Effects of AI, Human, and Hybrid Recommendation Sources Under Decision Urgency
Description
Research Hypothesis & Findings This study tests how recommendation source (AI, Human, Hybrid) and decision urgency impact perceived transparency, trust, and adoption. We hypothesize that urgency moderates the link between source and transparency (H1), and that transparency mediates the path to adoption via trust (H2), with urgency influencing this entire mechanism (H3). Key findings demonstrate that perceived transparency is a “situated evaluative judgment”: humans/hybrids are viewed as more transparent under low urgency, while AI/hybrids excel under high urgency. Mediation analysis confirms transparency drives trust, subsequently increasing adoption intention. Data Description & Methodology The dataset includes raw and processed responses from three experimental studies (N= 1160]) using simulated organizational decision scenarios. Collection: Data gathered via University of Electronic Science and Technology of China Platform from experienced professionals using validated scales for Transparency, Trust, and Adoption. Structure: Provided in [Format, e.g., CSV]. Files include: Independent: Source Type, Urgency Level. Mediators: Perceived Transparency, Trust. Dependent: Adoption Intention. Demographics: Control variables (age, experience, AI literacy). Interpretation & Use Use this data to replicate the reported ANOVAs and moderated sequential mediation models (e.g., via PROCESS macro or R). Refer to the accompanying Codebook.pdf for full scale items and variable mapping. This dataset enables further exploration into the “transparency-trust” link in hybrid human-AI decision architectures.