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The AMO theory: Solving the Black Box Problem for Data Scientists
When you have to explain how and why people behave in a certain way
In data science, we often want to test input or stimulus and see if that will have an impact on the outcome. But the black box problem sits in between to explain why the input has an impact on the outcome. AMO theory is a great way to solve this problem because AMO factors are thought of as a model for explaining the mechanism for predicting the outcome. AMO theory can be applied in many different areas such as human resource management (HRM), marketing, law enforcement, etc. In this blog, I will explain briefly what the theory is about and how it can be used in data science projects.
1. What is the AMO theory
The AMO stands for Ability, Motivation, and Opportunity. I learned about this theory while doing my PhD in human resource management. The theory was developed by combining extensive research done by industrial psychologists and social psychologists to understand the factors involved in employee performance.
In brief, AMO suggests that performance is a function of ability (training and selection), motivation (incentives and feedback), and opportunity (environment). These three factors affect employee performance. Often HRM…
