Saudi Cultural Missions Theses & Dissertations
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Item Restricted Assessing The Combined Impact of Blockchain, AI, And IoT on Operational Efficiency in Pharmaceutical Supply Chains: A Multi-Case Study Approach(Saudi Digital Library, 2025) Alonayzan, Lama; Dowsn, AltriciaThis thematic study examines the integration of blockchain, artificial intelligence (AI), and the Internet of Things (IoT) in transforming pharmaceutical supply chains (PSCs) by enhancing their operational efficiency and resilience, based on three case studies. Aim: This study aims to critically investigate how the integrated application of these digital technologies promotes operational efficiency in PSCs. It concentrates on the adoption patterns, performance metrics, and strategic alignment of these integrated technologies with AstraZeneca, Pfizer, and Johnson & Johnson as case studies. Design: This study has employed a qualitative multi-case study approach, using secondary data sources encompassing peer-reviewed academic research articles, industry reports, and company data. Thematic text analysis has been carried out to identify patterns and extract insights systematically. The analysis is grounded in three relevant theories: the Resource-Based View (RBV), the Technology Acceptance Model (TAM), and the Supply Chain Resilience (SCR). Guided by these theories, the study interprets strategic resource management, technology adoption behaviours, and resilience enhancement in PSCs. Findings: The integrated use of blockchain, AI, and IoT has created a cyber-physical ecosystem in PSCs that enormously enhances practical visibility, traceability, inventory optimisation, lead-time reduction, risk mitigation, and regulatory compliance. These digital resources, in combination, have fostered productivity and supply chain resilience, especially witnessed amid the COVID-19 pandemic. Nevertheless, this integration also encounters barriers encompassing technical issues in the form of interoperability, cybersecurity, organisational hurdles in the form of required skills and change resistance, and regulatory challenges in the form of data privacy and complex compliance frameworks. Originality: This research is unique in that the present literature has gaps, and it fills them with a focus on the integrated impact of the simultaneous adoption of these technologies instead of adopting them in isolation, as other studies have. This study scientifically validates integrated technology advantages via three case studies, and hence, it offers real-time strategic and managerial recommendations. This study also reveals the significant role of harmonised policies and cross-sector coordination to overcome barriers toward this technological integration, and hence it enriches academia, besides the convergence of three digital technologies for resilient and efficient PSCs.12 0Item Restricted The Impact of Blockchain-Enabled Supply Chain Transparency on Sustainable Purchase Intention: Evidence Based on Saudi Consumers.(Saudi Digital Library, 2025) Babaqi, Rawan Khaled; Arunachalam, DeepakWith the rapid developments in Blockchain in supply chains, there is still a need to study the impact of this technology, especially on individuals in the Kingdom of Saudi Arabia (KSA). The study presents a new evidence-based framework to measure the effect of Blockchain- enabled supply chain transparency (SCT) on Saudi consumers’ purchase intentions (PI) for sustainable products by developing the Theory of Planned Behaviour (TPB) framework. Data were collected electronically through a quantitative survey questionnaire completed by 200 participants of diverse educational and age backgrounds. The findings revealed that attitude is the primary and most significant driver of PI )β = 0.738, p = 0.001(, whereas subjective norms (SN) showed no significant effect )β = 0.088, p = 0.144(. This indicates that the effect of Blockchain transparency on PI is transmitted entirely through attitude. Demographic study analyses multiple cross-sectional positive correlations between knowledge of the technology and higher educational attainment, with the [30–44] age group demonstrating greater familiarity with Blockchain compared to younger groups. Based on these findings, targeted sustainable knowledge campaigns are recommended for the younger generation, who constituted most participants. The projects should particularly focus on enhancing Blockchain knowledge among women, of whom only )23 out of 93( were found to be familiar with the technology, given the observed knowledge gap compared to men )36 out of 48(. This would minimise the technological knowledge gap and narrow the intention gap in adopting sustainable SCT-enabled products. The study suggests that it is essential to create a measure for perceived behavioural control (PBC), as its current limitations hinder its ability to capture the local market context. Additionally, longitudinal studies should be conducted to track changes in consumer behaviour in line with the growing orientation and support for sustainability.7 0Item Restricted A blockchain-based Approach for Secure, Transparent and Accountable Distributed