Understanding people’s attitudes in IoT systems using wellness probes and TF-IDF data analysis

This study explores the enhancement of Internet of Things (IoT) product design and development through the integration of ethnography and big data analytics. A new model is proposed, recognizing the limitations of existing big data approaches in capturing nuanced and complex user needs. This model combines in-depth analysis of user attitudes with extensive review data from current IoT product users, aiming to uncover a more comprehensive understanding of the user experience beyond typical quantitative insights provided by big data. An IoT product named 'Zipband' has been developed as a practical application of this integrated research methodology. A combination of qualitative and quantitative research methods, including interviews and term frequency–inverse document frequency (TF-IDF) scheme analysis, was utilized to identify diverse user needs and uncover new opportunities for user experience improvement. This research contributes to the field by introducing a data-driven ethnographic approach that has the potential to inspire convergence research in IoT product design and user experience areas.