This thesis explores the potential of Virtual Reality (VR) as a tool for workforce reintegration after absenteeism, using a Value Sensitive Design (VSD) approach in the context of KLM, a major airline company. The study addresses the values and concerns of cabin crew employees undergoing traditional reintegration processes and examines how VR can be customized to better meet the diverse needs of employees returning to work after prolonged absence.

Background & Problem Area: Certain conditions may lead employees to stay away from their regular working environment for prolonged periods of time. This is called Absenteeism, and many employees stay for a significant time in the reintegration phase, where they work in alternative workplaces for much fewer hours per week before going back to their old duties. The one-size-fits-all designs of reintegration processes have proved unsuccessful considering their prolonged duration. The organizational turbulence & increased costs for companies caused by this phenomenon highlights the need for customized interventions for employees coming back to work.

Research Gap: As such, the first gap that this study will try to address is the lack of understanding of how to customize these processes and how to support employees coming back to work. The adaptability and applications of Virtual Reality makes it potent to be used in the context of workforce reintegration. However, this indicates a second gap regarding whether and how Virtual Reality technologies can be implemented in the workforce reintegration. The novelty of this study and the lack of an existing clear-cut approach, lead to a third research gap of how to implement a Value Sensitive Design approach, to redesign reintegration processes, through VR interventions.

Research Objectives & Questions: The first aim of this research is to explore the values in the context of workforce reintegration as a new way of understanding how to create value-sensitive technologies. The second aim is to understand the potential of using Virtual Reality in the reintegration process to best cater to the needs of their end-users. Therefore, the present study will seek to answer the following research question:

“How can the workforce be reintegrated back to work, by considering their values?”

Research Approach: The study employed a qualitative research approach using an adaptation of the Value Sensitive Design (VSD) framework. This included stakeholder mapping (direct & indirect), semi-structured value-oriented interviews, and VR user-testing sessions. The data was analyzed using a Thematic Content Analysis methodology.

Results: The stakeholder analysis revealed that the stakeholders that were mostly impacted by the technology were the ones with the least influence in the process. The data analysis unveiled that while VR showed potential for bridging the gap between reintegration and regular work environments, it also revealed the need for customization. The study identified the following key values in regards with the workplace: Organizational Support, Work-Life Balance, Autonomy, Safety, Physical Wellbeing, Mental Wellbeing, Purpose & Meaning Workplace Connection. Moreover, the study identified Privacy, Immersivity, Accessibility, Customization, Usability, Human Wellbeing and Autonomy as values implicated in VR design either through their presence or their absence in the technology. The study found that VR could mediate certain work values as depicted into the Virtual Vitality Model (VVM) developed on this basis. However, trade-offs, for example, between Workplace Connection and Physical Wellbeing, are to be considered. The value of Customization, a currently underexplored value, was investigated through the VSD prism, and seems promising to not only redesign processes through redesigning technologies, but also make this process more human-centered and inclusive.
Recommendations: The study recommends implementing VR as a supportive tool in the reintegration process, emphasizing the importance of customization to cater to individual needs and contexts. Customization in the context of reintegration through VR should consider factors such as people’s capabilities, experience with technology, their specific job duties and responsibilities. A transdisciplinary approach between managers and employees is essential for developing a more human-centric design that incorporates the values and needs of all stakeholders, particularly those most affected by the technology.

Conclusions: One of the key messages of this study, that despite people sharing similar values to a certain extent, their values vary among employees, work contexts, and even time periods. The study's strengths lie in its innovative methodology using the power-interest grid within VSD, the holistic approach on incorporating diverse stakeholder perspectives, and employing empathy and trust for data collection, leading to rich data. The weak external validity of this research should prevent us from generalizing the results to other populations and he cross-sectional design does not allow us to observe how VR usage impacts the reintegration process duration overtime. Job Satisfaction was a recurring pattern identified in the data, but it was outside the scope of this study. Future research can aim towards investigating how job satisfaction may impact the duration of the reintegration process and whether work values mediate this relationship.

Despite the potential of AI to significantly improve diagnostic accuracy, patient care, and operational efficiency in healthcare, ethical challenges such as biases inherent in AI systems can lead to unfair outcomes and exacerbate existing healthcare disparities. Drawing on insights from existing literature, this research aims to address the critical literature gap in the strategic and comprehensive integration of stakeholder collaboration with bias mitigation methodologies. Through a qualitative research design incorporating semi-structured expert interviews and literature analysis, this study searches to contribute valuable insights into the effective implementation of collaborative ethical AI development in healthcare. The result of this study presents an inclusive stakeholder collaboration framework designed to serve as a proactive guidance tool for stakeholders throughout the AI healthcare tool's lifecycle, with a focus on bias mitigation and ethical development. This framework outlines each phase of the AI lifecycle, and in each phase the algorithm's susceptibility to certain biases, bias mitigation strategies, ethical design strategies, and the responsible stakeholders are included.

