Because the Happiness Index, eNPS and the Score items are all collected continuously across the same population, we can calculate â at team and segment level â how each Score, factor and individual item correlates with the root indicators. This is the analytical core of the model.
What it is for
Ranking levers. Which dimension is most associated with the wellbeing or commitment of this team, now â not in a generic industry benchmark.
Standing on the broader evidence base. The engagement â outcomes chain is established meta-analytically: Harter et al. (2002) show that engagement correlates with productivity, profitability, retention and customer metrics at business-unit level. What our model does is locate that chain in your own organisation's data.
An honest note on causality
Correlations on observational data identify association, not causation. We treat them as prioritisation signals â where to look and act first â and we validate direction the only way observational settings allow: act on the lever and watch how the series responds.
Continuous measurement is precisely what makes that validation loop possible.
How to use it in practice
When you see a Score flagged as a driver of your HI or eNPS, read it as a prioritised hypothesis, not a proven conclusion. Act on it and check the series a few weeks later: that is the experiment available to you, and it is more than any annual survey allows.Keep reading
What are the Happyforce Scores? â the architecture and the foundation of each lever.
The Happiness Index: what it measures and what it doesn't â the root indicator the Scores explain.
Limitations of the model â why this is association, not causation.