Dataset on Success Factors of IoT in Entrepreneurship

This open-science dataset documents a hybrid evidence-synthesis study on the success factors of IoT-enabled digital tools in entrepreneurship, covering the Scopus-indexed literature published from 2020 to 2024. It was assembled to map and validate the current state of research on Research Question: What are the primary success factors that support IoT in the entrepreneurship industry? The underlying corpus comprises 23 peer-reviewed journal articles retrieved from Scopus and screened through a transparent PRISMA-guided workflow, producing a reproducible trail from identification to inclusion. To strengthen methodological traceability, the dataset captures both the review outputs and the theory-building steps that connect extracted evidence to higher-order constructs.The dataset integrates secondary evidence from the systematic literature review with primary expert-judgment data collected via a Delphi survey. First, success-factor statements were coded as initial first-order labels derived from the final SLR corpus. Next, these labels were operationalized into a structured questionnaire and disseminated to Delphi experts through Google Forms using a 5-point Likert scale (1–5). The Delphi responses were then used to generate validated first-order labels, which served as the empirical basis for developing second-order themes and consolidating them into aggregate dimensions following the Gioia grounded theory approach. This design preserves a clear chain of evidence—from published studies to expert validation to conceptual aggregation—supporting reuse in future replications, extensions, or comparative reviews.Files are provided in standard, interoperable formats to facilitate immediate reuse and inspection. The package includes (1) a PRISMA workflow figure (JPG) that visualizes the identification, screening, eligibility, and inclusion decisions for the success-factor review; (2) an SLR mapping document (DOCX) that structures the extracted success-factor evidence across the included studies; and (3) a Gioia grounded-theory output figure (JPG) that depicts the progression from first-order concepts to second-order themes and aggregate dimensions. Together, these artifacts are intended to support transparent reporting, enable auditability of analytic decisions, and provide a transferable foundation for scholars and practitioners investigating IoT adoption and performance in entrepreneurial contexts.