Can loyalty be pursued and achieved? An extended RFD model to understand and predict user loyalty to mobile apps

Although millions of mobile apps have been published in the app store, the majority are seldom downloaded or used. This phenomenon has intensified the competition among service providers for user loyalty. There were plenty of studies investigating user loyalty in the mobile‐app context; nevertheless, most failed to identify those true loyalty users who embraced attitudinal and behavioral loyalty. To address this research gap, this study aims to understand and predict user loyalty by an extended RFD model. We propose that recency, frequency, and duration are able to reflect behavioral loyalty, while category frequency rate and category duration rate are representations of attitudinal loyalty. Using the actual data collected from a third‐party app, we calculate the weights of each variable through the entropy weight method, evaluate users' loyalty in two dimensions, and classify users into four groups (i.e., true loyalty, latent loyalty, moderate loyalty, and no loyalty). We also conduct a dynamic analysis to investigate how users move across different loyalty conditions. The results indicate that majority of users tend to stay on their initial loyalty conditions. For those who have changed their loyalty conditions, it is found that true loyalty users are more likely to become latent loyalty users.