Digital innovation (DI) has enabled businesses to enhance their existing market offerings by integrating digital features. Despite advanced technologies, substantial marketing efforts, and global recognition, businesses can still struggle to convince customers to adopt their digital market offering. This process of spreading novel innovation is known as diffusion. In the fast-growing digital world, due to the unique characteristics of DI, traditional diffusion theories and models shows limited explanatory power, creating challenges for researchers and practitioners alike. With the aim to explore these challenges, we position our research within the IS literature with the following research question: "How and why do diffusion enablers and barriers emerge during digital innovation?". We conducted an interpretive case study of Company X, one of the world's largest consulting firms and an active DI practitioner. Our findings suggest that digital innovation diffusion can be enabled or hindered by several understudied interdependencies in its technological architecture. Furthermore, for successful diffusion, how DI distributed division of labor between layers must be effectively embedded and aligned for value in the clients' context. This study provides novel insights, exciting research avenues, and a diffusion strategy for DI practitioners.

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First-class software engineering is no longer enough for an information system product to gain success on a market. Developing successful information system products has become a challenging practice that requires an understanding of those who are going to use the products. As product innovation has become the lifeblood of companies competing in the fast- paced IT industry, the end users have ultimately become those who determine the success of these type of products. User research is conducted to gather insights of users’ contexts, behaviors and feelings when using products. It can be practiced to explore how to create products and features that end users will find useful. This thesis examines how methods and tools used in user research can expose opportunities for innovation. The study was conducted by a literature study and a case study, where user research methods were put to practice to discover opportunities for creating a concept for a new product. Emphasis was also put on studying how to provide utility when developing a new product. The case study was performed over a four month period at an e- commerce company called Swiss Clinic in Stockholm, Sweden. The study shows that opportunities for innovation in user research occur in the interplay between business, user research discoveries and iterative design and that effective communication and artifacts play essential parts for innovating successfully.

Background: In recent years, there has been considerable interest in the philosophical concept of Open Banking and its practical implementation, often associated with digital platforms. The term ‘Open Banking platforms' has been used to describe the complex intersection between the two concepts of Open Banking and digital platforms, and it sometimes embody a series of challenges and opportunities. Open Banking aims to increase competition, innovation, and interoperability in the financial sector, creating opportunities for new players while challenging established banks to rethink their business models and value propositions. However, few researchers have addressed the question of challenges and opportunities faced by the various stakeholders involved in Open Banking platforms. Purpose: The purpose of this study is to investigate and deepen the understanding of the challenges and opportunities faced by various stakeholders involved in Open Banking platforms. Methodology: In this study, a qualitative methodology with an abductive approach was used, and the study was designed as a case study. The empirical data consists of seven semi-structured interviews conducted in 2023, as well as supplementary data from relevant documents. Thematic analysis and triangulation were employed to analyze the collected data. Conclusion: The complexity of Open Banking platforms becomes apparent during their practical implementation within the financial sector, involving technical, functional, interactional, relational, economic, and regulatory dimensions, as well as various stakeholders. On the one hand, third-party providers see opportunities for innovation. On the other hand, banks face issues with outdated IT infrastructure and threats to their existing business models. Some of the challenges arise due to varying perspectives and technological frames. This study also found that interest organizations play an important role in the design of Open Banking platforms, and regulatory requirements are therefore indirectly influenced by interest organizations. It is however challenging for stakeholders with limited resources to actively participate in the discussions about Open Banking. Additionally, a clearer dialogue and balance between compliance, security, and innovation is needed. Despite the challenges, Open Banking is considered important for the future but requires time to mature.

