Short description
In the first Innovation Lab, students collaboratively develop innovative solutions for real-life business problems in product and process design.

Topics

• Creativity
• Innovation
• Problem-solving
• Project management
• Teamwork
• Presentation

Learning objectives

• Students will demonstrate their ability to work in a team to solve contemporary business problems.
• Students will show they can plan and organize projects under time pressure and competition.
• Students will use common creativity techniques and problem-solving tools and methodologies and demonstate they can think creatively to create innovative business solutions.
• Students will understand there are different ways of looking at new problems as they will develop alternative approaches to solving the problems they are assigned with.
• Students will deliver professional quality presentations to a demanding audience.

Methods

• The module involves interactive seminars with workshops and regular presentations.
• Together with the faculty, a jury of representatives from regional companies evaluates their solutions against innovativeness and usefulness and provides them with feedback and advice.
• The e-learning platform Moodle will be used for the dissemination of course material and discussions.

Compulsory reading

• The students will be provided with a reader and all lecture slides and supporting materials.

Short description
This course covers some statistical methods that can help to take decisions in business using data. These basic concepts of the statistical testing and estimating theory should – to a large extent - be known from an introductory course on probability theory and statistics in any bachelor program.

Topics

• Graphical and numerical characterizations of random variables and their distributions
• Framework and basic applications of testing hypotheses and estimating parameters
• Ordinary least squares method and its properties
• Simple linear regression including parameter estimation, diagnostic plots, hypothesis testing, predictions and model specifications using log-transformations
• Introduction to the software package R

Learning objectives

• Students present the distributions of random variables graphically, calculate and interpret their moments.
• Students can explain the framework of testing hypotheses and estimating parameters and apply basic procedures.
• Students criticize the assumptions of basic testing and estimating procedures and generalize the conclusions correctly.
• Students derive the minimal sample size for basic testing and estimating procedures.
• Students apply the ordinary least squares method to derive estimators and compare the statistical properties of different estimators.
• Students explain the classical linear model assumptions, run simple linear regressions, check the diagnostics plots, use log-transformations to specify models and interpret the results correctly.

Methods

• The e-learning platform Moodle will be used throughout the course for the dissemination of course material and discussions.
• Students are usually asked to read corresponding parts of the lecture notes or of the textbook in order to prepare for the upcoming lectures in advance.
• In the interactive lectures, statistical concepts will be introduced and motivated by discussing examples in detail. Assignments are offered to train these skills.
• During office hours, individual problems may be discussed with the lecturer.
• In order to analyse realistic data, the software package R will be used.

Entry requirements
We require basic knowledge of probability theory and statistics, which is usually presented in a basic course on these topics in any bachelor program. The module ''Statistik'' in the bachelor program at University of Liechtenstein serves as a guideline or benchmark for this previous knowledge.

This module is prerequisite for taking the Master’s thesis Module and writing the Master’s thesis


Compulsory reading

• Wooldridge, J.M. (2013). Introductory Econometrics. (International Student Edition, 5th edition). Mason: South Western Cengage Learning.

Further reading

• Sweeney, D.J., Williams, T.A., David R. Anderson, D.R. (2009). Fundamentals of Business Statistics (International Student Edition, 5th edition). Manson: South-Western Cengange Learning.
• Berensen, M.L., Levine, D.M., Krehbiel, T.C. (2012). Basic Business Statistics (Global Edition, 12th edition), Essex: Pearson Education Limited.

Short description
The module provides an introduction to research methods.

Topics

• Introduction to scientific research
• Literature reviews
• Qualitative research
• Quantitative research
• Design science research
• Theories used in IS research

Learning objectives

• Students will know and understand the historical development of scientific research.
• Students will know and understand the concept of scientific research.
• Students will identify appropriate theories to explain empirical phenomena.
• Students will identify suitable research methods in order to seek answers to specific research questions.
• Students will use appropriate qualitative, quantitative, and design-oriented approaches to scientific research.