System Environment(Saudi Digital Library, 2025) ALSHARIDAH, Ahmad Abdulrahman; Jha, Devki NandanRecent advances in distributed systems have revolutionised numerous industries by enabling efficient resource sharing and enhanced system scalability. These systems play a critical role in domains such as cloud and edge computing by supporting various applications and services. However, despite their many benefits, distributed systems face inherent trustworthiness challenges, including security threats, limited transparency, and the presence of untrusted entities. These challenges might threaten system reliability and present significant barriers to the broader adoption of distributed technologies. This thesis addresses these challenges by investigating the role of blockchain technology in enhancing trust within distributed systems. It presents three frameworks that work together to improve security, accountability, transparency, and fairness, creating a more resilient and reliable decentralised computing environment. I would like also to take the opportunity and thank the the members of my examining committee, Professor Ali Sadiq and Dr. Vlad Gonzalez for the insightful comments and valuable inputs. The first contribution is a blockchain-based auditing mechanism for cloud resource management. This system securely records resource allocation decisions, performance data, and policy compliance on an immutable ledger. By making these records transparent and tamper-proof, the mechanism provides clear evidence of service provider behaviour. This gives cloud customers and stakeholders confidence that scaling operations are conducted fairly and adhere to agreed-upon rules. The second contribution is RewardChain, an incentive mechanism for federated learning that addresses the lack of trust in traditional FL setups. By integrating blockchain to record each participant’s actions, RewardChain offers transparent and verifiable accountability. Using approximations of the Shapley value, it accurately evaluates and rewards honest contributions while identifying and penalising malicious behaviour. This approach ensures fair compensation, promotes long-term collaboration, and ultimately enhances the quality of the jointly trained models. The third framework, SecureFed, addresses data poisoning attacks in federated learning. It combines cosine similarity metrics for anomaly detection with blockchain-based validation to thoroughly analyse suspicious model updates before incorporating them into the global model. This hybrid defence strategy significantly strengthens model robustness, reducing the risk of adversarial interference and enhancing trust in learning. Together, these three frameworks illustrate how blockchain can transform distributed systems by embedding transparency, fairness, and security into their core. By ensuring accountable cloud operations, reinforcing fair incentive structures, and safeguarding models against attacks, this research paves the way for distributed systems that are more trustworthy, equitable, and robust. Ultimately, it highlights the foundation for the continued evolution of secure and reliable decentralised computing ecosystems.15 0Item Restricted Public-Private Partnerships (PPP) in Sustainable Infrastructure Development in Saudi Arabia: A Risk-Reward Analysis from a Project Management and Contracting Perspective(Saudi Digital Library, 2025) Alanzi, Mashal Johim; Hasan, FakhrulThis study investigates the dynamics of risk and reward allocation, governance, and technological enablers in Saudi Arabia’s Public-Private Partnerships (PPPs) within the framework of Vision 2030. Using a qualitative methodology based on semi-structured interviews and thematic analysis in NVivo, the research identified 13 initial codes consolidated into four overarching themes: risk–reward mechanisms, project management and governance, institutional and technological enablers, and international best practices. Findings reveal that while PPPs are central to infrastructure delivery, risk allocation in Saudi Arabia remains highly government-centric, often undermined by opaque communication and weak project management capacity. Governance reforms, such as the PSP Law, provide a legal basis, yet institutional fragmentation and regulatory overlaps persist. Technology, including blockchain and AI, is recognised as a potential enabler of transparency but remains at a largely symbolic stage. Comparative analysis highlights that international best practices can inform Saudi PPPs only when adapted to the Kingdom’s socio-economic and institutional context. The study advances PPP scholarship by providing a context-specific understanding of governance and risk-sharing in emerging markets, integrating technology as a dual enabler, and offering practical recommendations for policymakers, investors, and project managers.13 0Item Restricted Scalable Distributed Ledger Paradigms for Secure IoT-Driven Data Management in Smart Cities(Saudi Digital Library, 2025) Alruwaill, Musharraf; Mohanty, Saraju P; Kougianos, EliasBlockchain has become a cornerstone of trustworthy, decentralised information governance. Consensus protocols and cryptographic linkages guarantee