Big data approach to firm level innovation in manufacturing : industrial economics /
|Author / Creator:||Hosseini, Seyed Mehrshad Parvin.|
|Imprint:||Singapore : Springer, 2020.|
|Description:||1 online resource|
|Series:||SpringerBriefs in applied sciences and technology|
SpringerBriefs in applied sciences and technology.
Manufacturing processes -- Technological innovations.
Business & management.
Technology & Engineering -- Industrial Engineering.
Business & Economics -- Management Science.
Technology & Engineering -- Industrial Design -- Product.
Business & Economics -- Economics -- General.
Manufacturing processes -- Technological innovations
|URL for this record:||http://pi.lib.uchicago.edu/1001/cat/bib/12607109|
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.
|Physical Description:||1 online resource|
|Bibliography:||Includes bibliographical references.|