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
Educational methods continually evolve and adapt as its users become ever more digitally competent and dependent. It is therefore important to address the increasing need for digital and online learning options while engaging and motivating students. The recent pandemic made this fact excruciatingly clear as the majority of physical teaching had to be converted to online methods with very limited time for implementation.
Especially promising are recent developments in Virtual Reality (VR) technology as an educational tool where virtual environments can enhance the learning process in terms of motivation, engagement, and knowledge gain while also offering strong potential for remote learning.
This project aims to investigate the potential of VR technology based on a foundation of psychological theories of learning. For example, how can advances in VR technology be used to increase motivation, self-efficacy, or learning by continuously adapting the learning experience to the individual user. Specifically, the goal of the project is to conduct several experiments to test the use of psychophysiological measures, such as EEG and GSR, as tools to measure relevant learning variables and use these measurements to tailor the learning environment to the individual user.
Doing so will not only add value to education but is also relevant for industrial learning, which often use very similar methods. To support this connection between education and industry, part of the project includes an exploration of the generalizability of results from an educational setting to an industrial setting.
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.
The adoption of blockchain (and really new digital technologies in general) into business processes comes with challenges both from a technical side – in terms of what is possible – and from a social point of view – in terms of what is valuable for the organizations adopting new technology. Additionally there are considerations to be had in terms of how these two perspectives overlap (the socio-technical system) and this is really, where the PhD-research takes its place. By following the UnWind-project that is funded by the Danish Industrial Foundation in the period January 2020-June 2022, the PhD-research will have access to empirical findings on a developing blockchain use case. The UnWind-project aims to develop a use case for the wind industry supply chain that will enable enhanced collaboration for the members of the project. The PhD-research supports this process by applying design science research for the development of this use case (artifact), seeking to contribute both to practice (the wind industry) and theory (such as within the general fields of innovation and supply chain management) by contributing to the development of the use case.
The primary research question seeks to answer; what the primary barriers are for private, interorganizational blockchain adoption to overcome. Blockchain technology, that is the focus of the PhD-research (question), is a digital ledger, making use of cryptography (en- and decryption of data) and a distributed nature (i.e. everyone owns and have access to the stored data) to provide new methods for transparency of immutable, append-only information across traditional organizational bounds. The distinction private refers to the type of blockchain that is in focus, i.e. blockchains that has certain restrictions regarding the ability to join, read, write and validate data, while the distinction interorganizational refers to the focus of the research being on blockchains that is adopted by multiple businesses (such as supply chains). The focus on adoption is taken due to the nascent stage of maturity of blockchain in business processes, which is sought to be understood better by understanding the primary barriers perceived and experienced by practioners of business. Primary barriers of blockchain adoption are expected to be tied to aspects such as trust, collaboration, information-sharing, interoperability of organizational systems and business processes etc.
The PhD research is characterized by following an abductive logic, i.e. balancing theoretical and empirical fieldwork for the purpose of systematically combining the two through the development of the UnWind-case. The research will make use of a mixed methods approach, relying mostly on qualitative data from non- and semi-structured interviews with individuals and focus groups as well as observation during the development and testing of the use case all involving parties of the wind industry and blockchain-experts of various backgrounds. Furthermore, quantitative data acquired through one or more (wind) industry surveys will provide the research insights on the perceived pros and cons etc. on blockchain, while literature review of blockchain use cases from other industries will provide secondary, empirical data that can be analyzed for understanding the characteristics of existing blockchain use cases.
Before advanced technologies such as nanomaterials, Printed Electronics (PE), or Internet of Things (IoT) came to market, packaging served basic principles to contain, protect, preserve, and inform. However, the growing competitiveness, changes in consumer behavior and demand, emerging wireless and digital technologies have led to the improvement of the primary packaging functions and thereby to the emerge of smart packaging. Generally, the smart packaging incorporates advanced technologies to enhance its main functions and thus is divided into active, intelligent, and interactive packaging. Active and intelligent packaging related to food industry aims to prolong products’ shelf life, improve its quality, and inform the user about its current status. Contrary, interactive packaging extends the traditional communication aspects by triggering a conversation between the package and consumer. Recent advances in PE, AR, and IoT allow packaging to embrace the digital transformation and become network-connected. PE uses nanomaterials, as conductive inks, to produce electronics, as NFC tags, which can be printed on packaging, and thereby it highly increases the design freedom for new applications.
