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Organisational Leadership and Team Conflict: Hypothesizing Conflict Management Strategies under an AI Automated Leadership Setting
No full textThis study examines the influence of artificial intelligence (AI) on leadership in organisations and spotlights the emerging notion of automated leadership. Conventionally, leadership has been noted as human-focused, and thrives on human abilities like empathy, emotional intelligence, decision-making, and interpersonal skills. However, with an unprecedented rise in computer processing power and automation, AI is revolutionising leadership roles, with artificially intelligent agents now performing strategic-level functions hitherto conducted by human-leaders. However, despite the promised potentials and benefits, concerns have surfaced regarding the impacts of AI-automated leadership on team outcomes, particularly conflict management. Therefore, using phenomenology and case study design and focusing on UK organisations in the logistics and warehouse sector-where adoption of AI-powered automated leadership systems has surged-we examine the influence of AI-automated leadership agents on conflicts within working teams. After theoretically examining traditional leadership and team conflict models vis-à-vis AI-automated leadership, the study proceeded to hypothesise and operationalise efficient conflict management approaches for this emerging paradigm
Leadership and Leadership Development Critical Perspectives and Contemporary Approaches
No full textThis book provides a critical examination of leadership and leadership development, offering new insights and contemporary approaches that reflect the changing needs of organisations and societies
Self-Adaptive Optimization and Blockchain for Privacy Preservation in Cloud-Based Healthcare Systems
No full textHealth care is a critical point for cloud computing, where it offers affordable and optimal data management. Nevertheless, some challenges continue to arise regarding medical information security especially in the cloud. In the light of this, we put forward a Revolutionary Cloud Secure Healthcare Framework. Data anonymization, hybrid encryption, optimal key selection and blockchain integration technologies are easily integrated. This full framework is grounded with a sound foundation for secure medical data interchange, and it protects patient’s privacy. Thus, in our method, data aggregation and encryption preparation under the differential privacy-based anonymization chosen for Electronic Health Record (EHR) data are performed in a careful manner. Asikey encryption algorithm and key-agreement are being used; The Advanced Secure Elliptic Encryption (ASEE) model combines AES-256 with ECIES for powerful encryption, while the Self-Adaptive Wildebeest Herd Optimization (SA –WHO) method ensures secure key selection. The Ethereum blockchain allows both sensitive and non-sensitive EHR data to be securely encrypted as it is transmitted. A rigorous evaluation validates its effectiveness in the secure transfer of medical data in cloud-based healthcare. Metrics confirm effectiveness of the framework based on comparison with industry standards. This Cloud-based Secure Healthcare Framework guarantees unprecedented security and confidentiality for medical data in the cloud, which increases trust among healthcare stakeholders
Fatigue behavior of additively manufactured meta-biomaterials for biomedical applications: a review
No full textMetamaterials are engineered materials with unique properties arising from their structure rather than composition, featuring repeating patterns smaller than the wavelengths they affect. Meta-biomaterials are an important subset of metamaterials and have drawn increasing interest in recent times due to their favorable mechanical properties, biological properties and functional integrity. These exceptional properties have enabled their suitability for diverse biomedical applications, including orthopedic/dental implants, tissue engineering, and medical devices. These materials are generally subjected to cyclic musculoskeletal loads after implantation, making the study of their fatigue behavior critical for ensuring long-term reliability. The current review, therefore, focuses on the fatigue behavior of meta-biomaterials that are manufactured using different additive manufacturing techniques. Various factors like topological design, base material/alloy selection, type of fatigue loading, manufacturing and secondary treatment processes, etc., are carefully analysed, and their influence on fatigue performance is studied. Furthermore, the failure mechanisms of additively manufactured meta-biomaterials with different geometries, structures, and architectures are also analyzed. Thus, this comprehensive review not only elucidates the underlying fatigue mechanisms, but also establishes a framework for the rational design of next-generation of biomedical implants with enhanced durability and functionality
Prediction of Campus Energy Consumption Patterns Using Machine Learning Techniques
No full textThe exponential increase in campus energy consumption results from the rise in population density, leading to urbanisation and the use of higher energy-intensive devices within the environment. This study explored high-performance data analytics techniques to visualise energy consumption across buildings using datasets obtained from a load audit of the entire distribution network within the Federal University of Technology, Owerri. Advanced time series models were used to predict and forecast the consumption patterns for a year. Visualisations for this research provided detailed insights into the energy profile across all the clusters, while the SARIMA, ARIMA, and Prophet models predicted the energy demands. The heatmap for the correlation matrix reveals a constant energy scale throughout the week (weekend average energy usage is at least 40% of the weekday). A comparative performance was done to analyse the scalability and predictive abilities of the individual models. Results from the study indicate that SARIMA has the lowest mean square error (4.4896) and the highest R 2 score (0.8362). The study concludes that the adoption of machine learning models for energy forecasting and prediction is vital for modern-day energy management in the University
