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

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.

[1]  Samir Khuller,et al.  Improved Algorithms for Data Migration , 2006, APPROX-RANDOM.

[2]  Karl Rupp,et al.  ViennaCL - Linear Algebra Library for Multi- and Many-Core Architectures , 2016, SIAM J. Sci. Comput..

[3]  Yi-Ching Liaw,et al.  Can the technological impact of academic journals be evaluated? The practice of non-patent reference (NPR) analysis , 2014, Scientometrics.

[4]  Ronald Rousseau,et al.  Social network analysis: a powerful strategy, also for the information sciences , 2002, J. Inf. Sci..

[5]  Andrea Schiffauerova,et al.  How to boost scientific production? A statistical analysis of research funding and other influencing factors , 2016, Scientometrics.

[6]  Steven M. Goodreau,et al.  Understanding Classrooms through Social Network Analysis: A Primer for Social Network Analysis in Education Research , 2014, CBE life sciences education.

[7]  Chong Wang,et al.  The Discrete Infinite Logistic Normal Distribution for Mixed-Membership Modeling , 2011, AISTATS.

[8]  Fernanda Morillo.  Public–private interactions reflected through the funding acknowledgements , 2016, Scientometrics.

[9]  Avinash Kshitij,et al.  Functional information characteristics of large-scale research collaboration: network measures and implications , 2014, Scientometrics.

[10]  Naomi Fukuzawa,et al.  Effects of large-scale research funding programs: a Japanese case study , 2013, Scientometrics.

[11]  Magnus Gulbrandsen,et al.  Industry funding and university professors' research performance , 2005 .

[12]  Matthew E. Falagas,et al.  An analysis of factors contributing to PubMed's growth , 2015, J. Informetrics.

[13]  Xin Xu,et al.  Global pattern of science funding in economics , 2016, Scientometrics.

[14]  John Rigby,et al.  On the horns of a dilemma: does more funding for research lead to more research or a waste of resources that calls for optimization of researcher portfolios? An analysis using funding acknowledgement data , 2014, Scientometrics.

[15]  Wei Zhong-ming.  Quantitative Analysis upon Fund-sponsored Dissertations in 2006-2009 Library Tribune——Again Analysis Following 1993-2005 this Publication Fund-sponsored Dissertations , 2011 .

[16]  Yong-Ming Li,et al.  Interdisciplinarity research based on NSFC-sponsored projects: A case study of mathematics in Chinese universities , 2018, PloS one.

[17]  Brent Goldfarb,et al.  The effect of government contracting on academic research: Does the source of funding affect scientific output , 2008 .

[18]  Catherine Beaudry,et al.  The role of public funding in nanotechnology scientific production: Where Canada stands in comparison to the United States , 2014, Scientometrics.

[19]  Jörn Altmann,et al.  Identifying the effects of co-authorship networks on the performance of scholars: A correlation and regression analysis of performance measures and social network analysis measures , 2011, J. Informetrics.

[20]  Philip Shapira,et al.  Funding acknowledgement analysis: an enhanced tool to investigate research sponsorship impacts: the case of nanotechnology , 2011, Scientometrics.

[21]  Nicholas G. Polson,et al.  Tracking Epidemics With Google Flu Trends Data and a State-Space SEIR Model , 2012, Journal of the American Statistical Association.

[22]  Dangzhi Zhao,et al.  Characteristics and impact of grant-funded research: a case study of the library and information science field , 2010, Scientometrics.

[23]  L. Freeman,et al.  Centrality in social networks: ii. experimental results☆ , 1979 .

[24]  Kenton Kroker,et al.  Biomedicine , 2008, The Lancet.

[25]  Qingqiang Wu,et al.  Co-word analysis of the trends in stem cells field based on subject heading weighting , 2011, Scientometrics.

[26]  B. Looy,et al.  Combining entrepreneurial and scientific performance in academia: towards a compounded and reciprocal Matthew-effect? , 2004 .

[27]  Sina Farsiu,et al.  Open-source, machine and deep learning-based automated algorithm for gestational age estimation through smartphone lens imaging. , 2018, Biomedical optics express.

[28]  S. N. Singh,et al.  Mapping the intellectual structure of scientometrics: a co-word analysis of the journal Scientometrics (2005–2010) , 2014, Scientometrics.

[29]  D. Braun,et al.  The role of funding agencies in the cognitive development of science , 1998 .

[30]  Odilia Yim,et al.  Hierarchical Cluster Analysis: Comparison of Three Linkage Measures and Application to Psychological Data , 2015 .

[31]  Blake A. Richards,et al.  Irrelevance by inhibition: Learning, computation, and implications for schizophrenia , 2018, PLoS Comput. Biol..