Digital Repository of Theses

Offshore Green Hydrogen Production on the Dutch Continental Shelf: Techno-Economic Feasibility Assessment (2025–2050)

2026-04-03T08:18:16+00:00

The rapid expansion of offshore wind capacity in the Netherlands and across the North Sea is creating structural challenges for onshore electricity grids, while simultaneously offering an opportunity to produce renewable hydrogen offshore as an alternative energy transport vector. Offshore hydrogen production has the potential to relieve grid congestion, reuse existing offshore infrastructure, and support decarbonisation of hard-to-abate industrial sectors. However, its economic feasibility remains uncertain due to high capital intensity, policy dependence, and uncertainty in future electricity prices, technology costs, and utilisation regimes. This dissertation develops a replicable techno-economic decision-support framework to assess the feasibility of offshore green hydrogen production on the Dutch Continental Shelf over the period 2025–2050. A bottom-up levelised cost of hydrogen (LCOH) model is constructed and calibrated to Dutch offshore conditions, including wind resources, water depth, distance to shore, and existing oil and gas infrastructure. Three internally consistent scenarios (Conservative, Baseline, Optimistic) are analysed to capture plausible technology and market trajectories. Uncertainty is treated explicitly through one-at-a-time sensitivity analysis, Monte Carlo simulation, and break-even threshold analysis. Results are further integrated into a multi-criteria decision analysis (MCDA) framework comparing offshore hydrogen pathways with alternative decarbonisation options. Under baseline assumptions, the analysis demonstrates a 52% probability of achieving €2.0/kg LCOH by 2040, with outcomes highly sensitive to electricity price and capacity factor assumptions. Infrastructure repurposing provides substantial near-term economic and strategic advantage. The dissertation does not provide forecasts. However, it delivers a transparent and auditable framework for decision-making under uncertainty.
The baseline Monte Carlo analysis assumes statistical independence between input variables. Correlated inputs are examined exclusively as part of a dedicated sensitivity analysis using the Iman–Conover method.
Key words: Offshore green hydrogen; Dutch Continental Shelf (DCS); North Sea; Levelised Cost of Hydrogen (LCOH); PEM electrolysis; Electrolyser CAPEX; Capacity factor; Wake effects; Infrastructure repurposing; Hydrogen pipelines; HVDC transmission; Monte Carlo analysis; Sensitivity analysis; multi-criteria decision analysis (MCDA); Contracts for Difference (CfD); EU Hydrogen Bank; Grid congestion; TenneT; Gasunie;

A System Dynamics Approach to Catalyze Environmental Sustainability in Higher Education and Research Organizations

2026-04-03T08:16:26+00:00

Environmental sustainability has become a strategic imperative for higher education and research organizations (HEROs), yet many HEROs struggle to translate sustainability commitments into durable, system-wide outcomes. Existing research has largely relied on static frameworks, descriptive case studies, and performance rankings, offering limited insight into the dynamic processes through which sustainability strategies evolve, stabilize, or erode over time. This dissertation addresses this gap by examining sustainability transformation in higher education through the lenses of organizational change, strategic management, dynamic capabilities, and innovation and entrepreneurship. The dissertation develops and applies a System Dynamics (SD) modeling framework to capture the feedback mechanisms, delays, and path dependencies that shape sustainability performance in complex academic organizations. Five policy scenarios are simulated: governance and strategy integration, dynamic capability building, organizational change and cultural transformation, sustainability innovation ecosystem acceleration, and institutional context differences between U.S. and international higher education systems. Model calibration and validation draw on publicly available AASHE STARS data, enabling empirical grounding while preserving theoretical rigor. Results demonstrate that no single policy lever is sufficient to generate sustained sustainability performance. Instead, durable progress emerges from coordinated and sequenced interventions that align governance structures, organizational capabilities, cultural dynamics, and innovation ecosystems. The findings highlight complementarities and trade-offs among policy approaches and reveal how institutional context conditions the pace and stability of sustainability transitions. A case study application to the University System of Maryland illustrates how the SD model can function as a strategic learning and decision-support tool for system-level leadership. The dissertation contributes to theory by integrating dynamic capabilities and sustainability transitions within a formal dynamic framework, and to practice by offering actionable insights for university leaders and policymakers seeking to manage sustainability as a long-term organizational transformation.

