and AR (augmented reality), particularly in retail. However, extending activities through reality-mediation is still mostly believed to offer an inferior experience due to their shortcomings in usability, wearability, graphical fidelity, etc. This study aims to address the research gap by experimentally examining the acceptance of metaverse shopping.

This study conducts a 2 (VR: with vs. without) × 2 (AR: with vs. without) between-subjects laboratory experiment involving 157 participants in simulated daily shopping environments. This study builds a physical brick-and-mortar store at the campus and stocked it with approximately 600 products with accompanying product information and pricing. The XR devices and a 3D laser scanner were used in constructing the three XR shopping conditions.

Results indicate that XR can offer an experience comparable to, or even surpassing, traditional shopping in terms of its instrumental and hedonic aspects, regardless of a slightly reduced perception of usability. AR negatively affected perceived ease of use, while VR significantly increased perceived enjoyment. It is surprising that the lower perceived ease of use appeared to be disconnected from the attitude toward metaverse shopping.

This study provides important experimental evidence on the acceptance of XR shopping, and the finding that low perceived ease of use may not always be detrimental adds to the theory of technology adoption as a whole. Additionally, it provides an important reference point for future randomized controlled studies exploring the effects of technology on adoption.

This chapter explores the role of artificial intelligence (AI), particularly its subfield of machine learning (ML) methods, as a core technology of the fintech revolution in the financial services industry. It simplifies some of the complex concepts related to AI by introducing the main ML paradigms and related techno-methodic aspects. This chapter uses real-world examples to illustrate how next-generation AI powered by ML is transforming the financial services industry. Next, in illustrating the risks associated with AI adoption, this chapter discusses the need for regulation to address the essential facets of AI governance, including transparency, accountability, ethics, and responsible use. Lastly, it looks at emerging regulatory approaches across leading global jurisdictions. The primary goal is to give readers an initial understanding of AI's profound impact on the financial sector.

The paper proposes a framework for the successful deployment of Industry 4.0 (I4.0) principles in the aerospace industry, based on identified success factors. The paper challenges the perception of I4.0 being aligned with de-skilling and personnel reduction and instead promotes a route to successful deployment centred on upskilling and retaining personnel for future role requirements.

The research methodology involved a literature review and industrial data collection via questionnaires to develop and validate the framework. The questionnaire was sent to a purposive sample of 50 respondents working in operations, and a response rate of 90% was achieved. Content analysis was used to identify patterns, themes, or biases, and the data were tabulated based on specific common attributes. The proposed framework consists of a series of gates and criteria that must be met before progressing to the next gate.

The proposed framework provides a feedback mechanism to review minimum standards for successful deployment, aligned with new developments in capability and technology, and ensures quality assessment at each gate. The paper highlights the potential benefits of I4.0 implementation in the aerospace industry, including reducing operational costs and improving competitiveness by eliminating variation in manufacturing processes. The identified success factors were used to define the framework, and the identified failure points were used to form mitigation actions or controls for inclusion in the framework.

The paper provides a framework for the successful deployment of I4.0 principles in the aerospace industry, based on identified success factors. The framework challenges the perception of I4.0 as being aligned with de-skilling and personnel reduction and instead promotes a route to successful deployment centred on upskilling and retaining personnel for future role requirements. The framework can be used as a guideline for organizations to deploy I4.0 principles successfully and improve competitiveness.

This is an elementary and yet important chapter. In this chapter, the most important hindrances to rural development have been identified and how they usually hinder development has been explained. Various forms of bias that adversely affect a rural development process, namely, spatial bias, person bias, elite bias, male bias, user and adopter bias, and active present and living biases have also been briefly discussed.

The purpose of our research on blockchain technology is to unveil its immense potential, understand its applications and implications and identify opportunities to revolutionize existing systems and processes. This research aims to inspire the creation of new innovative solutions for industries. By harnessing blockchain technology, organizations can pinpoint key areas that could significantly benefit from its use, such as streamlining operations, providing secure and transparent digital solutions and fortifying data security.

This study presents a robust multi-criteria decision-making framework for assessing blockchain drivers in selected Indian industries. We initiated with an extensive literature review to identify potential drivers. We then sought the opinions of experts in the field to validate and refine our list. This meticulous process led us to identify 26 drivers, which we categorized into five main categories. Finally, we employed the Best-Worst Method to determine the relative importance of each criterion, ensuring a comprehensive and reliable assessment.