Students' perspectives on using GenAI technologies for formative preparation in higher education coursework
No full textThis study explores students' perceptions and experiences with the use of generative artificial intelligence (GenAI) technologies for formative assessment of learning in preparation for summative coursework in higher education. As GenAI tools, such as large language models, become increasingly accessible and integrated into academic settings, it is crucial to understand their impact on student learning and engagement. This case study investigates students' perspectives from a business management discipline to provide a comprehensive understanding of the opportunities and challenges presented by GenAI-powered formative assessment of learning.Formative assessment plays a vital role in supporting student learning by providing timely feedback, promoting self-regulation, and informing instructional strategies (Black & Wiliam, 1998; Sadler 1989). The integration of GenAI into formative assessment has the potential to enhance these processes by offering personalised, immediate, and scalable feedback to students (Dawson et al., 2019). However, the use of GenAI in educational contexts raises concerns about academic integrity, bias, and the potential displacement of human-led feedback (Winne, 2020).Informed by theories of assessment for learning and technology-enhanced assessment (Bienkowski et al., 2012), this study applied dialogic nature of the Conversational Framework by Laurillard (2002) to design formative learning activities using Gen AI technologies, and the TAM framework by Davis (1989) for qualitative comprehension of students' attitudes, perceived usefulness, and perceived ease of use of GenAI technologies as formative assessment tools.This case study employed a qualitative approach to capture the diverse perspectives of students across two academic disciplines with participants representing undergraduate and postgraduate programs in the subject areas of Business Management MBA. Data was collected using focus groups to delve deeper into students' experiences, concerns, and expectations regarding the use of GenAI in formative assessment. The focus groups provided a platform for students to share their personal narratives and engage in collective reflection. Students' interactions with GenAI powered tool were observed and documented during class sessions. Additionally, the interaction logs generated by the GenAI systems were observed to understand the nature and quality of the responses provided to students.Thematic content analysis (Braun & Clarke, 2006) identified patterns, themes, and relationships within the student responses and observation notes. Findings provide valuable insights into students' perceptions and experiences with the use of GenAI for formative assessment of learning in higher education. Initial findings indicate, students were reluctant to use responses generated by Gen AI tools verbatim but preferred to use Gen AI tools as an ‘agent’ to support with critical thinking and assessment of learning needs. The study contributes to the growing body of research on the integration of emerging technologies, such as GenAI, into teaching and learning practices by informing the design and implementation to better align with students' needs, preferences, and concerns. By exploring students' perceptions, this study offers a multidisciplinary perspective on the opportunities and challenges presented by the integration of GenAI into formative assessment practices in higher education. The findings can help inform the development of inclusive and effective technology-enhanced assessment strategies that empower students and promote their academic success
Recent advancement in Mxene-based nanomaterials for flame retardant polymers and composites
No full textThis review explores the advancements in MXene-based nanomaterials as flame-retardant additives for polymers and composites, driven by increasing fire safety demands across industries. It highlights the critical role of flame-retardant materials in mitigating fire hazards in structures, electronics, transportation, and textiles, emphasizing the need for innovative solutions due to stricter safety regulations. MXenes, a class of two-dimensional nanomaterials with unique structural properties such as high surface area, tunable composition, and superior thermal stability, are presented as promising candidates. The review discusses various synthesis and incorporation techniques for MXenes in polymer matrices, showcasing improvements in flame retardancy, mechanical properties, and thermal stability. Additionally, it emphasizes the multifunctionality of MXenes, which offer conductivity, electromagnetic shielding, and mechanical reinforcement alongside flame suppression. In conclusion, the review underscores MXenes' potential to address challenges in flame-retardant materials, advocating for further research to optimize their applications and explore synergies with other agents to enhance safety and sustainability in engineering materials
Determinants of effective participatory multi-actor climate change governance: Insights from Zambia’s environment and climate change actors
