Organizations today must deliver exceptional financial performance while simultaneously stewarding resources, minimizing waste, and advancing environmental and social sustainability. This dual imperative—maximizing outputs while minimizing inputs—is reshaping how incentives are designed across leading organizations.

In this course, students will critically explore how incentive and compensation systems can be re-engineered to balance these competing demands. We will integrate rigorous theoretical models with empirical case studies to understand how organizations can reward both financial outcomes and resource-efficient behavior—addressing productivity, ESG goals, and the urgent need to protect our planet earth.

Students will learn not only how incentive systems shape behavior, but also how poorly designed incentives can create systemic risks, inefficiencies, and unsustainable practices. We will work to re-engineer incentive contract designs that foster collaboration, accountability, long-term commitment, and sustainable value creation.

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

• Analyze how incentive systems influence individual and organizational behavior, including impacts on financial performance and sustainability.
• Critically assess the risks, inefficiencies, and unintended consequences of poorly designed incentive structures.
• Design sustainable incentive systems that foster collaboration, long-term commitment, and balance short-term performance with long-term value creation.
• Define and evaluate appropriate Key Performance Indicators (KPIs) aligned with financial objectives, ESG goals, and resource efficiency.
• Apply the controllability principle in performance measurement, ensuring accountability is fair and motivating.
• Identify and mitigate the entrepreneurial gap, addressing challenges in incentivizing innovation and dynamic work.
• Balance stretch targets with incremental progress strategies to drive sustainable growth while minimizing gaming, burnout, and resource depletion.
• Engineer incentive mechanisms that encourage creative problem-solving, cross-functional collaboration, and strategic thinking essential for sustainable leadership.
• Bridge theory and practice through critical evaluation and redesign of real-world corporate incentive systems.

Additionally, students will strengthen their skills in:

• Critical thinking and structured decision-making
• Professional communication (oral and written), including smart and responsible use of Large Language Models (LLMs)
• Teamwork and collaborative innovation

To successfully complete this course, students must attend at least 80% of all scheduled sessions.

This course is designed to move systematically from foundational theories toward applied, experiential learning, all delivered through in-person sessions.

We begin with lectures that establish the theoretical frameworks underpinning incentive system design, sustainability tradeoffs, and principal-agent dynamics. These initial sessions provide students with critical observational knowledge and a deep conceptual grounding.

As the course progresses, we transition to a "maker’s knowledge" approach, emphasizing experiential learning through group problem-solving, case studies, and practical coaching on how to engineer sustainable incentive systems. Students will actively apply theoretical insights to develop real-world solutions, critically analyzing current leading corporate practices and proposing innovative, sustainability-aligned incentive systems.

In the final phase, presentation sessions will offer students the opportunity to synthesize and showcase their expertise through comprehensive case study analyses. Students will demonstrate their ability to bridge theory and practice—delivering evidence-based evaluations and forward-looking strategies applicable to modern corporate environments.

This teaching approach ensures that students master core concepts while building the practical expertise necessary to design incentive systems that drive both financial performance and responsible resource stewardship.

Responsible Use of AI Tools

The responsible use of AI tools is encouraged to support individual academic learning and development, including (but not limited to) text summarization, data analysis, language refinement, and research support.

Students must:

• Transparently disclose any use of AI tools in assignments and projects.
• Critically evaluate AI outputs for relevance, accuracy, and alignment with academic standards.
• Uphold academic integrity and scientific rigor.

Students bear full responsibility for the content they submit. AI must enhance—not replace—critical thinking, analytical skill, and original work.

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