Big data approach to firm level innovation in manufacturing industrial economics /
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 undertake...
| Main Author: | Hosseini, Seyed Mehrshad Parvin. |
|---|---|
| Other Authors: | Azizi, Aydin., SpringerLink (Online service) |
| Format: | eBook |
| Language: | English |
| Published: |
Singapore :
Springer,
2020.
Singapore : 2020. |
| Physical Description: |
1 online resource. |
| Series: |
SpringerBriefs in applied sciences and technology.
|
| Subjects: |
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| 100 | 1 | |a Hosseini, Seyed Mehrshad Parvin. | |
| 245 | 1 | 0 | |a Big data approach to firm level innovation in manufacturing : |b industrial economics / |c Seyed Mehrshad Parvin Hosseini, Aydin Azizi. |
| 260 | |a Singapore : |b Springer, |c 2020. | ||
| 264 | 1 | |a Singapore : |b Springer, |c 2020. | |
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| 505 | 0 | |a 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. | |
| 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 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. | ||
| 650 | 0 | |a Production management. | |
| 650 | 0 | |a Manufacturing processes |x Technological innovations. | |
| 650 | 0 | |a Big data. | |
| 650 | 6 | |a Production |x Gestion. | |
| 650 | 6 | |a Fabrication |x Innovations. | |
| 650 | 6 | |a Données volumineuses. | |
| 650 | 7 | |a Production engineering. |2 bicssc. | |
| 650 | 7 | |a Business & management. |2 bicssc. | |
| 650 | 7 | |a Technical design. |2 bicssc. | |
| 650 | 7 | |a Economics. |2 bicssc. | |
| 650 | 7 | |a Technology & Engineering |x Industrial Engineering. |2 bisacsh. | |
| 650 | 7 | |a Business & Economics |x Management Science. |2 bisacsh. | |
| 650 | 7 | |a Technology & Engineering |x Industrial Design |x Product. |2 bisacsh. | |
| 650 | 7 | |a Business & Economics |x Economics |x General. |2 bisacsh. | |
| 650 | 7 | |a Big data. |2 fast. | |
| 650 | 7 | |a Manufacturing processes |x Technological innovations. |2 fast. | |
| 650 | 7 | |a Production management. |2 fast. | |
| 700 | 1 | |a Azizi, Aydin. | |
| 710 | 2 | |a SpringerLink (Online service) | |
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