Combined Framework of Multicriteria Methods to Identify Quality Attributes in Augmented Reality Applications

This study proposes a combined framework of multicriteria decision methods to describe, prioritize, and group the quality attributes related to the user experience of augmented reality applications. The attributes were identified based on studies of high-impact repositories. A hierarchy of the identified attributes was built through the multicriteria decision methods Fuzzy Cognitive Maps and DEMATEL. Additionally, a statistical analysis of clusters was developed to determine the most relevant attributes and apply these results in academic and industrial contexts. The main contribution of this study was the categorization of user-experience quality attributes in augmented reality applications, as well as the grouping proposal. Usability, Satisfaction, Stimulation, Engagement, and Aesthetics were found to be among the most relevant attributes. After carrying out the multivariate analysis, two clusters were found with the largest grouping of attributes, oriented to security, representation, social interaction, aesthetics, ergonomics of the application, and its relationship with the user’s emotions. In conclusion, the combination of the three methods helped to identify the importance of the attributes in training processes. The holistic and detailed vision of the causal, impact, and similarity relationships between the 87 attributes analyzed were also considered. This framework will allow the generation of a baseline for the use of multicriteria methods in research into relevant aspects of Augmented Reality.