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2020 | Buch

An Introduction on Models of Innovation and Analytical Frame Work Kapitel lesen Erstes Kapitel lesen

Big Data Approach to Firm Level Innovation in Manufacturing

Industrial Economics

verfasst von: Dr. Seyed Mehrshad Parvin Hosseini, Prof. Dr. Aydin Azizi

Verlag: Springer Singapore

Buchreihe : SpringerBriefs in Applied Sciences and Technology

Enthalten in: Springer Professional "Wirtschaft+Technik" , Springer Professional "Technik" , Springer Professional "Wirtschaft"

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Über dieses Buch

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.

Inhaltsverzeichnis

Frontmatter
1. An Introduction on Models of Innovation and Analytical Frame Work
Abstract
Mobility of technological changes driven by innovation is inevitably changing the form of the world rapidly so that firms cannot catch up with the fast-changing innovative technologies. This study addresses firm level innovation issues and provides an overview of current firm level innovation in developing industries. We showcasing situation of firm level innovation among manufacturing firms since the important of level of innovation has been underestimated in previous literature. The introductory section investigated several aspects of firm level innovation: the factors that influence the decision to invest in innovation; the extent of innovation; factors characterizing an innovating firm; the types of innovation and the factors that drive and enable them. A conceptual model and an associated cost-benefit framework was developed to explain a firm’s decision to invest in innovation. Then we provide details on the main drivers and enablers of innovation activities faced industrial developing countries.
Seyed Mehrshad Parvin Hosseini, Aydin Azizi
2. The Correlates of Firm-Level Innovation
Abstract
The lack of a satisfactory theory or framework to understand firm-level innovation has caused large body of literature examining the effect of different variables on firm-level innovation in an ad hoc fashion. The primary usefulness of this approach is that it has helped identify potential correlates of innovation. After surveying the literature, the correlates of firm-level innovation have been divided into two main groups for this study: factors that motivate or drive innovation and factors that support or enable innovation. The latter has been further subdivided into three groups of factors. One, firm-level characteristics that facilitate innovation; two, factors that lower the cost of innovation; and three, public policies that nurture innovation. Each of these groups is discussed in turn.
Seyed Mehrshad Parvin Hosseini, Aydin Azizi
3. Firm-Level Innovation: A Conceptual Model to Firm Level Innovation
Abstract
The review of literature confirms the presence of a large body of empirical work on the correlates of firm-level innovation but there is no conceptual framework that ties these correlates together into a coherent whole. The conceptual model provides the basis for developing an analytical framework to understand the role of the drivers and enablers in encouraging innovation. The underlying idea is that the firm does a cost-benefit calculation to make two decisions: (i) whether or not to invest in innovation; (ii) and, if it decides to do so, the level of innovation to be achieved. Based on the cost and benefit analysis of firm level innovation a conceptual model was therefore developed to better understand the links of these correlates to firm level innovation
Seyed Mehrshad Parvin Hosseini, Aydin Azizi
4. Machine Learning Approach to Identify Predictors in an Econometric Model of Innovation
Abstract
Two common methods in measuring cross sectional data of innovation will be discussed together with the short comings of these methods when dealing with large sample size. Further, we aim to demonstrate how machine learning application can help us selecting the best appropriate exploratory variables. We elucidate several machine learning applications for predicting the best independent variables. Further implication of Probit and Ordered Probit models were compared with machine learning techniques, by using the most common variables in the literature to analyse the firm level of innovation.
Seyed Mehrshad Parvin Hosseini, Aydin Azizi
5. Big Data and Innovation; A Case Study on Firm Level Innovation in Manufacturing
Abstract
This study investigated several aspects of firm level innovation in Malaysian manufacturing: the factors that influence the decision to invest in innovation activities; the extent of innovation; factors characterizing an innovating firm; the types of innovation and the factors that drive and enable them. Following the definition of Big Data, we drawn the data from a large representative survey from 2007 and 2015 of Malaysian manufacturing firms. The main findings unveil that while firm size, research and development investments, firms collaborative research, participation in international market through export among other indicators can positively influence firm level innovation. This section outlines the phases of the development of a coherent policy to foster, sustain and increase the level of innovation.
Seyed Mehrshad Parvin Hosseini, Aydin Azizi
Metadaten
Titel
Big Data Approach to Firm Level Innovation in Manufacturing
verfasst von
Dr. Seyed Mehrshad Parvin Hosseini
Prof. Dr. Aydin Azizi
Copyright-Jahr
2020
Verlag
Springer Singapore
Electronic ISBN
978-981-15-6300-3
Print ISBN
978-981-15-6299-0
DOI
https://doi.org/10.1007/978-981-15-6300-3

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