Talent Management for Digitized and Sustainability-Oriented Supply Chains in Traditional Manufacturing Firms

2026-04-03T08:14:23+00:00

This dissertation explores how Talent Management (TM) acts as a strategic enabler of Digital Transformation (DT) and Sustainability (SUS) within traditional manufacturing small and medium-sized enterprises (SMEs). By integrating perspectives from human resource management, organizational change, and operations strategy, this research develops a Human-Centered Transformation Framework (HCTF) that explains how SMEs can align people, technology, and purpose to achieve long-term competitiveness and resilience. A mixed-method approach—combining quantitative survey data with qualitative interviews—was employed to investigate the relationship between TM, DT, and SUS in the context of the European manufacturing sector. The findings reveal that while technology and sustainability represent the technical and ethical dimensions of transformation, the human factor—through leadership, culture, and continuous learning—serves as the critical link enabling integration. The study contributes to theory by merging fragmented research streams on digitalization, sustainability, and talent management into one unified model. Practically, it provides actionable insights for SME leaders and policymakers seeking to manage the “twin transitions” of Industry 4.0 and the European Green Deal. The dissertation concludes that technology enables change, but people make transformation happen.
Keywords: Talent Management, Digital Transformation, Sustainability, SMEs, Leadership, Organizational Culture, Industry 4.0, Human-Centered Transformation

Ethical Decision-Making Compass (EDMC): A Framework for Leadership in a Complex World

2026-04-03T08:12:17+00:00

In today’s fast, AI-intensive environments, leaders and teams must make principled choices under time pressure, complexity, and uncertainty. While many ethical frameworks illuminate parts of the journey, they often prove too siloed, slow, or abstract for high-velocity decisions—where ethical salience is easily missed and organizational learning is lost. This thesis develops and validates the Ethical Decision-Making Compass (EDMC): a first-tier, time-aware, auditable architecture structured by PASO—Principles (why), Actions (how), Skills (who), and Outcomes (what).
EDMC is doctrine-agnostic about which principles a community adopts, yet uncompromising that whatever is named must be enacted, owned, and evidenced. It functions as a Compass, not a checklist: orienting attention at the moment of choice, linking intent to safeguards and stewardship, and leaving a transparent record for review. Rather than cataloging gaps, this work synthesizes strengths from sixteen leading frameworks, integrating their most practical recurring elements into a unified, adaptable tool for diverse, AI-driven contexts.
Validation draws on two evidence streams: an international expert panel (N=30) and researcher-led applications across eight well-documented public-facts cases. Results show that EDMC clarifies criteria, counters ethical blindness with engineered salience cues and thresholds, and supports cautious, context-sensitive action. In AI-heavy settings, it translates principles into concrete guardrails, human-in-the-loop roles, bias audits, rollback conditions, and principle-linked KPIs, while remaining stable as priorities shift.
For leaders and teams, EDMC turns intent into safeguarded action with capable ownership and accountable outcomes. For boards and policymakers, it provides credible, pre-emptive diligence and proportionate disclosure. Repeated use builds capability and culture: the doctrine-agnostic PASO DNA, state the principle, specify the action, assign a skilled owner, define the check, functions as an operating code that creates a learning loop rather than paperwork. While practice-focused and modest in scope, the evidence indicates EDMC is adaptable, teachable, and ready for digital embedding and cross-context deployment.
In sum, EDMC reframes ethical decision-making as a disciplined, time-aware, and auditable, learnable practice. Used consistently, its PASO DNA makes ethical noticing unavoidable and turns principled intent into traceable action—offering a Compass for navigating uncertainty at the speed of modern work.
Keywords:
ethical decision-making, responsible leadership, decision architecture, PASO (Principles–Actions–Skills–Outcomes), Ethical Decision-Making Compass (EDMC), doctrine-agnostic, behavioral ethics, ethical blindness, time-pressure, traceability, auditability, AI governance, cross-cultural adaptability, human-in-the-loop (HITL), digital transformation

