College of Business and Law - Masters by Research Theses
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Item Coordinating without convergence: managing product architecture knowledge in emerging energy technologies(University College Cork, 2026-05-20) Tavares, Alba Francimara Gonçalves; Dooley, Lawrence; Barrett, Gillian; Rogan, Fionn; SFI Research Centre for Energy, Climate and MarineAfter five decades of development, wave energy remains uncommercialised. This is despite technical feasibility and substantial resource potential, e.g. 75% of Ireland's electricity demand could be met from wave energy alone. Unlike solar photovoltaics or other offshore renewable energy (ORE), which have converged around dominant designs that enable economies of scale, wave energy exhibits persistent technological diversity across fundamentally different device concepts. This research investigates how wave energy firms, characteristically small and medium-sized enterprises (SMEs), manage product-architecture knowledge coordination when technological consensus is absent and market-selection mechanisms provide weak guidance. Qualitative analysis of twelve Irish SMEs in the wave energy sector indicates that recognising architectural innovation requires inter-organisational coordination mechanisms that extend beyond conventional firm capabilities and rely on collective sense-making among various actors. Our contribution is to bridge system-level perspectives, which are already well established in the transitions literature, with the firm-level architectural knowledge tradition, which typically locates architectural recognition within the firm. We identify a management paradox: firms need deployment experience to validate architectural choices, but cannot deploy devices without standards. However, standards require consensus on architectural principles, which also need deployment experience to establish. Our findings connect dominant design theory from firm-centric to system-level analysis, demonstrating that when product architecture knowledge remains distributed across innovation ecosystems, coordination becomes a collective management challenge rather than an individual firm capability. This study contributes to innovation management scholarship by explaining how emerging technologies can stall despite technical maturity when the coordination mechanisms that enable collective architectural recognition remain underdeveloped.Item Sustainable optimisation of Cold Food Supply Chains: a bi-objective inventory–routing model under demand uncertainty(University College Cork, 2025-12-01) Jahdi, Soodeh; O'Reilly, Seamus; Tarim, Armagan; University College CorkCold Food Supply Chain (CFSC) plays a critical role in ensuring the availability, safety, and freshness of temperature-sensitive products, yet they remain particularly vulnerable to demand volatility, energy-intensive operations, and rising sustainability pressures. Maintaining precise temperature conditions throughout transportation and storage significantly increases energy consumption and operational cost, while also contributing to elevated Carbon Dioxide (CO2) emissions. These challenges are compounded by the fact that demand for perishable goods is frequently non-stationary and difficult to predict, with fluctuations driven by weather, promotions, consumer behaviour, and seasonal variations. Traditional replenishment policies such as the fixed-order (R, Q) approach lack the flexibility required in such uncertain environments and often produce inflated safety stocks and inefficient routing decisions. In cold chains, where excessive storage accelerates spoilage, increases energy use, and undermines product freshness, the consequences are particularly severe. Responding to these challenges, this thesis develops an integrated, bi-objective Mixed-Integer Programming (MIP) model based on the adaptive (R, S) replenishment policy. The model jointly optimises replenishment timing, order-up-to levels, and vehicle routes in a multi-period distribution environment characterised by non-stationary stochastic demand. Two conflicting objectives are addressed simultaneously: (I) minimising total expected cost, and (II) minimising total expected CO2 emissions arising from transportation activity and refrigeration load. Given the combinatorial complexity of the multi-period Inventory Routing Problem (IRP) under uncertainty, the Augmented Epsilon Constraint (AEC) method is employed to generate Pareto-efficient solutions, supported by lexicographic optimisation to construct an accurate payoff table. All models are solved using IBM ILOG CPLEX, and solution robustness is assessed through 10,000-run Monte Carlo simulations. The findings demonstrate that the (R, S) policy consistently outperforms the conventional (R, Q) policy across all examined demand patterns including constant, increasing, decreasing, life cycle, and erratic. In the case study, the (R, S) policy reduces total cost by 2.2% and CO2 emissions by 8.5%. Across 400 randomly generated problem instances, cost improvements grow with longer planning horizons and higher Coefficients of Variation (CV), highlighting the model’s effectiveness in volatile environments. The results also reveal that while modest emission reductions can be achieved with limited cost increases, deeper decarbonisation requires disproportionately higher investment, reflecting a clear non-linear trade-off along the Pareto frontier. Overall, the thesis provides an analytic and practical framework for uncertainty-aware, energy-efficient cold-chain planning. The contributions support managers and logistics planners in making informed decisions that balance cost efficiency, operational responsiveness, and environmental responsibility, ultimately fostering more sustainable and resilient CFSC.Item Core international crimes committed in Ukraine since the Russian full-scale invasion: complementarity, cooperation and Russian distortion(University College Cork, 2026-01-31) Sheehan, Rosalyn (Lynn); Cubie, Douglas; Zeffert, HenriettaThe thesis examines how complementarity between Ukrainian and international initiatives operates in terms of ensuring accountability for the Russian full-scale invasion of Ukraine which began on 24 February 2022. It investigates the capacity of Ukraine’s domestic criminal justice system to process the significant number of core international crimes generated by the Russian invasion and assesses the support provided by the international community. The thesis concludes that Ukraine possesses a functioning system capable of processing many cases with its EU accession process serving as an important driver. The thesis also analyses the range of international mechanisms operating alongside Ukraine’s domestic system. A central focus is the Special Tribunal for the Crime of Aggression, spearheaded by the Council of Europe in 2025, but also examines the role of the International Criminal Court, the Joint Investigation Team, the