{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T00:08:05Z","timestamp":1787011685262,"version":"3.56.0"},"reference-count":54,"publisher":"Emerald","issue":"2","license":[{"start":{"date-parts":[[2020,6,17]],"date-time":"2020-06-17T00:00:00Z","timestamp":1592352000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.emerald.com\/insight\/site-policies"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JKM"],"published-print":{"date-parts":[[2021,3,8]]},"abstract":"<jats:sec>\n                    <jats:title content-type=\"abstract-subheading\">Purpose<\/jats:title>\n                    <jats:p>This paper aims to present a methodology by which future knowledge flow can be predicted by predicting co-citations of patents within a technology domain using a link prediction algorithm applied to a co-citation network.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title content-type=\"abstract-subheading\">Design\/methodology\/approach<\/jats:title>\n                    <jats:p>Several methods and approaches are used: a dynamic analysis of a patent citation network to identify technology life cycle phases, patent co-citation network mapping from the patent citation network and the application of link prediction algorithms to the patent co-citation network.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title content-type=\"abstract-subheading\">Findings<\/jats:title>\n                    <jats:p>The results of the presented study indicate that future knowledge flow within a technology domain can be predicted by predicting patent co-citations using the preferential attachment link prediction algorithm. Furthermore, they indicate that the patent \u2013 co-citations occurring between the end of the growth life cycle phase and the start of the maturation life cycle phase contribute the most to the precision of the knowledge flow prediction. Finally, it is demonstrated that most of the predicted knowledge flow occurs in a time period closely following the application of the link \u2013 prediction algorithm.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title content-type=\"abstract-subheading\">Practical implications<\/jats:title>\n                    <jats:p>By having insight into future potential co-citations of patents, a firm can leverage its existing patent portfolio or asses the acquisition value of patents or the companies owning them.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title content-type=\"abstract-subheading\">Originality\/value<\/jats:title>\n                    <jats:p>It is demonstrated that the flow of knowledge in patent co-citation networks follows a rich get richer intuition. Moreover, it is show that the knowledge contained in younger patents has a greater chance of being cited again. Finally, it is demonstrated that these co-citations can be predicted in the short term when the preferential attachment algorithm is applied to a patent co-citation network.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1108\/jkm-01-2020-0079","type":"journal-article","created":{"date-parts":[[2020,6,17]],"date-time":"2020-06-17T13:20:22Z","timestamp":1592400022000},"page":"433-453","source":"Crossref","is-referenced-by-count":37,"title":["Exploring knowledge flow within a technology domain by conducting a dynamic analysis of a patent co-citation network"],"prefix":"10.1108","volume":"25","author":[{"given":"Vladimir","family":"Smojver","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mario","family":"\u0160torga","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Goran","family":"Zovak","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"140","published-online":{"date-parts":[[2020,6,17]]},"reference":[{"issue":"1","key":"key2021043009023821200_ref001","doi-asserted-by":"publisher","first-page":"101010","DOI":"10.1016\/J.JOI.2020.101010","article-title":"The role of geographical proximity in knowledge diffusion, measured by citations to scientific literature","volume":"14","year":"2020","journal-title":"Journal of Informetrics"},{"issue":"3","key":"key2021043009023821200_ref002","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1016\/S0378-8733(03)00009-1","article-title":"Friends and neighbors on the web","volume":"25","year":"2003","journal-title":"Social Networks"},{"issue":"3","key":"key2021043009023821200_ref003","doi-asserted-by":"publisher","first-page":"567","DOI":"10.1007\/s11192-005-0228-9","article-title":"Early citation counts correlate with accumulated impact","volume":"63","year":"2005","journal-title":"Scientometrics"},{"key":"key2021043009023821200_ref004","doi-asserted-by":"publisher","first-page":"202","DOI":"10.1016\/j.techfore.2015.03.011","article-title":"Forecasting technology success based on patent data","volume":"96","year":"2015","journal-title":"Technological Forecasting and Social Change"},{"issue":"3-4","key":"key2021043009023821200_ref005","doi-asserted-by":"publisher","first-page":"590","DOI":"10.1016\/S0378-4371(02)00736-7","article-title":"Evolution of the social network of scientific collaborations","volume":"311","year":"2002","journal-title":"Physica A: Statistical Mechanics and Its Applications"},{"key":"key2021043009023821200_ref006","doi-asserted-by":"publisher","volume-title":"Dynamical processes on complex networks","year":"2008","DOI":"10.1017\/CBO9780511791383"},{"issue":"1","key":"key2021043009023821200_ref007","doi-asserted-by":"publisher","first-page":"69","DOI":"10.1007\/s11192-012-0930-3","article-title":"A hybrid keyword and patent class methodology for selecting relevant sets of patents for a technological field","volume":"96","year":"2013","journal-title":"Scientometrics"},{"issue":"4","key":"key2021043009023821200_ref008","doi-asserted-by":"publisher","first-page":"233","DOI":"10.1002\/(SICI)1097-4571(199105)42:4<233::AID-ASI1>3.0.CO;2-I","article-title":"Mapping of science by combined co\u2010citation and word analysis. 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