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.本开源科学数据集记录了一项针对创业领域中物联网（IoT）赋能数字工具成功影响因素的混合证据合成研究，涵盖2020年至2024年间发表在Scopus索引数据库中的相关文献。本数据集旨在梳理并验证当前针对以下研究问题的研究现状：哪些是支撑创业领域物联网应用的核心成功影响因素？该数据集的核心文献库包含从Scopus数据库中检索得到的23篇同行评议期刊论文，并通过透明的PRISMA指南流程进行筛选，形成了从文献识别到最终纳入的可复现研究路径。为强化方法学可追溯性，本数据集同时收录了系统综述的产出结果，以及将提取的证据关联至高阶构念的理论构建流程。本数据集整合了来自系统文献综述（SLR）的二级证据，以及通过德尔菲（Delphi）调查收集的专家判断一级数据。首先，将成功影响因素表述编码为源自最终系统文献综述文献库的初始一阶标签；随后，将这些标签操作化为结构化问卷，并通过谷歌表单（Google Forms）向德尔菲专家发放，采用1至5分的李克特量表进行评分。随后基于德尔菲调查的反馈生成经过验证的一阶标签，以此作为经验基础，依据焦亚（Gioia）扎根理论方法构建二阶主题，并将其整合为聚合维度。该设计保留了从已发表研究到专家验证再到概念聚合的完整证据链，可为未来的复现研究、拓展研究或比较综述提供复用基础。本数据集以标准互操作格式提供，便于直接复用与检视。本数据包包含以下内容：(1) 一张PRISMA流程图（JPG格式），可视化展示了成功影响因素综述的文献识别、筛选、资格审查与最终纳入流程；(2) 一份系统文献综述映射文档（DOCX格式），对纳入研究中提取的成功影响因素证据进行结构化整理；(3) 一张焦亚扎根理论产出示意图（JPG格式），呈现了从一阶概念到二阶主题再到聚合维度的演进路径。上述所有文件旨在支持透明化报告，实现分析决策的可审计性，并为研究创业场景下物联网应用与绩效的学者与从业者提供可迁移的研究基础。