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Innovation and financial performance...

Lepore, Lisa D.

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Innovation and financial performance: A study of the effects of patent data from emerging cluster analysis.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Innovation and financial performance: A study of the effects of patent data from emerging cluster analysis./
Author:	Lepore, Lisa D.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2016,
Description:	157 p.
Notes:	Source: Dissertation Abstracts International, Volume: 78-06(E), Section: A.
Contained By:	Dissertation Abstracts International78-06A(E).
Subject:	Business administration. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10245840
ISBN:	9781369416817

Innovation and financial performance: A study of the effects of patent data from emerging cluster analysis.
Lepore, Lisa D.

Innovation and financial performance: A study of the effects of patent data from emerging cluster analysis. - Ann Arbor : ProQuest Dissertations & Theses, 2016 - 157 p.

Source: Dissertation Abstracts International, Volume: 78-06(E), Section: A.

Thesis (D.B.A.)--Capella University, 2016.

The rationale for this study was to understand how innovation, measured via cluster and patent data from emerging cluster analysis, correlates to the financial performance of the patenting company. Many researchers have studied the relationship between innovation, patents, and company financial performance, but none relied on emerging cluster analysis of patent data. The specific patent data under study, gleaned from emerging cluster methodology, might indicate the presence of potentially disruptive technology. Schumpeter originally framed the theories surrounding innovation and their incumbent economic value and this paper pondered the extent to which the emerging cluster patents and cluster data correlated to companies with a market capitalization of at least $1 billion. The companies were further divided into those with market capitalizations greater than $1 billion but less than or equal to $29.9 billion and those with market capitalizations greater than $30 billion. Various statistical methods were used to analyze the relationship between the cluster and patent data and the patenting companies' financial performance data, including regression analysis. There were statistically significant correlations found between clusters and patents as predictor variables and the financial performance metrics as criterion variables of the patenting companies. There were additional statistically significant correlations for the group of companies with the larger market capitalization, those above $30 billion, than those with the smaller market capitalization. Future research recommendations are provided in the conclusions of this study.

ISBN: 9781369416817Subjects--Topical Terms:

3168311
Business administration.

Innovation and financial performance: A study of the effects of patent data from emerging cluster analysis.
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245 1 0 $a Innovation and financial performance: A study of the effects of patent data from emerging cluster analysis.
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300 $a 157 p.
500 $a Source: Dissertation Abstracts International, Volume: 78-06(E), Section: A.
500 $a Adviser: Andrew Borchers.
502 $a Thesis (D.B.A.)--Capella University, 2016.
520 $a The rationale for this study was to understand how innovation, measured via cluster and patent data from emerging cluster analysis, correlates to the financial performance of the patenting company. Many researchers have studied the relationship between innovation, patents, and company financial performance, but none relied on emerging cluster analysis of patent data. The specific patent data under study, gleaned from emerging cluster methodology, might indicate the presence of potentially disruptive technology. Schumpeter originally framed the theories surrounding innovation and their incumbent economic value and this paper pondered the extent to which the emerging cluster patents and cluster data correlated to companies with a market capitalization of at least $1 billion. The companies were further divided into those with market capitalizations greater than $1 billion but less than or equal to $29.9 billion and those with market capitalizations greater than $30 billion. Various statistical methods were used to analyze the relationship between the cluster and patent data and the patenting companies' financial performance data, including regression analysis. There were statistically significant correlations found between clusters and patents as predictor variables and the financial performance metrics as criterion variables of the patenting companies. There were additional statistically significant correlations for the group of companies with the larger market capitalization, those above $30 billion, than those with the smaller market capitalization. Future research recommendations are provided in the conclusions of this study.
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