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<p>This study maps Iran’s entrepreneurial ecosystem through analysis of Instagram-mediated relationships among entrepreneurs, mentors, investors, media outlets, and support organizations. Integrating Actor-Network Theory (ANT) with Social Network Analysis (SNA), the research addresses three questions: How is the ecosystem structured as a network? Who are the central and influential actors? What distinct communities exist within the ecosystem? Data collection employed iterative snowball sampling from 40 expert-identified seed pages, yielding 134,236 nodes and 223,530 directed edges after systematic cleaning procedures. Findings reveal a scale-free network topology (γ ≈ 2.3) with small-world properties (average path length = 2.745), indicating concentrated influence and efficient information diffusion capacity. Centrality analysis identifies multidimensional influence patterns, with individual influencers dominating degree centrality while institutional actors achieve superior closeness centrality positions. Community detection using Louvain, Leiden, and Label Propagation algorithms identifies 48 communities (modularity Q = 0.627), with the ten largest spanning media and thought leadership, personal branding, business coaching, technology innovation, events, policy, creative industries, arts, management consulting, and venture capital domains. The study contributes methodologically by demonstrating social media data’s utility for ecosystem mapping, theoretically by explicating how platform algorithms co-constitute network topology as non-human actors, and practically by identifying leverage points for ecosystem development interventions.</p>

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Unveiling digital entrepreneurial networks: an actor-network and social network analysis approach using instagram data

  • 期刊论文

  • 发表日期：

    2026年2月28日

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  • Navid Mohammadi,
  • Mehrdad Maghsoudi,
  • Zahedeh Abarghouezade

摘要

This study maps Iran’s entrepreneurial ecosystem through analysis of Instagram-mediated relationships among entrepreneurs, mentors, investors, media outlets, and support organizations. Integrating Actor-Network Theory (ANT) with Social Network Analysis (SNA), the research addresses three questions: How is the ecosystem structured as a network? Who are the central and influential actors? What distinct communities exist within the ecosystem? Data collection employed iterative snowball sampling from 40 expert-identified seed pages, yielding 134,236 nodes and 223,530 directed edges after systematic cleaning procedures. Findings reveal a scale-free network topology (γ ≈ 2.3) with small-world properties (average path length = 2.745), indicating concentrated influence and efficient information diffusion capacity. Centrality analysis identifies multidimensional influence patterns, with individual influencers dominating degree centrality while institutional actors achieve superior closeness centrality positions. Community detection using Louvain, Leiden, and Label Propagation algorithms identifies 48 communities (modularity Q = 0.627), with the ten largest spanning media and thought leadership, personal branding, business coaching, technology innovation, events, policy, creative industries, arts, management consulting, and venture capital domains. The study contributes methodologically by demonstrating social media data’s utility for ecosystem mapping, theoretically by explicating how platform algorithms co-constitute network topology as non-human actors, and practically by identifying leverage points for ecosystem development interventions.