Next Generation Business Ecosystems: Engineering Decentralized Markets, Self-Sovereign Identities and Tokenization

Digital transformation research increasingly shifts from studying information systems within organizations towards adopting an ecosystem perspective, where multiple actors co-create value. While digital platforms have become a ubiquitous phenomenon in consumer-facing industries, organizations remain cautious about fully embracing the ecosystem concept and sharing data with external partners. Concerns about the market power of platform orchestrators and ongoing discussions on privacy, individual empowerment, and digital sovereignty further complicate the widespread adoption of business ecosystems, particularly in the European Union. In this context, technological innovations in Web3, including blockchain and other distributed ledger technologies, have emerged as potential catalysts for disrupting centralized gatekeepers and enabling a strategic shift towards user-centric, privacy-oriented next-generation business ecosystems. However, existing research efforts focus on decentralizing interactions through distributed network topologies and open protocols lack theoretical convergence, resulting in a fragmented and complex landscape that inadequately addresses the challenges organizations face when transitioning to an ecosystem strategy that harnesses the potential of disintermediation. To address these gaps and successfully engineer next-generation business ecosystems, a comprehensive approach is needed that encompasses the technical design, economic models, and socio-technical dynamics. This dissertation aims to contribute to this endeavor by exploring the implications of Web3 technologies on digital innovation and transformation paths. Drawing on a combination of qualitative and quantitative research, it makes three overarching contributions: First, a conceptual perspective on \u27tokenization\u27 in markets clarifies its ambiguity and provides a unified understanding of the role in ecosystems. This perspective includes frameworks on: (a) technological; (b) economic; and (c) governance aspects of tokenization. Second, a design perspective on \u27decentralized marketplaces\u27 highlights the need for an integrated understanding of micro-structures, business structures, and IT infrastructures in blockchain-enabled marketplaces. This perspective includes: (a) an explorative literature review on design factors; (b) case studies and insights from practitioners to develop requirements and design principles; and (c) a design science project with an interface design prototype of blockchain-enabled marketplaces. Third, an economic perspective on \u27self-sovereign identities\u27 (SSI) as micro-structural elements of decentralized markets. This perspective includes: (a) value creation mechanisms and business aspects of strategic alliances governing SSI ecosystems; (b) business model characteristics adopted by organizations leveraging SSI; and (c) business model archetypes and a framework for SSI ecosystem engineering efforts. The dissertation concludes by discussing limitations as well as outlining potential avenues for future research. These include, amongst others, exploring the challenges of ecosystem bootstrapping in the absence of intermediaries, examining the make-or-join decision in ecosystem emergence, addressing the multidimensional complexity of Web3-enabled ecosystems, investigating incentive mechanisms for inter-organizational collaboration, understanding the role of trust in decentralized environments, and exploring varying degrees of decentralization with potential transition pathways

Universell utforming : Inkluderende og tilgjengelige autentiseringsløsninger

Innlogging har vist seg å være en stor barriere for brukere av informasjonssystemer. Dette er spesielt et problem for personer med nedsatt sensorisk, motorisk eller kognitiv funksjon. I verste fall kan disse personene oppleve å bli stengt ute fra det digitale samfunnet og sentrale digitale tjenester som bank, handel, offentlige tjenester og sosial kontakt. Diskriminerings- og tilgjengelighetsloven setter krav til at IKT-tjenester skal ha en universell utforming og være tilgjengelig for flest mulig, uten ekstra tilpasning. Foreløpig mangler de offentlige tilsynsmyndighetene en forskrift til loven som kan fortelle hvilke krav loven setter til disse tjenestene. Det finnes lite forskning på universell utforming av autentiseringsløsninger, og i eksisterende retningslinjer for universell utforming finnes det ingen ingen direkte krav til autentiseringsløsninger. I denne oppgaven undersøkes rammevilkårene for å utvikle tilgjengelige autentiseringsløsninger. Det undersøkes og foreslås retningslinjer til slike løsninger, og til slutt vurderes det hvordan nærfeltskommunikasjon kan brukes for å skape en universelt utformet autentiseringsløsning. Noen sentrale funn er at man må se på hele brukerens belastning og ikke bare vurdere enkeltsituasjoner. I dag ligger kostnaden ved autentiseringsløsningene på brukerne. Gjennom krav til å håndtere passord, brukernavn og kontoer utnytter tjenesteleverandørene brukerens hukommelse som om det var en uregulert allmenning. Passord og brukernavn står ikke foran en snarlig død. Derfor må systemet håndtere og være kompatibelt med eksisterende løsninger. Tilgjengelige løsninger må være stabile og ikke kreve at brukeren stadig lærer seg nye metaforer, designkonvensjoner og terminologi. Kompleksiteten med å innføre stedet du er som en autentiseringsfaktor og håndtering av mange ulike kontoer på en enhet kan føre til at systemet blir for usikkert og umulig for brukeren å konfigurere på en sikker måte

Personality Identification from Social Media Using Deep Learning: A Review

Social media helps in sharing of ideas and information among people scattered around the world and thus helps in creating communities, groups, and virtual networks. Identification of personality is significant in many types of applications such as in detecting the mental state or character of a person, predicting job satisfaction, professional and personal relationship success, in recommendation systems. Personality is also an important factor to determine individual variation in thoughts, feelings, and conduct systems. According to the survey of Global social media research in 2018, approximately 3.196 billion social media users are in worldwide. The numbers are estimated to grow rapidly further with the use of mobile smart devices and advancement in technology. Support vector machine (SVM), Naive Bayes (NB), Multilayer perceptron neural network, and convolutional neural network (CNN) are some of the machine learning techniques used for personality identification in the literature review. This paper presents various studies conducted in identifying the personality of social media users with the help of machine learning approaches and the recent studies that targeted to predict the personality of online social media (OSM) users are reviewed