Die Vorstudie beschreibt den geplanten Forschungsweg im Rahmen des Doktoratsstudiums. Ihre Inhalte orientieren sich an den Curricula der jeweiligen Programme.

Zudem muss die Vorstudie den Richtlinien zum Verfassen wissenschaftlicher Arbeiten an der Universität Liechtenstein entsprechen.

Im Kolloquium zur Vorstudie präsentiert die Doktorandin oder der Doktorand das Dissertationsprojekt und erläutert die Gründe für den gewählten Forschungsansatz.

Die Note für die Vorstudie ergibt sich aus dem Durchschnitt der Bewertungen der Betreuerin bzw. des Betreuers und der Zweitbetreuerin bzw. des Zweitbetreuers.

• To register for modules in the specialization, students must have successfully completed the modules Statistics, Business Mathematics, and English I.
• In addition, to register for the IMIT specialization, students must have successfully completed the module Information Systems.

Grundlagen und Berufsbilder der Wirtschaftsinformatik, E-Business, E-Commerce, Collaborative Systems, Informationssysteme und -strategie, Enterprise Resource Planning, Geschäftsprozessmanagement, Informationsmanagement, Wissensmanagement, Benutzergerechte Gestaltung von Informationssystemen, IT-Projektmanagement, Nachhaltige Informationssystemgestaltung, Fallstudien zum Informationsmanagement

Das Modul Informationssysteme wird jeweils im Wintersemester auf Englisch und im Sommersemester auf Deutsch angeboten.

Information Systems Modelling focuses on systems analysis and design. In particular, the course covers methods of and approaches to modelling information systems in organisations. The course covers five primary topics:

• Introduction to object-oriented systems
• Project planning and initiation
• Requirements analysis (i.e. requirements gathering and structuring)
• Information systems modelling (i.e. UML modelling languages)
• Information systems documentation

• The course involves interactive lectures with exercises to integrate theoretical knowledge with practical design and analysis skills.
• The e-learning platform Moodle is used throughout the course to disseminate course material and for information and discussion.
• Case studies are used to show how the course contents are related.

After successful completion of the course, students will

• know how information systems can be modelled
• know and apply basic methods of systems modelling and design (i.e. UML modelling languages)
• use systems-modelling methods to analyse, design, and implement information systems

Written exam (60min)

Data Science and Artificial Intelligence covers statistical and exploratory techniques that are used to make sense of the vast and complex data sets that have emerged in business. Data Science and Artificial Intelligence is one of the core topics of the degree programme, so the course also provides a basis on which students can choose their electives. Students learn to detect patterns in large data sets in quantitative and qualitative formats to translate them into actionable insights. The course covers five primary topics, but also touches upon other topics such as contemporary ethical concerns. It is complemented by Hands-on labs with Python.

• Data visualisation and exploration
•Supervised learning techniques for regression and classification
• Un- and self-supervised learning techniques
• Deep learning fundamentals
• Generative artificial intelligence including large language models

• The course involves interactive lectures with exercises to integrate theoretical knowledge with practical design and analysis skills.

After successful completion of the course, students will

Professional competence
• understand the basic concepts and methods of data science and artificial intelligence
• be able to assess the assumptions and quality of machine learning models

Methodological competence
• know and be able to select and apply the right models for a given task or data set
• be able to derive actionable insights from data mining results
• know basic visualisation and storytelling techniques

Social competence
• communicate effectively using visualisations
• understand different stakeholder perspectives in a data science project

Personal competence
• critically reflect on analytical outcomes
• improve and mitigate self-inflicted errors

Technological competence
• be able to use Python including their libraries such as scikit-learn and matplotlib to apply machine learning and to create visualisations

• James, G., Witten, D., Hastie, T., Tibshirani, R., & Taylor, J. (2023). An Introduction to Statistical Learning: with Applications in Python (1st ed.). Springer Texts in Statistics. Springer. Bishop, C. M. (2024). Deep Learning Foundations and Concepts. Springer.
• Witten, H., Eibe, F., & Hall, M. (2016). Data Mining: Practical Machine Learning Tools and Techniques. San Francisco, CA: Morgan Kaufmann Publishers.
• Provost, F., & Fawcett, T. (2013). Data Science for Business. Sebastopol, CA: O’Reilly Media.

