Adaptive Strategies under Structural Mismatch: A Regional Heterogeneity Analysis of AI Talent Demand in Enterprises of the Guangdong-Hong Kong-Macao Greater Bay Area
Authors/Creators
Description
The rapid development of the artificial intelligence (AI) industry is profoundly reshaping the supply-demand dynamics of regional labor markets. This study focuses on Guangdong Province’s Guangdong-Hong Kong-Macao Greater Bay Area, a key hub of China’s AI industry, and aims to address a critical question: What kind of talent do enterprises truly need within this vast and diverse industrial ecosystem? By analyzing data from 213 AI-related job postings on the “Izhanchi” recruitment platform and employing descriptive statistics and text mining methods, the study reveals several key findings: First, talent demand is highly spatially concentrated within the Greater Bay Area, forming a pattern characterized by “Guangzhou and Shenzhen as dual cores leading the way, with Foshan and Dongguan collaborating in a tiered manner.” Each city develops differentiated human capital needs based on its industrial endowments. Second, the labor market demonstrates a preference for “young talent with engineering potential and high learning resilience.” The generalized label of “Algorithm Engineer” highlights the core demand for methodological capabilities during the industry’s implementation phase. Enterprises’ recruitment of recent graduates is, in essence, a long-term strategic investment aimed at building specific human capital. Third, the market signaling mechanism has undergone adaptive evolution, with practical signals such as “project experience” gaining prominence. Additionally, salary pricing indicates that the combination of technical proficiency, domain integration capability, and human-complementary skills is key to securing market premiums. This study underscores that the “shortage of effective supply of engineering-oriented human capital” is the underlying cause of current challenges and provides a foundation for policy insights aimed at constructing an industry-education collaborative human capital development ecosystem.
Files
ISRGJEBM5262025.pdf
Files
(822.3 kB)
| Name | Size | Download all |
|---|---|---|
|
md5:ebcc4c590d65668e52a5835b36e975ca
|
822.3 kB | Preview Download |