Topic of PhD project
Digital Servitization in SMEs: Managing Human Resources.
Research background/interests
My research explores the human resource (HR) practices in small and medium-sized manufacturing companies (SMEs) during their transition to digital servitization. SMEs face various challenges during this transition, including a lack of skilled workforce, informal HR practices, and the inapplicability of HR solutions designed for larger companies. As these firms integrate services and digital technologies in their traditional product-based business models, significant changes in organisational structure and processes require them to re-evaluate their HR capabilities.
By applying the strategy as practice lens, my study will focus on the micro-level practices and daily interactions within these organizations. My aim is to understand how and why HR strategies evolve, are modified, and are implemented in SMEs as they cope with service and technological challenges.
Theoretical background/interests
Practice theory especially strategy as practice
Paradox theory
Methodological background/interests
Process oriented approach
Interested in both organisational and individual level of analysis
Case studies (interviews, participant observation, archival data)
Open to other qualitative methods
Topic of PhD project
Sustainable Customisation: Integrating Product Configuration for Efficient Manufacturing.
Research background/interests
My research focus is within computer science and operation management in product configuration. This involves a complex expert system perspective and operational knowledge management dimension in evaluating "product sustainability". I try to explore and bridge the two dimensions of computational intelligence of a Product Solution Configuration Engine within its modelling environment based on prioritising sustainable product design choices for manufacturing.
My research interest falls within the following subjects:
Theoretical background/interests
My theoretical inspiration comes from industrial collaborations on a larger scale of product complexity. Within its core activities, the project is centralised within digital solutions and sustainability, seeking to increase industrial impact and contribute to academia. This is done by scoping real-world problems and intensifying well-informed actions and perspectives.
Methodological background/interests
I work with a mixed-method approach in my research using multiple or singular case studies. I use multiple methods and constantly seek to broaden my knowledge as I am doing multiple industrial collaborations.
Some of the areas can be:
Topic of PhD project:
Safe and secure drone design and operations.
Research background/interests
I specialise in wireless communication with an emphasis on physical layer security. My research focuses on drone technology as an enabler of wireless communication services where terrestrial communication infrastructure is either not available or destroyed due to natural disasters. Due to the broadcast nature of wireless communication, the information of legitimate users is vulnerable to security threats. I am trying to secure the legitimate information secure from the eavesdroppers.
Theoretical background/interests
My research is focused on, but not limited to, the following:
Methodological background/interests
My work includes simulations and mostly depends on information theory, algorithm designs, and optimisation.
Topic of PhD project
Data-driven decision-making across the product lifecycle (creating product service systems).
Research background/interests
The motivation for the PhD project is to understand how small and medium-sized enterprises can make their physical industrial products become a catalysator for gathering data across its product life cycles, thereby discovering and potentially improving the data foundations to make more data-driven decision-making for the continuous development and evolve towards product service system.
Theoretical background/interests
There is a potential for more knowledge discovery and identifying uncaptured value through extending the search of integrating literature across fields of research. There are both opportunities and challenges related to testing how dataflows that are based on all type of information and infrastructures can improve when travelling throughout the product lifecycle.
A gain for extending the amount of data to potentially be more informed in design and production of a product to the challenge of sharing the data across involved actors and users throughout the product life stages. Locating how more data awareness will help to evolve the use of data to enable a digital transformation and creating product services systems in (manufacturing) SMEs.
Methodological background/interests
The methodological approach in terms of empirical data collection will be done as collaborative work with different companies conducted inside their organisations. Several case studies will be conducted with a focus on directly experiencing and facilitating the development and testing of modular digital/physical artifacts. The empirical findings may have a potential for developing a dynamic tool and/or process. This may help manufacturing SMEs in similar challenges create a reproducible plan for independently further their digital transformation and data utilisation.
Topic of PhD project
Understanding the Role of Digital Twin Processes in Enhancing Organizational and Supply Chain Resilience.
