Benedict Lösser
Title
M.Sc.
Last Name
Lösser
First name
Benedict
Email
benedict.loesser@unisg.ch
Phone
+41 71 224 77 32
9 results
Now showing 1 - 9 of 9
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Item type:Publication, From zero to hero: ramp-up management as a new cross-cutting business process management capability(Springer Science and Business Media LLC, 2024-09); Röglinger, MaximilianChanging business environments challenge and motivate organizations to transform. To remain competitive, organizations need to embrace these dynamics and make radical changes to how work is performed. Business process management (BPM) as a holistic management discipline offers mature methods and end-to-end management activities. However, it is subject to the tension between stability and change. While change through the improvement of existing business processes is well understood, the implementation and scaling of novel business processes have been neglected in BPM research. Hence, this paper proposes business process ramp-up management (BPRUM) as a new cross-cutting capability area for contemporary and future BPM and explores relevant sub-capabilities. Our work synthesizes insights from an exploratory interview study with 21 subject matter experts to advance the understanding of BPM as a corporate capability regarding the implementation and scaling of novel processes. As a result, this study illustrates how BPRUM adds to modern BPM and presents 40 action-oriented sub-capabilities that provide hands-on knowledge and practical guidance for effective BPRUM. Thereby, it serves as a foundation for further theorizing on process ramp-up and for structuring discussions among BPM practitioners.Type:journal articleJournal:Information Systems and e-Business ManagementVolume:22Issue:3Scopus© Citations 4 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Design Principle Candidates for Steering Digital Transformation at the Enterprise-Level(2025-12-17); Digital Transformation (DT) challenges organizations by unfolding through distributed and loosely coordinated initiatives, often creating tensions between strategic intent and operational execution, as well as between legacy systems and emerging digital capabilities. Despite substantial investment, many DT efforts fail due to fragmented governance and weak enterprise-level coordination. This short paper introduces the concept of enterprise-level steering as a situated practice to support alignment of strategy and execution in complex DT contexts. Drawing from project and IS literature on steering committees, we extend the concept to the enterprise-level. As initial steps in a broader research project adopting the echelonized Design Science Research approach, we derive initial design requirements and propose early-stage design principle candidates. The research aims to contribute to the DT discourse by addressing key tensions through an integrative platform for different stakeholders, and to the steering literature by reframing steering as an enterprise-level practice rather than a project-bound function.Type:conference paper - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Data-Driven Steering of Digital Transformation: Case Insights from OTTO(2025-06-17); Enterprise-level Digital Transformation (DT) efforts often fall short of their intended outcomes, despite the availability of targeted solutions for individual challenges. This is frequently due to the complex and insufficiently coordinated interplay of initiatives, decisions, and stakeholders – driven in part by ineffective steering mechanisms and fragmented information logistics. Drawing on the revelatory case of OTTO, we demonstrate how enterprise-level steering can be systematically informed and supported through targeted business analytics, facilitated by enterprise architects. We show how business analytics not only inform but also support steering by shaping narratives and enabling timely interventions. Our findings contribute to the literature on steering committees by emphasizing their informational role and the subjective use of data in complex DT. We also outline future opportunities for design-oriented research, to further develop data-driven steering of DT.Type:conference paper - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Towards Design Patterns for Information Systems - Finding Suitable Artefact Type Candidates for a Design Problem(2025-01-07); The artefact is at the core of Design Science Research in Information Systems (DSR). The growing variety and complexity of designs makes selecting the best-suited artefact type in a research project more challenging. Extant classification approaches do not provide much support for identifying the most promising solution paths, as they focus on artefact properties and less on problem characteristics. As already illustrated in related disciplines like engineering science, differentiation of a functional (problem-oriented) and a construction (solution-oriented) perspective promises to be useful for supporting the design process. Our study intends to develop and utilize this differentiation for DSR projects. Based on associations between problem and solution classes, design patterns could help researchers to identify the most promising artefact type candidates, and thus avoid less promising con-struction paths. First, we develop a classification scheme for the functional design perspective. Second, we modify existing DSR artefact classifications to develop a scheme for the construction design perspective. Third, based on a polar sample, we identify initial functional clusters and develop preliminary design patterns as associations between functional clusters and construction types. Our contribution constitutes a first, exploratory step towards more effective design guidance in the early DSR stages.Type:conference paper - Some of the metrics are blocked by yourconsent settings
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Item type:Publication, Driving Factors in the Technology Acceptance of Generative Artificial Intelligence – Insights from an Exploratory Interview Study with Digital Leaders(2024-10-10); The adoption pace of Generative Artificial Intelligence (GAI) is swift, yet the factors specifically affecting GAI acceptance remain underexplored. Building on the long-standing tradition within information systems to elucidate technology acceptance, this paper adopts an exploratory approach given the novelty of GAI. An interview study involving thirteen experienced digital leaders from established companies has been conducted to understand GAI acceptance among human agents. The inquiry has led to developing the GAI Acceptance Model (GAIAM), which highlights trust, hedonic motivation, convenience, efficiency, and effectiveness as antecedent factors. The proposed model demonstrates that performance expectancy and perceived value creation, influenced by context factors, serve as appraisals that ultimately lead to behavioral intention and usage intensity. GAIAM may serve as an initial model for grounding the acceptance of GAI at an individual level of analysis and encourages further research in this area. Additionally, the hypothesized model provides practitioners with guidance on which levers they can proactively manage to increase the intensity of GAI usage among human agents.Type:conference paperJournal:itAIS Conference Proceeedings - Some of the metrics are blocked by yourconsent settings
Item type:Publication, State of the Art of Business Analytics for Enterprise Transformation SteeringAs Enterprise Transformations (ETs) pose significant challenges for companies, it is crucial to steer these resource-intensive endeavors rigorously. However, there is a substantial gap between ET’s operational implementation and their steering committees. While existing literature suggests comprehensive information factors to support ET steering, the extent to which these factors are applied in practice and standardized through business analytics remains unclear. This manuscript aims to analyze the current state of business analytics in ET steering through a descriptive interview study. Our findings reveal that these concepts have been utilized only to a limited extent in practice, particularly for context and ET process-related factors. We further highlight and advocate for the increased adoption and implementation of specific business analytics for ET steering, addressing information gaps and enhancing decision quality. This research contributes to the discourse on ET steering by demonstrating the practical applications and benefits of business analytics in improving ET decision-making processes.Type:book sectionJournal:Lecture Notes in Information Systems and OrganisationVolume:79 - Some of the metrics are blocked by yourconsent settings
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