Big Data Approach to Firm Level Innovation in Manufacturing (eBook)

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.

Dr. Mehrshad Parvin Hosseini is an Assistant Professor at the Faculty of Business at Sohar University. He holds master's and doctorate degree (Ph.D.) in Economics. He has served as a Lecturer at various international education institutions since 2013. His research include economics of innovation, economics of energy, technology transfer, manufacturing sector, service sector, labor economics, firm-level analysis, cross-sectional data and time series data, and he has published papers in major national and international and peer-reviewed journals, such as Asian Economic Papers, Economic Research-Ekonomska Istraživanja, New Zealand Economic Papers, and Institutions and Economies. 

Prof. Aydin Azizi holds a PhD degree in Mechanical Engineering. Certified as an official instructor for the Siemens Mechatronic Certification Program (SMSCP), he currently serves as a Senior Lecturer at the Oxford Brookes University. His current research focuses on investigating and developing novel techniques to model, control and optimize complex systems. Prof. Azizi's areas of expertise include Control & Automation, Artificial Intelligence and Simulation Techniques. Prof. Azizi is the recipient of the National Research Award of Oman for his AI-focused research, DELL EMC's 'Envision the Future' completion award in IoT for 'Automated Irrigation System', and 'Exceptional Talent' recognition by the British Royal Academy of Engineering.

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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.