Methods

• The module integrates theoretical knowledge and practical skills in an interactive lecture.
• The e-learning platform Moodle will be used throughout the course for the dissemination of course material and discussions.

This module is prerequisite for taking the Master’s thesis Module and writing the Master’s thesis

Compulsory reading

• Recker, J. (2012). Scientific Research in Information Systems: A Beginner’s Guide. Springer, Heidelberg, Germany.

Further reading

• Kumar, R. (2014). Research methodology: A step-by-step guide for beginners. London, UK: Sage Publications.
• Creswell, J. W. (2003). Research design: Qualitative, quantitative, and mixed methods approaches. London, UK: Sage Publications.
• Oates, B. J. (2006). Researching information systems and computing. London, UK: Sage Publications.
• Bryman, A. & Bell, E. (2011) Business research methods. Oxford, UK: Oxford University Press.
• Miles, M. B. & Huberman, A. M. (1994). Qualitative data analysis. An expanded sourcebook. London, UK: Sage Publications.

Short description
The course focuses on virtual collaboration, collaborative work, and modern collaboration tools in a business environment. Students will apply their knowledge in a hands-on collaboration project with partners.

Topics

• Understand the concepts of virtual collaboration and collaborative work
• Learn how IT can be used in order to support collaboration in a virtual environment
• Learn about the potentials and limits of collaboration technology
• Experience collaboration with team members from other countries

Learning objectives

• Students will repeat the fundamental concepts of collaboration and collaboration systems.
• Students will understand the benefits of collaboration and collaboration systems for sustainable competitive advantage.
• Students will solve assignments in the field of collaboration, especially collaborative research projects in the areas of current topics in IS.
• Students will identify relationships between different types of virtual collaboration systems. They compare solutions with regard to their value contribution.

Methods

• The module integrates theoretical knowledge and practical skills based on an interactive seminar that includes a hands-on collaboration project. The e-learning platform Moodle will be used throughout the course for the dissemination of course material and discussions.

Compulsory reading

• Davenport, T. H. (2005). Thinking for a living: how to get better performances and results from knowledge workers. Harvard Business Press.

Short description
The course focuses on data management and process management, which are complementary approaches for developing and implementing information systems in organizations.

Topics

• Introduction to process and data management
• Information management, data management, and IS strategy
• Process modeling
• Data modeling
• Reference models

Learning objectives

• Students will know how information systems can be described from different, complementary perspectives.
• Students will know basic methods of data and process modeling in order to analyze, design, and implement information systems in organizations.
• In exercises, students will use methods of data and process modeling in order to analyze, design, and implement information systems in organizations.

Methods

• The module integrates theoretical knowledge and practical skills in an interactive lecture.
• The e-learning platform Moodle will be used throughout the course for the dissemination of course material and discussions.

Compulsory reading

• Becker, J., Kugeler, M., & Rosemann, M. (Eds.). (2003). Process Management: a guide for the design of business processes: with 83 figures and 34 tables. Springer.
• Dumas, M., La Rosa, M., Mendling, J., & Reijers, H. A. (2013). Fundamentals of business process management (pp. I-XXVII). Heidelberg: Springer.
• Watson, R. T. (2008). Data management, databases and organizations. John Wiley & Sons.

Module 2 includes a number of topics ranging from the authorization of financial services providers and professional obligations (eg initial and minimum capital requirements, personal qualifications of persons who effectively conduct the business, legal aspects of risk management and compliance as well as issues of delegating financial services). All of these subjects will be discussed with a focus on Cross-Border activities, especially those of Liechtenstein, Switzerland, Germany, the United Kingdom, Ireland, Luxembourg, France and the United States. Furthermore, topics such as investor protection and specific corporate governance structures in financial institutions will be covered in this module. Students take part in a case study on the application of these notions to a financial services firm. Participants will also be given an overview of contemporary issues such as financial crime, the challenges of restructuring financial institutions as well as the realities of securities litigation.