{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,16]],"date-time":"2026-01-16T00:27:42Z","timestamp":1768523262814,"version":"3.49.0"},"reference-count":47,"publisher":"Emerald","issue":"2","license":[{"start":{"date-parts":[[2021,1,6]],"date-time":"2021-01-06T00:00:00Z","timestamp":1609891200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.emerald.com\/insight\/site-policies"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IMDS"],"published-print":{"date-parts":[[2021,1,6]]},"abstract":"<jats:sec><jats:title content-type=\"abstract-subheading\">Purpose<\/jats:title><jats:p>This study aims to explore the dependence structure among Chinese firms across the emerging 5G industry at different stages and to provide some strategic insights for market participants.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Design\/methodology\/approach<\/jats:title><jats:p>This study adopt macroeconomic fundamentals and the log-returns of 45 listed firms in the Chinese 5G industry to construct the weighted adjacency matrix by measuring the correlation parameters and then use the triangulated maximally filtered graph (TMFG) algorithm to construct the dependence network. It analyses the topological structure of the constructed networks to obtain the dependence characteristics for each firm in the whole industrial supply chain at different levels.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Findings<\/jats:title><jats:p>The empirical results provide a comprehensive and concise snapshot of the industrial structure, across the whole 5G industry at different levels, rather than just a \u201cone-to-one\u201d pattern. Specifically, the dependence characteristics of different firms are heterogeneous, and most firms are highly connected with partners in the whole industrial supply chain, whereas a few firms that are weakly connected. Those closely connected firms are usually in the midstream. In addition, compared with firms at different levels, downstream firms usually have closer dependencies and stronger influence capabilities.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Practical implications<\/jats:title><jats:p>Regulators not only should promote stability development for those firms most intensely connected with whole industry chain but also protect those firms with weak link in the whole industry chain. Investors should better understand the embedded ties among different firms to obtain effective market information and can select multiple firms with fewer connections as backup to conduct joint investment for risk mitigation. Mangers should give priority to the central players\/firms in the whole industrial supply chain and establish the alliances with closely connected firms.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Originality\/value<\/jats:title><jats:p>This study contributes to both the information system and operation management literature by constructing a new network method, Copula-TMFG, to capture the dependence structure among Chinese firms in 5G industry, empirically providing some strategic insights for 5G industry stakeholders, such as regulators, investors and managers.<\/jats:p><\/jats:sec>","DOI":"10.1108\/imds-06-2020-0323","type":"journal-article","created":{"date-parts":[[2021,1,5]],"date-time":"2021-01-05T16:19:19Z","timestamp":1609863559000},"page":"409-435","source":"Crossref","is-referenced-by-count":13,"title":["Exploring the dependence structure among Chinese firms in the 5G 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