Status Analysis on Talent Recruitment at Artificial Intelligence Industry in the Guangdong-Hong Kong-Macao Greater Bay Area

DOI

10.2991/assehr.k.200727.166How to use a DOI?

Keywords

Talent introduction, Talent recruitment, Talent demand, Artificial intelligence, High-end talents

Abstract

As the core of the industry in the new era and the strategic technology leading the future development, artificial intelligence all over the world attaches great importance to its development. This study looks at the characteristics of talent demand and the types of job functions required for the artificial intelligence industry in the Guangdong-Hong Kong-Macao Greater Bay Area, combined with the talent introduction policy of the local government in the Greater Bay Area. The study found that: (1) industry clusters can provide many local employment opportunities as one of the factors to attract talents; (2) Guangzhou and Shenzhen offer the best discounts for high-level doctoral talents, but the industry’s introduction of high-level doctoral talents is not effective Obviously, it only accounts for 0.81%; (3) At present, the industry mainly focuses on the introduction of bachelor’s degree, followed by those with a junior colleges and a certain working experience value; (4) The main factors that attract high-level talents are salary and benefits. The results of this study can be used as a reference for the industry in talent introduction.

Copyright

© 2020, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Fang Yu-Shen
AU  - Li Feng-Ping
AU  - Luo Ke-Yi
PY  - 2020
DA  - 2020/07/27
TI  - Status Analysis on Talent Recruitment at Artificial Intelligence Industry in the Guangdong-Hong Kong-Macao Greater Bay Area
BT  - Proceedings of the 2020 5th International Conference on Humanities Science and Society Development (ICHSSD 2020)
PB  - Atlantis Press
SP  - 526
EP  - 530
SN  - 2352-5398
UR  - https://doi.org/10.2991/assehr.k.200727.166
DO  - 10.2991/assehr.k.200727.166
ID  - Yu-Shen2020
ER  -