The European Union’s strategy to transition to sustainable and renewable energy systems emphasizes the importance of biogas in reducing greenhouse gas emissions. However, Greece remains behind other EU countries in biogas production. This study aims to identify systemic barriers hindering the growth of the Greek Biogas Innovation System (GBIS), focusing on the region of Thessaly. The primary research question targets the identification of these barriers, supplemented by sub-questions addressing the characteristics of GBIS, systemic failures, and community inclusion challenges. The study employs a qualitative case study approach, conducting semi-structured interviews with stakeholders such as biogas producers, biomass suppliers, research organizations, energy communities, funding organizations, natural gas experts, biogas entrepreneurs, and community members. The theoretical foundation is based on the Technological Innovation System (TIS) approach and the System Failure framework, which helps identify broad systemic issues affecting innovation systems. Stakeholder participation and participatory design are integral to the study, ensuring comprehensive consideration of perspectives from all parties involved. Findings characterize GBIS as a diverse network of stakeholders, including biogas companies, biomass suppliers, municipal and governmental bodies, financial and research organizations, and biogas associations, operating at international, national, and regional levels. Relationships among stakeholders vary from positive to negative, weak, competitive, and advocatory. Key functions such as entrepreneurial activities, knowledge development and diffusion, market formation, and resource mobilization are present but not fully optimized. Identified systemic failures include infrastructural failures, such as an outdated electricity grid, and institutional failures, such as complex bureaucratic processes, regulatory gaps, and inefficient waste management regulations. Interaction failures are seen in strong but closed networks that hinder collaboration and information exchange, while capability failures are due to a lack of organizational abilities to overcome obstacles like unstable feedstock supply. Market failures include the volatile price of biomass and the absence of major market players. Additionally, media portrayal, local opposition due to environmental concerns and odor issues, and misinformation about biogas technology create community-related barriers. Climatic risks due to natural disasters, such as recent flooding in Thessaly, further compound these systemic issues. Recommendations to address these barriers include public investments in upgrading the electricity grid and centralizing biomass gathering through third-party collaborations. Strengthening stakeholder cooperation, facilitated by the Hellenic Biogas Association and academia, is crucial for joint R&D projects. Developing educational tools and training programs by universities and biogas companies can build a skilled workforce. Institutional reforms are necessary to streamline bureaucratic processes, close regulatory gaps, and provide incentives for compliance with environmental regulations. Establishing a central organizational body for GBIS can streamline administrative procedures and improve resource management. For community engagement, biogas companies should address local needs before initiating projects to ensure acceptance. Strengthening regional energy communities can empower locals to produce their own energy and educate them on biogas benefits. Promoting public awareness through media and municipalities and involving communities in biogas projects can enhance public perception and acceptance. In conclusion, this study identifies and proposes solutions to systemic barriers in GBIS, aiming to boost biogas production and enhance community involvement and acceptance. The TIS and System Failure approach, combined with stakeholder participation, offers a comprehensive method to analyze and address these barriers across the entire value chain.

With growing tensions in today's world, new powers rising and contesting old powers, two nations risk ending up in a so called security dilemma: One nation's increase in security threatens its opposing nation. The consequence could be the escalation of military buildups into arms races. With ongoing technological advancements, militaries around the world are expanding on autonomous capabilities that need less and less human control. In this research, I investigate under which conditions arms races for these systems between two fictitious nations may occur and how the nations can influence these. For this, I construct a system dynamics model based on literature and expert consultations and conduct experiments to generate a wide range of possible scenarios. The model results suggest that different types of arms races for autonomous military capabilities occur under specific conditions: the inferior nation lacks behind the superior nation and fills this initial gap not with a greater number of autonomous systems, but with higher quality autonomous systems, leading to an arms race in quality. Bilateral disarmament in the form of regulations on the level of autonomy seem not to be the answer for the superior nation: some of the arms racing is reduced, but the superior nation now loses both racing and non-racing scenarios. Restrict an opponent in their technological development is more effective for the superior nation. Still, the inferior nation is able to win in the specific case, where the superior nation requires "too much" human control. Generally, arms races can further be reduced when both nations avoid to be too "reactive" to each other's arming actions. This suggests, that a third party could step in and aim at relaxing the tensions between the two nations. Further, this third party could also aim at encouraging the inferior nation to raise its ethical standards. To conclude, the model can give insight into which decisions a nation or a third party can take with different goals in mind. These include, for the nations involved in a competition as a result from tensions, to either win the race towards autonomy or to prevent its opponent from winning it. A third party, interested in a peaceful world without having nations piling up autonomous military capabilities, might aim at preventing arms races whatsoever.