Research background/interests
The growing application of digital twins (DT) in organizational behavior and supply chain contexts offers significant potential for enhancing resilience in response to disruptions. While much of the current research focuses on the strategic outcomes of DT deployment, the processual mechanisms through which organizational resilience (OR) and supply chain resilience (SCR) are constructed remain underexplored up to date. My PhD research aims to address this gap by investigating how DT facilitates the development of OR and SCR through the unfolding patterns of organizational processes and events. By adopting a process-oriented approach, this PhD research seeks to uncover the dynamic sequences that contribute to resilience building. Also, beyond the covariance-based explanations concerned with explaining causal mechanisms of relationships between variables, the study will provide a nuanced understanding of the multilevel processes involved in the emergence of OR and SCR. Explicating the emergence of the OR and SCR phenomenon through DT is expected to bear construct clarity one step further. The findings are expected to offer both theoretical and practical insights into how digitalization tools like DT can be leveraged to strengthen resilience in complex, dynamic and evolving organizational systems.
Theoretical background/interests
The theoretical basis of the research project is formed by the assumptions of resilience theory, dynamic capabilities theory and resource-based view. The theoretical basis of the project will be finalized after the systematic literature review, which is planned to be the first part of the project.
Methodological background/interests
I predominantly intend to undertake exploratory qualitative research with an inducive approach by adopting recursive process research model to explain the processual nature of the emergence and evolution of the OR and SCR through the application of DT. Besides, I also have a strong interest in quantitative methodology.
Topic of PhD project
The research on UAV autonomous guidance focuses on information provision, collision avoidance, and path planning.
Research background/interests
My research focuses on finding constraints for the autonomous guidance and critical analysis for the existing information provision at U-space.
One of the constraints is collision avoidance with uncooperative drone with unknown model. Unpredictive intentions of a rogue drone bring a significant threat to flight safety. Finding a solution to the problem is a priority of the study.
Path planning for UAVs is not a trivial task, as they operate at a very low level in 3-D space. Each model of UAV has its own aircraft performance, which imposes significant limitations and diversity for flight planning. The research contributes to addressing such issues, at least partially.
Theoretical background/interests
The study unites a few subjects – flight physics, flight safety, path planning techniques, artificial intelligence.
Methodological background/interests
The main methods are literature review, mathematical and physical modeling, testbed programming.
Topic of PhD project
Optimization of Environmental Conditions and Energy Consumption in Vertical Farms.
Research background/interests
I specialize in conducting simulations and optimization studies to design and develop indoor vertical farming systems. My primary research interests revolve around exploring key aspects in vertical farming, particularly in the contexts of energy consumption, and the design and control specific to air distribution systems. I also have a strong interest in the integration of Food-Energy systems and the intricate interrelationships that exist within this domain.
Theoretical background/interests
My research has focused on improving the following aspects:
Methodological background/interests
While I am open to various research methods, my primary focus lies in experiments and simulations. I am also interested in the concept of techno-economic analysis for indoor vertical farming systems. In the broader field, I emphasize the utilization of quantitative methods.
Topic of PhD project
Exploring the techno-economic feasibility of Floating Offshore and H2 systems.
Research background/interests
My PhD project is a part of TETHYS project and focuses on the integration of Floating Offshore Wind (FOW) and Hydrogen (H2) production systems, particularly assessing the economic feasibility and market dynamics in the Mediterranean region. The study aims to explore the financial viability, policy and subsidy frameworks and levelized cost of energy (LCOE), and hydrogen (LCOH) of the integrated systems to support large-scale implementation. The project aims to fill the gap between technological breakthroughs and financial sustainability by integrating techno-economic models and digital energy solutions and a result helping in the shift to a sustainable energy future.
Theoretical background/interests
My work is grounded in renewable energy economics, energy policy analysis, and computational finance. I am particularly interested in exploring levelized cost of energy (LCOE) and hydrogen (LCOH), market structures, and subsidy frameworks to understand the financial and regulatory landscape of FOW-H₂ systems. I also aim to expand my knowledge in systems integration and optimization models for energy infrastructure.
