Self-sovereign identity (SSI) is a promising paradigm for managing digital identities, offering individuals greater control while enabling more secure and efficient interactions across sectors. Yet, public sector innovation with SSI remains complex. In our paper (open access) that has been published in Government Information Quarterly, we explore how public sector organizations can experiment with and actualize the potential of SSI. Using the affordance-experimentation-actualization (A-E-A) framework, we present findings from a real-world case study on digital tax registration.

Over the past few years, Darmstadt-based artist Rainer Lind has created and published hundreds of video portraits. The films are based on lengthy interviews with people who have something special to say. Among them are many personalities who are active in cultural fields such as art, photography, theater, design, architecture, film, and music.

I feel very honored that Rainer Lind has also created a video portrait of me. In this portrait, I talk about my personal career, my passion for the information systems research field, and my views on the topic of artificial intelligence.

The video portrait can be viewed on the websites of Rainder Lind and the Bertolt Brecht School (German language).

Central Bank Digital Currencies (CBDCs) are reshaping the future of finance, but what role can private organizations play in this emerging ecosystem? In a our paper recently published in Communications of the Association for Information Systems (CAIS), we explore the value propositions that companies can offer in CBDC contexts, based on a multivocal literature review of academic and practitioner sources. Our findings provide a comprehensive framework for understanding how businesses can innovate and add value in a CBDC-driven financial landscape.

In today’s volatile environment, organizations are constantly striving to optimize their processes to maintain competitive advantages. Thereby, process mining supports data-driven process optimization, for example, through prescriptive process monitoring. So far, prescriptive process monitoring mainly focuses on data from single organizations.

However, there are often similar processes across several organizations with the same process goals but differing activities. This entails the potential for mutual learning from an interorganizational perspective. Yet, since process data contains sensitive information, data sovereignty represents a major requirement. 

Federated learning serves as a promising starting point to do so. Thus, following the design science paradigm, we developed, instantiated, and evaluated an approach for data-sovereign, interorganizational prescriptive process monitoring based on federated learning, providing a proof of concept and a foundation for future research.

I am happy that our paper “Toward Data-Sovereign Prescriptive Process Monitoring – A Federated Learning Approach” has been accepted for presentation at the 33rd European Conference on Information Systems (ECIS 2025). The conference will take place from June 12 to 18 in Amman, Jordan.

I am delighted to have received the Editor of Distinction award from SPRINGER NATURE in the category Author Service for my work as Senior Editor of Electronic Markets. The Springer Nature Author Service Award recognizes exceptional service in improving the author experience and ensuring the peer review process is efficient, constructive and fair.

The tourism industry is facing increasing demand for personalized, 24/7 services while simultaneously grappling with a shortage of skilled workers, rising operational costs, and high customer expectations. Digital solutions, particularly in marketing and sales, play a crucial role in increasing online visibility, enhancing customer engagement, and leveraging recommender systems to personalize offerings and improve decision-making. Generative AI Chatbots have emerged as a promising solution to address these challenges by automating processes, enhancing efficiency, and improving customer communication. However, their successful implementation requires a holistic approach that balances technical feasibility, economic sustainability, legal compliance, and social acceptance. At the same time, the role of digitalization extends beyond customer-facing applications; it is becoming increasingly relevant for back-office processes, helping businesses optimize operations and improve overall efficiency in the tourism sector.

Our recently published whitepaper, based on qualitative research, examines the opportunities and challenges associated with integrating Generative AI Chatbots into tourism businesses. Fifteen expert interviews with professionals from tourism, AI development, law, and marketing were conducted and analyzed using systematic content analysis to identify key factors influencing chatbot adoption. The findings highlight the potential of generative AI to streamline operations, provide immediate customer support, and optimize cost structures. Chatbots can automate repetitive tasks, reducing employee workload while ensuring uninterrupted service availability. They also enhance customer interaction by offering personalized recommendations, guiding users through booking processes, and answering inquiries with contextual relevance.

