Enablers of Big Data Analytics in Entrepreneurship from 43 Papers, 2021-2025

43 peer-reviewed papers from Scopus form the basis for this structured evidence on the enablers of Big Data Analytics in entrepreneurship. The dataset was created using a PRISMA-guided systematic review and Gioia-based grounded theory analysis, with findings validated via the Delphi Method. It was authored by Jesslyn Emmanuela Njoto Prawiro and last updated in May 2026.

Use Cases

• Identifying key factors that support Big Data Analytics adoption in entrepreneurship based on the documented enabler dimensions.
• Developing theoretical frameworks for data-driven entrepreneurship based on the synthesized higher-order conceptual structures.
• Validating or extending models of entrepreneurial decision-making based on the expert-validated enabler labels and descriptions.
• Conducting meta-analyses or comparative studies in digital transformation based on the mapped source information from 43 publications.

Strengths

• Evidence is derived from 43 peer-reviewed papers, providing a defined academic foundation.
• The review process followed PRISMA guidelines, suggesting a structured and transparent methodology.
• Enabler dimensions were developed through Gioia-based grounded theory analysis and validated via the Delphi Method.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment for large-scale analysis.
• The dataset is small at 379.6 KB, indicating a limited scope focused on literature synthesis rather than large empirical data.

Provenance

Source

Systematic literature review of papers indexed in Scopus.

Collection Method

PRISMA-guided review, Gioia-based grounded theory analysis, and Delphi Method validation.

Time Range

Publications from 2021 to 2025.

Freshness

Last updated 2026-05-26 09:13:52; freshness should be verified.