data integrity, immutability, and verifiable provenance, eliminating reliance on a single trusted authority and mitigating data fragmentation. Within smart‑healthcare ecosystems, these capabilities enable the shift from siloed, centralised repositories to distributed, patient‑centric infrastructures. Because clinical data are highly sensitive and strictly regulated, robust assurances of integrity, confidentiality, and fine‑grained authorisation are essential. Integrating blockchain and smart contracts with technologies such as distributed off‑chain storage and the Internet of Medical Things (IoMT) creates a resilient, scalable, and interoperable foundation for next‑generation healthcare data management. This research introduces hChain, a four‑generation family of distributed‑ledger frameworks that progressively strengthen security, intelligence, and scalability in smart‑healthcare environments. hChain 1.0 lays the groundwork with a blockchain architecture that safeguards patient data, supports real‑time clinical telemetry, and enables seamless inter‑institutional exchange. Building on this foundation, hChain 2.0 integrates InterPlanetary File System (IPFS) storage and smart‑contract enforcement to deliver tamper‑proof, fine‑grained access control. hChain 3.0 embeds on‑chain deep‑learning analytics, providing proactive, automated decision support across the care continuum while preserving data integrity. Finally, hChain 4.0 introduces a highly scalable, permissioned ledger augmented by an Attribute‑Based Access Control (ABAC) layer, ensuring dynamic, context‑aware authorisation in complex organisational settings. The results demonstrate practical solutions for transforming data infrastructures from centralised to decentralised architectures, providing techniques that facilitate seamless integration with existing systems while enhancing blockchain scalability and privacy.53 0Item Restricted The Application of Blockchains to Railway Condition Monitoring(University of Birmingham, 2025-05) Alzahrani, Rahma A; Easton, John MAgeing infrastructure and fragmented data ownership present major challenges to remote condition monitoring technologies in the European railway sector. Despite the potential of these technologies to improve efficiency and safety, their deployment is often limited by issues related to data silos, stakeholder mistrust, and the lack of transparent, enforceable cost attribution models. This thesis investigates how blockchain and smart contract technologies can be leveraged to address these challenges. The research focuses on key questions: how blockchain can reduce centralisation and mistrust; how it can improve transparency and compliance in data cost attribution; how smart contracts can automate and streamline the attribution process; how blockchain can ensure data integrity without storing large volumes of data; and what practical applications blockchain may have in railway operations. A blockchain-based framework was designed and implemented to enable fair, transparent, and legally compliant attribution of data costs across stakeholders. The system incorporates smart contracts to enforce agreement clauses without third-party involvement. The performance of the developed framework was tested under various scenarios to assess scalability, execution efficiency, and compliance with railway sector requirements. The primary contributions of this research are: the development of a cross-border data accounting framework; the establishment of operational links between the framework and real-world business and commercial processes; and a working proof-of-concept tailored to the European rail industry. These contributions demonstrate that blockchain can serve as a practical and scalable foundation for trusted, decentralised data management in multi-stakeholder transport environments.17 0Item Restricted A FRAMEWORK FOR SMART CONTRACT EVALUATION AND SELECTION USING MULTI-CRITERIA ANALYSIS(UNIVERSITI MALAYA, 2024) Alshahrani ,Norah Mohammad R; Mat Kiah, Miss LaihaNumerous smart contract frameworks have been proposed in academic literature and implemented in the industry, for a number pf blockchain platforms such as Ethereum, Corda, and Hyperledger Fabric. Choosing among these frameworks involves a multicriteria decision-making (MCDM) process with various evaluation and selection criteria, including security, financial aspects, and technical considerations. To address this complexity, this research aims to develop a comprehensive framework for standardizing the evaluation criteria and building a selection process for smart contracts. This framework will assist in evaluating critical criteria and selecting the most suitable smart contract framework from the available alternatives. Existing approaches for MCDM-based blockchain evaluation and selection are often not tailored specifically to smart contracts. These approaches are typically general and lack comprehensiveness, often designed for specific case studies. Multi-criteria analysis is employed in this research to determine the optimal option based on the decision-maker’s preferences. Among the available MCDM techniques, the Decision by Opinion Score Method (DOSM) was