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Titlebook: Big Data Approach to Firm Level Innovation in Manufacturing; Industrial Economics Seyed Mehrshad Parvin Hosseini,Aydin Azizi Book 2020 The

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发表于 2025-3-21 16:42:26 | 显示全部楼层 |阅读模式
期刊全称Big Data Approach to Firm Level Innovation in Manufacturing
期刊简称Industrial Economics
影响因子2023Seyed Mehrshad Parvin Hosseini,Aydin Azizi
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发行地址Presents five aspects of firm-level innovation in Southeast Asian manufacturing.Discusses factors associated with innovation among small and medium-sized enterprises (SMEs).Highlights how the findings
学科分类SpringerBriefs in Applied Sciences and Technology
图书封面Titlebook: Big Data Approach to Firm Level Innovation in Manufacturing; Industrial Economics Seyed Mehrshad Parvin Hosseini,Aydin Azizi Book 2020 The
影响因子.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..
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发表于 2025-3-21 22:21:06 | 显示全部楼层
The Correlates of Firm-Level Innovation,nt 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: factor
发表于 2025-3-22 02:12:25 | 显示全部楼层
Firm-Level Innovation: A Conceptual Model to Firm Level Innovation, 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 m
发表于 2025-3-22 07:18:56 | 显示全部楼层
Machine Learning Approach to Identify Predictors in an Econometric Model of Innovation,h 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 mo
发表于 2025-3-22 10:23:14 | 显示全部楼层
Big Data and Innovation; A Case Study on Firm Level Innovation in Manufacturing,vation 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.
发表于 2025-3-22 13:05:35 | 显示全部楼层
Firm-Level Innovation: A Conceptual Model to Firm Level Innovation,ake 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
发表于 2025-3-22 17:30:01 | 显示全部楼层
Book 2020factors 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
发表于 2025-3-22 23:51:49 | 显示全部楼层
发表于 2025-3-23 02:21:52 | 显示全部楼层
发表于 2025-3-23 07:10:42 | 显示全部楼层
Machine Learning Approach to Identify Predictors in an Econometric Model of Innovation, 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.
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