ScholarWorks@중앙대학교

초록

As digital platforms rapidly expand, the secondhand trading market has emerged as a key pillar of the circular economy. This study systematically identifies the knowledge structure of domestic secondhand trading research using text mining and network analysis. Analyzing secondhand trading-related papers through TF, TF-IDF, network centrality, and CONCOR analyses revealed ‘transaction’ (901 occurrences) as the most frequent keyword, with ‘secondhand’, ‘use’, ‘platform’, and ‘consumer’ ranking high, confirming platform-based digital environments and consumer behavior as central themes. TF-IDF analysis showed ‘automobile’ ranked highest, indicating secondhand car trading has developed into an independent specialized domain. CONCOR analysis identified four clusters: digital trading ecosystem (20 keywords), secondhand product distribution (6), finance-insurance system (2), and tax-business operator (2), revealing a dual structure where platform-based research coexists with independent institutional research for high-value durable goods. This study represents the first quantitative analysis of secondhand trading research structure, holding both academic and practical significance.

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