Research on The Influence of Digital Level on Knowledge Evolution Based on Threshold Regression Model
- DOI
- 10.2991/978-94-6463-056-5_113How to use a DOI?
- Keywords
- Digital level; Manufacturing digital innovation ecosystem; Dynamic capabilities; Knowledge evolution; Threshold effect
- Abstract
Manufacturing digital innovation ecosystem is an industrial organization form suitable for the digital age and an important way to realize high-quality development of manufacture. From the perspective of manufacturing digital innovation ecosystem, this paper takes the dynamic capabilities as the threshold variable, empirically testing the threshold effect of the impact of digital level on knowledge evolution based on the panel data of 28 China’s manufacturing industries from 2013 to 2020. The results show that the dynamic capabilities have a significant double-threshold effect in the model of digital level on knowledge evolution. With the improvement of dynamic capabilities, the influence of digital level on knowledge inheritance presents a J-shaped relationship, while digital level and knowledge variation show a S-shaped correlation.
- Copyright
- © 2023 The Author(s)
- Open Access
- Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
Cite this article
TY - CONF AU - Yu Liu AU - Yan Wang AU - Jieru Wu AU - Shuo Liu PY - 2022 DA - 2022/12/29 TI - Research on The Influence of Digital Level on Knowledge Evolution Based on Threshold Regression Model BT - Proceedings of the 2022 2nd International Conference on Management Science and Software Engineering (ICMSSE 2022) PB - Atlantis Press SP - 780 EP - 785 SN - 2589-4900 UR - https://doi.org/10.2991/978-94-6463-056-5_113 DO - 10.2991/978-94-6463-056-5_113 ID - Liu2022 ER -