• The purpose of this course is twofold: First, invited talks by researchers from other institutions (and, occasionally, from the University of Liechtenstein) bring students in touch with cutting-edge research in their field. Second, students learn how to give an academic presentation of one of their research papers. Students will receive feedback on their performance. Our goal is to provide research colloquia for all specializations, but the course may not be offered for all specializations in each academic year. Students from two specializations may be gathered into one colloquium for organizational reasons. Key topics covered are: Current research in business economics Structure and content of an academic paper discussion

Presentations, discussions with feedback.

• After successful completion of the course, students will Professional competence Prepare and deliver a presentation of a research paper at a research seminar. - Social competence Interact with scientists in different phases of their career. Formulate critical questions in a neutral and professional manner. - Personal Competence Reflect on one’s own performance and professional behavior.

For the specialisations in Economics and Finance the module is offered as part of the Finance Research Seminar. The module coordinator for ECO and FIN is Assistant Prof Dr Sebastian Stöckl. Please register for the desired module directly in the module.

Finance Research Seminar

For the specialisations in Entrepreneurship and Management and Information Systems the module is not offered internally at the moment. The process flow for external modules can be found on
uni.li/legal. It is not possible to register directly for the desired module here. The module coordinator for ENT and IS is Prof Dr Michael Hanke. If you have any further questions, please contact doktorat@uni.li.

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.

The course covers fundamental frameworks, models, and methods with regard to innovation. Topics include managing innovation within firms, process innovation, innovation decision-making, open innovation and technology transfer, and innovation culture. A real-life case which is set up in cooperation with a company partner serves to apply knowledge on innovation management in a practical project. The developed solution should be scientifically sound and applicable in practice.

• 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

The Information Systems module is offered in English in the winter semester and in German in the summer semester.

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

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

Written exam (60min)