@inproceedings{PreissWestner, author = {Preiß, Niklas and Westner, Markus}, title = {Towards a Taxonomy for Digital Assistant Technologies: Addressing the Jingle-Jangle Fallacies}, series = {38th Bled eConference: empowering transformation: shaping digital futures for all: conference proceedings, 8.-11.6.2025, Bled}, booktitle = {38th Bled eConference: empowering transformation: shaping digital futures for all: conference proceedings, 8.-11.6.2025, Bled}, publisher = {University of Maribor Press}, isbn = {9789612869984}, doi = {10.18690/um.fov.4.2025.1}, pages = {1 -- 20}, abstract = {This study proposes a unified taxonomy for Digital Assistant Technologies (DATs) to resolve terminological inconsistencies and eliminate »Jingle-Jangle fallacies.« By employing a systematic taxonomy development method on 137 papers, the framework categorizes DATs across four meta-characteristics: AI technology, context, intelligence, and interaction. This taxonomy facilitates the clear differentiation of three primary DAT concepts: assistant, chatbot, and agent. By providing a structured framework, the study enhances conceptual clarity, fosters more focused research, and ensures better alignment of DATs.}, language = {en} } @misc{CapellmannWestner, author = {Capellmann, Dominik and Westner, Markus}, title = {ChatGPT in Software Engineering: Potentials, Challenges and Possible Applications for Unit Testing and Code Debugging}, series = {21st International Conference on Applied Computing 2024}, journal = {21st International Conference on Applied Computing 2024}, language = {en} } @inproceedings{PreissWestner, author = {Preiß, Niklas and Westner, Markus}, title = {From Agents to Copilots: a Systematic Review of Digital Assistant Technology Adoption in Proprietary Productivity Software}, series = {Annals of Computer Science and Information Systems}, volume = {43}, booktitle = {Annals of Computer Science and Information Systems}, publisher = {Polish Information Processing Society}, issn = {2300-5963}, doi = {10.15439/2025F3271}, pages = {565 -- 576}, abstract = {This study provides a systematic review of how the impact and adaptation of digital assistant technologies (DATs) are defined, operationalized, and studied, synthesizing key domains where DATs generate or are expected to generate value. Based on an analysis of 61 articles published since 2013, it identifies five main areas of impact: productivity and efficiency, business development, resource optimization, quality enhancement, and the promotion of learning and creativity. The review highlights DAT adoption across various disciplines and industries, while revealing limited longitudinal research on benefits and adaptation. Key gaps remain in understanding strategic use and sustained impact. Future research should explore longitudinal comparisons of recently introduced generative AI-driven DATs and their organizational implications. This review contributes to information systems research by structuring current knowledge on DAT adoption and outcomes, and by proposing a research agenda to support deeper exploration of their value and long-term integration.}, language = {en} } @inproceedings{KoehlerHarlWestneretal., author = {K{\"o}hler, Jessica and Harl, Maximilian Victor and Westner, Markus and Strahringer, Susanne}, title = {Can AI be a Scholar? A Systematic Review on the Role of Generative AI in Systematic Literature Reviews}, series = {2025 27th International Conference on Business Informatics (CBI), 09-12. September 2025, Lisbon, Portugal}, booktitle = {2025 27th International Conference on Business Informatics (CBI), 09-12. September 2025, Lisbon, Portugal}, publisher = {IEEE}, doi = {10.1109/CBI68102.2025.00012}, pages = {11}, abstract = {Systematic literature reviews (SLRs) are foundational for research but resource-intensive to conduct. With the rise of large language models (LLMs) such as ChatGPT, generative AI (GenAI) tools are being increasingly explored for their potential to support and transform the SLR process. This study presents a systematic review of peerreviewed articles that examine how LLM-based GenAI tools are used in different SLR phases. Following the PRISMA 2020 guidelines, we screened 1,846 publications published since January 2021 until April 2025 and selected 54 for in-depth analysis. Each study was coded by review phase, prompting approach, automation level, validation type and challenges. Our findings show that GenAI is most often used to support in the screening, search, and writing phases, typically through Basic Prompting and under human oversight. While many studies report efficiency gains, concerns remain regarding validity, transparency, and methodological rigor. Moreover, GenAI is frequently applied to isolated tasks but is rarely embedded in a structured, methodologically guided review processhighlighting the need for clearer phase-specific guidance and standards. We offer a structured, phase-specific synthesis that highlights both the promise and the current limitations of GenAI in literature reviews and thereby offer practical recommendations for the responsible use of GenAI in literature reviews.