Clinician responses to AI clinical decision support: survey data on barriers, trust and adoption intention

Description This dataset contains the analysis file for a cross-sectional survey of 630 licensed clinicians directly involved in patient care, examining how perceived barriers to artificial-intelligence clinical decision support (AI-CDSS) relate to trust, resistance intention and intention to use, and how those relationships shift when the barrier is relevant to professional identity. Respondents were recruited purposively through professional gatekeepers and networks in 19 countries, with Malaysia (n = 317) and China (n = 193) predominating. The questionnaire was administered online in English and Chinese; the codebook reports the English form as fielded. The file holds only the variables entering the reported analysis: 630 rows and 47 columns. It contains an arbitrary respondent identifier; the indicators of eight measured constructs, namely attitude (3 items) and perceived clinical value (4), the lower-order dimensions of adoption favourability, clinical practice inertia (3), workflow barrier (4), perceived professional image and autonomy threat (4), trust (5), resistance intention (4), intention to use (4) and supervisory support (4); three marker-variable items for the common-method assessment; four controls entered in the intention-to-use equation alongside supervisory support, namely prior use of clinical AI tools, whether the workplace runs an AI-CDSS and two country indicators with Malaysia as the reference category; three demographic variables used to describe the sample and not entered in the model, namely age, gender and years of clinical experience; and a recruitment-group code used only for a sensitivity check. All focal items use seven-point scales; the marker items use five points. There are no missing values. The model was estimated by partial least squares structural equation modelling in SmartPLS 4.1.1. Adoption favourability is a Type I reflective-reflective higher-order construct estimated by the disjoint two-stage approach, so attitude and perceived clinical value enter the first stage as separate constructs and their latent variable scores serve as its indicators in the second stage. Inference uses 10,000 bootstrap subsamples with percentile intervals. No personal identifiers are included. The respondent identifier is arbitrary and cannot be traced to an individual; its values run from 1 to 673 with gaps because it was carried over from the full survey file before cleaning. The study was approved by the Universiti Malaya Research Ethics Committee (reference UM.TNC(P&I)/UMREC/2/6607, letter dated 26 June 2026) and all participants gave informed consent and took part anonymously. The data support the article "Which Barriers Matter When Professional Identity Is at Stake? Frontline Clinicians' Responses to AI Decision Support", submitted to the Journal of Service Management.