UN Commission of Inquiry, the Council of Europe Register of Damages and the role of the application of the Principle of Universal Jurisdiction. International support remains essential but is fundamentally constrained by Russia’s capacity to obstruct the international legal order. The situation has been further complicated by the changing political landscape since January 2025, including shifts in US policy towards Ukraine. Despite these geopolitical challenges, practical solutions within both the Ukrainian criminal justice system and International Criminal Law structures remain possible but international peace and security remains under threat.Item Investigating the future of digital assets in financial services(University College Cork, 2025) Stanley, Christopher; McAvoy, John; Feller, Joseph; State StreetThe growth of digital assets and the underlying technology that underpins them – distributed ledger technology – has been highlighted by their popularity amongst retail consumers and general popular culture throughout their emergence in the last decade. These novel forms of assets and their associated technology have significantly altered perspectives of many retail investors on how currency is managed and distributed in the modern technological era. However, there is still a lack of clarity on if and how large institutions across the financial services could leverage these assets and technology to their benefits. This lack of clarity is a key contributing factor to the overarching research objective of this paper. That being to understand digital assets on a more fundamental level, with a view to how they may be adopted and leveraged by organisations across the financial services industry. Existing literature on the area facilitates a more fundamental understanding of digital assets and associated processes across chapters 2 and 3, whilst chapter 4 delves deeper into their use by stakeholders across the financial services industry. Chapter 2 proposes a framework which can be used to classify digital assets and delves into some of the key detailed information around digital assets in the process. This framework adds to the existing body of information around digital assets and given the lack of an existing framework for classifying digital assets according to their structural properties, provides a useful means of doing so. Following this, chapter 3 further explores digital assets, but with a focus on their associated process of conversion called Tokenisation. Tokenisation is examined by comparing it to the process of digitalisation and a conceptual framework is derived which can be used to evaluate the suitability of traditional financial assets to be converted to digital assets through tokenisation. Once the fundamental concepts around digital assets are thoroughly understood, chapter 4 examines how companies within financial services could leverage these digital assets within their respective organisations. This chapter uses foresight research techniques to garner a more future oriented perspective on the application of digital assets within financial services. Results from chapter 2 show that the digital asset classification framework could be utilised for classification, ownership and regulatory purposes, and identifies a core issue for regulation in the area as being a lack of a consistent framework based on the structure of the assets themselves. Chapter 3 gives context to the landscape of tokenisation and demonstrates the importance of organisations considering the suitability of an asset to the process ahead of tokenising it. Chapter 4 finds that trust is the key factor to be considered by organisations operating in the financial services industry if they are to leverage the perceived value that digital assets offer, detailing ways that organisations could address this in the process.Item Machine Learning and its application in the Portfolio Management industry(University College Cork, 2024) Murphy, Conor C; McAvoy, John; Kiely, Gaye LouiseMachine Learning (ML) is a subdivision of Artificial Intelligence (AI). AI is a term used for technology that “enables machines to mimic human thoughts and behaviour” (Xu, 2021). Recently, there has been a significant increase in the use of ML techniques by finance professionals, primarily by the Portfolio Management industry (Perrin, 2019). This thesis reflects on ML’s application in the Portfolio Management industry. To further understand this relationship a literature review is carried out in Chapter 2 to identify the intersection of ML and Portfolio Management and to highlight key criteria required for ML’s application in the Portfolio Management industry. A key finding from Chapter 2 found that the lack of quality data is a critical barrier to ML’s application in the Portfolio Management industry. Chapter 2 examines the use of sentiment from unstructured data, in the main tweets, for stock market prediction in the Portfolio Management industry as alternative data source for ML. There are a variety of ML algorithms that can be applied for Twitter sentiment stock market prediction, Chapter 3 employs the Multilayer-Perceptron (MLP). MLP has been employed successfully in other studies for Twitter sentiment stock market prediction (Livingston, 2019; Turchenko, 2011). Before running the MLP prediction algorithm, EmoLex was employed to identify the underlying sentiment that may be apparent in the tweets. Subsequently, a prediction algorithm MLP Classifier was run to ascertain daily sentiment stock price predictions for the data. For the prediction analysis section the Vader Sentiment Analyser from the Natural Language Tool Kit (NLTK) in python was employed to split the Twitter sentiment into three categories (positive/neutral/negative), while the MLP classifier was run through SickIt-learn package in python. Interestingly, the performance of the MLP in Chapter 3 is not as accurate as other studies including, Kolasani (2020), Usmani (2016) & Khan (2020). Following the same guidelines as Pagolu (2016) and Mittal (2012) incorporating a new algorithm (the Random Forest algorithm) using the same methodology outlined in Chapter 3 may outperform the MLP. Studies such as Bollen (2011) indicate that positive Twitter sentiment will be reflected in the stock market by a positive increase in stock price and negative Twitter sentiment will result in a fall in stock price. The use of the Random Forest regressor in this study found that in the case of Black Swan events, Twitter sentiment has the same predictive power as a chance model. These results are integral to the Portfolio Management industry as it clearly indicates that Twitter sentiment cannot be used to gauge the severity of Black Swan events nor can it be used as a method to predict it. Only one of the Tweets in the database collected referenced that China allowed one of its banks to go into liquidation, indicating that the speed of stock price drop was ahead of the Twitter sentiment.