Human activity (e.g. heavy use of fossil fuel in buildings) is considered the main driver of climate change. Buildings (residential, commercial, or industrial) are significant energy consumers which share about 40% of the total global energy consumption. In this regard, various organizations explore the potential of renewable energy to reduce carbon footprints while maintaining reliable energy systems. However, the main concern in the future is not about producing more renewable energy but reducing energy consumption.
And as the global economy and consumers’ demands change rapidly, embracing innovative approaches to reduce energy consumption and improve energy efficiency are desired. Adoption to smart energy technologies can help reduce energy consumption and improve energy efficiency. However, the socio-economic and environmental benefits of smart energy technologies are not fully realized. Studies show low awareness and engagement to smart energy technologies due to several factors. For instance, older adults have very low engagement to smart technologies due to lack for technical skills and confidence of using smart technologies. In addition, demographics (e.g. age, education, and occupation), and lack of knowledge about the benefits of smart energy technologies affect users’ willingness to adopt smart energy technologies.
Certainly, financial incentives contribute significantly to changing social behavior. However, a sustainable growth requires knowledgeable consumers who can just simply engage with the smart energy technologies without even receiving financial rewards.
Changing social behavior by empowering energy consumers through education and information ensures the adoption of smart technologies. This could be done through strong collaboration among stakeholders (e.g. government, community and end-users). Sustainable business model encourages the involvement of multiple stakeholders that creates social, environmental and economic value.
Thus, this research aims at examining the potentials of sustainable business model in enhancing awareness and engagement to smart energy technology to improve energy efficiency.
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 focusses 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?
Denmark plays an active role in the transition toward a sustainable society, which has significant implications for enterprises. However, managing sustainable enterprise processes requires a change to provide decision-makers with sufficient data for monitoring environmental and sustainable footprints at the enterprise level. Industriens Fond highlights the importance of utilizing data measurements and data-driven decisions in the sustainable transition of enterprises. Currently, most sustainable performance data according to the use of resources, water, energy, CO2 emissions, and waste generation can only be obtained at the industry level but not at the enterprise level. Consequently, actions are needed to turn sustainable enterprise performance data into real-time data insights, and concrete data-driven actions, supporting enterprises in the sustainable transition.
This Ph.D. project aims to develop, implement, and evaluate data-driven decision-making, utilizing data-driven systems to visualize sustainable performance data at the enterprise level.
Central research question
Topic of PhD project
Supporting learning outcomes through virtual reality.
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:
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.
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.
The purpose of the PhD is to show how AR functions and services can be enhanced by AI and the potentials in developing intelligent athlete-centered applications for optimal physical and mental preparation. Currently, no solution has been implemented for an integrated use of AI and AR in sports training including biofeedback about arousal and anxiety levels in athletes. The main challenge is the difficulty to model human behavior and to realize its real-life re-enactment. Numerous peak performance studies have shown the benefits of linking the physical and the mental components however, no solution yet has been proposed on how to train both components together systematically.
The hypothesis for this research is that a fusion between AI and AR can enable to collect, transfer and process together physical performance and biofeedback data that can be used to predict future competitive performance and adjust training accordingly.
