Jan Marco Leimeister
Title
Prof. Dr.
Last Name
Leimeister
First name
Jan Marco
Email
janmarco.leimeister@unisg.ch
ORCID
Phone
+41 71 224 3330
869 results
Now showing 1 - 10 of 869
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Item type:Publication, Prediction, hype, and fear: Advancing Information Systems research in “the AI age”(2026-06-12) - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Continuous value shaping: A boundary concept for innovating service innovation approaches(2025-04-01)Scopus© Citations 4 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Rethinking technology regulation in the age of AI risks(2025-09-23); Leslie WillcocksThis paper argues that artificial intelligence exposes the shortcomings of traditional regulatory paradigms, challenging Easterbrook’s ‘Law of the Horse’ view that general legal principles suffice. AI’s opacity, autonomy, and systemic risks demand risk-informed, technology-specific governance. We identify the pacing problem, where innovation outstrips regulatory capacity, and propose a tripartite framework distinguishing functional, structural, and relational risks. Comparative analysis of EU, US, UK, and Chinese approaches highlights divergent logics of precaution, market oversight, hybrid flexibility, and state control. Effective governance requires embedding risk into policy design through adaptive, proportionate, and harmonised mechanisms, balancing innovation with accountability. The paper underscores the urgency of global coordination and calls for interdisciplinary IS research to inform anticipatory, participatory, and ethically grounded regulation.Type:journal articleJournal:Journal of Information TechnologyScopus© Citations 8 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, SynDEc: A Synthetic Data Ecosystem(2025-01-25); ; Given the critical role of data availability for growth and innovation in financial services, especially small and mid-sized banks lack the data volumes required to fully leverage AI advancements for enhancing fraud detection, operational efficiency, and risk management. With existing solutions facing challenges in scalability, inconsistent standards, and complex privacy regulations, we introduce a synthetic data sharing ecosystem (SynDEc) using generative AI. Employing design science research in collaboration with two banks, among them UnionBank of the Philippines, we developed and validated a synthetic data sharing ecosystem for financial institutions. The derived design principles highlight synthetic data setup, training configurations, and incentivization. Furthermore, our findings show that smaller banks benefit most from SynDEcs and our solution is viable even with limited participation. Thus, we advance data ecosystem design knowledge, show its viability for financial services, and offer practical guidance for privacy-resilient synthetic data sharing, laying groundwork for future applications of SynDEcs.Type:journal articleJournal:Electronic MarketsVolume:35Issue:1Scopus© Citations 13 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, A guide for structured literature reviews in business research: The state-of-the-art and how to integrate generative artificial intelligence(2025-03); Scopus© Citations 40 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, How Siemens Empowered Workforce Re- and Upskilling Through Digital Learning(2025-09); ; Gunter BeitingerThe accelerating digital transformation of manufacturing is enhancing automation and production efficiency while requiring employees to develop new skills to meet evolving demands. This case study examines how Siemens embedded a human-centric, bottom-up approach to empower employee re- and upskilling through innovative digital learning. Aligned with Siemens’s actions, we present a four-phase model on leveraging information systems to address skill gaps, enhance adaptability and tackle re- and upskilling challenges. We also provide five recommendations to help organizations foster lifelong learning in dynamic manufacturing environments.Type:journal articleJournal:MIS Quarterly ExecutiveVolume:24Issue:3 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Next-generation IS research methods – towards a better understanding of complex and dynamic phenomena … and generative AI as the elephant in the roomEmerging phenomena like the increasing volume and variety of available data, machine learning techniques, and, most recently, Generative AI are reshaping our research practices, affording new research methods for testing and developing theory. In this editorial, we discuss our two major observations from running the ‘Next-generation IS Research Methods’ Special Issue: (1) the need for research methods that enhance our understanding of complex and dynamic phenomena and (2) Generative AI as (potential) productivity enhancer. We compile these observations into an organizing framework and discuss possibilities for applying Generative AI in the fields of qualitative, quantitative, and engaged research. We highlight challenges that might occur when applying Generative AI in research and shed light on the changing role of researchers in such settings of human–AI collaboration.Type:journal articleJournal:Journal of Information Technology (JIT)Volume:40Issue:2Scopus© Citations 7 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Next-generation IS research methods – towards a better understanding of complex and dynamic phenomena … and generative AI as the elephant in the room(2025-06); Scopus© Citations 7 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Promoting students’ motivation in language education with gamified pedagogical conversational agents(2025-12)Scopus© Citations 12 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Long Live the Metaverse: Identifying the Potential for Market Disruption and Future Research(2025-02-17)Scopus© Citations 35