Globally the tourism sector is going through digital transformation. The develop a sustainable competitive advantage, the Indian tourism sector will have adopted emerging technologies for better customer experience and overall operational efficiency. Metaverse is emerging as a transformative force in the Indian tourism industry that can develop digital skills among leaders, help them improve employee performance and contribute to building an inclusive work culture. The chapter adopts a pragmatic and futuristic approach and aims to enable the readers to examine the role, strategies and actionable insights for leveraging metaverse as a virtual workspace by developing digital leadership, optimising performance management systems and creating an inclusive work environment in the Indian Tourism sector. The chapter provides a broad framework of actionable recommendations that can be used to leverage the metaverse technology. Ethical issues and risks are examined so that organisations are aware of the risks of adopting these new age technologies.

Artificial intelligence (AI) is rapidly transforming higher education, making it essential to understand how students adopt and benefit from AI-enabled learning tools in ways that advance SDG 4. This study develops and validates a structural model examining how AI literacy, perceived usefulness, perceived ease of use and institutional support influence students’ adoption of AI applications, and how such adoption contributes to future-ready skills and graduate employability. Using a cross-sectional survey of 211 university students and analysing the data through PLS-SEM, the findings reveal that institutional support and perceived ease of use significantly predict AI adoption, whereas AI literacy has a significant negative effect and perceived usefulness has no significant effect on AI adoption. AI adoption strongly enhances future-ready skills, which subsequently exert a positive effect on graduate employability and mediate the relationship between AI adoption and employability outcomes. These results highlight the critical role of supportive institutional environments and intuitive AI design while indicating shifts in the factors shaping technology acceptance among learners. Overall, the study provides theoretical and practical insights for integrating AI responsibly and effectively in higher education to support SDG 4.

This study examines the mediating role of digital transformation (DT) in the relationship between business intelligence (BI) and e-commerce development (ECD). Using survey data from 239 organizations in the Middle East and North Africa (MENA) region and applying partial least squares structural equation modeling (PLS-SEM), the findings confirm strong measurement reliability and significant structural relationships. Results indicate that BI positively influences both DT and ECD, while DT directly enhances e-commerce outcomes. DT partially mediates the BI–ECD relationship, and management support exerts a fully mediated effect through DT. In contrast, BI infrastructure and data collection alone show limited impact unless embedded within comprehensive digital transformation strategies. The study highlights BI as a strategic enabler rather than merely a technical resource, emphasizing the importance of leadership commitment, organizational readiness, and cultural alignment. Theoretically, it integrates the Technology–Organization–Environment (TOE) framework with the dynamic capabilities perspective. Practically, it offers guidance for managers and policymakers seeking to align BI investments with digital transformation initiatives to achieve sustainable e-commerce growth. Overall, the findings underscore the role of BI and DT in fostering resilience.

This study examines how autonomous digital technologies are associated with Sustainable Business Performance in circular-economy settings. Drawing on the Resource-Based View and dynamic capabilities theory, it develops a framework linking AI Agent Autonomy, Digital Twin Capability, Self-Optimization Capability, Circular Process Integration, Algorithmic Trust, and Sustainable Business Performance. Cross-sectional survey data were collected from 319 purposively selected managers and decision-makers in Jordanian organisations between January and April 2026 and analysed using Partial Least Squares Structural Equation Modelling in SmartPLS 4. The findings show that AI Agent Autonomy and Digital Twin Capability are positively associated with Self-Optimization Capability, which is subsequently associated with Circular Process Integration. Circular Process Integration and Algorithmic Trust are positively associated with Sustainable Business Performance. However, AI Agent Autonomy had no significant direct relationship with Sustainable Business Performance, while the direct relationship of Digital Twin Capability was weak. The simple and serial mediation results indicate that Self-Optimization Capability, Circular Process Integration, and Algorithmic Trust represent important mechanisms connecting autonomous technologies with sustainable performance. The study contributes by integrating technological, organisational, operational, and behavioural mechanisms within one circular-economy framework. The findings suggest that the organisational value of AI agents and digital twins depends on adaptive capability, circular-process implementation, and stakeholder trust rather than technology adoption alone.

Purpose: Internet of things (IoT) networks have grown cyber-attack exponentially, which demands efficient intrusion detection systems with high accuracy and interpretability. Existing deep learning (DL) techniques are usually heavily computationally expensive and non-interpretable and therefore are not viable in resource-constrained IoT scenarios. Design/methodology/approach: To bridge this gap, this paper introduces sustainable lightweight attention-based GhostNet-LSTM (LAGL), a novel hybrid model which consists of an efficient lightweight GhostNet structure for extracting features, a long short-term memory network to capture temporal information and an attention mechanism to enhance both detection accuracy and model interpretability. The LAGL model was evaluated on the large-scale CICIoT2023 dataset and showed improved performance over the baseline CNN-LSTM model. Findings: This essential reduction in model complexity directly explains lower memory requirements and faster processing. The model supports sustainability in computing of the IoT systems; thus, it is highly suitable for deployment on resource-constrained IoT gateways where traditional DL models are often infeasible. The attention weights also provide crucial insights into the model’s decision-making process, addressing the critical black-box problem. The first specific quantitative finding showed that the LAGL model achieved a higher F1 score (0.9844) than the baseline CNN-LSTM, demonstrating superior classification performance. This reduced the trainable parameters by 27.3%, a demonstration of its ability to provide the best balance solution for deploying real-world IoT security applications. Originality/value: In addition to computational effectiveness, in this paper, the model is viewed as a design that relies on limited intelligence, energy sensitivity, interpretability and long-term operation sustainability. LAGL model purposefully avoids extensive complexity, hence promoting responsible, goal-oriented AI implementation, but not just trying to maximize its performance. The proposed model can not only support explainable, real-time intrusion detection at the IoT edge but also support human-in-the-loop security processes and reduce energy usage, hardware reliance and lifecycle costs at the same time.

This study aims at exploring the impact of strategic adaptation on digital transformation in the oil and gas industry specifically within the case of Manaseer Oil and Gas Company in Jordan. The study adopts a perspective of strategic adaptation as a multidimensional capability of an organization, in accordance with the dynamic capabilities theory and contingency theory, which considers strategic adaptation as an ability of an organization to embrace strategic change, interact with environment, and maintain strategic coherence. Digital transformation is explored in four aspects: technology adoption; strategy and business models; corporate culture and human resources; business processes. The quantitative research design has been used, and a structured questionnaire was used for data collection, which was distributed to the employees in the administrative units of Manaseer Oil and Gas Company. 211 valid questionnaires were analysed and the analyses were carried out by using the partial least squares structural equation modelling (PLS-SEM) technique by SmartPLS 4 software. The results demonstrate that strategic adaptation is highly positive and statistically significant in overall digital transformation. The results also validate the high-impact positive effects on all four dimensions of digital transformation. The coefficient for business processes was the highest but the differences between the dimensions were very slight. The study enriches the field of strategic management and strategic digital transformation literature by showing the importance of strategic adaptation in the digital transformation of a traditional industrial environment.