Since the introduction in 2011, Industry 4.0 (I4.0) has promised to 'provide more value oriented and customer-centric products and services with a higher degree of efficiency', which would be possible through IT-integrations between planning, production, customers and vendors. High quality data, efficient data flows and data technologies now become necessities to remain competitive, as more and more product offerings and enterprise solutions, are built around data. It seems that it is imperative for companies to collect, structure and utilise data in their daily operations, as this will be the future prerequisite for delivering on customer expectations and operational efficiency. However, research has shown that companies are far away from intelligent and fully connected processes and machinery, and this is especially the case for small to medium enterprises (SMEs). Beyond innovative start-ups and tech-focused SMEs, there is a large group of industrial SMEs, that are slow to adopt digitalisation. For this type of SMEs, I4.0 technologies have been criticised as too abstract and for presenting many ideas but not enough results'. Furthermore, complaints have been made that SMEs lack of interest in digitization are increasing. This is extremely critical as SMEs are the backbone of the European economy, and because the successful implementation of an industrial revolution has to take place in large enterprises as well as in SMEs if this is meant to be more than false labelling. The lack of digitalisation in SMEs is not reported to be a product of unwillingness, but merely that they simply don’t know where to begin. There are many reasons why SMEs find it difficult to initiate innovation and digitalisation initiatives, but the most described barriers relate to lack of resources and competences. These scarcities makes many SMEs risk-averse, as poor investments will have high consequences, which often results in sticking with "business as usual". In the context of digitalisation, this is a major curtailment of potential, as SMEs are otherwise known for their agility and organic structure. In short, there is a clear research gap on how SMEs can adopt digital technologies and concepts and utilise them to increase their performance. This PhD will close this gap, through participatory and experimental research, by investigating how SMEs cam combine data and data technologies to increase their operational efficiency.
“Mobility and training for beyond 5G ecosystems” (MOTOR5G), an MSCA ITNA program, aims to efficiently design a Future Wireless Network (FWN) that shall provide diversified services such as enhanced mobile broadband access, ultra-reliable low-latency communications (URLLC), and massive machine-type communications. My research focuses on drone technology as an enabler of wireless communication services in scenarios where terrestrial communication infrastructure is either not available or destroyed due to natural disasters. My Ph.D. research objectives include, but are not limited to, the following:
The research objective of this work is to design and evaluate the UAVs architecture in wireless networks. UAVs, popularly known as drones, have widely been used in versatile fields for diverse applications and purposes in the past decade. Drones play a major role in the field of wireless communication and are considered as a key enabler for communication purposes in remote areas, catastrophic areas, and anywhere in bottleneck situations. These types of usages of UAVs are referred to as UAV-assisted wireless communication. Considering the potential of drone technology in enabling wireless communication, there is a need for an architecture where the spectrum can be reused without compromising on the Quality of the Service (QoS). The main objective of this work is to design a secure UAV-enabled communication network with low latency communication and high throughput with massive connectivity.
Topic of PhD project
Haptic systems for industrial virtual reality training.
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:
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.
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
Efficient matching of workers and patients in hospitals.
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:
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.
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:
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).
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
Exploring Open Innovation within the Context of Science among Academics and Institutions.
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.
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.
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.
The first part of the title reflects the dominant discourse seemingly taken by scholars, that ESG measurement is a single unified way of measuring corporations’ way of disclosing their environmental, social and governance data. First and foremost, Bloomberg which is the primary source observed in the literature review for this proposal, is only one out of approximately six major ESG data providers, which all have their respective ways of measuring, presenting and scoping ESG data. (Dieschbourg & Nussbaum 2017)
With the continuously increased focus on terms such as CSR and ESG, with the latter acting as a proxy of firms engagement in CSR, and CSR acting as a softer more fuzzy overarching notion, (Broadstock, Matousek, Meyer & Tzeremes, 2019) there is a need for a more critical view of the ESG concept, and to what extent it is used in academic papers. There has also been a Call for paper in the end of 2019, with one of its goals stressing out the need “to clarify the concepts of ESG in order to examine their overlaps, delineate their boundaries, and map out ESG research and practice.” (Cucari & Lagasio, 2019) A more practical example involves the ESG rankings of Volkswagen in the slipstream of its “dieselgate” scandal in 2015, where rating companies such as Sustainalytics gave the automobile maker a medium score, whereas Dow Jones Sustainability Index gave Volkswagen the Sustainability prize, and MSCI ranked the manufacturer a CCC ranking, which is one of its lowest rankings. (Dieschbourg & Nussbaum 2017)
By firstly understanding the complex landscape of ESG, from a share and stakeholder perspective, what traits can be identified in companies who can be characterized as successful innovators despite working in heavy ESG regulated industries?
