Category: Theses

Subject: Employee-AI Collaboration

Title: Socio-Technical Factors Shaping Employee-AI Collaboration

Abstract: 

Despite significant organisational investments in AI technologies, 70-85% of AI initiatives fail to achieve their projected value due to inadequate understanding of the human factors driving successful employee-AI collaboration. Using a socio-technical systems perspective to analyse intricate relationships between technological, organisational and individual components, this study explores the socio-technical characteristics that impact employee-AI collaboration in digital workplaces. This study examines five key factors: AI literacy, AI explainability, organisational support, task variety and paradoxical tensions. A quantitative cross-sectional survey was conducted at Doctolib, a European healthcare technology company that implemented enterprise-wide AI capabilities in February 2025. Data from 73 employees were analyzed using PLS-SEM to test relationships between five socio-technical factors and employee-AI collaboration outcomes. The key finding demonstrates that AI literacy serves as the most critical factor in determining collaborative success (β = 0.498, p = 0.016), providing empirical validation of Wang et al.’s (2022) four-dimensional AI literacy framework in a real organisational setting. However, contrary to theoretical expectations, AI explainability, task variety and paradoxical tensions showed no significant relationships with collaboration. Most surprisingly, organisational support failed to demonstrate any moderating effects, challenging traditional technology acceptance frameworks. The research validates AI literacy theory whilst challenging dominant narratives in explainable AI research. For practitioners, the findings suggest that organisations should prioritise comprehensive AI literacy programs addressing awareness, usage, evaluation and ethics dimensions rather than focusing on traditional organisational support mechanisms. This research demonstrates that successful employee-AI collaboration requires AI-specific approaches to implementation and capability development, highlighting the need for new theoretical frameworks that move beyond conventional technology adoption models.

Key words: Employee-AI collaboration, socio-technical systems, AI literacy, organisational support, PLS-SEM

Subject: Information System Science

Title: Onboarding Business Domains into a Data Mesh: A Kotter-Based Change Enablement Framework Tested at Toyota Motor Europe

Abstract: 

As organisations adopt decentralised data architectures like Data Mesh, many struggle to operationalise new roles such as Data Product Owner or Domain Data Steward. While technical aspects are well-documented, the organisational and behavioural dimensions, particularly the onboarding of business stakeholders, remain underexplored. This thesis investigates how large enterprises can enable successful role adoption during a Data Mesh transformation. Based on a qualitative case study at Toyota Motor Europe and using abductive reasoning, the study proposes the Data Mesh Change Enablement Framework, an adapted eight-step change model inspired by Kotter’s theory but tailored to decentralised contexts. The framework emphasises contextualised urgency, multi-level coalitions, co-created role narratives, peer-driven acceleration, and institutional anchoring. Grounded in empirical data, the framework offers a practical yet flexible tool for guiding change in large-scale data transformations.

Key words: Data Mesh, Change Management, Decentralised Data Governance, Business Stakeholder Onboarding, Data Product Owner, Federated Architecture, Kotter’s 8-Step Model, Qualitative Case Study, Toyota Motor Europe.

Subject: Information System Science

Title: The optimization of change management processes via automation and performance metrics in IT systems.

Abstract: 

The thesis investigates how organizations can manage new technology by adding automation and performance evaluation to what they already do. Since digital transformation is growing more complicated and happening faster, the research examines how technological tools, organizational methods and people are interacting. Using a qualitative method, the researcher study how institutions organize, execute and review changes in complicated conditions. Focus is placed on how firms deal with bringing together technological progress, strategic planning and staff cultural acceptance. The thesis adds to current debates on how data, systems and people help shape successful and lasting change.

Key words: IT Change Management, Automation, Performance metrics, Artificial Intelligence, Resistance to Change, Robotic Process Automation (RPA), Key Performance Indicators (KPIs).

Subject: OS Migrations

Title: Managing End-User Satisfaction and Compliance during OS Migrations in Multinational Research Organizations

Abstract: 

This thesis investigates how structured IT governance frameworks can support end-user compliance and satisfaction during operating system (OS) migrations, focusing on the case of ITER’s transition from Windows 10 to Windows 11. While existing governance models such as COBIT and ITIL provide structured guidance for managing risk and standardizing processes, their application during OS migrations remains underexplored. The study addresses this gap through a mixed methods case study, combining the analysis of 176 service desk tickets with seven semi-structured interviews conducted across IT and non-IT roles. The findings reveal that compliance emerged through negotiated behaviours shaped by user perception of change, and that workarounds bordering on non-compliance were also part of a healthy approach to change. While technical execution greatly improved over time, some persistent gaps in perspectives between end users and managers were observed. The study proposes that integrating governance frameworks with change management models (such as ADKAR and Kotter) enables organizations to adapt formal controls to contextual realities. This approach is especially relevant in multinational scientific institutions, where strict compliance requirements coexist with diverse user expectations and operational constraints.

