Digital knowledge orchestration in project management: Mapping AI-associated transformation through bibliometrics and BERTopic

This repository contains the analytical datasets, validation outputs, sensitivity-analysis materials, and supporting documentation associated with the study: Digital knowledge orchestration in project management: Mapping AI-associated transformation through bibliometrics and BERTopic The study combines bibliometric analysis and BERTopic modelling to examine AI-associated digital transformation in project management and related technology-management research. The final analytical corpus comprises 562 peer-reviewed journal and review articles published between 2017 and June 2026. The repository includes: a cleaned dataset prepared for BERTopic analysis; a bibliometric dataset prepared for VOSviewer analysis; archived outputs from five BERTopic sensitivity configurations; topic-coherence, NPMI, silhouette-score, topic-count, and outlier-count results; screenshots documenting alternative topic solutions; manuscript figures and, where available, their source data; a README file describing the files, analytical settings, and data restrictions. The final BERTopic configuration identified 16 substantive topics and retained 94 documents as Topic −1 outliers. The reported validation values for the final model were c_v = 0.433, NPMI = −0.126, and silhouette score = 0.048. The sensitivity analysis varied the UMAP n_neighbors parameter and the HDBSCAN min_cluster_size parameter. The archived outputs are provided to support transparency regarding model selection and the parameter dependence of smaller topic boundaries. The repository supports verification and transparency of the reported analytical outputs. It does not contain the complete executable analysis notebook or serialised alternative BERTopic models. The main modelling procedures, parameter settings, validation methods, and sensitivity configurations are documented in the associated article and supplementary materials. Some bibliographic metadata and abstracts were obtained from licensed databases, including Scopus, Web of Science, IEEE Xplore, and Google Scholar. Redistribution is therefore limited to the extent permitted by the relevant providers. Where full records cannot be shared, the repository provides cleaned or derived analytical data and aggregate outputs.