Internationally mobile scientists as knowledge transmitters: A lexical‐based approach to detect knowledge transfer

Abstract

This paper explores the knowledge transfer of internationally mobile scientists.It builds upon previous work on the development of methods for detecting the knowledge transfer of German scientists. Using abstract terms of publications covered in Scopus, this paper proposes a lexical-based approach to identify knowledge transmitters. These scientists are characterized by acquiring knowledge from their co-workers during their international stay and transferring it upon return to German co-workers. Knowledge is operationalized as the cooccurrence of rarely used abstract terms. Knowledge transfer is expressed as the diffusion of these term combinations in co-authorship networks. The method developed was validated by contacting the bibliometrically identified knowledge transmitters and asking them what they believe they learned during their stay abroad. A control group of internationally mobile scientists without traceable knowledge transfer was similarly asked to report on their knowledge acquisition. The findings suggest that bibliometric data are capable of detecting knowledge transmitters among German scientists who were internationally mobile. The juxtaposition of the responses on their perceived knowledge acquisition and the bibliometrically identified lexical terms shows that the method proposed is well suited to studying the knowledge transfer of internationally mobile scientists. The strength of the method is its simplicity and high precision.

A network coupling approach to detecting hierarchical linkages between science and technology

“…This implies that large‐scale network linkages are not stable enough, so we divide them into hierarchical subnetworks to measure the hierarchical coupling between S&T based on the internal characteristics of S&T knowledge networks to discover more stable and meaningful internal linkages of S&T networks. Moreover, the knowledge network created by the coupling of knowledge elements can operate as a proxy for S&T knowledge systems (Aman, 2022), based on which we entrain a temporal character to the network. Hence, in this paper, we propose a new hierarchical cascading temporal network coupling measurement to detect coupling associations between simultaneous S&T knowledge network levels to calculate S&T depth linkages.…”