Optimization of user experience in mobile application design by using a fuzzy analytic-network-process-based Taguchi method

Mobile application (app) design is an expanding research area, with user experience (UX) as its core. UX encompasses all aspects of human-computer interaction, and thus the optimization of UX has multiple objectives. Quality characteristics related to UX are subjective and even subconscious; moreover, there exists interdependence among UX quality characteristics. However, very little attention has been focused on these issues when optimizing UX based on multiple objectives. In this paper, a fuzzy analytic network process (ANP)-based Taguchi method is proposed for optimizing UX in mobile app design. First, design patterns and UX quality characteristics are determined. Subsequently, a Taguchi experiment is designed and carried out, and then signal-to-noise (S/N) ratios are calculated. A fuzzy ANP is adopted to derive the preference weights for the UX quality characteristics. Based on these weights, the S/N ratios are converted into a multiperformance characteristic index (MPCI) by using the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS). Finally, according to the MPCI, the significant design patterns are identified by using the analysis of variance, and the optimal design is obtained by using the response table and response graph. A mobile health app design was presented to illustrate the proposed approach. The results suggest that the proposed approach can effectively manage the interdependence among the subjective and even subconscious UX quality characteristics in the optimization process, and be used as a universal robust design approach to optimize UX in mobile app design. (C) 2019 Elsevier B.V. All rights reserved.