TY - CHAP A1 - Ho, Joe Ee A1 - Ooi, Boon Yaik A1 - Westner, Markus T1 - Application Integration Framework for Large Language Models T2 - 2024 5th International Conference on Artificial Intelligence and Data Sciences (AiDAS), Bangkok, Thailand, 03-04 September 2024 N2 - Large Language Models (LLMs) have unlocked new opportunities in processing non-structured information. However, integrating LLM into conventional applications poses challenges due to their non-deterministic nature. This paper introduces a framework designed to effectively integrate LLM into intermediate modules by ensuring more consistent and reliable outputs. The framework includes three key components: the Sieve, which captures and retries processing of incorrect outputs; the Circuit Breaker, which stops processing persistently incorrect outputs; and the Optimizer, which enhances processing efficiency by combining inputs into single prompts. Experimental results employing structured methodology demonstrate the framework's effectiveness, achieving significant improvements a 71.05% reduction in processing time and an 82.97% reduction in token usage while maintaining high accuracy. The proposed framework, agnostic to specific LLM implementations, aids the integration of LLMs into diverse applications, enhancing automation and efficiency in fields such as finance, healthcare, and education. KW - error handling KW - token efficiency KW - AI integration KW - data pipeline optimization KW - automation KW - data processing framework Y1 - 2024 SN - 979-8-3315-2855-3 U6 - https://doi.org/10.1109/AiDAS63860.2024.10730541 SP - 1 EP - 6 PB - IEEE ER - TY - CHAP A1 - Westner, Markus A1 - Preiss, Niklas T1 - Unraveling jingle-jangle fallacies in digital assistant technologies : a comprehensive systematic review and research agenda T2 - Proceedings of tenth international congress on information and communication technology : ICICT 2025, London, Volume 9 N2 - Digital assistant technologies (DATs) such as chatbots, virtual assistants, and intelligent agents have gained widespread attention, yet inconsistent terminology remains a critical challenge. The fragmented nature of previous research has led to significant confusion due to overlapping and interchangeable use of terms across industries. This systematic literature review, following the PRISMA protocol, consolidates the current state of knowledge on DATs and addresses the prevalent jingle-jangle fallacies in their terminology. Analysis of 137 articles identified keycharacteristics, applications, and conceptual overlaps of various DATs, uncovering 39 distinct technologies categorized under three overarching concepts: assistants, chatbots, and agents. Despite shared functionalities, terminological inconsistencies persist across different sectors, presenting challenges for both academic research and practical implementation. This review emphasizes the need for standardized terminology and clearer classification frameworks to facilitate broader DAT adoption across organizational contexts. Y1 - 2025 U6 - https://doi.org/10.1007/978-981-96-6438-2_15 SP - 199 EP - 227 PB - Springer ER - TY - CHAP A1 - Preiß, Niklas A1 - Westner, Markus T1 - Towards a Taxonomy for Digital Assistant Technologies: Addressing the Jingle-Jangle Fallacies T2 - 38th Bled eConference: empowering transformation: shaping digital futures for all: conference proceedings, 8.-11.6.2025, Bled N2 - 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. Y1 - 2025 SN - 9789612869984 U6 - https://doi.org/10.18690/um.fov.4.2025.1 SP - 1 EP - 20 PB - University of Maribor Press ER - TY - GEN A1 - Capellmann, Dominik A1 - Westner, Markus T1 - ChatGPT in Software Engineering: Potentials, Challenges and Possible Applications for Unit Testing and Code Debugging T2 - 21st International Conference on Applied Computing 2024 Y1 - 2024 ER - TY - CHAP A1 - Preiß, Niklas A1 - Westner, Markus T1 - From Agents to Copilots: a Systematic Review of Digital Assistant Technology Adoption in Proprietary Productivity Software T2 - Annals of Computer Science and Information Systems N2 - 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. KW - Digital Assistant Technology KW - Copilot KW - Generative AI KW - Technology Adoption KW - Productivity Y1 - 2025 U6 - https://doi.org/10.15439/2025F3271 SN - 2300-5963 VL - 43 SP - 565 EP - 576 PB - Polish Information Processing Society ER - TY - CHAP A1 - Köhler, Jessica A1 - Harl, Maximilian Victor A1 - Westner, Markus A1 - Strahringer, Susanne T1 - Can AI be a Scholar? A Systematic Review on the Role of Generative AI in Systematic Literature Reviews T2 - 2025 27th International Conference on Business Informatics (CBI), 09-12. September 2025, Lisbon, Portugal N2 - 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. KW - Generative AI KW - Large Language Models KW - ChatGPT KW - Systematic Literature Review KW - Evidence Synthesis KW - AI-assisted Literature Review Y1 - 2025 U6 - https://doi.org/10.1109/CBI68102.2025.00012 PB - IEEE ER - TY - JOUR A1 - Käss, Sebastian A1 - Strahringer, Susanne A1 - Westner, Markus T1 - Archetypes, Situations, and Practices : a Guide to Successful Low-Code Adoption JF - Information Resources Management Journal N2 - 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. KW - Business-managed IT KW - Low-Code Development KW - Citizen Development KW - Shadow IT KW - Multiple Mini Case Studies KW - Work Systems Theory KW - Good Practices Y1 - 2025 U6 - https://doi.org/10.4018/IRMJ.396005 SN - 1040-1628 VL - 38 IS - 1 PB - IGI Global ER - TY - CHAP A1 - Westner, Markus ED - Hofbauer, Günter T1 - Generative AI in Business BT - Opportunities, Challenges, and Strategic Implications Authors Markus Westner T2 - Artificial Intelligence in Business and Engineering N2 - 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. KW - Generative AI KW - Productivity KW - Technology Adoption Y1 - 2025 SN - 978-3-17-046742-2 SP - 38 EP - 50 PB - Kohlhammer CY - Stuttgart ER - TY - BOOK A1 - Westner, Markus A1 - Strasser, Artur T1 - Objectives and Key Results verstehen und anwenden N2 - Dieses essential gibt eine strukturierte und kompakte Einführung in die Objectives and Key Results-Managementmethode. Es erläutert 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 für alle, die OKR als Steuerungsinstrument in Organisationen verstehen und wirksam einsetzen möchten. KW - Objectives and Key Results (OKR) KW - Zielmanagement KW - Performance Management KW - Strategieumsetzung KW - Kennzahlen / KPI Y1 - 2025 SN - 978-3-658-50381-9 U6 - https://doi.org/10.1007/978-3-658-50382-6 PB - Springer Gabler CY - Wiesbaden ER -