Abstract Background:  Due to the COVID-19 pandemic a series of regulations and recommendations were imposed. This resulted in firms transitioning their co-located teams into virtual teams in an effort to slow down and prevent the spread of COVID-19. This research focuses on the individual team member’s experience of the transition. The transition is a digital transformation and organizational change and its effects on the individual team member is being studied.  Purpose:  The purpose of this research study is to explore how individual team members experience the transition from a co-located team into a virtual team. Trust, communication and social interactions are previously known challenges for virtual teams. Therefore, we strive to explore how these aspects have been affected by the transition and how the individual team member has experienced it.  Method:  This study follows a qualitative research design and the method of choice is semi-structured interviews that have been carried out in a virtual environment due to the current COVID-19 pandemic. Eleven interviewees have contributed to the empirical study. The main findings are presented in chapter 4 and analyzed against existing literature in chapter 5, to finally be concluded in chapter 6.  Conclusion:  Trust has previously been listed as a major challenge for virtual teams. However, our empirical findings have not identified trust as a major challenge and therefore contradict this. Communication has changed and the biggest two contributors to this is that the communication is digital and that body language becomes less effective in a virtual setting as compared to face-to-face communication. However, communication has not been a major issue during the transition but rather a complication. Social interactions were identified as the aspect affecting the team members the hardest since they had become non-existent except for in the virtual environment in many cases. A high level of digital maturity and the teams previously being co-located is seen to have made the transitions easier in terms of trust and communication. Furthermore, team members struggle to find their work life balance and often work more hours.

Depuis le Conseil européen de Lisbonne en 2000, l’Union européenne s’active à devenir une société de la connaissance leader dans l’hypercompétition mondiale. Elle s’attache à enrichir et structurer ses ressources de diversité et de créativité en restructurant sa méthode d’intégration suivant une approche plus ascendante. Elle pousse aussi au décloisonnement de tous les secteurs socio-économiques afin d’en libérer les potentiels d’innovation. Une société en réseau se tisse ainsi grâce au développement des acteurs locaux et d’une connivence public-privé. Toutefois, ces mutations juridiques exigent l’adhésion active des citoyens. Dès lors, à côté des politiques d’éducation, de recherche et d’innovation, l’Union favorise les synergies avec les domaines de l’emploi, de la jeunesse et de la culture. Tous ces changements entraînent une quête effrénée de cohérence globale nécessaire à la compétitivité et à la durabilité du modèle européen de société de la connaissance. Pour atteindre ces objectifs, le droit européen va puiser sa cohérence dans l’articulation des concepts d’Etat social actif, de méritocratie et de démocratisation. / Since the European Council of Lisbon in 2000, the European Union tends to become a knowledge society leader in the world competition. It enriches and structures its ressources of diversity and creativity by generating a renewal of its integration method around a bottom-up approach. It stimulates too a decompartmentalization of all the socio-economic sectors to free all the innovation potentials. So, a network society appears thanks to the development of regional actors and a public-private connivance. But, the legal evolutions need to get the adhesion of the citizens. That’s why, in addition to the policies of education, research and innovation, the European Union fosters synergies with the domains of employment, youth and culture. All these transitions trigger a search of global coherence to guarantee the competitiveness and the sustainability of the European model of knowledge society. To reach these goals, the European law finds its coherence by articulating the notions of active welfare State, meritocracy and democratization.

