Evolutionary modeling reveals that value-oriented knowledge creation behaviors reinvent jobs

Yihang Cheng, Zhaoqi Yang, Yuan Cheng, Yong Ge and Hengshu Zhu ()
Additional contact information
Yihang Cheng: Chinese Academy of Sciences
Zhaoqi Yang: The University of Arizona
Yuan Cheng: BOSS Zhipin
Yong Ge: The University of Arizona
Hengshu Zhu: Chinese Academy of Sciences

Palgrave Communications, 2025, vol. 12, issue 1, 1-14

Abstract: Abstract Recently, the strong artificial intelligence-based augmented capability enables the autonomous completion of traditional jobs devoid of human intervention, impacting labor markets. However, the underlying mechanisms have not been explored enough in prior research. In this research, we propose a computational model, focusing on the interplay between world knowledge networks and organizational knowledge sets, along with external labor market conditions. This model incorporates dynamic knowledge creation behaviors and is validated using a substantial dataset from a leading online recruitment platform in China, featuring over 20 million job postings and 1 million skill-related keywords. The results demonstrate that swift knowledge search and emulating knowledge within existing jobs are the main methods in the early developmental stage of organizations, accounting for about 75% of all simulation samples and forming the initial job evolution. As organizations progress, although fine-tuning the knowledge within existing jobs still remains significant, the intensity of knowledge search declines significantly, and the intensity of knowledge reuse surpasses that of knowledge search, reaching ~1.5 times its intensity during the stable phase. We also perform several parameter experiments and a case study to illustrate how jobs evolve in the labor market with different characteristics. The robustness tests demonstrate the model’s resilience across different simulation environments and organization strategies. Our study underlies the mechanisms of job evolution from the organizational level and provides empirical evidence and insights into the job evolution dynamics within knowledge networks.

Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1057/s41599-025-04706-1 Abstract (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:pal:palcom:v:12:y:2025:i:1:d:10.1057_s41599-025-04706-1

Ordering information: This journal article can be ordered from
https://www.nature.com/palcomms/about

DOI: 10.1057/s41599-025-04706-1

Access Statistics for this article

More articles in Palgrave Communications from Palgrave Macmillan
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().