Predicting app success in non-English markets

A deep learning approach using Self-Determination Theory

Bibliographic Data

ID16859102
AuthorsMahsa Aryan (0009-0002-4052-8398, Alzahra University), Neda Abdolvand (0000-0003-3623-1284, Alzahra University, corresponding author), Sajedeh Talebi (0009-0009-6761-6516, Alzahra University)
Year2025
Volume15
Issue1
Publication date2025-08-09
Open AccessYes
TypeARTICLE
VenueSocial Network Analysis and Mining (JOURNAL)
Journal identifiersISSN: 1869-5450 • E-ISSN: 1869-5469
PublisherSpringer Science and Business Media LLC (PUBLISHER)
DOI10.1007/s13278-025-01517-9
OpenAlexW4413130926
LanguageEN
References cited70

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

Data science · Deep learning · Machine learning · Natural language processing · Computer Science · Digital Marketing and Social Media · Digital Platforms and Economics · Technology Adoption and User Behaviour · Artificial Intelligence

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Open DOIOpen Access