Currently submitted to: Journal of Medical Internet Research
Date Submitted: Jun 10, 2019
Open Peer Review Period: Jun 13, 2019 - Aug 8, 2019
(currently open for review)
Understanding drivers of resistance towards implementation of online self-management tools in routine cancer care among oncology nurses
Supporting patients to engage in (online) self-management tools is increasingly gaining in importance, but the engagement of healthcare professionals lags behind. This can partly be explained by resistance among healthcare professionals.
The objective of this study was to investigate drivers of resistance among oncology nurses towards online self-management tools in cancer care.
Drawing from earlier research, combining clinical and marketing perspectives, we developed the Resistance to Innovation model (RTI-model). The RTI-model distinguishes between passive and active resistance, which can be enhanced or reduced by functional drivers (incompatibility, complexity, lack of value, risk) and psychological drivers (role ambiguity, social pressure from the institute, peers, and patients). Both types of drivers can be moderated by staff-, organization-, patient- and environment-related factors. We executed a survey covering all components of the RTI-model on a cross-sectional sample of nurses working in oncology in the Netherlands. Structural equation modelling was used to test the full model, using a hierarchical approach.
The goodness of fit statistic of the uncorrected base model of the RTI-model (n=239) was acceptable (χ2(df) = 9.243 (1); CFI=0.95; TLI=0.21; RMSEA=0.19; SRMR=0.016). In line with the RTI-model we indeed found that passive and active resistance among oncology nurses towards (online) self-management tools were driven by both functional and psychological drivers. Passive resistance was enhanced by complexity, lack of value, and risk, and reduced by institutional social pressure. Active resistance was enhanced by complexity, lack of value, and social pressure from peers, and reduced by social pressure from the institute and patients. Nurses’ expertise regarding (online) self-management moderated the effects of complexity, lack of value, risk, role ambiguity, and social pressure from thePassive and active resistance are driven by functional and psychological drivers, and these drivers are moderated by expertise, managerial support and governmental influence. institute, peers, and patients (P=.030). Managerial support moderated complexity, lack of value, role ambiguity, and social pressure from peers and the institute (P=.004). Governmental influence moderated the effects of complexity, lack of value, risk, role ambiguity, and social pressure from peers and the institute (P=.037).
Passive and active resistance are driven by functional and psychological drivers, and these drivers are moderated by expertise, managerial support and governmental influence.
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