A Dynamic Network Measure of Technological Change

Abstract

This article outlines a network approach to the study of technological change. We propose that new inventions reshape networks of interlinked technologies by shifting inventors' attention to or away from the knowledge on which those inventions build. Using this approach, we develop novel indexes of the extent to which a new invention consolidates or destabilizes existing technology streams. We apply these indexes in analyses of university research commercialization and find that, although federal research funding pushes campuses to create inventions that are more destabilizing, deeper commercial ties lead them to produce technologies that consolidate the status quo. By quantifying the effects that new technologies have on their predecessors, the indexes we propose allow patent-based studies of innovation to capture conceptually important phenomena that are not detectable with established measures. The measurement approach presented here offers empirical insights that support theoretical development in studies of innovation, entrepreneurship, technology strategy, science policy, and social network theory.

A Dynamic Network Measure of Knowledge Evolution: A Case Study of MIS Quarterly

“…the extent to which they are later used [2]. Although the real impact of an article is hard to assess, citations of articles are increasingly used as a criterion to assess the impact and the viability of research streams, the career success of scholars, and the quality and status of journals and academic units [6].…”

“…Despite the substantive and theoretical importance of differentiating between these two routes of knowledge creation, no quantitative measures exist to capture the distinction [2]. Systematic explanations for why, when, and how new knowledge has different effects on their context remain elusive.…”

“…However, the literature's focus on citation counts and citation-based measures of impact has a limitation. Although such measures can reflect the magnitude of a work's later use, they often fail to capture other dimensions of knowledge change in terms of differentiating revolutionary scientific breakthroughs from practice-oriented work [2]. Focusing on impact of a new work without considering how it relates to extant knowledge creates bias and incomplete understanding of knowledge evolution.…”

“…In this study, we build on Funk and Owen-Smith's dynamic measure of technological change [2] and extend it to a dynamic network measure of knowledge evolution, which captures the effects that a new article has on the use of its predecessors in the future development. Such a dynamic network measure is important to complement the citation impact because, unlike citation measures that only capture the magnitude of the later usage of an article, our new measure captures the effect of an article on its predecessor in future development.…”

“…Such a dynamic network measure is important to complement the citation impact because, unlike citation measures that only capture the magnitude of the later usage of an article, our new measure captures the effect of an article on its predecessor in future development. A new article as a new node can reshape the network of articles by shifting scholars' attention to or away from the knowledge they built on [2]. In other words, the new measure captures the direction of its effect related to extant knowledge: whether an article reinforces the status quo of the existing literature, or disrupts the existing literature with a new stream.…”