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?
Digitalization and Sustainability are both fast changing and uncertain environmental dynamics that encourage organizations to constantly reassess their strategies to align to those moving targets.
Digitalization and sustainability strategies are intertwined in the way they service each other and in the way they both require the development of new processes using new tools to propose, deliver, create and capture new value, challenging both the activity systems and value capture mechanisms of the companies. Yet, some might see two strategic foci as discordant directions and that the pursue of one must be done at the expense of the other.
The purpose of my research will be to follow the design and development of new digitalization and sustainability business models in a large Scandinavian contractor and observe how they interact with each other while understanding the interplay between the conceptualization and enactment of those new business models.
The first step of my research will be to capture the business models intention of the organization regarding digitalization and sustainability. What are their conceptualized business models? What is their implementation plan with its set of structures, controls and incentives? How do they frame this implementation? What are their social practices, discourse or rhetoric?
The second step will be to track the implementation or enactment of these business models intentions and identify how it influence the evolution of the business models conceptualization.
To identify the relationship between digitalization and sustainable business models, I intend to develop a Sustainability Business Model maturity model in order to assess the sustainability of new digitalization business models to capture the interaction between these two strategic directions.
The research will be carried out as part of GreenBizz a CTIF Global Capsule project in the department of Business and Technology.
Humanity is currently facing a combination of persistent and unprecedented societal challenges that are putting at risk its existence, ranging from social inequality to global warming. Even though the 4th Industrial Revolution has been providing new and promising technologies to adapt to future scenarios and reduce our footprint, technology alone cannot represent the solution to the root causes of this unsustainable development.
Against this background, the implementation of ground-breaking mission-oriented innovation policies (Deleidi & Mazzucato, 2021) has led to the development of new markets and organizational forms, embedding novel concepts such as circularity, inclusion and sharing. However, to spread and become effective, these new models require an increasing commitment and willingness of collaboration between all the involved stakeholders, as expressed in the last Davos Agenda (WEF, 2022). Yet, in the last decades, management scholars and practitioners have been mostly focusing on how to reduce the companies’ negative impacts on society, adopting a firm-level and rather reactive approach to sustainability matters (Biggi & Giuliani, 2021), while few studies have looked at how actors can collaborate to positively change the status-quo from a systemic perspective (Clarke & Crane, 2018).
Therefore, the goal of this research is to empirically explore the interaction between tech-based organizations and the surrounding stakeholders that aim at pursuing a common transformational societal mission (Avelino et al., 2019), taking an ecosystem perspective (Cobben et al., 2022).
Scholarship: MSCA, Project title: MOTOR5G, ESR10
The main objective is to extract monetary value from novel technologies. FWNs will enrich the mobile internet experience, and will therefore open new possibilities, applications, and services.  The blending of the physical, digital, and virtual worlds is pushing towards the rapid growth of new FWN-supported business model ecosystems.
New Business Models (BMs) are already emerging due to various aspects of networking. The cloud services market and the information market are becoming influential and critical in the routine sustainability of businesses. Low operational costs of IoT and Industry 4.0 driven future markets will redefine global economics and set the cornerstone of future BMs. Previous BMs are largely dependent upon traditional services such as messaging, voice and data. FWNs will bring a wave of societal changes due to the massive digitalization of services, which require novel BMs in addition to telecom regulation. These devices will be able to connect with one another forming a device-to-device (D2D), device-to-machine (D2M) or machine-to-machine (M2M) network. At present, there are no common BMs for advanced dynamic spectrum usage and charging network connections in D2D, D2M and M2M.
A modern innovative approach in the formation of new LSA algorithms are considered using the network slicing. Two different approaches using deep learning and blockchain licensing will be developed for LSA. These algorithms will then contribute to the construction of novel Business Models.
 P. Demestichas et al., “Intelligent 5G Networks: Managing 5G Wireless Mobile Broadband,” IEEE Veh. Tech. Mag., vol. 10, pp. 41-50, Sept. 2015.