MLapi: A Machine Learning API Tool for Data Analytics

2026-04-03T08:10:18+00:00

This study presents the design, development, and usability evaluation of MLapi, a novel machine learning API tool aimed at facilitating access to Python-based data analysis and machine learning techniques. MLapi was developed to address the growing need for user-friendly analytical tools that bridge the gap between technical complexity and accessibility, particularly for users with limited programming experience. The system architecture integrates Microsoft Excel as a front-end interface with a PHP-based API and a Python Anaconda back-end, forming a modular and scalable three-tier structure. MLapi offers pre-configured templates for statistical and machine learning methods, automatically generating results in Jupyter Notebook format to enhance transparency and educational value.
The empirical component of the study employed the System Usability Scale (SUS) to assess perceived usability among 150 data-analytics professionals in the Greek banking sector. MLapi achieved a mean SUS score of 90.0, exceeding the benchmark of 85 typically associated with excellent usability. Principal Component Analysis (PCA) of the SUS responses revealed three latent dimensions -Complexity, Agility, and Learnability- each demonstrating high internal consistency. Statistical analysis showed no significant differences in usability perceptions across gender, age, education level, or professional experience, indicating that MLapi provides a universally accessible user experience.
These findings suggest that MLapi is both technically robust and inclusive, offering intuitive workflows and minimizing cognitive load. The tool’s integration with Excel enhances accessibility, while its educational features support gradual skill development in Python and machine learning. The study contributes to the fields of educational technology and usability engineering by validating MLapi as a scalable and effective solution for data science education. Recommendations for future research include expanding the sample population, incorporating qualitative methods, and exploring integration with additional platforms to further enhance usability and applicability.

The Transition from Green to Blue IT and How Organisations Can Achieve Net Zero Goals

2026-04-03T08:08:08+00:00

As the world races toward net-zero goals, the role of IT is evolving from just being a support function to becoming a key player in sustainability. This research looks at how organizations can move from Green IT which focuses mainly on reducing energy use and waste to a more ambitious model called Blue IT. Blue IT isn’t just about being eco- friendly; it’s about embedding sustainability into every part of IT operation, from strategy and design to procurement and disposal.
The study takes a practical approach by combining interviews with IT leaders, government policymakers, and experts from Global Competency Centers with survey responses from professionals across industries. It explores what’s working, what’s not, and what’s missing when it comes to making IT truly sustainable. It also digs into how new technologies like AI, IoT, and blockchain can help optimize resources and support sustainability goals.
Through real-world case studies, the research highlights common challenges like budget constraints, inconsistent global policies, and lack of awareness especially in smaller organizations. It also emphasizes the importance of government policies, cross-functional teamwork, and building a culture that encourages “green thinking” across all levels.
One of the key takeaways is the need for better tools to measure and report on IT’s environmental impact, including emissions, energy use, and even the carbon footprint of supply chains. The research aims to provide a clear, flexible roadmap that organizations can actually use not just another theoretical model that sits on a shelf.
In the end, this work hopes to help IT leaders and decision-makers make sense of where they are today and what steps they can take next to align their digital strategies with long- term sustainability goals. It’s about turning good intentions into action and helping IT become a real force for climate progress.

Addressing Emotional Intelligence and Socio-Emotional Learning Gaps for Delivery Gig Workers in Chennai, Tamil Nadu: A Response to the Growing Gig Economy

2026-04-03T08:06:13+00:00

The study investigates the effect of Emotional Intelligence (EI) and Socio-Emotional Learning (SEL) programs on delivery workers' job satisfaction, emotional resilience, and customer service. Theoretical frameworks of EI and Self-Determination Theory guided the main research through advancing the understanding of socio-emotional issues using stress management, empathy, and self-regulation skills. The literature review offers a rigorous critique of the Western countries' gig economy growth and, at the same time, exposes the specific issues of delivery staff, such as gender-specific vulnerabilities and no informal emotional support. The gap in existing studies largely relates to the knowledge of the role that EI and SEL models play in the development of workers' well-being and thus the need for specific practitioner-oriented interventions. The mixed-methods research design was employed in the present study, comprising quantitative surveys and qualitative content analysis. To evaluate the dimensions of job satisfaction, stress, and emotional resilience, the quantitative data were gathered from a sample of 100 delivery workers and 200 consumers in Chennai, which were then subjected to descriptive and inferential statistical analyses. Qualitative insights were generated through the systematic content analysis of six secondary research papers selected for focusing on the gig economy worker experiences, emotional strains, and service performance. The findings showed that emotional intelligence, especially self-regulation and empathy, was positively linked to better job satisfaction and stronger coping skills among the delivery workers. The customers also identified emotionally intelligent workers as being the ones with high-quality service and good communication. Besides, the content analysis corroborated these results, indicating the emotional difficulties and the absence of a support structure throughout the industry. The study concludes that embedding mobile-first, context-sensitive EI and SEL learning programs into delivery platforms can foster psychosocial well-being and operational sustainability. These findings highlight the need for policy and platform-level reforms that prioritize emotional development alongside efficiency metrics.
Keywords: Gig Economy, Emotional Intelligence, Social-Emotional Learning, Delivery Partners, Gig Workers, Chennai.

