Users' intentions to use medical escort service platforms: Based on technology trust and psychological resistance

Dataset overview This record provides the anonymized survey dataset and supporting materials for the study “Users’ intentions to use medical escort service platforms: Based on technology trust and psychological resistance.” The dataset is intended to support transparency and reproducibility of the analyses reported in the manuscript. Data collection Data were collected via the professional online survey platform Credamo, targeting adult residents in Beijing, China. After a pilot study (N=50), the main survey was administered between April 17 and May 30, 2024. Participants received a small monetary incentive (average 5 RMB). All construct items were measured on a 7-point Likert scale. Sample and screening A total of 1,260 responses were collected. Quality screening excluded responses with extreme completion times (<120 seconds or >1,000 seconds) and straight-lining patterns (uniform answers across items). The deposited analysis dataset contains 904 valid responses (effective response rate 71.7%). Files included in this record data.csv: anonymized analysis dataset (N=904). codebook.xlsx: variable dictionary, response options, and coding scheme. readme.txt: documentation of study design, screening rules, and reproducibility instructions. questionnaire.pdf: survey questionnaire (English items as used in the study). Reproducibility / How to use To reproduce analyses reported in the manuscript: 1. Import data.csv into your statistical software. 2. Refer to Codebook.xlsx for variable definitions, response options, and coding. 3. Reconstruct the latent variables following the model specification in the manuscript (Section 3.1). 4. The confirmatory factor analysis / structural equation modeling was conducted in AMOS 26 (IBM SPSS AMOS). Ethics and privacy The dataset is anonymized and does not include direct personal identifiers. Only variables necessary to reproduce the reported analyses are shared. Correspondence For questions regarding the dataset, please contact the corresponding author: Yanfeng Liu. Related publication Manuscript under review. The citation will be updated once the article is published.