SACM - United Kingdom
Permanent URI for this collectionhttps://drepo.sdl.edu.sa/handle/20.500.14154/9667
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Item Restricted The Impact of Cloud Computing on the Skills, Autonomy, and Professional Identities of Junior External Auditors Case Studies of Three of the Big Four Audit Firms in Saudi Arabia(The University of Sheffield, 2024-01) Alromaihi, Alaa; Lee, Bill; Matos De, Juliana; Ji, JiaoThis thesis critically applies the Marxist (1954) analysis of capitalism and labour process theory (LPT) to investigate the impact of technological advances – specifically the introduction of Cloud Computing – on the work and experiences of junior external auditors at three of the Big Four audit firms in Saudi Arabia. While previous research has predominantly focused on the organisational level, this study shifts the focus to concentrates on the individual level. The study undertakes three case studies, 29 in-depth semi-structured interviews with junior and senior external auditors, managers, and partners providing a comprehansive perspective of the transformative effects of Cloud technology on the career development of jounior auditors. Responding to calls for more qualitative studies in the field of accounting research and more research in developing countries, this thesis expands the scope of Cloud Computing research. The findings reveal aspects of deskilling in the reduced demand for physical and mental effort, while the enhancement of juniors’ technical skills represents a form of reskilling. Notably, the decrease in autonomy among junior auditors, attributed to the centralising effect of the Cloud, provides support for LPT, illustrating how technological and procedural changes can reshape power dynamics in the workplace. The findings emphasis the dynamic interplay between technology and labour processes; highlighting significant shifts in the roles, behaviours, and attitudes of junior auditors due to the adoption of Cloud technology.57 0Item Restricted Modelling & Simulation the Performance of User Behaviour in Serious Contexts(Saudi Digital Library, 2023-09-27) Alkoradees, Ali Fayez; Thomas, Nigel; Harrison, Michael; Colquhoun, JohnReal-time experiments on healthcare procedural improvement can be infeasible due to the domain’s criticality and sensitivity. For instance, high morbidity rates and escalated patient treatment duration can, in some circumstances, be associated with medical resources exhaustion. Thus, formal methods can be an answer to lower the effects of experimentation within these healthcare domains as such an approach may be effective in deriving new insights and proposing further recommendations to the investigated domain. Specifically, performance modelling formalisms provide a rich theoretical foundation for dynamic systems, which are affected by an extensive collection of interventions, and supported by the existing formalisms toolsets. Hence, investigating healthcare system contexts involves several complex challenges. These challenges range from data collection methods and data analysis formalisms to optimising medical outcomes. This optimisation is beneficial to behaviour analysts and medical administrators. The current thesis contributes to addressing these challenges in many different ways: (i) By presenting an improved web-based version of a sketch simulation that collects the clinician behaviour during massive bleeding scenarios. This unconventional data collection method is proposed to minimise the need to observe the interventions in person where such treatment of these medical cases are performed; (ii) The modelling of two medical scenarios using different modelling formalisms for analysis and evaluation purposes, these modelling formalisms are Performance Evaluation Process Algebra (PEPA), Collective Adaptive Resource-sharing Markovian Agents (CARMA), and Stochastic Petri nets (SPN); (iii) A proposed tool to enhance the log analysis process. Doing so required the implementation of a trace-driven simulation tool. The tool simulates a clinical behaviour that has been recorded using a sketch simulation version. (iv) Proposing different suggestions to improve medical outcomes and to effectively reduce the cost of health resources.5 0Item Restricted The Adoption of CloudComputing: TowardsEnhancing EGovernment Systems in the Saudi Public Sector(2023-07-27) Alyami, Mohammed; Schaefer, DirkGovernments are always trying to find ways to improve their services to citizens; and in order to achieve this they need to restructure their processes and use information technology (IT) effectively. Pressure to do this comes from citizens who increasingly have access to digital technologies and expect better e-services from their governments. Public sector organisations in Saudi Arabia, therefore, need to proactively implement technological innovation to enhance their services. One way to achieve this is to develop a cloud computing infrastructure and the appropriate