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Replication Data and R Code for “How AI Governance Capability Enables Organizational AI Capability

Year: 2026
Type: dataset
Abstract: This dataset contains the data, R analysis code, documentation, and analytical outputs supporting the empirical study “How AI Governance Capability Enables Organizational AI Capability.” The study examines how organizational AI governance capabilities contribute to organizational AI capability using a partial least squares structural equation modeling (PLS-SEM) approach. The dataset contains survey responses measuring four dimensions of AI governance capability—Strategic AI Governance (SAG), Risk and Accountability Management (RAM), Institutional Data Governance (IDG), and Responsible AI Governance (RCG)—and Organizational AI Capability (OAC). The empirical analysis was conducted using R and the seminr package. The analytical workflow includes measurement-model assessment, higher-order construct modeling, bootstrapping, explanatory power assessment (R²), effect-size assessment (f²), predictive assessment, importance–performance map analysis (IPMA), and subsequent structural-model analyses. The repository is intended to support transparency, methodological reproducibility, and future replication or extension of the study. The accompanying R scripts document the analytical procedures and can be used to reproduce the reported analyses from the deposited data. The dataset includes a de-identified analytical dataset, R scripts, an RData workspace containing intermediate and final analytical objects, a codebook, questionnaire documentation, and a README describing the data structure and analytical workflow. The deposited data are provided for academic research, methodological learning, replication, and extension of the reported analysis. Users should consult the README and codebook before using the data.
DOI: 10.7910/dvn/6gsfuw
Language: English
Open Access status: green
OpenAlex ID: W7202272980
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