Defining a Resilient Next Gen Supply Chain Management to Design a Sustainable Model for Manufacturing Industries

2026-04-03T08:04:14+00:00

Background
Recently manufacturing industries have seen an era of significant innovations driving rapid growth in technological advancements that revolutionized the overall business processes. As the global industry witnessed a range of disruptions, i.e. COVID 19, it is important that industry champions should continue to innovate methodologies to redesign the process value chains by enabling real-time monitoring to increase productivity and overcome critical challenges. Traditionally organizations depend on manual inspection processes or rule-based engines to understand the problem scenarios and provide solutions.
Lately, industries understand the need for intelligent processes that support proliferation into problem scenarios and provide simplified solutions. This led to the emergence of multiple technological platforms, one of which is Generative AI that has propelled advancements into every section of manufacturing processes offering a variety of intelligent solutions that has fastened the delivery of services to its end customers.
Methods
The study used a qualitative case method to identify multiple factors that cause disruptions in a regular logistical operation. The primary source of research method is to gather information from the available literature and secondary sources and understand the inter-relationship and cause of concerns prevailing in the industry.
As part of the analysis, data classifications on lead indicators of disruptions are grouped to define the possible use of case scenarios to guide improvement in the daily operations.
Results
Leading trend data were analyzed based on which classification methods have been defined to group disruptions and conducted risk assessment reflecting possible impacts and related key performance indicators. As part of this research, a Global Maturity model for adopting the journey of Generative AI is proposed to develop resilient supply chain operations and a culture of customer centric transformations.
Conclusion
The study’s findings should be of most value to small, medium and large manufacturing organizations, that have embarked on a proactive journey to transform their business value chains and create a global network to overcome volatility and being customer value driven. Furthermore, it will help provide in-depth analysis and constant monitoring to bolster the organizations value chains with strength and flexibility required to withstand future market challenges and strengthen cross-collaboration to improve business efficiency.

Dehumanization and Limits of Artificial Intelligence Within Recruiting: The Optimal Interaction Between Humans and Artificial Intelligence

2026-04-03T08:02:11+00:00

Artificial intelligence (AI) is reshaping the recruitment landscape by enhancing efficiency, scalability, and accuracy. However, its adoption brings forth significant ethical dilemmas, such as dehumanization, bias, and transparency challenges. This dissertation delves into these issues within recruitment processes, aiming to find a harmonious interaction between AI and human recruiters.
The study emphasizes the advantages of AI, including the automation of repetitive tasks, improved candidate matching through predictive analytics, and cost savings. Simultaneously, it points out critical drawbacks, such as the reinforcement of biases from historical data, a lack of personalization, and concerns regarding candidate data privacy. Dehumanization—the decline of empathy and personal connection in hiring emerges as a significant concern, potentially harming employer-employee relationships and negatively impacting the candidate experience.
Employing a mixed-methods approach, the research combines qualitative and quantitative data to explore these issues. Interviews with HR professionals and AI specialists reveal a shared understanding of the necessity for a balanced strategy in AI integration. Statistical analyses further highlight the importance of transparency and fairness in reducing bias and building trust.
The dissertation suggests a hybrid recruitment model that merges AI’s efficiency with human insight. This model prioritizes fairness audits, explainable AI systems, and ethical oversight to strike a balance between automation and empathy. Practical recommendations include training AI systems on diverse datasets, utilizing fairness-aware algorithms, and encouraging collaboration between human recruiters and AI technologies.
This research adds to the academic and professional conversation by tackling the ethical and operational challenges that AI presents in recruitment. It provides actionable insights for HR professionals, AI developers, and policymakers to enhance recruitment processes while maintaining humanity and fairness.

The Impact of Implementing Formal HRM Practices on Small Firm Dynamics, Employee Motivation and Performance in Tourism and Hospitality Industry in Mallorca

2026-04-02T14:51:30+00:00

Formalization of Human Resource Management (HRM) practices is an important component of contemporary HRM. Formalized HRM practices enable improved operational performance and bring other benefits, including increased employee satisfaction, to the company. However, contemporary management science does not provide an answer to whether effective HRM practices are universally applicable or should be tailored to an organization and its characteristics. The empirical study included small tourism enterprises in Mallorca. The aim of the study was to assess the impact of the scope and intensity of HRM formalization on the firms surveyed. A combined quantitative and qualitative approach was used. The level of HRM formalization among the surveyed companies varied, mostly being moderate. Increased employee motivation and perceived benefits depended on interacting factors, including sector and company size. The findings indicate that a significant factor in the success of formalization is the lack of a gap between planned and actual HRM policies, as well as proper communication and a supportive attitude from managers..