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Research on Information Flow Mechanism of Manufacturing Enterprises from the Perspective of Innovation Value Chain.
- Source :
-
Proceedings of the Association for Information Science & Technology . Oct2021, Vol. 58 Issue 1, p390-399. 10p. - Publication Year :
- 2021
-
Abstract
- Manufacturing enterprises always generate, acquire, and transform large amount of information which flows across every link of their innovation process and value chain. Information flow is one of the basic conditions for information to be reproduced, utilized and value added. Unreasonable paths and transmission methods of information flow will lead to problems such as poor flow efficiency, low information sharing and even information silos, which will further restrict the R&D innovation and service upgrade. Optimizing information flow mechanisms and paths is a vital part for improving the innovation capability of manufacturing enterprises. In order to reflect the interactions, flow‐path differences and nonlinear laws among manufacturing enterprises' innovation units and to reveal relations between information‐flowing efficiency and innovation capabilities, the paper divides innovation activities of manufacturing enterprises into three phases, such as information acquisition, information transformation and information value addition based on the theory of innovation value chain, and puts forward a mathematical model of information absorption and transformation to ultimately reveal the mechanisms for information flow across inside and outside units. Further, the paper takes a non‐linear function for absorptive capacity based on level of experiential knowledge, continuous R&D intensity, and agent relation strength to quantify the information absorption and transformation process and takes index of innovation information amount and cumulative information amount to calculate innovation abilities and measure information value addition. Finally, the paper uses simulation tool to analyze the influencing factors of information absorption and transformation optimization. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 23739231
- Volume :
- 58
- Issue :
- 1
- Database :
- Academic Search Index
- Journal :
- Proceedings of the Association for Information Science & Technology
- Publication Type :
- Conference
- Accession number :
- 153009840
- Full Text :
- https://doi.org/10.1002/pra2.466