JOEBM 2024 Vol.12(2): 117-121
DOI: 10.18178/joebm.2024.12.2.785
A Study on the Prediction of the Quantity of Talents: A Case Study of Shandong Province
Sun Chenxi 1,
Zhao Shuai 2,
and
Liu Yuchuang 3
1.
School of Science, Shandong Jianzhu University, Jinan, 250101 China
2.
School of Civil Engineering, Shandong Jianzhu University, Jinan, 250101 China
3.
School of Thermal Engineering, Shandong Jianzhu University, Jinan, 250101 China
Email: 2993576476@qq.com (Z.S.)
*Corresponding author
Manuscript received December 11, 2023; revised December 24, 2023; accepted January 17, 2024; published April 17, 2024.
Abstract—Taking the sustainable development of R&D talents as an example, this paper proposes a prediction model based on Pearson correlation analysis and stepwise regression analysis. Using this model, it identifies the main factor affecting the quantity of technology-driven talents in Shandong Province from four dimensions: economic development, residents’ living standards, social development, and technological activities, which is the added value of the secondary industry. The model predicts a potential talent outflow in Shandong Province in the future. By comparing the influencing factors on the quantity of technology-driven talents in Shandong Province with those in Jiangsu Province and Guangdong Province, the paper reveals the advantages and disadvantages of Shandong Province in attracting technology-driven talents and provides two recommendations: (1) Increase R&D research funding reasonably (2) vigorously develop the secondary industry. Predicting the trend of the number of R&D talents can help countries to plan for strategy. This article will provide suggestions for the economic development of Shandong Province through the prediction of the number of talents in Shandong Province.
Keywords—R&D talents, Pearson correlation coefficient test, stepwise regression analysis
Cite: Sun Chenxi, Zhao Shuai, and Liu Yuchuang, "A Study on the Prediction of the Quantity of Talents: A Case Study of Shandong Province," Journal of Economics, Business and Management, vol. 12, no. 2, pp. 117-121, 2024.
Copyright © 2024 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).