Development strategy and collaboration preference in S&T of enterprises based on funded papers: a case study of Google

Rongying Zhao (), Xinlai Li (), Zhisen Liang and Danyang Li
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Rongying Zhao: Wuhan University
Xinlai Li: Wuhan University
Zhisen Liang: Wuhan University
Danyang Li: Wuhan University

Scientometrics, 2019, vol. 121, issue 1, No 14, 323-347

Abstract: Abstract Science funding plays a guiding role in the development direction of scientific innovation. As one of research funding providers, private companies influence the development of science and technology (S&T) through their selective support. Thus, strategy and layout of enterprises in S&T can be revealed by analysing their funded papers. Taking Google as the example, the paper proposes an analytical method of funded papers by the combination of co-word analysis, clusters analysis and social network analysis, so as to explore the scientific strategy and collaboration preference. The total 2162 valid bibliographic records of papers supported by Google from the Thomson Reuters Web of Science are divided into four groups according to discipline clusters using Hierarchical Clustering Algorithm. Social network analysis is conducted to detect communities among keywords and institutions. The results demonstrate that Google shows different funding patterns between traditional research fields and emerging industries. Famous universities are the main funding targets of Google, and the important institutions can be divided into two groups.

Keywords: Funded paper; Funding strategy; Collaboration preferences; Scientific collaboration; Hierarchical cluster analysis; Social network analysis (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (3)

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DOI: 10.1007/s11192-019-03182-0

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