Syllabus

Title
1206 Elective - Incentive Systems
Instructors
Univ.Prof. Dr. Anne d'Arcy, Stefan Edlinger-Bach, Ph.D.
Type
PI
Weekly hours
2
Language of instruction
Englisch
Registration
09/09/20 to 09/16/20
Registration via LPIS
Notes to the course
Dates
Day Date Time Room
Thursday 10/22/20 09:00 AM - 01:30 PM TC.5.03
Friday 11/06/20 09:00 AM - 01:30 PM Online-Einheit
Thursday 11/12/20 09:00 AM - 01:00 PM Online-Einheit
Friday 11/20/20 09:00 AM - 01:30 PM Online-Einheit
Thursday 12/03/20 09:00 AM - 02:00 PM Online-Einheit
Procedure for the course when limited activity on campus
Sessions will be held online (live online sessions via MS Teams), in case they cannot take place at Campus WU under the usual circumstances, the final exam will be designed as a remote take-home exam. However, if the sessions can take place at WU, then students are required to be present on Campus WU.
In case of the online option and if (requested) rooms that comply to social distancing regulations are available, several sessions (e.g. kickoff, group presentation, special events, exams) are to be held on-site.

 

Contents

Companies institute incentive systems to achieve goal congruence with employees. Incentives systems are reward systems that tie pay to performance. To be effective, incentives must be clearly defined and considered a viable, valuable reward for the associated workload. Designing suitable incentive systems is a challenging task that requires the definition of appropriate performance measures and effective means of control. This course stresses the importance of incentive systems to align diverging interests within organizations with particular focus on understanding how systems may also generate dysfunctional or unintended incentives or results.

The aim of this course is to develop a framework for analyzing incentive systems, which allows to evaluate compensation schemes, and the underlying governance in organizations. Students become familiarized with the theoretical principles on incentive systems and adopt these principles to explain recent trends in corporate governance and incentive compensation, e.g., say on pay provisions, pay disparities, relative performance evaluation, pay for performance, material risk takers. Students apply the acquired knowledge when analyzing and discussing current compensation practices within firms and screening them for potential problems.

Learning outcomes

Students will learn technical and organizational aspects of incentive systems. This includes deeper understanding of compensation and reward systems, incentives as a corporate governance issue, performance planning, goal setting, and the like. Students will learn which factors determine whether a performance measure is useful or not and to challenge applied incentive schemes.

After completing this course students will have the ability to:

  • Explain the basic principles of incentive systems
  • Explain how incentives guide principal-agent relationships
  • Deal with theoretical frameworks of agency theory
  • Analyze various aspects of incentive contracts in practice and consequences of incorrectly designed and implemented incentives systems

Apart from that, completing this course will contribute to students’ ability to:

  • Efficiently work and communicate in a team
  • Improve their oral and written communication and presentation skills
  • Learn to give and receive constructive feedback
Attendance requirements

In order to successfully pass this course, your absence is limited to 20 % of our appointments.

Teaching/learning method(s)

The course will combine various learning methods to deliver the different topics to the students. These will include readings, open class discussions, team work, case studies, exams and presentations.

Assessment

The final grade of the course will depend on:

  • Pre-assignment 10%
  • Group assignment 30%
  • solving a problem set/ small group assignment 15%
  • Exam 35%
  • Class participation 10%

Class participation is mandatory with continuous assessment of student Performance.

Grading key: 

Excellent (1)

≥ 87,5 %

Good (2)

≥ 75,0 %

Satisfactory (3)

≥ 62,5 %

Sufficient (4)

≥ 50,0 %

Fail (5)

< 50,0 %

Availability of lecturer(s)
Last edited: 2020-10-08



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