Methodological background/interests
I predominantly work with quantitative research, focusing on simulations and computational modelling. My methodology includes:
Topic of PhD project
Rethinking Data Collection: Myths and Privacy Challenges in Online Advertising.
Research background/interests
The online advertising industry operates on the assumption that more data leads to better precision and targeting—a belief that underpins its business models, technological infrastructures, and regulatory challenges. However, this assumption is rarely critically examined beyond industry narratives. My research investigates the myth of "more data = better advertising" by exploring how data flows through the online advertising ecosystem and the trade-offs between precision, privacy, and compliance. My research contributes to the development of privacy-preserving information flows and helps organizations build privacy-aware and compliant advertising systems.
Theoretical background/interests
My research is grounded in a Critical Data Studies perspective, challenging the assumption that data accumulation always leads to better outcomes. I integrate Contextual Integrity Theory to evaluate whether existing data practices align with societal and regulatory expectations of privacy. Additionally, I apply Systems Thinking to understand the complex, interdependent relationships between stakeholders in the advertising ecosystem.
Methodological background/interests
I adopt a mixed-methods approach that combines qualitative and design science research (DSR) methodologies to investigate current industry practices and develop actionable privacy-aware solutions. I seek to deepen my expertise in privacy-preserving advertising methodologies and advance practical solutions that challenge the industry's reliance on excessive data collection while maintaining commercial viability.
Topic of PhD project
Supporting learning outcomes through virtual reality.
Research background/interests
My research focus on the intersection between psychology and technology (mainly virtual reality) with the main goal of investigating ways to improve learning by utilizing the unique affordances of virtual reality. I am particularly interested in the role of technology in supporting learning, engagement, and motivation of students. With my studies, I explore the role of different factors, for example to identify ways to improve collaborative learning when designing virtual environments.
My research interest falls within the following subjects:
Theoretical background/interests
The theoretical foundation mainly comes from the field of educational psychology including the Cognitive Theory of Multimedia Learning and the Cognitive Affective Model of Immersive Learning. These approaches assume that learning is not automatically enhanced by technology, but rather that it's possible to extract fruitful outcomes when technology is used in meaningful ways, such as considering how virtual reality can create a learning environment that would be impossible in a traditional classroom.
Methodological background/interests
I predominantly work with quantitative research in the form of controlled lab experiments but occasionally also surveys. My interests also include open and reproducible science, such as working with pre-registrations and making data, scripts, and materials available after my studies.
Topic of PhD project
Cold-chain food transport based on battery-swapping electric trucks.
Research background/interests
Freight trucks are diesel-based and energy-intensive, emitting more CO2 than other transportation modes for cold-chain food transport. The outbreak of COVID-19 has further accentuated the importance of this sector with the increasement of e-business and retail outlets with fresh products contributing. Electric trucks have been introduced as a substitute for diesel-based ones to eliminate the emission of greenhouse gases from the transportation of food by trucks. Nevertheless, the implementation of electric freight vehicles is currently limited due to the high cost of batteries, the immature technology, the short lifespan of batteries, and the lack of fundamental infrastructure for electric vehicles.
Theoretical background/interests
To reduce acquisition costs and increase the utilisation of electric freight vehicles, battery-swapping technology has been developed to assume the responsibility of food freight and serve as an energy storage battery when the battery is not in use. My research will be mainly based on regional cold-chain food freight EV transport route and energy consumption prediction and the planning of battery swapping station along the motorway.
Methodological background/interests
My PhD project employs a mixed-method approach with both interviews from business side and optimization from route planning side.
Topic of PhD project
Efficient matching of workers and patients in hospitals.
Research background/interests
My research focus is on how to match workers and tasks in the most efficient way. With the use of employee data for nurses and admission data for patients, I try to develop a model capable of matching these while taking into account that the groups are both heterogeneous.
