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AI adoption and labor cost stickiness: based on natural language and machine learning

Author

Listed:
  • Haonan Wang

    (Xi’an Jiaotong University
    Xi’an University of Architecture and Technology)

  • Fangjuan Qiu

    (Xi’an Jiaotong University)

Abstract

Whether Artificial Intelligence (AI) displaces or augments employment is controversial. We add to this topical debate on AI-employment nexus by examining the effect of firms’ AI adoption on labor force decisions through the lens of cost stickiness. Utilizing a novel machine learning technique and textual analysis, we quantify and validate a firm-level AI adoption measure based on the annual reports of Chinese A-share listed firms during the period of 2006–2020. Then we employ this measure to empirically test the impact of AI adoption on labor cost stickiness. We find that firms’ AI adoption increases labor cost stickiness. The result is more significant for firms that have a higher share of employees with a higher educational degree, are more capital-intensive, and reside in regions with a higher degree of aging. Our results remain robust after addressing endogeneity concerns, controlling for alternative explanations and replacing main variables. Furthermore, our inferences persist for other cost categories, but the effect is more significant for SG&A than operating cost. This paper sheds light on AI’s firm-level consequence on the labor market and cost management.

Suggested Citation

  • Haonan Wang & Fangjuan Qiu, 2025. "AI adoption and labor cost stickiness: based on natural language and machine learning," Information Technology and Management, Springer, vol. 26(2), pages 163-184, June.
    • Handle: RePEc:spr:infotm:v:26:y:2025:i:2:d:10.1007_s10799-023-00408-9
    DOI: 10.1007/s10799-023-00408-9
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