What affects consumer behavior in mobile health professional diagnosis applications

In recent years, an increasing number of health diagnosis mobile apps have been developed and marketed to assist health professionals in the process of diagnosis. Yet, there is limited knowledge about the factors and app characteristics that affect their selection from health professionals. In this study, we investigate the specific apps' market that is addressed to medical professionals/students in order to explain how the specific consumers' behavior is affected by certain app characteristics and attributes. We based our model on the combination of two theoretical models, the Diffusion of Innovation (DOI) and the Technology Acceptance Model (TAM) to investigate the criteria for the intention of adoption of mobile apps in clinical routine. An evaluation framework (MARS) has been used to measure the quality of each app and text processing has been applied to retrieve and code additional informative variables from the descriptions and users' reviews. To investigate the relationships between app quality, downloads and users' ratings we used multiple linear regression statistical analysis. The results showed that the number of apps downloads is positively related to users' usefulness, star rating, and app quality while downloads are also correlated to the number of reviews, long app description, years since first release, and in-app ads. This study contributes to the information systems and mobile health literature in providing a better understanding of which quality characteristics of mobile apps have an impact on their popularity and evaluation and how their functionalities and quality affect the professionals' decision process.