What we already know
We already know why people behave in the way they currently do. What we don’t understand is why people change.
From now our theories and evaluation methods have to go beyond behaviour. New methods and more robust research designs that track individual people over time help us to learn why some people change behaviour and why others don’t.
What we have learned from 7 years of research
Since 2013 we have worked with our research partner’s data. Our scientific research program has focussed on:
- Analytical research methods that can be used to improve our understanding of how and why people change their behaviour.
- Building a theory (or theories) that practitioners can apply in future to change people’s behaviour.
In sum, our efforts are focussed on understanding how and why people change their behaviour. We are working as fast as we can to give you a road map that you can apply to reliably increase rates of behaviour change.
Why is theory important?
A theory is an explanation that outlines expected relationships. For example, the Theory of Planned Behaviour tells us that high and positive attitudes, social norms and perceived behavioural control lead to an increase in intentions to perform the desired behaviour and in turn, intentions are positively related to behaviour. From the base of what we currently know you may have been trained to think that by changing attitudes and social norms you can get behaviour change. Our science program has taught us this may not be the case at all.
Our work shows that cross-sectional research designs are frequently used to evaluate behaviour change program effectiveness. With these research designs, we can’t see how individual people have changed across time.
To understand how and why people change behaviours our evaluations need to monitor people across time using the same measures. By using repeated measure longitudinal research designs we can:
- examine behaviour at any point in time
- examine changes over time
- examine the direction of change – change can be in the desired and also in undesired direction or there can be no change at all
- examine factors that are associated with both behaviour and behavioural change
An example: Learning how food waste behaviours change
Waste Not Want Not is a food waste program trialled by one Social Marketing @ Griffith team in 2017. We draw on data from the repeated measure evaluation to show you how a new evaluation practice has given us a better understanding of rates and drivers of change.
We applied a Hidden Markov Model* (see Figure 1) to examine how and why behaviours did or did not change. The longitudinal repeated measure design meant we knew about food waste behaviour at two points (the amount of food wasted before and after the program), changes in the amount of food wasted reported over time for each household (more or less food wasted) and other factors (e.g. self-efficacy).
By using a new method we could extend our understanding beyond the overall effect (households in the Waste Not Want Not program group wasted less food after participating when compared to the control group). We learned that:
- Before the program, there were two different types of households (31.5% wasted fruit and vegetables)
- A behaviour change program helped 43.8% of the food waster households to waste less food.
- At the same time, people who were initially in the non-wasting group remained unchanged (94.7% remained in the non-waster group), which is another great outcome.
- Some households reported wasting more after the program than before (5.3%)
- People living in apartments (no private garden) are more likely to waste food
- The factors that explained the changes. For example, people from the food waste group who had high self-efficacy (the ability to use the food already in their fridge) changed for the better.
Overall, we’ve learned more about how and why people change. This deeper understanding is important. We now know the proportion of people in the target audience who changed their behaviour. We can use this new information to set improvement targets. Can future programs change 45 or 50% of households reached? And we can more confidently direct financial and human resources to the factors that we know will deliver the change we need to see.
If you are interested in learning more about repeated measure evaluation and how you can apply Hidden Markov Models to gain a deeper understanding of how and why people change please contact Sharyn Rundle-Thiele or Patricia David.
If you have longitudinal data from past projects or are planning future work join us in the Change Project.
*Hidden Markov Models examine the way individuals evolve over time. A HMM proposes there are sub-groups (or segments) of people who differ in ways that cause differences. HMM considers sub-groups as dynamic and captures the extent that individuals change group membership over time. For a detailed presentation of HMM see Visser (2011).
Read the original research article: Rundle-Thiele, S., David, P., Willmott, T., Pang, B., Eagle, L. & Hay, R. (2019). Social marketing theory development goals: an agenda to drive change. Journal of Marketing Management, 35(1-2), 160-181. https://doi.org/10.1080/0267257X.2018.1559871
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