Key words: IT governance, end-user compliance, OS migration, change management, Windows 11, ITER

Subject: Culture influence on ERP implementations in the MENA region

Title: Culture Influence on ERP implementation : a MENA perspective

Abstract: 

Enterprise Resource Planning (ERP) systems are widely adopted by multinational organizations to integrate business processes, yet their implementation success remains inconsistent across cultural contexts. This thesis investigates how national cultural differences influence ERP implementation outcomes in the Middle East and North Africa (MENA) region, with a specific focus on SAP projects.
While Critical Success Factors (CSFs) frameworks are frequently used to guide ERP deployment, most existing literature disregards culture or treats it as a peripheral factor. Moreover, these frameworks tend to be predominantly Western-oriented. Drawing on Hofstede’s cultural dimensions, this research explores how cultural values actively impact key CSFs such as top management support, project management, user training, ERP team composition and Business Process Reengineering. It employs a qualitative methodology based on semi-structured interviews with ERP consultants who have implemented SAP in Egypt, Jordan, Morocco, and Saudi Arabia.
This study reveals that Hofstede’s cultural dimensions translate into specific behavioral patterns that systematically influence Critical Success Factor effectiveness. Four consistent areas of influence emerged: hierarchical decision-making patterns, team dynamics, communication styles, and change resistance behaviors. The findings challenge the assumption that CSFs operate uniformly across cultures, revealing instead that cultural values fundamentally reshape how each success factor must be implemented.
This thesis extends ERP theory by positioning culture as a contingent factor that fundamentally shapes CSF effectiveness rather than treating it as a separate element. For practitioners, it offers concrete guidelines for adapting implementation approaches to local cultural contexts in the MENA region.

Key words: ERP implementation, SAP, National Culture, Critical Success Factors, MENA Region, Behaviours

Subject: Digital Addiction and Interface Design

Title: Designing for Control: Embedded Digital Interventions to Counter Compulsive Short-Form Video Use

Abstract: 

Short-form video (SFV) platforms like TikTok have become dominant in the digital media landscape, driven by immersive design features such as algorithmic personalization, continuous scroll, and high sensory stimulation. While these features maximize engagement, growing evidence links SFV use to compulsive behavior, impaired attention, and weakened self-regulation. Traditional awareness tools—such as screen time apps—often fail in such environments, as they rely on voluntary self-control despite the platform’s intent to suppress it. This thesis investigates whether embedded digital interventions, implemented directly within the consumption flow, can counteract compulsive SFV usage. A between-subjects experiment was conducted using a custom-built SFV platform, testing and comparing two conditions: a friction-based timer and a targeted motivational warning. The study measured behavioral outcomes, perceived control, and future intervention acceptance. Findings show that both intervention types significantly reduced perceived overconsumption and increased user reflection. Tailored warnings were also rated as more supportive, though behavioral outcomes were similar across types. These results suggest that embedded interventions can help restore user agency in digital environments explicitly designed to undermine it, offering practical insights for platform designers and policymakers.

Key words: Short-form video, Digital interventions, Interface design, Compulsive media use, Behavioral nudges, Friction awareness, Digital well-being

Subject: RFID in Retail

Title: Leveraging RFID in Retail: From Stock Management to Customer Experience Enhancement

Abstract: 

Radio-Frequency Identification (RFID) has long been associated with inventory management and operational efficiency in retail. However, recent developments show that RFID can also serve as a strategic enabler of customer experience, supporting digital transformation efforts. This thesis investigates how retailers can leverage RFID beyond stock optimization to enhance customer experience, while overcoming implementation challenges.
The study adopts a qualitative multiple case study approach, drawing on both primary and secondary data. Four companies from different retail sectors are examined: a luxury fashion brand (Luxury House), Decathlon, Uniqlo, and Kroger. Data were collected through semi-structured interviews and analysis of public sources, and were coded thematically across two dimensions: customer experience improvements and barriers to RFID implementation.
The findings highlight several best practices for enhancing customer experience through RFID, including real-time inventory visibility, personalized in-store services, seamless checkout, and omnichannel integration. The study also identifies recurring challenges, such as technical complexity, training needs, financial justification, and privacy concerns. Additional insights were observed around operational efficiency, data analytics, and sustainability.
Based on these findings, the thesis proposes a best practices framework to guide retailers in deploying RFID technology effectively. The framework outlines strategic, technical, and organizational components necessary to align RFID adoption with customer experience goals. This work contributes to the literature by bridging operational and experiential perspectives on RFID and offers actionable guidance for retail managers navigating RFID transformation initiatives.

