This lecture provides an introduction and overview of performance management. First, it begins with the nature, aims, characteristics, concerns and guiding principles of performance management. This lecture then will focus on the performance management at the organizational level.

In this mini lecture, we will briefly discuss the production process, which transforms resources (inputs) into production result. However, the result is categorized into output, outcome, and impact.

This lecture starts with discussion of previous studies related to organizational performance. The concept of organizational effectiveness is then provided, comprising steps to designing and implementing effective performance management system.

Accounting (financial) measures of performance have been the traditional mainstay of quantitative approaches to organizational performance measurement. This lecture briefly describes some  financial measures of corporate's performance management, including liquidity ratios, efficiency, leverage, coverage, profitability, as well as stockholder market value ratios. 

The lecture starts with the myths of performance measurement, e.g., more measures lead to better performance, by tying KPIs to remuneration you will increase performance, etc. Performance measures can be divided into: result indicators (i.e., result indicator and key result indicator) and performance indicators (i.e., performance indicator and key performance indicator).

The lecture first introduces the concept of variable, indicator, and index. Next, typology of indicators are presented, showing that composite indicators, leading indicators, and sentiment indicators are linked together. Finally, and primarily, the lecture will discuss how to construct a composite indicator.

This lecture offers an introduction to the topic of impact evaluation. First, it discusses why an impact evaluation might be undertaken and when it is worthwhile to do so. Next, it reviews various methods that can be used to estimate program impacts, i.e., randomized controlled trial, instrumental variables, regression discontinuity design, difference-in-differences, and matching. Lastly, it reviews common challenges encountered during implementation.