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Big data approach to firm level inno...

Hosseini, Seyed Mehrshad Parvin.

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Big data approach to firm level innovation in manufacturing = industrial economics /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Big data approach to firm level innovation in manufacturing/ by Seyed Mehrshad Parvin Hosseini, Aydin Azizi.
Reminder of title:	industrial economics /
Author:	Hosseini, Seyed Mehrshad Parvin.
other author:	Azizi, Aydin.
Published:	Singapore :Springer Singapore : : 2020.,
Description:	vii, 72 p. :ill., digital ;24 cm.
[NT 15003449]:	Chapter 1: Introduction to innovation activities -- Chapter 2: The role of SME's in innovation activities -- Chapter 3: Overview of innovation activities in Southeast Asia -- Chapter 4: From Linear model to Chain Linked model of innovation in reaching firm characteristics that facilitate and lowering the cost of innovation -- Chapter 5: Predicting level of innovation -- Chapter 6: Factors affecting the decision to innovate and related policies.
Contained By:	Springer Nature eBook
Subject:	Production management. -
Online resource:	https://doi.org/10.1007/978-981-15-6300-3
ISBN:	9789811563003

Big data approach to firm level innovation in manufacturing = industrial economics /
Hosseini, Seyed Mehrshad Parvin.

Big data approach to firm level innovation in manufacturingindustrial economics /[electronic resource] :by Seyed Mehrshad Parvin Hosseini, Aydin Azizi. - Singapore :Springer Singapore :2020. - vii, 72 p. :ill., digital ;24 cm. - SpringerBriefs in applied sciences and technology,2191-530X. - SpringerBriefs in applied sciences and technology..

Chapter 1: Introduction to innovation activities -- Chapter 2: The role of SME's in innovation activities -- Chapter 3: Overview of innovation activities in Southeast Asia -- Chapter 4: From Linear model to Chain Linked model of innovation in reaching firm characteristics that facilitate and lowering the cost of innovation -- Chapter 5: Predicting level of innovation -- Chapter 6: Factors affecting the decision to innovate and related policies.

This book discusses utilizing Big Data and Machine Learning approaches in investigating five aspects of firm level innovation in manufacturing; (1) factors that determine the decision to innovate (2) the extent of innovation (3) characteristics of an innovating firm (4) types of innovation undertaken and (5) the factors that drive and enable different types of innovation. A conceptual model and a cost-benefit framework were developed to explain a firm's decision to innovate. To empirically demonstrate these aspects, Big data and machine learning approaches were introduced in the form of a case study. The result of Big data analysis as an inferior method to analyse innovation data was also compared with the results of conventional statistical methods. The implications of the findings of the study for increasing the pace of innovation are also discussed.

ISBN: 9789811563003

Standard No.: 10.1007/978-981-15-6300-3doiSubjects--Topical Terms:

518951
Production management.

LC Class. No.: TS155 / .H677 2020

Dewey Class. No.: 658.514

Big data approach to firm level innovation in manufacturing = industrial economics /
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505 0 $a Chapter 1: Introduction to innovation activities -- Chapter 2: The role of SME's in innovation activities -- Chapter 3: Overview of innovation activities in Southeast Asia -- Chapter 4: From Linear model to Chain Linked model of innovation in reaching firm characteristics that facilitate and lowering the cost of innovation -- Chapter 5: Predicting level of innovation -- Chapter 6: Factors affecting the decision to innovate and related policies.
520 $a This book discusses utilizing Big Data and Machine Learning approaches in investigating five aspects of firm level innovation in manufacturing; (1) factors that determine the decision to innovate (2) the extent of innovation (3) characteristics of an innovating firm (4) types of innovation undertaken and (5) the factors that drive and enable different types of innovation. A conceptual model and a cost-benefit framework were developed to explain a firm's decision to innovate. To empirically demonstrate these aspects, Big data and machine learning approaches were introduced in the form of a case study. The result of Big data analysis as an inferior method to analyse innovation data was also compared with the results of conventional statistical methods. The implications of the findings of the study for increasing the pace of innovation are also discussed.
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