Written exam (90min)

Research Methods 1

• Planung und Umsetzung Forschungsprozess.
• Überblick über die Methoden der Sozialforschung.
• Definition der Forschungsfrage und Ableiten von Unterfragen.
• Forschungsdesigns zur Operationalisierung der Forschungsfrage.
• Forschungsrelevante Literatur zur Beantwortung der Forschungsfrage.
• Literaturarbeit (State of the Art) zu einem allgemeinen Forschungsthema.

BPM and Organisational Practice explores Business Process Management (BPM) through an organisational-studies lens, so it is a BPM elective. Emphasizing the duality of stability and change in organisational work, the course covers the factors, mechanisms, and interventions that affect how processes behave over time. The course covers six primary topics:

• Organisation theory
• Process- and practice-based research
• Organisational routines
• Intra-organisational dynamics and endogenous change
• Organisational learning, unlearning, and forgetting
• The role of agency and intention in the execution of organisational work

The course involves interactive lectures with exercises to integrate theoretical knowledge with practical design and analysis skills.

After successful completion of the course, students will

Professional competence
• understand the key assumptions and management implications of BPM
• understand key assumptions about process work from organisation theory
• understand the main concepts of (strong) process theory
• understand the main competence of routine dynamics theory

Methodological competence
• be able to synthesize the main tenets of two different scientific fields (BPM and routine dynamics)
• be able to analyse organisational phenomena through the lens of (strong) process theory
• be able to attend to (subtle) social dynamics evolving throughout organising processes

Social competence
• Be able to change roles when addressing managerial questions (role as BPM expert versus role as organisation theorist)
• Be able to work together with colleagues on case assignments

Personal competence
• Be able to find unconventional approaches to BPM-related question
• Be able to reflect on strengths and weaknesses from specific scientific fields

Technological competence
• Know about ways to observe and measure process dynamics

• Students are provided with the lecture slides and supplementary material (e.g., selected journal articles).
• Langley, A., & Tsoukas, H. (2017). The SAGE Handbook of Process Organization Studies. London, UK: SAGE Publications.

Written exam (60min)

Security Management covers technical and organisational methods for the definition and implementation of security policies. The course covers five primary topics:

• People, processes, and strategic planning
• Risk management
• Regulatory compliance, aw, and ethics
• Security analysis, safeguards, and frameworks
• Maturity and performance measurement

• The module involves interactive lectures with exercises to integrate theoretical knowledge with practical design and analysis skills.
• Homework assignments are used to deepen students’ understanding of the analytical methods of security management.

After successful completion of the course, students will

Professional competence
• understand the main security objectives and processes
• be able to initiate and lead basic security initiatives in smaller organisations

Methodological competence
• be able to set up and maintain basic information security management systems
• be able to apply correct metrics to measure security related KPIs

Social competence
• understand that security management always has an ethical part

Personal competence
• be able to identify emerging security issues
• be able to find and apply suitable standards, literature and frameworks

Technological competence
• be familiar with the main security related standards, guidelines, and frameworks

• Students are provided with the lecture slides and supplementary material (e.g., selected journal articles).

Written exam

In the Research Seminar course, students learn to apply in practice what they learned in the Research Methods course. The seminar covers issues related to identifying and formulating research questions, choosing a suitable research design to use in answering these questions, evaluating the feasibility of a planned research study, and writing research proposals. Together with faculty, students develop research proposals (so-called “exposés”) for their master’s theses

• The course involves interactive seminars with workshops and regular presentations.

After successful completion of the course, students will

Professional competence
• critical analyse state-of-the-art literature on selected research topics
• be able to write independently and scientifically about new research topics
• be able to present research ideas and designs

Methodological competence
• be able to understand and apply research methods in computer science and information systems
• be able to identify open research problems
• be able to identify feasible solution strategies for research problems

Social competence
• be able to effectively communicate their research ideas to their peers
• be able to effectively communicate their research ideas to their research supervisor(s)

Personal competence
• be able to plan and implement complex research tasks

Technological competence
• conduct proof-of-concept research experimentation and design

• Students are provided with the lecture slides and supplementary material (e.g., selected journal articles).

Seminar paper, presentation