However, the potential of such digital innovation is not yet fully explored, whereas the other smart packaging types, active and intelligent packaging is well-researched and already commercialized for food products. While this packaging ensures improved security and preservation of packed goods, brands are still in need to find better ways to connect with their consumers, to build stronger relationships and prolong consumers’ experience with their products. Especially, when packaging becomes an integral part of the product and is able to create strong emotional and memorable states or reactions. Consequently, new forms of packaging can contribute to retailer’s differentiation and connect in-store and at-home experiences with the brands’ digital marketing activities. The digital capabilities of smart interactive packaging to enhance consumers’ interaction are not well-explored. Researchers already approved packaging as a powerful communication tool for product positioning but did not consider the influence of information and communication technology. Furthermore the majority of researches designed applications for the improvement of logistics operations instead of consumer engagement in-store or at home. In response to this apparent lack of research, this project will aim to identify and examine the potential and capabilities of enabling communication technologies of smart interactive cardboard packaging to enhance consumer’s and product’s experiences at the point of purchase and utilization. More specifically this research will aim to:
To increase the speed, quality, and dissemination of research results scholars and policymakers call for implementing more collaborative approaches across the scientific research fields. Similarly, at a corporate level, solving large and complex global problems requires coordinated actions. These increasingly involve cross-industrial collaborations, and external knowledge sourcing (outside-in open innovation). Interestingly, Open Innovation (OI) literature reports much fewer examples of successful inside-out practices than other OI modes, while large research institutions and infrastructures would love their patents and inventions to be commercialized and utilized more often. Such phenomenon can be potentially related to redefined OI roles shifting the main focus from the internal innovation efforts to establishing external collaborations, university structures, and their control degree while conducting OI or intellectual property challenges when enabling value creation and value capture. The PhD project intends to explore the individual-level incentives and motivations of (not) implementing Open Innovation in Science (OIS) in research practice within the field of management where scientists, universities, and organizations preach openness, are required openness for their grant applications, but simultaneously raise the question – do they open up and share? The theoretical sample consists of top management scholars, who teach, study, and publish OI research, which will lead to the development of a unique data set.
Central research question: What are the individual-level incentives and motivations of (not) implementing OIS into research practice, and what strategies do innovation management scholars apply in order to overcome the potential challenges?
The project aims to respond to calls for further investigating the innovation scholars’ approach toward the implementation of OIS practices into their research, hence contribute to prior research on applying OI methods in the context of science by exploring the mentioned phenomenon on the individual context. Additionally, considering the emerging need for scientific cooperation outside the academia, the study intends to further develop a theory of scientific ambidexterity, and contribute to deepening the understanding of knowledge-based dynamic capabilities, as well as propose recommendations for policymakers.
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?
Project title: M&A as management and organisational competencies
The project is conducted with Norlys amba as partner and sponsor. Norlys a.m.b.a, headquartered in Silkeborg, is one of Denmark's largest energy companies, providing infrastructure, energy, data and TV packages to private and corporate customers in Denmark. Norlys' shareholders are customers in the company's infrastructure areas. The company has DKK 1.5 million customer relations, 709,000 shareholders, 2,500 employees and subsidiaries in 12 locations in Denmark (2019). Norlys has executed 40 mergers and takeovers the past 10 years, becoming the large entity it is today.
The number of transactions and value of M&A has been steadily increasing globally over the last 30 years. In 2017, about 52,000 transactions took place globally, corresponding to an aggregated transaction value of app. USD 4,000 bn. (IMAA, 2018). Still, after three decades of research, the success rate has not increased significantly. Depending on choice of literature, the failure rates are still app. 50-80% measured on financial values pre and post merger.
Based on the research in the past, where several disciplines and perspectives related to performance in M&A has been described separately (e.g., cultural phenomena, social processes, strategic management, M&A processes, financial modelling, post merger integration, power hierarchy) and the fact that success in M&A is still very low, there seems to be a basis for further research in the area.
I want to investigate why so many M&A transactions are still failing despite decades of research in the area.
The main question to be answered in this research project is:
What are the explanations for a continued low success rate in M&A?
Subsequent related questions to be answered are:
Expected contribution to research:
Given the vast expenditure of capital and human resources globally every year and previous research showing very little impact on increasing the value creation, despite significant research in the field, succeeding in developing a better understanding of M&A as a theoretical and empirical phenomenon would add significant value to organisations, companies and investors globally.
Servitization is an organizational transformation embracing the entire organization (T. S. Baines et al., 2009), from taking a product- to a service-centric approach, originating in the firms ´installed base´ (S. A. Brax & Visintin, 2017). Findings of the service paradox (Neely, 2008), the contradictory findings of servitization outcome by Eggert et al., (2011, 2015), and the numerous case studies of failed transformations (Sawhney et al., 2004), all points at the existence of the practical problem within servitization, namely the failed attempts to obtain profitable. The cost of such failure is the lost opportunities for competitive advantage, customer loyalty (Cusumano et al., 2014), inimitable solutions, and steady revenue streams (de Brentani, 1995) due to the retraction of servitization. Thus in time, include the consequences of commoditization, competitive pressure, and hence loss of market shares (Bustinza et al., 2018; Oliva & Kallenberg, 2003). For this reason, the practical problem is highly relevant to investigate from a managerial perspective.