 Study on Implications of 5G deployment on future Business Models, A report by DotEcon Ltd and Axon Partners Group 14-03-2018.
 P. Lindgren, “The Multi Business Model Innovation Approach : Part 1 The Multi Business Model Approach,” River Publishers, 2018.
 Study on Implications of 5G deployment on future Business Models, A report by DotEcon Ltd and Axon Partners Group 14-03-2018.
 https://www.wired.co.uk/article/ford-in-car-wi-fi-modem-vodafone-europe -dated on 28-03-2022
According to Single European Sky Air Traffic Management Research, the U-Space is a promising “set of new services and specific procedures designed to support safe, efficient and secure access to airspace for large numbers of drones” that promises significant economic contribution. It is expected that drones will do deliveries, surveillance, inspection, cinematography, communication networks, entertainment, etc.
However, due to the complexity of flight on the very low altitude, human factor threat, cost savings and other factors, the high level of automation is needed.
Large-scale autonomous and fully automated flights bring aviation to a new era; however, multiple constraints and limitations make such usage not trivial. For example, the most business-promising small goods delivery needs the lightweight drones (about 83% packages of Amazon is below 2.5 kg) due to an economical rationality. Therefore, it leads to unavoidable simplification of aircraft, including constraints for the onboard sensors. It must be noted that, manned aviation’s flight safety relies on multiple onboard sensors and critical systems’ reservation that barely possible for the drones of several kilograms.
Bearing in mind these factors, the author writes a PhD dissertation concerning information provisioning for the autonomous flight of drones within U-Space, which perfectly suits industrial needs and strategy of the EU Commission.
Topic of PhD project:
Exploration, Exploitation, and Group Dynamics: Experiments on Organizational Search and Creativity.
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.
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.
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 endeavor 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.
Investigating Data-Driven Business Models & Ecosystems in the Context of Privacy
Recently, due to the exponential technological development, the humanity found itself standing on the verge of the Fourth Industrial Revolution. The biological, physical and digital worlds are gradually fusing and people have never been so close to technology before. The numbers are staggering and barely imaginable. In fact, each of us is now a walking data generator. The data-driven technologies have significantly impacted the way of how business is conducted and companies started to innovate their business models through digitalization. The importance of leveraging data for the commercial purpose is so far-reaching, that some even call it data capitalism. Companies now co-specialize through creating bonds that promote collaboration without excluding competition and form business ecosystems that gradually take over the world. Without exaggeration, literally every aspect of the business landscape has radically shifted and besides the undoubtable ubiquitous benefits, the exponential data-driven progress encompasses number of substantial concerns, with privacy being one of the most critical. The power of digital became so promising that organizations started to abuse the customers’ personal data, capitalize on them and use them without their permission or awareness – on a massive scale.
The notorious scandals of data-mining tech behemoths have drawn attention to the colossal imbalance of profit and privacy. Despite the regulatory mechanisms being employed, it is apparent that the digital business models of many precedent-setting companies practically stand upon the premise of exploiting personal data and mitigation through external intervention can be ineffective or even counterproductive for innovation. Moreover, the newly redefined status quo of competition dominated by technology platforms only emphasizes the infamous trade-off between customers’ security, convenience and privacy, a fundamental human right recognized in the UN Declaration of Human Rights. According to Tim Berners-Lee himself, the current form of internet basically suffers from two key myths – “advertising is the only possible business model for online companies” and that “it is too late to change the way platforms operate”. Since it is time to stop applying intrusive techniques and find a safer way to develop business, an extensive ecosystem of decentralized open-source disruptors tackles the incompatibility of the current Web environment with technological solutions centred around human-centric principles of privacy. Nonetheless, technology by itself has no single objective value - the privacy-by-default commercial alternatives need to embrace sustainable business models and secure their roles in competitive ecosystems in order to ensure viability of the technology that has a chance to disrupt the unsustainable state of affairs per se.
