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Die Universität Paderborn im Februar 2023 Bildinformationen anzeigen

Die Universität Paderborn im Februar 2023

Foto: Universität Paderborn, Hannah Brauckhoff

Katharina Brennig, M.Sc.

 Katharina Brennig, M.Sc.

Wirtschaftsinformatik, insb. Data Analytics

Wissenschaftliche Mitarbeiterin

Warburger Str. 100
33098 Paderborn
 Katharina Brennig, M.Sc.
09/2020 - 06/2021

Masterandin - IT Business Solutions GmbH & Co. KG

- Masterandin im Bereich der Implementierung von Process Mining

- Thema der Masterarbeit: Prozessverbesserung in mittelständischen Unternehmen - Gestaltung einer Process Mining Methode für das ERP-System SEMA

10/2018 - 06/2021

M.Sc. Management Information Systems

07/2018 - 06/2021

Werksstudentin - Optano GmbH

09/2019 - 05/2020

Individual Study or Research - Option GmbH

- Betreuung der Studienarbeit durch die Optano GmbH

- Thema der Studienarbeit: Algorithm Runtime Prediction: Are machine learning techniques suitable for predicting the runtime of optimization models?

03/2018 - 07/2018

Praktikum im Supply Chain Management - Daimler AG

10/2014 - 04/2018

B.Sc. International Business Studies

09/2014 - 02/2018

Werksstudentin - net-m Privatbank 1891 AG

05/2017 - 07/2017


08/2016 - 02/2017

Auslandssemester - Universidad de Chile

10/2015 - 12/2015

Propädeutik - Universität Paderborn

11/2013 - 04/2014

Language Assistant Madrid - IST Sprachreisen

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More Isn’t Always Better – Measuring Customers’ Preferences for Digital Process Transparency

K. Brennig, O. Müller, in: Proceedings of the 56th Hawaii International Conference on System Sciences, 2023

Digital technologies have made the line of visibility more transparent, enabling customers to get deeper insights into an organization’s core operations than ever before. This creates new challenges for organizations trying to consistently deliver high-quality customer experiences. In this paper we conduct an empirical analysis of customers’ preferences and their willingness-to-pay for different degrees of process transparency, using the example of digitally-enabled business-to-customer delivery services. Applying conjoint analysis, we quantify customers’ preferences and willingness-to-pay for different service attributes and levels. Our contributions are two-fold: For research, we provide empirical measurements of customers’ preferences and their willingness-to-pay for process transparency, suggesting that more is not always better. Additionally, we provide a blueprint of how conjoint analysis can be applied to study design decisions regarding changing an organization’s digital line of visibility. For practice, our findings enable service managers to make decisions about process transparency and establishing different levels of service quality.


Process Mining of Knowledge-Intensive Processes: An Action Design Research Study in Manufacturing

B. Löhr, K. Brennig, C. Bartelheimer, D. Beverungen, O. Müller, in: Business Process Management, Springer International Publishing, 2022, pp. 251–267

Existing process mining methods are primarily designed for processes that have reached a high degree of digitalization and standardization. In contrast, the literature has only begun to discuss how process mining can be applied to knowledge-intensive processes—such as product innovation processes—that involve creative activities, require organizational flexibility, depend on single actors’ decision autonomy, and target process-external goals such as customer satisfaction. Due to these differences, existing Process Mining methods cannot be applied out-of-the-box to analyze knowledge-intensive processes. In this paper, we employ Action Design Research (ADR) to design and evaluate a process mining approach for knowledge-intensive processes. More specifically, we draw on the two processes of product innovation and engineer-to-order in manufacturing contexts. We collected data from 27 interviews and conducted 49 workshops to evaluate our IT artifact at different stages in the ADR process. From a theoretical perspective, we contribute five design principles and a conceptual artifact that prescribe how process mining ought to be designed for knowledge-intensive processes in manufacturing. From a managerial perspective, we demonstrate how enacting these principles enables their application in practice.

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