The applications of artificial intelligence (AI) are significant on a social and economic scale, and they can offer businesses great value and opportunities. However, because of AI's varied application areas, its inherent complexity, and the new organizational requirements that result from AI adoption, companies encounter pitfalls when deploying the technology. Potential implementation scenarios are not always clear. Understanding the AI readiness on an organizational level, which shows “the extent to which an organization has the ability to reap the benefits of AI” can improve the chances of effective AI deployment. This study aims to bridge the gap between academia and practice as most attention to artificial intelligence was paid to modeling steps in academia. Research on applying AI models to real-life problems and realizing business value has received insufficient attention. The current frameworks do not incorporate context-specific considerations for organizational readiness. This research investigates the organizational readiness of AI in the semiconductor industry. It is a critical step in avoiding costly failures, considering the capital-intensive characteristic of the semiconductor industry. A literature study has been conducted at first to review the existing AI organizational readiness framework and typical readiness factors. Then eight industry expert interviews are conducted to give a more holistic view of AI use cases across the semiconductor value chain as identifying the opportunity is the first step to establish AI readiness. Three challenges of AI deployment are summarized and the potential AI organizational readiness factors are listed to guide the case study interviews. The case study is carried out in ASML, one of the leading producers of chip-making equipment in the world. Through fourteen case study interviews, this research proposes an AI organizational readiness framework with six dimensions and conceptualizes twenty readiness factors. In the strategic alignment dimension, there are needs and added-value assessment, bottom-up proposal/innovation lab, top management support, business model innovation. In the resource dimension, there are talents, financial budget, IT infrastructure, competence center. In the process dimension, there are multidisciplinary team/collaboration, agile way of working, employee training, business process standardization. Regarding data dimension, there are data availability, data governance, data platform. In the AI model cluster, there are explainable AI with domain experts, context-aware modeling, model operation. In the external business environment, there are peers/competitors/software vendors and customer demand. Furthermore, twenty propositions on AI readiness in semiconductor organizations are given indicating their positive or negative influence on AI organizational readiness. This study contributes to the emerging literature on AI organizational readiness with the developed multi-dimension framework. It identifies AI-specific readiness factors under the dimensions of "Data" and "AI model". Moreover, among 20 readiness factors, 10 readiness factors are newly identified such as the agile way of working, competence center, context-aware modeling. Companies that seek to implement AI can use the proposed framework with readiness factors as a tool for assessment to help decision-makers, managers, and project teams to develop and deploy AI faster and more effectively.

Data has often been stated to be “the new oil”. This is said because both oil and data can be very valuable but only after they have been pre-processed. Among other things, a challenge for creating value from data lies in realizing data exchange. This is what data marketplaces aim to do. The number of shutdowns of data marketplaces during the past years implies that their business models are not viable and that there are obstacles that should be overcome to create a data marketplace that is robust in the long run. In this research, we explore a new research context: the relationships between business models of data marketplaces and data marketplace obstacles. To do this, by the means of explorative theorizing, we first provide an overview of the obstacle categories for data marketplaces: trust, lack of governance mechanisms, privacy and control, product quality and product description, matching data providers and data buyers, data pricing mechanism, and ‘other’ that do not fall within a specific category. We then followed a multiple-case studying approach in which we subject three different real-life data marketplaces to a Business Model (BM) Stress Test on the identified obstacles. The cases were selected by theoretical sampling of the types of data marketplaces: personal data marketplaces, Internet-Of-Things (IoT) data marketplaces, and Business-to-Business (B2B) data marketplaces. By reflecting upon the results, we conclude that the assessment of the true viability of a data marketplace business model goes way beyond just the relationships between business model choices and obstacles. It is concerned with the business model strategy and dynamic capabilities of a data marketplace. We add to the literature by presenting a first explanatory model on the relationships between data marketplace business models and data marketplace obstacles. This model leads us to the practical implications of our results. These include the considerations that a data marketplace owner can take into account when making business model choices. Based on the results, we are also able to give recommendations that go beyond certain business model choices.