Previous literature has emphasized that, the lack of profitability are influenced by the intensified investments (Visnjic Kastalli & Van Looy, 2013), the volume of service ratio (Nezami et al., 2016), as well as the capabilities within the organization (Eggert et al., 2011, 2015) and execution of building the right organizational capabilities and culture (Neely et al., 2011; Tenucci & Supino, 2019). While academics seems to agree on the curvilinear relation of profitability throughout the transformation presented by Nezami et al., (2016), a greater focus into how to optimize the position on the curve through the second stage of the deliberately servitization transformation, is insufficient. Furthermore, additional calls have been made for further investigations into the multi-dimensional perspective of the operationalization of servitization. However, although some dimensions have been identified within the field of servitization (Capabilities, organisational orientation, etc.) (Adrodegari et al., 2018; Coreynen et al., 2017), academia lacks insight into which dimensions occur in the field, and how these impacts the profitability of servitization. For this reason, the research problem is the ignorance of academia on how the servitization dimensions impact the profitability of manufacturing firms, in their deliberate operationalization of it.
The aim of this PhD project is to study the multi-dimensional operationalization of servitization among SMEs, in order to find out what impact the dimensions have on the servitization success, and thus help the readers understand how to increase the likelihood for a successful and profitable transformation. This investigation is following three main objectives:
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.
Individual and collective human search: Conditions for how to balance exploration and exploitation
Many organizational challenges, e.g., restructuring an organization, hiring an employee, or developing a product, can be understood as a search process that aims to balance the two distinct processes of exploration and exploitation. The early development of a theory of search in organizational contexts mainly relied on abstract modeling and simulations. Recently, a shift towards experimental approaches has become prominent, both by describing the mechanisms of the individual search process and demonstrating the beneficial structures and compositions for groups while.
However, there has been no direct experimental comparison of the search behaviors of individuals and groups and how they are affected in their outcomes across different problem complexities. Different search behaviors have various advantages and disadvantages depending on the complexity of the search e.g., a pure exploitive hill-climbing strategy might be advantageous in simple problems but fail with complex problems. There are theoretical considerations for why the search behavior of groups and individuals would differ. In groups, everyone brings their unique perspective and preferences, which could lead to a more thorough investigation of the problem, increasing the amount of exploration compared to individuals. At the same time, members in a group also tend to conform to each other, i.e., investigating the same local area of the solutions space, leading to less exploration. It is not straightforward to disentangle the tension between search processes’ heterogeneity and conformity as their contribution to the overall search process depends on the landscape characteristics. If the problem is simple, conformity can limit unnecessary exploration On the other hand, conformity can lead to premature convergence on a suboptimal solution for more complex problems. Heterogeneity could counteract both of these effects by increasing the amount of exploration. For simple problems, the increased exploration investigates multiple solutions that, through incremental improvements, would lead toward the same result. In complex problems, the increased exploration could find new promising solutions that would otherwise not have been investigated.
The PhD research will apply both experiments and simulations in order to understand the balance between the underlying mechanisms of search and how they are affected by e.g., problem complexity or exogenous shocks. Obtaining an understanding of these dependences can help managers determine the most appropriate search strategy for individuals and groups.
Skill training is of paramount importance to the success of businesses, and because of this, the training industry is projected to hit 487.3 billion USD by 2030 with a CAGR of 8% . In this, technical training has the highest market share with a growth rate of 6.9% per year till 2030 . The use of Virtual Reality (VR) technologies for training and education is growing at an even faster pace, projected to grow from 6.37 billion USD in 2021 to around 33 billion USD in 2026 with a CAGR of 39.7% . While the industry is adopting VR technologies for training in the workplace, academic research is still inconclusive in answering the question of how these technologies can be made more effective than conventional training with contradictory findings from controlled experiments . In addition to traditional VR technologies, both industry and academia are also exploring the application of newer technologies for enhancing training, for example, haptic feedback technologies which reproduce touch and weight sensations on the trainee's body and are being explored primarily for training in fine motor skills for surgical skills . Another promising area of research is the use of biosensors that objectively measure the physiological signals from the trainees, which might indicate stress, attention, cognition, among other mental states of the trainee . These and other technologies can potentially be used for creating adaptive VR training systems which will be an advance over the current generation of non-adaptive systems which offer the same training experience for all trainees .
The objectives of this PhD research are as follows -
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?
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: