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

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    Submitted Manuscript (SM)
    Author(s)
    David, Patricia
    Rundle-Thiele, Sharyn
    Pallant, Jason Ian
    Griffith University Author(s)
    Tavares de Lima David, Patricia
    Rundle-Thiele, Sharyn
    Year published
    2019
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    Abstract
    Purpose: 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). Design/methodology/approach: Data were drawn from a 2017 food waste ...
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    Purpose: 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). Design/methodology/approach: 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). Findings: 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. Originality/value: 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.
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    Journal Title
    Journal of Social Marketing
    Volume
    9
    Issue
    2
    DOI
    https://doi.org/10.1108/JSOCM-04-2018-0038
    Copyright Statement
    © 2019 Emerald. This is the author-manuscript version of this paper. Reproduced in accordance with the copyright policy of the publisher. Please refer to the journal's website for access to the definitive, published version.
    Subject
    Sociology
    Publication URI
    http://hdl.handle.net/10072/385869
    Collection
    • Journal articles

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