Despite these advantages, several challenges hinder widespread adoption. Technical barriers include ensuring chatbot accuracy, managing real-time data integration, and preventing issues such as hallucinations or inconsistent responses, all of which require continuous monitoring and system updates. Economic constraints present another obstacle, as the high initial investment, ongoing maintenance costs, and unclear return on investment make companies hesitant to commit to chatbot implementation. Legal and compliance issues, such as adherence to AI regulations, GDPR requirements, and liability concerns, further complicate deployment. Additionally, social resistance remains a significant factor, with employees fearing job displacement and customers displaying reluctance to engage with AI-driven services due to skepticism about reliability and usability.

For Generative AI Chatbots to be successfully integrated into tourism businesses, a balanced and strategic approach is essential. Technological readiness must be ensured through high-quality training data, enhanced chatbot response accuracy, and seamless system integration. Organizational change management plays a crucial role in addressing employee concerns through training and transparent communication. Legal and ethical compliance must be prioritized by adhering to regulations, clearly labeling AI-generated content, and ensuring consumer protection. Furthermore, economic viability should be carefully assessed through a thorough cost-benefit analysis and scalable implementation strategies.

While the adoption of Generative AI Chatbots comes with challenges, it also holds substantial potential to improve service efficiency, lower operational costs, and enhance customer experience. By proactively tackling technical, economic, legal, and social obstacles, businesses can fully leverage AI-driven chatbots and strengthen their competitive edge in the rapidly evolving tourism industry.

Current approaches to managing digital identities struggle to meet the demands of ongoing digital transformation. They either create fragmented identities tied to specific online services, making it difficult for users to manage, or they raise concerns about being locked into corporate identity providers and data protection issues. Additionally, they provide limited support for machine-verifiable identity attributes. This reliance on third parties for managing machine identities can put companies at a market disadvantage. Therefore, there is a pressing need for a unified identity management solution that allows for the portable and interoperable use of verifiable identity data across services.

The recently announced European Digital Identity Wallet marks a significant step forward in digital identity management. This initiative aims to provide EU citizens with a unified, secure, and convenient way to access both public and private online services, thereby enhancing the efficiency and security of digital interactions and prioritizing user needs. Self-sovereign identity (SSI) forms the basis for such a wallet-based identity ecosystem that supports electronic market growth. However, as a relatively new concept, SSI still lacks a unified theoretical analysis and a thorough exploration of its value propositions for digital ecosystems and networked businesses.

I am happy that our fundamentals paper “Self-Sovereign Identity and Digital Wallets" has been accepted for publication in Electronic Markets and is now available online (Open Access): https://link.springer.com/article/10.1007/s12525-025-00772-0

Machine learning (ML) applications face many new, hardly predictable aspects in their production environments. Detecting new aspects in an ML production environment and understanding their impacts on the ML application is crucial if organizations are to ensure ML applications’ functionality. A monitoring entity is essential if one is to monitor ML applications in their production environments, to both continually minimize risks and improve ML application’s performance. But existing monitoring approaches are struggling to deal with specifics that arise from ML applications.

In our recently published research, we aimed at deriving monitoring practices and providing a holistic view over the required steps in successful ML applications monitoring. Since there has been little research on this topic, we followed a qualitative research approach, i.e., we conducted an interview study combined with a multivocal literature review. Thus, we provide a theoretical framework of an ML-enabled agent in its production environment, five characteristics of ML applications’ production environments and 17 monitoring practices – 14 practices arranged sequentially on a typical quality management cycle and three cross-sectional practices. To outline the ML specifics that arise in monitoring ML applications, we investigate the five ML production environment characteristics’ influences on the ML monitoring practices.

I am happy that our paper “What Gets Measured Gets Improved: Monitoring Machine Learning Applications in their Production Environments” has been accepted for publication in IEEE Access and is now available online (Open Access): https://ieeexplore.ieee.org/document/10886935

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