Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Wang, Chun-Hsien | en_US |
dc.contributor.author | Chin, Yang-Chieh | en_US |
dc.contributor.author | Tzeng, Gwo-Hshiung | en_US |
dc.date.accessioned | 2014-12-08T15:06:39Z | - |
dc.date.available | 2014-12-08T15:06:39Z | - |
dc.date.issued | 2010-07-01 | en_US |
dc.identifier.issn | 0166-4972 | en_US |
dc.identifier.uri | http://dx.doi.org/10.1016/j.technovation.2009.11.001 | en_US |
dc.identifier.uri | http://hdl.handle.net/11536/5204 | - |
dc.description.abstract | The research and development (R&D) innovation of firms continues to be viewed as an important source of competitive advantage to academics and practitioners. To explore and extract the R&D innovation decision rules, it is important to understand how the R&D innovation rule-base works. However, many studies have not yet adequately induced and extracted the decision rule of R&D innovation and performance based on the characteristics and components of the original data rather than on post-determination models. The analysis of this study is grounded in the taxonomy of induction-related activities using a rough set theory approach or rule-based decision-making technique to infer R&D innovation decision rules and models linking R&D innovation to sales growth. The rules developed using rough set theory can be directly translated into a path-dependent flow network to infer decision paths and parameters. The flow network graph and cause-and-effect relationship of decision rules are heavily exploited in R&D innovation characteristics. In addition, an empirical case of R&D innovation performance will be illustrated to show that the rough sets model and the flow network graph are useful and efficient tools for building R&D innovation decision rules and providing predictions. We will then illustrate that integrating the flow network graph with rough set theory can fully reflect the characteristics of R&D innovation, and, through the established model, we can obtain a more reasonable result than with artificial influence. Crown Copyright (C) 2009 Published by Elsevier Ltd. All rights reserved. | en_US |
dc.language.iso | en_US | en_US |
dc.subject | R&D innovation | en_US |
dc.subject | Rule induction | en_US |
dc.subject | Cause-and-effect relationship | en_US |
dc.subject | Rough set theory | en_US |
dc.subject | Flow network graph | en_US |
dc.title | Mining the R&D innovation performance processes for high-tech firms based on rough set theory | en_US |
dc.type | Article | en_US |
dc.identifier.doi | 10.1016/j.technovation.2009.11.001 | en_US |
dc.identifier.journal | TECHNOVATION | en_US |
dc.citation.volume | 30 | en_US |
dc.citation.issue | 7-8 | en_US |
dc.citation.spage | 447 | en_US |
dc.citation.epage | 458 | en_US |
dc.contributor.department | 管理科學系 | zh_TW |
dc.contributor.department | 科技管理研究所 | zh_TW |
dc.contributor.department | Department of Management Science | en_US |
dc.contributor.department | Institute of Management of Technology | en_US |
dc.identifier.wosnumber | WOS:000279235400007 | - |
dc.citation.woscount | 11 | - |
Appears in Collections: | Articles |
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