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When compared to other industries, the construction industry has been slow to adopt digital technologies. BIM stands for Building Information Modeling (hereon referred to as BIM) and it represents a turning point when it comes to digitalization in the AEC sector. Despite BIM’s proven potential to reduce costs and improve the efficiency of construction projects, widescale adoption, and implementation of construction projects using BIM hasn’t happened yet. This research aims to conduct an innovation analysis of adoption of BIM in Europe using innovation theories such as Rogers’s diffusion theory and Crossing the Chasm by Moore. We hope the reader will have an understanding of the various adoption barriers for BIM in Europe after reading this research paper. / Jämfört med andra branscher har byggbranschen varit långsam med att använda digital teknik. BIM står för Building Information Modeling (nedan kallad BIM) och representerar en vändpunkt när det gäller digitalisering inom AEC-sektorn. Trots BIM: s beprövade potential att minska kostnaderna och förbättra effektiviteten i byggprojekt, har vidsträckt antagande och genomförande av byggprojekt med BIM ännu inte hänt. Denna forskning syftar till att göra en innovationsanalys av antagandet av BIM i Europa med hjälp av innovationsteorier som Rogers diffusionsteori och Crossing the Chasm av Moore. Vi hoppas att läsaren kommer att ha en förståelse för de olika adoptionsbarriärerna för BIM i Europa efter att ha läst detta forskningsdokument.

Integrated STEM education and Universal Design for Learning (UDL) have been often theorized as compatible. However, there has been little research done to understand how UDL is used in real-world integrated STEM classrooms. Our study aimed to understand how current practicing educators in integrated STEM classrooms combine these teaching methodologies. This was done through a combination of interviews and document analysis of lesson plans, and supplementary information. To evaluate what elements of UDL were used in the documents, researchers developed a UDL codebook based off of the 31 checkpoints in the Center for Applied Special Technology (CAST) UDL guidelines. The goal of the study was to understand how the adoption of UDL could spread across all integrated STEM educators. Therefore, our study viewed the use of UDL in an integrated STEM classroom as an 'innovation' and analyzed our results through Diffusion of Innovation theory. Specifically looking to providing understanding to the 'innovation' through Rogers 5 Attributes of innovations. The study found that all except two UDL checkpoints were proved to be compatible within integrated STEM classrooms, and were categories developed to explain how the participant achieved these checkpoints. The findings also show that not all UDL checkpoints occur at the same frequency. Through Diffusion of Innovation theory, our study showed that Integrated STEM educators believe that UDL is automatically adopted by educators using Integrated STEM teaching methodologies. They expressed problems associated with implementing some UDL checkpoints, and providing overall themes of complexity when implement UDL in an Integrated STEM classroom. / Master of Science in Life Sciences / Integrated STEM education and Universal Design for Learning (UDL) are two teaching methodologies that have been often theorized to be compatible. However, there has been little research done to understand how UDL is used in real-world integrated STEM classrooms. The study aimed to understand how current practicing educators in integrated STEM classrooms combine these teaching methodologies. This was done through a combination of interviews and document analysis of lesson plans, and supplementary information. To evaluate what elements of UDL were used in the documents, researchers developed a UDL codebook based off of the 31 checkpoints in the Center for Applied Special Technology (CAST) UDL guidelines. The goal of the study was to understand how the adoption of UDL could spread across all integrated STEM educators. Therefore, our study viewed the use of UDL in an integrated STEM classroom as an ‘innovation' and analyzed our results through Diffusion of Innovation theory, which conceptualizes an innovation spread through a population. The study found that all except two UDL checkpoints were proved to be compatible within integrated STEM classrooms, and were categories developed to explain how the participant achieved these checkpoints. The findings also show that not all UDL checkpoints occur at the same frequency. Through Diffusion of Innovation theory, our study showed that Integrated STEM educators believe that UDL is automatically adopted by educators using Integrated STEM teaching methodologies, but when discussing the implementation of specific UDL checkpoints themes about the complexity of the innovation emerged.

