Background: Supporting patients to engage in (Web-based) self-management tools is increasingly gaining importance, but the engagement of health care professionals is lagging behind. This can partly be explained by resistance among health care professionals. Objective: The aim of this study was to investigate drivers of resistance among oncology nurses toward Web-based self-management tools in cancer care. Methods: Drawing from previous research, combining clinical and marketing perspectives, and several variables and instruments, 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, and 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 modeling was used to test the full model, using a hierarchical approach. In total, 2500 nurses were approached, out of which 285 (11.40%) nurses responded. Results: The goodness of fit statistic of the uncorrected base model of the RTI-model (n=239) was acceptable (Χ 2 1=9.2; Comparative Fit Index=0.95; Tucker Lewis index=0.21; Root Mean Square Error of Approximation=0.19; Standardized Root Mean Square=0.016). In line with the RTI-model, we found that both passive and active resistance among oncology nurses toward (Web-based) self-management tools were driven by both functional and psychological drivers. Passive resistance toward Web-based self-management tools was enhanced by complexity, lack of value, and role ambiguity, and it was reduced by institutional social pressure. Active resistance was enhanced by complexity, lack of value, and social pressure from peers, and it was reduced by social pressure from the institute and patients. In contrast to what we expected, incompatibility with current routines was not a significant driver of either passive or active resistance. This study further showed that these drivers of resistance were moderated by expertise (P=.03), managerial support (P=.004), and influence from external stakeholders (government; P=.04). Conclusions: Both passive and active resistance in oncology nurses toward Web-based self-management tools for patients with cancer are driven by functional and psychological drivers, which may be more or less strong, depending on expertise, managerial support, and governmental influence.
- Health-related quality of life
- Implementation science
- Resistance to innovations