No full textParticipatory governance has widely been emphasised as essential to achieving SDG 13. However, recent studies have tended to focus on climate change impacts or global-level politics and governance, to the exclusion of providing practical country-level multi-actor climate governance solutions. Our study bridges this gap by examining the determinants of effective participatory multi-actor climate change governance. The objectives were to examine the current state of Zambia’s climate change governance and policy environment, to examine the elements required to actualise participatory multi-actor climate change governance, and to develop a Climate Action Coordination (CAC) Model of participatory multi-actor governance. Using semi-structured interviews with policy-level actors and a survey of implementation-level actors, we find that Zambia’s current climate governance architecture is characterised by intricate political, policy, institutional, and coordination challenges. Despite these complexities, our study reveals that effective participatory multi-actor climate change governance is contingent upon a deep understanding of the prevailing political dynamics and the effective navigation of political interference by climate actor institutions. Within such a political context, a multi-tiered governance institutional framework is essential, anchored on both an influential political authority and robust multi-level technical autonomy. Our results also identify various determinants such as: broad stakeholder inclusion; clarity of roles; decentralisation of decision making, with safeguards to limit policy reversals; harnessing of indigenous knowledge; alignment to the broader national development agenda; adequate financing; leveraging the influence of global commitments; and establishing parliamentary oversight mechanisms, among others. We synthesised these determinants into a practical CAC Model that cuts across the different administrative and sectoral tiers of climate change governance. Our study is unique as it offers a broad, multifaceted, and practical consideration of the determinants of climate change governance. This is particularly useful for a country like Zambia that has embarked on ambitious environmental and climate change sector reforms.•We obtained data from the makers and implementers of environmental and climate change policy in Zambia.•Climate change policy uptake requires wide multistakeholder inclusion in formulation processes and implementation.•Success of climate governance is contingent upon understanding and navigating prevailing political dynamics.•Robust climate change governance is a precursor and necessity to achieving effective climate action
Nexus Between Exposure to Natural Outdoor Environments and Cognitive Competence among Older Adults in China
No full textWith a rapidly aging population and an increasing number of older people with cognitive impairment, a multitude of social problems could arise. The natural outdoor environments (NOEs) surrounding residential areas are important environments as they directly influence the residents’ quality of life and even physical health. A plethora of studies have suggested the definitive impact of NOE on the physical and mental health of older people. There have been very few studies, however, investigating the nexus between exposure to NOE, especially blue spaces, and cognitive competence among older populations. Our study aims to explore the effects of exposure to residential NOE on cognition by investigating the correlations between exposure to the county-level NOE (i.e., green and blue spaces) and cognitive competence of older Chinese adults. Using regression methods and stratified analysis, we demonstrate that there exists a positive relation between access to blue-green spaces and cognitive competence among older Chinese adults. The cognitive competence of older adults could benefit from increased exposure to NOEs with abundant blue-green spaces. Path analysis has been further conducted to investigate the pathways through which exposure to NOE affects cognitive ability and identify the mediating effects of population density and PM2.5 concentration. Under the person–environment framework, the findings of this study highlight the importance of well-designed NOEs for senior citizens and provide a strong theoretical foundation to support the building of age-friendly cities that could provide a healthier environment for aging in place. Finally, the limitations of this study and a few potential directions for future research are also discussed
AI-Powered Business Intelligence for Modern Organizations
No full textTechnology's rapid advancement has revolutionized how organizations gather, analyze, and utilize data. In this dynamic landscape, integrating artificial intelligence (AI) into business intelligence (BI) systems has emerged as a critical factor for driving informed decision-making and maintaining competitive advantage. This integration allows business to respond quickly to market changes, personalize customer experiences, and optimize operations with greater precision. As AI-driven BI tools continue to evolve, they empower organizations to harness vast amounts of data more effectively, making strategic decisions that are both timely and data-driven, thereby securing their position in an increasingly competitive marketplace. AI-Powered Business Intelligence for Modern Organizations provides a comprehensive overview of this transformative intersection, addressing the diverse challenges, opportunities, and future trends in this field. By exploring the integration of AI into BI systems, the text delves into how advanced analytics, machine learning, and automation are reshaping the way businesses operate. Covering topics such as augmented analytics, decision-making, and sustainability metrics, this book is an excellent resource for business leaders and executives, data scientists and analysts, IT and technology managers, academicians, researchers, graduate and postgraduate students, consultants, industry experts, and more