AI as an Intelligent Control: Survey Data from Italy on Accounting Governance and Risk Management

Description This dataset accompanies the study “From Manual to Intelligent Accounting: AI Adoption, Substitution Effects, and Governance Implications—Evidence from Italy.” It provides the anonymized raw survey data, documentation, and instrument used in the empirical analysis of Artificial Intelligence (AI) adoption in accounting among students and practitioners in Northeast Italy. The dataset explores how AI tools are perceived as substitutes for traditional accounting procedures and as enablers of improved governance and risk management. It operationalizes key constructs from the Technology Acceptance Model (TAM) and Organizational Information Processing Theory (OIPT) through a structured 1–5 Likert scale (1 = strongly disagree, 5 = strongly agree). Contents AI_Accounting_Italy_Data.xlsx – Contains the original anonymized survey responses (Sheet: Raw). Variables: TAM constructs: Perceived Usefulness (PU1–PU4), Perceived Ease of Use (PEOU1–PEOU4), AI Literacy (AL1–AL3), Technology Readiness (TR1–TR3), Social Influence (SI1–SI3), Facilitating Conditions (FC1–FC3) Mechanism & outcomes: Perceived Substitution Benefit (PSB1–PSB4), AI Intention (AI_INT1–AI_INT3), Governance/Risk Outcomes (GRO1–GRO4) Sample size: N = [insert actual number of non-header rows from the Raw sheet] (all genuine responses; no duplicates or synthetic data). Scale: 1–5 Likert (integer values only). AI_in_Accounting_Survey_Instrument_English.docx – Full English version of the administered questionnaire, including all 31 indicator items, two contextual vignettes, and demographic questions AI_in_Accounting_Survey_Instrum… . AI_accounting_Italy.docx – Study manuscript describing the theoretical framework, empirical results, and conceptual integration of TAM and OIPT AI_accounting_Italy . Data provenance Data were collected online via Google Forms between March–June 2025. Participation was voluntary and limited to respondents aged 18 or older with prior accounting coursework or ≥6 months of relevant professional experience. Ethics and anonymization All participants provided informed consent for academic use. No personal identifiers, email addresses, IPs, or free-text responses are included. The dataset was exported directly from Google Forms and contains only anonymized responses. Intended use The dataset can be used for: Descriptive and reliability analysis (e.g., Cronbach’s α, composite reliability, AVE) Structural equation modeling (PLS-SEM, CB-SEM) Replication and cross-country comparative studies on AI adoption in accounting and governance Researchers should report the actual observed N from the Raw sheet and not treat any expanded or resampled data as observed responses.