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Title
Big data approach to firm level innovation in manufacturing : industrial economics / Seyed Mehrshad Parvin Hosseini, Aydin Azizi.
ISBN
9789811563003 (electronic book)
9811563004 (electronic book)
9811562997
9789811562990
Imprint
Singapore : Springer, 2020.
Language
English
Description
1 online resource
Other Standard Identifiers
10.1007/978-981-15-6
Call Number
TS155
Dewey Decimal Classification
658.5/14
Summary
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 firms 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.
Bibliography, etc. Note
Incldues bibliographical references.
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Access limited to authorized users.
Series
SpringerBriefs in applied sciences and technology.
Available in Other Form
Print version: 9789811562990
Chapter 1: Introduction to innovation activities
Chapter 2: The role of SMEs 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.
