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The role of innovation in SMEs in the UAE: Reselling and recycling mobile phones in emerging markets.
No full textThe thesis explores the pivotal role of Small and Medium-sized Enterprises (SMEs) in shaping themarket dynamics of the United Arab Emirates (UAE), with a specific focus on the wireless industry.Grounded in an extensive review of past research, this study delves into the profound impact of SMEson the economic development of the UAE. Central to the investigation is the integration of innovationwithin the SME sector, particularly in the realm of wireless technology. The research underscores theburgeoning interest in understanding how innovation catalyses economic growth, with SMEs emergingas crucial players in this transformative process. Drawing from existing literature, the study illuminatesSMEs as instrumental agents in fostering economic stability and expansion. In the contemporarylandscape marked by rapid changes and intense market competition, the influence of emerging SMEson national economies worldwide becomes increasingly pronounced. This study reveals a positivecorrelation between innovation adoption and the growth of the mobile phone industry in the UAE.However, it also sheds light on the intricate nature of the relationship between innovation and growthwithin firms. The research underscores the complexity of this connection, which is significantlyinfluenced by the nature of innovation itself. By critically examining the interplay between innovation,SMEs, and market growth, this thesis contributes valuable insights to the ongoing discourse oneconomic development in the UAE. Through meticulous analysis and nuanced exploration, it offers acomprehensive understanding of how SMEs, driven by innovative practices, play a pivotal role inshaping the economic landscape of the nation. Moreover, this study provides a foundation for futureresearch endeavours, offering a framework for policymakers and industry stakeholders to harness thefull potential of SMEs in propelling the UAE’s economic progress in the wireless industry and beyond.In this study, a conceptual framework of innovation in the mobile phone industry, focusing on SMEsand market segmentation is presented. The intricate interplay between market segmentation and thepivotal role of innovation in the Mobile Phone industry, particularly among mobile traders in the UnitedArab Emirates is illustrated: providing crucial insights into the innovative depth adopted by SMEs,shedding light on the dynamic landscape of mobile phone innovation in the UA
Exploring the role of entrepreneurial leadership in circular economy adoption: A conceptual model
No full textCircular Economy (CE) is an emerging research phenomenon that has garnered significantattention in policy and research spheres in recent times. The evidence suggests that the adoptionof CE involves a myriad of internal and external factors, including firm resources, organisationalculture, leadership skills, environmental challenges, and government policies. Among thesefactors, leadership emerges as a crucial element in CE adoption and sustainable development.However, the existing literature lacks in-depth discussion on the specific role of leadership in CEadoption. To address this gap, this paper explores the intricate relationship between entrepreneurialleadership and CE adoption and proposes a conceptual model. Through a thorough examination ofcurrent research, we identify distinct competencies inherent in entrepreneurial leaders, such asrisk-taking, innovativeness, and proactiveness, which play pivotal roles in fostering CE practices.The proposed conceptual model delineates how entrepreneurial leaders can effectively navigatethe complexities of CE adoption. This model not only contributes to the theoretical discoursesurrounding the intersection of entrepreneurial leadership and CE but also offers practical insightsfor organisations seeking to embrace sustainable practices and actively contribute to the circulareconomy paradigm
A critical assessment of mangers’ perceptions of supply chain performance: a case study of international oil companies in Iraq
No full textFor Iraq, the oil industry is the booming sector that massively promotes national and international economic development; it presents the infrastructure that allows Iraq to develop its economy. Various studies have been conducted on Iraq's oil sector, primarily addressing sustainability, logistics efficiency and examining the challenges Iraq faces as a result of internal and external conflicts, as well as a security situation involving war and terrorism impacting investments. Thus, this study conducted a critical investigation of managers’ perceptions of all the oil supply chain to identify the performance issues and challenges that the previous studies failed to capture. Moreover, this thesis sheds light on the International Oil Companies’ (IOCs’) managers’ perceptions of the barriers and enablers of the entire supply chain performance in the Iraqi oil sector. IOCs’ managers’ perceptions of the barriers and enablers of supply chain performance in the Iraqi oil sector have so far been missing, and as a result, this research filled a major gap in academic literature. In order to examine the strategies that might be adopted to mitigate the barriers and promote the enablers to Supply Chain (SC) performance in Iraq’s oil sector, the perspectives of global practices and academic views of the oil supply chain in other countries are synthesised in this thesis’ theoretical framework, which provides strategic insights for explaining and understanding the phenomenon surrounding the generic barriers and enablers to the entire supply chain performance that can be later assessed in light of the analysis of IOCs’ Iraqi managers’ perceptions of the entire supply chain. The research adopts an interpretivism philosophy, focusing on understanding managers’ perceptions of working across the Iraqi supply chain. The research design adopted is descriptive with a qualitative approach. The study uses primarily the deductive method, where data sources from semi-structured interviews are used. A deductive approach is used to critically investigate the supply chain segments and related issues as perceived by IOC managers in Iraq's oil industry for the first time. Qualitative methods are used as they are the best methods for identifying concepts and understanding the entirety of problems and complexities in the "natural setting" of IOCs in Iraq. The sampling design chosen was judgemental, and NVivo software used to analyse the data collected from interviews supplemented by corresponding secondary data. Key findings reveal that the Iraqi oil supply chain currently faces significant challenges, including inefficiencies due to reliance on road transportation, improper management, inconsistent industrial norms, lack of standardisation, and political decisions overriding technical considerations. Key barriers identified by managers include cost increases from Covid-19, delays in customs clearance, interference from neighbouring countries, and a shortage of skilled local workers. However, strong country infrastructure, low extraction costs, vast oil reserves, and potential for rapid return on investment were seen as major enablers. To mitigate barriers, managers suggested strategies such as digitalisation, enhancing stability, improving security, forming an international technical advisory board, reducing corruption, and ensuring a transparent risk mitigation approach. Strategies to promote enablers included constructing pipelines, improving infrastructure, enacting a generic oil and gas law, hiring trained workers, leveraging oil resources, minimizing uncertainty, providing incentives, reducing financial instability, and implementing clear and transparent laws. It is crucial to manage risk and build resilient supply chains to ensure operational continuity, which requires anticipating disruptions and developing robust contingency plans
