Abstract
To keep up with the technology reform, “absorbing, transferring and re-innovating” is a feasible and effective approach to technology improvement for late developing countries. However, it remains unclear about the mechanism and influencing factors in knowledge transfer of individual engineer, leading to the incapability in tackling the low-quality and low-efficiency transfer in this process. To solve this problem, this paper proposes an eye-tracking experiment to concentrate on the transfer of empirical engineering knowledge under technological paradigm shift, based on the transfer of learning theory and “Concept-Knowledge” theory. An experiment conducts in the conceptual design of new type accelerator, and measures the performance of engineers in the process of knowledge transfer. The result shows that the quality of transfer is positively affected by the creativity facet in self-direct learning, accuracy of mastering an original technological paradigm, and important noun concepts in accessing a new technological paradigm; while the efficiency of transfer is negatively affected by the integrity of mastering an original technological paradigm, and unrelated noun concepts and engineering plots in accessing a new technological paradigm. The output of this paper is supportive for constructing the transfer mechanism of empirical engineering knowledge under technological paradigm shift, and improving the performance of knowledge transfer in knowledge management aspect.
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Acknowledgment
This research was supported by National Natural Science Foundation of China (Nos. 70971085, 71271133, 71671113, 71601113), Shanghai Science and Technology Commission (No. 13111104500), and Shanghai Municipal Education Commission (13ZZ012). The authors would like to express great gratitude to all the editors and reviewers for their fair, encouraging and constructive advice.
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Li, X., Jiang, Z., Guan, Y., Li, G. (2019). Transfer of Empirical Engineering Knowledge Under Technological Paradigm Shift. In: Arai, K., Kapoor, S., Bhatia, R. (eds) Intelligent Computing. SAI 2018. Advances in Intelligent Systems and Computing, vol 857. Springer, Cham. https://doi.org/10.1007/978-3-030-01177-2_17
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DOI: https://doi.org/10.1007/978-3-030-01177-2_17
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