"Alexa, não era isso": analisando a usabilidade e aspectos de comunicabilidade da Amazon Alexa

Abstract

Ubiquitous computing proposes the imperceptible integration of technology into users’ everyday lives through intelligent and interconnected devices that operate in context-aware ways. However, traditional evaluation methods in Human–Computer Interaction (HCI), originally developed for desktop systems, have proven insufficient to capture specific characteristics of these environments, such as context-awareness, invisibility, attention, calmness, mobility, and synchronicity. Amazon Alexa, as a voice-based ubiquitous virtual assistant, exemplifies these challenges, revealing recurring issues in public reviews related to synchronization, communicability, and user experience. Addressing this gap, this study evaluates the usability and communicability aspects of Alexa using the Extended MALTU model, integrated with the labels of the Communicability Evaluation Method (CEM), in order to identify communicative breakdowns in ubiquitous contexts. A total of 598 User-Related Posts were analyzed, extracted from spontaneous reviews on Google Play and the App Store, applying a triangulated analysis between MALTU, Extended MALTU, and CEM labels. The results highlight problems related to synchronicity (36.8%), invisibility (15.9%), and limited context-awareness (13.5%), with moderate inter-rater agreement (Kappa 0.214–0.443). The study validates the Extended MALTU as an effective instrument for the initial screening of problems in ubiquitous systems, while also demonstrating the need for qualitative complementation. This work contributes to Semiotic Engineering by demonstrating the feasibility of analyzing early communicability aspects through spontaneous user comments and provides feedback for the development of more accessible and communicative voice assistants.