# Artificial Intelligence and Employment: A Delicate Balance Between Progress and Quality in China
## 2 Literature Review, Theoretical Analysis, and Research Hypotheses
### Theoretical Foundation and Framework  
This study is grounded in three fundamental theories--Technology Diffusion Theory, Labor Market Segmentation Theory, and Human Capital Theory--which provide a robust framework for understanding how AI adoption influences employment quality [19; 20; 21].  
Technology Diffusion Theory: This theory explains how new technologies, such as AI, spread across regions and industries. Regions with better infrastructure and higher levels of investment are more likely to adopt AI, leading to improved employment quality through the creation of high-skilled jobs. In contrast, less developed regions may face challenges in adopting AI, resulting in job displacement and skill polarization [22].  
Labor Market Segmentation Theory: This theory divides the labor market into primary (high-skilled, secure jobs) and secondary (low-skilled, precarious jobs) sectors. AI adoption is likely to exacerbate this segmentation by increasing demand for high-skilled workers while displacing low-skilled workers in routine occupations [23].  
Human Capital Theory: This theory emphasizes the importance of education and training in preparing workers for an AI-driven economy. Regions that invest in human capital development are better equipped to mitigate the negative effects of AI on employment quality by reskilling workers and creating new opportunities in high-skilled sectors [24].  
These theories inform the study's hypotheses and provide a framework for analyzing the heterogeneous effects of AI adoption on employment quality across different regions in China.  
Key hypotheses and the relationships between them make up the study's theoretical framework as shown in Figure 1, which is as follows: While Technology Diffusion and Labor Market Segmentation theories are well-established, their intersection in China's context reveals unique mechanisms--regions with advanced infrastructure (e.g., eastern provinces) experience AI-driven skill polarization, whereas underdeveloped regions (central/western China) leverage AI for industrial upgrading, moderated by policy interventions. This tripartite interaction offers a novel lens for analyzing developing economies.  
The conceptual model of Figure 2 illustrates how the correlations between AI adoption and employment quality are moderated by factors such as skill bias, productivity gains, geographic inequities, economic development, and governmental interventions. AI adoption has a direct impact on employment quality, which can have both positive and negative outcomes (e.g., the displacement of low-skilled workers against the development of high-skilled positions).  
Figure 1: Theoretical framework: AI’s impact on employment quality.  
Economic development is fueled by the adoption of AI, which drives innovation and efficiency. In consequence, economic development shapes regional disparities; stronger infrastructure in affluent areas leads to greater benefits, which in turn leads to uneven employment results. In addition, growth and better work prospects are two ways in which AI-driven productivity increases indirectly improve employment quality.  
Adopting AI, on the other hand, brings a competence bias that polarizes workers based on their level of expertise. The quality of employment is impacted as a result of the widening gap in salary disparities, job insecurity, and opportunities. Policy measures, such as social protections and reskilling programs, can adapt the workforce and lessen the negative effects of AI adoption.  
In the end, the model emphasizes how these components interact with each other. Adopting AI could boost productivity and the economy, but the advantages are not going to all workers. We need tailored measures to make sure that everyone gets a fair shot at better jobs.