Through a series of research articles, Fabien’s PhD project contributes to the state-of-the-art research enquiry by investigating how privacy-centric focus impacts the business model development and innovation of companies, what are the characteristics of their current ecosystems and how can they sustainably enhance privacy while achieving competitiveness.
New robotic technologies, such as mobile telepresence robots (MTRs), are changing organizational dynamics, work practices and processes, occupations, and challenging the psycho-social contingencies in the workplace. This brings challenges related to interconnected aspects such as work coordination, learning processes and occupational boundary relations that may impact the stakeholders’ physical, mental and social wellbeing. Novel empirical studies are thus needed to better understand social robots, such as MTRs, and their organizational, managerial as well as psycho-social and socio-cultural effects in different organizational contexts.
The research project will examine how the use and the design of mobile telepresence robots (MTRs) may improve the overall conditions of the pilot and the local users, focusing on aspects such as roles reconfiguration and well-being in the context of job transformation and human-robot interaction. The project will follow a qualitative approach with field study, possibly followed by confirmatory laboratory studies.
The Internet of Things (IoT) and Artificial Intelligence (AI) technologies are contributing significantly in businesses towards their sustainable growth and bringing digitalization in Industry 4.0 (I4.0). The emerging IoT ecosystem with AI capabilities and mature industries in I4.0 are adopting a new distributed edge architecture to gain benefits of computing closer to the device layer. This offers high bandwidth, low latency, increased security, expanded interoperability, reduced storage costs and improved connectivity Beside benefits, edge computing also has certain challenges such as constraint resources, trust, privacy, discovery, monitoring and fault tolerance. Recent technologies advancement like Distributed Ledger Technology (DLT) can address many of the security issues through tamper proof encryption, integrity, transparent, immutable, and auditable functionality. Therefore, it motivates to investigate and experiment on the integration and/or of AI, IoT, DLT and Edge (hereinafter called as AIDE) to exploit the best from fusion of these technologies and bring new values to the industry. These values could be distributed security features, digital traceability of events, seamless sharing data and service chaining of resources/systems/services via resource pooling at edge, optimum utilization of resources and unlocking new IoT business ecosystem constellations wherein actors can engage directly at the edge to get benefits of a transformed business ecosystems.
Therefore, this PhD is investigating on developing a model/framework for the convergence and integration of AIDE technological services considering technical and business ecosystem for different industry use cases to offer new values to industry 4.0 and beyond. For this purpose, different industry and academic -research use cases have been identified given as follows:
This PhD will focus on researching AIDE model technologies considering hypothesis-based research approach and experimentation through prototyping by proposing the following main research question:
Through practical experimentation and investigation, how can AIDE technologies model be applied in Industry 4.0 contexts and offer new values to the industry?
Topic of PhD project
Optimization of Environmental Conditions and Energy Consumption in Vertical Farms.
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.
My research has focused on improving the following aspects:
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.
Global warming and energy security issues together with increasing energy consumptions make renewable energy more attractive than ever before. Even though Denmark is one of the main players and pioneering countries within the wind turbine industry, there is still a long way to go in order to reach the zero-emission target by 2050. This means that improvements need to come from different areas of a wind turbine project.
Machine Learning (ML) is an emerging technology that has successfully been used in different industries and also within the wind energy industry this is used, however mainly for predictive maintenance, forecasting, as well as to create surrogate models. Given this, experts within the industry see huge potential for ML techniques within other stages of the project as well and stress that there hasn’t been much focus on ML in connection with wind resource assessment. The wind turbine site assessment process is one of the processes that is very time and resource consuming and therefore the main aim of this research is to investigate if and how ML techniques can be used to enhance this process. As there is a vast amount of data including historical data about installed turbines available, there is a possibility to use this data to make decisions in connection with future turbine locations and types. Consequently, the main research question that will lead this project is:
This will be done by: