As more and more organizations continue to expand globally, the need to manage and sustain a global workforce has become vital. Increased digitization and technological advancements have made it possible to have a data-driven approach to making strategic decisions regarding multinational organizations’ human resources. This course will introduce you to global HR strategic planning and will explore methods and tools used to make data-driven decisions about recruiting, selecting, developing, evaluating, and retaining employees. Ethical considerations in collecting and using employee-related data will also be discussed.

This is a highly interactive and hands-on course that gives you the opportunity to learn how to analyze and interpret data for making strategic HR decisions. We will use R Studio to analyze data. You need to bring a personal laptop to each session. Make sure that the laptop can run R Studio.  

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

• Identify and describe the central aspects of the strategic approach to HR.
• Demonstrate an improved understanding of methodologies for analyzing data.
• Apply the skills and tools you learned in this course to make accurate data driven decisions.
• Analyze and interpret data to aid various HR functions.
• Improve your research and critical thinking skills.

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. 

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

1. Individual final exam: 35% of total grade
2. Team project presentation (final grade partially depends on peer evaluations): 35% of total grade
3. 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. 

Be aware that a significant portion of the course is dedicated to analyzing data. Those who do not have prior experience with statistics and/or empirical analysis will thus need to dedicate more time to the course. A short tutorial on R Studio and basic statistics will be provided at the beginning of the course, but much of the learning in the course is through individual and teamwork rather than through lectures. 

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)