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 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. Contents 6 1 An Introduction on Models of Innovation and Analytical Frame Work 9 1.1 Introduction 9 1.2 The Role of Eco-innovation 11 1.3 Indicators and Measurement of Innovation 12 1.4 Models of Innovation 13 1.4.1 The Linear Model of Innovation 14 1.4.2 The Chain Linked Model 15 1.5 Conclusion 16 References 17 2 The Correlates of Firm-Level Innovation 20 2.1 Introduction 20 2.2 Conditions in Individual Subsectors 21 2.3 Export Orientation 21 2.4 Market Concentration 22 2.5 Firm Characteristics that Facilitate Innovation 23 2.5.1 Firm-Size 23 2.5.2 Age of Firm 24 2.5.3 Foreign Ownership 24 2.5.4 Organizational Characteristics 25 2.6 Factors Lowering Innovation Costs 25 2.6.1 Access to Technology 25 2.6.2 Skilled Workers 26 2.6.3 Industrial Clustering 26 2.6.4 Government Policies and Incentives 27 2.7 Conclusion 28 References 28 3 Firm-Level Innovation: A Conceptual Model to Firm Level Innovation 34 3.1 Introduction 34 3.2 Driver of Innovation 35 3.3 Enablers of Innovation 37 3.4 An Analytical Framework: The Cost and Benefits of Firm-Level Innovation 37 3.5 The Decision to Innovate 38 3.6 Facilitators of Innovation 39 3.7 Costs of Innovation 39 3.8 Policy Environment 41 3.9 The Benefits of Innovation 42 3.10 Classifying the Types of Innovating Firms 43 3.11 Conclusion 45 References 45 4 Machine Learning Approach to Identify Predictors in an Econometric Model of Innovation 47 4.1 Introduction 47 4.1.1 Regression Based Analysis 48 4.2 Probit Model 49 4.3 Ordered Probit Model 50 4.4 The Expected Signs for the Explanatory Variables 51 4.5 How Machine Learning Techniques Can Identify Predictor of Innovation 53 4.5.1 Classification and Regression Trees 54 4.5.2 Lasso Regression 57 4.6 Conclusion 58 References 58 5 Big Data and Innovation; A Case Study on Firm Level Innovation in Manufacturing 59 5.1 Introduction 59 5.2 New Measures of Firm Level Innovation Activities 60 5.3 Factors Affecting the Decision to Innovate in Malaysian Context 61 5.4 Extent of Firm Level Innovation 61 5.5 Characteristics of Innovating and Non-innovating Firms 62 5.5.1 Innovation by Firm Size 63 5.5.2 Innovation by Ownership 64 5.5.3 Innovation by Age of Establishment 64 5.5.4 Innovation by Subsector 65 5.5.5 Mode of Access to Technology 66 5.5.6 Innovation by Other Key Variables 68 5.6 Factors Motivating Innovation 70 5.7 Pooled Sample 71 5.8 SMEs and Large Firms 74 5.9 Summary and Discussion 75 References 78 Front Matter ....Pages i-vii An Introduction on Models of Innovation and Analytical Frame Work (Seyed Mehrshad Parvin Hosseini, Aydin Azizi)....Pages 1-11 The Correlates of Firm-Level Innovation (Seyed Mehrshad Parvin Hosseini, Aydin Azizi)....Pages 13-26 Firm-Level Innovation: A Conceptual Model to Firm Level Innovation (Seyed Mehrshad Parvin Hosseini, Aydin Azizi)....Pages 27-39 Machine Learning Approach to Identify Predictors in an Econometric Model of Innovation (Seyed Mehrshad Parvin Hosseini, Aydin Azizi)....Pages 41-52 Big Data and Innovation; A Case Study on Firm Level Innovation in Manufacturing (Seyed Mehrshad Parvin Hosseini, Aydin Azizi)....Pages 53-72