Yes / With the expected increase in the numbers of persons with dementia, providing timely, adequate, and affordable care and support is challenging. Assistive and health technologies may be a valuable contribution in dementia care, but new challenges may emerge. The aim of our study was to review the state of the art of technologies for persons with dementia regarding issues on development, usability, effectiveness and cost-effectiveness, deployment, and ethics in 3 fields of application of technologies: (1) support with managing everyday life, (2) support with participating in pleasurable and meaningful activities, and (3) support with dementia health and social care provision. The study also aimed to identify gaps in the evidence and challenges for future research. Methods: Reviews of literature and expert opinions were used in our study. Literature searches were conducted on usability, effectiveness and cost-effectiveness, and ethics using PubMed, Embase, CINAHL, and PsycINFO databases with no time limit. Selection criteria in our selected technology fields were reviews in English for community-dwelling persons with dementia. Regarding deployment issues, searches were done in Health Technology Assessment databases. Results: According to our results, persons with dementia want to be included in the development of technologies; there is little research on the usability of assistive technologies; various benefits are reported but are mainly based on low-quality studies; barriers to deployment of technologies in dementia care were identified, and ethical issues were raised by researchers but often not studied. Many challenges remain such as including the target group more often in development, performing more high-quality studies on usability and effectiveness and cost-effectiveness, creating and having access to high-quality datasets on existing technologies to enable adequate deployment of technologies in dementia care, and ensuring that ethical issues are considered an important topic for researchers to include in their evaluation of assistive technologies. Conclusions: Based on these findings, various actions are recommended for development, usability, effectiveness and cost-effectiveness, deployment, and ethics of assistive and health technologies across Europe. These include avoiding replication of technology development that is unhelpful or ineffective and focusing on how technologies succeed in addressing individual needs of persons with dementia. Furthermore, it is suggested to include these recommendations in national and international calls for funding and assistive technology research programs. Finally, practitioners, policy makers, care insurers, and care providers should work together with technology enterprises and researchers to prepare strategies for the implementation of assistive technologies in different care settings. This may help future generations of persons with dementia to utilize available and affordable technologies and, ultimately, to benefit from them.

No / This paper examines the continuous intention by healthcare professionals to use the Internet of Medical Things (IoMT) in combination with responsible artificial intelligence (AI). Using the theory of Diffusion of Innovation (DOI), a model was developed to determine the continuous intention to use IoMT taking into account the risks and complexity involved in using AI. Data was gathered from 276 healthcare professionals through a survey questionnaire across hospitals in Bahrain. Empirical outcomes reveal nine significant relationships amongst the constructs. The findings show that despite contradictions associated with AI, continuous intention to use behaviour can be predicted during the diffusion of IoMT. This study advances the under- standing of the role of responsible AI in the continued use of IoMT in healthcare and extends DOI to address the diffusion of two innovations concurrently.

The purpose of this descriptive case study was to identify the strategies used by post-secondary military institutions to adopt, implement and train faculty for the use of new instructional technologies in the learning environment. Termed the Innovation Migration Process, it includes: 1) the adoption decision (selection of the innovation), 2) strategies for implementation and, 3) how faculty are trained on its use. The study was a two phased, explanatory, mixed-methods design beginning with a quantitative survey, followed by twelve qualitative interviews conducted at two exemplary institutions. The study identified two strategies are used to adopt new technology: 1) authoritative decisions from the "top-down" and 2) a bottom-up strategy where new technology is first used by innovators who work with a central organization to adopt the change. Five strategies were identified to implement the innovation: 1) centralized training; 2) leadership commitment; 3) tapping expertise; 4) well defined support for pedagogy and technical issues; and 5) a robust infrastructure. Four strategies were found for training faculty: 1) tapping expertise (indicating training and implementation are interwoven); 2) formal training; and 4) dedicated training time. The fourth strategy, incentives and rewards, was used successfully by one of the two exemplary institutions, but few of the other institutions offered either of these for training. Suggested guidelines for post-secondary, military institutions include: create a culture of innovativeness; demonstrated commitment by the leadership; follow Ely's Eight Conditions for Implementation; develop a centralized training organization; develop a robust technical support organization; invest in the infrastructure; seek out and support innovators; use a formal faculty development program. / Ph. D.

