As organizations increasingly expand across borders, effectively managing and sustaining a global workforce has become essential. Advances in digitization and technology now enable a data-driven approach to strategic decision-making in multinational human resource management. This course introduces global HR strategic planning and explores methods and tools for making data-driven decisions in recruiting, selecting, developing, evaluating, and retaining employees. Ethical considerations surrounding the collection and use of employee data will also be examined.

This highly interactive, hands-on course provides practical experience in analyzing and interpreting HR data to support strategic decisions. We will use R Studio for data analysis, so you must bring a personal laptop capable of running R Studio to every session.

Upon successful completion of this course, you will be able to:

• Identify and explain key elements of the strategic approach to global HR management.
• Demonstrate a solid understanding of data analysis methodologies relevant to HR.
• Apply analytical tools and techniques to make informed, data-driven HR decisions.
• Analyze and interpret HR data to support various functions such as recruitment, development, and retention.
• Enhance your research capabilities and critical thinking skills in the context of global human capital analytics.

This course is to be held in a blended format. Students will have to complete a series of short exercises online as well as attend the in-person class sessions. As per WU's guidelines for PI courses, you will fail the course if you miss more than 20% of the course, this includes both in-person sessions and online activities. Please note that any absences will also negatively impact your participation grade. Please also note that the final class meeting date will include an exam. 

Attendance of the first session is mandatory. 

The course is designed in a way to maximize your learning by balancing between lecture and your involvement in discussions, cases, and exercises. You will also regularly work hands-on with data, giving you an opportunity to develop your analytic skills and gain experience in using data to make strategic HR decisions. The course will additionally include an exam, which will cover core course concepts and test your ability to analyze data using statistical methods and interpret the results.

Due to the blended format of the course, success depends to a large part on your self-directed learning at home. Working together with other classmates in small groups to complete homework exercises and to prepare for the final exam can also be very helpful. 

AI tools will be integrated to support your learning and assist in the effective analysis of empirical data.

Assessment will be based on both individual and team performance. Breakdown of assignments with percent of total grade:

1. Individual final exam: 30% of total grade
2. Team project presentation: 30 % of total grade
3. Team peer evaluations: 10% of total grade
4. Participation: 30% of total grade

Please note that successful participation involves thoughtfully contributing to class discussion, engaging in thoughtful analysis of any cases, actively participating in class activities, and synthesizing across readings. You are expected to have read in advance any assigned readings and cases and be prepared to discuss them.

More information on assignments and required course readings will be provided at the start of the course.

Grading Key:

90-100% = 1

80-89% = 2

70-79% = 3

60 - 69% = 4

59% and below = 5

Please log in with your WU account to use all functionalities of read!t. For off-campus access to our licensed electronic resources, remember to activate your VPN connection connection. In case you encounter any technical problems or have questions regarding read!t, please feel free to contact the library at readinglists@wu.ac.at.

Prior knowledge of human resource management is helpful but not necessary. 

Prior statistical knowledge is helpful but not necessary. However, those who do not have prior experience with statistics and/or empirical analysis may need to dedicate more time to the course. AI tools will be integrated to support your learning and assist in the effective analysis of empirical data.

Assoc. Prof. Mihaela Dimitrova, Ph.D.

Department of Global Business and Trade

Office: D1 3.032

I am available for individual meetings by appointment. Please email to set up an appointment (mdimitro@wu.ac.at)