utilized, which has been applied across diverse fields, including financial institutions and operating businesses. However, this technique does not provide the functionality of explicit criteria for weight measurement. Therefore, a modified version of DOSM that incorporates explicit weight measurement is proposed in this research. The proposed framework consists of five phases. In the first phase, the final set of criteria is identified and examined through expert opinions and the Fuzzy-Delphi method. An improved version of the fuzzy DOSM technique with an explicit weight mechanism is employed in the second stage. The third phase involves identifying and evaluating smart contract alternatives using the criteria identified in phase one. In the iv fourth phase, developed and assessed the opinion score weighting algorithm (NS-WFDOSM), This algorithm modifies FDOSM and incorporates a Neutrosophic Fuzzy Sets (NFSs) environment to measure the weights explicitly and rank the alternatives. In the final phase, a sensitivity analysis module to study the behavior of the new weight technique and its impact on alternative ranking is developed. The inclusion of weight parameters in the proposed framework facilitates the identification of influential criteria in the ranking procedure. The research methodology adopts a quantitative approach to determine critical criteria for smart contracts. Different numerical samples obtained through a closed-ended questionnaire survey are applied in various scenarios to collect data regarding the essential criteria of the subject under investigation. Results indicated the following: (1) The FW-DOSM method efficiently weights the criteria for smart contract blockchain. (2) The NS-FW-DOSM method successfully ranks smart contract blockchain frameworks. (3) Given the binary nature of the selected data, the variation of the criteria, and an increase in the number of alternatives, no significant changes were observed in the final framework among different 𝛼 values. The validation of the framework results was confirmed using sensitivity analysis. The implications of this study can assist administrators in various organizations in selecting the most appropriate and confident smart contract framework and guide future directions for system developers. In conclusion, NS-WF-DOSM is extensively discussed and compared with different MCDM methods from ranking and weighting perspectives. The results demonstrate that NS-WF-WDOSM produces more logical outcomes than other MCDM methods.5 0Item Restricted Blindly Backrunning Private Transactions With Fully Homomorphic Encryption(Imperial College London, 2024-09) Althonayan, Majed; Passerat-Palmbach, JonathanBlockchains and cryptocurrencies have experienced a monumental rise over the past decade. With Ethereum alone having around 1 million transactions per day [1], making it increasingly more attractive to opportunists who attempt to extract monetary value from transactions. This is, however, often at the expense of the user. As a result, it is of paramount importance to ensure that users are protected from malicious agents who exploit the public, transparent nature of Blockchains for individual gain. A Blockchain is a chain of blocks, each of which consisting of transactions that are executed sequentially. The ability to alter this order of transactions (by insertion, removal and re-ordering of transactions) can lead to the extraction of additional value commonly referred to as Maximal Extractable Value (MEV). MEV has led to the extraction of $750 million from Ethereum before the merge [2]. Although certain forms of MEV are universally considered to have adverse effects on users and their experience, other forms of MEV, such as arbitrage and liquidations, are believed to have a positive effect in regulating the markets. This research introduces a promising solution that allows searchers to backrun transactions, leveraging the effects of arbitrage while mitigating the harmful effects of MEV. It expands on the work done by Flashbots by utilising fully homomorphic encryption to enable the blind backrunning of transactions by searchers through the fhEVM framework [3] on the UniswapV2 decentralised exchange. This paper also addresses the challenges faced by previous works, aiming to reduce the computational overhead and enhance the solution’s usability. Despite computational constraints, this paper presents a novel solution to the outlined aims through the advancement of known solutions by allowing searchers to combine multiple transactions and accept a greater number of UniswapV2 methods, thereby allowing searchers to generate complex and novel arbitrage opportunities. This advancement is aided with use of the fhEVM framework [3] which was utilised to build and deploy the solution on the public network. This paper represents a solid foundation for future research with the aim of further enhancing the use of fully homomorphic encryption in decentralised finance to create a fairer, more ethical ecosystem.16 0Item Restricted Explainability Requirement in Blockchain Smart Contracts: A Human-Centred Approach(The university of Birmingham, 2024-07) Alghanmi, Hanouf; Bahsoon, RamiBlockchain