My research interest falls within the following streams:
Theoretical background/interests
My theoretical inspiration comes from the field of labour economics where topics such as job assignment theory and search and matching theory is a foundational part of my PhD project. I work, however, with a “micro” perspective trying to match the individual worker to the individual patient in a ward functioning as a platform.
Methodological background/interests
I work with a quantitative approach in my research and draw inspiration from different fields hereof. I use a wide range of methods and will obtain a broad knowledge within these.
Some of the areas I work with are:
Engineering asset management (EAM) is the discipline of addressing the sustained and deliberate value contributions of the technical assets in an organization. Companies are experiencing a rising complexity and volume of their overall technical asset portfolio. Companies are further dealing with an increase in workload, knowledge, and skill requirements in contrast to shrinking budgets and rising cost complexity for their technical asset management. With Industry 4.0 approaches, assets are increasingly retrofitted and digitized with new technologies, providing new opportunities for the asset management department (AMDept), but increasing complexity experienced by the employees and asset managers. New and improved methodologies and processes are necessary to investigate for EAM in modern organizations, to better alleviate that complexity.
Agile approaches has demonstrated improvements in competitiveness, effectiveness, learning, and flexibility in a multitude of different organizational contexts, ranging from manufacturing to product/system development and services. EAM is currently categorized by a blend of planned activities and rapid interventions; however, in many cases individual and intra-organizational learning and sustainability are neglected or dismissed. Hence, the idea for this project is to identify, characterize, review, experiment, exercise, and evaluate the feasibility of agile approaches in EAM. Agile methods are rooted in running a sustainable and profitable business, this is assumed to be transferable to EAM with appropriate design and adaptation to EAM practices.
This project focuses on the potential of augmenting EAM with agile approaches. Given that agile methods have proven successful in the development part of the assets life cycle, the assumption is that agile approaches should provide a useful addition to EAM, being the operational part of the assets life cycle. The professionalization and digitalization of EAM changes EAM to a complex knowledge driven business function, thereby further supporting the suggestion of agile as a means of change. This project will focus on alleviating the needs represented in the AMDept’s concerning improvements towards effectiveness, flexibility, learning and knowledge. These considerations lead to the following research question:
How can agile methods in EAM positively influence value, through the parameters of learning, knowledge, effectiveness and flexibility?
Topic of PhD project
Investigating approaches for first time-right implementation of production line equipment.
Research background/interests
My project primarily concerns technology implementation within manufacturing companies, especially those using line-based layouts. The companies face complex issues when replacing or introducing new equipment whether it is new units, new processes, or additional lines. This project explores the complexities of implementation, examines current practices, and sets the stage for a transformative approach to a more successful production line equipment implementation.
Theoretical background/interests
My theoretical inspiration comes from structured decision-making based on quantitative modelling with holistic understanding from systems thinking to create a robust framework for seamless implementation in manufacturing. This project aims at exploring computational techniques applying theoretical principles from physics, statistics, and mathematics to create and analyse distinct virtual models of production machines. Also, theoretical principles from engineering economics, operations research and operations management are the basis for performing structured decision-making. Systems theory is also applied in this project to allow analysis of complex systems.
Methodological background/interests
The quantitative modelling techniques used in this project implies cyber physical modelling through computational simulation and the creation of digital twins. This project also extends from digital twin to use quantitative modelling and systems engineering as key techniques for automated and integrated manufacturing processes.
This project seeks to combine digital twins and virtual factory acceptance testing alongside Model-Based Systems Engineering (MBSE) and Techno-Economic Analysis (TEA). Furthermore, Design of Experiments and statistical analysis founds practical investigations of production systems.
Topic of PhD project:
Exploration, Exploitation, and Group Dynamics: Experiments on Organizational Search and Creativity.
