{
  "abstract": "This study develops a mediation framework grounded in dynamic capability and innovation theories to unpack how big data capabilities shape new venture performance through business model innovation. Valid survey responses from 400 Chinese startups operating less than eight years across seven core economic zones were gathered via stratified random sampling. We employed SPSS 24.0 and AMOS 24.0 to examine scale reliability, validity and confirmatory factor structures, alongside hierarchical regression and 5,000 bootstrap PROCESS simulations to estimate direct and mediating paths. Empirical outcomes confirm big data capabilities significantly lift venture performance and foster business model innovation. Meanwhile, business model innovation positively contributes to growth and partially mediates the focal relationship, with median split and outlier removal robustness tests validating all proposed hypotheses. This research extends dynamic capability theory to digital entrepreneurship within China’s emerging market context, unpacks the full transmission chain linking data capacity, business model innovation and venture growth, adds localized empirical evidence to relevant entrepreneurship literature, and offers actionable strategies for resource-constrained startups to build competitiveness via digital upgrading and business model restructuring.",
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  "affiliations": [
    "Nanfang College, Guangzhou, China"
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  "article_type": "Research Article",
  "arxiv": null,
  "author_total": 1,
  "authors": [
    {
      "affs": [
        0
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      "name": "Xiaofang Xiong"
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  "doi": "10.1371/journal.pone.0358363",
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  "licence": "CC-BY-4.0",
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  "pmcid": null,
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  "published": "2026-09-17",
  "publisher": "Public Library of Science",
  "ris": "https://research.draconitic.com/paper/cgwgfak2cs3bz.ris",
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  "title": "The impact of big data capabilities and business model innovation on new venture performance",
  "title_text": "The impact of big data capabilities and business model innovation on new venture performance",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:17:49Z",
  "url": "https://research.draconitic.com/paper/cgwgfak2cs3bz",
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