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Unravelling SW industry trading networks: the role of reciprocity, transitivity, and firm characteristics

Sang Hyeok Han & Min Jae Park

Science and Public Policy2026https://doi.org/10.1093/scipol/scag003article
AJG 2ABDC C
Weight
0.50

What the paper says

Abstract In today’s digital economy, the software (SW) industry acts as a core engine of technological progress and competitiveness. Rather than simple transactions, trading networks amongst SW firms generate complex interdependencies that shape market behaviour, innovation diffusion, and firm-level opportunities. This study applies the Exponential Random Graph Model to Korean SW transaction data, integrating firm-level attributes with structural features such as reciprocity and transitivity. The results reveal that reciprocity and transitivity strongly increase the likelihood of partnership formation, whilst homophily effects—similarity in size, founding time, or location—play little role. Instead, network positioning and scalability advantages drive new exchanges, highlighting the strategic importance of centrality in digital ecosystems. Theoretically, the study advances embeddedness theory by showing that structural interdependence outweighs firm similarity in digital industries. Practically, it offers policy insights: governments should support small and medium-sized enterprises by leveraging trust-based, transitive ties rather than relying on size-based classifications.

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https://doi.org/https://doi.org/10.1093/scipol/scag003

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@article{sang2026,
  title        = {{Unravelling SW industry trading networks: the role of reciprocity, transitivity, and firm characteristics}},
  author       = {Sang Hyeok Han & Min Jae Park},
  journal      = {Science and Public Policy},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1093/scipol/scag003},
}

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.