In the highly regulated and competitive European Union (EU) lighting industry, effective product portfolio management (PPM) is crucial for companies to maintain market share and profitability. This thesis focuses on identifying key factors impacting profitability and market share, including technical specifications, pricing strategies, market positioning, competition benchmarking, and environmental considerations.

A notable gap in the existing literature is the lack of industry-specific frameworks and qualitative assessments for identifying these factors. This study addresses this gap by developing a comprehensive guiding framework, informed by theoretical models such as SWOT analysis and Porter’s Five Forces. The framework guides the qualitative research design, which involves semi-structured interviews with PPM experts.

The empirical analysis, employing axial coding techniques, reveals critical insights into how these factors influence PPM. Key findings highlight the importance of market-oriented pricing and strategic targeting of product segments with low differentiation potential to enhance long-term profitability. The study's conclusions provide practical and theoretical implications, offering actionable insights for industry stakeholders and addressing the limitations of the current research landscape.

The thesis explores the complexities and risks associated with Automated Decision-Making (ADM) systems in social welfare, focusing on the phenomenon of ”digital cages.” The digital cage concept refers to a situation where rigid systems, perpetuated through algorithmic misclassification, inadvertently trap citizens in bureaucratic complications without recourse. This thesis analyzes the digital cages concept from a system safety perspective, on the example of the Dutch Toeslagenaffair. The thesis centers on understanding how digital cages form within social welfare systems and seeks methods to mitigate their emergence through targeted interventions using system safety theory. The pivotal question addressed is: ”What are safety control interventions to curb the emergence of algorithmic decision-making systems-induced digital cages in the context of national social welfare administration in the Netherlands?” The research concludes by highlighting the potential of assumption-based leading indicators. Assumptionbased leading indicators are predictive measures that rely on underlying assumptions about how certain inputs or actions correlate with future outcomes. The leading indicators provide a novel approach to enhancing system safety in social welfare administration by addressing the underlying assumptions of the system in operation. This offers a substantial potential to transform how social welfare systems manage and utilize ADM, potentially reducing the incidence of digital cages. By detecting early signs of hazardous systems states digital cages can be mitigated before they solidify into systemic issues. The thesis not only addresses the immediate concerns regarding digital cages but also opens pathways for future inquiries into safer and more equitable administrative practices. Future research is suggested to further refine these indicators and explore their applicability in diverse administrative contexts, ensuring they can effectively adapt to the evolving landscape of public administration and technology.

Corporate venturing practices have been one of the most successful practices in terms of shared innovation or leveraging outside talent to further one’s competitive edge in the rapidly growing and evolving industry of technology. Since, technology has been one of the main driving factors in providing solutions for achievement of short term and long term goals in the space, organizations are interested in finding out potential research findings and data that will enable them to maximize their probabilities of success for the particular venture. Streamlining such points of interest will improve the innovative capabilities of all participating organizations. A repository of literature was combed for identification of key factors that influence success in ventures. For this research BWM methodology has been employed to assess the importance therefore the prioritization of these factors and their impact through a set of curated list of respondents from the industry.

This master's thesis addresses a common problem of the adoption of IT automation; the nonoptimal use of the technology due to ineffective managerial communication. It searches for a solution by using the Design Science Research methodology to create a design artifact in the form of a managerial communication framework. This framework provides values to all stakeholders in the IT automation adoption process; the managerial level, the employees, and the technology provider. It improves managerial communication during the adoption process and identifies actionable guidelines for managers.

Narrowing down the scope of the thesis to the adoption of Robotic Process Automation (RPA) within the recruitment industry allows the research to be manageable within the constraints of a master's thesis. A literature review is used to investigate the knowledge base of the topic of the thesis. Additionally, semi-structured interviews with managers and employees are used as a means of collecting data from the environment. Combining these inputs, this research designs a model that explains the role of managerial communication in the effective adoption of IT automation. The model consists of four main components: managerial communication, motivation & vision, impact awareness, and the effective adoption of IT automation. It explains the constructs that were found relevant in this process and how they relate to each other. Several root constructs are used as a basis to create actionable guidelines to facilitate an effective adoption process. Together with the explanatory model, these guidelines form the managerial communication framework. The main goal of the framework is to bridge the gap between the manager's and the employee's views on the nature of the change. This study finds that managers often view the change as a process optimization, whereas employees view it as an organizational change. The managerial communication framework allows to identify these differences and act upon them.

This study not only designs the communication framework, but also evaluates its defined objectives with several experts. The results of these evaluation interviews demonstrate the validity and usability of the research conducted and the design artifact. Future research can extend the framework to other domains & technologies and tailor it to specific problem cases. Furthermore, the framework can be researched and evaluated in practice by conducting a case study.

In recent years, there has been a growing interest in entrepreneurial education among students, emphasizing the significance of an entrepreneurial mindset. Introducing the EnteComp framework in 2016 has provided a standardized approach for evaluating interdisciplinary competences in students. This study aims to address this issue by examining the optimal utilization and integration of the EntreComp framework in the educational practices of Delft Center for Entrepreneurship (DCE). By conducting a comprehensive literature review and analyzing data from a large-scale survey involving 119 questions among DCE students, the research investigates the most effective methods for measuring the development of entrepreneurship competences among students. The findings indicate EntreComp offers a shared language and a holistic approach for assessing entrepreneurial competences. The analysis also reveals a lack of consensus and practical implementation in utilizing the EntreComp framework. Therefore, it is crucial to focus on the longitudinal development of EntreComp competencies over time and to employ various survey types and statistical tests to make the use of EntreComp more common.

