User requirements mining methods for scenario design using Quantitative Ethnography——data disclosure

The study commenced with a questionnaire survey, which yielded a total of 1,742 initial demands from the user group. Subsequently, 231 invalid sample data were eliminated, and 1,511 valid data were obtained. To further enhance the quality of the sample, 120 users were selected for field household research and in-depth interviews using a random sampling method. One-on-one structured interviews were conducted between 10 May 2022 and 25 June 2024 with 120 participants. Additionally, the content of the interviews was adapted to align with the participants' preferences. The duration of each interview was approximately 180 minutes, with the interviews themselves taking place approximately two weeks after the field study. The study yielded a final sample of 120 kitchen space needs explorations, with each participant's statements being coded. In order to safeguard the anonymity of the interviewees and to adhere to data protection regulations, only the 1,000 coded data points, excluding the interviews, are presented in this report. These data are displayed in the form of a statistical table, entitled 'Coded Data from the Research Sample'.In the data processing and analysis stages, an epistemic network analysis Web Tool (version 1.7.0) is employed for the processing and analysis of the coded data. The length of the sliding window is set to six lines, comprising the current line and the preceding five lines. This signifies that the co-occurrence of requisite elements is calculated for each six adjacent interview data lines. An adjacency matrix is constructed, and the resulting adjacency vectors are subsequently accumulated. In order to accommodate the potential discrepancy in the number of data coding rows across different analysis units, a process of normalization is applied to all network data prior to dimension reduction. The singular value decomposition (SVD) method is employed to generate orthogonal dimensions, thereby maximising the variance explained by each dimension. The final map of the kitchen space demand network model has been produced and can be seen in Figures 3 to 6 of the paper.