Professional skills and expertise
In this module, students gain expertise in selecting and applying data analytics and artificial intelligence tools in business cases. This module also includes an introduction to research methods for international management research and requires students to write a proposal for their master thesis project (master thesis synopsis).
On successful completion of this module, students will be able to:

• explain how data analytics/business intelligence and artificial intelligence tools can be used to support decision making processes in internationally active companies
• recognize the importance of data quality and data governance
• understand the manager's role in formulating hypotheses and interpreting data
• identify the right research method for answering international management-related research questions
• create a research proposal for a master thesis project (master thesis synopsis)
• apply selected data analytics/business intelligence tools and interpret results

• critically discuss potential downsides and ethical issues involved in using data analytics and artificial intelligence systems
• suggest appropriate data analytics and artificial intelligence tools for solving decision problems in an international management context

• apply data analytics/business intelligence software in business cases
• recognize the possibilities for applying artificial intelligence in data analytics and predictive analytics
• conduct basic visualizations and reporting with business intelligence software tools

None

This module covers the following topics:
I. Fundamentals of data analytics and business intelligence (BI)
• The importance of data analytics and BI for making well-informed decisions
• Assessing data quality
• Emerging trends in data analytics and BI
• Data governance, data protection, and ethical issues
II. Applying data analytics/business intelligence tools
• The functionality of data analytics/BI software
• Applying data analytics/ BI software tools (including results visualization and reporting) in business cases
• Visualization and reporting with data analytics/BI tools
III. Integration of artificial intelligence and data analytics/BI
• Artificial intelligence concepts and technologies
• Applications of artificial intelligence in business
• Machine learning and predictive analytics
• Emerging trends in using artificial intelligence in a business environment
IV. The international management research process
• Elements of a research paper
• Conducting a literature review (including sources of relevant academic literature)
• Creating a conceptual framework
• Qualitative and quantitative empirical research methods
• Research design (method selection, sample selection, data collection and analysis methods)
• Writing a master thesis synopsis (including requirements of a master thesis, formulating a research question, developing a chain of reasoning)

FH Kärnten (2020). Guidelines on Academic Writing School of Management(WI-R03). Spittal an der Drau: Fachhochschule Kärnten.
Jackson, T. W. & Lockwood, S. (2018). Business Analytics: A Contemporary Approach. London: Macmillan International Higher Education.
Miles, M. B., Huberman, A. M. & Saldana, J. (2020). Qualitative Data Analysis: A Methods Sourcebook. 4th ed. Thousand Oaks: Sage Publications.
Saunders, M. N. K., Lewis, P., & Thornhill, A. (2019). Research Methods for Business Students. 8th ed. Harlow: Pearson.
Sharda, R., Delen, D., & Turban, E. (2020). Analytics, Data Science, & Artificial Intelligence. 11th ed. Harlow: Pearson.
Yin, R. K. (2017). Case Study Research and Applications: Design and Methods. 6th ed. Thousand Oaks: Sage Publications.

• Lecture and discussion (in-class or online)
• Application of data analytics/business intelligence tools in business cases
• Lecturer and peer feedback on research project design

Integrated module exam (immanent examination character):

• Written reports on case studies (data analytics business cases) (50%)
• Submission of a synopsis for the master thesis (50%)