}, language = {en} } @article{KaessStrahringerWestner, author = {K{\"a}ss, Sebastian and Strahringer, Susanne and Westner, Markus}, title = {Archetypes, Situations, and Practices : a Guide to Successful Low-Code Adoption}, series = {Information Resources Management Journal}, volume = {38}, journal = {Information Resources Management Journal}, number = {1}, publisher = {IGI Global}, issn = {1040-1628}, doi = {10.4018/IRMJ.396005}, pages = {26}, abstract = {This paper extends prior work on low-code by explaining when adoption archetypes occur and how to use them. Motivated by information technology (IT) talent shortages and uneven low-code development platform (LCDP) outcomes, the paper seeks practical guidance for post-adoption choices in work systems. Using a multiple mini case study of 36 cases in large German organizations, this study analyzes interviews and context questionnaires with within-/cross-case coding and pattern matching against a 13-factor model. This analysis identifies situations that trigger three adoption archetypes—application development democratizers, synergy realizers, and IT resource shortage mitigators—and one non-adoption archetype, intricacy adversaries. The analysis also maps advantages and disadvantages and distills 12 good practices. Across adoption cases, efficiency is the dominant goal, whereas non-adoption stems from high application sophistication. The results give actionable guidance: align goals to an archetype, stick to LCDP standards, involve IT and foster an open culture, invest in skilling, reuse platform components, and reserve LCDPs for less-complex apps while planning architecture early.}, language = {en} } @incollection{Westner, author = {Westner, Markus}, title = {Generative AI in Business}, series = {Artificial Intelligence in Business and Engineering}, booktitle = {Artificial Intelligence in Business and Engineering}, editor = {Hofbauer, G{\"u}nter}, publisher = {Kohlhammer}, address = {Stuttgart}, isbn = {978-3-17-046742-2}, pages = {38 -- 50}, abstract = {Generative Artificial Intelligence (GenAI) is transforming industries at an unprecedented rate, offering novel opportunities for productivity and innovation. This article explores the adoption of GenAI, highlighting its accelerated uptake compared to previous technologies. Key topics include productivity gains through GenAI tools (e.g., ChatGPT, Klarna's AI assistant), challenges such as data quality and organizational readiness, and the implications for business strategy. Practical recommendations for managing generative AI adoption and maximizing its impact on both individual and organizational levels are also provided. The analysis underscores the necessity of aligning AI capabilities with customer needs and creating data-driven, adaptable business models.}, language = {en} } @book{WestnerStrasser, author = {Westner, Markus and Strasser, Artur}, title = {Objectives and Key Results verstehen und anwenden}, publisher = {Springer Gabler}, address = {Wiesbaden}, isbn = {978-3-658-50381-9}, doi = {10.1007/978-3-658-50382-6}, pages = {47}, abstract = {Dieses essential gibt eine strukturierte und kompakte Einf{\"u}hrung in die Objectives and Key Results-Managementmethode. Es erl{\"a}utert Herkunft, Definition und Einsatzzweck von Objectives and Key Results (OKR) und beschreibt deren zentrale Bestandteile. Leser:innen erfahren, wie gute Objectives und Key Results formuliert werden und welche Strategien sich zur erfolgreichen Implementierung eignen. Zudem werden praxisrelevante Erfolgs- und Misserfolgsfaktoren analysiert, die den Einsatz von OKR maßgeblich beeinflussen. Ein fundierter Leitfaden f{\"u}r alle, die OKR als Steuerungsinstrument in Organisationen verstehen und wirksam einsetzen m{\"o}chten.}, language = {en} } @article{BuerknerWestner, author = {B{\"u}rkner, Leonhard and Westner, Markus}, title = {KI erfolgreich einf{\"u}hren: Status quo und Erfolgsfaktoren in deutschen Großunternehmen}, series = {Wirtschaftsinformatik \& Management}, journal = {Wirtschaftsinformatik \& Management}, publisher = {Springer}, doi = {10.1365/s35764-026-00593-6}, pages = {10}, abstract = {Viele deutsche Großunternehmen experimentieren derzeit intensiv mit k{\"u}nstlicher Intelligenz (KI), stehen aber vor der Frage, wie sich erste Pilotprojekte in einen nachhaltigen, wirtschaftlich wirksamen Einsatz {\"u}berf{\"u}hren lassen. Eine empirische Studie mit 34 Chief Information Officers (CIO) und IT-Entscheidern in deutschen Großunternehmen zeigt: 112 identifizierte KI-Use-Cases, ein klar erkennbarer Reifezuwachs - aber auch deutliche H{\"u}rden bei Daten, Kompetenzen und Akzeptanz. Der Beitrag fasst den Status quo zusammen, validiert zentrale Erfolgsfaktoren aus der Forschung und leitet konkrete Empfehlungen f{\"u}r die Praxis von IT- und Fachbereichsverantwortlichen ab.}, subject = {K{\"u}nstliche Intelligenz}, language = {de} }