Vol.42, No.2, 2022, pp.639-658, doi:10.32604/csse.2022.022519
OPEN ACCESS
ARTICLE
Predicting Mobile Cross-Platform Adaptation Using a Hybrid Sem–ANN Approach
  • Ali Alkhalifah*
Department of Information Technology, College of Computer, Qassim University, Buraydah, Saudi Arabia
* Corresponding Author: Ali Alkhalifah. Email:
Received 10 August 2021; Accepted 23 September 2021; Issue published 04 January 2022
Abstract
Owing to constant changes in user needs, new technologies have been introduced to keep pace by building sustainable applications. Researchers and practitioners are keen to understand the factors that create an attractive user interface. Although the use of cross-platform applications and services is increasing, limited research has examined and evaluated cross-platforms for developing mobile applications for different operating systems. This study evaluates cross-platform features, identifying the main factors that help to create an attractive user adaptation when building sustainable applications for both Android and iOS. Flutter and React Native were selected so end-users could test their features using the cross-platform usability assessment model. Usability, satisfaction, and navigation were tested to measure the cross-platform adaptation and end-user experience. The data were analyzed using hybrid structural equation modeling (SEM) and artificial neural network (ANN) approaches. The study results show that usability and navigation both have a positive effect on adaptation on Flutter and React Native, while satisfaction only has an effect on Flutter. The navigation variable was also the most significant predictor of adaptation for both models. This study has several implications and makes contributions to the research field, to developers, and to end-users.
Keywords
Mobile application; user experience; Flutter; React Native; cross-platform; artificial neural network
Cite This Article
A. Alkhalifah, "Predicting mobile cross-platform adaptation using a hybrid sem–ann approach," Computer Systems Science and Engineering, vol. 42, no.2, pp. 639–658, 2022.
This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.