Teknologiers transformativa förmågor har stor potential att bidra till förändring som gynnar samhällen, miljön och mänskligt välbefinnande. Världen står idag inför många stora utmaningar som klimatförändringar och omfattande digitala förändringar. Investerare påverkar vilka teknologier som formar våra samhällen, och uppdragsdrivna aktörer, snarare än endast vinstdrivna, kan påverka framtida paradigmskiften. Trots ett ökat intresse för området ”transformativa teknologier” saknas konsensus kring grundläggande egenskaper och vad begreppet inbegriper. Avsaknaden av definierande egenskaper resulterar i att begreppet löper risk att urvattnas, vilket vidare medför negativa effekter kopplade till beslutsfattares avsaknad av praxis för att utvärdera transformativa förmågor hos teknologier. Beslutsfattare kan hjälpas av ett tydligare definierat begrepp och genom applicerbara metoder kan beslutsfattare hjälpas nå sina mål. Befintliga metoder för utvärdering av ansvarsfulla investeringar som ESG och Impact investing är inte fokuserade på teknologier och därtill finns begränsningar som gör de mindre tillämpbara i fall där transformativa teknologier är relevant. Studien bidrar till en ökad förståelse för begreppet och tillhandahåller metoder för utvärdering genom att analysera transformativa egenskaper i Industrifondens portföljbolag inom området transformativ teknologi. Det empiriska underlaget utgörs av 12 portföljbolag. Studien visar på tre dimensioner av transformativa egenskaper, value changes, mimicry och diffusion, samt bidrar med förståelse kring dess innebörd. Vidare visar studien på återkommande mönster i termer av transformativa egenskaper. Utöver bidraget kopplat till en ökad förståelse inom området ger studien förslag på hur dessa teknologier kan utvärderas samt hur utvärderingsmetoder kan tillämpas vid investeringsstrategier. / The transformative capabilities of technologies have great potential contributing to change that benefits society, the environment and human well-being. The world faces many grand challenges today, such as climate change and extensive digital change. Investors affect which technologies shaping our societies and organizations that are mission-driven, rather than entirely profit-driven, can affect the paradigm shifts that are to come. Despite an increased interest in the field of “transformative technologies”, there is a lack of consensus on basic characteristics and what the term entails. The lack of defining characteristics results in a risk of the term being diluted, which further has negative effects linked to decision-makers’ lack of approaches to evaluate transformative abilities in technologies. Decision-makers can be helped by a more defined concept, and through applicable methods, decision-makers can be helped to achieve their goals. Existing methods for evaluation of so-called “responsible investments” such as ESG and Impact investing are not focused on transformative capabilities of technologies. Furthermore, some limitations make them less applicable in cases where transformative technologies are relevant. The study contributes to an increased understanding of the concept and provides methods for evaluation by analyzing transformative capacities in Industrifonden's portfolio companies in the field of transformative technology. The empirical basis consists of 12 portfolio companies. The study highlights three dimensions of characteristics for transformative technologies, value changes, mimicry and diffusion, and contributes to an understanding of their meaning. Furthermore, the study shows recurring patterns in terms of transformative characteristics. In addition to the contribution linked to an increased understanding in the area, the study provides suggestions on how these technologies can be evaluated and how evaluation methods can be applied to investment strategies.

The cyber world is growing as more information is converted from analogue to digital form. While convenience has been the main driver for this change little effort has been made on securing the data. Data breaches are growing in number and each breach is growing in severity. Combined with regulatory pressure organizations are starting to realize the importance of security. The increased threat level is also driving the security market for more potent solutions and artificial intelligence (AI) have in recent years been implemented to enhance the capabilities of security technologies.  The thesis aims to investigate the adoption of AI enabled cybersecurity technologies within the financial industry which is often perceived as the market leader regarding security. Using a qualitative method through a multiple case study, valuable insights were gained regarding how firms are working with security and what needs they have. To identify factors that influence the rate of diffusion of AI enabled security technologies the diffusion of innovation theory combined with the TOE framework was used in this study.  The thesis has contributed to the field of innovation management by enriching an area within IT innovation management by bridging the gap between security innovation and AI innovation. The study revealed that environmental factors, such as regulations and threat landscape, are forcing organizations to take action and control both how organizations work with security but also what technological attributes are perceived as advantageous. Detection and automation are two technological attributes that the companies are looking for to fill their needs. AI solutions are already being implemented to increase detection and automation we believe that the rate of adoption for AI enabled security innovation will only continue to grow. The results and findings contribute to an expanded understanding on the factors that affect adoption of AI security innovations within the financial industry. / Den digitala världen fortsätter att växa eftersom mer information omvandlas från analog till digital form. Medan bekvämlighet har varit den viktigaste drivkraften för denna förändring, så har lite ansträngning gjorts för att säkra upp den data som företagen besitter. Dataintrången växer i antal, och varje ny incident får allvarligare konsekvenser än den förra. Detta faktum kombinerat med strängare regelverk har fått företagen att inse vikten av att säkra sin miljö. Den förstärkta hotbilden driver också säkerhetsmarknaden framåt med nya lösningar, och artificiell intelligens (AI) har under de senaste åren i allt större utsträckning implementerats i säkerhetslösningar för att förstärka skyddet. Uppsatsens syfte är att undersöka spridningen av AI-säkerhetsinnovationer inom finansbranschen, som ofta uppfattas som marknadsledande när det gäller säkerhet. Med hjälp av en kvalitativ metod genom en fallstudie på tre företag erhölls värdefulla insikter om hur företagen arbetar med säkerhet och vilka behov de har. För att identifiera faktorer som påverkar spridningshastigheten för AI-säkerhetslösningar användes ”diffusion of innovation”-teorin i kombination med TOE-ramverket i denna studie. Uppsatsen har bidragit till innovation management-området genom att berika ett område inom IT-innovation genom att brygga mellan säkerhetsinnovation och AI-innovation. Studien visade att miljöfaktorer, såsom regelverk och hotbild, kontrollerar både hur organisationer arbetar med säkerhet och vilka tekniska egenskaper som uppfattas som fördelaktiga. Detektion och automatisering är två tekniska egenskaper som företagen har stora behov av. AI-lösningar implementeras redan för att öka de egenskaperna. Vi argumenterar för att, utifrån de behoven som företagen har kombinerat med miljöfaktorerna, kommer spridningstakten att öka för AI-säkerhetsinnovation.

