Predicting app success in non-English markets: a deep learning approach using Self-Determination Theory

Abstract

This study investigates the factors influencing mobile app success in non-English markets by examining Iran’s Café Bazaar, a major Persian-language app marketplace. Grounded in Self-Determination Theory (SDT), it proposes a two-stage framework to predict app success by analyzing developers’ decisions, such as category and pricing, and user feedback derived from Persian-language reviews. Moving beyond traditional metrics, such as download counts, the study incorporates a Persian-specific sentiment analysis tool and neural network models to assess success. The two-dimensional Convolutional Neural Networks (2D-CNNs) achieved high accuracies. They identified key success factors like the Number of Comments, Category, Package Volume, Number of Images in Description, and free status. The novel “Data Flow” metric, adjusting installs by app size, enhances engagement measurement. This SDT-driven approach offers theoretical insights into developer-user dynamics and practical strategies for optimising app performance in regional markets, such as Iran.