Pierre Maier

Wissenschaftlicher Mitarbeiter

Pierre Maier, M. Sc.

R09 R04 H38
+49 201 18-34150



10/2015 - 04/2019: Studium der Wirtschaftsinformatik, B. Sc. an der Universität Duisburg-Essen

  • Bachelorarbeit: "Design Thinking: Theoretischer Hintergrund, grundlegende Konzepte und Anwendungspotenziale im Kontext der Softwareentwicklung"

04/2019 - 05/2021: Studium der Wirtschaftsinformatik, M. Sc. an der Universität Duisburg-Essen

  • Masterarbeit: "Contingency Adaption Through Deep Learning: A Critical Reflection on the Applicability of Deep Learning for Organizational Problem-Solving"

Berufliche Positionen

seit 08/2021: Wissenschaftlicher Mitarbeiter am Lehrstuhl für Wirtschaftsinformatik und Unternehmensmodellierung, Universität Duisburg-Essen

10/2019 - 07/2021: Wissenschaftliche Hilfskraft am Lehrstuhl für Wirtschaftsinformatik und Unternehmensmodellierung, Universität Duisburg-Essen

03/2018 - 01/2020: Tätigkeit im Bereich Metadaten-Management, Data Governance und Datenlizenz-Management bei der E.ON Digital Technology GmbH, Essen

07/2017 - 12/2017: Tätigkeit im Bereich Datenbank-Testing bei der Finanz-Informatik GmbH & Ko. KG, Münster


  • Frank, Ulrich; Maier, Pierre; Bock, Alexander: Low Code Platforms: Promises, Concepts and Prospects: A Comparative Study of Ten Systems - ICB Research Report, 70. Essen 2021. doi:10.17185/duepublico/75244) VolltextBIB DownloadDetails
    Low Code Platforms: Promises, Concepts and Prospects: A Comparative Study of Ten Systems

    In recent years, the catchword “low‐code” has evolved into what can be seen as a major trend
    in software development platforms. A growing number of vendors respond to this trend by
    offering software development platforms that promise limited need for coding only and a tremendous
    boost in productivity. Both aspects have been the subject of intensive research over
    many years in areas such as domain‐specific modeling languages, model‐driven software development,
    or generative programming. Therefore, the obvious question is how ʺlow codeʺ
    platforms differ from such approaches and what specific performance features they offer.
    Since there is no unified definition of “low‐code”, the only way to develop an elaborate understanding
    of what it is – and might be – is to analyze the actual use of the term. For obvious
    reasons, it is not promising in this respect to rely on marketing announcements made by vendors.
    Instead, it seems more appropriate to examine “low‐code” platforms. This research report
    presents a study of 10 relevant platforms, capturing and assessing common characteristics
    as well as specific features of individual tools. The study is guided by a method that consists
    of a conceptual framework, which provides a uniform structure to describe and compare “lowcode”
    platforms, and a process model that describes the sequence of steps.

Betreute Abschlussarbeiten:

  • Deep Learning in Organisationen: Entwurf und Anwendung eines Bewertungsrahmen zum Vergleich von Explainable Artificial Intelligence-Methoden (Bachelorarbeit Wirtschaftsinformatik, in Bearbeitung)
  • Problemstrukturierungsmethoden im Requirements Engineering – Entwicklung eines Eignungsrahmens für den Einsatz in der Anforderungserhebung (Bachelorarbeit Betriebswirtschaftslehre, in Bearbeitung)