A Semantic Network Analysis as a Method for Understanding Qualitative User Experience in Product Interactions

Abstract

Qualitative research provides useful insights with which to analyze the User Experience (UX). This is distinguished from quantitative research by its inductive form of logic and the research aim of understanding holistic phenomena. Since qualitative research aims to identify intangible factors and explore phenomena without simplifying contextual information, it is difficult to exclude a researchers subjectivity during their analysis. In addition, interpreting and analyzing qualitative materials requires much time and effort. Therefore, this dissertation suggests a systematic research method that utilizes user expression data to understand UX. The research starts by transforming textual data into numerical representations using semantic network analysis

three major issues were elucidated from the limitations of existing methods: (1) examining the representativeness of the sample size, (2) eliciting important user values (UV), and (3) evaluating product attributes (PA) with numerical inferences. First, the representativeness of sample size was examined by observing the stability of a semantic network. Among the semantic networks generated from the text, subnetworks were sampled from the original network to vary the sample size. Then, similarities between subnetworks and the original were calculated by applying correlation analysis to node-level centralities. Three case studies that were composed of two interview datasets and one online review data were presented

these proved that this method could be applicable for both small and large samples. Second, a mixed-method research approach was introduced to suggest appropriate camera shutter press sounds. In qualitative research, important UVs were elicited by analyzing terms with high centralities in a semantic network. The elicited UVs were then used as questionnaire items in quantitative research to represent UV with numerical values. The result demonstrated user satisfaction models for shutter press sounds and the relationships between UV and PA by adopting the concept of psychoacoustic variables. Third, the importance of UV and their relations to PA were examined based on qualitative research on vacuum cleaners. Seven types of network centrality were used to weight the UVs, which resulted in UX quantification models. These models goodness-of-fit were compared to the results of quantitative research. Then, the links between UV and PA nodes were identified. Since statistical analysis without a proper theoretical interpretation may mislead users, qualitative data can assist quantitative research by examining the sematic associations between UV and PA. Compared to traditional qualitative studies, the proposed method in this dissertation has a competitive edge for reducing the cost, effort, and subjectivity. Determining the smallest sample size that can achieve network stability is a novel data collection strategy that attempts to maximize effectiveness while minimizing both cost and effort. Utilizing this method allows UX researchers and practitioners to collect the optimal sample size by gradually increasing their sample sizes. Important UVs were elicited in the process of evaluating UX, and their importance was quantified to build a UX quantification model. Transferring qualitative descriptions to the quantitative models allows researchers to understand UX more efficiently by reducing the process of collecting numerical data on each UV. Lastly, important PA and their relations to UV were identified. Although centrality measures were not proportional to the correlation level, semantic associations between UV and PA could be identified. Considering that huge amounts of text data are being generated and collected every day, the suggested method is expected to be useful for practical applications when developing products.