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Endogenous Technology Space

Authors: Nikolas Kuhlen;

Endogenous Technology Space

Abstract

We use probabilistic machine learning to span a new endogenous technology space from patent texts. We then rely on information-theoretic methods to construct measures of technological firm distances -- both fixed and time-varying. Using the latter, we present three sets of findings. First, we observe that industries are becoming more technologically specialised and segregated over time. Second, we identify the emergence of internet companies in the mid-1990s as a distinct group of firms with roots in traditional information and communication technologies. Third, we determine the unique set of time-varying rivals surrounding a focal firm in the endogenous technology space. We demonstrate the validity of this approach by means of a case study of the software company Oracle.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
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Average
Average
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