Integrating social psychological theories of motivation and intention to explain health and safety behaviours

Abstract

The work within the thesis aimed to integrate concepts from three psychological frameworks, including self-determination theory (SDT), the theory of planned behaviour (TPB), and the hierarchical model of motivation (HMM), into a model to understand the processes that underpin motivation and intention toward health and safety behaviours. The first tenet of the model (derived from SDT and HMM), namely the trans-contextual effect of motivation, hypothesised that self-determined motivation for a given activity related to self-determined motivation for undertaking health-promoting behaviour associated with the activity. The second tenet of the model (derived from the strength, limitation, and theoretical assumptions of SDT and the TPB) speculates that the effects of self-determined motivation for health and safety behaviour on intention and behaviour were mediated by social cognitive variables. A total of eight studies were employed to test the two tenets of the integrated model across various health contexts (i.e., sport injury rehabilitation and prevention (Study 1 to 5), occupational injury rehabilitation and prevention (Study 6 and 7), and myopia prevention (Study 8), and these studies are presented in five related research chapters (Chapters 2 to 6) in this thesis. The results provided preliminary evidence in support of both tenets of the integrated model, in which motivation from a general life domain is transferred to motivation, and antecedent social cognitive variables, for behaviour in a health and safety domain. The final chapter (Chapter 7) of the thesis summarises the findings of the eight studies and offers explanations and interpretations of the overall pattern of results. Conclusions were then drawn with respect to the theoretical and practical implications of the findings. Consideration was also given to the methodological limitations of the thesis and the scope for further studies to improve the predictive power, utility, measurement reliability, and evidence base for the model.