Federated Learning for Optimized Communication in Industry 5.0
No full textThe rapid advancements in Industry 5.0 necessitate efficientcommunication optimization techniques to facilitate seamless collaboration between humans and advanced technologies. This book chapter explores the potential of Federated Learning (FL) as a solution to addressthe challenges faced by current communication optimization techniquesin Industry 5.0. FL enables collaborative model training while preservingdata privacy by keeping sensitive data decentralized. By leveraging FL,organizations can achieve data efficiency by training models locally ondevices or edge servers, reducing the need for extensive data transfer. FLpromotes collaboration and knowledge sharing without sharing raw data,fostering collective intelligence. Moreover, FL operates in a decentralizedmanner, reducing infrastructure costs and enabling efficient communication in distributed environments. The continuous learning capabilities ofFL ensure that models stay up-to-date with changing circumstances. Byminimizing communication overhead through the sharing of encryptedmodel parameters or gradients, FL enhances communication efficiencyin Industry 5.0. However, challenges related to managing model updatesand ensuring fairness should be carefully addressed. This book chapterpresents FL as a promising approach to optimize communication in Industry 5.0, offering privacy preservation, data efficiency, collaboration,decentralized infrastructure, and continuous learning
AI-powered leadership : a systemic literature review
No full textPurpose - In this era of rapid technological advancement, Artificial Intelligence (AI) has emergedas a crucial factor in reshaping organisational dynamics, notably in the realm of leadership. Thissystematic literature review (SLR) aims to investigate the emerging relationship between AI andleadership, focusing on defining AI-powered leadership, identifying prevalent themes, exploringchallenges, and uncovering research gaps within the relevant literature.Design/methodology/approach - A sample of 73 papers was chosen after carefully applying theinclusion and exclusion criteria to 1387 research articles that were initially sought. Using themethodological framework presented by Denyer and Tranfield (2009), our study adopted a fourstep procedure to obtain insights from the corpus of literature. The papers were analysed byemploying content and thematic analysis to address four key questions.Findings - The review explores various definitions of AI-powered leadership proposed in theliterature bas
Data Set from GREAT Case Study 1
No full textThis open data set contains the raw CSV files that were generated in the first GREAT case study, carried out in collaboration with UNDP and using the infrastructure developed by PlanetPlay. A merged file is also provided that may be more convenient for some users who wish to carry out their own analysis
Cutting-edge deep learning approaches to predict thyroid hormonal disorder for the healthcare sector
No full textSeveral researchers have used a range of Machine Learning (ML) and a few Deep Learning (DL) approaches to predict thyroid hormonal disorders over the years. However, these researchers have recommended a need for the re-evaluation of the ML models and the use of more DL models with feature selection techniques to improve the accuracy of predicting thyroid hormonal disorders. Therefore, this study fills the identified gaps in the literature by comprehensively discussing the data understanding and pre-processing of a reconciled large-sized thyroid disease dataset obtained from Kaggle, which is a secondary source and uses the cleaned dataset with the application of an embedded method that is a features selection technique to develop three ML models, a hybrid model, and four modern DL models, to improve the accuracy of predicting thyroid hormonal disorder by using 80% of the cleaned and balanced dataset for model training, and 20% of the dataset for testing. Based on the findings attained and comparisons of the performances of the developed models using Mean Absolute Error (MAE), BiLSTM is the best-fit model because it has a minimum MAE value of 4.9202. Therefore, this study concludes and recommends BiLSTM as the DL model for the healthcare sector to adopt and be deployed to produce an intelligent medical diagnosis system for an improved prediction of thyroid hormonal disorders
Exploring the concept of Mini Data Sprints as a methodology to assess data validity and stimulate climate conversation
No full textThe GREAT (Games Realising Effective and Affective Transformation) project explores new approaches that foster climate change discussion and stimulate citizen reflection. However, some citizens have limited resources for participation, even though their engagement and contributions are crucial. To address this challenge, the authors present two studies that have deployed mini data sprints (MDS). The MDS approach uses interactive data applications and visualisations to provoke citizens? feelings, knowledge, and perspectives towards the climate conversation and presented data. These studies highlight how the MDS approach can provide data set recommendations, facilitate efficient and focused climate conversation, and improve the data literacy of the cohort
Entrepreneurship education and internationalisation : Cases, collaborations and contexts
No full textEmpirical evaluation of deep learning approaches for predicting cervical cancer in the health care sector
No full textThis research paper addresses the urgent need to combat the escalating mortality rates in cervical cancer, impacting 570,000 women, with 311,000 fatalities, as reported by the World Health Organization. Recognizing the potential of digital solutions, we explore deep learning’s untapped power for early diagnosis. Amidst healthcare challenges due to population growth and disease spread, traditional methods prove inadequate. To bridge this gap, we introduce novel techniques: Long Short-term Memory Networks and Bidirectional Long Short-term Memory Networks. Leveraging a comprehensive dataset of 15 attributes, including age, pregnancies, partners, smoking, cytology, and biopsy, our model achieves a noteworthy 97% accuracy, signifying a ground-breaking advancement in cervical cancer management