The paradigm of diffusion research can be traced back all the way to the 1940s when Ryan and Gross investigated the diffusion of hybrid seed among farmers in Iowa. Since the 1960s diffusion research has been applied in a wide variety of disciplines, for instance, to study the diffusion of the Internet and the non-diffusion of the Dvorak keyboard. Currently, the technologies that are on top of the Gartner Hype Cycle are all associated with Artificial Intelligence (AI), which shortly can be defined as learning devices that perceive their environment and take actions to maximize their success at some goal. Consequently, some people suggest that the current hype surrounding AI can be the end of the human kind, while others believe it will give way for millions of fresh jobs and cleverer decision-making. In recent years both media and political organizations have shown great interest in AI. In addition, the industry is captivated by the potential uses of AI. In the last years, AI-related companies in the US have raised billions of dollars in the stock market together with a large number of acquisitions. The large flow of capital into AI technology underpins the fast development of AI solutions. The purpose of this study is to investigate how groups approach AI. What can be concluded after reviewing different sectors is that organizations seem to share a common interest of AI. Furthermore, organizations share the opinion that eventually AI will be a more natural part of their processes. Organizations investing a larger share of their budget in R&D have a longer experience of using AI and are currently doing projects utilizing more advanced technologies within AI. In organizations from other sectors, the investments in AI depend on the people with the authority to invest money in projects and their view on AI. Organizations generally seem to approach AI in a similar way. Firstly, they evaluate what AI is. Secondly, they find areas to make small iterative PoC-projects utilizing AI, usually with machine learning. Finally, more money is invested if the PoC-projects were successful and the organization starts looking at how to acquire more competence within the area to fully exploit the value of AI. / Paradigmet för innovationsspridning kan spåras ända tillbaka till 1940-talet när Ryan och Gross undersökte spridningen av hybridfrön bland bönder i Iowa. Sedan 1960-talet har forskningen tillämpats inom en mängd olika discipliner, till exempel för att studera spridningen av Internet och icke-spridningen av Dvorak-tangentbordet. För närvarande är teknologierna som ligger på toppen av Gartner Hype-cykeln alla förknippade med artificiell intelligens (AI), som kan definieras som lärande enheter som uppfattar sin miljö och vidtar åtgärder för att maximera sin framgång gällande något mål. Hypen som nu finns kring AI har lett till att vissa människor tror att det kan innebära slutet för mänskligheten medan andra tror att det kommer att ge plats för miljoner nya jobb och smartare beslutsfattande. Under de senaste åren har både medier och politiska organisationer visat stort intresse för AI samt visat intresse för potentiella användningsområden av AI. AI-relaterade företag i USA har under de senaste åren har tagit in miljarder dollar i riskkapital. Ett stort antal förvärv och kapitalflödet till AI-teknik ökar den snabba utvecklingen av AI-lösningar. Syftet med denna studie är att beskriva spridningen av AI i organisationer från ett antal olika sektorer. Vad som kan sägas efter att ha studerat olika sektorer är att organisationer delar en gemensam nyfikenhet för AI och att de tror att AI kommer bli en allt mer naturlig del av sina processer. De företag som spenderar mycket pengar på FoU har längre erfarenhet av att använda AI och gör för närvarande projekt som använder mer avancerade tekniker. I andra organisationer är investeringarna inom AI beroende av de anställda som har rätt att investera pengar i projekt och deras syn på AI. Organisationer verkar allmänt närma sig AI på ett liknande sätt där de först utvärderar vad AI är. Därefter väljer de ett antal områden där de gör små iterativa projekt där de utnyttjar AI, vanligtvis via ML. Därefter investerades mer pengar om de små projekten lyckas och företaget börjar titta på hur man kan förvärva mer kompetens inom området.