Today market success and competitive advantage depends on information, and the proper flow of information using information technologies is critical to maintain competitive advantage. To improve their business processes and networks, organizations need to leverage new information systems/information technologies (IS/IT). These new IT systems, which see a constant flux of innovations in software, computational and automation capabilities, include technologies such as robotic process automation (RPA), chatbots, analytics using machine learning and artificial intelligence to name a few. These technologies are currently gaining a lot of traction and popularity as solutions to bolstering an organization’s competitiveness. Organizations are struggling with managing these technologies to maintain competitive advantage. A challenge for organizations is to judge the effectiveness and the impact the new technology would have on their respective business processes before implementing the IT system. Organizations lack a framework that encompasses both the short-term and the long-term view of implementing these technologies as well as the challenges they would face because of these technologies. Through this research, we hope to allay this issue by asking and answering: How can an organization effectively evaluate the impact of new IT systems to improve its business processes? There exist various frameworks and measures in management literature that help organizations choose between multiple IT investments and there exists frameworks such as the balanced scorecard that help organization as performance measuring tools. The IT scorecard, a subset of the balance scorecard, was developed to measure performance of existing IT products and services in use. However the use of existing frameworks to evaluate new IT systems has not been empirically recorded. Existing frameworks also do not take into consideration the uniqueness of individual IT systems and the challenges that are associated with these systems. Other challenges include a greater focus on short- term monetary gains rather than long-term benefits, a lack of standardised parameters for accurate measurements of performance and the ethical challenges that come with using these new controversial technologies. The proposed evaluation framework aims to accommodate the weaknesses of the earlier frameworks. This is why an integrated approach has been taken in the framework. The framework consists of three levels with the evaluation of the IT system conducted at the final level. The top most strategic level offers the organization a perspective on the needs and goals of the business. This allows them to see if the IT system truly aligns with the organization’s strategic objectives. The next level is the business process level. The reason IT systems are introduced into organizational business processes is to improve efficiency of the business process by automating certain activities or reducing certain steps in the process, improve performance of employees or enhance customer experience thereby boosting overall productivity. However, if the IT systems are introduced into the existing business process without correcting for inefficiencies, these inefficiencies are carried into the new business process (the existing business process with the IT system introduced) which is detrimental to the organization in the long run. Therefore analysing the business process helps us identify these gaps existing in the process, solve for them and redesign the business process with the implementation of the IT system. This is achieved at the business process level of the framework. Finally the level of the IT system where its impact on the business process is evaluated. The IT system is evaluated from different perspectives which include the customer, the business value the system provides, the internal processes that are affected by the IT system and the future readiness of the organization to the IT system. This integrated approach, similar to the Balanced and IT scorecards, is augmented and made more dynamic and robust enough to evaluate new IT systems by using KPIs derived by the system developers and engineers as metrics of measurement. The framework also brings to light the ethical values and challenges that come with using new IT systems. To the initial four perspectives, the two new perspectives will provide a more complete view of the impact the IT system has on the business process. The better the evaluation, the better the decision making of the organization. To demonstrate the utility of the evaluation framework, it is applied to the incident management process which is a common business process of the service based enterprises. This business process is analysed and the common challenges found in this process are discussed. In order to improve the business process, two new IT systems are introduced separately; the robotic process automation (RPA) and the chatbot. The proposed evaluation framework is applied to both the situations and the results are compared to see which IT system better improves the business process. This work has shown that including the distinctive nature of the new IT systems such as RPA and chatbots improves the results of the impact evaluation. In case of RPA, aside from the obvious benefits of the technology organizations might not be aware of the improvement in compliance the system provides or the increased quality of data being processed due to fewer errors or the heightened security risks that increased automation can cause. Organizations must also be wary of any underlying biases and stereotyping when implementing chatbots into their business processes. The idiosyncratic properties of each technology can influence the evaluation results and hence the decision on whether the IT system will be implemented. Therefore, by including the indicators specific to these systems, organizations can be aware of the potential impact these systems have on a particular business process. In this way undesired effects such as the inherent challenges of the IT system as well as the consequences of ethical issues are captured. Besides the impact evaluation of the IT system itself, the analysis at the strategic level ensures that there is a strategic alignment with the objectives of the business and the IT system and the modelling of the business process ensures that a detailed analysis can be done to find the inefficiencies and challenges in the business process. Through the proposed framework, this study focuses largely on the IT system and the impact it has on a business process of an organization. Once these IT systems are more prolific in their applications and use, higher levels of evaluation such as at the strategic level will be possible. This will also be very useful for organizations interested in choosing the right IT system for their organizational structure, culture and business processes and therefore be an interesting field for future research. A limitation of this research is that while this paper explains the working of the evaluation framework, a detailed analysis of the application of the framework has not been made. This can be captured by future research through in depth empirical studies with organizations implementing these new IT systems and using this framework to conduct the impact evaluation on the business process. Comparative studies can also be conducted such as comparing this framework with other integrative evaluation approaches such as the multi-attribute utility theory, the voting analytic hierarchy process and information economics.