Digital Tax Transformation Through Technology Adoption and Citizen Satisfaction Analytics

Respondent-level file constructed to reflect the survey data structure of the study "Digital Tax Transformation Through Technology Adoption and Citizen Satisfaction Analytics," conducted within the PBB-P2 (Land and Building Tax) service context of Bekasi Regency, West Java, Indonesia. It comprises 696 citizen respondents proportionally distributed across 23 districts, collected through online and paper questionnaires between May and July 2025, consistent with the sampling design reported in the source study. Each respondent provided answers to 16 indicator items on a 5-point Likert scale, measuring five latent constructs: Civil Servant Training (CT1–CT3), Technology-Based Service Strategy (SS1–SS3), Technology Adoption (TA1–TA4), Increase in PBB-P2 Revenue/Service Improvement (RP1–RP3), and Citizen Satisfaction (CS1–CS3). Item-level scores were generated using a reflective factor model with loadings consistent with the Confirmatory Factor Analysis (CFA) range reported in the original manuscript (0.52–0.88), while inter-construct relationships follow the standardized structural path coefficients from the Structural Equation Modeling (SEM) results across the study's seven hypotheses (H1–H7), including civil servant training as the strongest predictor of technology adoption (β = 0.424). The dataset includes a variable codebook and a formula-based district-distribution table, making it suitable for SEM/CFA practice, methodological replication exercises, or statistics instruction within the context of local digital tax service transformation.