L’avènement de l’utilisation de systèmes d’intelligence artificielle (IA) en santé s’inscrit dans le cadre d’une nouvelle médecine « haute définition » qui se veut prédictive, préventive et personnalisée en tirant partie d’une quantité inédite de données aujourd’hui disponibles. Au cœur de l’innovation numérique en santé, le développement de systèmes d’IA est à la base d’un système de santé interconnecté et auto-apprenant qui permettrait, entre autres, de redéfinir la classification des maladies, de générer de nouvelles connaissances médicales, ou de prédire les trajectoires de santé des individus en vue d’une meilleure prévention. Différentes applications en santé de la recherche en IA sont envisagées, allant de l’aide à la décision médicale par des systèmes experts à la médecine de précision (ex. ciblage pharmacologique), en passant par la prévention individualisée grâce à des trajectoires de santé élaborées sur la base de marqueurs biologiques. Des préoccupations éthiques pressantes relatives à l’impact de l’IA sur nos sociétés émergent avec le recours grandissant aux algorithmes pour analyser un nombre croissant de données relatives à la santé (souvent personnelles, sinon sensibles) ainsi que la réduction de la supervision humaine de nombreux processus automatisés. Les limites de l’analyse des données massives, la nécessité de partage et l’opacité des décisions algorithmiques sont à la source de différentes préoccupations éthiques relatives à la protection de la vie privée et de l’intimité, au consentement libre et éclairé, à la justice sociale, à la déshumanisation des soins et du patient, ou encore à la sécurité. Pour répondre à ces enjeux, de nombreuses initiatives se sont penchées sur la définition et l’application de principes directeurs en vue d’une gouvernance éthique de l’IA. L’opérationnalisation de ces principes s’accompagne cependant de différentes difficultés de l’éthique appliquée, tant relatives à la portée (universelle ou plurielle) desdits principes qu’à la façon de les mettre en pratique (des méthodes inductives ou déductives). S’il semble que ces difficultés trouvent des réponses dans la démarche éthique (soit une approche sensible aux contextes d’application), cette manière de faire se heurte à différents défis. L’analyse des craintes et des attentes citoyennes qui émanent des discussions ayant eu lieu lors de la coconstruction de la Déclaration de Montréal relativement au développement responsable de l’IA permet d’en dessiner les contours. Cette analyse a permis de mettre en évidence trois principaux défis relatifs à l’exercice de la responsabilité qui pourrait nuire à la mise en place d’une gouvernance éthique de l’IA en santé : l’incapacitation des professionnels de santé et des patients, le problème des mains multiples et l’agentivité artificielle. Ces défis demandent de se pencher sur la création de systèmes d’IA capacitants et de préserver l’agentivité humaine afin de favoriser le développement d’une responsabilité (pragmatique) partagée entre les différentes parties prenantes du développement des systèmes d’IA en santé. Répondre à ces différents défis est essentiel afin d’adapter les mécanismes de gouvernance existants et de permettre le développement d’une innovation numérique en santé responsable, qui doit garder l’humain au centre de ses développements. / The use of artificial intelligence (AI) systems in health is part of the advent of a new "high definition" medicine that is predictive, preventive and personalized, benefiting from the unprecedented amount of data that is today available. At the heart of digital health innovation, the development of AI systems promises to lead to an interconnected and self-learning healthcare system. AI systems could thus help to redefine the classification of diseases, generate new medical knowledge, or predict the health trajectories of individuals for prevention purposes. Today, various applications in healthcare are being considered, ranging from assistance to medical decision-making through expert systems to precision medicine (e.g. pharmacological targeting), as well as individualized prevention through health trajectories developed on the basis of biological markers. However, urgent ethical concerns emerge with the increasing use of algorithms to analyze a growing number of data related to health (often personal and sensitive) as well as the reduction of human intervention in many automated processes. From the limitations of big data analysis, the need for data sharing and the algorithmic decision ‘opacity’ stems various ethical concerns relating to the protection of privacy and intimacy, free and informed consent, social justice, dehumanization of care and patients, and/or security. To address these challenges, many initiatives have focused on defining and applying principles for an ethical governance of AI. However, the operationalization of these principles faces various difficulties inherent to applied ethics, which originate either from the scope (universal or plural) of these principles or the way these principles are put into practice (inductive or deductive methods). These issues can be addressed with context-specific or bottom-up approaches of applied ethics. However, people who embrace these approaches still face several challenges. From an analysis of citizens' fears and expectations emerging from the discussions that took place during the coconstruction of the Montreal Declaration for a Responsible Development of AI, it is possible to get a sense of what these difficulties look like. From this analysis, three main challenges emerge: the incapacitation of health professionals and patients, the many hands problem, and artificial agency. These challenges call for AI systems that empower people and that allow to maintain human agency, in order to foster the development of (pragmatic) shared responsibility among the various stakeholders involved in the development of healthcare AI systems. Meeting these challenges is essential in order to adapt existing governance mechanisms and enable the development of a responsible digital innovation in healthcare and research that allows human beings to remain at the center of its development.

