2019
DOI: 10.3390/su11226207
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Identification of Overall Innovation Behavior by Using a Decision Tree: The Case of a Korean Manufacturer

Abstract: Based on the two recent consecutive Korean Innovation Surveys in 2014 and 2016, this research empirically identifies the influencing factors and overall behavior of innovation success and failure in the manufacturing industry by using decision-making tree analysis (DT). The influencing factors and behavior of a successful innovator are also investigated from the perspectives of financial contribution, innovation activity, and research and development (R&D) activity. By using DT, this study acquires compreh… Show more

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Cited by 5 publications
(3 citation statements)
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References 178 publications
(626 reference statements)
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“…First, their wide application in studies involving students' academic achievement, psychological states, and behaviors demonstrates their relevance and effectiveness in educational contexts [ 81 , 82 ]. Some studies have used decision tree models to identify innovative behavior [ 83 , 84 ] and accurately predict the factors influencing the success and failure of innovation in the Korean manufacturing industry [ 85 ].…”
Section: Methodsmentioning
confidence: 99%
“…First, their wide application in studies involving students' academic achievement, psychological states, and behaviors demonstrates their relevance and effectiveness in educational contexts [ 81 , 82 ]. Some studies have used decision tree models to identify innovative behavior [ 83 , 84 ] and accurately predict the factors influencing the success and failure of innovation in the Korean manufacturing industry [ 85 ].…”
Section: Methodsmentioning
confidence: 99%
“…The decision tree methodology encompasses three core steps: feature selection, decision tree construction, and pruning of the decision tree. This approach identifies information that maximally reduces uncertainty by analyzing the variability of random variables under specific conditions, thereby aiding decision-makers in formulating informed decisions and plans [21]. ML, in tandem with decision trees, enables manufacturers to navigate complex and dynamic environments, making informed decisions to minimize resource use and reduce costs.…”
Section: Prediction and Planningmentioning
confidence: 99%
“…Decision-tree models, one of the data-mining algorithms in machine learning, have high predictive accuracy and the ability to decompose a complex decision process into a series of simpler decisions, thus providing a more easily interpretable solution [ 10 , 11 , 12 ]. Researchers have used decision-tree models to accurately predict the factors influencing the success and failure of innovation in the Korean manufacturing industry [ 13 ]. Other researchers have utilised decision-tree models in marketing and psychology to predict the response rate of consumer satisfaction, attitude, and loyalty surveys [ 14 ].…”
Section: Introductionmentioning
confidence: 99%