Team

Michael Harr

Wissenschaftlicher Mitarbeiter

Michael Harr, M.Sc.

Raum:
R09 R03 H18
Telefon:
+49 201 18-32847
E-Mail:

Lebenslauf:

Seit März 2023: Wissenschaftlicher Mitarbeiter/Doktorand am Lehrstuhl für Wirtschaftsinformatik und integrierte Informationssysteme der Universität Duisburg-Essen von Prof. Dr. Reinhard Schütte.

Juli 2021 – September 2021: Freier wissenschaftlicher Autor für die netStart GmbH – Verschriftlichung des deutschen Startup-Monitors 2021 in Zusammenarbeit mit PricewaterhouseCoopers und dem Bundesverband deutscher Startups e. V.

Oktober 2020 – April 2022: Wissenschaftliche Hilfskraft am Lehrstuhl für Digital Business und Digital Entrepreneurship der Universität Duisburg-Essen von Prof. Dr. Tobias Kollmann.

April 2020 – Februar 2023: Studium der Wirtschaftsinformatik (M. Sc.) an der Universität Duisburg-Essen in Essen.

  • Studienschwerpunkt: IT-Projektmanagement und Digital Entrepreneurship 
  • Thema der Abschlussarbeit: Selecting appropriate process models for IT projects: Towards a tool-supported decision approach

August 2017 – September 2020: Studentische Hilfskraft in der Fachbibliothek Geisteswissenschaften/Gesellschaftswissenschaften der Universität Duisburg-Essen.

Oktober 2016 – März 2020: Studium der Wirtschaftsinformatik (B. Sc.) an der Universität Duisburg-Essen in Essen.

  • Studienschwerpunkt: IT-Management 
  • Thema der Abschlussarbeit: Plattform-Governance-Dimensionen – fokussiert auf potenzielle Plattform-Preispolitiken und deren Umsetzung im E-Commerce

Publikationen:

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  • Strauss, Christina; Harr, Michael Dominic; Schütte, Reinhard; Wimmer, Simon: "Hey Siri, Don't Make Me Mad" - Overcomming User Annoyances With Voice Assistants. In: Proceedings of the European Conference on Information Systems (2024). PDFVolltextBIB DownloadDetails

    This study examines the effects of integrating a technically advanced and more human-like large language model into a voice assistant to assess, how technical advancements mitigate user annoyances. Therefore, a generative pre-trained transformer was integrated into Siri and made available to 23 interview participants. Preliminary results reveal a decrease in user-reported annoyances, showing that the integration not only improves technical accuracy but also enhances the perceived humanness of interactions. However, subsequent interviews indicated that the distinction between the effects of technical advancements and the infusion of humanness emerged as critical, indicating a complex interplay between these factors. It is therefore planned to differentiate between technical and human improvements in the further development of this article. The results contribute to the discourse on optimizing voice assistants by pinpointing the reduction of user annoyances as a pivotal factor in improving user experience, suggesting pathways for future enhancements in voice assistant platforms.

  • Harr, Michael; Seufert, Sarah: Selecting appropriate process models for IT projects: Towards a tool-supported decision approach. In: Wirtschaftsinformatik (WI) 2023 Proceedings (2023). PDFVolltextBIB DownloadDetails

    The appropriate selection of suitable process models plays an important role for IT project success. To aid in decision-making, IT project management literature offers a plethora of decision models for selecting suitable process models, however, hybrid process models are often neglected and adoption in practice is low or non-existent. To address this challenge, we draw on contingency theory to develop and implement a tool-supported decision model for the selection and evaluation of appropriate process models for IT projects, thereby leveraging artificial intelligence and machine learning in the context of a self-enforcing network. Our model provides an objective tool to assess process model suitability. Results from a conducted online survey with project management experts indicate high validity. Therefore, we contribute to the field of IT project management by expanding AI-based decision models for selecting and evaluating process models through extending the range of covered models and implementing inherent weighting of criteria.

  • Kollmann, Tobias; Kleine-Stegemann, Lukas; Then-Bergh, Christina; Harr, Michael Dominic; Hirschfeld, A.; Gilde, J; Wald, V.: Deutscher Startup Monitor 2021 - Nie war mehr möglich, PricewaterhouseCoopers; Bundesverband deutscher Startups e. V. (Hrsg.), Berlin 2022. PDFVolltextBIB DownloadDetails

Vorträge:

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  • Harr, Michael; Seufert, Sarah: Selecting appropriate process models for IT-Projects: Towards a tool-assisted decision approach, Wirtschaftsinformatik Conference (2023), 21.09.2023, Paderborn. PDFDetails
    Selecting appropriate process models for IT-Projects: Towards a tool-assisted decision approach

Begleitete Abschlussarbeiten:

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  • Exploration der Fähigkeiten von ChatGPT für bessere Lernerfahrungen in MOOCs: Eine systematische Übersicht (Bachelorarbeit Wirtschaftsinformatik, in Bearbeitung)
  • Einsatz von Verfahren der künstlichen Intelligenz bei Recruiting potenzieller Bewerber: Evaluation der Einsatzpotenziale (Bachelorarbeit Wirtschaftsinformatik, 2023)
  • State-of-the-Art-Analyse des Recruiting von Talenten auf sozialen Netzwerken (Bachelorarbeit Wirtschaftsinformatik, 2023)