Key words: RFID, retail, customer experience, best practices, framework, digital transformation, case study, inventory accuracy, checkout process, implementation challenges.

Subject: Information System Science

Title: Benefits from implementing a data labelling tool

Abstract: 

The amount of data in the world is constantly increasing, making data management more complex and demanding. Effective data utilization is crucial for in-depth analysis, logical reasoning, and decision-making processes. Data labelling is an essential part of this process, but it has traditionally been labour-intensive and resource-consuming. To manage always scarce resources more efficiently, companies are turning to data labelling tools to automate the process, enhance data management, and extract more value from their data.

This thesis aims to reason the benefits and risks associated with implementing a data labelling tool, specifically Microsoft Purview. The study employs a benefit measurement model and includes a pilot project conducted in a case company. Additionally, interviews with company professionals were conducted to provide further validation and professional insights into the benefits of data labelling.

The findings reveal several notable benefits of data labelling and data labelling tools. Firstly, labelling tools improve the quality and understanding of the data in hand, enhancing its utility. Secondly, automated labelling tools significantly accelerate the labelling process, reducing resource consumption compared to manual methods. Thirdly, data labelling offers broad advantages in data management, data governance, data loss prevention, data security and compliance management and data lifecycle management. Risks related to data labelling tool implementation includes accuracy of labelling, user adoption and engagement and beneficial resource allocation.

Key words: Data labelling, Benefit management, Data labelling tool

Subject: International Master in Management of IT

Title: Enhancing Financial Audits through Deep Learning: Addressing Key Challenges and Improving Efficiency

Abstract: 

The financial audit (FA) process, traditionally based on manual procedures and reliant on professional judgment, faces challenges in the era of digitalization, due to the requirement of analyzing large volumes of complex data. This thesis investigates how deep learning (DL) can address challenges in the FA process, particularly focusing on large data volumes, manual procedures, and the subjectivity of professional judgment. Using the Task-Technology Fit (TTF) theory as a guiding framework, the study explores DL’s potential through a comprehensive research approach.

Through 13 EY expert interviews across various global locations, and a qualitative survey, the research identifies key challenges in current FA practices, and shows a fit with DL applications. DL shows promise in addressing these issues by automating tasks, managing data complexity and large data volumes, and providing auditors with data-driven recommendation.

Findings reveal that DL’s capabilities in natural language processing (NLP), computer vision, anomaly detection, recommendation systems, and big data analytics can address the identified FA challenges. Additionally, DL models are suggested for alleviating each challenge.

This study not only validates existing DL applications, but also introduces up to date FA challenges. This thesis provides a solid foundation for future research and practical applications in the field of financial auditing. The implications of these findings suggest that adopting DL can lead to more efficient and accurate FA processes.

Key words: Deep Learning (DL), Financial Audit (FA), Task-Technology Fit (TTF)

Subject: Information Systems Science

Title: Adoption and Governance of AI-Powered Dashboards in Executive-Level Decision-Making

Abstract: 

AI-powered dashboards are business intelligence tools that collect and present data in a comprehendible manner to enhance decision-making. The integration of artificial intelligence (AI) in data-driven decision-making (DDDM) processes is continuously increasing in modern organizations, however, current research examining AI-powered dashboards does not address the deployment of the technology. To complement prior research, this thesis aims to provide a foundation for the implementation of AI-powered dashboards by investigating the current dashboard best practices, as well as the adoption and governance of AI-powered dashboards in the context of executive decision-making. The concepts of DDDM and AI function as a foundation regarding the purpose and functionality of AI-powered dashboards, whereas IT governance provides a basis for their adoption and governance. The research methodology encompasses a review of scientific literature addressing these concepts to enable the proposal of two distinct theoretical frameworks addressing the adoption and governance of AI-powered dashboards.

The results of this study highlight three critical areas regarding AI-powered dashboards: current best practices, adoption processes, and governance. Best practices show that AI-enabled dashboards are dynamic and versatile BI tools that enhance decision-making and operational efficiency through detailed visualizations and data analysis. Concerning adoption, the study emphasizes the importance of selecting a suitable framework tailored to organizational needs, suggesting that a combination of existing models might often be necessary to integrate AI and BI within organizational environments effectively. Results addressing the governance of AI-powered dashboards emphasize the importance of BI and AI governance. Although the development of AI governance frameworks is still in its early stages compared to BI and IT governance, the findings suggest that adopting flexible and diverse governance structures enables organizations to manage the security, transparency, and accuracy risks associated with AI technologies. 

Key words: Dashboard, Business intelligence, Artificial intelligence, Data-driven decision-making.