applications. Cloud computing is understood, however, more needs to be known about how it impacts public service organisations and the provision of services. This research aims to identify and discuss the importance of particular factors pertaining to the fitness and viability of adopting the cloud for Saudi public organisations. The model that forms the theoretical framework for this integrates the Diffusion of Innovation (DOI) theory and the Fit Viability Model (FVM). The cloud computing adoption performance within Saudi public organizations, together with determining the best cloud model for these organizations are also discussed in this research. This research adopts a mixed methodology which includes one survey conducted with 408 IT staff and 21 IT experts for the second and third surveys. The analysis of quantitative data was processed by structural equation modelling (SEM) and descriptive statistics. The qualitative analysis phase was conducted using semi-structured interviews with IT heads and experts in four government organizations to deeply understand and analyse the research problem and to find the optimal solution that would lead decision makers to cloud adoption. The thematic analysis approach was chosen to analyse the qualitative data. The outcomes confirmed that the proposed model worked well and the quantitative data collected showed that fit, viability, task, relative advantage, compatibility, trialability, top management support, IT skills, ROI and asset specificity had a direct and significant effect on the adoption of cloud computing while IT policy, IT infrastructure, cloud knowledge, security, complexity and uncertainty had no direct and significant effect. The qualitative data largely confirmed these findings but shed further light on cloud adoption and suggested that other factors such as trust, service quality, accessibility and ease of interaction also needed to be considered.24 0Item Restricted Self-adaptive System Supporting Elasticity and Quality of Service in Edge Computing(2023-05-30) Aljulayfi, Abdullah Fawaz A; Djemame, Karim; Xu, JieThe Edge Computing (EC) paradigm is seen as a promising paradigm to address the Internet of Things’ (IoT) application requirements, such as low latency to support responsiveness. It is a complementary paradigm of the Cloud Computing (CC) which leverages CC’s resources to the network proximity closer the data source in a distributed fashion. EC is a complex operational environment due to its nature which consists of limited resources and experiences a highly dynamic workload. This complexity is also augmented by the massive growth of the number of end devices, e.g., IoT. Such complexity requires efficient resource and task management to support both the elasticity, which aims to provision and deprovision the resources in order to adapt with the workload dynamicity and cope with the massive growth of the number of IoT devices in such resource restricted environment, and the Quality of service (QoS) in terms of the latency, which aims to manage the tasks by avoiding resource overutilisation to support the fulfilment of the latency requirement as EC has limited resources, experiences a high workload, and emerged to support such requirement. Such management requires a continuous monitoring of the operation environment, including the behaviour of the end users and their applications’ requirements, in order to have a full control over the EC infrastructure. This can be performed using a Self-adaptive System (SAS) which enables the system to monitor the operational environment, hence, adapt to response to the environment changes without human interaction. To this end, this thesis proposes a novel SAS for EC environment. The proposed SAS consists of three frameworks which are the elasticity framework, that aims to provision and deprovision the containerised applications in accordance to the workload dynamicity using Machine Learning (ML) algorithms, the QoS framework, whereby it is responsible for performing efficient task management by avoid resource overutilisation to support the latency requirement, and the offloading framework, which aims to consider the cloud layer by offloading some workload to extend the edge capability as it has limited resources. Moreover, a simulation-based environment is used to implement and evaluate the proposed SAS under different scenarios to demonstrate its effectiveness. The performance evaluation results show that it is essential to study and understand the operational environment, such as workload and applications scenarios, in order to have a robust SAS that can support both elasticity and QoS. For instance, the improvement of the SAS performance in the acceptance rate can reach ~70% in supporting the elasticity once a suitable adaptive approach is selected. Additionally, the internal design of the SAS to support the latency requirement can significantly improve the system objectives fulfilment, which can reach 50%.35 0