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(Re)Focussing on behavioural change: an examination of the utility of hidden Markov modelling

Patricia David (Department of Marketing, Griffith University, Gold Coast, Australia)
Sharyn Rundle-Thiele (Department of Marketing, Griffith University, Gold Coast, Australia)
Jason Ian Pallant (Swinburne University of Technology, Hawthorn, Australia)

Journal of Social Marketing

ISSN: 2042-6763

Article publication date: 5 June 2019

Issue publication date: 10 June 2019




Behavioural change practice has focussed attention on understanding behaviour; failing to apply dynamic approaches that capture the underlying determinants of behavioural change. Following recommendations to direct analytical focus towards understanding both the causal factors of behaviour and behavioural change to enhance intervention practice, this paper aims to apply a hidden Markov model (HMM) approach to understand why people transition from one state to another (e.g. reporting changes from wasting food to not wasting food or vice versa).


Data were drawn from a 2017 food waste programme that aimed to reduce waste of fruit and vegetables by increasing self-efficacy through a two-week pilot, featuring recipes and in-store cooking demonstrations. A repeated measure longitudinal research design was used. In total, 314 households completed a phone survey prior to the two-week pilot and 244 completed the survey in the weeks following the intervention (77% retention in the evaluation study).


Two behavioural states were identified, namely, fruit and vegetable (FV) wasters and non-FV wasters. Age was identified as a causal factor for FV food wasting prior to the campaign (45-54 years were most likely to waste FV). Following the intervention, a total of 43.8% transitioned away from FV wasters to non-wasters, and attitudes and self-efficacy were indicated as potential causal factors of this change in FV waste behaviour.


Through this application, it is demonstrated how HMM can identify behavioural states, rates of behaviour change and importantly how HMM can identify both causal determinants of behaviour and behavioural change. Implications, limitations and future research directions are outlined.



The data on which this article was based were funded by Redland City Council. The funders played no role in study design, collection, analysis, interpretation of data or in the decision to submit the paper for publication. They accept no responsibility for contents.


David, P., Rundle-Thiele, S. and Pallant, J.I. (2019), "(Re)Focussing on behavioural change: an examination of the utility of hidden Markov modelling", Journal of Social Marketing, Vol. 9 No. 2, pp. 130-145.



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