Background: The contextualisation of models of acceptance in health has resulted in a research environment that is highly fragmented. This makes it difficult to consolidate ideas and understand the most significant factors to assess when planning to implement new technology. Given that the context of health care is unique but also highly influenced, it can be difficult to understand which constructs will be significant in implementation. Aims: To use meta-analysis to synthesise various works of literature and understand the construct relationships in existing technology adoption models that are significant across hospital contexts.
Methods: Meta-analysis with R using Pearson’s correlation effect size was chosen to address inconsistent evidence. A record search was conducted on the Web of Science platform and yielded 343 results, after screening, 31 papers were included in the qualitative analysis and then 14 for the quantitative analysis. Each individual hypothesis which correlated to a popular construct relationship was assessed.
Results: All effect values reported positive associations. According to the results (shown in the table below), the relationships, PEOU-PU, PU-BI, SN-BI, PEOU-BI, PU-ATT, SP-PU, ATT-BI, PU-SQ, PEOU-ATT, SP-PEOU and CMP-PU are supported, TN-PEOU, TNPU and PU-SQ are not supported.
Conclusions: The results provide an indication of the significant construct relationships that should be included in future models assessing health technology adoption. Ten hypotheses were found to produce significant conclusions and should be considered when developing future models of adoption in hospitals. No mediating effects were found to be significant. Further research is required to mediate the high heterogeneity and reduce publication bias.