The authors have ranked the blockchain drivers based on their degree of importance using the Best-Worst Method. This study reveals the priority of BC implementation, with the retail industry identified as the most in need, followed by the Banking and Healthcare industries. Various critical factors are identified where blockchain technology could help reduce costs, increase efficiency and enable new innovative business models.

While this study acknowledges potential bias in driver assessment relying on literature and expert opinions, its findings carry significant practical implications. We have identified key areas where blockchain technology could be transformative by focusing on select industries. Future research should encompass other industries and real-world case studies for practical insights that could delve into the adoption challenges and benefits of blockchain technology in many other industries, thereby amplifying the relevance of our findings.

Blockchain is a groundbreaking, innovative technology with immense potential to revolutionize industries. Past research has explored the benefits and challenges of blockchain implementation in specific industries or sectors. This creates a gap in research regarding systematically classifying and ranking the importance of blockchain across different Indian industries. Our research seeks to address this gap by using advanced multi-criteria decision-making techniques. We aim to provide a comprehensive understanding of the significance of blockchain technology in critical Indian industries, offering valuable insights that can inform strategic decision-making and drive innovation in the country’s business landscape.

Managers must make numerous strategic decisions in order to initiate and implement a business model innovation (BMI). This paper examines how managers perceive the management team interacts when making BMI decisions. The paper also investigates how group biases and board members’ risk willingness affect this process.

Empirical data were collected through 26 in-depth interviews with German managing directors from 13 companies in four industries (mobility, manufacturing, healthcare and energy) to explore three research questions: (1) What group effects are prevalent in BMI group decision-making? (2) What are the key characteristics of BMI group decisions? And (3) what are the potential relationships between BMI group decision-making and managers' risk willingness? A thematic analysis based on Gioia's guidelines was conducted to identify themes in the comprehensive dataset.

First, the results show four typical group biases in BMI group decisions: Groupthink, social influence, hidden profile and group polarization. Findings show that the hidden profile paradigm and groupthink theory are essential in the context of BMI decisions. Second, we developed a BMI decision matrix, including the following key characteristics of BMI group decision-making managerial cohesion, conflict readiness and information- and emotion-based decision behavior. Third, in contrast to previous literature, we found that individual risk aversion can improve the quality of BMI decisions.

This paper provides managers with an opportunity to become aware of group biases that may impede their strategic BMI decisions. Specifically, it points out that managers should consider the key cognitive constraints due to their interactions when making BMI decisions. This work also highlights the importance of risk-averse decision-makers on boards.

This qualitative study contributes to the literature on decision-making by revealing key cognitive group biases in strategic decision-making. This study also enriches the behavioral science research stream of the BMI literature by attributing a critical influence on the quality of BMI decisions to managers' group interactions. In addition, this article provides new perspectives on managers' risk aversion in strategic decision-making.

This study explored the themes and sentiments of online learners regarding the use of Generative Artificial Intelligence (AI) or “generative AI” technology in higher education.

English-language tweets were subjected to topic modelling and sentiment analysis. Three prevalent themes were identified and discussed: curriculum development opportunities, lifelong learning prospects and challenges associated with generative AI use.

The results also indicated a range of topics and emotions towards generative AI in education, which were predominantly positive but also varied across male and female users.

The findings provide insights for educators, policymakers and researchers on the opportunities and challenges associated with the integration of generative AI in educational settings. This includes the importance of identifying AI-supported learning and teaching practices that align with gender-specific preferences to offer a more inclusive and tailored approach to learning.

This chapter examines the world of risk management within fintech. It initiates by emphasizing the crucial role of technology and risk assessment in shaping the fintech landscape. It discusses various risk categories prevalent in fintech operations, elucidating the nuances of technology, operational, compliance, strategic, and reputational risks. A comparative analysis across different fintech sub-sectors unveils their distinct risk profiles. The narrative extends to proactive risk management frameworks, contrasting prominent models like the COSO ERM, FAIR Risk Quantification, and NIST Cybersecurity Frameworks. Integral defense measures are scrutinized, encompassing data encryption, access controls, vulnerability assessments, and incident response plans. This chapter underscores the significance of building operational resilience through robust technology infrastructure, regular system updates, disaster recovery planning, and business continuity measures. Ultimately, this chapter culminates in a comprehensive summary, offering pragmatic recommendations to fortify technology risk management in fintech.