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

• Foundations of software engineering
• Introduction to programming in Phyton
• Relational data modeling (Entity-Relationship Models (ERM), Structured Query Language (SQL), Relational Databases)
• Object-oriented systems modeling (Object Orientation, Unified Modeling Language (UML))
• Best practices of software engineering and programming
• Group Project
• Basics of Networking

Enrollment from WS23/24
These admission requirements:

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

Enrollment before WS23/24
Either the above admission requirements or:

• To register for fifth semester modules, all modules from the first year of study must be successfully completed.
• Elective courses are not taken into account for these regulations.

• Technik des Jahresabschlusses (mit/ohne Abschlusstabelle),
• Elemente des Jahresabschlusses (Bilanz, Gewinn- & Verlustrechnung, Anhang, Lagebericht),
• Bilanzierung im Allgemeinen,
• Bewertungsmassstäbe (Anschaffungskosten, Herstellungskosten, beizulegender Wert),
• Bilanzierungsgrundsätze (Vorsichtsprinzip, Realisationsprinzip, Niederstwertprinzip, Höchstwertprinzip),
• Gewinn- & Verlustrechnung nach Gesamtkosten- und Umsatzkostenverfahren,
• Ergebnisverwendung bei Kapitalgesellschaften (mit/ohne Ausschüttungssperren) und bei Personengesellschaften,
• Aktivierungswahlrechte, Passivierungswahlrechte,
• Anlagenspiegel, Forderungenspiegel,
• Fremdwährungsforderungen und -verbindlichkeiten,
• Rückstellungenspiegel, Verbindlichkeitenspiegel,
• ausgewählte Sonderformen der Rückstellungen,
• Eventualverbindlichkeiten,
• Verbuchung verschiedener Zuschüsse,
• gewinnabhängige Steuern und Mehr-Weniger-Rechnung,
• Unternehmen in der Krise (Überschuldung),
• latente Steuern (aktive und passive latente Steuern).

Vorlesung

Practise listening, reading, speaking, and writing skills and competences to reach an advanced C1 level (cf. CEFR descriptors) and be able to take an English certificate exam at C1 level

Interaction, coaching

Develop skills and competences to reach an advanced C1 level (cf. CEFR descriptors), be able to take an English certificate exam at C1 level

• listening:

o understand extended speech even when it is not clearly structured and when relationships are only implied and not signalled explicitly

• reading:

o understand long and complex factual and literary texts, appreciating distinctions of style
o understand specialised articles and longer technical instructions

• speaking:

o express oneself fluently and spontaneously without much obvious searching for expressions
o use language flexibly and effectively for social and professional purposes
o formulate ideas and opinions with precision and relate contribution skillfully to those of other speakers
o present clear, detailed descriptions of complex subjects integrating sub-themes, developing particular points and rounding off with an appropriate conclusion

• writing:

o express oneself in clear, well-structured text, expressing points of view at some length
o write about complex subjects in a letter, an essay or a report, underlining what are considered the salient issues
o select style appropriate to the reader in mind

Available on Moodle

Parts of assessment
A: written exam
B: oral exam

Attendance: minimum 80% required

Grading

Fakultätsübergreifendes Wahlfach:
Regeln für die Anmeldung: www.uni.li/cross-faculty

The course is designed to provide students with hands-on experience in managing a wealth management firm through a comprehensive investment management game simulation. The course focuses on strategic and tactical asset allocation, implementing strategies based on financial outlooks and forecasts, evaluating performance (performance attribution), and adapting strategies in the context of existing but uncertain information. Developed through an Erasmus+ project by the University of Liechtenstein and the Free University of Bolzano in collaboration with Cesim, this simulation provides a competitive and realistic environment for students to apply theoretical knowledge to practical scenarios.

Key topics covered in this course include:

• Strategic and Tactical Asset Allocation
• Financial Forecasting and Outlook
• Performance Evaluation and Attribution
• Investment Decision-Making under Uncertainty
• Wealth Management Firm Operations

• Interactive simulation-based learning
• Group discussions and strategic planning sessions
• Performance review and feedback sessions
• Case studies and analysis of wealth management strategies

After successful completion of the course, students will

Professional competence

• understand the principles of strategic and tactical asset allocation.
• gain insights into the operations and challenges of running a wealth management firm.
• develop the ability to make informed investment decisions based on financial forecasts.

Methodological competence

• apply financial outlook and forecasting techniques to portfolio management.
• conduct performance evaluation and attribution to assess investment strategies.
• adapt investment strategies in response to uncertain and evolving market information.

Social competence

• collaborate effectively within a team in a competitive simulation environment.
• communicate investment strategies and performance outcomes clearly.
• engage in strategic discussions and decision-making processes.Personal competence
• develop critical thinking and problem-solving skills in portfolio management.
• enhance decision-making abilities under uncertainty.
• reflect on the implications of investment decisions and strategies.

Technological competence

• analyse financial data and performance metrics using relevant tools (Excel, R).
• Leverage technology to enhance strategic decision-making in portfolio management.

