Emergent affordances and potential challenges of mobile learning apps: insights from online reviews

Purpose Through a data-driven theory development approach, this study builds on affordance theory and demonstrates how online mobile app reviews can be analyzed to understand the drivers of informal mobile learning success. Design/methodology/approach Textual big data provide a wealth of information regarding user-app relationships and various facets of user engagement. Adopting structural topic modeling and sentiment analysis, the authors extract latent topics from reviews of two educational apps: Duolingo and Photomath. Findings The findings suggest that the quality of the relationship between users and mobile learning apps is significantly reliant on how underlying affordances have been actualized. While affordances can leverage satisfaction, they may also be a source of frustration in case of any failure in their design or integration. The analysis reveals eight emergent affordances: practicality, affordability, information reliability, instruction integrity, hedonic experience, user-friendliness, interactive input and iterative upgrading. Research limitations/implications Since affordances of a technology entail both enablement and constraint and are best studied as a bundle of connected elements influencing each other reciprocally, the authors discuss how to address potential challenges from technical aspects to the added value of using mobile learning apps. Originality/value The results demonstrate that qualitative information in online reviews about mobile learning app experiences is of significant value. The approach demonstrates how advanced analytics can provide business value by addressing the evolving nature of customer needs and expectations. It proves the value of online reviews in discovering underlying technology affordances and their potential boundaries and challenges.