With the current challenges for the healthcare such as increased demand for care, financial and resource constraints along with rapid changes and complexity there is high believe in digital innovation and digitalisation to efficacy resources and aid in delivering a safer, more accessible and patient centred valuable care. There is a digitalisation that is ongoing, being used and implemented over several different areas of healthcare. Since healthcare can be seen as a complex adaptive system, there is a need to understand several agents. The aim is to gather more knowledge about perceptions within the physiotherapy staff and give recommendations and directions for improvements regarding digital innovation. Opinions about digital innovation have been gathered with open interviews and a semisystematic literature review with focus on physiotherapy. Too find subjective data the mixed method Q methodology was applied. The open interviews resulted in eight categories: digital innovation, digital innovation being used, digital innovation not used, management, obstacles, education, wishful thinking, applications and systems and associated opinions. The semi-systematic literature review showed on a rapid scientifically development, 25 articles was found and thematically analysed. 140 cited viewpoints and facts was merged with the results from the open interviews. Ten physiotherapists performed the q-sort consisting of 25 statements. Three factors were found. Interpreted as digital innovation optimism & patient oriented, digital innovation scepticism & management oriented and digital innovation sceptical optimism. Video-call technique is strongly encouraged by factor one contrary to factor two. Integrity is the major conflicting viewpoint between the factors. The result shows that gender can affect if a physiotherapist is either optimistic or sceptical to digital innovation. Using existing models such as UTAUT could improve acceptance about digital innovation. Education is perceived as important among all factors. Nine participants responded on baseline questions showing low knowledge of the term mHealth and little communication with IT departments.

The advancements in Artificial Intelligence (AI) have received large attention in recent years and increased awareness has led to massive societal benefits and new opportunities for industries able to capitalize on these emerging technologies. The pulp and paper industry is going through one of the most considerable transformations into Industry 4.0. Integrating AI technology in the manufacturing process of the pulp and paper industry has shown great potential, but there are uncertainties which direction companies are heading. This study is an investigation of the pulp and paper industry in collaboration with IBM that aims to fill a gap between academia and the progress companies are making. More specifically, this thesis is a multiple case study of the current state and barriers of AI technology in the Swedish pulp and paper industry, the future trends and expectations of AI and the way organizations are managing AI initiatives Semi-structured interviews were conducted with 11 participants from three perspectives and the data was thematically coded. Our analysis shows that the use of AI varies, and companies are primarily experimenting with a still immature technology. Several trends and areas with future potential were identified and it was shown that digital innovation management is highly regarded. We conclude that there are several barriers hindering further use of AI. However, continued progress with AI will provide large benefit long term in areas such as predictive maintenance and process optimization. Several measures taken to support initiatives with AI were identified and discussed. We encourage managers to take appropriate actions in the continued work toward AI integration and encourage further research in the area of potential reworks in R&D. / Framgångarna inom Artificiell Intelligens (AI) har fått stor uppmärksamhet de senaste åren och ökad medvetenhet har lett till stora fördelar för samhället liksom nya möjligheter för industrier som tar vara på dessa nya teknologier. Pappers- och massa industrin genomgår en av de mest omfattande transformationerna mot Industri 4.0. Integreringen av AI-teknologi i industrins tillverkningsprocesser has visat stor potential, men också osäkerhet kring vilken riktning företag är på väg mot. Denna studie är en undersökning av den svenska pappers- och massaindustrin, i samarbete med IBM, som syftar till att minska gapet mellan akademin och framstegen företag inom industrin tar. Mer specifikt är denna uppsats en kombinerad fallstudie av det nuvarande läget, barriärerna till AI-teknik i den svenska pappers- och massa industrin, de framtida trenderna och förväntningarna på AI och metoderna företag använder för att stötta AI-initiativ. Semi-strukturerade intervjuer genomfördes med 11 deltagare från tre olika perspektiv och datan var tematiskt kodad. Vår analys visar att användning av AI varierar och företag experimenterar huvudsakligen med omogen teknik. Flera trender och områden med potential för framtiden identifierades och det visades att digital innovationshantering är högt ansedd. Vi sammanfattar med att det finns flera barriärer som hindrar fortsatt användning av AI. Fortsatt arbete med AI-tekniken kommer leda till stora fördelar på lång sikt inom områden som prediktivt underhåll och fortsatt processoptimering. Flera åtgärder för att stötta AI-initiativ var identifierade och diskuterades. Vi uppmuntrar industrin att genomföra lämpliga åtgärder i det fortsatta arbetet mot AI-integration och uppmuntrar fortsatt forskning inom potentiella omstruktureringar inom FoU.