Research background/interests
My project investigates the complexities of decision-making and creativity during search in both individual and collaborative contexts. Central to this exploration is understanding how different search strategies, encompassing exploration and exploitation, affect performance outcomes amidst varying problem complexities.
A significant aspect of my work involves examining how individuals and groups adapt their search behaviors, whether through direct experience or via social learning from AI-driven agents. This approach not only sheds light on the dynamics of decision-making but also probes into the potential of hybrid human-algorithm collaborations.
Additionally, the project explores the influence of social robots as potential facilitators in group settings, particularly focusing on their role as emotional contagions. By investigating whether these robots can induce affective shifts, the study aims to understand their impact on enhancing creativity and group performance.
Overall, my PhD aims to provide a comprehensive understanding of the factors influencing organizational problem-solving in various contexts, from individual cognition to the complex dynamics of group interactions and the integration of technological agents.
Theoretical background/interests
I am engaged with theories related to exploration and exploitation in organizational search, drawing upon concepts from behavioral economics and cognitive psychology. A significant portion of my research revolves around understanding how groups and individuals navigate complex problems, leveraging theories of group dynamics, creativity, and innovation.
In exploring these areas, I employ theories of organizational learning, particularly in the context of how individuals and groups adapt to and learn from their environment, including AI agents and robotic facilitators. This involves integrating perspectives from the fields of human-computer interaction into traditional organizational and psychological theories.
I am keen to expand my knowledge in the realm of computational social science, aiming to deepen my understanding of how computational models can simulate and predict complex human behaviors in organizational settings. Additionally, I am interested in further exploring the implications of artificial intelligence in organizational research, particularly how AI can transform traditional models of decision-making and group interaction.
Overall, my theoretical interests lie at the nexus of human behavior, technology, and organizational dynamics.
Methodological background/interests
My research integrates experimental approaches with computational modeling. I utilize simulations as a tool to refine and develop theoretical concepts. These ideas are then empirically tested through online gamified experiments, which have been the primary focus of my work to date. Currently, I am expanding my experimental repertoire to include laboratory-based experiments, an endeavour that is introducing me to the distinct challenges and nuances of in-person research settings. Additionally, I am exploring the use of artificial intelligence in my studies, specifically investigating its potential and limitations in experiments and computational models related to organizational search behaviors.
Topic of PhD project
Measuring and Visualizing sustainable performance data utilizing data-driven systems.
Research background/interests
Theoretical background/interests
Methodological background/interests
Topic of PhD project
How can the integration of virtual reality (VR) and performance measuring tools enhance the training and performance of ice hockey players?
Research background/interest
As an avid enthusiast of ice hockey with a profound interest in sports psychology and performance management, my research focus revolves around leveraging cutting-edge technologies to enhance athlete performance and well-being. Ice hockey serves as an intricate domain where the amalgamation of physical prowess, cognitive strategies, and psychological resilience profoundly influences performance outcomes. Moreover, the skills needed in ice hockey provide a strong foundation that can translate to success in various other sports, owing to the multifaceted nature of its demands. My overarching goal is to bridge the gap between traditional training methodologies and contemporary advancements in technology, such as Virtual Reality (VR) and Artificial Intelligence (AI), to optimize athlete development and performance in the realm of ice hockey, while also recognizing its potential to serve as a foundational platform for other sports.
Theoretical background/interests
My theoretical background is deeply rooted in engineering principles, complemented by a keen interest in the theories of sports psychology and user experience (UX) design. Through an interdisciplinary lens, I aim to synergize these seemingly disparate fields to explore novel approaches in enhancing athlete performance and well-being.
Engineering foundation:
With a solid foundation in engineering, I bring analytical rigor, problem-solving skills, and a systems-thinking approach to my research endeavours. My engineering background equips me with the ability to dissect complex problems, identify patterns, and develop innovative solutions—a skill set invaluable in the realm of sports performance optimization.
Sports psychology:
Drawing from theories in sports psychology, I am intrigued by the intricate interplay between cognitive processes, emotional regulation, and performance outcomes in athletic endeavours. By delving into topics such as motivation, goal setting, self-efficacy, and resilience, I seek to unravel the psychological mechanisms underlying peak performance and mental well-being in athletes.
User experience design:
In parallel, my interest in user experience design stems from a fascination with how technology interfaces with human behavior and cognition. I believe that the principles of UX design can be seamlessly integrated into sports technology to create intuitive, engaging, and impactful training environments that facilitate skill acquisition, performance feedback, and athlete empowerment.
By synthesizing insights from engineering, sports psychology, and UX design, I aspire to pioneer holistic approaches to athlete development and performance optimization. Whether through the design of innovative training tools, the implementation of immersive virtual environments, or the creation of personalized feedback systems, my research endeavours aim to bridge the gap between theory and practice, ultimately empowering athletes to reach their full potential.
Methodological background/interests
In addition to my interdisciplinary theoretical foundation, my methodological approach is characterized by a commitment to employing innovative research methodologies that bridge quantitative analysis with qualitative insights. Drawing from my engineering background and research experience, as well as insights from sports psychology and UX design, I embrace a multi-faceted approach to data collection and analysis tailored to the complexities of athlete performance and user experience.
In summary, my methodological background and interests underscore a holistic approach to research that blends quantitative rigor with qualitative depth, leveraging diverse methodologies to advance knowledge and innovation at the intersection of engineering, sports psychology, and UX design.
Applying Artificial Intelligence in the building design industry for autonomizing design processes
This project is an investigation of the theoretical field about applying Artificial Intelligence (AI) in the building design industry for Single Family Homes (SFH). The focus area will be on the “missing gap” between the use of AI for customer clarification and the design process using generative CAD Software (GCS) for drawing up final design solutions. The benefit of using AI in the building design process is to automate costly and time-consuming processes that engineers and architects today are struggling with.
Over the years it has been heavily discussed whether AI would replace jobs for engineers, architects etc., but it seems like there is still a huge gap between this discussed topic and the current reality. The artist and designer S. Errazuriz has stated that 90 % or more of architects will lose their job to AI, due to the generality in building architecture that Machine Learning systems can handle more efficiently by using data based on previous building cases.
Compared to the number of resources used for private building constructions alone in the US, it will have a huge potential if just some of the architectural design processes can be supported by AI. However, it still needs clarification on how to address AI in the house developing processes to gain full potential.
This project will aim to identify and examine the potential and capabilities of making a design process which integrate AI technologies and lead to the main research question:
How can AI and GCS contribute to a more autonomous design process for SFH?
Topic of PhD project
Navigating adverse selection in Decentralized Autonomous Organizations (DAOs) funding markets.
Research background/interests
The funding markets of DAOs are characterized by high levels of information asymmetry between token issuers and outside investors, due to the lack of formal disclosure requirements. Without credible separation, the investors will face a lemons problem, i.e., being unable to distinguish high- from low-quality DAOs. By drawing on signaling theory, my research aims to investigate the efficacy of DAO-specific factors at mitigating this information asymmetry and agency conflicts associated with token financing. With credible separation, high-quality DAOs will have access to more efficient funding contracts, attract top talent, and build trust with investors and regulators.
Theoretical background/interests
My research builds on the classic theory of the firm and applies it to Decentralized Autonomous Organizations (DAOs). This includes, but is not limited to, agency theory, signaling theory, and the theory of financing ventures with crypto tokens.
Methodological background/interests
I have a strong interest in empirical research with theoretical implications. My PhD project employs a mixed-method approach, with a particular focus on leveraging blockchain data.
Topic of PhD project
Haptic systems for industrial virtual reality training.
Research background/interests
My research focuses on understanding the impact that utilizing haptic systems has in virtual reality training scenarios. With collaboration with industry, I aim to develop a system to enhance training performance.
My research interests focus on:
Theoretical background/interests
My theoretical background comes from theories in industrial training, Immersive Virtual Reality training and haptic training systems for medical applications. My research combines the learning from the medical field and applies the learning to industrial scenarios.
Methodological background/interests
I predominantly work with pilot studies and controlled experiments. I use haptic systems in combination with virtual reality training scenarios to improve training performance.
Topic of PhD project
Investigating organizing in open-source software development. The curse or the blessing of non-traditional organizations.
Research background/interests
My research explores non-traditional organizations in the open-source software industry. Open-source software immensely impacts how the software industry works, and it is also a unique field with an amorphous, decentralized, and fast-growing group of contributors. With their tendency for fluidity and sometimes incomplete structural elements, open-source software organizations' boundaries shift and affect the coordination within the organization and with external actors. The inability to manage effectively within these boundaries poses a significant risk, especially in collaborations that involve multiple organizations. Therefore, I investigate the dynamic nature of these organizations through the process and temporality lens to understand how organizational structure shifts over time.
Focus areas of my research:
Theoretical background/interests
This research aims to delve into the organizational boundaries of open-source software organizations, examining them through the lens of organizational temporality. I am applying the integrative view, where continuity and change are not mutually exclusive but interdependent aspects within organizational dynamics. Considering that organizations constantly change, research treats time and process as connected.
Methodological background/interests
I am applying a qualitative approach to my research. To investigate organizing in the open-source software industry from the point of view of the subjects of the research, the ethnographic approach is adopted. Etnography will allow deeper exploration of open-source software organizations' cultural, social, and operational dynamics through the perspective of the individuals. To capture historical and contemporary aspects, data for this research will be collected from interviews, observations, internal communication documentation, and records in open-source software repositories.
Topic of PhD project
Machine Learning based Site Assessment for Onshore Wind Energy Projects.
Research background/interests
My research focuses on the utilization of machine learning techniques, particularly explainable AI methods, to accelerate the site assessment process of wind turbine projects. While machine learning and AI are widely employed in the wind energy industry, there is an imbalance in their application across all phases of a wind turbine project’s lifecycle. Consequently, my research targets one of the early phases, specifically the planning phase. By integrating explainable AI techniques with traditional machine learning methods, my aim is to enable more informed decision-making.
The primary subjects of my research interest are:
Theoretical background/interests
The theoretical foundation of my research is rooted in the field of machine learning. Here, explainable AI methods contribute to comprehending the black box nature of machine learning algorithms. This understanding enhances the interpretability of model predictions, facilitating more informed human decision-making. Additionally, theoretical insights from current site assessment practices are incorporated to identify the essential features that must be included in the dataset for training machine learning models.
Methodological background/interests
I predominantly have a quantitative approach in my research, where I utilize secondary data and apply data analysis and machine learning methods. However, to better understand the wind energy industry and to identify the most relevant data from the stakeholders’ perspective, different qualitative methods like semi-structured interviews and focus group interviews are also applied.
The PhD project aims to address the limited engagement by SMEs in the field of organisational resilience and sustainability. The project will develop an integrated approach that can support SMEs in making informed decisions to enhance their competitiveness, resource efficiency and resilience to disruption.
A constantly changing environment means businesses operate in a highly complex context. They must deal with threats such as pandemics, geopolitical tensions and cyberthreats. As the significance of uncertainty continues to rise, there is a greater focus on organisational resilience as a critical factor for businesses to prosper. At the same time, they are also under pressure from both customers and (national/regional) regulations to undertake a transformation to become more (environmentally) sustainable. The concepts of sustainability and organisational resilience have received increased attention from both researchers and practitioners in recent years and have also been linked to business model innovation (BMI) and as factors that can drive or impact innovation in a firm. The increased focus on topics such as climate change and the covid-19 pandemic has resulted in a significant increase of studies on the topics, investigating the relationship between sustainability and organisational resilience. For instance, studies have concluded that sustainable business practices can contribute to organisational resilience and that sustainable practices not only benefit organisational resilience but can even impact resilience on the community level. At the same time, there is limited research on developing synergies and integrating sustainability and resilience practices in a business context. This research gap highlights the need for a deeper understanding of how businesses can develop synergies between sustainability and resilience practices to achieve long-term success. Thus, the primary objective of this research is to investigate the following research question:
How do organizations pursue the dual priorities of sustainability and organizational resilience, and how can organizations successfully integrate these priorities?
Small and medium-sized enterprises (SMEs), which play a significant role in driving economic growth, employment, and total value added, continue to face high vulnerability, and lack preparedness when confronted with environmental perturbations. Hence the focus on SMEs (in a Danish context).
Background
The project is an industrial-PhD collaboration between Dansk Brand- og sikrings Institut (DBI) and Aarhus University, funded by the Innovation Foundation.
Topic of PhD project
Techno-economic performance optimization of a wave energy converter design.
Research background/interests
My research interest is on wave energy. Ocean waves represent a vast yet untapped renewable energy resource, as the harsh conditions at sea makes the development of durable and economically viable wave energy converters (WECs) highly challenged. WEC technologies are still under-researched, however, the Danish WEC design, Crestwing, has shown a potential for high energy efficiency as well as high capacity. Through techno-economic performance assessment and the development of an optimization framework for the design, I seek to explore how to advance the technology to become a reliable and competitive contributor to the renewable energy system.
Theoretical background/interests
My research is focused on examining and identifying the determining parameters for techno-economic optimization of the Crestwing WEC design, in order to follow a development trajectory that prioritizes advancement of the Technological Performance Level (TPL) - a holistic, approach encompassing all economic, environmental, and social aspects – as to consider the competitiveness of the technology throughout the development process.
Methodological background/interests
The research methods include mathematical calculations and statistical methods to validate the techno-economic performance of the WEC design expressed through the Levelized Cost of Energy (LCoE). For this, data such as energy capacity and efficiency, costs, and met-ocean data are used. In addition, I also look into Life Cycle Assessment (LCA) and social acceptance. The emphasis is on quantitative methods, while qualitative methods are used to a limited extent.
Topic of PhD project
Exploring Open Innovation within the Context of Science among Academics and Institutions.
Research background/interests
As there have been increasing calls to implement more openness and collaboration in research activities across scientific fields, promising enhanced quality, speed, and dissemination of research results, my research project delves into open and collaborative research practices among academics and institutions.
My particular interest lies in individual-level motivations and incentives for (not) implementing open and collaborative research practices and I aim to explore the circumstances under which the (non)implementation of such practices occurs.
Overall, my research is focused on understanding how scholars and their respective institutions approach the implementation of open and collaborative research. My ambition is to deepen the understanding of individual-level implementation or resistance towards such practices by uncovering previously unknown concepts and mechanisms. Additionally, I aim to provide insights and recommendations for academic and research institutions, policymakers, as well as research funding schemes that promote open and collaborative research practices.
Theoretical background/interests
My project is situated within the framework of Open Innovation in Science, recognized as a phenomenon or concept rather than a theory itself. This framework considers open and collaborative practices to be observable at every stage of the scientific research process, extending beyond data sharing. It encompasses formulating research questions, securing funding, developing research methods, collecting, and analyzing data, and disseminating new knowledge or results. Consequently, considering my research interest in the individual-level implementation of open and collaborative practices among academics and their respective institutions, the main theoretical foundations of my project are rooted in the theories of professional identity, scientific ambidexterity, and institutional theory.
Methodological background/interests
In my research project, I primarily employ a qualitative approach. I utilize various data collection methods, such as semi-structured interviews (including interviews with elite informants) and various types of observations. Additionally, I integrate secondary data, such as academics' CVs, publication lists, webpage data, and information about the tenure system at scholars' respective universities. To conduct data analysis, I use NVivo, a qualitative data analysis software.