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

Class participation (60%), Group project documentation (20%), Final presentation (20%); Attendance is mandatory (80%)

• Communication skills
• Writing skills
• Business English

• Exercise with max. 30 participants
• Group and individual work
• Discussions and role plays
• Presentations and mini-lectures

• Einführung in die Steuerwissenschaften
• Steuersysteme: Liechtenstein, Deutschland, Österreich, Schweiz
• Lösungsschema und Zusammenspiel: Nationales und Internationales Steuerrecht
• Nationales und internationales Steuerrecht: Liechtenstein, Deutschland, Österreich, Schweiz
• Fallstudien: Besteuerung natürlicher und juristischer Personen (Liechtenstein, Deutschland, Österreich, Schweiz)
• Vergleichende Analyse der Steuersysteme
• Anwendung von Doppelbesteuerungsabkommen

Vorlesung

• This course provides a comprehensive introduction to the application of econometric techniques in finance. Stu-dents will delve into both univariate and multivariate time-series analysis, gaining insights into key concepts of modern econometrics. Overall, students will be equipped with the necessary skills to analyse and interpret com-plex financial time-series data effectively. This course combines theoretical knowledge with practical application using R. Key topics covered are:Stationarity, differencing, and co-integrationHeteroskedasticity and volatility clusteringSelf-dependence and endogeneityVector auto regressions.Time series modelling, including ARMA and GARCHImplement and empirically test the above mentioned in R

• Interactive lectures combined with coding sessions (exercises).

• After successful completion of the course, students willProfessional competenceunderstand the complexity and pitfalls of financial data.apply advanced econometric techniques to analyse that complexity.formulate predictive models for quantitative and unbiased forecasts. - Methodological competenceformulate predictive models for financial time-series. - · understand and know how to implement models of univariate and multivariate volatility.· know how to detect, test and handle stationarity, heteroscedasticity and auto-correlation.· understand when to use univariate end multivariate time series models, know how to test and implement them and interpret the output of such models.Technological competenceconduct econometric analyses and forecast financial data using R. - Social competencesolve complex econometric problems as a team.

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

Project (50%), Written exam (50%); Attendance is mandatory (80%)

Students will explore how to establish effective governance frameworks that enable businesses to leverage AI while managing risks. The curriculum also delves into strategic planning for generative AI, illustrating its potential to revolutionize business models and enhance operational efficiency. Additionally, students will learn about trans-forming companies towards AI companies, including changing organizational culture to thrive in the AI era. Key points are:

• Data and AI governance: Frameworks for businesses for seizing opportunities while mitigating risks
• Data and AI strategy with focus on Generative AI
• AI Transformation: Leading a company into the AI area
• Ethical considerations in AI

• 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
• Gain an understanding of how AI can be integrated into business strategies and operations effectively and ethically.

Methodological competence
• Develop skills in designing and implementing AI governance frameworks and strategies that align with business goals and ethical standards.

Social competence
• Learn to navigate the ethical, social, and cultural challenges associated with AI technologies in a work-place, fostering responsible AI use.

Personal competence
• Enhance critical thinking and ethical reasoning abilities in the context of AI deployment and management.

Technological competence
• Understand implications and handling of weaknesses and strengths of AI technology

• No text book, but papers, e.g.,
• Abraham, R., Schneider, J., & Vom Brocke, J. (2019). Data governance: A conceptual framework, structured review, and research agenda. International journal of information management, 49, 424-438.
• Schneider, J., Abraham, R., Meske, C., & Vom Brocke, J. (2023). Artificial intelligence governance for busi-nesses. Information Systems Management, 40(3), 229-249.

Written exam

This optional module allows for various kinds of research studies. It is closely connected to the five units of the Liechtenstein School of Architecture and is usually part of ongoing research projects. The supervision consists of directing the students towards clear results within a given field of research. The individual study is reviewed within the respective unit.

Sustainable Design Unit:
Research in Sustainable Architecture - Focus on Fluidglass This elective module invites participants to explore the transformative potential of Fluidglass technology within the realm of sustainable architecture. In alignment with the ongoing research at the LSA, this module allows you to examine how innovative facade technologies such as Fluidglass can influence the future of construction. Participants will have a unique opportunity to contribute to the advancement of Fluidglass in the context of sustainability and contemporary building practices. Depending on the desired credit allocation, tailored work packages will be developed. These may include an exploration of current advancements in facade technology, competitor and market analyses, or technical and cost simulations. Your interests and prior knowledge will be taken into account during the initial phase of the project. Participants will engage in self-directed research, working either individually or in collaborative groups. The scope of your work will be customized to meet the specific requirements of your selected work packages and will be guided by the research unit. While there are no formal prerequisites for this module, it is advisable to undertake it in the third semester to build upon foundational knowledge. Essential literature and resources will be provided at the commencement of the course, with regular updates throughout the semester to support your work and great importance is placed on equipping the participants with a strong foundation for future projects and scientific work in the field of sustainability.

Self-defined design or research studies, developed individually or in groups agreed upon with research units and under the guidance of mentors. The size of the module is determined by the respective unit.

After successful completion of the course, students will be able to

Relevant reading will be made available at the beginning of the course. A list of recommended literature will be announced in the course and updated on an ongoing basis.

Minimum 75% compulsory attendance, regular meetings with instructors, continuous assessment, portfolio and final review.
The final grade is calculated according to the weighting of the following components: final submission (80%) and oral presentation (20%).

Individual appointments will be set with the tutor.