Analyzing the nonlinear effects of firm size, profitability, and employee productivity on patent citations of the US pharmaceutical companies by using artificial neural network

Yu-Shan Chen; Ke-Chiun Chang

doi:10.1007/s11192-009-0034-x

Scientometrics

Volume/Issue:: Volume 82: Issue 1

Analyzing the nonlinear effects of firm size, profitability, and employee productivity on patent citations of the US pharmaceutical companies by using artificial neural network

Authors:

Yu-Shan Chen

Yu-Shan Chen National Yunlin University of Science and Technology Yunlin Taiwan, ROC

Search for other papers by Yu-Shan Chen in
Current site
Google Scholar
PubMed

and

Ke-Chiun Chang

Ke-Chiun Chang National Yunlin University of Science and Technology Yunlin Taiwan, ROC

Search for other papers by Ke-Chiun Chang in
Current site
Google Scholar
PubMed

View More View Less

Pages:: 75–82

Online Publication Date:: 12 Jun 2009

Publication Date:: 01 Jan 2010

Article Category:: Research Article

DOI:: https://doi.org/10.1007/s11192-009-0034-x

Keywords:: Patent citations; Firm size; Profitability; Employee productivity

Restricted access

Abstract

This study utilized artificial neural network (ANN) to explore the nonlinear influences of firm size, profitability, and employee productivity upon patent citations of the US pharmaceutical companies. The results showed that firm size, profitability, and employee productivity of the US pharmaceutical companies had the nonlinearly and monotonically positive influences upon their patent citations. Therefore, if US pharmaceutical companies want to enhance their innovation performance, they should pay attention on their firm size, profitability, and employee productivity.

Save
Cite
Email this content

Share Link

Copy this link, or click below to email it to a friend
Email this content
or copy the link directly:

https://akjournals.com/abstract/journals/11192/82/1/article-p75.xml
The link was not copied. Your current browser may not support copying via this button.

Link copied successfully

Collapse
Expand

Scientometrics

Print ISSN:: 0138-9130

Online ISSN:: 1588-2861

To see the editorial board, please visit the website of Springer Nature.

Manuscript submission: http://www.editorialmanager.com/scim/

For subscription options, please visit the website of Springer Nature.

Scientometrics
Language	English
Size	B5
Year of Foundation	1978
Volumes per Year	1
Issues per Year	12
Founder	Akadémiai Kiadó
Founder's Address	H-1117 Budapest, Hungary 1516 Budapest, PO Box 245.
Publisher	Akadémiai Kiadó Springer Nature Switzerland AG
Publisher's Address	H-1117 Budapest, Hungary 1516 Budapest, PO Box 245. CH-6330 Cham, Switzerland Gewerbestrasse 11.
Responsible Publisher	Chief Executive Officer, Akadémiai Kiadó
ISSN	0138-9130 (Print)
ISSN	1588-2861 (Online)