AI-Driven Digital Transformation in E-Commerce Platforms — Supplementary Analysis Dataset

This deposit contains the open-access analysis dataset and the full Boolean search strategy for a PRISMA-compliant systematic literature review of 371 peer-reviewed studies from Scopus and Web of Science, published between 2021 and 2026, on AI-driven digital transformation in e-commerce platforms.

The review synthesizes the corpus into the AI-Commerce Maturity Model (ACMM), a four-stage capability framework covering rule-based automation (S1), machine-learning optimization (S2), generative-AI augmentation (S3), and agentic operations (S4). Its central finding is that the binding constraint on stage progression shifts predictably across the maturity curve: technology-dominant at S1 to S2, organization-dominant at S2 to S3, and environment/governance-dominant at S3 to S4. This reframes the Technology-Organization-Environment (TOE) framework from a static checklist into a stage-conditional diagnostic.

The deposit contains:

• corpus-included-papers.csv: 371 included papers with bibliographic metadata, stage assignment, quality-appraisal scores, and analytical tags.• enabler-barrier-codings.csv: 2,846 enabler and barrier entries coded under the Technology-Organization-Environment framework across 30 canonical factor categories.• capability-codings.csv: primary and secondary AI capability categories across 23 capability categories for the included papers.• business-outcome-codings.csv: 1,418 quantitative business-outcome entries extracted from the corpus and coded into 11 outcome categories.• prisma-flow-counts.csv: counts at each PRISMA 2020 flow stage.• search-queries.txt: the eight Boolean queries, consisting of the Q0 anchor query, Q1 to Q4 stage-specific queries, and Q5 to Q7 supplementary queries, used in Scopus and Web of Science.• dataset-README.pdf: a full description of the dataset, methodology summary, and the mapping between each CSV file and the figures and tables in the related paper.

This deposit accompanies a paper submitted to the 2nd International Conference on Intelligent Systems and Sustainability (ICISS 2026). The deposit DOI is referenced in the Methodology section of the manuscript.