This research focuses on the effects of the socio-technical boundary conditions on the success of chatbot implementation in organizations. To do so, different actor perspectives on "successful implementation" are explored and 33 boundary conditions are proposed. The findings suggest that satisfying the boundary conditions has a positive effect on the success of the chatbot implementation. This indicates that the suggested boundary conditions can be used as a tool to guide future chatbot implementations. By doing so, the social and technical requirements can be ensured.

Banks have started adopting machine learning to reinvent their business functions and improve their decision-making capabilities. However, operationalizing machine learning. i.e. converting machine learning experiments into sustainable production-grade applications, remains a key challenge, which limits banks from realizing the true business value of machine learning. Despite its significance, the scholarly literature on machine learning operationalization is scant and predominantly technical. The main objective of this research is to develop a socio-technical framework, that supports the understanding and implementation of the machine learning operationalization process in the banking sector. This research combines an extensive literature study and 15 expert interviews to identify nine socio-technical factors that influence the operationalization of machine learning applications. The identified factors are then validated and applied to a real-world context through a case study analysis. The findings suggest that risk management is one of the most crucial yet challenging aspects of the process. To investigate this further, the research analyzes the socio-technical challenges of risk management and proposes four strategic guidelines to address the same. This leads to the development of a conceptual model which illustrates how factors such as shared knowledge and controls reduce the challenges of risk management and thereby support the machine learning operationalization process.

The financial domain is losing ground to rapid-developing fraud schemes. It puts intense pressure on organizations such as banks to find new approaches to tackle financial misconduct. This financial crime has always existed and is present in the financial industry. However, the rise of technology and the use of online transactions has enhanced the presence of the impact of fraud in this industry. The increase in financial fraud cases in a technological era result from a lack of inter-organizational synergy and the privacy concerns that entail by making data available. On top of the increasing fraud cases, organizations are exposed to increasing regulatory, financial, reputational, and legal risks. Hence, the financial crime industry and fraud prevention organizations must act on this threat. Therefore, these actors need to improve their current workflow continuously to keep up with the new developments. Different studies propose that it is a potential opportunity to take the chance and bundle data together to learn from the existing environment and improve their workflows and prediction models. However, the main concern is that parties are reluctant to share data as it involves confidential and sensitive data, which malicious parties can leverage and abuse. Also, the increasing focus on privacy protection regulations makes it complex and challenging to exchange data easily. The dataset that actors are providing will contain personally identifiable information, which in fact cannot be shared and proposed without any legitimate reason and is subjected to the data privacy regulations.
The existing set of techniques for sharing and analysing data securely, such as differential privacy, homomorphic encryption and federated learning have been proposed in studies and use cases are built in the real world. However, these techniques are insufficient and capable enough to facilitate multi-actor (data owners) data exchange and analysis. Secure Multiparty Computation, however, is capable of having multiple data owners securely perform a joint analysis. For this reason, this study has been focused on SMPC. In the case of the financial crime industry, it requires the involvement of multiple stakeholders sharing data simultaneously. Especially in the case of banks, it is essential to have bundled data for all transactions as these are connected.
To understand the purpose and the concept of the study, it is essential to have a basic understanding of SMPC. Secure Multiparty Computation is a cryptographic method for parties to mutually compute a function over all parties' acquired inputs while keeping the intake private throughout the entire process. Independent computation nodes will perform and provide analysis outcomes to designated parties. The concept of Secure Multiparty Computation has been studied by academia since the 1980s (Yao, 1982). However, the applications and introduction of SMPC are relativity novel to organizations and will not be immediately accepted. A technique such as SMPC will require participating parties to share a mutual interest and willingness to contribute continuously. It is uncertain if organizations will accept and adopt SMPC as mentioned before. Therefore, the study will also incorporate the concept and theory of collective action to understand the motives and the common goal for stakeholders in the anti-fraud industry to accept the technique and collaborate. A common goal, also known as a collective goal or interest, would create acceptance among the group. In this case, it will help to identify the factors and interests that influence an organization's decision to engage in collective action for developing MPC for fraud detection in the financial industry...

Data Marketplaces are emerging digital platforms in data management. These platforms enable the infrastructure for data exchange, offering the opportunity for data providers to commercialize their datasets and save time for data consumers in identifying appropriate datasets. Conceptually, data marketplaces provide data exchange services as their core activity for the business community. However, in practice, organizations may be reluctant to exchange data through intermediaries as marketplaces instead of exchanging data bilaterally. The bilateral exchange of data gives more control to providers and consumers over the exchange process. For this purpose, a decentralized architecture could represent the peer-to-peer technical solution for the bilateral exchange. Therefore, what was previously called intermediaries, in the decentralized environment, data marketplaces are communication structures facilitating the commercial exchange of data between organizations. In this thesis, a specific focus on a decentralized architecture, namely blockchain technology, is considered for data marketplaces. The research follows the firm belief that both the technical and business specifications are essential in understanding the data marketplaces with blockchain architecture. Therefore, the decentralized architecture and the business models for data marketplaces, are researched with the overarching objective to investigate the impact of a decentralized architecture on the business models of data marketplaces. This research contributes to the literature by improving the general understanding of decentralized data marketplaces and by providing technical and business frameworks that can be utilized to investigate the application of blockchain technology in data trading platforms as well as analyze their business model characteristics.