smart contracts have emerged as a transformative technology, enabling the automation and execution of contractual agreements. These self-executing software programs leverage blockchain's distributed and immutable nature to eliminate the need for third-party intermediaries. However, this new paradigm of automation and authority introduces a complex environment with technical intricacies that users are expected to understand and trust. The irreversible nature of blockchain decisions exacerbates these issues, as any mistake or misuse cannot be rectified. Current smart contract designs often neglect human-centric approaches and the exploration of trustworthiness characteristics, such as explainability. Explainability, a renowned requirement in Explainable Artificial Intelligence (XAI) aimed at enhancing human understandability, transparency and trust, has yet to be thoroughly examined in the context of smart contracts. A noticeable gap exists in the literature concerning the early development of explainability requirements, including established methods and frameworks for addressing requirements analysis phases, design principles, evaluation of their necessity and trade-offs. Therefore, this thesis aims to advance the field of blockchain smart contract systems by introducing explainability as a design concern, fundamentally prompting requirements engineers and designers to cater to this concern during the early development phases. Specifically, we provide guidelines for explainability requirements analysis, addressing what, why, when and to whom to explain. We propose design principles for integrating explainability into the early stages of development. To tailor explainability further, we propose a human-centred framework for determining information requirements in smart contract explanations, utilising situational awareness theories to address the `what to explain' aspect. Additionally, we present `explainability purposes' as an integral resource in evaluating and designing explainability. Our approach includes a novel evaluation framework inspired by the metacognitive explanation-based theory of surprise, addressing the `why to explain' aspect. The proposed approaches have been evaluated through qualitative validations and expert feedback. We have illustrated the added value and constraints of explainability requirements in smart contracts by presenting case studies drawn from literature, industry scenarios and real-world projects. This study informs requirements engineers and designers regarding how to elicit, design and evaluate the need for explainability requirements, contributing to the advancement of the early development of smart contracts.38 0Item Restricted A Simulation Framework for Evaluating the Performance of Blockchain-based IoT Ecosystems(Newcastle University, 2024-09-05) Albshri, Adel; Solaiman, EllisRecently, it has been appealing to integrate Blockchain with IoT in several domains, such as healthcare and smart cities. This integration facilitates the decentralized processing of IoT data, enhancing cybersecurity by ensuring data integrity, preventing tampering, and strengthening privacy through decentralized trust mechanisms and resilient security measures. These features create a secure and reliable environment, mitigating potential cyber threats while ensuring non-repudiation and higher availability. However, Blockchain performance is questionable when handling massive data sets generated by complex and heterogeneous IoT applications. Thus, whether the Blockchain performance meets expectations will significantly influence the overall viability of integration. Therefore, it is crucial to evaluate the feasibility of integrating IoT and Blockchain and examine the technology readiness level before the production stage. This thesis addresses this matter by extensively investigating approaches to the performance evaluation of Blockchain-based IoT solutions. Firstly, it systematically reviews existing Blockchain simulators and identifies their strengths and limitations. Secondly, due to the lack of existing blockchain simulators specifically tailored for IoT, this thesis contributes a novel blockchain-based IoT simulator which enables investigation of blockchain performance based on adaptable design configuration choices of IoT infrastructure. The simulator benefits from lessons learnt about the strengths and limitations of existing works and considers various design requirements and views collected through questioners and focus groups of domain experts. Third, the thesis recognises the shortcomings of blockchain simulators, such as support for smart contracts. Therefore, it contributes a middleware that leverages IoT simulators to benchmark real blockchain platforms' performance, namely Hyperledger Fabric. It resolves challenges related to integrating distinctive environments: simulated IoT models with real Blockchain ecosystems. Lastly, this thesis employs Machine Learning (ML) techniques for predicting blockchain performance based on predetermined configurations. Contrariwise, it also utilises ML techniques to recommend the optimal configurations for achieving the desired level of blockchain performance.74 0