Saudi Cultural Missions Theses & Dissertations
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Item Restricted Decoding the Impact of Leadership Multiplicity on Innovation Adoption: The Role of Dual Leadership in Data-Supported Decision-Making Adoption in the UK Local Government(University of Reading, 2024-06-28) Jad, Sumayya; Nakata, KeiichiData adoption in decision-making has been identified as a primary solution for the increasing challenges confronted by local government authorities in the United Kingdom, thus contributing to the improvement of public service provision. Consequently, numerous research is conducted to investigate data adoption in the UK local government. However, little is known about the impact of the dual leadership hierarchies on the adoption of data-supported decision-making (DSDM) within the specified context. Therefore, this thesis aims to investigate the role of dual leadership in the adoption DSDM in the UK local government. To achieve this, the thesis conducts an inductive qualitative comparative approach, where data is collected from 13 local authorities in the form of documentation and semi-structured interviews. As thematic analysis and constant comparative analysis methods are applied to analyse the data, it is found that there are three coexisting decision-making logics in the UK local government. Moreover, based on the Institutional Logics Perspective, it is found that the higher the instantiation of the profession institutional order in the decision-making logics, the higher the adoption of data-supported decision-making in local authorities. Furthermore, based on the Diffusion of Innovation in Organizations, it is found that the dual leadership schemes manifesting as a result of interactions occurring among the decision-making logics significantly impact the level of data-supported decision-making adoption within local authorities. In addition, five leadership-related factors are found to influence a local authority’s level of DSDM adoption: level of delegation, dual leadership relationship direction, political arrangement and stability of a local authority, and the political experience of local authorities’ leading councillors. These results contribute empirically to the research context by exploring the different dual leadership schemes and explaining each’s influence on the adoption of the phenomenon. Moreover, this thesis contributes theoretically to literature by extending the Diffusion of Innovation in Organizations theory to include organizations with multiple leadership hierarchies by adding the multiple leadership schemes as a construct under the leadership dimension. Practical implication of the research is presented by proposing an enhancement to a data maturity model for local government.18 0Item Restricted DETECTING MANIPULATED AND ADVERSARIAL IMAGES: A COMPREHENSIVE STUDY OF REAL-WORLD APPLICATIONS(UCF STARS, 2023-11-06) Alkhowaiter, Mohammed; Zou, CliffThe great advance of communication technology comes with a rapid increase of disinformation in many kinds and shapes; manipulated images are one of the primary examples of disinformation that can affect many users. Such activity can severely impact public behavior, attitude, and be- lief or sway the viewers’ perception in any malicious or benign direction. Additionally, adversarial attacks targeting deep learning models pose a severe risk to computer vision applications. This dissertation explores ways of detecting and resisting manipulated or adversarial attack images. The first contribution evaluates perceptual hashing (pHash) algorithms for detecting image manipulation on social media platforms like Facebook and Twitter. The study demonstrates the differences in image processing between the two platforms and proposes a new approach to find the optimal detection threshold for each algorithm. The next contribution develops a new pHash authentication to detect fake imagery on social media networks, using a self-supervised learning framework and contrastive loss. In addition, a fake image sample generator is developed to cover three major image manipulating operations (copy-move, splicing, removal). The proposed authentication technique outperforms the state-of-the-art pHash methods. The third contribution addresses the challenges of adversarial attacks to deep learning models. A new adversarial-aware deep learning system is proposed using a classical machine learning model as the secondary verification system to complement the primary deep learning model in image classification. The proposed approach outperforms current state-of-the-art adversarial defense systems. Finally, the fourth contribution fuses big data from Extra-Military resources to support military decision-making. The study pro- poses a workflow, reviews data availability, security, privacy, and integrity challenges, and suggests solutions. A demonstration of the proposed image authentication is introduced to prevent wrong decisions and increase integrity. Overall, the dissertation provides practical solutions for detect- ing manipulated and adversarial attack images and integrates our proposed solutions in supporting military decision-making workflow.30 0