People Analytics: Prediction of Performance & Prescription of Policy
- Develop high-impact People Analytics in order to generate business value from the Big Data and little data available to the organization.
- Identify types of people analytics used in a company.
- Obtain valuable people analytics to improve the efficiency of workforce planning, hiring, placing and retaining the best employees.
- Choose relevant data science methods for specific problems in human resource (HR) management.
- Analyze outcomes of HR practices and identify their effects on both the staff members’ attitudes toward the company and the organizational performance.
- Explain how data-driven decision-making affects organizational performance and HR practices.
- Able to develop his/her intellectual and cultural level, build a trajectory of career development
- Able to identify the data required for the solution of research tasks in management; to gather data from both the field research and desk research as well as from the social and economic sources
- Able to prepare and manage the consultancy project.
- Introduction to People Analytics
- Workforce Planning Analytics
- Sourcing Analytics
- Talen Acquisition Analytics
- Analytics for Onboarding and Organizational Culture Fit
- Employee Engagement Analytics
- Employee Life Time Value and Cost Modeling
- Retention Analytics
- Employee Wellness, Health, and Safety Analytics.
- Individual-based project “Examining the role of talent management practices using fsQCA"The individual-based project includes two parts: 10-page paper (50% in grading) and presentation in the class (50% in grading). Both files must be uploaded at Ms Teams not later than the deadline agreed at the beginning of the course and set at Teams. No grades will be given if files are not uploaded. The paper should include the following parts: 1. Introduction with a problem statement (research motivation and research question) 2. Brief review and analysis of academic literature, proposition development. 3. Methodology: Sample description, data collection approach, short description of the statistical analysis method(s). 4. Findings and theoretical/practical implications. 5. Conclusions References. The paper should be single-spaced throughout; Times New Roman 12-point font (except for the title page); A4 size page formatting; 2.5 cm margins on all sides.
- Class activities(1) Kahoot game. At the beginning or at the end of some lectures a 15-minute Kahoot game is conducted. If the student missed the class, there is no option to participate in this activity. (2) Individual assignment during seminars. At the beginning or at the end of some seminars students will get a written task for 10-15 min based on their home reading or materials discussed during the previous classes. If the student missed the class, there is no option to rewrite this task, except for sickness absence. In this case, the student should notify the instructor about his/her sick leave.
- Exam in the Ms TeamsThe exam consists of 22 test questions (up to 2 marks for each positive answer), 2 open questions with the explanation of your ideas (up to 3 marks for each positive answer), and one practical exercise (up to 4 marks). No negative marks for wrong answers. Total time: 60 min. Total questions: 25
- Coursera course "Qualitative Comparative Analysis (QCA)"All students are required to take the Coursera course "Qualitative Comparative Analysis (QCA" , https://www.coursera.org/learn/qualitative-comparative-analysis?
- 2021/2022 2nd module0.4 * Individual-based project “Examining the role of talent management practices using fsQCA" + 0.3 * Exam in the Ms Teams + 0.3 * Class activities
- Isson, J. P., & Harriott, J. (2016). People Analytics in the Era of Big Data : Changing the Way You Attract, Acquire, Develop, and Retain Talent (Vol. 1). Hoboken: Wiley. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1226538
- Edwards, M. R., & Edwards, K. (2019). Predictive HR Analytics : Mastering the HR Metric (Vol. 2nd Edition). New York: Kogan Page. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=2037089
- Fitz-enz, J., & Mattox, J. (2014). Predictive Analytics for Human Resources. Hoboken, New Jersey: Wiley. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=812792